
Citation 
 Permanent Link:
 http://ufdc.ufl.edu/AA00025728/00001
Material Information
 Title:
 Human information processing for decisions to investigate cost variances
 Creator:
 Brown, Clifton E., 1947
 Publication Date:
 1978
 Language:
 English
 Physical Description:
 x, 193 leaves : ill. ; 28 cm.
Subjects
 Subjects / Keywords:
 Conservatism ( jstor )
Cost accounting ( jstor ) Cost allocation ( jstor ) Cost efficiency ( jstor ) Efficiency decisions ( jstor ) Employee efficiency ( jstor ) Motivation ( jstor ) Simulations ( jstor ) Statistical discrepancies ( jstor ) Variable costs ( jstor ) Accounting thesis Ph. D ( lcsh ) Dissertations, Academic  Accounting  UF ( lcsh ) Industrial management  Decision making ( lcsh )
 Genre:
 bibliography ( marcgt )
theses ( marcgt ) nonfiction ( marcgt )
Notes
 Thesis:
 ThesisUniversity of Florida.
 Bibliography:
 Includes bibliographical references (leaves 188192).
 Additional Physical Form:
 Also available online.
 General Note:
 Typescript.
 General Note:
 Vita.
 Statement of Responsibility:
 by Clifton E. Brown.
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 Source Institution:
 University of Florida
 Holding Location:
 University of Florida
 Rights Management:
 Copyright [name of dissertation author]. Permission granted to the University of Florida to digitize, archive and distribute this item for nonprofit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
 Resource Identifier:
 022909022 ( ALEPH )
05267304 ( OCLC )

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Full Text 
HUMAN INFORMATION PROCESSING FOR DECISIONS
TO INVESTIGATE COST VARIANCES
By
CLIFTON E. BRO14N
A DISSERTATION PRESENTED TO THE GRADUATE COUNCIL OF
THE UNIVERSITY OF FLORIDA
,IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA 1978
HUMAN INFORMATION PROCESSING FOR DECISIONS
TO INVESTIGATE COST VARIANCES
By
CLIFTON E. BROWN
A DISSERTATION PRESENTED TO THE GRADUATE COUNCIL OF
THE UNIVERSITY OF FLORIDA
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1978
Copyright 1978
By
Clifton E.
Brown
ACKNOWLEDGEMENTS
I am grateful to my supervisory committee; Rashad Abdelkhalik,
Douglas Snowball, Gary Hoi strum, and Richard Griggs. Without their
valuable contributions this dissertation would have had far more
obscurities.
I am indebted to my experimental assistants; Doug Snowball,
Gary Hoi strum, Ron Teichman, Ed Bailey, John Wragge, Robert Thompson,
Nancy Hetsko, Andy Judd, and Michael Gift (even though he was not on
time). Without their help the experimental procedures used within
this study would not have been possible.
Finally, my greatest debt is to my wife, Sandy. Without her
encouragement and support this dissertation never would have been done.
TABLE OF CONTENTS
ACKNOWLEDGEMENTS iii
ABSTRACT viii
CHAPTER I. INTRODUCTION AND OBJECTIVES 1
Variance Investigation Decision Processes 1
The Research Objectives A
Dissertation Organization 6
CHAPTER II. AREA OF INVESTIGATION 8
Managerial Accounting Concepts 8
Managerial Decisions 8
Management Task Planning and Control 9
Standard Cost Variance Investigation 11
Psychological Concepts 16
Psychophysics 16
Human Information Processing 20
CHAPTER III. RESEARCH METHODOLOGY AND DESIGN 23
General Conceptual Development 23
Decision Situation Structure 23
Available Information Set 25
Individual Information Processing Efficiency 26
Individual Ability to Expand the Information Set 27
General Research Design 28
Selection of Independent Variables 29
General Research Methodology 33
General Experimental Environment and Task 34
Operationalization of Variables 35
Hypothesis Formation 46
Simulation of Optimal Model Performances 46
Simulation of Investigation Decision Performances 49
CHAPTER IV. THE EXPERIMENT 76
Experimental Environment 76
Subjects 79
Experimental Materials 80
Background Information 80
Variance Investigation Decisions 80
Elicitation of Heuristics 83
%
Elicitation of Subject Motivations . . 83
Experimental Procedures 83
Assignment of Subjects to Treatment Conditions 84
Training Phase 85
Experimental Phase 86
Final Debriefing 87
CHAPTER V. ANALYSIS AND RESULTS 88
Summary of Results 88
General Method of Analysis 88
Training Phase Analysis and Results 98
Initial Decision Anchors 98
Decision Anchor Adjustment 102
Individual Decision Model Sensitivity 106
The dj Dependent Variable 106
v
Relative Decision Model Sensitivity 107
Intrinsic Motivation and Decision Model Sensitivity 114
Individual Decision Criteria 116
Relative Decision Criteria 116
Relative Decision Criteria Conservatism 121
Individual Attributes and Decision Criteria 124
Individual LongRun Decision Efficiency 126
Relative Decision Costs 126
Individual Attributes and LongRun Decision Efficiency ... 138
LongRun Decision Efficiency and Training Phase Performance 140
Individual Attributes 141
Subject Grade Point Average 141
Subject Motivations 142
CHAPTER VI. DISCUSSION OF RESULTS 146
Conceptual Development Revi si ted 146
Overall Results 148
Discussion of Results 150
Decision Anchor Selection and Adjustment 151
Individual Decision Model Sensitivity 153
Individual Decision Criteria 156
Individual LongRun Decision Efficiency 159
Limitations 163
Implications for Accounting 166
Value of Additional Information 166
General Standard Setting Process 167
Future Research 168
APPENDIX A. THEORY OF SIGNAL DETECTION DERIVATIONS 172
vi
APPENDIX B. ORAL PRESENTATION TO ELICITE VOLUNTEER SUBJECTS .... 174
APPENDIX C. BACKGROUND INFORMATION BOOKLET 176
APPENDIX D. PRIOR INFORMATION SHEETS 182
APPENDIX E. SUBJECT HEURISTIC ELICITATION QUESTIONNAIRE 184
APPENDIX F. SUBJECT MOTIVATION QUESTIONNAIRE 186
BIBLIOGRAPHY 188
BIOGRAPHICAL SKETCH 193
Abstract of Dissertation Presented to the Graduate Council
of the University of Florida in Partial Fulfillment of the Requirements
for the Degree of Doctor of Philosophy
HUMAN INFORMATION PROCESSING FOR DECISIONS
TO INVESTIGATE COST VARIANCES
By
Clifton E. Brown
December 1978
Chairman: A. Rashad Abdelkhalik
Major Department: Accounting
Two major functions of management are those of planning and con
trol. Broadly defined, control is the management process which assures
that selected alternatives are implemented and executed in accordance
with plans. Important aspects of the control process concern analysis
and investigation of standard cost variances provided within accounting
reports. A substantial portion of the accounting variance investigation
literature has employed the normative model approach researchers have
created variance investigation models which a manager should use. Rarely
has attention been given to how the manager would interprete and integrate
information required by the various normative models.
The principal focus of this dissertation was on the effects of
specific situational variables upon a manager's information processing
for purposes of making variance investigation decisions. The specific
objectives were 1) tc develop a conceptual framework which will predict
effects cf specific situational variables on a manager's relative effi
ciency in information processing and in variance investigation decision
making, and 2) to empirically test some implications of this conceptual
framework.
viii
Variables that can affect the manager's variance investigation
decision process include 1) structure of the decision situation, 2)
contents of the available information set, 3) manager's information
processing efficiency, and 4) manager's learning efficiency. The
structure of the decision situation depends upon number possible states
of control, relative frequencies of the states, various statistical
relationships among the states, and various relationships between the
decision/state outcomes. The contents of the available information set
include information known by the manager prior to his decision. The
manager's information processing efficiency relates to the particular
strategies employed in combining and weighting various items of infor
mation. The manager's learning efficiency refers to the manager's
ability to learn from his experiences with the controlled process. Both
information processing and learning efficiency are the results of the
manager's particular decision strategies. These decision strategies
were expected to adapt a general form, referred to as anchoring and ad
justment. A natural starting point is used as a first approximation for
the investigation decision rule and is then adjusted as the manager
learns from his experiences.
Two experimental methods were used to derive and test implications
of the conceptual framework. Simulation techniques were used to opera
tionalize hypotheses and a laboratory experiment was used to test these
hypotheses.
The experimental environment was that of an assembly department
within a simulated manufacturing company. The assembly department as
sembled a single product and performance of this department was deter
mined completely by the assembly workers' labor efficiency. Student
IX
subjects, who assumed the role of assembly department operational manager,
made labor efficiency variance investigation decisions based upon a series
of independent standard cost variance reports. The manipulated indepen
dent variables included contents of the available information set,
distributional properties of the states of control, and cost effects of
the investigation decision errors. Parameters of the psychological theory
of signal detection permitted measurement of sensitivity and criteria of
a subject's decision model. The subject's investigation decision costs
compared to normative investigation decision costs derived under similar
situations were employed as a relative measure of decision efficiency.
Overall, the implications of the conceptual framework were supported
by the obtained results. To explain the few major deviations from
expectations, an ex post hypothesis was introduced. This hypothesis
posits that the standard introduces a subjective adjustment bias. Depend
ing upon the direction of adjustment the standard subjectively inhibits
complete adjustment either to optimal decision values close to the
standard or to optimal decision values distant from the standard.
x
CHAPTER I
INTRODUCTION AND OBJECTIVES
Business organizations generate internal accounting reports for
use by managers for purposes of evaluation and decision making.1 One
important evaluation process and decision task of management is that of
standard cost variance analysis and investigation. Based in part upon
standard cost variance reports generated by the internal accounting
system, managers estimate the likelihoods that various production pro
cesses remain under control and decide whether to investigate particu
lar variances. The present study concerns the effects of selected
variables on standard cost variance investigation decision making by
operational control managers. This chapter discusses general aspects
of the standard cost variance investigation decision process and the
variables which may affect the process and presents the research
objectives of the study.
Variance Investigation Decision Processes
An important factor in the evaluation and use of standard cost
variance reports is the manager's perception of the validity of the
xThe American Accounting Association 1966 Statement of Basic Accounting
Theory states that "the objective of accounting for internal use is to
provide information to persons within an organization that enables them
to make informed judgements and effective decisions which further the
organization's goals" (p. 38).
1
2
information provided. In particular, the manager's perception of the
standard setting process will have considerable influence upon his
variance investigation decision process. If the manager believes that
the standards are unrealistic (i.e., that the standards have been placed
too far from the incontrol distribution) he may rescale either the
standards or the variances to correspond with his own perception of the
incontrol distribution.2
If it is assumed that a manager accepts the standard setting pro
cess as realistic and does not rescale the standards or variances, his
variance investigation decisions may be viewed as the culmination of a
twostage process. The first stage concerns the detection of the
particular distribution (e.g., incontrol or outofcontrol) that gen
erated the variance. The manager's performance of this task is a
function of the sensitivity of his decision process (model). This
sensitivity is affected by the structure of the particular situation
and by the manager's knowledge of this structure. The extent of the
manager's knowledge of the situation, in turn, is affected by the
available information (both contained within the variance report and
provided from other sources), and by the manager's ability to learn
from his experiences with the processes being controlled. In situations
where the controlled process distributions have some area of overlap
the results of a manager's detection process are probabilistic (i.e.,
prior to completing an investigation the existence of any given state
is uncertain). Furthermore, the greater the area of overlap, the more
difficult the discrimination task becomes.
2Within this context, an incontrol distribution concerns statistical
congruence of production output and planned output in terms of
controllable resource utilization.
3
The second stage concerns the manager's investigation decision
criteria. Having arrived at a conclusion (albeit probabilistic) about
the distribution that generated the variance, the manager must integrate
and process various objective function parameters in order to arrive at
his variance investigation decision (these parameters can belong to
either the manager's objective function, the organization's objective
function, or both if the same). The potential investigation criteria
can be divided into two major categories: structural criteria and
behavioral criteria. The first category can be divided further into
structural cost criteria and structural probability criteria. The
structural cost criteria include such variables as the additional cost
of operating an outofcontrol process (given that the process can be
returned to the original incontrol state), and the costs of variance
investigation and correction (which can differ depending upon the
actual state that generated the variance). The structural probability
criteria include such variables as the probability that the source of
the variance is controllable (i.e., that it can be returned to the
original incontrol state through managerial action), the probability
that the process will return to the incontrol state without managerial
action, and the prior probabilities of each state distribution. The
behavioral criteria include such variables as the manager's perception
of the effect of the investigation upon his performance evaluation and
reward structure, and the manager's perception of the effect of the
decision upon employee performances and attitudes.
Although the variance investigation decision process has been
described as two stages, the manager may not actually utilize such a
sequential stage process. The manager's actual decision process is
4
labeled a heuristic. Within this context, heuristic refers to the
learned set of rules or principles that are utilized by the individual
in making the particular decisions required of him. The manager's
specific heuristic can be affected substantially by his individual
characteristics (e.g., intelligence, cognitive complexity and style,
decision process sensitivity, motivations, etc.). However, a general
heuristic, labeled anchoring and adjustment (Tversky and Kahnemar,
1974), is expected to describe the general form of the manager's vari
ance investigation decision process.3
The above examination of the manager's variance investigation
decision process indicates that the variables which can affect both
the process and the results of the process include 1) the structure
of the decision situation, 2) the contents of the available information
set, 3) the manager's information processing efficiency, and 4) the
manager's learning efficiency (from his experiences with the controlled
process). The manager's knowledge concerning the structure of the
decision situation may be limited by the contents of the available
information set and by his information processing and learning effici
encies. The accountant, to a large extent, has control over the con
tents of the available information set.
The Research Objectives
Many problems in accounting reduce to one of choosing among
alternative information sets that could be provided to a decision maker
(American Accounting Association, 1972). Two basic approaches to
3The anchoring and adjustment heuristic is described in greater detail
within Chapter II.
5
deciding on the information set to be presented within the cost variance
report have been advocated. First, the accountant can determine those
models which managers use in making variance investigation decisions and
provide an information set which would permit the implementation of
these models. Problems with this individual model approach are 1) the
possibility exists that different individual models use a wide range of
different information sets (which are not costless), and 2) the indi
vidual models may not be optimal (i.e., they could be inefficient with
respect to other decision models). The individual model approach cen
ters on the psychological question of what the manager is doing (or
would do) with the available (or additional) information. Second, the
accountant could create a normative model of the variance investigation
decision and provide the information required by that model. Problems
with the normative model approach are 1) the information provided may
not optimize the individual's investigation decisions (which may con
tinue to be made using the individual's model), and 2) the costs
associated with operationalization of the normative model may exceed
the benefits (cost savings as a result of more optimal investigation
decisions and greater congruence of manager goals with overall organi
zation goals) offered by the model. The normative model approach
centers on the analytical question of what the manager should be doing
(or should do) with the available (or additional) information.
A substantial portion of the accounting variance investigation
literature has focused upon the second approach the normative model
approach.4 However, rarely has attention been given to how the manager
4This literature is reviewed within Chapter II.
6
would interprete and integrate the information sets of the various
normative (optimal) variance investigation models. In many instances
the implicit assumption has been that the manager would process the
information with the same efficiency as the normative model.
The principal focus of this research is the effects of specific
situational variables on a manager's variance investigation decisions
(relative to the investigation decisions of an optimal model) and on
a manager's processing of available information (relative to the infor
mation processing of an optimal model). Specific objectives are:
1) To develop a conceptual framework which will predict the
effects of situational variables on a manager's relative
information processing efficiency and to empirically test
the implications of this conceptual framework.
2) To develop a conceptual framework which will predict the
effects of situational variables on a manager's relative
variance investigation decision efficiency and to
empirically test the implications of this conceptual framework.
Dissertation Organization
Chapter II presents certain general concepts from accounting and
psychology. These concepts are necessary for the development of the
specific environment studied in this research, and for the development
of a conceptual information processing and decision making framework
within this environment. The accounting concepts are concerned with the
nature of managerial decisions, managerial task planning and control,
and standard cost variance investigation. The psychological concepts
are concerned with the theory of signal detection and with cognitive
7
aspects of human information processing such as the general heuristic
of anchoring and adjustment.
Chapter III synthesizes the general concepts of the previous
chapter and develops a conceptual framework of standard cost variance
investigation employing the methodology of the psychological concepts.
This conceptual framework was operationalized using simulation tech
niques, and experimental hypotheses were derived from the simulations.
The details of a laboratory experiment designed to test the
hypotheses derived from the conceptual variance investigation framework
are presented in Chapter IV. The experiment employed a betweensubjects
design and was manipulated factorially using specific variance investi
gation situation variables. Modified parameters from the psychological
theory of signal detection were employed to measure subject's information
processing efficiency and decision model sensitivity.
The results obtained from the laboratory experiment are presented
in Chapter V. The model comparison procedure of nonorthogonal analysis
of variance was the primary method of analysis employed. Chapter VI
discusses the results, develops a modification (ex post) of the
original conceptual framework, and discusses some implications for
accounting and for future accounting research.
CHAPTER II
AREA OF INVESTIGATION
Managerial Accounting Concepts
This study relies on the synthesis of certain psychological con
cepts with certain accounting concepts. This section discusses the
accounting concepts: the nature of managerial decisions, managerial
task planning and control, and standard cost variance investigation.
Managerial Decisions
Since the information set (accounting report) exists to support
the manager's tasks, a conceptual framework of managerial decisions would
facilitate the accountant's information set selection. A conceptual
framework of managerial decisions should differentiate managerial de
cisions along dimensions that allow insights into the informational
needs of those decisions.
Anthony (1965) provides one such dimension along which he iden
tifies the purposes or orientation of managerial activities: strategic
planning, management control, and operational control. Another dimen
sion is provided by Simon (1966) who distinguishes between programmed
and nonprogrammed decisions. The underlying dimension is concerned
with the manner in which managers deal with their problems. The cri
teria for classifying a decision consist of the extent of structure
associated with the problem solving phases of the decision.
8
9
A conceptual framework of managerial decisions that synthesizes
these dimensions is presented by Gorry and Morton (1971). Simon's
dimension is modified by replacing the terms "programmed" and "non
programmed" with the terms "structured" and "unstructured" and adding
a third category labeled semistructured. Gorry and Morton's conceptual
framework is used in this research as a model that will permit the
identification of a standard cost system within the overall managerial
decision framework.
Management Task Planning and Control
Standard cost systems present information sets to managers to
support various decisions concerning task planning and control.
Demski (1967) provides an excellent conceptual discussion of the manage
ment task planning and control process, and his approach (with some
modification) is adopted in this research.
Figure 1 presents a model of the management task planning and
control process. Environmental information, largely external to the
standard cost system, facilitates the planning of overall goals and
policies within the constraints imposed by the environment. Overall
objective control feedback 1) provides evidence for the ccntinuted
validity of overall assumptions made in forming the organization ob
jectives, and 2) facilitates strategic planning evaluation of organi
zation performance.
Both the environmental variables and the overall task control
feedback of the management control activity 1) provide evidence for
the continued validity of the overall task assumptions made in forming
Overall
Objective
Control
Strategic
Planning
Overall
Objective
Planning
Overal1
Task
Control
Management
Control
Overall and
Specific Task
Planning
Specific
Task
Control
V
Operational
J
Control
Specific Task
Structuring
Source: Modified from Demski, 1967.
Environment
Output
FIGURE 1
MANAGEMENT TASK PLANNING AND CONTROL PROCESS
11
the task plans, and 2) facilitate management control evaluation of
operational control performance.
The operational control manager uses the specific task control
feedback to 1) decide whether the physical system performance is in
agreement with the specific task plan, and 2) to decide whether the
task can be restructured to bring performance back into agreement with
the plan (if performance and the plan differ). A task that can not be
restructured could have significance for the overall task control feed
back to the management control activity and possibly may lead to
modification of the original task plans. Such modification could in
turn have significance for the overall objective control feedback to
the strategic planning activity, and a modification of the original
overall objectives may follow.
Standard Cost Variance Investigation
Within the operational control activity the specific task plan
provides standards of performance in terms of expected component costs
and usages, and the desired physical outputs. The operational control
manager structures the tasks within the physical system and periodically
receives a standard cost variance report describing the system output
in terms of the task plan and the actual results. Upon receiving a vari
ance report the manager must decide the nature of the given variances.
Dopuch et al. (1967) present a classification of standard cost
variances that is based on the expected source of the variances. A
variance resulting from a random fluctuation of the physical system,
labeled a Type 1 variance, requires no operational control response if
12
not statistically significant. Whether a variance resulting from a
change in the physical system, labeled a Type 2 variance, requires an
operational control response depends on whether the underlying cause of
the change is a temporary rather than a permanent phenomena. A tem
porary or controllable variance, labeled a Type 2a variance, is one
that operational control can correct in the future (i.e., the physical
system can be returned to the previous incontrol condition). A per
manent or noncontrollable variance, labeled a Type 2b variance, is one
that operational control can not correct in the future.
If the manager decides that the variance is of Type 1 no action
is required. If, however, the manager decides that the variance is of
Type 2 he must then decide if the variance should be investigated for
its underlying causes. Should the variance be of Type 2b it would
have little operational control significance; should the variance be
of Type 2a the cause of the variance could be eliminated by restructur
ing the physical system. The present study confines itself to the
assumption that all variances which result from a change in the physical
system are controllable by the operational control activity. In other
words, it assumes that the standard cost variances have no significant
affect on either the strategic planning or the management control
activities.
The information set contained in a standard cost variance report
can be viewed as relating to one of two categories of information:
distributional properties of system states and investigation decision
criteria. The distributional properties category can include such
variables as 1) the mean and standard deviation of the output when the
system is known to be incontrol, 2) the mean and standard deviation of
13
the output when the system is known to be outofcontrol, 3) the actual
output for the period, and 4) the deviation between the standard output
and the actual output for the period (the standard output variance).
The investigation decision criteria category can include such variables
as 1) the prior probabilities of the system's states, and 2) the
costs and benefits associated with each possible investigation decision
in combination with each possible system state.
Information relating to investigation decision criteria that is
not typically presented within the variance report includes 1) the
probability that the cause of the variance is controllable (assumed by
this research to equal one), 2) the probability that the system will
return to the incontrol state without managerial action (assumed by
this research to equal zero), 3) the perceived effect of the investi
gation decision on manager performance evaluation, and 4) the perceived
effect of the investigation on employee performances and attitudes.
The variance investigation literature is concerned mainly with
the investigation significance of Type 1 and Type 2a variances. A
general approach described within the literature is that of the Shewhart
X chart procedure (Probst, 1971; Koehler, 1968; Luh, 1968; Jeurs, 1967;
Zannetos, 1964). This approach, based upon classical statistics,
involves sampling the system to construct an incontrol mean and stan
dard deviation. Arbitrary control limits (generally plus or minus three
standard deviations) are used as the criteria for making a variance
investigation decision. Only one of the above studies considered the
effects of the information provided on the manager's investigation
decision. Probst (1971), in an industrial field study, found foremen
unwilling to accept the procedure as their investigation decision rule
14
when the procedure was constructed objectively (the foremen indicated
the reason for not accepting the procedure was that they felt it
ignored their experience). However, Probst did not analyze his results
in terms of the performance efficiency of the foremen.
Other research has noted what are considered to be significant
drawbacks to the Shewhart X chart procedure: the failure to consider
the costs and benefits associated with the various investigation de
cision outcomes and the failure to consider information from prior
periods. Bierman et al. (1961) proposed the incorporation of investi
gation decision costs and benefits within a classical statistics frame
work. Thus managers may use statistical information to calculate the
probability that the variance reflects the incontrol state, and com
bine this probability with the investigation decision costs and benefits.
Most of this information is assumed to be provided by the accountant
and accurately processed by the manager.
Kaplan (1975) and Jacobs (1978) have considered another deficiency
of the classical statistics approach the failure to use prior period
information. They analyze the use of the cummulative sum procedure,
whereby the cumulative sum of the variance is charted for each period.
Theoretically, under a stable state these sums should follow a random
walk, and any drift would indicate the system is outofcontrol. Eval
uation of such a drift would be accomplished on the basis of information
provided by the accountant or derived from the manager's experience.
The economic cumulative sum procedure incorporates the effects of
estimated investigation decision costs within the drift evaluation.
Several studies in the variance investigation literature use
decision theory to construct a variance investigation model (Kaplan,
15
1969; Dyckman, 1969; Kaplan, 1975; Dittman and Prakash, 1978). If
all the parameters required by these models were available these
models could replace the manager as the variance investigation decision
maker. However, to the extent that not all the parameters are opera
tionalizable (given seme cost constraint), the manager must continue
to make the variance investigation decisions using the information
provided by the accountant and by his own experiences.
A conclusion that can be drawn from the variance investigation
literature is that the additional information proposed for the use of
managers is becoming both diverse and complex. Furthermore, there is
a paucity of research relating to the manager's ability and efficiency
to interpret and integrate this additional information. Demski (1970,
1971) was one of the first to note the problem associated with the
decisionimplementation interface (i.e., the effect of the control
system information on individual behavior and performance). Some
analytic research on this problem has followed. Using simulation
techniques, Magee (1976) compared average total cost for seven decision
rules under various operational conditions. Although the simpler
decision models (rules) tended to have larger average costs than the
more sophisticated decision models, the difference in average cost was
relatively small. Indeed, if model implementation and information costs
are considered the simpler models may be more efficient than the
sophisticated models. Magee also noted that because of the use of
different manager performance measures (average operating costs, average
number of months below standard, etc.) simpler decision models may re
sult in rational choices (i.e., choices that maximize the manager's
expected utility). Closely related to Magee's research is an empirical
16
study by Magee and Dickhaut (1977). Using human subjects, Magee and
Dickhaut found support for the proposition that different manager per
formance (payoff) measures will affect the specific decision rules
(heuristics) employed by the decision maker (as a result of the decision
maker attempting to maximize his subjective utility).
Psychological Concepts
This section discusses the psychological concepts employed in
developing a conceptual framework of manager standard cost variance
investigation decisions. These concepts include the psychophysical
theory of signal detection and the human information processing con
cepts of decision heuristics.
Psychophysics
Psychophysics studies the relationships between physical and
psychological scales of measurement. Modern psychophysics adopts the
view that subjects can make meaningful evaluations of the magnitudes of
their sensory experiences, and therefore sensory magnitudes, as well
as physical magnitudes, can be quantified. One approach of modern
psychophysics is based upon the theory of signal detection (TSD). TSD
permits the separation of the decision maker's ability to discriminate
between classes of stimuli (sensitivity) from his motivational response
biases (decision criteria). Two comprehensive theoretical descriptions
of TSD are presented by Green and Swets (1974) and Egan (1975); general
surveys of the TSD theory are presented by Coombs et al. (1970), Watson
(1973), and Pastore and Scheirer (1974).
17
The basic TSD experiment utilizes the singleinterval procedure.
This procedure consists of a series of trials, each trial consisting of
an observation interval and a response interval. The possible stimulus
events during the observation interval are 1) the observation contains
a meaningful signal added to a background of noise (sn trial), and 2)
the observation contains only a background of noise (n trial). It is
assumed that each trial is independent of all other trials and that
the prior probabilities of n and sn are given and remain constant. The
background noise fluctuates at random from trial to trial; the stimulus
(usually a fixed level) is added to the noise. Therefore, the obser
vation fluctuates randomly from trial to trial. The task of the subject
is to detect whether the observation was generated by the signal plus
noise (sn) or by the noise alone (n) distribution. That is, in the
response interval the subject will respond with either "Yes, the signal
was present" (Y response), or "No, the signal was not present" (N re
sponse).
On any trial there exist four possible outcomes of the subject's
decision in conjunction with the actual distribution: 1) sn was pre
sented and the subject said "Yes" (a hit), 2) sn was presented and the
subject said "No" (a miss), 3) n was presented and the subject said
"Yes" (a false alarm), and 4) n was presented and the subject said
"No" (a correct rejection). A conditional probability matrix for a
series of these events is given by the following (Green and Swets, 1974):
RESPONSE
Yes No
:3 sn P(Ysn) P(Nsn)
rs
~ n P(Yn) P(Nn)
UO
18
Since the cells of this matrix are both exhaustive and mutually exclu
sive, the rowwise conditional probabilities must sum to one (this does
not necessarily hold for the sum of the columnwise conditional
probabilities). All parameters of the TSD model are derived from this
conditional probability matrix (see Appendix A for a more detailed
discussion of the conditional probability matrix including its rela
tionship with Bayes1 theorem).
In the singleinterval task the subject analyzes the evidence
and classifies the stimulus into one of two categories according to
his criteria. The criteria are determined by his objective function.
The objective funcition of interest within this research is the
maximization of expected value. Assume that the subject has some value
(utility) fqr each of the four event outcomes. A payoff matrix of these
values related to the four outcomes is. given by the following (Egan,
1975):
DECISION
No
Yes
sn ^sn,Y ^sn,N
n vn,Y vn,N
The decision rule for maximizing the expected value (see Appendix A
for a derivation of this decision rule) is:
P(xsn) P(n) V,N Vn.Y
P(xjn) P(sn) V$n,Y ^sn,N
At the point at which the above expression is an equality the subject
should be indifferent between saying "Yes" or saying "No." This point
can be considered the critical value of the likelihood ratio of the
observations, L(x0). The decision rule for saying "Yes" is expressed
by the relation L(x) > L(x0).
19
The critical value L(x0) for this decision rule has two possible
values: a theoretical value which is a measure of the criteria of an
optimal (or ideal) subject and a subjective value which is a measure
of the criteria of an actual subject.
One set of TSD models assumes that both conditional probability
distributions are Gaussian; i.e., $(xn) and $(xsn) are normally
distributed. With the additional assumption of equal variance for both
distributions, the parameters which measure individual discrimination
sensitivity and individual decision criteria are labeled d1 and g,
respectively. The discriminability measure, d', has the following
theoretical definition:
where, ysn= the mean of the signal plus noise distribution;
vn = the mean of the noise alone distribution;
a = the standard deviation of both distributions;
zn = the value of the normal distribution function associated
with the noise alone distribution and any decision axis
cutoff value common to both distributions; and
z = the value of the normal distribution function associated
sn
with the signal plus noise distribution and any decision
axis cutoff value common to both distributions.
The d' measure theoretically is independent of the decision criteria
measure.
The decision criteria measure, 3> has the following theoretical
definition:
3 = i>(zsn) / 4>(zn)
20
where <Â¡>( ) denotes the normal density function for the point in
parentheses and the z parameters are the same as defined for the d'
measure.
Although the assumption of equal variance normal distributions
is employed within this research, such an assumption is not necessary
to employ TSD. Egan (1975) demonstrates the use of TSD with exponential
distributions, chisquare distributions, Bernoulli distributions, and
Poisson distributions. Grier (1971) develops nonparametric measures
of discriminability and decision criteria.
Traditionally, psychophysics has employed TSD to study perceptual
processes: i.e., sensory processes such as audition and vision. Over
the last decade, however, TSD has been applied to conceptual processes.
These extensions to conceptual processes have included numerical
processing (Lieblich and Lieblich, 1969; Hammerton, 1970; Weissmar. et al.,
1975), medical diagnosis (Lusted, 1969; Lusted, 1971; Swets, 1972),
conceptual judgement (Ulehla et al., 1967a; Ulehla et al., 1967b), and
memory (Bernbach, 1967; Banks, 1970).
Human Information Processing
Human information processing (HIP), a subset of cognitive psy
chology, studies human judgement and decision making with particular
emphasis on the processing of information that determines these
activities. An area within HIP is the construction of models of human
decision making. The work within this area is typically classified
into schools of research which employ different paradigms, the two
major paradigms being the Bayesian and regression approaches (Slovic
and Lichtenstein, 1971). The TSD model is related to the Bayesian approach.
21
The Bayesian approach is a normative model specifying how a
decision should be made given certain internally consistent relation
ships among probabilistic beliefs. The basic beliefs of this approach
are that decisions should be based on subjective probabilities and
that these probabilities should be revised upon the receipt of addi
tional information in accordance with Bayes's theorem.
The major findings of Bayesian research are labeled conservatism.
The subjects, after receiving additional information, revise their
posterior probabilities in the same direction as the optimal model but
the revision is insufficient. Much of the research has focused on an
explanation of the cause of conservatism, the major explanations being
misperception (Peterson et al., 1968), misaggregation (DuCharme and
Peterson, 1968), and response bias (DuCharme, 1970).
Another area within HIP is the study of subjective information
processing principles and decision rules, labeled heuristics. A
heuristic, within this context, refers to a learned set of rules or
principles which are utilized by individuals in making the particular
decisions required of them. Research in this area has been concerned
with identifying systematic biases (relative to some definition of
optimal) of subjective heuristics within certain types of decision
tasks. Those information processing and decision rule biases iden
tified thus far have been labeled as an anchoring and adjustment
heuristic (Tversky and Kahneman, 1973), a representative heuristic
(Kahneman and Tversky, 1972; Swieringa et al., 1976), an avail
ablility heuristic (Tversky and Kahneman, 1973), and the law of small
numbers (Tversky and Kahneman, 1971).
22
This study will make particular use of the anchoring and
adjustment heuristic. In many situations, individuals first make
decisions by starting with an initial anchor (decision point) and then
adjust this initial anchor as they learn from their experiences. The
initial anchor can be suggested by the structure of the decision
situation, or can be the result of a partial computation or estimate.
Empirical tests involving the anchoring and adjustment heuristic in
dicate individuals do not sufficiently adjust their initial decision
point. That is, their adjustment is less than that which would allow
optimal processing of the available information (Slovic and Lichtenstein,
1971; Slovic, 1972; Alpert and Raiffa, 1968; Tversky and Kahneman,
1974).
CHAPTER III
RESEARCH METHODOLOGY AND DESIGN
General Conceptual Development
A general problem confronting decision makers is that the indi
vidual must decide subjectively which state of nature is most probable
based upon some incomplete set of information. When an individual
deals repetitively with a similar situation his longrun decision
efficiency or "decision correctness" can be affected by 1) the struc
ture of the particular decision situation, 2) the contents of the set
of available information, 3) his efficiency in processing the available
information, and 4) his ability to expand the available information
through experience with, and observations of, the various states of
nature. Each of these four elements is discussed below with specific
reference to the objectives of this research.
Decision Situation Structure
The structure of the particular decision situation primarily de
pends upon several key variables. These variables include the number of
possible states of nature, the relative frequencies of the states, the
various statistical relationships among the states, and the relation
ships between the various decision outcomes (the costs incurred given a
specific decision and the existence of a specific state). Depending on
23
24
the specific values which these variables may assume, the structure of
the particular decision situation can affect both the difficulty and
the importance of the individual's discrimination among the states. In
general, as the number of possible states increases and as the area of
distributional overlap of these states increases the discrimination task
becomes more difficult. Furthermore, the discrimination task becomes
more important (in terms of incurred costs) as the relative frequencies
of the states become equal and as the costs associated with the possible
decision outcomes which involve decision errors (an incorrect decision
given the existence of a specific state) become unequal.
The general situation employed in this research, selected for
its relevance to the accounting discipline, is the cost variance
investigation decision. In this situation two states of nature are
possible: 1) incontrol (the underlying physical process described by
the standard cost variance report is functioning as planned), and 2)
outofcontrol (the underlying physical process described by the stan
dard cost variance report is not functioning as planned). In reality,
the possible states of nature may be located on a continuum whose end
points are the states of incontrol and outofcontrol. Between these
two end points are any number of states that take the general form of
partially outofcontrol or moving outofcontrol. In the present
research the possible states of nature are confined to the two end
points. The relative frequencies of the two states are controlled as
constants with their values being close to equal. The statistical
relationships among the two states and the relationships between the
various decision outcomes are manipulated as independent variables.
25
Available Information Set
The contents of the available information set refers to the infor
mation known by the individual prior to his decision. Such information
can be of two types singular or distributional (Tversky and Kahneman,
1977). Singular information consists of information that specifically
relates to the current decision. Distributional information consists
of information that relates to the relative frequencies and to the
statistical relationships among the states of nature.1 The difficulty
of the discrimination task may be affected by the presence or absence
of certain items of information. In general, the less information con
tained in the available set the more difficult the discrimination task,
for missing information required by a decision model must be estimated
by the individual. Compared to statistically derived estimates, these
subjective estimates are likely to have greater uncertainty and in
efficiency associated with them.
Within the variance investigation situation, the information con
tained on each variance report constitutes the singular information.
Two types of singular information are employed in this research the
actual results of the physical process and its variance from a standard,
and the marginal costs associated with each of the two possible decisions
in combination with each of the two possible states of nature. The
presence of both these singular information types is controlled as a
constant within this research. The presence or absence of specific
xThis definition of distributional information implicitly assumes that
the relative frequencies and statistical relationships are stable over
the relevant time frame. If these variables were nonstable, revised
estimates of their specific values would be required prior to each
decision, thus classifying them as singular information. This research
assumes that both of these variables are stable across all decisions.
26
distributional information items (the statistical means, variances, and
distributional shapes of the two states) is manipulated as an indepen
dent variable. The presence of other distributional information items
(the relative frequencies and the allowed standard) is controlled as a
constant.
Individual Information Processing Efficiency
The individual's efficiency in processing the available infor
mation relates to the particular heuristics or strategies employed in
combining and weighting the various items of information. The term
efficiency implies a relationship between the individual's process
output (his decision) and a normatively correct or optimal decision.
The individual's decision and information processing performance can be
evaluated by comparing his performance against an optimal model.
Optimality refers to the best possible performance under given condi
tions. Since the optimal decision model relies on an incomplete infor
mation set rather than certain knowledge,even its performance can be
affected by both the structure of the situation and the contents of the
available information set.
The optimal models used in this research are a function of the
experimental environment: the singleinterval procedure found within
the psychological theory of signal detection and the basic decision
situation of standard variance investigation. Within this environment
the optimal decision rule for minimizing (maximizing) the expected cost
(value) of a set of decisions is based upon an extension of Bayes'
theorem that takes into account the relative costs (values) of various
27
possible decision outcomes. The parameters required to fit the optimal
model include 1) the relative frequencies of the two states of nature,
2) the mean of each state, 3) the statistical variance of each state,
and 4) the costs associated with each of the two possible decisions in
combination with each of the two possible states of nature. Since the
individual's decisions are to be evaluated by comparisons with the out
puts of the optimal model, it would seem reasonable that the information
available to the optimal model be the same as the information available
to the individual. Consequently, for decision situations in which some
of the parameters required by the optimal model are not contained in the
available information set the optimal model must make estimates of the
missing parameters.
Individual Ability to Expand the Information Set
The individual's ability to expand the available information set
over time refers to his ability to learn from his experiences with the
states of nature. Such learning can occur through improved estimates
of unknown items of distributional information and through modifications
of information processing strategies to incorporate state relationships
which were unknown or undetected previously.
The general form of the expected individual information processing
strategy is described by the heuristic of anchoring and adjustment
(Tversky and Kahneman, 1974).2 A natural starting point, or anchor, is
used as the first approximation for the decision. This anchor is then
2A heuristic, within this context, refers to a learned set of rules or
principles which are utilized by an individual in making the particular
decisions required of him.
28
adjusted as the individual learns from his experiences with the states
of nature.
Within the present study the initial decision points or anchors
were expected to fall near the geometric intersection (either actual or
estimated, depending upon the available information) of the distribu
tional curves of the two states. Adjustments from these initial deci
sion points by the individual were expected to occur during the train
ing phase of the experiment as the individual gained experience with
the states and received feedback as to his performance (relative to an
optimal model). The extent of an individual's adjustments (his learn
ing efficiency) is measured using several variables. Each variable
measures the relative extent of adjustment (or lack of adjustment) from
the original decision anchor tov/ard the optimal value of that variable.
General Research Design
As stated previously, the focus of this research is on human
decision making and information processing within a particular decision
context that of standard cost variance investigation. Much of the
variance investigation literature within accounting has focused on how
the decision maker should integrate the available information and make
the investigation decision (see Kaplan, 1975 for a review of this
literature). Very little research has been concerned with how the
decision maker does accomplish these processes. The major objective
of this research is to study the processes used by the decision maker
in reaching variance investigation decisions. In particular, this re
search will examine within the conceptual framework discussed previously:
29
1) the effects of the decision situation structure, the available infor
mation set contents, the individual's information processing efficiency,
and the individual's learning efficiency on the individual's longrun
decision efficiency; and 2) the effects of the decision situation
structure and the available information set contents on the individual's
information processing and learning efficiency.
Selection of Independent Variables
The effects of two types of variables are of primary interest
within this study: these are the situation variables and the process
variables. The following discussion describes the selection of each
of the variables employed in this study.
The situation variables
The situation variables are the quantity of information available
to the individual prior to his decision, the statistical structure of
the two states of nature, and the cost structure of the possible decision
outcomes.
The available information set. The effects of the contents of
the available information set are studied by manipulating the presence
and absence of certain distributional information. This involves the
specification of two levels of available information set content. Since
the difficultly of the discrimination task may be increased by the
absence of certain information, one level of the information variable
has less distributional information than the other level. This inde
pendent variable is labeled the information variable.
30
The statistical structure. The effects of the statistical struc
ture of the decision situation are studied by manipulating a distribu
tional information variable. This involves the specification of two
levels of statistical relationship among the two states. Since the
difficulty of the discrimination task increases as the area of dis
tributional overlap between the two states increases, one level of the
distribution variable will have a greater area of overlap than will
the other level. This independent variable is labeled the distribution
variable. All other distributional information items, if presented,
will be controlled as constants.
The cost structure. The effects of the cost structure of the
possible decision outcomes are studied by manipulating a singular in
formation variable. The manipulation of this variable involves the
specification of two levels of decision outcome relationships (in terms
of incurred costs). This independent variable is labeled the cost
variable. Since the importance of the discrimination task increases as
the costs associated with decision outcomes that involve decision errors
become unequal, the different levels of the cost variable will be
associated with different decision error costs. One level of the cost
variable is structured in favor of more variance investigations and the
other level of the cost variable is structured in favor of fewer vari
ance investigations. The only other singular information variable, the
actual results of the physical process and its cost variance, is the
primary experimental stimulus. This information item will be a random
variable whose distributions, given either of the two states of nature,
will be normally shaped with parameter values defined by the appropriate
level of the distribution variable.
31
Independent variables interaction. The research design of this
study is a factorial design in which the above three independent vari
ables, each at two levels, are fully crossed, thus producing eight (23)
independent variables combinations or treatments. The factorial
combination of the different levels of the independent variables adds
power to the research design by permitting the examination of the
effects of interactions upon the dependent variables.
The process variables
The effects of an individual's information processing and learn
ing efficiency are studied using certain observation variables measured
on a continuous (ratio) scale. Continuous measurement facilitates the
use of these variables as both independent and dependent variables.
When used as independent variables the measures are treated as random
components rather than as discrete classification levels. The effects
are studied using three major types of variables: 1) individual de
cision model sensitivity (relative effect of a variable response range),
2) individual decision criteria (relative effect of conservative ad
justment), and 3) relative initial decision anchor and final decision
anchor relationships. The first two types of variables are derived
from model parameters within the theory of signal detection.
A summary of the general research design
The general research design is depicted in Table 1. The design
is presented in terms of the dependent and independent variables to be
included within the research. More specific extensions arid
32
TABLE 1
GENERAL RESEARCH DESIGN IN TERMS OF EXPERIMENTAL VARIABLES
Dependent Variables
Independent Variables
Individual
LongRun
Decision
Efficiency
Individual
Information
Processing and
Learning
Efficiency
Contents of the available infor
mation set
X
X
Structure of the decision situation:
Statistical relationships
X
X
Decision outcome relationships
X
X
Individual information processing
and learning efficiency
X
Note: An "X" within a cell indicates that the relationship of the
variables concerned are included within the experimental design.
33
operationalizations of these concepts are discussed in a later section
of this chapter. The objective at this point is to summarize in general
terms the relationships which will be included within the experimental
design.
General Research Methodology
Research methodology pertains to the general procedures or
methods employed in conducting research. Two general methods are
employed in this research simulation and laboratory experimentation.
The major objective of the simulation is to produce hypotheses which
will predict the behavior of human decision makers within the decision
situation assumed by the simulation. The simulation is based upon
assumptions derived from the conceptual development and from the gen
eral task environment of the laboratory experimentation. These
assumptions and the task environment are discussed in greater detail
in a later section of this chapter. The major objective of the
laboratory experiment is to test the conceptual development (through
the hypotheses derived by the simulation) of the effects of the vari
ous independent variables on the various observation variables.
The general and specific research design employed in the labora
tory experiment is the same as that employed within the simulation.
The major difference between the simulation and the laboratory experi
ment is the use of human subjects. This difference creates two major
sources of incompatability betv/een the simulation and the laboratory
experiment. First, the simulation makes certain assumptions concerning
human behavior and applies these assumptions consistently. Within
34
the laboratory experiment such behavior is not necessarily applied
consistently. Consequently, greater variance of results is expected
within the laboratory experiment than within the simulation. Second,
it should be noted that certain variables are largely affected by
individual attributes. Without a theory of the effect of individual
attributes any simulation of these variables would be arbitrary.
Accordingly, not all of the experimental variables are included in the
simulation. Hypotheses concerning the variables not present within
the simulation either are derived from limited concepts concerning the
effects of individual attributes or are stated in an exploratory manner
(no expected difference).
General Experimental Environment and Task
The standard cost variance investigation situation studied within
this research is set in an environment of a manufacturing company.
More specifically, the subjects are asked to assume the role of the
operational manager of an assembly department which assembles a single
product, a metal folding chair. The operating efficiency of the
assembly department is determined completely by the labor efficiency of
the assembly workers.
Each subject receives a sequential series of standard cost vari
ance reports and is asked for each report to decide whether to investi
gate or not to investigate the reported labor efficiency variance.
The efficiency of a subject's decision performance and the amount of
payment he will receive for participating in the experiment is based
upon the total investigation decisions cost which he incurs over the
series of variance reports.
35
Each standard variance report is concerned with the results of a
single joborder to produce a constant number of chairs and reports
only aggregate (overall assembly department) results. Each report
contains the aggregate standard assembly time allowed per chair, the
actual assembly time incurred per chair, the overall labor efficiency
variance per chair, the total number of chairs produced, and the costs
associated with each possible decision in combination with each
possible state of nature. All time units are presented in minutes.
The singular information contained in a variance report is independent
of that contained in previous variance reports.
The experiment is conducted in two phases a training phase
and an experimental phase. The training phase consists of three
contiguous sessions in which the subject learns his role and presumably
develops his decision strategy. Performance feedback is given at the #
completion of each training session. The experimental phase consists
of a single session in which the subject receives a series of variance
reports similar to those presented in his training session. In this
phase no performance feedback is given until after the completion of
the entire experiment. The subject is paid according to his performance
in the experimental phase.3
Operationalization of Variables
This research employs the following three decision situation
independent variables, each measured using a discrete classification:
3Greater detail concerning the experimental environment, task, and
procedures is presented in Chapter V.
36
1) the information variable, 2) the distribution variable, and 3) the
cost variable. As indicated previously, each is varied across two levels.
The individual process variable types, measured on a continuous scale,
include the following: 1) individual decision model sensitivity, 2)
individual decision criteria, and 3) relative initial and final decision
anchor relationships. The major dependent variable is the individual
longrun decision efficiency (in terms of incurred costs). The
following discussion describes each of the variables or variable types
as they are employed in this study.
The information variable
The first level, labeled Jl_, is derived from the set of infor
mation assumed to come from individual experience with the physical
system. It includes the following items: 1) the historically derived
portion of time in which the process has been found to fall in each of
the two states, 2) the assumption that the random variable of interest
(actual minutes incurred per chair or its associated standard vari
ance) is normally distributed for both states, 3) the lowest observed
value of the random variable, 4) the highest observed value of the
random variable, 5) the maximum costs associated with each state, and
6) the minimum costs associated with each state.
The second level of the information variable, labeled 12^, includes
additional distributional information. In addition to the six items
contained in the II information set, the following two items are
included: 1) the mean of the random variable within each state, and
2) the standard deviation of the random variable within each state.
37
The distribution variable
Manipulation of the distribution variable involves two factors:
1)the distributional parameters of each state, and 2) the statistical
relationship between the states. The two levels of this variable are
generated through a change in the variance and a change in the stan
dardized distance between the means of the two states. The first
level of the distribution variable, labeled Sl_, has the following
parameters and relationships:
1) y1 = 36.0 actual minutes incurred per chair;
2) crn = a =
3) u12 = y + 1.5a = 40.5 actual minutes incurred per chair,
where, y = the mean of the jth state of nature (incontrol = 1 and
* J
outofcontrol = 2) given the ith distribution level
(SI = 1 and S2 = 2);
a.. = the standard deviation of the jth state of nature given
' J
the ith distribution level; and
= the standard deviation common to both states of nature
given the ith distribution level.
The second level of the distribution variable, labeled S2^, has
the following parameters and relationships:
1) y = 36.0 actual minutes incurred per chair;
2) 021 = 022 = 09 = 5.0 actual minutes incurred per chair; and
3) y2 = y21 + 1.80 = 45.0 actual minutes incurred per chair,
where y^., 0^, and 0^ are defined the same as in the SI level. The
third and fourth statistical moments of each state within each dis
tribution level are uncontrolled except that their deviations from a
normal distribution are minimized.
38
The cost variable
Given the two states of nature (the realization of which is un
known to the individual) and two possible decisions, there follows that
two types of errors can be made in reaching a decision. The first type
of decision error, labeled type A, is the decision to investigate the
physical process when the incontrol state exists. The second type of
decision error, labeled type B, is the decision not to investigate when
the outofcontrol state exists. The marginal cost of either error
type equals the cost that would have been incurred had the correct
decision been made minus the cost that was incurred by the incorrect
decision. Thus, the marginal costs of the two decision error types,
labeled MC^ and MCg, are:
= C(decision=not investigate  state=incontrol) 
C(decision=investigate  state=incontrol), and
MCg = C(decision=investigate  state=outofcontrol) 
C(decision=not investigate  state=outofcontrol),
where C( ) represents the decision cost for the situation within the
parentheses.
When the marginal cost of a type A error equals the marginal
cost of a type B error the cost structure should be ignored when making
an investigation decision. Of greater interest are situations in which
the marginal error costs not equal. In the present study, each level
of the cost variable takes one of the following forms: 1) the
marginal cost of a type B decision error equals three times the marginal
cost of a type A error (labeled level CJ_), and 2) the marginal cost
of a type A error equals three times the marginal cost of a type B
error (labeled level C2).
39
Individual decision model sensitivity variables
The sensitivity of the individual's decision model relative to the
decision situation is measured using the TSD parameter d. The theo
retical definition of d is:
d = (p2 Pj)/ = zi z2
where y. = the mean of the jth state of nature (incontrol = 1 and
outofcontrol = 2);
a = the common standard deviation of the states of nature; and
z. = the value of the normal distribution function associated
J
with the jth state of nature and any decision cutoff value
common to both states.
An empirical estimate of the d1 for an individual, labeled d 1,
is obtained using the individual's conditional probabilities P(decision=
investigate  state=outofcontrol) and P(decision=investigate  state=
incontrol) to calculate a subjective z2 and z2> As previously pointed
out, d' is relative to the decision situation. In particular, d is
relative to the distribution variable. To gain comparability across
situations the following measure is used:
DN, = di / d
where d^ is generated using optimal model k. The measure decreases
from a value of one as the result of several factors: 1) the individual
is inconsistent in his use of his cutoff value (i.e., there exists a
range around his cutoff value within which decisions are not made
using a strict relation to this cutoff value) or the individual makes
one or more temporary processing errors, and 2) the individual
utilizes more than one cutoff value.
40
Based upon a subjective analysis of each individual's decisions,
the measure DN^ can be adjusted for the effects of using multiple cut
off values. Defining d^a to be the individual's decision model sensi
tivity with the effects of multiple cutoff values eliminated:
DNAi = d\a / d'k
The difference DNA^ DN^ approaches zero as the effects of multiple
cutoff values decreases and becomes zero when the individual uses a
single cutoff value.
Individual decision criteria variables
The criteria the individual adopts in making his decisions are
measured using the TSD parameter 3. The theoretical definition of 3 is:
3 = d>(z2) / (z1)
where ( ) denotes the normal density function for the point in the
parentheses and 2. is defined the same as for the d'. variable.
J
An empirical estimate for the 3 of an individual, labeled 3 ,
is obtained using the z, and z2 values associated with the individual's
conditional probabilities employed in estimating dl. The measure 3..
is relative to the decision situation. In particular, it is relative
to those variables which affect the point on the decision axis which
the individual selects as his investigation decision cutoff value. A
measure of individual criteria comparable across situations is defined
as:
BN. = 3. / 3.
i i k
where 3^ is generated using optimal model k. The measure approaches a
value of one as 3.. approaches 3^ (i.e., as the individual's cutoff
decision value approaches the optimal model's cutoff decision value).
The measure approaches either a value of zero or positive infinity as
the result of several factors: 1) the individual does not process
properly the effects of the relative costs of the two types of decision
errors, 2) the individual does not process properly the effects of the
relative frequencies of the two states, and 3) the individual uses more
than one cutoff value.
Using the same subjective analysis as that used in adjusting the
DN measure, the BN^ measure can be adjusted for the effects of multipl
cutoff values. Defining B^ to be the individual's criteria measure
with the effects of multiple cutoff values eliminated:
BNA, B* / Bk
The difference BNA^ BN^ approaches a value of zero as the effects of
multiple cutoff values decrease and becomes zero when the individual
uses a single cutoff value.
Another measure derived from the BN measure can be considered
a measure of individual conservatism. In this study, conservatism
refers to incomplete adjustment from an initial decision anchor towards
the optimal cutoff value. Nonconservatism refers to a more that
complete adjustment from the initial decision anchor past the optimal
cutoff value. The extent of conservatism is measured by the relative
distance between the individual's cutoff value and the optimal model's
cutoff value. Since the measure is dependent on the direction of ad
justment it is conditional upon the level of the cost variable. Using
BNC.Â¡ to denote tne extent of an individual's conservatism:
BNC.Â¡ = (3? B<) / B^ given the Cl cost level; and
BNC.Â¡ = (b^ Bj) / B^ given the C2 cost level.
42
BNC.J approaches either positive infinity or a value of one as the extent
of conservatism increases, approaches a value of zero as the extent of
conservatism decreases, and approaches either a value of negative one
or negative infinity as the extent of nonconservatism increases.
Initial and final decision anchor variables
The relative relationships between an individual's initial and
final decision anchors can be measured in terms of the relative linear
distance between these two decision anchors. A measure of the relative
adjustment for individual i, labeled RA^, is conditional on the direc
tion of adjustment along the relevant decision axis. This direction
of adjustment is in turn conditional on the level of the cost variable.
Given the Cl cost level the initial decision anchor is greater than the
optimal model cutoff value, and given the C2 cost level the initial
decision anchor is less than the optimal model cutoff value. The RA^
measure is defined as:
RAi = (EDVi 0DVk) / (TDVi 0DVk) ,given the Cl cost level;
RA.j = (0DVk EDV.j) / (0DVk TDV.Â¡) ,given the C2 cost level,
where EDV^ = individual i's final (experiment) decision anchor;
TDV. = individual i's initial (training) decision anchor; and
0DVk = optimal model k's final (experiment) cutoff value.
The RA.j measure has the following relationships: 1) if no
adjustment occurs between the training and the experiment phases the
measure will equal a value of one, 2) if the adjustment is in the wrong
direction (away from the optimal model's experiment cutoff value) the
measure will be greater than a value of one, 3) if the adjustment is in
43
the proper direction but incomplete the measure will be less than a
value of one but greater than a value of zero, 4) if the adjustment
is in the proper direction and is complete the measure will equal a
value of zero, and 5) if the adjustment is in the proper direction but
more than complete the measure will be less than a value of zero.
Individual longrun decision efficiency
A major dependent variable of interest in this research is the
cost incurred as a result of the individual's variance investigation
decisions. The experimental objective function for all decision
situations is to minimize these costs (labeled investigation decisions
costs). The individual's longrun decision efficiency is measured in
terms of his minimization of the investigation decisions costs summed
over all decisions.
Given a pair of decision situations which hold constant all of
i
the independent variables except for the cost variable,the correct
decision within each situation will lead to different investigation
decisions costs. Consequently, absolute investigation decisions costs
are not comparable between decision situations: a relative measure
must be obtained. One such relative measure involves the comparison of
the individual's investigation decision costs with that of an optimal
model which uses the same information as that available to the indi
vidual. Denoting such a measure G:
Gi: = / SMCk
where IC = individual i's investigation decision cost for decision j;
MC^j = optimal model k's investigation decision cost for decision
j; and
44
SMC^ = the sum of optimal model k's investigation decisions costs
over all decisions (m in number).
The G measure can be classified into one of three submeasures
' J
where the classification is dependent upon the algebraic sign of G^j.
The classifications are 1) G.Â¡ > 0 the individual's investigation
decision cost for decision j is greater than that of the optimal model,
2) G^j = 0 the individual's investigation decision cost for decision
j is equal to that of the optimal model, and 3) G < 0 the indi
* \J
vidual's investigation decision cost for decision j is less than that
of the optimal model.
Summing those G.s with identical algebraic signs, the summations
* 0
are defined as follows:
GP.j = the sum of those Gs with a positive algebraic sign;
GN^ = the sum of those Gs with a negative algebraic sign; and
GZ.j = the sum of those Gs with a value of zero.
It is easily shown that the following relation holds:
m
E G.. = GP, + GN, + GZ,
j=l J
where m equals the total number of decisions made by individual i. Since
the GZ.j summation equals zero, the sum of the G over all j = l,m
decisions, denoted G, is:
G = GP + GN.
i i i
Conceptually, GP^ is the relative additional cost of those de
cisions made by individual i which are greater in value than that of
the optimal model. The GN^ is the relative savings of those decisions
made by individual i which are less in value than that of the optimal
model.
45
The anchoring and adjustment process may occur over three training
sessions and one experiment session. Using the first training session
as an estimate of the initial decision anchor and the experiment
session as an estimate of the final decision anchor, there remains two
training sessions within which the adjustment process itself can be
analyzed. In particular, the effects of the pattern of adjustments
within the training sessions are expected to influence significantly
the magnitude of an individual's experiment Gj measure. The adjustment
process can be measured in the training sessions by computing the
individual's Gj measure at the completion of each session. The
measures, labeled TG^, TG.Â¡0, and TG.Â¡3, are defined in the same manner
as the G. measure discussed above (with the exception that they are
computed from the appropriate training session data instead of the
experiment session data). The differences between these training
measures reflect the effects of the individual's adjustments in the
training sessions. These differences are defined as follows:
DTGii = TGn TGi2
DTGi2 = TGi2 TGi3
where, DTGÂ¡j = the difference between the GÂ¡ measure for the jth and
the jth + 1 training sessions of individual i; and
TG^j = the G.j measure for the jth training session of indi
vidual i.
Negative DTGÂ¡jvalues indicate an increase in the TGjj measure between
training sessions, and positive DTGjj values indicate the reverse.
The effects of the directions of these adjustments should be
related to the effects of the final anchor on the individual's GÂ¡
measure (in the experiment). The effects of the adjustment measures
46
on tha experiment measure can be analyzed using a discrete classifica
tion of the direction of the adjustments. This classification of the
measure is accomplished using its algebraic sign. The second and
third training session results can be grouped into one of four
classes: PP(+,+), PM(+,), MP(,+), and MM(,). Subjects within
the MM classification have constantly increasing TGjj measures and
subjects within the PP classification have constantly decreasing TG^j
measures.
Hypotheses Formation
As previously discussed, the formation of the experimental
hypotheses is accomplished primarily using the method of simulation.
Two general types of simulations are performed simulation of optimal
model performances and simulation of subjective investigation decision
performances. The first part of this section discusses the assumptions
and presents the results of the simulation of optimal model perform
ances. The second part of this section discusses certain assumptions
derived from the conceptual development that form the basis for the
simulation of subjective investigation decision performances. It also
presents the results of this simulation and the derivation of the ex
perimental hypotheses.
Simulation of Optimal Model Performances
The decision rule of the optimal model (given the objective func
tion of minimizing incurred costs) employed in this research is to
investigate the reported labor efficiency variance when:
47
Equation 1
where x = the actual minutes incurred per chair;
out = the state of outofcontrol;
in
= the state of incontrol;
Vin9N = the cost associated with not investigating an incontrol
variance;
VÂ¡nsy = the cost associated with investigating an incontrol
variance;
V0Ut,Y = the cost associated with investigating an outofcontrol
variance; and
Vout,N = the cost associated with not investigating and
outofcontrol variance.
The manipulation of the cost variable produces a constant cost ratio
at each of the two levels of this variable. When the cost variable
level is Cl the constant cost ratio is equal to onethird, and when the
cost variable level is C2 the constant cost ratio is equal to three.
The optimal model investigation decision rule expressed in equation 1
can be restate as:
LRq > PR.j CR Equation 2
where LRQ = the likelihood odds of the reported labor efficiency
variance being outofcontrol (P(xout)/P(xin));
PR.j = the prior odds of the incontrol state (P(in)/P(out)); and
CR = the constant cost ratio associated with the appropriate
level of the cost variable.
Assuming that both states of nature are normally distributed with equal
variance the .optimal model investigation decision rule expressed in
48
equation 2 can be restated in the following mathematical terms:
exp (x y2)2/2cr2
: > PRi CR
exp (x \il)2/2a2
where x = actual minutes incurred per chair;
Vj = the mean of the incontrol state;
y2 = the mean of the outofcontrcl state;
a = the common standard deviation of both states; and
exp = the notation for the exponential funcition (e).
If the above equation was changed to an equality and solved for x the
result would be the optimal cutoff value in terms of actual minutes
incurred. This solution takes the general form:
G2(ln(PRÂ¡) + ln(CR))
x = + 0.50(y2 + y,) Equation 3
y2 yi
where x = the optimal cutoff value in terms of actual minutes incurred;
and
In = the notation for the natural logarithm function (loge).
With the decision rule in the form of equation 3 the parameters required
to fit the optimal model become identifiable. These parameters include
the mean of both states of nature, the common variance of the two
states, the prior odds of the incontrol state, and the appropriate
value of the cost ratio.
With knowledge of the parameters required to fit the optimal
model the criteria employed for the simulation of optimal model per
formances can be discussed. The first criterion is that information
available to the optimal model be the same as the information avail
able to the individual. The information available to the individual
is manipulated by the levels of the information variable. Within the 12
49
information level the available information set contains statistical
estimates of all the parameters required to fit the optimal model.
However, within the II information level the available information
set does not contain all the required parameter estimates. Therefore,
within the II information level the optimal model must use the training
sessions to estimate the missing parameters. The parameter estimates,
both given and estimated, are presented in Table 2.
The second criterion is that the labor efficiency variance re
ports presented to the subjects in the experiment be the same reports
used in the simulation of optimal model performances. The appropriate
parameter estimates presented in Table 2 were used in the optimal
model decision rule expressed by equation 2 to simulate optimal model
investigation decisions. Table 3 presents some performance results
of this optimal model simulation.
Simulation of Subjective Investigation Decision Performances
This section discusses assumptions derived from the conceptual
development that form the basis for the simulation of subjective investi
gation decision performances. It also presents the results of this
simulation and the derivation of the experimental hypotheses.
Assumptions of simulation
The first assumption employed for the simulation of subjective
investigation decision performances is that the subjects will behave
as if they use the anchoring and adjustment heuristic during the
training phase. The anchoring and adjustment heuristic proposes that
TABLE 2
ESTIMATES OF THE PARAMETERS REQUIRED TO FIT THE OPTIMAL MODEL GIVEN THE VARIOUS CONDITIONS
Information
Experimental
Variable 11
Parameters
Information
Variable 12
Actual Underlying
Data Structure Parameters
Cost
Parameter
Distribution Variable
Distribution Variable
Distribution Variable
Variable
Estimate
SI
S2
SI
S2
SI
S2
35.99
36.26
36.00
36.00
35.975
35.943
y2
40.21
44.24
40.50
45.00
40.492
44.995
Cl
al
8.88
29.42
9.00
25.00
9.157
25.060
al
7.38
19.32
9.00
25.00
9.437
25.002
PRi
1.50
1.50
1.50
1.50
1.500
1.500
CR
0.33
0.33
0.33
0.33
0.333
0.333
C2a
CR
3.00
3.00
3.00
3.00
3.000
3.000
aThe parameter estimates
given the Cl cost level.
for u1 ,y2,cj2
,and PR.Â¡
given the C2
cost level are
the same as those
TABLE 3
RESULTS OF THE SIMULATION OF OPTIMAL MODEL PERFORMANCES GIVEN THE VARIOUS CONDITIONS
k Information
Distribution Cost
P(investigate
outofcontrol)
P( investigate
incontrol)
Optimal Cutoff
Value (actual
minutes incurred)
dk
6k
1
11
SI
Cl
0.875
0.383
36.90
1.447
0.539
2
11
SI
C2
0.400
0.033
41.29
1.581
5.206
3
11
S2
Cl
0.875
0.300
38.70
1.674
0.592
4
11
S2
C2
0.425
0.017
45.88
1.939
9.453
5
12
SI
Cl
0.900
0.400
36.86
1.535
0.454
6
12
SI
C2
0.400
0.033
41.26
1.581
5.206
7
12
S2
Cl
0.900
0.300
38.57
1.806
0.504
8
12
S2
C2
0.525
0.033
44.68
1.897
5.364
cn
52
a subject will select an initial anchor as a first decision approxi
mation and will adjust this decision anchor toward an optimally correct
point as he learns from his experiences with the states of nature.
However, the individual's adjustment process will not be sufficient:
i.e., he will tend to approach the optimally correct point but will not
adjust completely to that point.
The second assumption is that the location of the initial de
cision anchor on the actual minutes incurred continuum (axis) will be
conditional upon the level of the distribution variable. The reason
for this is that the initial decision anchor is expected to be located
at a central point between the means of the two states. Since the
mean of the outofcontrol distribution shifts with the level of the
distribution variable, the central point between the means of the two
states also shifts with the level of the distribution variable. The
initial decision anchor used in the simulation of subjective investi
gation decision performances is the geometric intersection point of the
two states' distribution curves. This particular initial decision
anchor is used because it is the exact central point between the means
of the two states. However, it is selected for operational purposes
and is not necessary to the conceptual development. Any point of
central tendency would be satisfactory.
The final assumption is that the subjects' adjustment process
will be approximately equal over the various treatment conditions. The
adjustment process is defined as a linear movement along the actual
minutes incurred axis from the initial decision anchor towards the
appropriate optimal model decision cutoff. The magnitude of the linear
movement used in the simulation of subjective investigation decision
53
performances is 50 percent of the distance between the initial decision
anchor and the appropriate optimal decision value. The 50 percent
adjustment value, selected for operational purposes, is completely
arbitrary and is not necessary to the conceptual development. The only
restriction is that it be less than 100 percent. Furthermore, the
general assumption of equal adjustment over the various conditions is
not necessary to the conceptual development: the objective of this
general assumption is to facilitate a simple operationalization of the
simulation.
Training phase simulation and hypotheses formation
The first stage of the simulation involves simulating the
anchoring and adjustment process in the training sessions for each
combination of independent variables. A simulated subject sample size
of one is used for each condition (resulting in a total simulated sub
ject sample of eight). The results of this first stage simulation are
presented in graphical form in Figure 2. This figure depicts the
simulated initial decision anchors (points A), the simulated final
decision anchors (points B), and the simulated optimal cutoff values
(points C) for each of the eight combinations of independent variables.
The assumptions used in simulating the training phase can be
investigated using the results of the experimental training phase. The
assumptions are i) that the subjects will behave as if they used the
anchoring and adjustment heuristic, 2) that the initial decision anchor
will be located centrally between the means of the two states and will
be dependent on the level of the distribution variable, and 3) that the
II Information Level
Cost
Cl
Cost
C2
Distribution
SI
1 1 i
36.90 37.575 38.25
+
39.77 41.29
Distribution
S2
C B A
h
38.7 39.6 40.50
43.19
H
45.88
Distribution
SI
12 Information Level
Cost
Cl
B A
36.86 37.555 38.25
Cost
C2
39.755 41.26
C B A
Distribution 1 } 1
S2 38.57 39.535 40.50
+
42.59
H
44.68
Note: All scales are actual minutes incurred per chair produced.
FIGURE 2
SIMULATED INITIAL DECISION ANCHORS (POINTS A), FINAL DECISION
ANCHORS (POINTS B), AND OPTIMAL CUTOFF VALUES (POINTS C)
55
adjustment process will be approximately equal over the various con
ditions. The location of the initial decision anchor can be investigated
by examining the relationship between the individual's initial cutoff
value (in the first training session) and the intersection point of
the appropriate two distribution curves (the TDV.Â¡ measure). These
intersection points are conditional upon the level of the distribution
variable. Given the SI distribution level the intersection point is
38.25 actual minutes incurred, and given the S2 distribution level the
intersection point is 40.5 actual minutes incurred (see Figure 2 for a
graphical presentation of these points). The expected relationship is
that the mean initial decision anchor (TDV.) of the individuals, given
a level of the distribution variable, will not be significantly differ
ent from the intersection point of the appropriate two distribution
curves.
The assumption of approximately equal adjustment over the various
conditions can be investigated by examining the relationship between
the individual's adjustment over the complete experiment and the total
adjustment that would be required to reach the optimal model's decision
cutoff value. A measure of the relative adjustment for individual i,
RA.j, is conditional upon the direction of adjustment along the relevant
decision axis (i.e., the actual minutes incurred per chair produced).
The expected relationship is that the mean RA^ will not differ signifi
cantly between the levels of the various conditions.
Given the above simulation and discussion the following hypo
theses can be derived:
HI.la (TDVjISl) = 38.25.
The mean initial decision anchor given the SI distribution level
56
will equal the intersection point of the two distribution curves
within that level.
HI.lb (TDVi S2) = 40.5.
The mean initial decision anchor given the S2 distribution level
will equal the intersection point of the two distribution curves
within that level.
HI.2a (TDV.]I1) = (157^12).
The mean initial decision anchor given the II information level
will equal the mean initial decision anchor given the 12 infor
mation level.
HI.2b (TDViiCl) = (TViÂ¡C2).
The mean initial decision anchor given the Cl cost level will
equal the mean initial decision anchor given the C2 cost level.
HI.3a (RAi111) = (RAi112).
The mean relative adjustment given the II information level will
equal the mean relative adjustment given the 12 information level.
HI.3b (RA^Sl) = (RAi Â¡S2).
The mean relative adjustment given the SI distribution level will
equal the mean relative adjustment given the S2 distribution
level.
HI.3c (RAiCl) = (RAC2).
The mean relative adjustment given the Cl cost level will equal
the mean relative adjustment given the C2 cost level.
Individual decision model sensitivity hypotheses formation
The measures of individual decision model sensitivity should
generally be unaffected by the independent variables. These measures
57
have a greater relationship with individual decision processes than
with the specific conditions of a general decision situation. The
individual decision model sensitivity measure di should have a re
lationship with the distribution variable only. The theoretical d1
measure under the SI distribution level is 1.5 and under the S2 dis
tribution level is 1.8. Thus, the average dl under the S2 distribu
tion level should be greater than under the SI distribution level.
The DNA^ measure should have no systematic relationships with
the three independent variables. This measure of the relative de
viation of the individuals decision model sensitivity from that of
the optimal model's generally is due to individual decision incon
sistencies and temporary processing errors. If individuals are
randomized into the specific decision situations there are no a
priori reasons for expecting significant differences in this measure
due to the independent variables.
The difference between DNA^ and DNÂ¡ measures the effects of
multiple decision anchors upon the individual's relative decision
model sensitivity. Relationships between this difference and the in
dependent variables depend upon whether the independent variables are
related to the causes underlying an individual's use of more than one
cutoff value. A possible explanation involves the distribution vari
able. As the variance of the incontrol state increases the absolute
distance between the lowest values of the random variable of interest
(actual minutes incurred) and the mean of the incontrol state in
creases. In turn, as this distance increases subjects may be more
inclined to perceive that a second outofcontrol distribution overlaps
the lower range of the incontrol state. If such perceptions underly
58
the use of multiple cutoff values, the average difference between DNA.Â¡
and DN.j should differ between the two levels of the distribution vari
able. A second possible explanation involves the cost variable. As
the individual's final cutoff value increases, the absolute distance
between the individual's final cutoff value and the lowest values of
the random variable of interest increases. In turn, as this distance
increases subjects again may be more inclined to perceive that a sec
ond outofcontrol distribution overlaps the lower range of the
incontrol state. If such perceptions underly the use of multiple
cutoff values, the average difference between DNA and DN.Â¡ should
differ between the two levels of the cost variable.
Based on the above discussion the effects of the independent
variables on the individual decision model sensitivity measures can
be hypothesized as follows:
H2.la (dj SI) < (dj S2).
Those subjects within the SI distribution level will have
significantly smaller mean djs then those subjects within the
S2 level.
H2.lb (di Â¡II) = (d! Â¡12).
Those subjects within the II information level and within the
12 information level will have equal mean d^s.
H2.1c (d* Cl) = (d! C2).
Those subjects within the Cl cost level and within the C2 cost
level will have equal mean d^s.
H2.2a (DNAilll) = (DAj Â¡ 12).
Those subjects within the II information level and within the 12
information level will have equal mean DNAÂ¡s.
59
H2.2b (DNAj  SI) = (DNAj S2).
Those subjects within the SI distribution level and within the
S2 distribution level will have equal mean DNAjS.
H2.2c (DA,1 Cl) = (DAj IC2).
Those subjects within the Cl cost level and within the C2 cost
level will have equal mean DNAÂ¡s.
H2.3a (DNAjDNj Â¡SI) < (DNAÂ¡DNj S2).
Those subjects within the SI distribution level will have
significantly smaller mean DNAÂ¡DNÂ¡s than will those subjects
within the S2 distribution level.
H2.3b (DNAjDNjCl) <(DNAiDNiC2).
Those subjects within the Cl cost level will have significantly
smaller mean DNAÂ¡DNÂ¡s than will those subjects within the C2
cost level.
Individual decision criteria simulation and hypotheses formation
The variables which affect the BNj measure should be those factors
related to the individual's selection of a cutoff value. The most
significant variable affecting the individual's cutoff value should be
the cost variable. Within this variable the initial decision anchor
given the Cl level is much closer to the optimal cutoff value than is
the initial decision anchor given the C2 level. Consequently, the BNj
measure should be closer to a value of one under the Cl level than
under the C2 level.
The difference between the BNAÂ¡ and the BNj measures should
relate to the same variables as those discussed for the difference
60
between the DNA.Â¡ and DN^ measures. The two possible explanations in
clude the distribution variable and the cost variable.
Theoretically, the more extreme the optimal cutoff value relative
to a central point between the distributions of the two states, the
higher should be the individual conservatism. The decision situations
with the most extreme optimal cutoff values are those within the C2
cost level; the situations with the least extreme optimal cutoff values
are those within the Cl cost level. Consequently, the BNCj measure
should be larger within the C2 cost level than within the Cl cost level.
The simulation and assumptions used in developing the training
phase hypotheses can be extended to enable formation of hypotheses con
cerning the individual decision criteria. The earlier simulation de
rived the following final decision cutoff values: 1) 37.555 actual
minutes incurred for the Cl cost level given the 12 information level
and the SI distribution level, and 2) 39.755 actual minutes incurred
for the C2 cost level given the 12 information level and the SI dis
tribution level. Given these cutoff values a f(e^) measure can be
calculated directly (the 3^ measure is used rather than the en measure
due to the assumption of a single cutoff value). The functional
notation, f( ), is employed to indicate these estimates are based upon
a simulation rather than upon empirical observation. For this simu
lation the f(3?)s are as follows:
f(3*Cl,Sl,I2) = 4>(zout) /
f(B*C2,Sl,I2) = (zQut) / *(z.n) = 2.11774
where ( ) indicates the value of the normal density function at the
point in parentheses. Given the f(B^) measures the f(BNA^) measures
can be calculated. For this simulation the f(BNA^)s are as follows:
61
f(BNAiCl,SI,12) = f(B) / f(6k) = 1.41898
f(BNAiC2,Sl,I2) = f(e?) / f(fJk) = 0.47017
where f($k) is the simulated measure at the cutoff value of the kth
optimal model.
This simulation can be extended to the S2 level of the distribu
tion variable given the 12 information level and can be extended to
both the SI and S2 levels of the distribution variable given the II
information level. The f(BNA) measures obtained under the 12 infor
mation and S2 distribution conditions are as follows:
f(BNAiJCl,S2,12) = 1.42318
f(BNAiÂ¡C2,S2,I2) = 0.47300
The f(BNA) measures given the II information level are as follows:
f(BNAiCl,Sl,Il) = 1.40126
f(BNA 1C2,SI,11) = 0.46993
f(BNAiCl,S2,Il) = 1.38273
f(BNA.C2,S2,Il) = 0.38094
Based on these f(BNA) measures, hypotheses can be derived con
cerning the effects of the independent variables upon the BNA. measure.
The f(BNA^) measures can be averaged over the appropriate conditionals
to determine these effects. Averaging over all conditionals except the
information variable gives the following results:
f(BAiIII) = 0.90871
f(BNAi12) = 0.94633
Although there is a difference in the f(BNA) measures due to the infor
mation variable this difference is small compared to the standard error
of these estimates (the difference divided by the standard error is
0.03762/0.39157 = 0.0961). Therefore, a significant effect due to the
information variable would not be expected.
62
Averaging over all conditionals except the distribution variable
gives the following results:
f(BAÂ¡ 1 SI) = 0.94009
f(BNAiS2) = 0.91496
Again, the difference in the f(BNAj) measures due to the distribution
variable is small compared to the standard error of the estimates (the
difference divided by the standard error is 0.02513/0.39174 = 0.C642).
A significant effect due to the distribution variable would not be
expected.
Averaging over all conditionals except the cost variable gives
the following results:
f (BAj Cl) = 1.40654
f(BAÂ¡ C2) = 0.44851
The difference in the f(BNAj) measures due to the cost variable is
large compared to the standard error of the estimates (the difference
divided by the standard error is 0.95803/0.02436 = 39.3308). Con
sequently, a significant effect would be expected for the cost
variable.
Another implication drawn from the f(BNAj) measures concerns the
homogeneity of variance for the measures given different levels of the
cost variable. The variance for the f(BNAÂ¡  Cl) is .00034 and the vari
ance for the f(BNAÂ¡ C2) is .00203. Using the F test for equal variances,
F=5.9344 (3 and 3 d.f.) which indicates the variances may not be equal.
The variance of (BNAÂ¡[C1) would be expected to be less than the vari
ance of (BNAjÂ¡C2).
This simulation can be extended to enable the formation of
hypotheses concerning the effects of the independent variables on the
63
conservatism measure BNCÂ¡. This extension employs the f(Bj) and fU^)
measures used above. The results of this extended simulation averaged
over all conditionals except for the information variable are as follows:
f(BNCi  II) = 0.48328
f(BC.Â¡I2) = 0.47475
The difference in the f(BNC^) measures due to the information variable
is small compared to the standard error of the estimates (the differ
ence divided by the standard error is 0.00853/0.06390 = 0.1335). A
significant effect due to the information variable would not be expected.
Averaging over all conditionals except for the distribution
variable gives the following results:
f(BCiSI) = 0.47004
f (BNCiS2) = 0.48799
The difference in the f (BNC.Â¡) measures due to the distribution vari
able is small compared to the standard error of the estimates (the
difference divided by the standard error is 0.01795/0.06357 = 0.2824).
A significant effect due to the distribution variable would not be
expected.
Finally, averaging over all conditionals except for the cost
variable gives the following results:
f(BC_Â¡ i Cl) = 0.40654
f(BCiC2) = 0.55149
The difference in the f(BC.Â¡) measure due to the cost variable is large
compared to the standard error of the estimates (the difference divided
by the standard error is 0.14495/0.02436 = 5.9503). Consequently, a
significant effect would be expected for the cost variable.
64
The above discussion concerning the individual decision criteria
measures can be summarized as follows:
H3.1a (B.Â¡lll) = (BAj 112).
The mean BNAj of those subjects within the II information level
and within the 12 information level will be equal.
H3.lb (BilSl) = (BAÂ¡  S2).
The mean BNAÂ¡ of those subjects within the SI distribution level
and within the S2 distribution level will be equal.
H3.1c (BAj  Cl) > (BAÂ¡  C2).
Those subjects within the Cl cost level will have a significantly
larger mean BNAj than will those subjects within the C2 level.
H3.2 a2(BNAj [Cl) < a2(BNAÂ¡  C2).
The BNAÂ¡ of those subjects within the Cl cost level will have a
significantly smaller variance than will the BNAÂ¡ of those sub
jects within the C2 cost level.
H3.3a (BCj Â¡II) = (BCj 112).
The mean BNCÂ¡ of those subjects within the II information level
and within the 12 information level will be equal.
H3.3b (BCjSI) = (BCjS2).
The mean BNCj of those subjects within the SI distribution level
and within the S2 distribution level will be equal.
H3.3c (BCjCl) < (BCj j C2).
The mean BNCj of those subjects within the Cl cost level will be
significantly smaller than the mean BNCj of those subjects within
the C2 level.
65
H3.4a (BNAiBNiSl) < (BNA.jBN.Â¡S2).
The mean BNA.BN^ of those subjects within the SI distribution
level will be significantly smaller than the mean BNA^BN^ of
those subjects within the S2 distribution level.
H3.4b (BNA.BN.C1) <(BNAiBN^C2).
The mean BNA^BN^ of those subjects within the Cl cost level
will be significantly smaller than the mean BNA.BN. of those
subjects within the C2 cost level.
Individual longrun decision efficiency simulation and hypotheses
The simulation and assumptions used in developing the training
phase hypotheses enable the formation of hypotheses concerning the
G.j variable. The graph presented in Figure 3 depicts the relations of
the values derived within the training phase simulation given the
condition (12,SI). The interval between the final cutoff value and
the appropriate optimal model cutoff value represents the areas
associated with the 6. measure. For the Cl cost level the area of the
interval under the incontrol distribution is the area associated with
the GN. measure; the area of the interval under the outofcontrol
distribution is the area associated with the GP. measure. These areas
are the leftmost shaded areas in Figure 3. For the C2 cost level the
area of the interval under the outofcontrol distribution is the area
associated with the GN. measure; the area of the interval under the
incontrol distribution is the area associated with the GP measure.
These areas are the rightmost shaded areas in Figure 3. The areas
under these curves for this simulation are as follows:
66
o
o
LD
o
LD
o
o
o
LO
LO
LD
LD
o
LO
o
CO
ID
C\J
LD
CM
'
LO
LD
r^
CO
cr>
o
H
CO
CO
CO
CO
CO
*3
FIGURE 3
GRAPH OF THE SIMULATION USED IN THE CONDITION
(12 INFORMATION, SI DISTRIBUTION)
67
Area(GNj Cl,SI,12) = 0.08483
Area(GPÂ¡ Cl,SI, 12) = 0.05048
AreaiGNiC2,S1,I2) = 0.19780
Area(GPiC2,Sl,I2) = 0.06549
These areas must be adjusted for the relative frequency differ
ences and the relative cost differences between the various conditions.
If the incontrol distribution is given a relative frequency weight of
one,then the relative frequency weight of the outofcontrol distribu
tion would equal twothirds (P(incontrol) = 0.60 and P(outofcontrol) =
0.40). The relative cost weights can be obtained directly from the
manipulation of the cost variable. Given the Cl cost level, errors
under the outofcontrol distribution equal three times the errors under
the incontrol distribution. Given the C2 cost level, errors under the
incontrol distribution are equal to three times the errors under the
outofcontrol distribution. The four areas associated with the
various GÂ¡ measures can be converted into relative relations by taking
into account both of these relative weights (frequency and cost). These
relative relations are as follows:
f(GN.Â¡  Cl,S1,12) = (0.08483) (1) (1) = 0.08483
f(GPj  Cl,SI, 12) = (0.05048) (2/3) (3) = 0.10096
f(GNiC2,Sl,I2) = (0.19780)(2/3)(1) = 0.13187
f(GPiC2,Sl,I2) = (0.06549) (1) (3) = 0.19647
The difference between the relative relations of GP.Â¡ and GNj
given a level of the cost variable indicates the relative effect of
these intervals upon the Gj measure. The differences for this simu
lation are as follows:
68
f(6iCl,Sl,I2) = f(GPiC1,S1,12) f(GN^C1,S1,I2)
= 0.10096 0.08483 = 0.01613
f(Gi C2,S1,12) = f(GPi C2,S1,12) f(GN .C2,S1,12)
= 0.19647 0.13187 = 0.06460
This simulation can be extended to the S2 level of the distribu
tion variable given the 12 information level and can be extended to
both the SI and S2 distribution levels given the II information level.
The relative relations of the G measure obtained under the 12 infor
mation and S2 distribution conditions are as follows:
f(GiCl,S2,I2) = 0.07596 0.06385 = 0.01211
f(GiC2,S2,I2) = 0.15741 0.10639 = 0.05102
The relative relations for the G.Â¡ measure given the II information
level are as follows:
f(GjC1,S1,I1) = 0.09942 0.08230 = 0.01712
f(GiC2,S1,II) = 0.19629 0.13146 = 0.06483
f(G1Cl,S2,Il) = 0.07246 0.05884 = 0.01362
f(GiC2,S2,Il) = 0.15342 0.14078 = 0.01264
Based upon these relative relations hypotheses can be derived
concerning the effects of the independent variables on the G^ measure.
These relations can be averaged over the appropriate conditionals to
determine the effects. Averaging over all conditionals except the
information variable gives the following results:
f(GiII) = 0.02705
f(CTj 112) = 0.03596
Although there is a difference in the f(G) due to the information vari
able,this difference is small compared to the standard error of the
estimates (the difference divided by the standard error is
69
0.0089/0.01808 = 0.49226). Therefore, a significant effect due to the
information variable would not be expected.
Averaging over all conditionals except the distribution variable
gives the following results:
f((^ SI) = 0.04152
f(G.S2) = 0.02235
Although the difference between these measures is much larger than that
above, this difference is only slightly larger than the standard error
of the estimates (the difference divided by the standard error is
0.01832/0.01658 = 1.10495). The direction of the f(G^) differences
would be expected to be as predicted by the simulation but may not be
significant.
Averaging over all conditionals except the cost variable gives
the following results:
f(G.Cl) = 0.01475
f(G.C2) = 0.04827
The difference between these measures is large compared to the
standard error of the estimates (the difference divided by the standard
error is 0.03353/0.00951 = 3.5258). Consequently, a significant effect
would be expected for the cost variable.
These relative relations can be averaged over all but pairs of
conditionals. Such measures could indicate the presence of interaction
effects of the independent variables. The results of averaging the
pairwise conditionals indicate only one interaction of possible
significance the distribution by cost interaction. The f(G^) measures
conditional to these variables are as follows:
f (&.SI,Cl) = 0.01663
70
f((xSl,C2) = 0.06471
f(GiS2,Cl) = 0.01287
f(GiS2,C2) = 0.03185
A graphic representation of this interaction is presented in Figure 4.
The difference between the two f(G.Cl) measures is much smaller than
the difference between the two f(^C2) measures. The differences are
0.00376 and 0.03286, respectively. This would indicate that the
distribution variable has a significant effect only when given the
C2 cost level. Such q result would explain the larger but not signifi
cant effect of the SI distribution level on the f(G^S ) measures.
Other implications which can be derived from these relative
relations involve relationships between f(GP) and f(GN^) measures.
Two such relationships appear to be of possible significance. Both
involve the ratio f(GP^)/f(GN^) (i.e., the effect of additional errors
relative to the effect of additional correct decisions). The results
of the first relationship are as follows:
f(GPi/GNiC1) = 1.20480
f(GPi/GNiC2) = 1.38337
The difference between these measures is large compared to the standard
error of the estimates (the difference divided by the standard error is
0.17857/0.07621 = 2.34313). Consequently, the GP./GN. ratio is ex
pected to be larger given the C2 cost level than given the Cl cost
level.
The results of the second relationship are as follows:
f(GPi/GNiSl,Cl) = 1.19908
f(GPi/GNiSl,C2) = 1.48206
f(GP./GiNiS2,Cl) = 1.21057
71
f(G.)
Distribution
Variable
FIGURE 4
SIMULATED COST BY DISTRIBUTION VARIABLE INTERACTION ON f(GÂ¡)
72
f(GP j/GNÂ¡ S2,C2) = 1.28465
The difference between the two f(GPÂ¡/GNÂ¡ S1) is much smaller than the
difference between the two f (GP Â¡/GN j S2). This would indicate the
presence of a distribution by cost interaction in which the effect of
the cost variable is significant only under the SI distribution level.
A graphical representation of this interaction is presented in Figure 5.
A final implication derived from these relative relations in
volves the homogeneity of variance for the f(G^) given the different
levels of the cost variable. The variance for the f(G^Cl) is equal to
5.26 x 106 for this simulation, and the variance for the f(G^[C2) is
equal to 6.06 x 10"4 for this simulation. Using the F test for equal
variances, F=115.24 (3 and 3 d.f.) which indicates the variances are
not equal. Consequently, the variance of (GÂ¡  Cl) is expected to be less
than the variance of (GÂ¡C2).
Given the above simulation the effects of the independent vari
ables on the GÂ¡ variable can be hypothesized as follows:
H4.1 a2(GjCl) < a2(GiC2).
The GÂ¡ of those subjects within the Cl cost level will have
significantly smaller variance than will the GÂ¡ of those subjects
within the C2 cost level.
H4.2a (GilCl) < (&C2).
Those subjects within the Cl cost level will have a significantly
smaller mean Gj than will those subjects within the C2 level.
H4.2b (GiS2) < (GiSl).
Those subjects within the S2 distribution level will have smaller
mean Gj than will those subjects within the SI level. Signifi
cance is not predicted due to the interaction effect with the
cost variable.
73
Distribution
Variable
FIGURE 5
SIMULATED COST BY DISTRIBUTION VARIABLE INTERACTION ON f(GPi/GNi)
74
H4.2c (Gj  II) = (Gi 112).
The mean Gj of those subjects within the II information level
and within the 12 information level will be equal.
H4.3 (Gi SI,Cl) (Gj S2,C1) < (Gi]Sl,C2) (G'i JS2,C2) .
There will be a significant interaction of the distribution and
cost variables in which the distribution variable will have no
significant effect given the Cl cost level but will have a
significant effect given the C2 cost level.
H4.4a (GPj/GNj Cl) < (GP1/GNi C2).
Those subjects within the Cl cost level will have a significantly
smaller mean GPj/GNj than will those subjects within the C2 level.
H4.4 (GPj/GNj C2,S2) (GPj/GNj  Cl,S2) <
(GPj/GNj Â¡C2,S1) (GPj/GNj ] Cl,S1) .
There will be a significant interaction of the distribution and
cost variables in which the cost variable will have no signifi
cant effect given the S2 distribution level but will have a
significant effect given the SI level.
Individual longrun decision efficiency and the training phase
Since performance within the experiment is assumed to be a
function of the training adjustment process, it would be expected that
the subjects within the PP classification of training adjustment
pattern would have a lower average experiment GÂ¡ measure than those
subjects within the MM classification. Furthermore, the mean expected
Gj measure for the mixed classifications (PM and MP) would be expected
to fall between those of the nonmixed classifications. The mixed
75
classifications have one positive adjustment which would improve per
formance above that of subjects who make only negative adjustments. On
the other hand, the mixed classifications have one negative adjustment
which would decrease performance below that of subjects who make only
positive adjustments.
The following hypotheses predict the effects of the training
adjustment pattern on the Gi variable:
H4.5 (GjPP) < (G^MM).
Those subjects within the PP classification of training adjust
ment pattern will have a significantly smaller mean G measure
than those subjects within the MM classification.
H4.6 PP) < (G.j MP) = ((Tj PM) < (GTj MM).
Those subjects within the mixed classifications of training
adjustment pattern will have mean G^s that fall between those of
the nonmixed classifications. The mean G^s of the mixed classi
fications are not expected to be significantly different.
CHAPTER IV
THE EXPERIMENT
Experimental Environment
The decision situation studied within this research was standard
cost variance investigation decision making at the operational manage
ment level. The experimental setting was the simulated environment of
a manufacturing company, and the subjects were requested to assume the
role of an operational manager of an assembly department within this
environment. The assembly department assembled a single product, a
metal folding chair. Subjects were presented with a series of standard
cost variance reports for the department and were asked to decide for
each report whether the underlying physical process should be investi
gated to correct an outofcontrol situation. Each series of variance
reports were crosssectional in nature: i.e., all reports within a
series were assumed to have occurred simultaneously and were independent
of each other.
The physical process within the simulated environment was a
laborpaced process (Barefield, 1972): i.e., the operating efficiency
of the department was determined completely by the labor efficiency of
the workers. The labor efficiency standard (stated in terms of time
per unit assembled) was based on engineering estimates that allowed for
unavoidable labor inefficiencies and reasonable variation in worker
76
77
performance (i.e., the standards were currently attainable). The sub
jects were instructed to accept the labor efficiency standard as fair
in terms of control and performance goals.
The physical labor process of the department could be in one of
two mutually exclusive states of nature: either incontrol or outof
control. The overall labor process consisted of many individual
physical labor operations: the expected aggregate of these procedures
was represented by the overall labor efficiency standard. The variance
reports, however, included only the aggregate standard and labor
efficiency variance. The labor process was defined to be incontrol
when all of the individual physical procedures were being performed as
expected. The labor process was defined to be outofcontrol when one
or more of these procedures was not being performed as expected.
The task of the subject was to decide whether to investigate each
labor efficiency variance for its underlying causes. The purpose of
investigation was to facilitate correction of those individual pro
cedures which were not operating as expected. Tv/o assumptions were pro
vided to aid the subject's decision making process. First, if they
decided to investigate a variance and the labor process turned out to
be outofcontrol, the process would be returned to the original
incontrol state with certainty. Second, if they decided not to in
vestigate a variance and the labor process was outofcontrol, the process
would remain outofcontrol with certainty.
Various costs were associated with the variance investigation
decision. These costs depended upon the subject's decision (either in
vestigate or do not investigate) and upon the actual state of the labor
process (either incontrol or outofcontrol). Estimates of these
78
costs were presented to the subject on the face of each variance report
using the following format:
If Your
Investigation
Decision Is
And If The
Assembly Line
State Is
Then
Your
Costs
Are
Investigation
Production
Total
Yes
Incontrol
$
X
$
0.00
$ X
Yes
Outofcontrol
$
Y
$
0.00
$ Y
No
Incontrol
$
0.00
$
0.00
$ 0.00
No
Outofcontrol
$
0.00
$
Z
$ z
The letter X represented the estimated investigation cost when the
assembly line was incontrol. The cost was related to the size of the
labor efficiency variance: the more negative (unfavorable) the variance
the larger the cost. The letter Y represented the estimated investi
gation cost when the assembly line was outofcontrol. The cost was
related to the size of the labor efficiency variance: the more negative
(unfavorable) the variance the smaller the cost. Given any particular
labor efficiency variance, the investigation cost when outofcontrol
(Y) was always larger than the investigation cost when incontrol (X).
This was because the investigation cost included correction costs when
the assembly line was outofcontrol. The letter Z represented the
estimated marginal production cost of operating the next period in the
outofcontrol state. The cost was constant for all variance reports.
The subjects were told that their immediate supervisor, the pro
duct section manager, would evaluate their control performance in terms
of their minimization of both investigation and production costs above
the expected standard (labeled the total investigation decision cost).
A cash bonus was promised to the subjects, the size of the bonus being
contingent upon the extent to which they minimized their total investi
gation decisions cost. The measure of a subject's control performance,
labeled TIDCmin, was determined by summing the total investigation
79
decision costs incurred by the subject over the series of variance re
ports and dividing this sum by the sum of the total investigation de
cision costs incurred by an optimal model over the same series of vari
ance reports. The subject's cash bonus function, constant for all sub
jects, was inversely related to this measure. As TIDCmin approached
one, the payoff approached the maximum: as TIDCmin became larger than
one, the payoff approached the minimum.
Subjects
The subjects were 86 senior year undergraduate and master's level
graduate students enrolled in the business college at the University of
Florida. The subjects participated in the experiment during a two week
period; 47 participated in the first week and 39 participated in the
second week. A total of 92 subjects initially volunteered to participate
but six subjects failed to complete the experiment. The 86 subjects
who completed the experiment consisted of 63 males and 23 females.
Three subject selection criteria were applied: 1) the subject
must have completed an intermediatelevel managerial accounting course,
2) the subject must have completed an introductorylevel statistics
course, and 3) the subject must have earned an overall grade point
average (GPA) of at least 2.0 on a 4.0 scale.
Subjects initially were contacted within senior level and graduate
accounting classes. The contact was made by the experimenter giving a
brief oral presentation followed by passing signup sheets around each
class (a copy of the oral presentation is presented in Appendix B). To
motivate volunteering, the presentation focused on the student's pro
fessional responsibilities as future accountants and on the monetary
80
benefits that would accrue to those who volunteered. The subjects were
told that they would be given a cash payment for participating in the
experiment and that the amount of a subject's payment would depend upon
his performance. The minimum payment was set at $2.00 and the maximum
at $10.00.
Experimental Materials
The experimental materials included a background information book
let, variance investigation decision stimuli, heuristics questionnaire,
and motivations questionnaire.
Background Information
A background information booklet (see Appendix C) was designed
to provide the subjects with a common experimental environment. The
booklet provided the subject with general company information, general
product information, general manufacturing process information, and
specific assembly department information. The specific assembly depart
ment information included information concerning the employees, the
physical process, the accounting control system, the subject's task as
the operational manager, and the subject's performance evaluation as
the operational manager.
Variance Investigation Decisions
Various information constant over all decision trials within a
treatment condition was presented on a separate page prior to the start
81
of the decision trials. The extent of such information depended upon the
treatment condition but could include information such as the prior
probabilities of both states, the means of both states, the standard
deviations of both states, the shape of the distributions of both states,
the range of past labor efficiency variances, and the range of past
investigation costs. Copies of the prior information pages for two
specific treatment conditions are presented in Appendix D.
Each variance investigation decision trial consisted of the pre
sentation of a labor efficiency variance report and a subject's re
sponse to two questions. The questions were 1) would you investi
gate this reported variance, and 2) how strongly do you feel about your
decision? During the training phase each decision trial was followed
by feedback concerning the actual state of the assembly line and the
actual costs incurred for each possible decision given the actual state.
Decision trials were presented in booklets of 33 trials (each trial
included the report with questions followed by the feedback). Within
the experimental phase decision trials were presented in booklets of
50 trials (each trial included only the report with questions). In
both the training and the experimental phases, answer sheets were pro
vided for the subject to record his responses.
An example of a labor efficiency variance report with the set of
questions is presented in Figure 6. The format of the report and
questions was constant for all treatment conditions: the only variation
related to the distribution of the actual minutes incurred per chair
and the labor efficiency variance, and to the magnitude and structure
of the costs of investigation.
82
AMSECO
Metal Folding Chair Assembly Department
Labor Efficiency Variance Report For Job 5247
Standard Minutes
Allowed Per Chair
Actual Minutes
Incurred Per Chair
Labor Efficiency Total Chairs
Variance Per Chair Produced
36.0
44.0
8.0
200
The Costs Associated With Investigation Are:
If Your
And If The
Then
Your Costs
Are
Investigation
Assembly Line
ieie 'A'* irirkkie + kkkkirkkkk
Decision Is
State Is
Investigation
Production
Total
'kk'trirkirk'kirkirkk
**************
*********
Yes
InControl
$ 28.33
$ 0.00
$ 28.33
Yes
0ut0fControl
$ 90.00
$ 0.00
$ 90.00
No
InControl
$ 0.00
$ 0.00
$ 0.00
No
0u0fControl
S 0.00
$ 175.00
$ 175.00
Please answer the following questions placing your answers on the answer sheet:
A. Would you investigate this reported variance /circle the appropriate
response on the answer sheet/
NO
YES
B. How strongly do you feel about your decision /select a number between
0 and 100 which indicates the strength of your feeling and place this
number on the answer sheet/
0 10
20
30
40 50 60
70
80
90
100
*
* + *
k
*
k
k kkiek kkkkklrkkk kkkk kkkkkkk k k kkkkk
k
*
k
k
k
k
Uncertain
Reasonably
Almost
Certain
Certain
FIGURE 6
VARIANCE INVESTIGATION DECISION TRIAL
83
Elicitation of Heuristics
The elicitation of each subject's heuristics was accomplished using
a predominately openended questionnaire (see Appendix E). The objec
tives of this questionnaire were 1) to determine the strategy the sub
ject used in answering the variance investigation questions within the
experimental phase, 2) to determine, where appropriate, the exact values
of any numerical rules used by the subject, 3) to determine whether the
subject thought he used his strategy consistently within the experimental
phase, 4) to determine the method or approach used by the subject in
forming his strategy within the training phase, and 5) to determine the
subjective importance attached to each information item presented to
the subject.
Elicitation of Subject Motivations
Subject motivations were elicited using a motivation questionnaire
(see Appendix F) developed by Snowball and Brown (1977). The question
naire is a ten item Likerttype scale which has submeasures for both
intrinsic and extrinsic motivation.
Experimental Procedures
Experimental procedures included assignment of subjects to
treatment conditions, adminstration of a training phase, administration
of an experimental phase, and final debriefing.
84
Assignment of Subjects to Treatment Conditions
Since each of the 86 subjects was assigned to one of eight groups,
randomization per se can not be relied upon to control for individual
attribute differences between groups. An alternative is to block the
randomization process onindi vidual attribute dimensions assumed to
significantly affect the subject's information processing within the
task required by the experiment.
The literature relating to individual attributes has not pro
duced conclusive results concerning individual attributes effect on
decision processes. The most comprehensive study is that of Taylor and
Dunnette (1974) who analyzed the effect of 16 decision maker attributes
upon eight measures of predecisional, decisionpoint, and post decisional
processes within a personnel decision simulation experiment. Of con
siderable interest is that the relevant decision maker attributes
accounted for a generally small portion of the total decision variance
within the various processes (the range was from 8 percent to 33 percent).
The decision maker attribute with the greatest predictive capacity was
intelligence. In two of the predecisional processes (diagonosticity
and information processing rate) and in two of the decisionpoint and
post decisional processes (information retention and decision accuracy),
intelligence accounted for from onehalf to almost all of the variance
explained by individual characteristics.
In the present study, the randomization process of assigning
subjects to treatment conditions was blocked on individual intelligence.
Ideally, individual intelligence should be measured using some validated
instrument (e.g., the Wesman Personnel Classification Test or the
85
Wechsler Adult Intelligence Scale). Due to resource limitations, however,
subject grade point average (GPA) was used as a surrogate for such a
measure. A median GPA was identified, and those subjects with a GPA
above the median were categorized as above average intelligence and
those subjects with a GPA below the median were categorized as average
intelligence. Each subject within an intelligence category then was
assigned randomly to one of the treatment conditions with the restric
tion that each intelligence group contributed an equal number of sub
jects to each condition.1 Upon assignment to a treatment condition each
subject received the background information booklet.
Training Phase
%
Each subject received training within the treatment condition to
*
which he was assigned. Training was conducted in groups of two subjects
within a 50 minute session administered by either the experimenter or
by an experimental assistant.2 Subjects were assigned randomly to the
experimental assistants subject to two restrictions. The restrictions
were that the experimental assistant was not currently the subject's
teacher for an academic course and that the subject's available time
coincided with that of the experimental assistant. Training of all
!The assignment process involved two procedures. First, the assignment
to the cost variable levels was by the week in which the subject par
ticipated in the experiment. Those subjects who participated during
the first week were assigned to the Cl cost level, and those subjects
who participated during the second week were assigned to the C2 cost
level. Second, the assignment to the information variable and the
distribution variable levels was by random selection based upon a ran
dom number table.
2The experimental assistants were not paid. The author believes, how
ever, that this is an exception to the aphorism that price reflects
value.
86
subjects (within each week) was completed over two contiguous days. The
training phase consisted of 99 decision trials with feedback. The
experimental materials used in the training phase were similar to those
used in the experimental phase. The decision trials with feedback were
presented in three booklets of 33 trials and the subject was provided
an answer sheet on which to record his responses. Additional perfor
mance feedback was given at the completion of each book of 33 decision
trials. This performance feedback was the subjects's TIDC^p for that
particular book.
Experimental Phase
The experimental session lasted one hour and was administered by
the experimenter. The experimental phase (within each week) was com
pleted over two contiguous days immediately following the training phase.
The experimental session consisted of three parts. The first
part was the presentation of 100 decision trials. The decision trials
were presented in two booklets of 50 trials each and the subject re
corded his responses on a separate answer sheet. The subjects were
allowed twenty minutes to complete the 100 decision trials. The second
part of the experimental phase was the elicitation of the subject's
heuristics. This elicitation was accomplished using the openended
questionnaire (see Appendix E). The third part of the experimental
phase was the administration of the motivation questionnaire (see
Appendix F).
87
Final Debriefing
Each subject's final performance measure (TIDCmi.n) for the
variance investigation decisions part of the experimental phase was
presented individually at a later date. At this time his cash pay
ment was determined, he was debriefed as to the purpose of the experi
ment, and any questions were answered. Additional data were collected
from those subjects not responding completely to the heuristic elicita
tion questionnaire.
CHAPTER V
ANALYSES AND RESULTS
Summary of Results
A summary of the analyses and results contained in this chapter
is presented in Table 4. The objective of this summary is to provide
a brief statement of each hypothesis together with the results of analy
ses for that hypothesis. Such a summary will facilitate the presenta
tion of the analyses and results and will serve as a reference for the
discussion and conclusions contained in the next chapter.
General Method of Analysis
Although several methods of analysis are employed, the most
prevalent method is analysis of variance using the model comparison
procedure (Appelbaum and Cramer, 1974; Lewis and Keren, 1977). This
method of analysis is employed due to the nonorthogonality of the data
structure. The problem of nonorthogonality arises in this instance as
a result of nonequal cell frequencies.
The model comparison procedure involves fitting a linear model
allowing for certain effects and then comparing the obtained fit to
that of a linear model which omits one or more of the effects. The
objective is to find the simplest model that adequately fits the data.
The procedure begins with the complete or full model (which allows for
88
TABLE 4
SUMMARY OF ANALYSES AND RESULTS
Hypotheses
Analysis
Method(s)
Test
Statistic(s)
Results
1.1a (TDVi1 SI)=38.5
Twotailed Z test
Z=0.9912
(n.s.)
The mean initial decision anchor of
those subjects within the SI distribu
tion level was not significantly
different from 38,5
1.1b (TDViS2)=40.5
Twotailed Z test
Z=1.5681
(p<.10)
The mean initial decision anchor of
those subjects within the S2 distribu
tion level was significantly less than
40.5
1.2a (TDV.Il)=(TDVi
112)
Model comparison
Twotailed Z test
F= 1.0944
Z=0.7007
(n.s.)
(n.s.)
The mean initial decision anchor of
those subjects within the reduced infor
mation level was not significantly dif
ferent from that of those subjects with
in the expanded information level.
1.2b (TDViCl)=(TDVi
1 C2)
Model comparison
Twotailed Z test
F=3.3931
Z=1.4362
(p<.10)
(p<.10)
The mean initial decision anchor of
those subjects within the Cl cost level
was significantly smaller than that of
those subjects within the C2 cost level.
1.3a (RA.I1)=(RA.I2)
Model comparison
Twotailed Z test
F=0.0903
Z=0.4432
(n.s.)
(n.s.)
The mean relative decision anchor ad
justment of those subjects within the
reduced information level was not sig
nificantly different from that of those
subjects within the expanded information
level.

Full Text 
subjects, who assumed the role of assembly department operational manager,
made labor efficiency variance investigation decisions based upon a series
of independent standard cost variance reports. The manipulated indepen
dent variables included contents of the available information set,
distributional properties of the states of control, and cost effects of
the investigation decision errors. Parameters of the psychological theory
of signal detection permitted measurement of sensitivity and criteria of
a subject's decision model. The subject's investigation decision costs
compared to normative investigation decision costs derived under similar
situations were employed as a relative measure of decision efficiency.
Overall, the implications of the conceptual framework were supported
by the obtained results. To explain the few major deviations from
expectations, an ex post hypothesis was introduced. This hypothesis
posits that the standard introduces a subjective adjustment bias. Depend
ing upon the direction of adjustment the standard subjectively inhibits
complete adjustment either to optimal decision values close to the
standard or to optimal decision values distant from the standard.
x
135
TABLE 21
MODEL COMPARISON PROCEDURE RESULTS FOR THE GPi/GN1 DEPENDENT VARIABLE
Model
SSe
d.f.(F)
F
Ful 1b
66.265
12,66
6.91a
Without 3way interaction
67.538
1,66
1.27
Without distribution x cost
interaction
76.352
1,67
8.74a
Without information x distribution
interaction
67.607
1,67
0.07
Without information x cost
interaction
69.242
1,67
1.69
C
Without information
70.493
1,69
1.17
ap<.01
bfhe F value associated with the full
model is the
test of
the full
model and not a model comparison test,
c
The test of this model assumes the effects of those interactions
that include this variable are equal to zero.
140
increase at a rate greater than the relative additional decision savings
the subjects' variable response ranges increase.
The negative association between the G.Â¡ variable and the intrinsic
motivation factor indicates that as the relative net decision costs
increase the subjects' intrinsic motivation measures decrease. The same
association exists between the GP.Â¡ variable and the intrinsic motivation
factor: as the relative additional decision costs increase the subjects'
intrinsic motivation measures decrease. The GN.Â¡ variable and the in
trinsic motivation factor were associated positively: as the relative
additional decision savings increase the subjects' intrinsic motivation
measures increase.
Similar associations exist for the extrinsic (monetary) motivation
factor. Thus, as the relative additional decision costs increase (GP^
increases) the subjects' extrinsic (monetary) motivation measures de
crease. Conversely, as the relative additional decision savings in
crease (GN.Â¡ increases) the subjects' extrinsic (monetary) motivation
measures increase.
The negative association between the GP^ variable and the GPA
variable indicates that as subjects' GPAs decrease relative additional
decision costs increase. Likewise, the positive association between the
GN.j variable and the GPA variable indicates that as subjects' GPAs in
crease relative additional decision savings increase.
LongRun Decision Efficiency and Training Phase Performance
Hypothesis 4.5 predicted that those subjects in the PP classi
fication of training adjustment direction would have a signficantly
smaller mean G.Â¡ than those subjects in the MM classification. A ttest
81
of the decision trials. The extent of such information depended upon the
treatment condition but could include information such as the prior
probabilities of both states, the means of both states, the standard
deviations of both states, the shape of the distributions of both states,
the range of past labor efficiency variances, and the range of past
investigation costs. Copies of the prior information pages for two
specific treatment conditions are presented in Appendix D.
Each variance investigation decision trial consisted of the pre
sentation of a labor efficiency variance report and a subject's re
sponse to two questions. The questions were 1) would you investi
gate this reported variance, and 2) how strongly do you feel about your
decision? During the training phase each decision trial was followed
by feedback concerning the actual state of the assembly line and the
actual costs incurred for each possible decision given the actual state.
Decision trials were presented in booklets of 33 trials (each trial
included the report with questions followed by the feedback). Within
the experimental phase decision trials were presented in booklets of
50 trials (each trial included only the report with questions). In
both the training and the experimental phases, answer sheets were pro
vided for the subject to record his responses.
An example of a labor efficiency variance report with the set of
questions is presented in Figure 6. The format of the report and
questions was constant for all treatment conditions: the only variation
related to the distribution of the actual minutes incurred per chair
and the labor efficiency variance, and to the magnitude and structure
of the costs of investigation.
59
H2.2b (DNAj  SI) = (DNAj S2).
Those subjects within the SI distribution level and within the
S2 distribution level will have equal mean DNAjS.
H2.2c (DAÂ¡ 1 Cl) = (DAÂ¡ IC2).
Those subjects within the Cl cost level and within the C2 cost
level will have equal mean DNAÂ¡s.
H2.3a (DNAÂ¡DNÂ¡ Â¡SI) < (DNAiDNi S2).
Those subjects within the SI distribution level will have
significantly smaller mean DNAÂ¡DNÂ¡s than will those subjects
within the S2 distribution level.
H2.3b (DNAjDN.Â¡ Cl) <(DNAiDNi  C2).
Those subjects within the Cl cost level will have significantly
smaller mean DNAÂ¡DNÂ¡s than will those subjects within the C2
cost level.
Individual decision criteria simulation and hypotheses formation
The variables which affect the BNj measure should be those factors
related to the individual's selection of a cutoff value. The most
significant variable affecting the individual's cutoff value should be
the cost variable. Within this variable the initial decision anchor
given the Cl level is much closer to the optimal cutoff value than is
the initial decision anchor given the C2 level. Consequently, the BNj
measure should be closer to a value of one under the Cl level than
under the C2 level.
The difference between the BNAÂ¡ and the BNj measures should
relate to the same variables as those discussed for the difference
TABLE 4
SUMMARY OF ANALYSES AND RESULTS
Hypotheses
Analysis
Method(s)
Test
Statistic(s)
Results
1.1a (TDVi1 SI)=38.5
Twotailed Z test
Z=0.9912
(n.s.)
The mean initial decision anchor of
those subjects within the SI distribu
tion level was not significantly
different from 38,5
1.1b (TDViS2)=40.5
Twotailed Z test
Z=1.5681
(p<.10)
The mean initial decision anchor of
those subjects within the S2 distribu
tion level was significantly less than
40.5
1.2a (TDV.Il)=(TDVi
112)
Model comparison
Twotailed Z test
F= 1.0944
Z=0.7007
(n.s.)
(n.s.)
The mean initial decision anchor of
those subjects within the reduced infor
mation level was not significantly dif
ferent from that of those subjects with
in the expanded information level.
1.2b (TDViCl)=(TDV.
1 C2)
Model comparison
Twotailed Z test
F=3.3931
Z=1.4362
(p<.10)
(p<.10)
The mean initial decision anchor of
those subjects within the Cl cost level
was significantly smaller than that of
those subjects within the C2 cost level.
1.3a (RAill)=(RA.l2)
Model comparison
Twotailed Z test
F=0.0903
Z=0.4432
(n.s.)
(n.s.)
The mean relative decision anchor ad
justment of those subjects within the
reduced information level was not sig
nificantly different from that of those
subjects within the expanded information
level.
25
Available Information Set
The contents of the available information set refers to the infor
mation known by the individual prior to his decision. Such information
can be of two types singular or distributional (Tversky and Kahneman,
1977). Singular information consists of information that specifically
relates to the current decision. Distributional information consists
of information that relates to the relative frequencies and to the
statistical relationships among the states of nature.1 The difficulty
of the discrimination task may be affected by the presence or absence
of certain items of information. In general, the less information con
tained in the available set the more difficult the discrimination task,
for missing information required by a decision model must be estimated
by the individual. Compared to statistically derived estimates, these
subjective estimates are likely to have greater uncertainty and in
efficiency associated with them.
Within the variance investigation situation, the information con
tained on each variance report constitutes the singular information.
Two types of singular information are employed in this research the
actual results of the physical process and its variance from a standard,
and the marginal costs associated with each of the two possible decisions
in combination with each of the two possible states of nature. The
presence of both these singular information types is controlled as a
constant within this research. The presence or absence of specific
xThis definition of distributional information implicitly assumes that
the relative frequencies and statistical relationships are stable over
the relevant time frame. If these variables were nonstable, revised
estimates of their specific values would be required prior to each
decision, thus classifying them as singular information. This research
assumes that both of these variables are stable across all decisions.
86
subjects (within each week) was completed over two contiguous days. The
training phase consisted of 99 decision trials with feedback. The
experimental materials used in the training phase were similar to those
used in the experimental phase. The decision trials with feedback were
presented in three booklets of 33 trials and the subject was provided
an answer sheet on which to record his responses. Additional perfor
mance feedback was given at the completion of each book of 33 decision
trials. This performance feedback was the subjects's TIDC^^ for that
particular book.
Experimental Phase
The experimental session lasted one hour and was administered by
the experimenter. The experimental phase (within each week) was com
pleted over two contiguous days immediately following the training phase.
The experimental session consisted of three parts. The first
part was the presentation of 100 decision trials. The decision trials
were presented in two booklets of 50 trials each and the subject re
corded his responses on a separate answer sheet. The subjects were
allowed twenty minutes to complete the 100 decision trials. The second
part of the experimental phase was the elicitation of the subject's
heuristics. This elicitation was accomplished using the openended
questionnaire (see Appendix E). The third part of the experimental
phase was the administration of the motivation questionnaire (see
Appendix F).
117
interactions), the three motivation factors, and the GPA^ variable. The
three full models are:
 p + aj + Bk + Yi + 0Sjk + ctYj, + eykl + aSYjkl + Â£j
+ Ti + xi + 6i
The three model comparison procedure results are presented in
Table 14, and the F values associated with the sources of the reduced
models are presented in Table 15. The results indicate that the reduced
BN.j model contains the distribution variable, the cost variable, and
the extrinsic (monetary) motivation factor. Both the cost variable main
effect and the motivation factor are significant at the p<.01 level,
and the distribution variable main effect is significant at the p<.10
level. The reduced BNA model contains the cost variable and the
extrinsic (monetary) motivation factor, and both are significant at the
p<.01 level. The reduced BNABN.Â¡ model contains the cost variable and
the GPA.j variable. The cost variable main effect is significant at the
pc.01 level, and the GPA.Â¡ variable is significant at the p<.10 level.
The means, variances, and sample sizes for the dependent variables
given the levels of the significant situation variables are included in
Table 15. The F test of equal variances indicated that the following
variances differ significantly: 1) within the BN variable between the
levels of the distribution variable (F=1.51, p<.10), 2) within the BN
variable between the levels of the cost variable (F=6.11, p<.01),
3) within the BNA^ variable between the levels of the cost variable
(F=9.36, p<.01), and 4) within the BNA BN^ variable between the levels
of the cost variable (F=39.74, p<.01). Using a Z test of equal means,
the means of the various levels were found to differ significantly
BNAi
BNA,BN,
16
study by Magee and Dickhaut (1977). Using human subjects, Magee and
Dickhaut found support for the proposition that different manager per
formance (payoff) measures will affect the specific decision rules
(heuristics) employed by the decision maker (as a result of the decision
maker attempting to maximize his subjective utility).
Psychological Concepts
This section discusses the psychological concepts employed in
developing a conceptual framework of manager standard cost variance
investigation decisions. These concepts include the psychophysical
theory of signal detection and the human information processing con
cepts of decision heuristics.
Psychophysics
Psychophysics studies the relationships between physical and
psychological scales of measurement. Modern psychophysics adopts the
view that subjects can make meaningful evaluations of the magnitudes of
their sensory experiences, and therefore sensory magnitudes, as well
as physical magnitudes, can be quantified. One approach of modern
psychophysics is based upon the theory of signal detection (TSD). TSD
permits the separation of the decision maker's ability to discriminate
between classes of stimuli (sensitivity) from his motivational response
biases (decision criteria). Two comprehensive theoretical descriptions
of TSD are presented by Green and Swets (1974) and Egan (1975); general
surveys of the TSD theory are presented by Coombs et al. (1970), Watson
(1973), and Pastore and Scheirer (1974).
72
f(GPi/GN1Â¡S2,C2) = 1.28465
The difference between the two f(GPj/GNÂ¡ ]SI) is much smaller than the
difference between the two f(GPÂ¡/GNj S2). This would indicate the
presence of a distribution by cost interaction in which the effect of
the cost variable is significant only under the SI distribution level.
A graphical representation of this interaction is presented in Figure 5.
A final implication derived from these relative relations in
volves the homogeneity of variance for the f(G^) given the different
levels of the cost variable. The variance for the f(G^Cl) is equal to
5.26 x 106 for this simulation, and the variance for the f(Gj [C2) is
equal to 6.06 x 10"4 for this simulation. Using the F test for equal
variances, F=115.24 (3 and 3 d.f.) which indicates the variances are
not equal. Consequently, the variance of (Gj  Cl) is expected to be less
than the variance of (GÂ¡C2).
Given the above simulation the effects of the independent vari
ables on the GÂ¡ variable can be hypothesized as follows:
H4.1 a2(Gj  Cl) < a2(Gj C2).
The Gj of those subjects within the Cl cost level will have
significantly smaller variance than will the GÂ¡ of those subjects
within the C2 cost level.
H4.2a (GilCl) < (OfC2).
Those subjects within the Cl cost level will have a significantly
smaller mean Gj than will those subjects within the C2 level.
H4.2b (GÂ¡iS2) < (GiSl).
Those subjects within the S2 distribution level will have smaller
mean Gj than will those subjects within the SI level. Signifi
cance is not predicted due to the interaction effect with the
cost variable.
73
Distribution
Variable
FIGURE 5
SIMULATED COST BY DISTRIBUTION VARIABLE INTERACTION ON f(GPi/GNi)
181
Extent of Total Investi
gation Decisions Cost
Minimization Cash Bonus
1.021
to
1.030
$ 7.00
1.031
to
1.040
6.00
1.041
to
1.050
5.00
1.051
to
1.100
4.00
1.011
to
1.150
3.00
1.151
2.00
Again, I rely on your not discussing this experiment with other people.
104
TABLE 7
MODEL COMPARISON PROCEDURE RESULTS FOR THE RAj DEPENDENT VARIABLE
Model
SSe
d.f.(F)
F
a
Full
67.8716
8,73
0.58
Without 3way interaction
67.8852
1,73
0.01
Without distribution x cost
interaction
67.8852
1,74
0.00
Without information x distribu
tion interaction
67.8889
1,74
0.00
Without information x cost
interaction
68.2633
1,74
0.41
b
Without information
68.3470
1,77
0.09
b
Without distribution
69.8016
1,77
1.73
b
Without cost
68.4208
1,77
0.17
a
The F value associated with the full
model is the
test of
the full
model and not a model comparison test.
I
The test of this model assumes the effects of those interactions
that include this variable are equal to zero.
38
The cost variable
Given the two states of nature (the realization of which is un
known to the individual) and two possible decisions, there follows that
two types of errors can be made in reaching a decision. The first type
of decision error, labeled type A, is the decision to investigate the
physical process when the incontrol state exists. The second type of
decision error, labeled type B, is the decision not to investigate when
the outofcontrol state exists. The marginal cost of either error
type equals the cost that would have been incurred had the correct
decision been made minus the cost that was incurred by the incorrect
decision. Thus, the marginal costs of the two decision error types,
labeled MC^ and MCg, are:
= C(decision=not investigate  state=incontrol) 
C(decision=investigate  state=incontrol), and
MCg = C(decision=investigate  state=outofcontrol) 
C(decision=not investigate  state=outofcontrol),
where C( ) represents the decision cost for the situation within the
parentheses.
When the marginal cost of a type A error equals the marginal
cost of a type B error the cost structure should be ignored when making
an investigation decision. Of greater interest are situations in which
the marginal error costs not equal. In the present study, each level
of the cost variable takes one of the following forms: 1) the
marginal cost of a type B decision error equals three times the marginal
cost of a type A error (labeled level CJ_), and 2) the marginal cost
of a type A error equals three times the marginal cost of a type B
error (labeled level C2).
Abstract of Dissertation Presented to the Graduate Council
of the University of Florida in Partial Fulfillment of the Requirements
for the Degree of Doctor of Philosophy
HUMAN INFORMATION PROCESSING FOR DECISIONS
TO INVESTIGATE COST VARIANCES
By
Clifton E. Brown
December 1978
Chairman: A. Rashad Abdelkhalik
Major Department: Accounting
Two major functions of management are those of planning and con
trol. Broadly defined, control is the management process which assures
that selected alternatives are implemented and executed in accordance
with plans. Important aspects of the control process concern analysis
and investigation of standard cost variances provided within accounting
reports. A substantial portion of the accounting variance investigation
literature has employed the normative model approach researchers have
created variance investigation models which a manager should use. Rarely
has attention been given to how the manager would interprete and integrate
information required by the various normative models.
The principal focus of this dissertation was on the effects of
specific situational variables upon a manager's information processing
for purposes of making variance investigation decisions. The specific
objectives were 1) tc develop a conceptual framework which will predict
effects cf specific situational variables on a manager's relative effi
ciency in information processing and in variance investigation decision
making, and 2) to empirically test some implications of this conceptual
framework.
viii
133
which the (Gj S1 ,C1 )(G.Â¡ S2,C1) would be smaller than the (G^ [SI ,C2)
(Gj S2,C2). The results of the model comparison procedure indicated a
significant distribution by cost interaction (pc.Ol), with the
I SI ,C1)(Gj Â¡S2,C1) equal to 0.0074 and the (Gj S1 ,C2)(Gi S2,C2)
equal to 0.0346. Hypothesis 4.2a predicted that (G^C1) would be
significantly smaller than (G^C2). A Z test of equal means indicated
that the means differ significantly (Z=2.4420, p<.01 onetailed), with
the Cl level having the smaller mean. The lack of significance for
the cost variable main effect within the model comparison procedure
could be the result of the distribution by cost interaction. There
exists substantially less difference between the levels of the cost
variable when given S2 distribution level than when given the SI dis
tribution level. Hypothesis 4.2b predicted that (G.jS2) would be
smaller than (G^S1). A Z test of equal means indicated that the means
differ significantly (Z=1.7452, p<.05 onetailed), with the S2 level
having the smaller mean. The lack of significance within the model
comparison procedure for the distribution main effect was predicted by
hypothesis 4.2b. Hypothesis 4.2c predicted that (G^II) would not
differ significantly from (G^ j12). Using a Z test of equal means, the
difference was found to be not significant (Z=0.4528, n.s. twotailed).
Hypotheses 4.4a and 4.4b relate to the ratio of the individual's
relative additional decision costs to the individual's relative addi
tional decision savings. The method of analysis was the model compari
son procedure where the dependent variable is the GP^/GN^ variable, and
the independent variables are the same as for the full G^ model. The
full GP.Â¡/GN.Â¡ model is:
Gpi/GN1 = + j + Bk + Y1 + aSjk + aYjl + 8Yk1 + a8Yjkl + +1 +
+ t + X. + 5.
1 l l
159
variance investigation decision behavior observed within this research.
Generalization of the modified conceptual development would involve
extensions aimed at reducing the limitations of the existing study.
The primary ex post concept developed within this research is
the decision behavior magnet view (incorporating the concept of sub
jective limits). Future research could directly test this concept.
Such research could involve variance investigation decision situations
in which the standard was not the same value as the mean of the
incontrol state of nature. If this ex post concept is valid, then
subjects within situations involving convergent adjustment and a
strict standard should have less decision criteria conservatism than
subjects within situations involving convergent adjustment and a lax
standard. Similarly, subjects within situations involving divergent
adjustment and a lax standard should have less decision criteria
conservatism than subjects within situations involving divergent ad
justment and a strict standard. Additional research could test
whether the biases implied by the decision behavior magnet concept can
be reduced or eliminated by extended training within the particular
decision situation.
Generalizations of the conceptual development would require
extensions concerning both the experimental subjects and the experimental
environment. Extensions concerning the experimental subjects could
involve the use of nonstudents, perferably actual operational managers.
Such extensions could include managers within familiar experimental
environments and within nonfamiliar experimental environments. Be
yond extending the decision criteria conservatism effects, the central
question would be whether differential information processing
3
The second stage concerns the manager's investigation decision
criteria. Having arrived at a conclusion (albeit probabilistic) about
the distribution that generated the variance, the manager must integrate
and process various objective function parameters in order to arrive at
his variance investigation decision (these parameters can belong to
either the manager's objective function, the organization's objective
function, or both if the same). The potential investigation criteria
can be divided into two major categories: structural criteria and
behavioral criteria. The first category can be divided further into
structural cost criteria and structural probability criteria. The
structural cost criteria include such variables as the additional cost
of operating an outofcontrol process (given that the process can be
returned to the original incontrol state), and the costs of variance
investigation and correction (which can differ depending upon the
actual state that generated the variance). The structural probability
criteria include such variables as the probability that the source of
the variance is controllable (i.e., that it can be returned to the
original incontrol state through managerial action), the probability
that the process will return to the incontrol state without managerial
action, and the prior probabilities of each state distribution. The
behavioral criteria include such variables as the manager's perception
of the effect of the investigation upon his performance evaluation and
reward structure, and the manager's perception of the effect of the
decision upon employee performances and attitudes.
Although the variance investigation decision process has been
described as two stages, the manager may not actually utilize such a
sequential stage process. The manager's actual decision process is
39
Individual decision model sensitivity variables
The sensitivity of the individual's decision model relative to the
decision situation is measured using the TSD parameter d1. The theo
retical definition of d1 is:
d = (p2 Pj)/o = zi Z2
where y. = the mean of the jth state of nature (incontrol = 1 and
outofcontrol = 2);
a = the common standard deviation of the states of nature; and
z. = the value of the normal distribution function associated
J
with the jth state of nature and any decision cutoff value
common to both states.
An empirical estimate of the d1 for an individual, labeled d 1,
is obtained using the individual's conditional probabilities P(decision=
investigate  state=outofcontrol) and P(decision=investigate  state=
incontrol) to calculate a subjective z1 and zÂ£. As previously pointed
out, d' is relative to the decision situation. In particular, d is
relative to the distribution variable. To gain comparability across
situations the following measure is used:
DN, / dÂ¡
where d^ is generated using optimal model k. The measure decreases
from a value of one as the result of several factors: 1) the individual
is inconsistent in his use of his cutoff value (i.e., there exists a
range around his cutoff value within which decisions are not made
using a strict relation to this cutoff value) or the individual makes
one or more temporary processing errors, and 2) the individual
utilizes more than one cutoff value.
APPENDIX C
BACKGROUND INFORMATION BOOKLET
The following represents background information concerning the role
that you will be asked to assume in the business experiment for which
you have volunteered to participate. Please read this information
several times, familarizing yourself with the general aspects of this
role.
More detailed information together with specific instructions will be
presented at the inception of the actual experiment. You may use this
booklet when you participate in the experiment.
PLEASE DO NOT DISCUSS THIS EXPERIMENT WITH OTHER PEOPLE. If you have
questions contact Cl if Brown (Bryan 214E or telephone 20155).
My Training Session is:
Date:
Time:
Room:
My Experiment Session is:
Date: Time:
Room:
176
20
where <Â¡>( ) denotes the normal density function for the point in
parentheses and the z parameters are the same as defined for the d'
measure.
Although the assumption of equal variance normal distributions
is employed within this research, such an assumption is not necessary
to employ TSD. Egan (1975) demonstrates the use of TSD with exponential
distributions, chisquare distributions, Bernoulli distributions, and
Poisson distributions. Grier (1971) develops nonparametric measures
of discriminability and decision criteria.
Traditionally, psychophysics has employed TSD to study perceptual
processes: i.e., sensory processes such as audition and vision. Over
the last decade, however, TSD has been applied to conceptual processes.
These extensions to conceptual processes have included numerical
processing (Lieblich and Lieblich, 1969; Hammerton, 1970; Weissmar. et al.,
1975), medical diagnosis (Lusted, 1969; Lusted, 1971; Swets, 1972),
conceptual judgement (Ulehla et al., 1967a; Ulehla et al., 1967b), and
memory (Bernbach, 1967; Banks, 1970).
Human Information Processing
Human information processing (HIP), a subset of cognitive psy
chology, studies human judgement and decision making with particular
emphasis on the processing of information that determines these
activities. An area within HIP is the construction of models of human
decision making. The work within this area is typically classified
into schools of research which employ different paradigms, the two
major paradigms being the Bayesian and regression approaches (Slovic
and Lichtenstein, 1971). The TSD model is related to the Bayesian approach.
Copyright 1978
By
Clifton E.
Brown
113
significant at the pc.05 level, and the motivation factor is significant
at the p<.10 level.
Means, variances, and sample sizes of the three dependent variables
given the levels of the significant situation variables are included in
Table 12. Using the F test of equal variances, the following variances
were found to differ significantly: 1) within the DN.Â¡ variable between
the levels of the distribution variable (F=1.63, p<.10), 2) within the
DNA.j variable between the levels of the cost variable (F=2.57, pc.01),
and 3) within the DNA^DN^ variable between the levels of the distribu
tion variable (F=2.20, pc.Ol). The Z test of equal means indicated that
the means of the variable levels differ significantly (pc.Ol) for each
of the situation variables found to be significant by the above sources
F values (see Table 12).
Hypothesis 2.2a predicted that (DNAjll) would not differ signifi
cantly from (DNA.jI2). A Z test v/as employed to test this hypothesis
and the difference was found to be not significant (Z=0.0010, n.s.
twotailed). Hypothesis 2.2b predicted that (DNAjSl) would not differ
significantly from (DAijS2). Again, a Z test indicated that the
difference was not significant (Z=0.4662, n.s. twotailed). Hypothesis
2.2c predicted that (DNA^Â¡Cl) would not differ significantly from
(DNA^C2). The results of the model comparison procedure and Z test
indicated that the difference was significant at the pc.Ol level, with
the Cl level having the greater mean.
Hypothesis 2.3a predicted that (DNADN^ jSl) would be significantly
smaller than (DNAÂ¡DN.Â¡ S2). Although the results of the model comparison
procedure and Z test indicated that there was a significant difference
between the means, the S2 distribution level had the smaller mean.
CHAPTER II
AREA OF INVESTIGATION
Managerial Accounting Concepts
This study relies on the synthesis of certain psychological con
cepts with certain accounting concepts. This section discusses the
accounting concepts: the nature of managerial decisions, managerial
task planning and control, and standard cost variance investigation.
Managerial Decisions
Since the information set (accounting report) exists to support
the manager's tasks, a conceptual framework of managerial decisions would
facilitate the accountant's information set selection. A conceptual
framework of managerial decisions should differentiate managerial de
cisions along dimensions that allow insights into the informational
needs of those decisions.
Anthony (1365) provides one such dimension along which he iden
tifies the purposes or orientation of managerial activities: strategic
planning, management control, and operational control. Another dimen
sion is provided by Simon (1966) who distinguishes between programmed
and nonprogrammed decisions. The underlying dimension is concerned
with the manner in which managers deal with their problems. The cri
teria for classifying a decision consist of the extent of structure
associated with the problem solving phases of the decision.
8
160
to the optimal model's total decision costs (GPj) and the total
additional individual decision savings relative to the optimal model's
total decision costs (GNÂ¡). The hypotheses concerning these variables
were derived using a simulation which assumed a set of initial decision
anchors and assumed equal learning efficiency (in terms of relative
linear adjustments along the random variable axis). Given these
assumptions, specific variations in the decision situation were ex
pected to have specific effects on a subject's relative decision costs
(see Table 4 in Chapter V for a summary of these expected effects and
the obtained results). All of these expected decision situation effects
were supported, at least in part, by the obtained results. However,
the results of the individual decision model sensitivity and the indi
vidual decision criteria analyses indicated that learning efficiency
was not equal between the levels of the situation variables.
The support obtained for the hypotheses concerning decision
efficiency variables would suggest that the unequal learning efficiency
did not have a significant effect on the relative decision costs. The
majority of the unequal learning efficiency occurred between the levels
of the cost variable (the most significant effect, decision criteria
conservatism, was greater within the Cl cost level than within the C2
level). A closer examination of both the simulated and the obtained
cost variable effects on the GÂ¡ variable indicated that the obtained
effects were not as strong as the simulated effects (note that (GjC2)/
(GilCl) equalled 1.61 whereas f(GjC2)/f(GjCl) equalled 3.27). The
simulated effects may be adjusted for unequal learning efficiency by
assuming the relative decision costs within the Cl cost level would be
increased by a factor of two (relative to the C2 cost level). After
This dissertation was submitted to the Graduate Faculty of the Department
of Accounting in the College of Business Administration and to the
Graduate Council, and was accepted as partial fulfillment of the require
ments for the degree of Doctor of Philosophy.
August 1978
Dean, Graduate School
187
6. Did you feel tense during the experiment?
kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
k k k k k k k
Definitely Yes Probably Don't Probably No Definitely
Yes Yes Know No No
7. Did your desire to cooperate with the experimenter cause you to try
very hard?
*************************************************************
*******
Definitely No Probably Don't Probably Yes Definitely
No No Know Yes Yes
8. Do you think the money rewards caused you to try harder than you
would have without them?
kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
k k k k k k k
Definitely Yes Probably Don't Probably No Definitely
Yes Yes Know No No
9. Did you enjoy the overall experience of participating in the
experiment?
kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
k k k k k k k
Definitely No Probably Don't Probably Yes Definitely
No No Know Yes Yes
10.Did your desire to contribute to research knowledge cause you to try
very hard?
kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
k k k k k k k
Definitely Yes Probably Don't Probably No Definitely
Yes Yes Know No No
18
Since the cells of this matrix are both exhaustive and mutually exclu
sive, the rowwise conditional probabilities must sum to one (this does
not necessarily hold for the sum of the columnwise conditional
probabilities). All parameters of the TSD model are derived from this
conditional probability matrix (see Appendix A for a more detailed
discussion of the conditional probability matrix including its rela
tionship with Bayes1 theorem).
In the singleinterval task the subject analyzes the evidence
and classifies the stimulus into one of two categories according to
his criteria. The criteria are determined by his objective function.
The objective funcition of interest within this research is the
maximization of expected value. Assume that the subject has some value
(utility) fqr each of the four event outcomes. A payoff matrix of these
values related to the four outcomes is. given by the following (Egan,
1975):
DECISION
No
Yes
sn ^sn,Y ^sn,N
n vn,Y vn,N
The decision rule for maximizing the expected value (see Appendix A
for a derivation of this decision rule) is:
P(xsn) P(n) Vn,W VV
P(xjn) P(sn) V$n,Y ^sn,N
At the point at which the above expression is an equality the subject
should be indifferent between saying "Yes" or saying "No." This point
can be considered the critical value of the likelihood ratio of the
observations, L(x0). The decision rule for saying "Yes" is expressed
by the relation L(x) > L(x0).
121
Relative Decision Criteria Conservatism
Hypothesis 3.3 relates to the effect of conservatism on the
relative individual decision criteria. The hypothesis proposes that
the greater the difference between the individual's initial decision
anchor and the optimal cutoff value, the higher the level of conser
vatism. The situation variable most related to such a difference is
the cost variable.
The method of analysis was the model comparison procedure where
the dependent variable is the BNCÂ¡ measure and the independent vari
ables are the three situation variables (with their interactions), the
three motivation factors, and the GPAj variable. The full BNCj model
is:
BNCi = p + aj + ek + Y, + aSjk + Yji + BYk) + aSYjkl + Ej
+ t, + X, + ,
The model comparison procedure results are presented in Table
16, and the F values associated with the sources of the reduced BNCi
model are presented in Table 17. The results indicate that the re
duced BNCn model contains the cost variable, the extrinsic (monetary)
motivation factor, and the GPA.Â¡ variable. Both the cost variable
main effect and the GPA^ variable are significant at the p<.05 level.
The extrinsic (monetary) motivation factor is significant at the
p<.01 level.
The means, variances, and sample sizes of the BNC^ measure given
the levels of the cost variable are included in Table 17. Using the
F test of equal variances, the variances of the BNC^ given the levels
of the cost variable were found to differ significantly (F=9.36, p<.01).
61
f(BNAiCl,Sl,I2) = f(6*) / f(6k) = 1.41898
f(BNAiC2,Sl,I2) = f(e?) / f(f?k) = 0.47017
where f(ek) is the simulated measure at the cutoff value of the kth
optimal model.
This simulation can be extended to the S2 level of the distribu
tion variable given the 12 information level and can be extended to
both the SI and S2 levels of the distribution variable given the II
information level. The f(BNA) measures obtained under the 12 infor
mation and S2 distribution conditions are as follows:
f(BNAiJCl,S2,12) = 1.42318
f(BNAiÂ¡C2,S2,I2) = 0.47300
The f(BNA) measures given the II information level are as follows:
f(BNAiCl,Sl,Il) = 1.40126
f(BNAiC2,Sl,Il) = 0.46993
f(BNAiCl,S2,Il) = 1.38273
f(BNA.C2,S2,Il) = 0.38094
Based on these f(BNA) measures, hypotheses can be derived con
cerning the effects of the independent variables upon the BNA. measure.
The f(BNA^) measures can be averaged over the appropriate conditionals
to determine these effects. Averaging over all conditionals except the
information variable gives the following results:
f(BAiIII) = 0.90871
f(BAi12) = 0.94633
Although there is a difference in the f(BNA) measures due to the infor
mation variable this difference is small compared to the standard error
of these estimates (the difference divided by the standard error is
0.03762/0.39157 = 0.0961). Therefore, a significant effect due to the
information variable would not be expected.
122
TABLE 16
MODEL COMPARISON PROCEDURE RESULTS FOR THE BNCj DEPENDENT VARIABLE
Model
SSe
d.f.(F)
F
b
Full
38.249
11,74
a
2.35
Without 3way interaction
38.510
1,74
0.51
Without distribution x cost
interaction
39.291
1,75
1.52
Without information x distribu
tion interaction
38.543
1,75
0.06
Without information x cost
interaction
38.624
1,75
0.22
c
Without information
39.895
1,78
0.91
c
Without distribution
39.607
1,78
0.34
c
Without cost
42.778
1,78
a
6.61
a
p<.05
b
The F value associated with the full
model is the
test of
the full
model and not a model comparison test.
The test of this model assumes the effects of those interactions
that include this variable are equal to zero.
139
TABLE 23
CORRELATION COEFFICIENTS FOR INDIVIDUAL ATTRIBUTES WITH
LONGRUN DECISION EFFICIENCY
Individual Attribute
G
GP
GN
GP /GN
DN
0.7616
(.001)
0.6528
(.001)
0.1868
(.085)
0.6613
(.001)
Intrinsic motivation
0.2854
(.008)
0.3044
(.004)
0.2399
(.026)
0.1758
(.121)
Extrinsic (monetary)
motivation
0.1071
(.327)
0.1887
(.081)
0.3018
(.005)
0.0185
(.871)
GPA
0.1737
(.110)
0.2526
(.019)
0.3377
(.002)
0.0861
(.450)
Note: The numbers within the parentheses are the exact levels
associated with the rejection of the null hypothesis that the
correlation coefficient is equal to zero.
TABLE OF CONTENTS
ACKNOWLEDGEMENTS iii
ABSTRACT viii
CHAPTER I. INTRODUCTION AND OBJECTIVES 1
Variance Investigation Decision Processes 1
The Research Objectives A
Dissertation Organization 6
CHAPTER II. AREA OF INVESTIGATION 8
Managerial Accounting Concepts 8
Managerial Decisions 8
Management Task Planning and Control 9
Standard Cost Variance Investigation 11
Psychological Concepts 16
Psychophysics 16
Human Information Processing 20
CHAPTER III. RESEARCH METHODOLOGY AND DESIGN 23
General Conceptual Development 23
Decision Situation Structure 23
Available Information Set 25
Individual Information Processing Efficiency 26
Individual Ability to Expand the Information Set 27
General Research Design 28
Selection of Independent Variables 29
I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Philosophy.
A. Rashad Abdelffhali k, Chairman
Professor of Accounting
I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Philosophy.
Gary L. irHstrum
Associate Professor of Accounting
I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Philosophy.
Dougla<Â£^ T. STiowLalT1^"
Assistant Professor of Accounting
I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Philosophy.
Richard A. Griggs ^ //
Assistant Professor of Psychology
TABLE 8
OBSERVED MEAN
EDV.jS AND
TDV.jS GIVEN THE
VARIOUS LEVELS OF THE
DISTRIBUTION AND COST VARIABLES
Distribution
Cl Cost
Variable Level
Expected Initial
C2 Cost
Variable Level
Variable
DV
TV
Decision Anchor
TDV
EDV
SI
37.83
38.16
38.25
38.62
39.01
S2
39.62
39.98
40.50
40.41
42.43
o
en
26
distributional information items (the statistical means, variances, and
distributional shapes of the two states) is manipulated as an indepen
dent variable. The presence of other distributional information items
(the relative frequencies and the allowed standard) is controlled as a
constant.
Individual Information Processing Efficiency
The individual's efficiency in processing the available infor
mation relates to the particular heuristics or strategies employed in
combining and weighting the various items of information. The term
efficiency implies a relationship between the individual's process
output (his decision) and a normatively correct or optimal decision.
The individual's decision and information processing performance can be
evaluated by comparing his performance against an optimal model.
Optimality refers to the best possible performance under given condi
tions. Since the optimal decision model relies on an incomplete infor
mation set rather than certain knowledge,even its performance can be
affected by both the structure of the situation and the contents of the
available information set.
The optimal models used in this research are a function of the
experimental environment: the singleinterval procedure found within
the psychological theory of signal detection and the basic decision
situation of standard variance investigation. Within this environment
the optimal decision rule for minimizing (maximizing) the expected cost
(value) of a set of decisions is based upon an extension of Bayes'
theorem that takes into account the relative costs (values) of various
2
information provided. In particular, the manager's perception of the
standard setting process will have considerable influence upon his
variance investigation decision process. If the manager believes that
the standards are unrealistic (i.e., that the standards have been placed
too far from the incontrol distribution) he may rescale either the
standards or the variances to correspond with his own perception of the
incontrol distribution.2
If it is assumed that a manager accepts the standard setting pro
cess as realistic and does not rescale the standards or variances, his
variance investigation decisions may be viewed as the culmination of a
twostage process. The first stage concerns the detection of the
particular distribution (e.g., incontrol or outofcontrol) that gen
erated the variance. The manager's performance of this task is a
function of the sensitivity of his decision process (model). This
sensitivity is affected by the structure of the particular situation
and by the manager's knowledge of this structure. The extent of the
manager's knowledge of the situation, in turn, is affected by the
available information (both contained within the variance report and
provided from other sources), and by the manager's ability to learn
from his experiences with the processes being controlled. In situations
where the controlled process distributions have some area of overlap
the results of a manager's detection process are probabilistic (i.e.,
prior to completing an investigation the existence of any given state
is uncertain). Furthermore, the greater the area of overlap, the more
difficult the discrimination task becomes.
2Within this context, an incontrol distribution concerns statistical
congruence of production output and planned output in terms of
controllable resource utilization.
II Information Level
Cost
Cl
Cost
C2
Distribution
SI
1 1 i
36.90 37.575 38.25
+
39.77 41.29
Distribution
S2
C B A
h
38.7 39.6 40.50
43.19
H
45.88
Distribution
SI
12 Information Level
Cost
Cl
B A
+
36.86 37.555 38.25
Cost
C2
4
39.755 41.26
C B A
Distribution 1 } 1
S2 38.57 39.535 40.50
4
42.59
4
44.68
Note: All scales are actual minutes incurred per chair produced.
FIGURE 2
SIMULATED INITIAL DECISION ANCHORS (POINTS A), FINAL DECISION
ANCHORS (POINTS B), AND OPTIMAL CUTOFF VALUES (POINTS C)
159
and thus parallel those obtained with the DNA^DN^ measure. Both the
DNA^DN^ and the BNA^BN^ measures obtained results that indicated
greater multiple decision anchor usage when the subjects' initial de
cision anchors were closer to the standard (the SI distribution level
within the DNA^DN^ measure and the Cl cost level within the BNA^BN^
measure). No plausible explanation for these results is readily
apparent.
The obtained associations between the individual decision criteria
measures and the individual attributes of extrinsic (monetary) moti
vation and subject GPA have intuitive interpretation. As both the
extrinsic (monetary) motivation measures and the subjects' GPAs de
creased the level of conservatism increased. Furthermore, as the sub
jects' GPAs decreased the use of multiple decision anchors increased.
Of the two individual process variables (decision model sensitivity
and decision criteria), the decision criteria had the more signifi
cant affect on a subject's performance feedback measure and, in turn,
on the level of his monetary rewards. However, since motivation
pretests were not administered,causal relationships (i.e., decreased
motivation led to increased conservatism, or increased conservatism led
to decreased motivation) can not be claimed.
Individual LongRun Decision Efficiency
The overall efficiency of an individual's decision making within
the experiment was measured using the G.Â¡ variable. The G variable was
the individual's net additional decision costs relative to the optimal
model's total decision costs. The G^ variable was disaggregated into
two measures the total additional individual decision costs relative
21
The Bayesian approach is a normative model specifying how a
decision should be made given certain internally consistent relation
ships among probabilistic beliefs. The basic beliefs of this approach
are that decisions should be based on subjective probabilities and
that these probabilities should be revised upon the receipt of addi
tional information in accordance with Bayes's theorem.
The major findings of Bayesian research are labeled conservatism.
The subjects, after receiving additional information, revise their
posterior probabilities in the same direction as the optimal model but
the revision is insufficient. Much of the research has focused on an
explanation of the cause of conservatism, the major explanations being
misperception (Peterson et al., 1968), misaggregation (DuCharme and
Peterson, 1968), and response bias (DuCharme, 1970).
Another area within HIP is the study of subjective information
processing principles and decision rules, labeled heuristics. A
heuristic, within this context, refers to a learned set of rules or
principles which are utilized by individuals in making the particular
decisions required of them. Research in this area has been concerned
with identifying systematic biases (relative to some definition of
optimal) of subjective heuristics within certain types of decision
tasks. Those information processing and decision rule biases iden
tified thus far have been labeled as an anchoring and adjustment
heuristic (Tversky and Kahneman, 1973), a representative heuristic
(Kahneman and Tversky, 1972; Swieringa et al., 1976), an avail
ablility heuristic (Tversky and Kahneman, 1973), and the law of small
numbers (Tversky and Kahneman, 1971).
19
The critical value L(xQ) for this decision rule has two possible
values: a theoretical value which is a measure of the criteria of an
optimal (or ideal) subject and a subjective value which is a measure
of the criteria of an actual subject.
One set of TSD models assumes that both conditional probability
distributions are Gaussian; i.e., $(xn) and $(xsn) are normally
distributed. With the additional assumption of equal variance for both
distributions, the parameters which measure individual discrimination
sensitivity and individual decision criteria are labeled d1 and g,
respectively. The discriminability measure, d', has the following
theoretical definition:
where, ysn= the mean of the signal plus noise distribution;
vn = the mean of the noise alone distribution;
a = the standard deviation of both distributions;
zn = the value of the normal distribution function associated
with the noise alone distribution and any decision axis
cutoff value common to both distributions; and
z = the value of the normal distribution function associated
sn
with the signal plus noise distribution and any decision
axis cutoff value common to both distributions.
The d' measure theoretically is independent of the decision criteria
measure.
The decision criteria measure, B, has the following theoretical
definition:
B = i>(zsn) / i>(zn)
11
the task plans, and 2) facilitate management control evaluation of
operational control performance.
The operational control manager uses the specific task control
feedback to 1) decide whether the physical system performance is in
agreement with the specific task plan, and 2) to decide whether the
task can be restructured to bring performance back into agreement with
the plan (if performance and the plan differ). A task that can not be
restructured could have significance for the overall task control feed
back to the management control activity and possibly may lead to
modification of the original task plans. Such modification could in
turn have significance for the overall objective control feedback to
the strategic planning activity, and a modification of the original
overall objectives may follow.
Standard Cost Variance Investigation
Within the operational control activity the specific task plan
provides standards of performance in terms of expected component costs
and usages, and the desired physical outputs. The operational control
manager structures the tasks within the physical system and periodically
receives a standard cost variance report describing the system output
in terms of the task plan and the actual results. Upon receiving a vari
ance report the manager must decide the nature of the given variances.
Dopuch et al. (1967) present a classification of standard cost
variances that is based on the expected source of the variances. A
variance resulting from a random fluctuation of the physical system,
labeled a Type 1 variance, requires no operational control response if
180
If Your
Investigation
Decision Is
And If The
Assembly Line
State Is
Then
Your
Costs
Are
Investigation
Production
Total
Investigate
Incontrol
$
X
5
0.00
$ X
Investigate
Outofcontrol
$
Y
$
0.00
$ Y
Not Investigate
Incontrol
$
0.00
$
0.00
$ 0.00
Not Investigate
Outofcontrol
$
0.00
$ 175.00
$ 175.00
X represents the variable investigation cost when the assembly
line is incontrol. This investigation cost is related to the size of
the labor efficiency variance; the more negative (unfavorable) the
variance the larger this cost.
Y represents the variable investigation cost when the assembly
line is outofcontrol. This investigation cost also is related to the
size of the labor efficiency variance; the more negative (unfavorable)
the variance the smaller this cost. For any given labor efficiency
variance the investigation cost if the assembly line is outofcontrol,
Y, is always larger than the investigation cost if the assembly line is
incontrol, X. This is due to the investigation cost including the cost
of correcting the outofcontrol labor procedure when the assembly line
is outofcontrol.
The $175.00 cost is the expected marginal production cost of
operating next period in the outofcontrol state. This cost is fixed
for all labor efficiency variance reports.
Your Performance Measure as the Operational Control Manager
The metal folding chair section manager (your immediate supervisor)
evaluates your control performance in terms of the minimization of both
investigation costs and production costs above the expected standard
(the total of these costs is called the Total Investigation Decisions
Cost). The extent to which your investigation decisions for a specified
period of time minimizes the Total Investigation Decision Cost is
determined as follows:
Extent of Total Investigation
Decisions Cost Minimization =
Total Investigation Decisions Cost of your decisions
Optimal Total Investigation Decisions Cost
The optimal Total Investigation Decisions Cost is determined by the
section manager at the end of the specified period of time. It i_s_
possible for your Total Investigation Decisions Cost to equal the optimal
Total Investigation Decisions Cost. AMSECO pays a cash bonus based on
this measure of the extent of Total Investigation Decisions Cost
minimization. The closer this measure is to one or less than one the
larger the cash bonus.
The cash bonus for 100 labor efficiency variance reports is as
fol 1ows:
Extent of Total Investi
gation Decisions Cost
Minimization
1.000
1.001 to 1.010
1.011 to 1.020
Cash Bonus
$ 10.00
9.00
8.00
175
2) A chance to earn up to $10.00 as payment for participating in
the experiment.
3) A chance to gain firsthand experience with an accounting
research project.
Your participation in the experiment would not involve a great
sacrifice of your time. The entire experiment requires only two
oneperiod sessions. The times of these sessions are flexible to meet
the requirements of your personal schedule. The first oneperiod
session, a training session, would be scheduled sometime on Monday or
Tuesday, April 10th and 11th. The second oneperiod session, the actual
experiment, would be scheduled sometime on Wednesday or Thursday,
April 12th and 13th.
I can assure you that your responses within the experiment will
be held strictly confidential. Only aggregate or anomymous results
will be made pub!ic.
I will pass around a signup sheet; I would appreciate it if
everyone would print their name and indicate with a checkmark whether
you are willing to participate. If you are willing to participate,
fill in your phone number and indicate some times that I can contact
you to schedule your participation.
I would like to thank you in advance for your time. The overall
success of this study is dependent upon a positive response from most
of you in this room. I am confident that as professionals you will
exceed my expectations.
Does anyone have any general questions?
7
aspects of human information processing such as the general heuristic
of anchoring and adjustment.
Chapter III synthesizes the general concepts of the previous
chapter and develops a conceptual framework of standard cost variance
investigation employing the methodology of the psychological concepts.
This conceptual framework was operationalized using simulation tech
niques, and experimental hypotheses were derived from the simulations.
The details of a laboratory experiment designed to test the
hypotheses derived from the conceptual variance investigation framework
are presented in Chapter IV. The experiment employed a betweensubjects
design and was manipulated factorially using specific variance investi
gation situation variables. Modified parameters from the psychological
theory of signal detection were employed to measure subject's information
processing efficiency and decision model sensitivity.
The results obtained from the laboratory experiment are presented
in Chapter V. The model comparison procedure of nonorthogonal analysis
of variance was the primary method of analysis employed. Chapter VI
discusses the results, develops a modification (ex post) of the
original conceptual framework, and discusses some implications for
accounting and for future accounting research.
37
The distribution variable
Manipulation of the distribution variable involves two factors:
1)the distributional parameters of each state, and 2) the statistical
relationship between the states. The two levels of this variable are
generated through a change in the variance and a change in the stan
dardized distance between the means of the two states. The first
level of the distribution variable, labeled Sl_, has the following
parameters and relationships:
1) y1 = 36.0 actual minutes incurred per chair;
2) crn = a = Gj = 3.0 actual minutes incurred per chair; and
3) u12 = y + 1.5a, = 40.5 actual minutes incurred per chair,
where, y = the mean of the jth state of nature (incontrol = 1 and
* J
outofcontrol = 2) given the ith distribution level
(SI = 1 and S2 = 2);
a = the standard deviation of the jth state of nature given
' J
the ith distribution level; and
a = the standard deviation common to both states of nature
given the ith distribution level.
The second level of the distribution variable, labeled S2^, has
the following parameters and relationships:
1) y = 36.0 actual minutes incurred per chair;
2) a21 = a22 = a9 = 5.0 actual minutes incurred per chair; and
3) y2 = y21 + 1.8a = 45.0 actual minutes incurred per chair,
where y , a^, and a^ are defined the same as in the SI level. The
third and fourth statistical moments of each state within each dis
tribution level are uncontrolled except that their deviations from a
normal distribution are minimized.
125
TABLE 18
CORRELATION COEFFICIENTS FOR EXTRINSIC (MONETARY) MOTIVATION AND
SUBJECT GPA WITH INDIVIDUAL DECISION CRITERIA VARIABLES
Model
Extrinsic (Monetary)
Motivation
Subject GPA
BNÂ¡
0.1975 (.068)
0.0468 (.669)
BNAj
0.1916 (.077)
0.1000 (.360)
BNAjBNj
0.0564 (.606)
0.1896 (.080)
BNCj
0.2951 (.006)
0.2346 (.030)
Note: The numbers within the parentheses are the exact alevels
associated with the rejection of the null hypothesis that the
correlation coefficient is equal to zero.
102
of the distribution variable differ significantly (Z=7.6346, p<.01
twotailed), with the S2 level having the larger mean. Differences in
the means of the levels of the cost variable are also significant
(Z=1.4362, p<.10 twotailed), with the C2 level having the larger mean.
Hypothesis 1.1a predicted that (TDV^S1) would not differ signifi
cantly from 38.25 actual minutes incurred. A singlesample Z test was
employed to test this hypothesis, and the difference was found to be not
significant (Z=0.9912, n.s. twotailed). Hypothesis 1.1b predicted
that (TWi S2) would not differ significantly from 40.5 actual minutes
incurred. A singlesample Z test indicated that the difference was
significant (Z=1.5681, p<.10 twotailed). Hypothesis 1.2a predicted
that (TDV.j  II) would not differ significantly from (TDV^]I2). The Z
test used to test this hypothesis indicated no significant difference
(Z=0.7007, n.s. twotailed). Hypothesis 1.2b predicted that (TDV^Cl)
would not differ significantly from (TDV.Â¡C2). This hypothesis was
tested by the above model comparison procedure and related Z test, and
the results indicated that the difference was significant (p<.10).
Decision Anchor Adjustment
Hypothesis 1.3 relates to an assumption made in the training phase
simulation: i.e., that subjects would have equal relative decision
anchor adjustment between the levels of the various experiment conditions.
The simulation arbitrarily used an adjustment of onehalf the linear
distance between the appropriate expected initial decision anchor and
the appropriate optimal model decision cutoff value.
The method of analysis was the model comparison procedure where
the dependent variable is the RA^ measure and the independent variables
Hypotheses
Analysis
Method(s)
3.4b (BNAÂ¡BN,1Cl) <
Model comparison
(BNAiBN1C2)
Onetailed Z test
4.1 a2(GiCl)
F test of equal
variances
4.2a (GiCl)<(GiC2)
Sources F value
Onetailed Z test
4.2b (Gj1S2)<(GiSI)
Sources F value
Onetailed Z test
4.2c (GiIl)=(GiI2)
Model comparison
Twotailed Z test
4 Continued
Test
Statistic(s)
F=11.4812 (pc.Ol)
Z=3.7090 (p<.01)
F=2.43 (p<.01)
F=2.56 (n.s.)
Z=2.442 (p<.01)
F=0.51 (n.s.)
Z=1.7452 (p<.05)
F=1.0488 (n.s.)
Z=0.4528 (n.s.)
Results
The mean multiple decision anchor effect
on relative decision criteria of those
subjects within the Cl cost level was
significantly larger than that of those
subjects within the C2 cost level.
The relative net decision costs variance
of those subjects within the Cl cost
level was significantly smaller than that
of those subjects within the C2 cost level.
The mean relative net decision costs of
those subjects within the C2 cost level
was larger than that of those subjects
within the Cl cost level.
The mean relative net decision costs of
those subjects within the SI distribution
level was larger than that of those sub
jects within the S2 distribution level.
The mean relative net decision costs of
those subjects within the reduced infor
mation level was not significantly dif
ferent than that of those subjects with
in the expanded information level.
100
TABLE 5
MODEL COMPARISON PROCEDURE RESULTS
FOR THE TDVj
DEPENDENT
VARIABLE
Model
SSe
d.f.(F)
F
Ful 1d
91.6549
8,77
a
8.19
Without 3way interaction
93.1248
1,77
1.23
Without distribution x cost
interaction
93.1426
1,78
0.01
Without information x distribu
tion interaction
93.1463
1,78
0.02
Without information x cost
interaction
93.7287
1,78
0.51
e
Without information
95.0374
1,81
1.09
e
Without distribution
164.6998
1,81
61.27a
. e
Without cost
97.6986
1,81
3.39C
a
p<.01
b
pc.05
Cp<.10
d
The F value associated with the full model is the test of the full
model and not a model comparison test.
e
The test of this model assumes the effects of those interactions
that include this variable are equal to zero.
127
models. Both the stated objective function and subject payoff function
involved the minimization of the decision costs. A subject's longrun
decision efficiency was measured in terms of his minimization of his
decision costs relative to those of an optimal model.
The method of analysis was the model comparison procedure where
the dependent variables are the GP.Â¡, and GN.Â¡ measures. The in
dependent variables are the three situation variables (with their
interactions), the DN.Â¡ variable, the three motivation factors, and the
GPA.j variable. Ideally, both the DN.Â¡ and BN^ variables should be
included in the model: however, the BN.Â¡ variable had significantly
greater association with the other independent variables than did the
DN.j variable (the R2 for the full BN.Â¡ model was 0.6474, whereas the
R2 for the full DN^ model was 0.2192). Inclusion of the BN.Â¡ variable
would produce a significant problem of multi colinearity. The three
full models are:
Gi
GPi = y + j + Bk + Y1 + a6jk + fjl + 6Ykl + a6Yjkl + *1 +
GN + t. + X. +6.
1 ill
The three model comparison procedure results are presented in
Table 19, and the F values associated with the sources of the reduced
models are presented in Table 20. The results indicate that the re
duced G. model contains the distribution by cost interaction and the
DN. variable. Both the interaction and the DN. variable are siqnifi
l i a
cant at the p<.01 level. The reduced GP.. model contains the distribu
tion by cost interaction, the DN. variable, the intrinsic motivation
factor, the extrinsic (monetary) motivation factor, and the GPA.
variable. The interaction, extrinsic motivation factor, and GPA.
l
variable are significant at the p<.05 level. The interaction,
56
will equal the intersection point of the two distribution curves
within that level.
HI.lb (TV.S2) = 40.5.
The mean initial decision anchor given the S2 distribution level
will equal the intersection point of the two distribution curves
within that level.
HI.2a (TDV.I1) = (157^12).
The mean initial decision anchor given the II information level
will equal the mean initial decision anchor given the 12 infor
mation level.
HI.2b (TDViiCl) = (TViC2).
The mean initial decision anchor given the Cl cost level will
equal the mean initial decision anchor given the C2 cost level.
HI.3a (RAi111) = (RAi112).
The mean relative adjustment given the II information level will
equal the mean relative adjustment given the 12 information level.
HI.3b (RA^Sl) = (RAi Â¡S2).
The mean relative adjustment given the SI distribution level will
equal the mean relative adjustment given the S2 distribution
level.
HI.3c (RAiCl) = (RA.C2).
The mean relative adjustment given the Cl cost level will equal
the mean relative adjustment given the C2 cost level.
Individual decision model sensitivity hypotheses formation
The measures of individual decision model sensitivity should
generally be unaffected by the independent variables. These measures
APPENDIX E
SUBJECT HEURISTIC ELICITATION QUESTIONNAIRE
Please answer the following questions in the spaces provided. This is
not a test; there are no right or wrong answers. It is important that
you try to answer each question honestly. If you need additional space
use the back of this questionnaire.
1. What rule or set of rules did you use to make your investigation
decisions in the last 50 labor efficiency variance reports (the
second block)? Please include any numerical values that were part
of this rule or set of rules.
2. Do you feel you used the same rule or set of rules as you gave in
question one to make your variance investigation decisions in the
first 50 labor efficiency variance reports (the first block)? If
not the same, how does the rule or set of rules used in the first
50 reports differ from what you gave in question one? Please in
clude any numerical values used as part of the rules or sets of
rules that were different.
3.Rate the following items of information in terms of their importance
to you in making your variance investigation decisions (do not rate
the 'other' item unless you have specified some other information).
Circle the appropriate response.
A.The background information booklet.
irklrkk'k'kk'k'kk'k'k'kk'k'k'k'kk'k'k'k'k'k'k'k'k'k'k'k'k'k'k'kk'kkk'k'k'k'k'k'k'k'k.'kk
k
*
Not Slightly
Important Important
Moderately Very
Important Important
Extremely
Important
B. The information based upon past experience and past observation
which was presented in the front of each experiment booklet.
k'kk'kk'kkk'kk'kkkkic'k'k'kkkk'kk'k'kkk'k'k'k'k'k'kk'kkkkk'k'kk'k'k'k'kk'k'k
* * *
Not Slightly Moderately Very Extremely
Important Important Important Important Important
C. The standard minutes allowed per chair.
**************************************************
k
k
*
*
*
Not Slightly
Important Important
Moderately Very
Important Important
Extremely
Important
184
TABLE 4 Continued
Analysis Test
Hypotheses Method(s) Statistic(s) Resul ts
3.2 a2(BNA.Cl) <
F test of equal
F9.36 (p
K.01)
The adjusted relative decision criteria
variances
variance of those subjects within the Cl
cost level was significantly larger than
that of those subjects within the C2
cost level.
3.3a (MCiIl) = (BCiI2)
Model comparison
F=0.9075
(n.s.)
The mean decision criteria conservatism
of those subjects within the reduced
Twotailed Z test
Z=0.6802
(n.s.)
information level was not significantly
different from that of those subjects
within the expanded information level.
3.3b (BNCiSl)=(BNC.S2)
Model comparison
F=0.3377
(n.s.)
The mean decision criteria conservatism
of those subjects within the SI distribu
Twotailed Z test
Z=0.3295
(n.s.)
tion level was not significantly differ
ent from that of those subjects within
the S2 distribution level.
3.3c (BNCiCl)<(BNCiC2)
Model comparison
F=6.6104
(p<.05)
The mean decision criteria conservatism
of those subjects within the Cl cost
Onetailed Z test
Z=2.5598
(pc.05)
level was significantly larger than that
of those subjects within the C2 cost
level.
3.4a (BNAiBN.Sl) <
Model comparison
F=0.0924
(n.s.)
The mean multiple decision anchor effect
on relative decision criteria of those
(BNAiBNiS2)
Onetailed Z test
Z=0.3892
(n.s.)
subjects within the SI distribution level
was not significantly different from that
of those subjects within the S2 distribu
tion level.
CO
149
Third, the obtained effects of the situation variables on the
relative decision costs were weaker than those predicted. The pre
dicted effects were based, in part, upon the assumption of equal
learning efficiency between the levels of the various situation vari
ables. However, as indicated earlier the obtained learning efficien
cies were not equal between various situation variable levels. A
plausible explanation for the weaker obtained effects of the situation
variables on the relative decision costs utilizes the unequal learning
efficiency results. That is, the incorporation of unequal learning
efficiencies within the prediction of the effects of the situation
variables on the relative decision costs will produce expected effects
with strengths similar to the obtained effects.
Finally, multiple decision anchor usage had greater effects upon
individual decision model sensitivity within situations where the state
distributions were closer together and had smaller variance. It had
greater effects on individual decision criteria within situations where
the decision costs favored making investigation decisions for smaller
(in absolute value) labor efficiency variances. Both of these results
are contrary to those predicted, and no explanations are offered.
Overall, the variable which had the largest impact on the relative
decision costs was the subjects' decision criteria conservatism. The
mean DNA^ measure over all subjects was 0.926. Considering that a
value of one would indicate variable response ranges were not used, the
subjects' overall decision model sensitiviy was approximately that of
the optimal model. The mean BNC^ over all subjects was 0.828. This
indicated that the average distance between the subjects' decision
criteria and the optimal model's decision criteria was 82.8 percent
of that of the optimal model. This was supported by the mean RA.Â¡ over
67
Area(GNj Cl,SI, 12) = 0.08483
Area(GPj (Cl,S1,12) = 0.05048
AreaiGNiC2,S1,I2) = 0.19780
Area(GPiC2,Sl,I2) = 0.06549
These areas must be adjusted for the relative frequency differ
ences and the relative cost differences between the various conditions.
If the incontrol distribution is given a relative frequency weight of
one,then the relative frequency weight of the outofcontrol distribu
tion would equal twothirds (P(incontrol) = 0.60 and P(outofcontrol) =
0.40). The relative cost weights can be obtained directly from the
manipulation of the cost variable. Given the Cl cost level, errors
under the outofcontrol distribution equal three times the errors under
the incontrol distribution. Given the C2 cost level, errors under the
incontrol distribution are equal to three times the errors under the
outofcontrol distribution. The four areas associated with the
various GÂ¡ measures can be converted into relative relations by taking
into account both of these relative weights (frequency and cost). These
relative relations are as follows:
f(GN.Â¡  Cl,S1,12) = (0.08483) (1) (1) = 0.08483
f(GPj  Cl,SI, 12) = (0.05048) (2/3) (3) = 0.10096
f(GNiC2,Sl,I2) = (0.19780)(2/3)(1) = 0.13187
f(GPiC2,Sl,I2) = (0.06549)(1)(3) = 0.19647
The difference between the relative relations of GP.Â¡ and GNj
given a level of the cost variable indicates the relative effect of
these intervals upon the Gj measure. The differences for this simu
lation are as follows:
22
This study will make particular use of the anchoring and
adjustment heuristic. In many situations, individuals first make
decisions by starting with an initial anchor (decision point) and then
adjust this initial anchor as they learn from their experiences. The
initial anchor can be suggested by the structure of the decision
situation, or can be the result of a partial computation or estimate.
Empirical tests involving the anchoring and adjustment heuristic in
dicate individuals do not sufficiently adjust their initial decision
point. That is, their adjustment is less than that which would allow
optimal processing of the available information (Slovic and Lichtenstein,
1971; Slovic, 1972; Alpert and Raiffa, 1968; Tversky and Kahneman,
1974).
130
extrinsic motivation factor, and GPA^ variable are significant at
the p<.05 level. The DN^ variable is significant at the p<.01 level
and the intrinsic motivation factor is significant at the p<.10 level.
The reduced GN.Â¡ model contains the intrinsic motivation factor, the
extrinsic (monetary) motivation factor, and the GPA^ variable. Both the
extrinsic motivation factor and the GPA.Â¡ variable are significant at the
pc.Ol level and the intrinsic motivation factor is significant at the
p<.05 level.
The means, variances, and sample sizes for the distribution by
cost interaction within the G^ and GP^ models are included in Table 20.
Figure 7 presents a graphic representation of both distribution by
cost interactions. Bartlett's test for homogeneity of variances in
dicted that the variances within the G^ model distribution by cost
interaction are significantly heterogeneous (x2=l0.66, p<.05 with
3 d.f.). The variances within the GP model distribution by cost inter
action are considered homogeneous (x2=2.52, p<.25 with 3 d.f.).
Duncan's multiple range test of equal means indicated that the means
within the G^ model distribution by cost interaction form two groups:
the means within a group do not differ significantly (p=.01) but a
significant difference (p=.01) exists for the means between groups.
These groups are 1) (G.S1,C2) and (G.Â¡ S2,C2), and 2) S2,C1),
(G\j SI,C1), and (G.S2,C2).
Hypothesis 4.1 predicted that the variance of (G^C1) would be
significantly smaller tha n the variance of (G.jC2). Using the F test
of equal variances, the variances differ significantly (F=2.43, pc.Ol),
with (G^ Cl) having the smaller variance. Hypothesis 4.3 predicted a
significant distribution by cost interaction within the G model in
TABLE 11
MODEL COMPARISON PROCEDURE RESULTS FOR THE DNi, DNAi, AND DNAiDMi DEPENDENT VARIABLES
DNi
DNAi
DNAiDMi
Model
SSe
d.f.(FT~
F
SSe
d.TTUT
F
SSe
d.f.(F)
F
Ful 1d
2.1852
11,74
4.66a
1.1753
11,74
2.03b
1.4200
11,74
0.97
Without 3way interaction
2.2031
1,74
0.61
1.1758
1,74
0.03
1.4327
1,74
0.66
Without distribution x
cost interaction
2.2236
1,75
0.70
1.2105
1,75
2.22
1.4345
1,75
0.10
Without information x dis
tribution interaction
2.2032
1,75
0.00
1.1783
1,75
0.16
1.4343
1,75
0.09
Without information x
cost interaction
2.2123
1,75
0.31
1.1758
1,75
0.00
1.4412
1,75
0.45
. e
Without information
2.2324
1,78
0.00
1.2140
1,78
0.01
1.4453
1,78
0.01
. 6
Without distribution
2.4330
1,78
7.01a
1.2269
1,78
0.84
1.5566
1,78
6.01b
. e
Without cost
2.3748
1,78
4.98b
1.4522
1,78
15.31a
1.4575
1,78
0.66
V.Ol bp<.05 Cp<.10
^The F value associated with the full model is the test of the full model and not a model comparison test,
e
The test of this model assumes the effects of those interactions that include this variable are equal to
zero.
APPENDIX A
THEORY OF SIGNAL DETECTION DERIVATIONS
Relationship of Conditional Probability Matrix and Bayes1 Theorem
The unconditional probabilities of the four event outcomes can be
stated in terms of the conditional probabilities and the prior proba
bilities of the sn and n distributions: 1) the probability of a hit,
P(sn,Y) equals P(Ysn)P(sn); 2) the probability of a miss, P(sn,N)
equals P(N sn)P(sn); 3) the probability of a false alarm, P(n,Y) equals
P(Yn)P(n); and 4) the probability of a correct rejection, P(n,N)
equals P(Nn)P(n).
Egan (1975, pp. 1213) demonstrates the relationship between the
singleinterval procedure and Bayes1 theorem. The posterior probability
of the event sn, given the discrete observation x, can be represented
as P(snx). Since the two events sn and n are exhaustive the addition
of P(snx) and P(njx) equals one. The posterior probability of the
event sn can be stated as P(sn,x) which equals:
P(xsn)P(sn) = P(snx)P(x).
Rearranging the terms of this equality and expanding the P(x):
n/ i P(xsn)P(sn)
P(sn x)= iÂ¡!
P(x]sn)P(sn) + P(x[n)P(n)
This equation is a simple expression of Bayes' theorem. It can be ex
tended to the odds form, the posterior odds equals the prior odds times
the likelihood ratio of the observation x:
P(sn[x) P(sn) P(xsn)
P(nx) P(n) P(xn)
172
192
Weissman, S. M.; Hollingworth, S. R.; Baird, J. C. "Psychophysical
Study of Numbers: III. Methodological Applications."
Psychological Research, 38 (1975), 97115.
Zannetos, Z. A. "Standard Cost as a First Step to Probabilistic Control:
A Theoretical Justification, and Extension and Implications."
The Accounting Review, 39 (1964), 296304.
Zigler, E. "Metatheoretical Issues in Developmental Psychology." In
Theories in Contemporary Psychology, M. Marx (Ed.). New York:
Macmillan, 1963.
BIOGRAPHICAL SKETCH
Clifton Edward Brown was born on May 17, 1947,in Kansas City,
Kansas. In June 1965 he graduated from St. Petersburg High School,
St. Petersburg, Florida. Mr. Brown enlisted in the United States Air
Force from 1965 through 1968, and was honorably separated with the
rank of Sergeant. He was married in December 1968 to Sandra Cornell.
Mr. Brown attended the University of South Florida from which
he graduated in June 1971 with a Bachelor of Arts degree majoring in
accounting. From graduation until 1974 Mr. Brown was employed as an
industrial accountant, during which time he held positions of con
troller and vicepresident of finance. From September 1974 until
the present Mr. Brown has attended the University of Florida where
he has studied for a Doctor of Philosophy degree in business admin
istration (accounting).
While attending the University of Florida, Mr. Brown was the
recipent of the Price Waterhouse Foundation Grant, the American
Accounting Association Fellowship Grant, and the Haskin and Sells
Fellowship Grant. Mr. Brown was elected by the faculty as the
university representative to the 1975 American Accounting Association
Doctoral Consortium.
193
168
(or push) of the standard, subjectively limiting a complete (convergent)
adjustment to the optimal decision value. The decision behavior magnet
view represents a generalization of the subjective adjustment limit
concept. The subjective limit concept as presented earlier dealt with
decision criteria conservatism involving only convergent adjustment.
The question of whether decision makers could learn (either through
training or experience) to reduce the decision biases implied by the
decision behavior magnet concept remains unanswered. An alternative
approach, however, involves the standard setting process itself.
Previous research involving the nature of standards (e.g., strict
standards, currently attainable standards, lax standards) has primarily
dealt with the standard's motivational affects. Other things being
equal, the nature of the standard may affect the decision behavior
magnet biases.. Within situations requiring adjustment, lax standards
(which have values greater than the mean of the incontrol state) may
reduce substantially divergent decision criteria conservatism. Within
situations requiring convergent adjustment, strict standards (which
have values less than the mean of the incontrol state) may reduce
substantially convergent decision criteria conservatism.
Future Research
The implications of this study for future accounting research
take two general forms: modification of the existing conceptual
development and generalizations of the modified conceptual development.
Modifications of the existing conceptual development would involve
incorporating and testing the ex post explanations offered for certain
CHAPTER III
RESEARCH METHODOLOGY AND DESIGN
General Conceptual Development
A general problem confronting decision makers is that the indi
vidual must decide subjectively which state of nature is most probable
based upon some incomplete set of information. When an individual
deals repetitively with a similar situation his longrun decision
efficiency or "decision correctness" can be affected by 1) the struc
ture of the particular decision situation, 2) the contents of the set
of available information, 3) his efficiency in processing the available
information, and 4) his ability to expand the available information
through experience with, and observations of, the various states of
nature. Each of these four elements is discussed below with specific
reference to the objectives of this research.
Decision Situation Structure
The structure of the particular decision situation primarily de
pends upon several key variables. These variables include the number of
possible states of nature, the relative frequencies of the states, the
various statistical relationships among the states, and the relation
ships between the various decision outcomes (the costs incurred given a
specific decision and the existence of a specific state). Depending on
23
155
(DNA.jC2). The subjective limit also should have had the effect of
skewing the measure away from those values which indicated larger
variable response ranges. The third moment (as expressed by the co
efficient of skewness) of the DNA measure indicated that 1) the
skewness of the (DNA^C1) distribution was positive (skewed away from
values which indicated larger variable response ranges), and 2) the
skewness of the (DNAC2) distribution was negative (skewed toward
values which indicated larger variable response ranges).
The multiple decision anchor variable (the DNA^DN^ measure) was
affected primarily by the distribution variable. Those subjects within
the SI distribution level employed multiple decision anchors to a
larger extent than did those subjects within the S2 level. The hypo
theses posited a relation between multiple decision anchors and the
%
distance separating the lower tail of the incontrol state from^the
subject's final decision anchor. It was expected that the larger this
distance (found within the C2 cost level and the S2 distribution level)
the greater would have been the subject's propensity to have perceived
a second outofcontrol state below the existing incontrol state
(and used multiple decision anchors). However, the obtained results
do not support this proposed relation.
The observed associations between the individual decision model
sensitivity measures and intrinsic motivation have intuitive inter
pretations. The intrinsic motivation factor measured experimental
task and experimental environment enjoyment as modified by decision
performance satisfaction. As the subjects use of multiple decision
anchors and variable response ranges increased, their intrinsic moti
vation appeared to decrease. Since the use of multiple decision
TABLE 4 Continued
2.3b
3.1a
3.1b
3.1c
Hypotheses
Analysis
Method(s)
Test
Statistic(s)
Results
(DNAjDNj  Cl) <
(DNAiDNiC2)
Model comparison
Onetailed Z test
F=0.6618 (n.s.)
Z=0.8642 (n.s.)
The mean multiple decision anchor effect
on relative decision model sensitivity
of those subjects within the Cl cost
level was not significantly different
from that of those subjects within the
C2 cost level.
(BNAiIl)=(BNAi
12)
Model comparison
Twotailed Z test
F=2.6738 (n.s.)
Z=0.7891 (n.s.)
The mean adjusted relative decision
criteria of those subjects within the
reduced information level was not sig
nificantly different from that of those
subjects within the expanded information
level.
(BNAiSl)=(BNAi
S2)
Model comparison
Twotailed Z test
F=1.9398 (n.s.)
Z=0.7048 (n.s.)
The mean adjusted relative decision
criteria of those subjects within the SI
distribution level was not significantly
different from that of those subjects
within the S2 distribution level.
(BNAiCl)>(BNAi
C2)
Model comparison
Onetailed Z test
F=107.4386 (p<.01)
Z=10.6306 (p<.01)
The mean adjusted relative decision
criteria of those subjects within the Cl
cost level was significantly larger than
that of those subjects within the C2
cost level.
115
TABLE 13
CORRELATION COEFFICIENTS FOR INTRINSIC MOTIVATION WITH
INDIVIDUAL DECISION MODEL SENSITIVITY VARIABLES
Model
Intrinsic Motivation
d!
i
0.2045 (.059)
DNj
0.2549 (.018)
DNAÂ¡
0.1724 (.113)
DNAjDNÂ¡
0.1672 (.124)
Note: The numbers within the parentheses are the exact alevels
associated with the rejection of the null hypothesis that the
correlation coefficient is equal to zero.
138
significance for the cost variable main effect within the model com
parison procedure could be the result of the distribution by cost
interaction. The cost variable has a significant effect only when given
the SI distribution level.
Individual Attributes and LongRun Decision Efficiency
The model comparison procedure results indicated that the DN
variable, the intrinsic motivation factor, the extrinsic (monetary)
motivation factor, and the GPA variable had significant effects upon
various individual longrun decision efficiency measures.3 The
analysis of these effects required additional tests. A productmoment
correlation was computed for each of these individual attributes with
each of the dependent variables. The results of these correlations
are presented in Table 23.
The negative association between the variable and the DN
variable indicates that as the relative net decision costs increase the
subjects' variable response ranges increase (DN increases). The
same association exists between the GP.Â¡ variable and the DN.Â¡ variable:
as relative additional decision costs increase the subjects' variable
response ranges increase. The positive association between the GN^
variable and the DN.Â¡ variable indicates that as the relative additional
decision savings increase the subjects' variable response ranges decrease
(DN.j decreases). The GP^/GN^ variable and the DN^ variable were
associated negatively: as the relative additional decision costs
3See the last sections in this chapter for definitions and additional
analyses concerning these individual attributes.
TABLE 4 Continued
Hypotheses
4.6 (GiPP)<(GiMP)=
(Gj  PM)<(G^11 MM)
Analysis Test
Method(s) Statistic(s)
Results
The mean relative net decision costs of
those subjects within the mixed training
session performances categories were less
than that of those subjects within the
constantly increasing training session
performances category and greater than
that of those subjects within the con
stantly decreasing training session
performances category.
LO
G">
62
Averaging over all conditionals except the distribution variable
gives the following results:
f(BAÂ¡ 1 SI) = 0.94009
f (BAÂ¡ 1S2) = 0.91496
Again, the difference in the f(BNAj) measures due to the distribution
variable is small compared to the standard error of the estimates (the
difference divided by the standard error is 0.02513/0.39174 = 0.C642).
A significant effect due to the distribution variable would not be
expected.
Averaging over all conditionals except the cost variable gives
the following results:
f(BAÂ¡ Cl) = 1.40654
f(BAÂ¡ C2) = 0.44851
The difference in the f(BNAj) measures due to the cost variable is
large compared to the standard error of the estimates (the difference
divided by the standard error is 0.95803/0.02436 = 39.3308). Con
sequently, a significant effect would be expected for the cost
variable.
Another implication drawn from the f(BNAj) measures concerns the
homogeneity of variance for the measures given different levels of the
cost variable. The variance for the f(BNAÂ¡  Cl) is .00034 and the vari
ance for the f(BNAj C2) is .00203. Using the F test for equal variances,
F=5.9344 (3 and 3 d.f.) which indicates the variances may not be equal.
The variance of (BNAÂ¡ [Cl) would be expected to be less than the vari
ance of (BNAj[C2).
This simulation can be extended to enable the formation of
hypotheses concerning the effects of the independent variables on the
TABLE 12
PARAMETER VALUES ASSOCIATED WITH THE REDUCED DNj, DNAj, AND DNAjDNj MODELS
Source
F Values Associated
With
the Sources of the Reducec
1 Models
DNi
DNi
DNAi
DNAi
d.f.
F
d.f.
F
d.f.
F
Distribution
1,79
7.10a
1,80
6.17 b
Cost
1,79
5.05b
1,80
15.52 a
Intrinsic motivation
1,79
8.64a
1,80
3.55C
1,80
3.59C
Extrinsic (nonmonetary) motivation 1,79
1.68
1,80
1.38
1,80
0.24
Extrinsic (monetary) motivation 1,79
0.04
1,80
0.07
1,80
0.01
GPA
1,79
0.02
1,80
0.14
1,80
0.27
Means and Variances
Associated With
the Siqnificant
Variables
DNi
DNAi
DNAiDNi
Variable Level N
Mean Variance
N
Mean
Variance
N
Mean
Variance
SI 44
0.8052 0.0391
44
0.9193
0.0183
44
0.1141
0.0249
S2 42
0.8845 0.0240
42
0.9329
0.0180
42
0.0484
0.0113
Cl 47
0.8789 0.0276
47
0.9726
0.0091
47
0.0938
0.0197
C2 39
0.8017 0.0369
39
0.8696
0.0234
39
0.0679
0.0185
9P<.01 bp<.05 Cp<.10
64
The above discussion concerning the individual decision criteria
measures can be summarized as follows:
H3.1a (BffjIll) = (BAÂ¡ 112).
The mean BNAj of those subjects within the II information level
and within the 12 information level will be equal.
H3.1b (BAÂ¡ SI) = (BAÂ¡ S2).
The mean BNAj of those subjects within the SI distribution level
and within the S2 distribution level will be equal.
H3.1c (BNAj  Cl) > (BAÂ¡  C2).
Those subjects within the Cl cost level will have a significantly
larger mean BNAj than will those subjects within the C2 level.
H3.2 a2(BNAj Â¡C1) < a2(BNAÂ¡  C2).
The BNAÂ¡ of those subjects within the Cl cost level will have a
significantly smaller variance than will the BNAj of those sub
jects within the C2 cost level.
H3.3a (BCj Â¡II) = (BCj 112).
The mean BNCÂ¡ of those subjects within the II information level
and within the 12 information level will be equal.
H3.3b (BNCjSl) = (BCj S2).
The mean BNCj of those subjects within the SI distribution level
and within the S2 distribution level will be equal.
H3.3c (BCjCl) < (BCj j C2).
The mean BNCj of those subjects within the Cl cost level will be
significantly smaller than the mean BNCj of those subjects within
the C2 level.
46
on tha experiment measure can be analyzed using a discrete classifica
tion of the direction of the adjustments. This classification of the
measure is accomplished using its algebraic sign. The second and
third training session results can be grouped into one of four
classes: PP(+,+), PM(+,), MP(,+), and MMSubjects within
the MM classification have constantly increasing TGjj measures and
subjects within the PP classification have constantly decreasing TG^j
measures.
Hypotheses Formation
As previously discussed, the formation of the experimental
hypotheses is accomplished primarily using the method of simulation.
Two general types of simulations are performed simulation of optimal
model performances and simulation of subjective investigation decision
performances. The first part of this section discusses the assumptions
and presents the results of the simulation of optimal model perform
ances. The second part of this section discusses certain assumptions
derived from the conceptual development that form the basis for the
simulation of subjective investigation decision performances. It also
presents the results of this simulation and the derivation of the ex
perimental hypotheses.
Simulation of Optimal Model Performances
The decision rule of the optimal model (given the objective func
tion of minimizing incurred costs) employed in this research is to
investigate the reported labor efficiency variance when:
137
GPi/GNi
Variable
FIGURE 8
DISTRIBUTION BY COST INTERACTION WITHIN THE GPi/GNi MODEL
TABLE 19
MODEL COMPARISON PROCEDURE RESULTS FOR THE GÂ¡, GPÂ¡, AND GNÂ¡ DEPENDENT VARIABLES
Gi
GPi
GNi
Model
SSe
d. f. ( F)
F
SSe
dTfTFT
F
SSe
error
F
c
Full
0.0809
12,73
12.08a
0.1824
12,73
a
7.46
0.0349
12,73
a
2.67
Without 3way interaction
0.0811
1,73
0.17
0.1824
1,73
0.00
0.0352
1,73
0.52
Without distribution x
cost interaction
0.0895
1,74
a
7.71
0.1953
1,74
b
5.21
0.0356
1,74
0.96
Without information x dis
tribution interaction
0.0812
1,74
0.09
0.1824
1,74
0.01
0.0352
1,74
0.06
Without information x
cost interaction
0.0814
1,74
0.26
0.1826
1,74
0.07
0.0352
1,74
0.03
d
Without information
0.0826
1,76
1.05
0.1835
1,76
0.36
0.0357
1,77
0.05
d
Without distribution
0.0360
1,77
0.71
d
Without cost
0.0369
1,77
2.60
a b
p<.01 p<.05
c
The F value associated with the full model is the test of the full model and not a model comparison test,
d
The test of this model assumes the effects of those interactions that include this variable are equal to
zero.
97
all effects) and eliminates effects starting with the highest order
interactions. The model comparisons utilize the general linear model
theory (the method of least squares) as described by Scheffe" (1959)
and the F test as described by Lewis and Keren (1977) is used to test
the fit of the various models. Defining two models as ft and w, the
F test is:
d.f.(ft) SSe(w) SSe(ft)
F =
d.f. (ajft) SSe(ft)
where w is the more restricted model and the degrees of freedom associ
ated with the numerator and the denominator of the F statistic are
d.f.(coft) and d.f. (ft), respectively.
Given a dependent variable, the model comparison procedure is
used to find the simplest model which adequately fits the data. After
the simplest (or reduced) model is found the sources of this model are
presented with their corresponding F values. These source F values are
the same as model comparison F tests where the comparison models are the
reduced model and the reduced model without the corresponding source.
Within this research the source F values are also the same as the Type IV
sum of squares (Barr et al., 1976).
Unless otherwise specified, the a levels for the rejection of the
null hypothesis are pc.10 for main effects and p<.05 for interactions.
The selection of these a values is largely arbitrary: the values reflect
the preliminary nature of this research. Also, unless otherwise speci
fied, all 2way interactions are tested against the without 3way
interaction model, employing the assumption that the 3way interaction
effect is equal to zero within the subject population. All the individual
attributes are retained in each model, employing the assumption that
35
Each standard variance report is concerned with the results of a
single joborder to produce a constant number of chairs and reports
only aggregate (overall assembly department) results. Each report
contains the aggregate standard assembly time allowed per chair, the
actual assembly time incurred per chair, the overall labor efficiency
variance per chair, the total number of chairs produced, and the costs
associated with each possible decision in combination with each
possible state of nature. All time units are presented in minutes.
The singular information contained in a variance report is independent
of that contained in previous variance reports.
The experiment is conducted in two phases a training phase
and an experimental phase. The training phase consists of three
contiguous sessions in which the subject learns his role and presumably
develops his decision strategy. Performance feedback is given at the #
completion of each training session. The experimental phase consists
of a single session in which the subject receives a series of variance
reports similar to those presented in his training session. In this
phase no performance feedback is given until after the completion of
the entire experiment. The subject is paid according to his performance
in the experimental phase.3
Operationalization of Variables
This research employs the following three decision situation
independent variables, each measured using a discrete classification:
3Greater detail concerning the experimental environment, task, and
procedures is presented in Chapter V.
116
the association between the motivation factor and individual decision
model sensitivity is only partially a result of the variable response
range (the remaining effect within the DNA^ measure). This drop in the
significance level could be explained by the negative association (albeit
an equally low significance level) between the motivation factor and
the DNA^DN^ measure. As the use of multiple cutoff values decreases
(DNA^DN^ decreases) the subjects' intrinsic motivation measures
increase.
Individual Decision Criteria
The following sections report the analyses and results of the
individual decision criteria variables. The variables include the
relative decision criteria, the relative decision criteria conservatism,
and the relationships between individual attributes and decision criteria.
Relative Decision Criteria
Hypotheses 3.1 and 3.2 relate to the adjusted relative individual
decision criteria. The adjustment concerns the elimination of the
effects of multiple cutoff values on individual decision criteria. The
variables which affect the adjusted relative decision criteria should be
those related to the individual's selection of a cutoff value. Of the
three situation variables the one most related to this selection is
the cost variable.
The method of analysis was the model comparison procedure where
the dependent variables are the BN.Â¡, BNA^, and BNABN measures. The
independent variables are the three situation variables (with their
BIBLIOGRAPHY
Al pert, M., and Raiffa, H. "A Progress Report on the Training of
Probability Assessors." Unpublished manuscript, Harvard
University, 1968.
American Accounting Association. A Statement of Basic Accounting
Theory. Evanston, Ill.: The Association, 1966.
. "Report of the 196970, 197071 Committee on Managerial
Accounting." The Accounting Review Supplement, 47 (1972),
31735.
Anthony, R. N. Planning and Control Systems: A Framework for
Analysis. Boston: Harvard University Press, 1965.
Appelbaum, M. I., and Cramer, E. M. "Some Problems in the Non
orthoaonal Analysis of Variance." Psychological Bulletin,
81 (1974), 33543.
Banks, W. P. "Signal Detection Theory and Human Memory." Psychological
Bulletin, 74 (1970), 8199.
Barefield, R. M. "The Effect of Aggregation of Decision Making Success:
A Laboratory Study." Journal of Accounting Research, 10 (1972),
22942.
Barr, A. J.; Goodnight, J. H.; Sail, J. P.; and Helwig, J. T.
A User's Guide to SAS 76. Raleigh, N.C.: SAS Institute, Inc.,
1976.
Bernbach, H. A. "Decision Processes in Memory." Psychological Review,
74 (1967), 46280.
Bierman, H.; Fouraker, L. E.; and Jaedicke, R. K. "A Use Probability
and Statistics in Performance Evaluation." The Accounting
Review, 36 (1961), 40917.
Coombs, C. H.; Dawes, R. M.; and Tversky, A. Mathematical Psychology:
An Elementary Introduction. Englewood Cliffs, N.J.:
PrenticeHall, Inc., 1970.
188
143
clearly associated with any one of the previous sources. The rotated
factor pattern for the ten scale items is presented in Table 24.
Analyzing the eigenvalues of these factors, the sharpest break (or
decline in slope) occurred between the third and fourth factor. For
this reason only the first three factors are included within the
subject motivation analysis. The first three factors account for 62.1
percent of the variability in the motivation scale items.
The first motivation factor had heavy positive loadings on the
scale items "enjoy making the decisions," "satisfied with performance,"
and "enjoy overall experience of participating." The first motivation
factor was interpreted as basically measuring intrinsic motivation
stimulated by enjoyment of the experiment and the required task.
The second motivation factor had heavy positive loadings on the
scale items "desire to perform well," "desire to cooperate with the
experimenter," and "desire to contribute to research knowledge." This
factor was interpreted as basically measuring extrinsic motivation
stimulated by nonmonetary sources.
The third motivation factor had heavy positive loadings on the
scale items "try less hard if money rewards halved" and "money rewards
caused you to try harder than without them." The third motivation
factor was interpreted as basically measuring extrinsic motivation
stimulated by monetary sources.
Using an orthogonal transformation matrix the individual scale
items were transformed into individual factor scores for each of the
first three motivation factors. Two methods of analysis were used to
test the association of subject motivation with the independent
variables: Duncan's multiple range test and Bartlett's test of homo
geneity of variance.
179
The
ment are:
per chair labor efficiency standards for the assembly depart
Process
Standard Time Allowed
Assembly
Packaging
Total
The variance report contains
variance is reported only in
33
3
minutes
minutes
36 minutes
only the labor efficiency variance and this
aggregate form (i.e., the variance is not
broken into the assembly and packaging processes). The accounting
system is computerized and the variance report is in computer output
format.
The physical labor process of the assembly department can be in one
of two states; either incontrol or outofcontrol. The overall labor
process is made up of many individual physical labor procedures; the
expected aggregate of these procedures is represented by the labor
efficiency standard of 36 minutes per chair. The department is defined
to be incontrol when all of these physical procedures are performed as
expected. The department is defined to be outofcontrol when one or
more of these physical labor procedures are not performed as expected.
Since the overall labor process is made up of many individual labor
procedures it is possible for the department to be outofcontrol while
at the same time the reported labor efficiency variance is small. This
would occur when some of the individual procedures are performed at
higher than normal efficiency while other procedures are performed at
subnormal (outofcontrol) efficiency. It is also possible for the
department to be incontrol while at the same time the reported labor
efficiency variance is large. This would occur when most of the indivd
ual procedures are performed at slightly below normal efficiency. These
examples represent two extreme situations; the probability of occurance
for either of them is low when compared to the probabilities of occur
ance for situations that fall inbetween the two extreme examples.
Your Task as the Operational Control Manager
Based upon the variance report you must decide whether to investi
gate the particular labor efficiency variance for its underlying causes.
The purpose of investigation is to facilitate correcting those indivdual
labor procedures which are not operating as expected. The following
assumptions will aid your decision making:
1) If you decide to investigate the variance and the assembly
department turns out to be outofcontrol, the department will
be returned to the original incontrol state with certainty.
2) If you decide not to investigate the variance and the assembly
department turns out to be outofcontrol, the department will
remain outofcontrol with certainty.
There are certain marginal costs associated with the variance
investigation decision. These costs depend upon your decision (either
investigate or do not investigate) and upon the actual state of the
assembly department (either incontrol or outofcontrol). These mar
ginal costs have the following values and relationships:
132
0.03 1 J
SI S2
Distribution
Variable
0.13
0.12
0.11
GPi 0.10
0.09
0.08
0.07
C2
Cl
h
SI S2
Distribution
Variable
Cost
Variable
Cost
Variable
87
Final Debriefing
Each subject's final performance measure (TIDCmin) for the
variance investigation decisions part of the experimental phase was
presented individually at a later date. At this time his cash pay
ment was determined, he was debriefed as to the purpose of the experi
ment, and any questions were answered. Additional data were collected
from those subjects not responding completely to the heuristic elicita
tion questionnaire.
TABLE 20
PARAMETER VALUES ASSOCIATED WITH THE REDUCED G.Â¡, GPi, AND GNj MODELS
F Values
Associated
! With the
Sources of
the Reduced
Models
Source
Gi
GPi
GNi
d.f.
F
d.f.
F
d.f.
F
Distribution
1,70
0.51
1,70
0.77
Cost
1,70
2.56
1,70
0.11
Distribution x cost
1,70
7.92a
1,70
5.39b
DNi
1,70
88.76a
1,70
46.39a
1,80
0.90
Intrinsic motivation
1,70
2.40
1,70
3.95C
1,80
5.00b
Extrinsic (nonmonetary) motivation 1,70
0.00
1,70
0.01
1,80
0.05
Extrinsic (monetary) motivation
1,70
1.71
1,70
4.33b
1,80
7.59a
GPA
1,70
2.39
1,70
5.93b
1,80
9.82a
Means and
Variances
Associated
With the
Significant
Variables
... Gi
GP
j
Variable Level
N
Mean
Variance
N
Mean
Variance
si,
Cl
24
0.0504
0.00229
24
0.0987
0.00456
SI,
C2
20
0.0921
0.00371
20
0.1372
0.00538
S2,
Cl
23
0.0430
0.00097
23
0.0886
0.00297
S2,
C2
19
0.0575
0.00375
19
0.0934
0.00576
3p<.01
V.05
Cp<.10
ro
CO
106
The expected pattern given the SI distribution level was obtained. How
ever, the expected pattern given the S2 distribution level was not con
sistent with respect to the expected initial decision anchor (40.5).
Individual Decision Model Sensitivity
The following sections report the analyses and results of the
individual decision model sensitivity variables. The variables include
the d.! dependent variable, the relative decision model sensitivity, and
the relationships between intrinsic motivation and decision model
sensitivity.
The dj Dependent Variable
Hypothesis 2.1 relates to the fit of the subjects' responses to
the TSD model. The dependent variable d.! should have little relation
with the information and cost situation variables, but should have a
relation with the distribution variable. The relation with the dis
tribution variable is derived from the optimal model decision sensi
tivity measure, d'. Within the SI level of the distribution variable
the theoretical value of d^ is 1.5, and within the SI level of the dis
tribution variable the theoretical value of d.1 is 1.8.
k
The method of analysis was the model comparison procedure where
the dependent variable is the d.! measure and the independent variables
are the three situation variables (with their interactions), the three
motivation factors, and the GPA. variable. The full d! model is:
i i
di + j + Bk + Y, + aBjk + otYj, + BYkl + aByjkl + C, + T,
103
are the three situation variables (with their interactions) and the
GP/l variable. The full RAj model is:
RAj = ii + a, + ^ + Y, + aeJk + otyj, + BYkl + aBYjkl + 6,
The model comparison procedure results for this analysis are
presented in Table 7. The results indicate that the reduced model con
taines only the p parameter: i.e., none of the situation variables had
a significant effect on an individual's relative decision anchor adjust
ment. Using the F test of equal variances, the variances between the
levels of the distribution variable and between the levels of the cost
variable were found to differ significantly (F=5.22, p<.01; F=6.23,
pc.01). The S2 distribution level and the Cl cost level had the greater
variances.
Hypothesis 1.3a predicted that (RAI1) would not differ signifi
cantly from (RA^I2). A Z test indicated that the difference was not
significant (Z=0.4432, n.s. twotailed). Hypothesis 1.3b predicted
that (RA.JS1) would not differ significantly from (RA^S2). The
application of the Z test revealed no significant difference (Z=1.3052,
n.s. twotailed). Hypothesis 1.3c predicted that (RA.Â¡C1) would not
differ significantly from (RAC2). A Z test was employed to test this
hypothesis: again, the difference was found to be not significant
(Z=0.4865, n.s. twotailed).
Table 8 presents the actual patterns of the TDVi and EDV.Â¡ measures
given the various levels of the distribution and cost variables. Given
that the TDV.Â¡ measures have incorporated some initial adjustment (during
the first training session), the expected TDVi and EDVi patterns are
1) (DV1C1,S1) < (TViCl,Sl) < 38.25 < (TDViC2,Sl) < (EDViC2,S1),
and 2) (EDV.C1,S2) < (TDVi Cl ,S2) < 40.5 < (TDVi C2,S2) < (EDVjC2,S2).
66
o
o
LO
o
LO
o
o
o
LO
LO
LO
LO
o
LO
o
CO
LO
C\J
r.
LO
(NJ
'
LO
LO
r^
CO
CT>
o
CO
CO
CO
CO
CO
*3
FIGURE 3
GRAPH OF THE SIMULATION USED IN THE CONDITION
(12 INFORMATION, SI DISTRIBUTION)
13
the output when the system is known to be outofcontrol, 3) the actual
output for the period, and 4) the deviation between the standard output
and the actual output for the period (the standard output variance).
The investigation decision criteria category can include such variables
as 1) the prior probabilities of the system's states, and 2) the
costs and benefits associated with each possible investigation decision
in combination with each possible system state.
Information relating to investigation decision criteria that is
not typically presented within the variance report includes 1) the
probability that the cause of the variance is controllable (assumed by
this research to equal one), 2) the probability that the system will
return to the incontrol state without managerial action (assumed by
this research to equal zero), 3) the perceived effect of the investi
gation decision on manager performance evaluation, and 4) the perceived
effect of the investigation on employee performances and attitudes.
The variance investigation literature is concerned mainly with
the investigation significance of Type 1 and Type 2a variances. A
general approach described within the literature is that of the Shewhart
X chart procedure (Probst, 1971; Koehler, 1968; Luh, 1968; Jeurs, 1967;
Zannetos, 1964). This approach, based upon classical statistics,
involves sampling the system to construct an incontrol mean and stan
dard deviation. Arbitrary control limits (generally plus or minus three
standard deviations) are used as the criteria for making a variance
investigation decision. Only one of the above studies considered the
effects of the information provided on the manager's investigation
decision. Probst (1971), in an industrial field study, found foremen
unwilling to accept the procedure as their investigation decision rule
183
The two assembly line state means, in minutes per chair, are:
Incontrol 36.0 Outofcontrol 45.0
The two assembly line state standard deviations, in minutes per chair:
Incontorl 5.0 Outofcontrol 5.0
The portion of time the assembly line was:
Incontrol 60% Outofcontrol 40%
The actual minutes per chair for both states are normally distributed.
The most favorable labor efficiency variance was:
10.0 minutes per chair (26.0 actual minutes incurred per chair)
The most unfavorable labor efficiency variance was:
19.0 minutes per chair (55.0 actual minutes incurred per chair)
The maximum investigation cost when the assembly line state was:
Incontrol $ 98.75 Outofcontrol $ 154.17
The minimum investigation cost when the assembly line state was:
Incontrol $ 62.50 Outofcontrol $ 142.08
29
1) the effects of the decision situation structure, the available infor
mation set contents, the individual's information processing efficiency,
and the individual's learning efficiency on the individual's longrun
decision efficiency; and 2) the effects of the decision situation
structure and the available information set contents on the individual's
information processing and learning efficiency.
Selection of Independent Variables
The effects of two types of variables are of primary interest
within this study: these are the situation variables and the process
variables. The following discussion describes the selection of each
of the variables employed in this study.
The situation variables
The situation variables are the quantity of information available
to the individual prior to his decision, the statistical structure of
the two states of nature, and the cost structure of the possible decision
outcomes.
The available information set. The effects of the contents of
the available information set are studied by manipulating the presence
and absence of certain distributional information. This involves the
specification of two levels of available information set content. Since
the difficultly of the discrimination task may be increased by the
absence of certain information, one level of the information variable
has less distributional information than the other level. This inde
pendent variable is labeled the information variable.
108
TABLE 9
MODEL COMPARISON PROCEDURE RESULTS
FOR THE d'.
i
DEPENDENT
VARIABLE
Model
SSe
d.f.(F)
F
Full5
5.8767
11,74
4.66a
Without 3way interaction
5.8926
1,74
0.20
Without distribution x cost
interaction
5.9032
1,75
0.13
Without information x distribu
tion interaction
5.8947
1,75
0.03
Without information x cost
interaction
5.8969
1,75
0.05
Without information0
5.9950
1,78
0.59
Without distribution0
9.4035
1,78
46.10a
Without cost0
5.9304
1,78
0.26
V.oi
bThe F value associated with the full
model is the
test of
the full
model and not a model comparison test,
c
The test of this model assumes the effects of those interactions
that include this variable are equal to zero.
Relative Decision Model Sensitivity 107
Intrinsic Motivation and Decision Model Sensitivity 114
Individual Decision Criteria 116
Relative Decision Criteria 116
Relative Decision Criteria Conservatism 121
Individual Attributes and Decision Criteria 124
Individual LongRun Decision Efficiency 126
Relative Decision Costs 126
Individual Attributes and LongRun Decision Efficiency ... 138
LongRun Decision Efficiency and Training Phase Performance 140
Individual Attributes 141
Subject Grade Poir.t Average 141
Subject Motivations 142
CHAPTER VI. DISCUSSION OF RESULTS 146
Conceptual Development Revi si ted 146
Overall Results 148
Discussion of Results 150
Decision Anchor Selection and Adjustment 151
Individual Decision Model Sensitivity 153
Individual Decision Criteria 156
Individual LongRun Decision Efficiency 159
Limitations 163
Implications for Accounting 166
Value of Additional Information 166
General Standard Setting Process 167
Future Research 168
APPENDIX A. THEORY OF SIGNAL DETECTION DERIVATIONS 172
vi
57
have a greater relationship with individual decision processes than
with the specific conditions of a general decision situation. The
individual decision model sensitivity measure dj should have a re
lationship with the distribution variable only. The theoretical d1
measure under the SI distribution level is 1.5 and under the S2 dis
tribution level is 1.8. Thus, the average dl under the S2 distribu
tion level should be greater than under the SI distribution level.
The DNA.Â¡ measure should have no systematic relationships with
the three independent variables. This measure of the relative de
viation of the individuals decision model sensitivity from that of
the optimal model's generally is due to individual decision incon
sistencies and temporary processing errors. If individuals are
randomized into the specific decision situations there are no a
priori reasons for expecting significant differences in this measure
due to the independent variables.
The difference between DNA^ and DNÂ¡ measures the effects of
multiple decision anchors upon the individual's relative decision
model sensitivity. Relationships between this difference and the in
dependent variables depend upon whether the independent variables are
related to the causes underlying an individual's use of more than one
cutoff value. A possible explanation involves the distribution vari
able. As the variance of the incontrol state increases the absolute
distance between the lowest values of the random variable of interest
(actual minutes incurred) and the mean of the incontrol state in
creases. In turn, as this distance increases subjects may be more
inclined to perceive that a second outofcontrol distribution overlaps
the lower range of the incontrol state. If such perceptions underly
85
Wechsler Adult Intelligence Scale). Due to resource limitations, however,
subject grade point average (GPA) was used as a surrogate for such a
measure. A median GPA was identified, and those subjects with a GPA
above the median were categorized as above average intelligence and
those subjects with a GPA below the median were categorized as average
intelligence. Each subject within an intelligence category then was
assigned randomly to one of the treatment conditions with the restric
tion that each intelligence group contributed an equal number of sub
jects to each condition.1 Upon assignment to a treatment condition each
subject received the background information booklet.
Training Phase
%
Each subject received training within the treatment condition to
*
which he was assigned. Training was conducted in groups of two subjects
within a 50 minute session administered by either the experimenter or
by an experimental assistant.2 Subjects were assigned randomly to the
experimental assistants subject to two restrictions. The restrictions
were that the experimental assistant was not currently the subject's
teacher for an academic course and that the subject's available time
coincided with that of the experimental assistant. Training of all
!The assignment process involved two procedures. First, the assignment
to the cost variable levels was by the week in which the subject par
ticipated in the experiment. Those subjects who participated during
the first week were assigned to the Cl cost level, and those subjects
who participated during the second week were assigned to the C2 cost
level. Second, the assignment to the information variable and the
distribution variable levels was by random selection based upon a ran
dom number table.
2The experimental assistants were not paid. The author believes, how
ever, that this is an exception to the aphorism that price reflects
value.
147
(incontrol and outofcontrol) for a given production labor process.
A random variable produced by the accountant provided evidence con
cerning state existance at particular points in time. This random
variable (the labor efficiency variance) had certain statistical
properties within the states of nature: the decision maker either
had or did not have knowledge of these particular properties. The
decision task was complicated by the existence of unequal values (costs)
associated with the various possible decision outcomes.
The form of the general heuristic proposed to describe decision
making behavior was that of anchoring and adjustment. Anchoring and
adjustment first requires the selection of an initial decision value
(anchor) from the continuum of possible decision values that are
located within the domain of the random variable (the labor efficiency
variance in the case of the present study). Once an initial decision
anchor is selected by the decision maker, training and experience
(learning) will lead to adjustments in the value of the anchor. Such
adjustments will be motivated primarily by feedback of various decision
performance measures. Should a particular decision performance measure
indicate less than satisfactory decision performance, the decision
maker will be motivated to adjust his decision anchor in an attempt to
improve decision performance measures in the future. The concept of
less than satisfactory decision performance assumes that the decision
maker has internalized the objective function from which the decision
performance measure was derived. Within this research the decision
performance objective function had a homomorphic relation with the
decision maker's payoff function.
APPENDIX F
SUBJECT MOTIVATION QUESTIONNAIRE
Please respond to the following questions by circling the appropriate
response. This is not a test; there are no right or wrong answers. It
is important that you try to answer each question honestly.
1. Suppose this same experiment was to be repeated on a number of future
occasions. How many more times would you be prepared to participate
in the experiment under circumstances similar to the present?
kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
*
*
* *
*
No 1
2
3
4 5
More
More More
More
More
More More
Than 5
Times Time
Ti mes
Times
Times Times
Times
Did your desire to perform well
in undertaking a challenging
task
cause you to try
very hard?
******************************* ******************************
k
* *
*
Definitely Yes
Probably
Don t
Probably No
Definitely
Yes
Yes
Know
No
No
Did you enjoy making the decisions requi
red in the experiment?
***************************************** ********************
* *
k
*
* *
*
Definitely No
Probably
Don' t
Probably Yes
Definitely
No
No
Know
Yes
Yes
Are you satisfied
with your performance
in the experiment?
*************************************************************
k k
*
*
k k
*
Definitely Yes
Probably
Don't
Probably No
Definitely
Yes
Yes
Know
No
No
Do you think you
would have tried less hard if the money rewards had
been halved?
*************************************************************
* *
*
*
* *
*
Definitely No
Probably
Don't
Probably Yes
Definitely
No
No
Know
Yes
Yes
186
32
TABLE 1
GENERAL RESEARCH DESIGN IN TERMS OF EXPERIMENTAL VARIABLES
Dependent Variables
Independent Variables
Individual
LongRun
Decision
Efficiency
Individual
Information
Processing and
Learning
Efficiency
Contents of the available infor
mation set
X
X
Structure of the decision situation:
Statistical relationships
X
X
Decision outcome relationships
X
X
Individual information processing
and learning efficiency
X
Note: An "X" within a cell indicates that the relationship of the
variables concerned are included within the experimental design.
33
operationalizations of these concepts are discussed in a later section
of this chapter. The objective at this point is to summarize in general
terms the relationships which will be included within the experimental
design.
General Research Methodology
Research methodology pertains to the general procedures or
methods employed in conducting research. Two general methods are
employed in this research simulation and laboratory experimentation.
The major objective of the simulation is to produce hypotheses which
will predict the behavior of human decision makers within the decision
situation assumed by the simulation. The simulation is based upon
assumptions derived from the conceptual development and from the gen
eral task environment of the laboratory experimentation. These
assumptions and the task environment are discussed in greater detail
in a later section of this chapter. The major objective of the
laboratory experiment is to test the conceptual development (through
the hypotheses derived by the simulation) of the effects of the vari
ous independent variables on the various observation variables.
The general and specific research design employed in the labora
tory experiment is the same as that employed within the simulation.
The major difference between the simulation and the laboratory experi
ment is the use of human subjects. This difference creates two major
sources of incompatability betv/een the simulation and the laboratory
experiment. First, the simulation makes certain assumptions concerning
human behavior and applies these assumptions consistently. Within
34
the laboratory experiment such behavior is not necessarily applied
consistently. Consequently, greater variance of results is expected
within the laboratory experiment than within the simulation. Second,
it should be noted that certain variables are largely affected by
individual attributes. Without a theory of the effect of individual
attributes any simulation of these variables would be arbitrary.
Accordingly, not all of the experimental variables are included in the
simulation. Hypotheses concerning the variables not present within
the simulation either are derived from limited concepts concerning the
effects of individual attributes or are stated in an exploratory manner
(no expected difference).
General Experimental Environment and Task
The standard cost variance investigation situation studied within
this research is set in an environment of a manufacturing company.
More specifically, the subjects are asked to assume the role of the
operational manager of an assembly department which assembles a single
product, a metal folding chair. The operating efficiency of the
assembly department is determined completely by the labor efficiency of
the assembly workers.
Each subject receives a sequential series of standard cost vari
ance reports and is asked for each report to decide whether to investi
gate or not to investigate the reported labor efficiency variance.
The efficiency of a subject's decision performance and the amount of
payment he will receive for participating in the experiment is based
upon the total investigation decisions cost which he incurs over the
series of variance reports.
40
Based upon a subjective analysis of each individual's decisions,
the measure DN^ can be adjusted for the effects of using multiple cut
off values. Defining d^a to be the individual's decision model sensi
tivity with the effects of multiple cutoff values eliminated:
DNAi = d\a / d'k
The difference DNA DN approaches zero as the effects of multiple
cutoff values decreases and becomes zero when the individual uses a
single cutoff value.
Individual decision criteria variables
The criteria the individual adopts in making his decisions are
measured using the TSD parameter 3. The theoretical definition of 3 is:
3 = d>(z2) /
where ( ) denotes the normal density function for the point in the
parentheses and z. is defined the same as for the d'. variable.
J
An empirical estimate for the 3 of an individual, labeled 3.5
is obtained using the z1 and z2 values associated with the individual's
conditional probabilities employed in estimating d^.. The measure 3..
is relative to the decision situation. In particular, it is relative
to those variables which affect the point on the decision axis which
the individual selects as his investigation decision cutoff value. A
measure of individual criteria comparable across situations is defined
as:
BN. = 3. / 3.
i i k
where 3^ is generated using optimal model k. The measure approaches a
value of one as 3_. approaches 3^ (i.e., as the individual's cutoff
decision value approaches the optimal model's cutoff decision value).
HUMAN INFORMATION PROCESSING FOR DECISIONS
TO INVESTIGATE COST VARIANCES
By
CLIFTON E. BROWN
A DISSERTATION PRESENTED TO THE GRADUATE COUNCIL OF
THE UNIVERSITY OF FLORIDA
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1978
Copyright 1978
By
Clifton E.
Brown
ACKNOWLEDGEMENTS
I am grateful to my supervisory committee; Rashad Abdelkhalik,
Douglas Snowball, Gary Hoi strum, and Richard Griggs. Without their
valuable contributions this dissertation would have had far more
obscurities.
I am indebted to my experimental assistants: Doug Snowball,
Gary Hoi strum, Ron Teichman, Ed Bailey, John Wragge, Robert Thompson,
Nancy Hetsko, Andy Judd, and Michael Gift (even though he was not on
time). Without their help the experimental procedures used within
this study would not have been possible.
Finally, my greatest debt is to my wife, Sandy. Without her
encouragement and support this dissertation never would have been done.
TABLE OF CONTENTS
ACKNOWLEDGEMENTS iii
ABSTRACT viii
CHAPTER I. INTRODUCTION AND OBJECTIVES 1
Variance Investigation Decision Processes 1
The Research Objectives A
Dissertation Organization 6
CHAPTER II. AREA OF INVESTIGATION 8
Managerial Accounting Concepts 8
Managerial Decisions 8
Management Task Planning and Control 9
Standard Cost Variance Investigation 11
Psychological Concepts 16
Psychophysics 16
Human Information Processing 20
CHAPTER III. RESEARCH METHODOLOGY AND DESIGN 23
General Conceptual Development 23
Decision Situation Structure 23
Available Information Set 25
Individual Information Processing Efficiency 26
Individual Ability to Expand the Information Set 27
General Research Design 28
Selection of Independent Variables 29
General Research Methodology 33
General Experimental Environment and Task 34
Operationalization of Variables 35
Hypothesis Formation 46
Simulation of Optimal Model Performances 46
Simulation of Investigation Decision Performances 49
CHAPTER IV. THE EXPERIMENT 76
Experimental Environment 76
Subjects 79
Experimental Materials 80
Background Information 80
Variance Investigation Decisions 80
Elicitation of Heuristics 83
%
Elicitation of Subject Motivations . . 83
Experimental Procedures 83
Assignment of Subjects to Treatment Conditions 84
Training Phase 85
Experimental Phase 86
Final Debriefing 87
CHAPTER V. ANALYSIS AND RESULTS 88
Summary of Results 88
General Method of Analysis 88
Training Phase Analysis and Results 98
Initial Decision Anchors 98
Decision Anchor Adjustment 102
Individual Decision Model Sensitivity 106
The dj Dependent Variable 106
v
Relative Decision Model Sensitivity 107
Intrinsic Motivation and Decision Model Sensitivity 114
Individual Decision Criteria 116
Relative Decision Criteria 116
Relative Decision Criteria Conservatism 121
Individual Attributes and Decision Criteria 124
Individual LongRun Decision Efficiency 126
Relative Decision Costs 126
Individual Attributes and LongRun Decision Efficiency ... 138
LongRun Decision Efficiency and Training Phase Performance 140
Individual Attributes 141
Subject Grade Poir.t Average 141
Subject Motivations 142
CHAPTER VI. DISCUSSION OF RESULTS 146
Conceptual Development Revi si ted 146
Overall Results 148
Discussion of Results 150
Decision Anchor Selection and Adjustment 151
Individual Decision Model Sensitivity 153
Individual Decision Criteria 156
Individual LongRun Decision Efficiency 159
Limitations 163
Implications for Accounting 166
Value of Additional Information 166
General Standard Setting Process 167
Future Research 168
APPENDIX A. THEORY OF SIGNAL DETECTION DERIVATIONS 172
vi
APPENDIX B. ORAL PRESENTATION TO ELICITE VOLUNTEER SUBJECTS .... 174
APPENDIX C. BACKGROUND INFORMATION BOOKLET 176
APPENDIX D. PRIOR INFORMATION SHEETS 182
APPENDIX E. SUBJECT HEURISTIC ELICITATION QUESTIONNAIRE 184
APPENDIX F. SUBJECT MOTIVATION QUESTIONNAIRE 186
BIBLIOGRAPHY 188
BIOGRAPHICAL SKETCH 193
Abstract of Dissertation Presented to the Graduate Council
of the University of Florida in Partial Fulfillment of the Requirements
for the Degree of Doctor of Philosophy
HUMAN INFORMATION PROCESSING FOR DECISIONS
TO INVESTIGATE COST VARIANCES
By
Clifton E. Brown
December 1978
Chairman: A. Rashad Abdelkhalik
Major Department: Accounting
Two major functions of management are those of planning and con
trol. Broadly defined, control is the management process which assures
that selected alternatives are implemented and executed in accordance
with plans. Important aspects of the control process concern analysis
and investigation of standard cost variances provided within accounting
reports. A substantial portion of the accounting variance investigation
literature has employed the normative model approach researchers have
created variance investigation models which a manager should use. Rarely
has attention been given to how the manager would interprete and integrate
information required by the various normative models.
The principal focus of this dissertation was on the effects of
specific situational variables upon a manager's information processing
for purposes of making variance investigation decisions. The specific
objectives were 1) tc develop a conceptual framework which will predict
effects cf specific situational variables on a manager's relative effi
ciency in information processing and in variance investigation decision
making, and 2) to empirically test some implications of this conceptual
framework.
viii
Variables that can affect the manager's variance investigation
decision process include 1) structure of the decision situation, 2)
contents of the available information set, 3) manager's information
processing efficiency, and 4) manager's learning efficiency. The
structure of the decision situation depends upon number possible states
of control, relative frequencies of the states, various statistical
relationships among the states, and various relationships between the
decision/state outcomes. The contents of the available information set
include information known by the manager prior to his decision. The
manager's information processing efficiency relates to the particular
strategies employed in combining and weighting various items of infor
mation. The manager's learning efficiency refers to the manager's
ability to learn from his experiences with the controlled process. Both
information processing and learning efficiency are the results of the
manager's particular decision strategies. These decision strategies
were expected to adapt a general form, referred to as anchoring and ad
justment. A natural starting point is used as a first approximation for
the investigation decision rule and is then adjusted as the manager
learns from his experiences.
Two experimental methods were used to derive and test implications
of the conceptual framework. Simulation techniques were used to opera
tionalize hypotheses and a laboratory experiment was used to test these
hypotheses.
The experimental environment was that of an assembly department
within a simulated manufacturing company. The assembly department as
sembled a single product and performance of this department was deter
mined completely by the assembly workers' labor efficiency. Student
IX
subjects, who assumed the role of assembly department operational manager,
made labor efficiency variance investigation decisions based upon a series
of independent standard cost variance reports. The manipulated indepen
dent variables included contents of the available information set,
distributional properties of the states of control, and cost effects of
the investigation decision errors. Parameters of the psychological theory
of signal detection permitted measurement of sensitivity and criteria of
a subject's decision model. The subject's investigation decision costs
compared to normative investigation decision costs derived under similar
situations were employed as a relative measure of decision efficiency.
Overall, the implications of the conceptual framework were supported
by the obtained results. To explain the few major deviations from
expectations, an ex post hypothesis was introduced. This hypothesis
posits that the standard introduces a subjective adjustment bias. Depend
ing upon the direction of adjustment the standard subjectively inhibits
complete adjustment either to optimal decision values close to the
standard or to optimal decision values distant from the standard.
x
CHAPTER I
INTRODUCTION AND OBJECTIVES
Business organizations generate internal accounting reports for
use by managers for purposes of evaluation and decision making.1 One
important evaluation process and decision task of management is that of
standard cost variance analysis and investigation. Based in part upon
standard cost variance reports generated by the internal accounting
system, managers estimate the likelihoods that various production pro
cesses remain under control and decide whether to investigate particu
lar variances. The present study concerns the effects of selected
variables on standard cost variance investigation decision making by
operational control managers. This chapter discusses general aspects
of the standard cost variance investigation decision process and the
variables which may affect the process and presents the research
objectives of the study.
Variance Investigation Decision Processes
An important factor in the evaluation and use of standard cost
variance reports is the manager's perception of the validity of the
xThe American Accounting Association 1966 Statement of Basic Accounting
Theory states that "the objective of accounting for internal use is to
provide information to persons within an organization that enables them
to make informed judgements and effective decisions which further the
organization's goals" (p. 38).
1
2
information provided. In particular, the manager's perception of the
standard setting process will have considerable influence upon his
variance investigation decision process. If the manager believes that
the standards are unrealistic (i.e., that the standards have been placed
too far from the incontrol distribution) he may rescale either the
standards or the variances to correspond with his own perception of the
incontrol distribution.2
If it is assumed that a manager accepts the standard setting pro
cess as realistic and does not rescale the standards or variances, his
variance investigation decisions may be viewed as the culmination of a
twostage process. The first stage concerns the detection of the
particular distribution (e.g., incontrol or outofcontrol) that gen
erated the variance. The manager's performance of this task is a
function of the sensitivity of his decision process (model). This
sensitivity is affected by the structure of the particular situation
and by the manager's knowledge of this structure. The extent of the
manager's knowledge of the situation, in turn, is affected by the
available information (both contained within the variance report and
provided from other sources), and by the manager's ability to learn
from his experiences with the processes being controlled. In situations
where the controlled process distributions have some area of overlap
the results of a manager's detection process are probabilistic (i.e.,
prior to completing an investigation the existence of any given state
is uncertain). Furthermore, the greater the area of overlap, the more
difficult the discrimination task becomes.
2Within this context, an incontrol distribution concerns statistical
congruence of production output and planned output in terms of
controllable resource utilization.
3
The second stage concerns the manager's investigation decision
criteria. Having arrived at a conclusion (albeit probabilistic) about
the distribution that generated the variance, the manager must integrate
and process various objective function parameters in order to arrive at
his variance investigation decision (these parameters can belong to
either the manager's objective function, the organization's objective
function, or both if the same). The potential investigation criteria
can be divided into two major categories: structural criteria and
behavioral criteria. The first category can be divided further into
structural cost criteria and structural probability criteria. The
structural cost criteria include such variables as the additional cost
of operating an outofcontrol process (given that the process can be
returned to the original incontrol state), and the costs of variance
investigation and correction (which can differ depending upon the
actual state that generated the variance). The structural probability
criteria include such variables as the probability that the source of
the variance is controllable (i.e., that it can be returned to the
original incontrol state through managerial action), the probability
that the process will return to the incontrol state without managerial
action, and the prior probabilities of each state distribution. The
behavioral criteria include such variables as the manager's perception
of the effect of the investigation upon his performance evaluation and
reward structure, and the manager's perception of the effect of the
decision upon employee performances and attitudes.
Although the variance investigation decision process has been
described as two stages, the manager may not actually utilize such a
sequential stage process. The manager's actual decision process is
4
labeled a heuristic. Within this context, heuristic refers to the
learned set of rules or principles that are utilized by the individual
in making the particular decisions required of him. The manager's
specific heuristic can be affected substantially by his individual
characteristics (e.g., intelligence, cognitive complexity and style,
decision process sensitivity, motivations, etc.). However, a general
heuristic, labeled anchoring and adjustment (Tversky and Kahnemar,
1974), is expected to describe the general form of the manager's vari
ance investigation decision process.3
The above examination of the manager's variance investigation
decision process indicates that the variables which can affect both
the process and the results of the process include 1) the structure
of the decision situation, 2) the contents of the available information
set, 3) the manager's information processing efficiency, and 4) the
manager's learning efficiency (from his experiences with the controlled
process). The manager's knowledge concerning the structure of the
decision situation may be limited by the contents of the available
information set and by his information processing and learning effici
encies. The accountant, to a large extent, has control over the con
tents of the available information set.
The Research Objectives
Many problems in accounting reduce to one of choosing among
alternative information sets that could be provided to a decision maker
(American Accounting Association, 1972). Two basic approaches to
3The anchoring and adjustment heuristic is described in greater detail
within Chapter II.
5
deciding on the information set to be presented within the cost variance
report have been advocated. First, the accountant can determine those
models which managers use in making variance investigation decisions and
provide an information set which would permit the implementation of
these models. Problems with this individual model approach are 1) the
possibility exists that different individual models use a wide range of
different information sets (which are not costless), and 2) the indi
vidual models may not be optimal (i.e., they could be inefficient with
respect to other decision models). The individual model approach cen
ters on the psychological question of what the manager is doing (or
would do) with the available (or additional) information. Second, the
accountant could create a normative model of the variance investigation
decision and provide the information required by that model. Problems
with the normative model approach are 1) the information provided may
not optimize the individual's investigation decisions (which may con
tinue to be made using the individual's model), and 2) the costs
associated with operationalization of the normative model may exceed
the benefits (cost savings as a result of more optimal investigation
decisions and greater congruence of manager goals with overall organi
zation goals) offered by the model. The normative model approach
centers on the analytical question of what the manager should be doing
(or should do) with the available (or additional) information.
A substantial portion of the accounting variance investigation
literature has focused upon the second approach the normative model
approach.4 However, rarely has attention been given to how the manager
4This literature is reviewed within Chapter II.
6
would interprete and integrate the information sets of the various
normative (optimal) variance investigation models. In many instances
the implicit assumption has been that the manager would process the
information with the same efficiency as the normative model.
The principal focus of this research is the effects of specific
situational variables on a manager's variance investigation decisions
(relative to the investigation decisions of an optimal model) and on
a manager's processing of available information (relative to the infor
mation processing of an optimal model). Specific objectives are:
1) To develop a conceptual framework which will predict the
effects of situational variables on a manager's relative
information processing efficiency and to empirically test
the implications of this conceptual framework.
2) To develop a conceptual framework which will predict the
effects of situational variables on a manager's relative
variance investigation decision efficiency and to
empirically test the implications of this conceptual framework.
Dissertation Organization
Chapter II presents certain general concepts from accounting and
psychology. These concepts are necessary for the development of the
specific environment studied in this research, and for the development
of a conceptual information processing and decision making framework
within this environment. The accounting concepts are concerned with the
nature of managerial decisions, managerial task planning and control,
and standard cost variance investigation. The psychological concepts
are concerned with the theory of signal detection and with cognitive
7
aspects of human information processing such as the general heuristic
of anchoring and adjustment.
Chapter III synthesizes the general concepts of the previous
chapter and develops a conceptual framework of standard cost variance
investigation employing the methodology of the psychological concepts.
This conceptual framework was operationalized using simulation tech
niques, and experimental hypotheses were derived from the simulations.
The details of a laboratory experiment designed to test the
hypotheses derived from the conceptual variance investigation framework
are presented in Chapter IV. The experiment employed a betweensubjects
design and was manipulated factorially using specific variance investi
gation situation variables. Modified parameters from the psychological
theory of signal detection were employed to measure subject's information
processing efficiency and decision model sensitivity.
The results obtained from the laboratory experiment are presented
in Chapter V. The model comparison procedure of nonorthogonal analysis
of variance was the primary method of analysis employed. Chapter VI
discusses the results, develops a modification (ex post) of the
original conceptual framework, and discusses some implications for
accounting and for future accounting research.
CHAPTER II
AREA OF INVESTIGATION
Managerial Accounting Concepts
This study relies on the synthesis of certain psychological con
cepts with certain accounting concepts. This section discusses the
accounting concepts: the nature of managerial decisions, managerial
task planning and control, and standard cost variance investigation.
Managerial Decisions
Since the information set (accounting report) exists to support
the manager's tasks, a conceptual framework of managerial decisions would
facilitate the accountant's information set selection. A conceptual
framework of managerial decisions should differentiate managerial de
cisions along dimensions that allow insights into the informational
needs of those decisions.
Anthony (1365) provides one such dimension along which he iden
tifies the purposes or orientation of managerial activities: strategic
planning, management control, and operational control. Another dimen
sion is provided by Simon (1966) who distinguishes between programmed
and nonprogrammed decisions. The underlying dimension is concerned
with the manner in which managers deal with their problems. The cri
teria for classifying a decision consist of the extent of structure
associated with the problem solving phases of the decision.
8
9
A conceptual framework of managerial decisions that synthesizes
these dimensions is presented by Gorry and Morton (1971). Simon's
dimension is modified by replacing the terms "programmed" and "non
programmed" with the terms "structured" and "unstructured" and adding
a third category labeled semistructured. Gorry and Morton's conceptual
framework is used in this research as a model that will permit the
identification of a standard cost system within the overall managerial
decision framework.
Management Task Planning and Control
Standard cost systems present information sets to managers to
support various decisions concerning task planning and control.
Demski (1967) provides an excellent conceptual discussion of the manage
ment task planning and control process, and his approach (with some
modification) is adopted in this research.
Figure 1 presents a model of the management task planning and
control process. Environmental information, largely external to the
standard cost system, facilitates the planning of overall goals and
policies within the constraints imposed by the environment. Overall
objective control feedback 1) provides evidence for the ccntinuted
validity of overall assumptions made in forming the organization ob
jectives, and 2) facilitates strategic planning evaluation of organi
zation performance.
Both the environmental variables and the overall task control
feedback of the management control activity 1) provide evidence for
the continued validity of the overall task assumptions made in forming
Overall
Objective
Control
Strategic
Planning
Overall
Objective
Planning
Overal1
Task
Control
Management
Control
Overall and
Specific Task
Planning
Specific
Task
Control
V
Operational
Control
Specific Task
Structuring
Source: Modified from Demski, 1967.
Environment
Output
FIGURE 1
MANAGEMENT TASK PLANNING AND CONTROL PROCESS
11
the task plans, and 2) facilitate management control evaluation of
operational control performance.
The operational control manager uses the specific task control
feedback to 1) decide whether the physical system performance is in
agreement with the specific task plan, and 2) to decide whether the
task can be restructured to bring performance back into agreement with
the plan (if performance and the plan differ). A task that can not be
restructured could have significance for the overall task control feed
back to the management control activity and possibly may lead to
modification of the original task plans. Such modification could in
turn have significance for the overall objective control feedback to
the strategic planning activity, and a modification of the original
overall objectives may follow.
Standard Cost Variance Investigation
Within the operational control activity the specific task plan
provides standards of performance in terms of expected component costs
and usages, and the desired physical outputs. The operational control
manager structures the tasks within the physical system and periodically
receives a standard cost variance report describing the system output
in terms of the task plan and the actual results. Upon receiving a vari
ance report the manager must decide the nature of the given variances.
Dopuch et al. (1967) present a classification of standard cost
variances that is based on the expected source of the variances. A
variance resulting from a random fluctuation of the physical system,
labeled a Type 1 variance, requires no operational control response if
12
not statistically significant. Whether a variance resulting from a
change in the physical system, labeled a Type 2 variance, requires an
operational control response depends on whether the underlying cause of
the change is a temporary rather than a permanent phenomena. A tem
porary or controllable variance, labeled a Type 2a variance, is one
that operational control can correct in the future (i.e., the physical
system can be returned to the previous incontrol condition). A per
manent or noncontrollable variance, labeled a Type 2b variance, is one
that operational control can not correct in the future.
If the manager decides that the variance is of Type 1 no action
is required. If, however, the manager decides that the variance is of
Type 2 he must then decide if the variance should be investigated for
its underlying causes. Should the variance be of Type 2b it would
have little operational control significance; should the variance be
of Type 2a the cause of the variance could be eliminated by restructur
ing the physical system. The present study confines itself to the
assumption that all variances which result from a change in the physical
system are controllable by the operational control activity. In other
words, it assumes that the standard cost variances have no significant
affect on either the strategic planning or the management control
activities.
The information set contained in a standard cost variance report
can be viewed as relating to one of two categories of information:
distributional properties of system states and investigation decision
criteria. The distributional properties category can include such
variables as 1) the mean and standard deviation of the output when the
system is known to be incontrol, 2) the mean and standard deviation of
13
the output when the system is known to be outofcontrol, 3) the actual
output for the period, and 4) the deviation between the standard output
and the actual output for the period (the standard output variance).
The investigation decision criteria category can include such variables
as 1) the prior probabilities of the system's states, and 2) the
costs and benefits associated with each possible investigation decision
in combination with each possible system state.
Information relating to investigation decision criteria that is
not typically presented within the variance report includes 1) the
probability that the cause of the variance is controllable (assumed by
this research to equal one), 2) the probability that the system will
return to the incontrol state without managerial action (assumed by
this research to equal zero), 3) the perceived effect of the investi
gation decision on manager performance evaluation, and 4) the perceived
effect of the investigation on employee performances and attitudes.
The variance investigation literature is concerned mainly with
the investigation significance of Type 1 and Type 2a variances. A
general approach described within the literature is that of the Shewhart
X chart procedure (Probst, 1971; Koehler, 1968; Luh, 1968; Jeurs, 1967;
Zannetos, 1964). This approach, based upon classical statistics,
involves sampling the system to construct an incontrol mean and stan
dard deviation. Arbitrary control limits (generally plus or minus three
standard deviations) are used as the criteria for making a variance
investigation decision. Only one of the above studies considered the
effects of the information provided on the manager's investigation
decision. Probst (1971), in an industrial field study, found foremen
unwilling to accept the procedure as their investigation decision rule
14
when the procedure was constructed objectively (the foremen indicated
the reason for not accepting the procedure was that they felt it
ignored their experience). However, Probst did not analyze his results
in terms of the performance efficiency of the foremen.
Other research has noted what are considered to be significant
drawbacks to the Shewhart X chart procedure: the failure to consider
the costs and benefits associated with the various investigation de
cision outcomes and the failure to consider information from prior
periods. Bierman et al. (1961) proposed the incorporation of investi
gation decision costs and benefits within a classical statistics frame
work. Thus managers may use statistical information to calculate the
probability that the variance reflects the incontrol state, and com
bine this probability with the investigation decision costs and benefits.
Most of this information is assumed to be provided by the accountant
and accurately processed by the manager.
Kaplan (1975) and Jacobs (1978) have considered another deficiency
of the classical statistics approach the failure to use prior period
information. They analyze the use of the cummulative sum procedure,
whereby the cumulative sum of the variance is charted for each period.
Theoretically, under a stable state these sums should follow a random
walk, and any drift would indicate the system is outofcontrol. Eval
uation of such a drift would be accomplished on the basis of information
provided by the accountant or derived from the manager's experience.
The economic cumulative sum procedure incorporates the effects of
estimated investigation decision costs within the drift evaluation.
Several studies in the variance investigation literature use
decision theory to construct a variance investigation model (Kaplan,
15
1969; Dyckman, 1969; Kaplan, 1975; Dittman and Prakash, 1978). If
all the parameters required by these models were available these
models could replace the manager as the variance investigation decision
maker. However, to the extent that not all the parameters are opera
tionalizable (given seme cost constraint), the manager must continue
to make the variance investigation decisions using the information
provided by the accountant and by his own experiences.
A conclusion that can be drawn from the variance investigation
literature is that the additional information proposed for the use of
managers is becoming both diverse and complex. Furthermore, there is
a paucity of research relating to the manager's ability and efficiency
to interpret and integrate this additional information. Demski (1970,
1971) was one of the first to note the problem associated with the
decisionimplementation interface (i.e., the effect of the control
system information on individual behavior and performance). Some
analytic research on this problem has followed. Using simulation
techniques, Magee (1976) compared average total cost for seven decision
rules under various operational conditions. Although the simpler
decision models (rules) tended to have larger average costs than the
more sophisticated decision models, the difference in average cost was
relatively small. Indeed, if model implementation and information costs
are considered the simpler models may be more efficient than the
sophisticated models. Magee also noted that because of the use of
different manager performance measures (average operating costs, average
number of months below standard, etc.) simpler decision models may re
sult in rational choices (i.e., choices that maximize the manager's
expected utility). Closely related to Magee's research is an empirical
16
study by Magee and Dickhaut (1977). Using human subjects, Magee and
Dickhaut found support for the proposition that different manager per
formance (payoff) measures will affect the specific decision rules
(heuristics) employed by the decision maker (as a result of the decision
maker attempting to maximize his subjective utility).
Psychological Concepts
This section discusses the psychological concepts employed in
developing a conceptual framework of manager standard cost variance
investigation decisions. These concepts include the psychophysical
theory of signal detection and the human information processing con
cepts of decision heuristics.
Psychophysics
Psychophysics studies the relationships between physical and
psychological scales of measurement. Modern psychophysics adopts the
view that subjects can make meaningful evaluations of the magnitudes of
their sensory experiences, and therefore sensory magnitudes, as well
as physical magnitudes, can be quantified. One approach of modern
psychophysics is based upon the theory of signal detection (TSD). TSD
permits the separation of the decision maker's ability to discriminate
between classes of stimuli (sensitivity) from his motivational response
biases (decision criteria). Two comprehensive theoretical descriptions
of TSD are presented by Green and Swets (1974) and Egan (1975); general
surveys of the TSD theory are presented by Coombs et al. (1970), Watson
(1973), and Pastore and Scheirer (1974).
17
The basic TSD experiment utilizes the singleinterval procedure.
This procedure consists of a series of trials, each trial consisting of
an observation interval and a response interval. The possible stimulus
events during the observation interval are 1) the observation contains
a meaningful signal added to a background of noise (sn trial), and 2)
the observation contains only a background of noise (n trial). It is
assumed that each trial is independent of all other trials and that
the prior probabilities of n and sn are given and remain constant. The
background noise fluctuates at random from trial to trial; the stimulus
(usually a fixed level) is added to the noise. Therefore, the obser
vation fluctuates randomly from trial to trial. The task of the subject
is to detect whether the observation was generated by the signal plus
noise (sn) or by the noise alone (n) distribution. That is, in the
response interval the subject will respond with either "Yes, the signal
was present" (Y response), or "No, the signal was not present" (N re
sponse).
On any trial there exist four possible outcomes of the subject's
decision in conjunction with the actual distribution: 1) sn was pre
sented and the subject said "Yes" (a hit), 2) sn was presented and the
subject said "No" (a miss), 3) n was presented and the subject said
"Yes" (a false alarm), and 4) n was presented and the subject said
"No" (a correct rejection). A conditional probability matrix for a
series of these events is given by the following (Green and Swets, 1974):
RESPONSE
Yes No
^ sn P(Ysn) P(Nsn)
rs
~ n P(Yn) P(Nn)
UO
18
Since the cells of this matrix are both exhaustive and mutually exclu
sive, the rowwise conditional probabilities must sum to one (this does
not necessarily hold for the sum of the columnwise conditional
probabilities). All parameters of the TSD model are derived from this
conditional probability matrix (see Appendix A for a more detailed
discussion of the conditional probability matrix including its rela
tionship with Bayes1 theorem).
In the singleinterval task the subject analyzes the evidence
and classifies the stimulus into one of two categories according to
his criteria. The criteria are determined by his objective function.
The objective funcition of interest within this research is the
maximization of expected value. Assume that the subject has some value
(utility) fqr each of the four event outcomes. A payoff matrix of these
values related to the four outcomes is. given by the following (Egan,
1975):
DECISION
No
Yes
sn ^sn,Y ^sn,N
n vn,Y vn,N
The decision rule for maximizing the expected value (see Appendix A
for a derivation of this decision rule) is:
P(xsn) P(n) Vn,W VV
P(xjn) P(sn) V$n,Y ^sn,N
At the point at which the above expression is an equality the subject
should be indifferent between saying "Yes" or saying "No." This point
can be considered the critical value of the likelihood ratio of the
observations, L(x0). The decision rule for saying "Yes" is expressed
by the relation L(x) > L(x0).
19
The critical value L(xQ) for this decision rule has two possible
values: a theoretical value which is a measure of the criteria of an
optimal (or ideal) subject and a subjective value which is a measure
of the criteria of an actual subject.
One set of TSD models assumes that both conditional probability
distributions are Gaussian; i.e., $(xn) and $(xsn) are normally
distributed. With the additional assumption of equal variance for both
distributions, the parameters which measure individual discrimination
sensitivity and individual decision criteria are labeled d1 and g,
respectively. The discriminability measure, d', has the following
theoretical definition:
where, ysn= the mean of the signal plus noise distribution;
vn = the mean of the noise alone distribution;
a = the standard deviation of both distributions;
zn = the value of the normal distribution function associated
with the noise alone distribution and any decision axis
cutoff value common to both distributions; and
z = the value of the normal distribution function associated
sn
with the signal plus noise distribution and any decision
axis cutoff value common to both distributions.
The d' measure theoretically is independent of the decision criteria
measure.
The decision criteria measure, B, has the following theoretical
definition:
B = i>(zsn) / i>(zn)
20
where <Â¡>( ) denotes the normal density function for the point in
parentheses and the z parameters are the same as defined for the d'
measure.
Although the assumption of equal variance normal distributions
is employed within this research, such an assumption is not necessary
to employ TSD. Egan (1975) demonstrates the use of TSD with exponential
distributions, chisquare distributions, Bernoulli distributions, and
Poisson distributions. Grier (1971) develops nonparametric measures
of discriminability and decision criteria.
Traditionally, psychophysics has employed TSD to study perceptual
processes: i.e., sensory processes such as audition and vision. Over
the last decade, however, TSD has been applied to conceptual processes.
These extensions to conceptual processes have included numerical
processing (Lieblich and Lieblich, 1969; Hammerton, 1970; Weissmar. et al.,
1975), medical diagnosis (Lusted, 1969; Lusted, 1971; Swets, 1972),
conceptual judgement (Ulehla et al., 1967a; Ulehla et al., 1967b), and
memory (Bernbach, 1967; Banks, 1970).
Human Information Processing
Human information processing (HIP), a subset of cognitive psy
chology, studies human judgement and decision making with particular
emphasis on the processing of information that determines these
activities. An area within HIP is the construction of models of human
decision making. The work within this area is typically classified
into schools of research which employ different paradigms, the two
major paradigms being the Bayesian and regression approaches (Slovic
and Lichtenstein, 1971). The TSD model is related to the Bayesian approach.
21
The Bayesian approach is a normative model specifying how a
decision should be made given certain internally consistent relation
ships among probabilistic beliefs. The basic beliefs of this approach
are that decisions should be based on subjective probabilities and
that these probabilities should be revised upon the receipt of addi
tional information in accordance with Bayes's theorem.
The major findings of Bayesian research are labeled conservatism.
The subjects, after receiving additional information, revise their
posterior probabilities in the same direction as the optimal model but
the revision is insufficient. Much of the research has focused on an
explanation of the cause of conservatism, the major explanations being
misperception (Peterson et al., 1968), misaggregation (DuCharme and
Peterson, 1968), and response bias (DuCharme, 1970).
Another area within HIP is the study of subjective information
processing principles and decision rules, labeled heuristics. A
heuristic, within this context, refers to a learned set of rules or
principles which are utilized by individuals in making the particular
decisions required of them. Research in this area has been concerned
with identifying systematic biases (relative to some definition of
optimal) of subjective heuristics within certain types of decision
tasks. Those information processing and decision rule biases iden
tified thus far have been labeled as an anchoring and adjustment
heuristic (Tversky and Kahneman, 1973), a representative heuristic
(Kahneman and Tversky, 1972; Swieringa et al., 1976), an avail
ablility heuristic (Tversky and Kahneman, 1973), and the law of small
numbers (Tversky and Kahneman, 1971).
22
This study will make particular use of the anchoring and
adjustment heuristic. In many situations, individuals first make
decisions by starting with an initial anchor (decision point) and then
adjust this initial anchor as they learn from their experiences. The
initial anchor can be suggested by the structure of the decision
situation, or can be the result of a partial computation or estimate.
Empirical tests involving the anchoring and adjustment heuristic in
dicate individuals do not sufficiently adjust their initial decision
point. That is, their adjustment is less than that which would allow
optimal processing of the available information (Slovic and Lichtenstein,
1971; Slovic, 1972; Alpert and Raiffa, 1968; Tversky and Kahneman,
1974).
CHAPTER III
RESEARCH METHODOLOGY AND DESIGN
General Conceptual Development
A general problem confronting decision makers is that the indi
vidual must decide subjectively which state of nature is most probable
based upon some incomplete set of information. When an individual
deals repetitively with a similar situation his longrun decision
efficiency or "decision correctness" can be affected by 1) the struc
ture of the particular decision situation, 2) the contents of the set
of available information, 3) his efficiency in processing the available
information, and 4) his ability to expand the available information
through experience with, and observations of, the various states of
nature. Each of these four elements is discussed below with specific
reference to the objectives of this research.
Decision Situation Structure
The structure of the particular decision situation primarily de
pends upon several key variables. These variables include the number of
possible states of nature, the relative frequencies of the states, the
various statistical relationships among the states, and the relation
ships between the various decision outcomes (the costs incurred given a
specific decision and the existence of a specific state). Depending on
23
24
the specific values which these variables may assume, the structure of
the particular decision situation can affect both the difficulty and
the importance of the individual's discrimination among the states. In
general, as the number of possible states increases and as the area of
distributional overlap of these states increases the discrimination task
becomes more difficult. Furthermore, the discrimination task becomes
more important (in terms of incurred costs) as the relative frequencies
of the states become equal and as the costs associated with the possible
decision outcomes which involve decision errors (an incorrect decision
given the existence of a specific state) become unequal.
The general situation employed in this research, selected for
its relevance to the accounting discipline, is the cost variance
investigation decision. In this situation two states of nature are
possible: 1) incontrol (the underlying physical process described by
the standard cost variance report is functioning as planned), and 2)
outofcontrol (the underlying physical process described by the stan
dard cost variance report is not functioning as planned). In reality,
the possible states of nature may be located on a continuum whose end
points are the states of incontrol and outofcontrol. Between these
two end points are any number of states that take the general form of
partially outofcontrol or moving outofcontrol. In the present
research the possible states of nature are confined to the two end
points. The relative frequencies of the two states are controlled as
constants with their values being close to equal. The statistical
relationships among the two states and the relationships between the
various decision outcomes are manipulated as independent variables.
25
Available Information Set
The contents of the available information set refers to the infor
mation known by the individual prior to his decision. Such information
can be of two types singular or distributional (Tversky and Kahneman,
1977). Singular information consists of information that specifically
relates to the current decision. Distributional information consists
of information that relates to the relative frequencies and to the
statistical relationships among the states of nature.1 The difficulty
of the discrimination task may be affected by the presence or absence
of certain items of information. In general, the less information con
tained in the available set the more difficult the discrimination task,
for missing information required by a decision model must be estimated
by the individual. Compared to statistically derived estimates, these
subjective estimates are likely to have greater uncertainty and in
efficiency associated with them.
Within the variance investigation situation, the information con
tained on each variance report constitutes the singular information.
Two types of singular information are employed in this research the
actual results of the physical process and its variance from a standard,
and the marginal costs associated with each of the two possible decisions
in combination with each of the two possible states of nature. The
presence of both these singular information types is controlled as a
constant within this research. The presence or absence of specific
xThis definition of distributional information implicitly assumes that
the relative frequencies and statistical relationships are stable over
the relevant time frame. If these variables were nonstable, revised
estimates of their specific values would be required prior to each
decision, thus classifying them as singular information. This research
assumes that both of these variables are stable across all decisions.
26
distributional information items (the statistical means, variances, and
distributional shapes of the two states) is manipulated as an indepen
dent variable. The presence of other distributional information items
(the relative frequencies and the allowed standard) is controlled as a
constant.
Individual Information Processing Efficiency
The individual's efficiency in processing the available infor
mation relates to the particular heuristics or strategies employed in
combining and weighting the various items of information. The term
efficiency implies a relationship between the individual's process
output (his decision) and a normatively correct or optimal decision.
The individual's decision and information processing performance can be
evaluated by comparing his performance against an optimal model.
Optimality refers to the best possible performance under given condi
tions. Since the optimal decision model relies on an incomplete infor
mation set rather than certain knowledge,even its performance can be
affected by both the structure of the situation and the contents of the
available information set.
The optimal models used in this research are a function of the
experimental environment: the singleinterval procedure found within
the psychological theory of signal detection and the basic decision
situation of standard variance investigation. Within this environment
the optimal decision rule for minimizing (maximizing) the expected cost
(value) of a set of decisions is based upon an extension of Bayes'
theorem that takes into account the relative costs (values) of various
27
possible decision outcomes. The parameters required to fit the optimal
model include 1) the relative frequencies of the two states of nature,
2) the mean of each state, 3) the statistical variance of each state,
and 4) the costs associated with each of the two possible decisions in
combination with each of the two possible states of nature. Since the
individual's decisions are to be evaluated by comparisons with the out
puts of the optimal model, it would seem reasonable that the information
available to the optimal model be the same as the information available
to the individual. Consequently, for decision situations in which some
of the parameters required by the optimal model are not contained in the
available information set the optimal model must make estimates of the
missing parameters.
Individual Ability to Expand the Information Set
The individual's ability to expand the available information set
over time refers to his ability to learn from his experiences with the
states of nature. Such learning can occur through improved estimates
of unknown items of distributional information and through modifications
of information processing strategies to incorporate state relationships
which were unknown or undetected previously.
The general form of the expected individual information processing
strategy is described by the heuristic of anchoring and adjustment
(Tversky and Kahneman, 1974).2 A natural starting point, or anchor, is
used as the first approximation for the decision. This anchor is then
2A heuristic, within this context, refers to a learned set of rules or
principles which are utilized by an individual in making the particular
decisions required of him.
28
adjusted as the individual learns from his experiences with the states
of nature.
Within the present study the initial decision points or anchors
were expected to fall near the geometric intersection (either actual or
estimated, depending upon the available information) of the distribu
tional curves of the two states. Adjustments from these initial deci
sion points by the individual were expected to occur during the train
ing phase of the experiment as the individual gained experience with
the states and received feedback as to his performance (relative to an
optimal model). The extent of an individual's adjustments (his learn
ing efficiency) is measured using several variables. Each variable
measures the relative extent of adjustment (or lack of adjustment) from
the original decision anchor tov/ard the optimal value of that variable.
General Research Design
As stated previously, the focus of this research is on human
decision making and information processing within a particular decision
context that of standard cost variance investigation. Much of the
variance investigation literature within accounting has focused on how
the decision maker should integrate the available information and make
the investigation decision (see Kaplan, 1975 for a review of this
literature). Very little research has been concerned with how the
decision maker does accomplish these processes. The major objective
of this research is to study the processes used by the decision maker
in reaching variance investigation decisions. In particular, this re
search will examine within the conceptual framework discussed previously:
29
1) the effects of the decision situation structure, the available infor
mation set contents, the individual's information processing efficiency,
and the individual's learning efficiency on the individual's longrun
decision efficiency; and 2) the effects of the decision situation
structure and the available information set contents on the individual's
information processing and learning efficiency.
Selection of Independent Variables
The effects of two types of variables are of primary interest
within this study: these are the situation variables and the process
variables. The following discussion describes the selection of each
of the variables employed in this study.
The situation variables
The situation variables are the quantity of information available
to the individual prior to his decision, the statistical structure of
the two states of nature, and the cost structure of the possible decision
outcomes.
The available information set. The effects of the contents of
the available information set are studied by manipulating the presence
and absence of certain distributional information. This involves the
specification of two levels of available information set content. Since
the difficultly of the discrimination task may be increased by the
absence of certain information, one level of the information variable
has less distributional information than the other level. This inde
pendent variable is labeled the information variable.
30
The statistical structure. The effects of the statistical struc
ture of the decision situation are studied by manipulating a distribu
tional information variable. This involves the specification of two
levels of statistical relationship among the two states. Since the
difficulty of the discrimination task increases as the area of dis
tributional overlap between the two states increases, one level of the
distribution variable will have a greater area of overlap than will
the other level. This independent variable is labeled the distribution
variable. All other distributional information items, if presented,
will be controlled as constants.
The cost structure. The effects of the cost structure of the
possible decision outcomes are studied by manipulating a singular in
formation variable. The manipulation of this variable involves the
specification of two levels of decision outcome relationships (in terms
of incurred costs). This independent variable is labeled the cost
variable. Since the importance of the discrimination task increases as
the costs associated with decision outcomes that involve decision errors
become unequal, the different levels of the cost variable will be
associated with different decision error costs. One level of the cost
variable is structured in favor of more variance investigations and the
other level of the cost variable is structured in favor of fewer vari
ance investigations. The only other singular information variable, the
actual results of the physical process and its cost variance, is the
primary experimental stimulus. This information item will be a random
variable whose distributions, given either of the two states of nature,
will be normally shaped with parameter values defined by the appropriate
level of the distribution variable.
31
Independent variables interaction. The research design of this
study is a factorial design in which the above three independent vari
ables, each at two levels, are fully crossed, thus producing eight (23)
independent variables combinations or treatments. The factorial
combination of the different levels of the independent variables adds
power to the research design by permitting the examination of the
effects of interactions upon the dependent variables.
The process variables
The effects of an individual's information processing and learn
ing efficiency are studied using certain observation variables measured
on a continuous (ratio) scale. Continuous measurement facilitates the
use of these variables as both independent and dependent variables.
When used as independent variables the measures are treated as random
components rather than as discrete classification levels. The effects
are studied using three major types of variables: 1) individual de
cision model sensitivity (relative effect of a variable response range),
2) individual decision criteria (relative effect of conservative ad
justment), and 3) relative initial decision anchor and final decision
anchor relationships. The first two types of variables are derived
from model parameters within the theory of signal detection.
A summary of the general research design
The general research design is depicted in Table 1. The design
is presented in terms of the dependent and independent variables to be
included within the research. More specific extensions arid
32
TABLE 1
GENERAL RESEARCH DESIGN IN TERMS OF EXPERIMENTAL VARIABLES
Dependent Variables
Independent Variables
Individual
LongRun
Decision
Efficiency
Individual
Information
Processing and
Learning
Efficiency
Contents of the available infor
mation set
X
X
Structure of the decision situation:
Statistical relationships
X
X
Decision outcome relationships
X
X
Individual information processing
and learning efficiency
X
Note: An "X" within a cell indicates that the relationship of the
variables concerned are included within the experimental design.
33
operationalizations of these concepts are discussed in a later section
of this chapter. The objective at this point is to summarize in general
terms the relationships which will be included within the experimental
design.
General Research Methodology
Research methodology pertains to the general procedures or
methods employed in conducting research. Two general methods are
employed in this research simulation and laboratory experimentation.
The major objective of the simulation is to produce hypotheses which
will predict the behavior of human decision makers within the decision
situation assumed by the simulation. The simulation is based upon
assumptions derived from the conceptual development and from the gen
eral task environment of the laboratory experimentation. These
assumptions and the task environment are discussed in greater detail
in a later section of this chapter. The major objective of the
laboratory experiment is to test the conceptual development (through
the hypotheses derived by the simulation) of the effects of the vari
ous independent variables on the various observation variables.
The general and specific research design employed in the labora
tory experiment is the same as that employed within the simulation.
The major difference between the simulation and the laboratory experi
ment is the use of human subjects. This difference creates two major
sources of incompatability betv/een the simulation and the laboratory
experiment. First, the simulation makes certain assumptions concerning
human behavior and applies these assumptions consistently. Within
34
the laboratory experiment such behavior is not necessarily applied
consistently. Consequently, greater variance of results is expected
within the laboratory experiment than within the simulation. Second,
it should be noted that certain variables are largely affected by
individual attributes. Without a theory of the effect of individual
attributes any simulation of these variables would be arbitrary.
Accordingly, not all of the experimental variables are included in the
simulation. Hypotheses concerning the variables not present within
the simulation either are derived from limited concepts concerning the
effects of individual attributes or are stated in an exploratory manner
(no expected difference).
General Experimental Environment and Task
The standard cost variance investigation situation studied within
this research is set in an environment of a manufacturing company.
More specifically, the subjects are asked to assume the role of the
operational manager of an assembly department which assembles a single
product, a metal folding chair. The operating efficiency of the
assembly department is determined completely by the labor efficiency of
the assembly workers.
Each subject receives a sequential series of standard cost vari
ance reports and is asked for each report to decide whether to investi
gate or not to investigate the reported labor efficiency variance.
The efficiency of a subject's decision performance and the amount of
payment he will receive for participating in the experiment is based
upon the total investigation decisions cost which he incurs over the
series of variance reports.
35
Each standard variance report is concerned with the results of a
single joborder to produce a constant number of chairs and reports
only aggregate (overall assembly department) results. Each report
contains the aggregate standard assembly time allowed per chair, the
actual assembly time incurred per chair, the overall labor efficiency
variance per chair, the total number of chairs produced, and the costs
associated with each possible decision in combination with each
possible state of nature. All time units are presented in minutes.
The singular information contained in a variance report is independent
of that contained in previous variance reports.
The experiment is conducted in two phases a training phase
and an experimental phase. The training phase consists of three
contiguous sessions in which the subject learns his role and presumably
develops his decision strategy. Performance feedback is given at the #
completion of each training session. The experimental phase consists
of a single session in which the subject receives a series of variance
reports similar to those presented in his training session. In this
phase no performance feedback is given until after the completion of
the entire experiment. The subject is paid according to his performance
in the experimental phase.3
Operationalization of Variables
This research employs the following three decision situation
independent variables, each measured using a discrete classification:
3Greater detail concerning the experimental environment, task, and
procedures is presented in Chapter V.
36
1) the information variable, 2) the distribution variable, and 3) the
cost variable. As indicated previously, each is varied across two levels.
The individual process variable types, measured on a continuous scale,
include the following: 1) individual decision model sensitivity, 2)
individual decision criteria, and 3) relative initial and final decision
anchor relationships. The major dependent variable is the individual
longrun decision efficiency (in terms of incurred costs). The
following discussion describes each of the variables or variable types
as they are employed in this study.
The information variable
The first level, labeled Jl_, is derived from the set of infor
mation assumed to come from individual experience with the physical
system. It includes the following items: 1) the historically derived
portion of time in which the process has been found to fall in each of
the two states, 2) the assumption that the random variable of interest
(actual minutes incurred per chair or its associated standard vari
ance) is normally distributed for both states, 3) the lowest observed
value of the random variable, 4) the highest observed value of the
random variable, 5) the maximum costs associated with each state, and
6) the minimum costs associated with each state.
The second level of the information variable, labeled 12^, includes
additional distributional information. In addition to the six items
contained in the II information set, the following two items are
included: 1) the mean of the random variable within each state, and
2) the standard deviation of the random variable within each state.
37
The distribution variable
Manipulation of the distribution variable involves two factors:
1)the distributional parameters of each state, and 2) the statistical
relationship between the states. The two levels of this variable are
generated through a change in the variance and a change in the stan
dardized distance between the means of the two states. The first
level of the distribution variable, labeled Sl_, has the following
parameters and relationships:
1) y1 = 36.0 actual minutes incurred per chair;
2) crn = a = Gj = 3.0 actual minutes incurred per chair; and
3) u12 = y + 1.5a, = 40.5 actual minutes incurred per chair,
where, y = the mean of the jth state of nature (incontrol = 1 and
* J
outofcontrol = 2) given the ith distribution level
(SI = 1 and S2 = 2);
a = the standard deviation of the jth state of nature given
' J
the ith distribution level; and
a = the standard deviation common to both states of nature
given the ith distribution level.
The second level of the distribution variable, labeled S2^, has
the following parameters and relationships:
1) y = 36.0 actual minutes incurred per chair;
2) a21 = a22 = a9 = 5.0 actual minutes incurred per chair; and
3) y2 = y21 + 1.8a = 45.0 actual minutes incurred per chair,
where y , a^, and a^ are defined the same as in the SI level. The
third and fourth statistical moments of each state within each dis
tribution level are uncontrolled except that their deviations from a
normal distribution are minimized.
38
The cost variable
Given the two states of nature (the realization of which is un
known to the individual) and two possible decisions, there follows that
two types of errors can be made in reaching a decision. The first type
of decision error, labeled type A, is the decision to investigate the
physical process when the incontrol state exists. The second type of
decision error, labeled type B, is the decision not to investigate when
the outofcontrol state exists. The marginal cost of either error
type equals the cost that would have been incurred had the correct
decision been made minus the cost that was incurred by the incorrect
decision. Thus, the marginal costs of the two decision error types,
labeled MC^ and MCg, are:
= C(decision=not investigate  state=incontrol) 
C(decision=investigate  state=incontrol), and
MCg = C(decision=investigate  state=outofcontrol) 
C(decision=not investigate  state=outofcontrol),
where C( ) represents the decision cost for the situation within the
parentheses.
When the marginal cost of a type A error equals the marginal
cost of a type B error the cost structure should be ignored when making
an investigation decision. Of greater interest are situations in which
the marginal error costs not equal. In the present study, each level
of the cost variable takes one of the following forms: 1) the
marginal cost of a type B decision error equals three times the marginal
cost of a type A error (labeled level CJ_), and 2) the marginal cost
of a type A error equals three times the marginal cost of a type B
error (labeled level C2).
39
Individual decision model sensitivity variables
The sensitivity of the individual's decision model relative to the
decision situation is measured using the TSD parameter d1. The theo
retical definition of d1 is:
d = (p2 Pj)/o = zi Z2
where y. = the mean of the jth state of nature (incontrol = 1 and
outofcontrol = 2);
a = the common standard deviation of the states of nature; and
z. = the value of the normal distribution function associated
J
with the jth state of nature and any decision cutoff value
common to both states.
An empirical estimate of the d1 for an individual, labeled d 1,
is obtained using the individual's conditional probabilities P(decision=
investigate  state=outofcontrol) and P(decision=investigate  state=
incontrol) to calculate a subjective z1 and zÂ£. As previously pointed
out, d' is relative to the decision situation. In particular, d is
relative to the distribution variable. To gain comparability across
situations the following measure is used:
DN, / dÂ¡
where d^ is generated using optimal model k. The measure decreases
from a value of one as the result of several factors: 1) the individual
is inconsistent in his use of his cutoff value (i.e., there exists a
range around his cutoff value within which decisions are not made
using a strict relation to this cutoff value) or the individual makes
one or more temporary processing errors, and 2) the individual
utilizes more than one cutoff value.
40
Based upon a subjective analysis of each individual's decisions,
the measure DN^ can be adjusted for the effects of using multiple cut
off values. Defining d^a to be the individual's decision model sensi
tivity with the effects of multiple cutoff values eliminated:
DNAi = d\a / d'k
The difference DNA DN approaches zero as the effects of multiple
cutoff values decreases and becomes zero when the individual uses a
single cutoff value.
Individual decision criteria variables
The criteria the individual adopts in making his decisions are
measured using the TSD parameter 3. The theoretical definition of 3 is:
3 = d>(z2) /
where ( ) denotes the normal density function for the point in the
parentheses and z. is defined the same as for the d'. variable.
J
An empirical estimate for the 3 of an individual, labeled 3.5
is obtained using the z1 and z2 values associated with the individual's
conditional probabilities employed in estimating d^.. The measure 3..
is relative to the decision situation. In particular, it is relative
to those variables which affect the point on the decision axis which
the individual selects as his investigation decision cutoff value. A
measure of individual criteria comparable across situations is defined
as:
BN. = 3. / 3.
i i k
where 3^ is generated using optimal model k. The measure approaches a
value of one as 3_. approaches 3^ (i.e., as the individual's cutoff
decision value approaches the optimal model's cutoff decision value).
The measure approaches either a value of zero or positive infinity as
the result of several factors: 1) the individual does not process
properly the effects of the relative costs of the two types of decision
errors, 2) the individual does not process properly the effects of the
relative frequencies of the two states, and 3) the individual uses more
than one cutoff value.
Using the same subjective analysis as that used in adjusting the
DN measure, the BN^ measure can be adjusted for the effects of multipl
cutoff values. Defining B^ to be the individual's criteria measure
with the effects of multiple cutoff values eliminated:
BNAi B* / Bk
The difference BNA^ BN^ approaches a value of zero as the effects of
multiple cutoff values decrease and becomes zero when the individual
uses a single cutoff value.
Another measure derived from the BN measure can be considered
a measure of individual conservatism. In this study, conservatism
refers to incomplete adjustment from an initial decision anchor towards
the optimal cutoff value. Nonconservatism refers to a more that
complete adjustment from the initial decision anchor past the optimal
cutoff value. The extent of conservatism is measured by the relative
distance between the individual's cutoff value and the optimal model's
cutoff value. Since the measure is dependent on the direction of ad
justment it is conditional upon the level of the cost variable. Using
BNC.Â¡ to denote tne extent of an individual's conservatism:
BNC.Â¡ = (3? B<) / B^ given the Cl cost level; and
BNC.j = (b^ Bj) / B^ given the C2 cost level.
42
BNC.J approaches either positive infinity or a value of one as the extent
of conservatism increases, approaches a value of zero as the extent of
conservatism decreases, and approaches either a value of negative one
or negative infinity as the extent of nonconservatism increases.
Initial and final decision anchor variables
The relative relationships between an individual's initial and
final decision anchors can be measured in terms of the relative linear
distance between these two decision anchors. A measure of the relative
adjustment for individual i, labeled RA^, is conditional on the direc
tion of adjustment along the relevant decision axis. This direction
of adjustment is in turn conditional on the level of the cost variable.
Given the Cl cost level the initial decision anchor is greater than the
optimal model cutoff value, and given the C2 cost level the initial
decision anchor is less than the optimal model cutoff value. The RA.
measure is defined as:
RAi = (EDVi 0DVk) / (TDVi 0DVk) ,given the Cl cost level;
RA.. = (0DVk EDV.Â¡) / (0DVk TDV.Â¡) ,given the C2 cost level,
where EDV^ = individual i's final (experiment) decision anchor;
TDV. = individual i's initial (training) decision anchor; and
0DVk = optimal model k's final (experiment) cutoff value.
The RA.j measure has the following relationships: 1) if no
adjustment occurs between the training and the experiment phases the
measure will equal a value of one, 2) if the adjustment is in the wrong
direction (away from the optimal model's experiment cutoff value) the
measure will be greater than a value of one, 3) if the adjustment is in
43
the proper direction but incomplete the measure will be less than a
value of one but greater than a value of zero, 4) if the adjustment
is in the proper direction and is complete the measure will equal a
value of zero, and 5) if the adjustment is in the proper direction but
more than complete the measure will be less than a value of zero.
Individual longrun decision efficiency
A major dependent variable of interest in this research is the
cost incurred as a result of the individual's variance investigation
decisions. The experimental objective function for all decision
situations is to minimize these costs (labeled investigation decisions
costs). The individual's longrun decision efficiency is measured in
terms of his minimization of the investigation decisions costs summed
over all decisions.
Given a pair of decision situations which hold constant all of
(
the independent variables except for the cost variable,the correct
decision within each situation will lead to different investigation
decisions costs. Consequently, absolute investigation decisions costs
are not comparable between decision situations: a relative measure
must be obtained. One such relative measure involves the comparison of
the individual's investigation decision costs with that of an optimal
model which uses the same information as that available to the indi
vidual. Denoting such a measure G:
Gij = (ICi0 MCko> / SMCk
where IC = individual i's investigation decision cost for decision j;
MC^j = optimal model k's investigation decision cost for decision
j; and
44
SMC= the sum of optimal model k's investigation decisions costs
over all decisions (m in number).
The G. measure can be classified into one of three submeasures
' J
where the classification is dependent upon the algebraic sign of G.Â¡j.
The classifications are 1) G > 0 the individual's investigation
decision cost for decision j is greater than that of the optimal model,
2) G^j = 0 the individual's investigation decision cost for decision
j is equal to that of the optimal model, and 3) G < 0 the indi
* J
vidual's investigation decision cost for decision j is less than that
of the optimal model.
Summing those Gs with identical algebraic signs, the summations
' 0
are defined as follows:
GP.j = the sum of those Gs with a positive algebraic sign;
GN^ = the sum of those Gs with a negative algebraic sign; and
GZ.j = the sum of those Gs with a value of zero.
It is easily shown that the following relation holds:
m
E G.. = GP, + GN, + GZ,
j=l J
where m equals the total number of decisions made by individual i. Since
the GZ.Â¡ summation equals zero, the sum of the G over all j = l,m
decisions, denoted G, is:
G = GP + GN.
i i i
Conceptually, GP^ is the relative additional cost of those de
cisions made by individual i which are greater in value than that of
the optimal model. The GN^ is the relative savings of those decisions
made by individual i which are less in value than that of the optimal
model.
45
The anchoring and adjustment process may occur over three training
sessions and one experiment session. Using the first training session
as an estimate of the initial decision anchor and the experiment
session as an estimate of the final decision anchor, there remains two
training sessions within which the adjustment process itself can be
analyzed. In particular, the effects of the pattern of adjustments
within the training sessions are expected to influence significantly
the magnitude of an individual's experiment Gj measure. The adjustment
process can be measured in the training sessions by computing the
individual's Gj measure at the completion of each session. The
measures, labeled TG^, TG.Â¡0, and TG.Â¡3, are defined in the same manner
as the G. measure discussed above (with the exception that they are
computed from the appropriate training session data instead of the
experiment session data). The differences between these training
measures reflect the effects of the individual's adjustments in the
training sessions. These differences are defined as follows:
DTGii = TG11 TGi2
DTGi2 = TGi2 TGi3
where, DTGjj = the difference between the GÂ¡ measure for the jth and
the jth + 1 training sessions of individual i; and
TG^j = the G.j measure for the jth training session of indi
vidual i.
Negative DTGjjvalues indicate an increase in the TGjj measure between
training sessions, and positive DTGjj values indicate the reverse.
The effects of the directions of these adjustments should be
related to the effects of the final anchor on the individual's Gj
measure (in the experiment). The effects of the adjustment measures
46
on tha experiment measure can be analyzed using a discrete classifica
tion of the direction of the adjustments. This classification of the
measure is accomplished using its algebraic sign. The second and
third training session results can be grouped into one of four
classes: PP(+,+), PM(+,), MP(,+), and MMSubjects within
the MM classification have constantly increasing TGjj measures and
subjects within the PP classification have constantly decreasing TG^j
measures.
Hypotheses Formation
As previously discussed, the formation of the experimental
hypotheses is accomplished primarily using the method of simulation.
Two general types of simulations are performed simulation of optimal
model performances and simulation of subjective investigation decision
performances. The first part of this section discusses the assumptions
and presents the results of the simulation of optimal model perform
ances. The second part of this section discusses certain assumptions
derived from the conceptual development that form the basis for the
simulation of subjective investigation decision performances. It also
presents the results of this simulation and the derivation of the ex
perimental hypotheses.
Simulation of Optimal Model Performances
The decision rule of the optimal model (given the objective func
tion of minimizing incurred costs) employed in this research is to
investigate the reported labor efficiency variance when:
47
P(xout) P(in) V1r>N Vin>Y
Equation 1
where x = the actual minutes incurred per chair;
out = the state of outofcontrol;
in
= the state of incontrol;
Vin,N = ^e cost associated with not investigating an incontrol
variance;
VÂ¡nsy = the cost associated with investigating an incontrol
variance;
V0Ut,Y = the cost associated with investigating an outofcontrol
variance; and
Vout,N = the cost associated with not investigating and
outofcontrol variance.
The manipulation of the cost variable produces a constant cost ratio
at each of the two levels of this variable. When the cost variable
level is Cl the constant cost ratio is equal to onethird, and when the
cost variable level is C2 the constant cost ratio is equal to three.
The optimal model investigation decision rule expressed in equation 1
can be restate as:
LRq > PR.j CR Equation 2
where LRQ = the likelihood odds of the reported labor efficiency
variance being outofcontrol (P(xout)/P(xin));
PR.j = the prior odds of the incontrol state (P(in)/P(out)); and
CR = the constant cost ratio associated with the appropriate
level of the cost variable.
Assuming that both states of nature are normally distributed with equal
variance the .optimal model investigation decision rule expressed in
48
equation 2 can be restated in the following mathematical terms:
exp (x y2)2/2cr2
: r > PRi CR
exp (x yj^cr2
where x = actual minutes incurred per chair;
Vj = the mean of the incontrol state;
y2 = the mean of the outofcontrcl state;
a = the common standard deviation of both states; and
exp = the notation for the exponential funcition (e).
If the above equation was changed to an equality and solved for x the
result would be the optimal cutoff value in terms of actual minutes
incurred. This solution takes the general form:
G2(ln(PRÂ¡) + ln(CR))
x = + 0.50(y2 + y,) Equation 3
y2 yi
where x = the optimal cutoff value in terms of actual minutes incurred;
and
In = the notation for the natural logarithm function (loge).
With the decision rule in the form of equation 3 the parameters required
to fit the optimal model become identifiable. These parameters include
the mean of both states of nature, the common variance of the two
states, the prior odds of the incontrol state, and the appropriate
value of the cost ratio.
With knowledge of the parameters required to fit the optimal
model the criteria employed for the simulation of optimal model per
formances can be discussed. The first criterion is that information
available to the optimal model be the same as the information avail
able to the individual. The information available to the individual
is manipulated by the levels of the information variable. Within the 12
49
information level the available information set contains statistical
estimates of all the parameters required to fit the optimal model.
However, within the II information level the available information
set does not contain all the required parameter estimates. Therefore,
within the II information level the optimal model must use the training
sessions to estimate the missing parameters. The parameter estimates,
both given and estimated, are presented in Table 2.
The second criterion is that the labor efficiency variance re
ports presented to the subjects in the experiment be the same reports
used in the simulation of optimal model performances. The appropriate
parameter estimates presented in Table 2 were used in the optimal
model decision rule expressed by equation 2 to simulate optimal model
investigation decisions. Table 3 presents some performance results
of this optimal model simulation.
Simulation of Subjective Investigation Decision Performances
This section discusses assumptions derived from the conceptual
development that form the basis for the simulation of subjective investi
gation decision performances. It also presents the results of this
simulation and the derivation of the experimental hypotheses.
Assumptions of simulation
The first assumption employed for the simulation of subjective
investigation decision performances is that the subjects will behave
as if they use the anchoring and adjustment heuristic during the
training phase. The anchoring and adjustment heuristic proposes that
TABLE 2
ESTIMATES OF THE PARAMETERS REQUIRED TO FIT THE OPTIMAL MODEL GIVEN THE VARIOUS CONDITIONS
Information
Experimental
Variable 11
Parameters
Information
Variable 12
Actual Underlying
Data Structure Parameters
Cost
Parameter
Distribution Variable
Distribution Variable
Distribution Variable
Variable
Estimate
SI
S2
SI
S2
SI
S2
35.99
36.26
36.00
36.00
35.975
35.943
y2
40.21
44.24
40.50
45.00
40.492
44.995
Cl
CT?
8.88
29.42
9.00
25.00
9.157
25.060
7.38
19.32
9.00
25.00
9.437
25.002
PRi
1.50
1.50
1.50
1.50
1.500
1.500
CR
0.33
0.33
0.33
0.33
0.333
0.333
C2a
CR
3.00
3.00
3.00
3.00
3.000
3.000
aThe parameter estimates
given the Cl cost level.
for u1 ,y2,cj2
,and PR.Â¡
given the C2
cost level are
the same as those
TABLE 3
RESULTS OF THE SIMULATION OF OPTIMAL MODEL PERFORMANCES GIVEN THE VARIOUS CONDITIONS
k Information
Distribution Cost
P(investigate
outofcontrol)
P( investigate
incontrol)
Optimal Cutoff
Value (actual
minutes incurred)
dk
6k
1
11
SI
Cl
0.875
0.383
36.90
1.447
0.539
2
11
SI
C2
0.400
0.033
41.29
1.581
5.206
3
11
S2
Cl
0.875
0.300
38.70
1.674
0.592
4
11
S2
C2
0.425
0.017
45.88
1.939
9.453
5
12
SI
Cl
0.900
0.400
36.86
1.535
0.454
6
12
SI
C2
0.400
0.033
41.26
1.581
5.206
7
12
S2
Cl
0.900
0.300
38.57
1.806
0.504
8
12
S2
C2
0.525
0.033
44.68
1.897
5.364
cn
52
a subject will select an initial anchor as a first decision approxi
mation and will adjust this decision anchor toward an optimally correct
point as he learns from his experiences with the states of nature.
However, the individual's adjustment process will not be sufficient:
i.e., he will tend to approach the optimally correct point but will not
adjust completely to that point.
The second assumption is that the location of the initial de
cision anchor on the actual minutes incurred continuum (axis) will be
conditional upon the level of the distribution variable. The reason
for this is that the initial decision anchor is expected to be located
at a central point between the means of the two states. Since the
mean of the outofcontrol distribution shifts with the level of the
distribution variable, the central point between the means of the two
states also shifts with the level of the distribution variable. The
initial decision anchor used in the simulation of subjective investi
gation decision performances is the geometric intersection point of the
two states' distribution curves. This particular initial decision
anchor is used because it is the exact central point between the means
of the two states. However, it is selected for operational purposes
and is not necessary to the conceptual development. Any point of
central tendency would be satisfactory.
The final assumption is that the subjects' adjustment process
will be approximately equal over the various treatment conditions. The
adjustment process is defined as a linear movement along the actual
minutes incurred axis from the initial decision anchor towards the
appropriate optimal model decision cutoff. The magnitude of the linear
movement used in the simulation of subjective investigation decision
53
performances is 50 percent of the distance between the initial decision
anchor and the appropriate optimal decision value. The 50 percent
adjustment value, selected for operational purposes, is completely
arbitrary and is not necessary to the conceptual development. The only
restriction is that it be less than 100 percent. Furthermore, the
general assumption of equal adjustment over the various conditions is
not necessary to the conceptual development: the objective of this
general assumption is to facilitate a simple operationalization of the
simulation.
Training phase simulation and hypotheses formation
The first stage of the simulation involves simulating the
anchoring and adjustment process in the training sessions for each
combination of independent variables. A simulated subject sample size
of one is used for each condition (resulting in a total simulated sub
ject sample of eight). The results of this first stage simulation are
presented in graphical form in Figure 2. This figure depicts the
simulated initial decision anchors (points A), the simulated final
decision anchors (points B), and the simulated optimal cutoff values
(points C) for each of the eight combinations of independent variables.
The assumptions used in simulating the training phase can be
investigated using the results of the experimental training phase. The
assumptions are i) that the subjects will behave as if they used the
anchoring and adjustment heuristic, 2) that the initial decision anchor
will be located centrally between the means of the two states and will
be dependent on the level of the distribution variable, and 3) that the
II Information Level
Cost
Cl
Cost
C2
Distribution
SI
1 1 i
36.90 37.575 38.25
+
39.77 41.29
Distribution
S2
C B A
h
38.7 39.6 40.50
43.19
H
45.88
Distribution
SI
12 Information Level
Cost
Cl
B A
+
36.86 37.555 38.25
Cost
C2
4
39.755 41.26
C B A
Distribution 1 } 1
S2 38.57 39.535 40.50
4
42.59
4
44.68
Note: All scales are actual minutes incurred per chair produced.
FIGURE 2
SIMULATED INITIAL DECISION ANCHORS (POINTS A), FINAL DECISION
ANCHORS (POINTS B), AND OPTIMAL CUTOFF VALUES (POINTS C)
55
adjustment process will be approximately equal over the various con
ditions. The location of the initial decision anchor can be investigated
by examining the relationship between the individual's initial cutoff
value (in the first training session) and the intersection point of
the appropriate two distribution curves (the TDV.Â¡ measure). These
intersection points are conditional upon the level of the distribution
variable. Given the SI distribution level the intersection point is
38.25 actual minutes incurred, and given the S2 distribution level the
intersection point is 40.5 actual minutes incurred (see Figure 2 for a
graphical presentation of these points). The expected relationship is
that the mean initial decision anchor (TDV.) of the individuals, given
a level of the distribution variable, will not be significantly differ
ent from the intersection point of the appropriate two distribution
curves.
The assumption of approximately equal adjustment over the various
conditions can be investigated by examining the relationship between
the individual's adjustment over the complete experiment and the total
adjustment that would be required to reach the optimal model's decision
cutoff value. A measure of the relative adjustment for individual i,
RA.j, is conditional upon the direction of adjustment along the relevant
decision axis (i.e., the actual minutes incurred per chair produced).
The expected relationship is that the mean RA^ will not differ signifi
cantly between the levels of the various conditions.
Given the above simulation and discussion the following hypo
theses can be derived:
HI.la (TDV.S1) = 38.25.
The mean initial decision anchor given the SI distribution level
56
will equal the intersection point of the two distribution curves
within that level.
HI.lb (TV.S2) = 40.5.
The mean initial decision anchor given the S2 distribution level
will equal the intersection point of the two distribution curves
within that level.
HI.2a (TDV.I1) = (157^12).
The mean initial decision anchor given the II information level
will equal the mean initial decision anchor given the 12 infor
mation level.
HI.2b (TDViiCl) = (TViC2).
The mean initial decision anchor given the Cl cost level will
equal the mean initial decision anchor given the C2 cost level.
HI.3a (RAi111) = (RAi112).
The mean relative adjustment given the II information level will
equal the mean relative adjustment given the 12 information level.
HI.3b (RA^Sl) = (RAi Â¡S2).
The mean relative adjustment given the SI distribution level will
equal the mean relative adjustment given the S2 distribution
level.
HI.3c (RAiCl) = (RA.C2).
The mean relative adjustment given the Cl cost level will equal
the mean relative adjustment given the C2 cost level.
Individual decision model sensitivity hypotheses formation
The measures of individual decision model sensitivity should
generally be unaffected by the independent variables. These measures
57
have a greater relationship with individual decision processes than
with the specific conditions of a general decision situation. The
individual decision model sensitivity measure dj should have a re
lationship with the distribution variable only. The theoretical d1
measure under the SI distribution level is 1.5 and under the S2 dis
tribution level is 1.8. Thus, the average dl under the S2 distribu
tion level should be greater than under the SI distribution level.
The DNA.Â¡ measure should have no systematic relationships with
the three independent variables. This measure of the relative de
viation of the individuals decision model sensitivity from that of
the optimal model's generally is due to individual decision incon
sistencies and temporary processing errors. If individuals are
randomized into the specific decision situations there are no a
priori reasons for expecting significant differences in this measure
due to the independent variables.
The difference between DNA^ and DNÂ¡ measures the effects of
multiple decision anchors upon the individual's relative decision
model sensitivity. Relationships between this difference and the in
dependent variables depend upon whether the independent variables are
related to the causes underlying an individual's use of more than one
cutoff value. A possible explanation involves the distribution vari
able. As the variance of the incontrol state increases the absolute
distance between the lowest values of the random variable of interest
(actual minutes incurred) and the mean of the incontrol state in
creases. In turn, as this distance increases subjects may be more
inclined to perceive that a second outofcontrol distribution overlaps
the lower range of the incontrol state. If such perceptions underly
58
the use of multiple cutoff values, the average difference between DNA.Â¡
and DN.Â¡ should differ between the two levels of the distribution vari
able. A second possible explanation involves the cost variable. As
the individual's final cutoff value increases, the absolute distance
between the individual's final cutoff value and the lowest values of
the random variable of interest increases. In turn, as this distance
increases subjects again may be more inclined to perceive that a sec
ond outofcontrol distribution overlaps the lower range of the
incontrol state. If such perceptions underly the use of multiple
cutoff values, the average difference between DNA and DN.Â¡ should
differ between the two levels of the cost variable.
Based on the above discussion the effects of the independent
variables on the individual decision model sensitivity measures can
be hypothesized as follows:
H2.la (di SI) < (di S2).
Those subjects within the SI distribution level will have
significantly smaller mean djs then those subjects within the
S2 level.
H2.lb (di Â¡II) = (di Â¡12).
Those subjects within the II information level and within the
12 information level will have equal mean d^s.
H2.1c (di 1 Cl) = (di C2).
Those subjects within the Cl cost level and within the C2 cost
level will have equal mean dis.
H2.2a (DNAilll) = (DAj ] 12).
Those subjects within the II information level and within the 12
information level will have equal mean DNAjS.
59
H2.2b (DNAj  SI) = (DNAj S2).
Those subjects within the SI distribution level and within the
S2 distribution level will have equal mean DNAjS.
H2.2c (DAÂ¡ 1 Cl) = (DAÂ¡ IC2).
Those subjects within the Cl cost level and within the C2 cost
level will have equal mean DNAÂ¡s.
H2.3a (DNAÂ¡DNÂ¡ Â¡SI) < (DNAiDNi S2).
Those subjects within the SI distribution level will have
significantly smaller mean DNAÂ¡DNÂ¡s than will those subjects
within the S2 distribution level.
H2.3b (DNAjDN.Â¡ Cl) <(DNAiDNi  C2).
Those subjects within the Cl cost level will have significantly
smaller mean DNAÂ¡DNÂ¡s than will those subjects within the C2
cost level.
Individual decision criteria simulation and hypotheses formation
The variables which affect the BNj measure should be those factors
related to the individual's selection of a cutoff value. The most
significant variable affecting the individual's cutoff value should be
the cost variable. Within this variable the initial decision anchor
given the Cl level is much closer to the optimal cutoff value than is
the initial decision anchor given the C2 level. Consequently, the BNj
measure should be closer to a value of one under the Cl level than
under the C2 level.
The difference between the BNAÂ¡ and the BNj measures should
relate to the same variables as those discussed for the difference
60
between the DNA.Â¡ and DN^ measures. The two possible explanations in
clude the distribution variable and the cost variable.
Theoretically, the more extreme the optimal cutoff value relative
to a central point between the distributions of the two states, the
higher should be the individual conservatism. The decision situations
with the most extreme optimal cutoff values are those within the C2
cost level; the situations with the least extreme optimal cutoff values
are those within the Cl cost level. Consequently, the BNCj measure
should be larger within the C2 cost level than within the Cl cost level.
The simulation and assumptions used in developing the training
phase hypotheses can be extended to enable formation of hypotheses con
cerning the individual decision criteria. The earlier simulation de
rived the following final decision cutoff values: 1) 37.555 actual
minutes incurred for the Cl cost level given the 12 information level
and the SI distribution level, and 2) 39.755 actual minutes incurred
for the C2 cost level given the 12 information level and the SI dis
tribution level. Given these cutoff values a f(e^) measure can be
calculated directly (the measure is used rather than the 3. measure
due to the assumption of a single cutoff value). The functional
notation, f( ), is employed to indicate these estimates are based upon
a simulation rather than upon empirical observation. For this simu
lation the f(3?)s are as follows:
f(B*Cl,Sl,I2) = (zout) / <Â¡>(zin) = 0.70818
f(B*C2,Sl,I2) = (zQut) / *(zin) = 2.11774
where ( ) indicates the value of the normal density function at the
point in parentheses. Given the f(B^) measures the f(BNA^) measures
can be calculated. For this simulation the f(BNA^)s are as follows:
61
f(BNAiCl,Sl,I2) = f(6*) / f(6k) = 1.41898
f(BNAiC2,Sl,I2) = f(e?) / f(f?k) = 0.47017
where f(ek) is the simulated measure at the cutoff value of the kth
optimal model.
This simulation can be extended to the S2 level of the distribu
tion variable given the 12 information level and can be extended to
both the SI and S2 levels of the distribution variable given the II
information level. The f(BNA) measures obtained under the 12 infor
mation and S2 distribution conditions are as follows:
f(BNAiJCl,S2,12) = 1.42318
f(BNAiÂ¡C2,S2,I2) = 0.47300
The f(BNA) measures given the II information level are as follows:
f(BNAiCl,Sl,Il) = 1.40126
f(BNAiC2,Sl,Il) = 0.46993
f(BNAiCl,S2,Il) = 1.38273
f(BNA.C2,S2,Il) = 0.38094
Based on these f(BNA) measures, hypotheses can be derived con
cerning the effects of the independent variables upon the BNA. measure.
The f(BNA^) measures can be averaged over the appropriate conditionals
to determine these effects. Averaging over all conditionals except the
information variable gives the following results:
f(BAiIII) = 0.90871
f(BAi12) = 0.94633
Although there is a difference in the f(BNA) measures due to the infor
mation variable this difference is small compared to the standard error
of these estimates (the difference divided by the standard error is
0.03762/0.39157 = 0.0961). Therefore, a significant effect due to the
information variable would not be expected.
62
Averaging over all conditionals except the distribution variable
gives the following results:
f(BAÂ¡ 1 SI) = 0.94009
f (BAÂ¡ 1S2) = 0.91496
Again, the difference in the f(BNAj) measures due to the distribution
variable is small compared to the standard error of the estimates (the
difference divided by the standard error is 0.02513/0.39174 = 0.C642).
A significant effect due to the distribution variable would not be
expected.
Averaging over all conditionals except the cost variable gives
the following results:
f(BAÂ¡ Cl) = 1.40654
f(BAÂ¡ C2) = 0.44851
The difference in the f(BNAj) measures due to the cost variable is
large compared to the standard error of the estimates (the difference
divided by the standard error is 0.95803/0.02436 = 39.3308). Con
sequently, a significant effect would be expected for the cost
variable.
Another implication drawn from the f(BNAj) measures concerns the
homogeneity of variance for the measures given different levels of the
cost variable. The variance for the f(BNAÂ¡  Cl) is .00034 and the vari
ance for the f(BNAj C2) is .00203. Using the F test for equal variances,
F=5.9344 (3 and 3 d.f.) which indicates the variances may not be equal.
The variance of (BNAÂ¡ [Cl) would be expected to be less than the vari
ance of (BNAj[C2).
This simulation can be extended to enable the formation of
hypotheses concerning the effects of the independent variables on the
63
conservatism measure BNCÂ¡. This extension employs the f(Bj) and fU^)
measures used above. The results of this extended simulation averaged
over all conditionals except for the information variable are as follows:
f(BNCi  II) = 0.48328
f(MCi 112) = 0.47475
The difference in the f(BNC^) measures due to the information variable
is small compared to the standard error of the estimates (the differ
ence divided by the standard error is 0.00853/0.06390 = 0.1335). A
significant effect due to the information variable would not be expected.
Averaging over all conditionals except for the distribution
variable gives the following results:
f (BCiSI) = 0.47004
f (BICi S2) = 0.48799
The difference in the f(BNC^) measures due to the distribution vari
able is small compared to the standard error of the estimates (the
difference divided by the standard error is 0.01795/0.06357 = 0.2824).
A significant effect due to the distribution variable would not be
expected.
Finally, averaging over all conditionals except for the cost
variable gives the following results:
f(BC_Â¡ i Cl) = 0.40654
f(BCiC2) = 0.55149
The difference in the f(BNC.j) measure due to the cost variable is large
compared to the standard error of the estimates (the difference divided
by the standard error is 0.14495/0.02436 = 5.9503). Consequently, a
significant effect would be expected for the cost variable.
64
The above discussion concerning the individual decision criteria
measures can be summarized as follows:
H3.1a (BffjIll) = (BAÂ¡ 112).
The mean BNAj of those subjects within the II information level
and within the 12 information level will be equal.
H3.1b (BAÂ¡ SI) = (BAÂ¡ S2).
The mean BNAj of those subjects within the SI distribution level
and within the S2 distribution level will be equal.
H3.1c (BNAj  Cl) > (BAÂ¡  C2).
Those subjects within the Cl cost level will have a significantly
larger mean BNAj than will those subjects within the C2 level.
H3.2 a2(BNAj Â¡C1) < a2(BNAÂ¡  C2).
The BNAÂ¡ of those subjects within the Cl cost level will have a
significantly smaller variance than will the BNAj of those sub
jects within the C2 cost level.
H3.3a (BCj Â¡II) = (BCj 112).
The mean BNCÂ¡ of those subjects within the II information level
and within the 12 information level will be equal.
H3.3b (BNCjSl) = (BCj S2).
The mean BNCj of those subjects within the SI distribution level
and within the S2 distribution level will be equal.
H3.3c (BCjCl) < (BCj j C2).
The mean BNCj of those subjects within the Cl cost level will be
significantly smaller than the mean BNCj of those subjects within
the C2 level.
65
H3.4a (BNAiBNiSl) < (BNAiBNiS2).
The mean BNA.BN.. of those subjects within the SI distribution
level will be significantly smaller than the mean BNA^BN. of
those subjects within the S2 distribution level.
H3.4b (BNA.BN.Cl) <(BNAiBN^C2).
The mean BNA^BN^ of those subjects within the Cl cost level
will be significantly smaller than the mean BNA.BN. of those
subjects within the C2 cost level.
Individual longrun decision efficiency simulation and hypotheses
The simulation and assumptions used in developing the training
phase hypotheses enable the formation of hypotheses concerning the
G.j variable. The graph presented in Figure 3 depicts the relations of
the values derived within the training phase simulation given the
condition (12,SI). The interval between the final cutoff value and
the appropriate optimal model cutoff value represents the areas
associated with the measure. For the Cl cost level the area of the
interval under the incontrol distribution is the area associated with
the GN measure; the area of the interval under the outofcontrol
distribution is the area associated with the GP. measure. These areas
are the leftmost shaded areas in Figure 3. For the C2 cost level the
area of the interval under the outofcontrol distribution is the area
associated with the GN. measure; the area of the interval under the
incontrol distribution is the area associated with the GP^ measure.
These areas are the rightmost shaded areas in Figure 3. The areas
under these curves for this simulation are as follows:
66
o
o
LO
o
LO
o
o
o
LO
LO
LO
LO
o
LO
o
CO
LO
C\J
r.
LO
(NJ
'
LO
LO
r^
CO
CT>
o
CO
CO
CO
CO
CO
*3
FIGURE 3
GRAPH OF THE SIMULATION USED IN THE CONDITION
(12 INFORMATION, SI DISTRIBUTION)
67
Area(GNj Cl,SI, 12) = 0.08483
Area(GPj (Cl,S1,12) = 0.05048
AreaiGNiC2,S1,I2) = 0.19780
Area(GPiC2,Sl,I2) = 0.06549
These areas must be adjusted for the relative frequency differ
ences and the relative cost differences between the various conditions.
If the incontrol distribution is given a relative frequency weight of
one,then the relative frequency weight of the outofcontrol distribu
tion would equal twothirds (P(incontrol) = 0.60 and P(outofcontrol) =
0.40). The relative cost weights can be obtained directly from the
manipulation of the cost variable. Given the Cl cost level, errors
under the outofcontrol distribution equal three times the errors under
the incontrol distribution. Given the C2 cost level, errors under the
incontrol distribution are equal to three times the errors under the
outofcontrol distribution. The four areas associated with the
various GÂ¡ measures can be converted into relative relations by taking
into account both of these relative weights (frequency and cost). These
relative relations are as follows:
f(GN.Â¡  Cl,S1,12) = (0.08483) (1) (1) = 0.08483
f(GPj  Cl,SI, 12) = (0.05048) (2/3) (3) = 0.10096
f(GNiC2,Sl,I2) = (0.19780)(2/3)(1) = 0.13187
f(GPiC2,Sl,I2) = (0.06549)(1)(3) = 0.19647
The difference between the relative relations of GP.Â¡ and GNj
given a level of the cost variable indicates the relative effect of
these intervals upon the Gj measure. The differences for this simu
lation are as follows:
68
f(GiI Cl ,S1,12) = f(GPjCl,Sl,I2) f(GNiCl,Sl,I2)
= 0.10096 0.08483 = 0.01613
f(G*C2,S1,12) = f(GPiC2,S1,12) f(GN.C2,$l,I2)
= 0.19647 0.13187 = 0.06460
This simulation can be extended to the S2 level of the distribu
tion variable given the 12 information level and can be extended to
both the SI and S2 distribution levels given the II information level.
The relative relations of the G.Â¡ measure obtained under the 12 infor
mation and S2 distribution conditions are as follows:
f(GiCl,S2,I2) = 0.07596 0.06385 = 0.01211
f(GiC2,S2,I2) = 0.15741 0.10639 = 0.05102
The relative relations for the G.Â¡ measure given the II information
level are as follows:
f(G1Cl,SI,II) = 0.09942 0.08230 = 0.01712
f(GiC2,Sl,Il) = 0.19629 0.13146 = 0.06483
f(GiCl,S2,Il) = 0.07246 0.05884 = 0.01362
f(GiC2,S2,Il) = 0.15342 0.14078 = 0.01264
Based upon these relative relations hypotheses can be derived
concerning the effects of the independent variables on the G^ measure.
These relations can be averaged over the appropriate conditionals to
determine the effects. Averaging over all conditionals except the
information variable gives the following results:
f(GiII) = 0.02705
f(G.Â¡l2) = 0.03596
Although there is a difference in the f(G.j) due to the information vari
able,this difference is small compared to the standard error of the
estimates (the difference divided by the standard error is
69
0.0089/0.01808 = 0.49226). Therefore, a significant effect due to the
information variable would not be expected.
Averaging over all conditionals except the distribution variable
gives the following results:
fiG^Sl) = 0.04152
f(G.S2) = 0.02235
Although the difference between these measures is much larger than that
above, this difference is only slightly larger than the standard error
of the estimates (the difference divided by the standard error is
0.01832/0.01658 = 1.10495). The direction of the f(G^) differences
would be expected to be as predicted by the simulation but may not be
significant.
Averaging over all conditionals except the cost variable gives
the following results:
f(G.Cl) = 0.01475
f(GiC2) = 0.04827
The difference between these measures is large compared to the
standard error of the estimates (the difference divided by the standard
error is 0.03353/0.00951 = 3.5258). Consequently, a significant effect
would be expected for the cost variable.
These relative relations can be averaged over all but pairs of
conditionals. Such measures could indicate the presence of interaction
effects of the independent variables. The results of averaging the
pairwise conditionals indicate only one interaction of possible
significance the distribution by cost interaction. The f(G^) measures
conditional to these variables are as follows:
ftf^ISl.Cl) = 0.01663
70
f(G.Â¡Sl,C2) = 0.06471
f(GiS2,Cl) = 0.01287
f(GiS2,C2) = 0.03185
A graphic representation of this interaction is presented in Figure 4.
The difference between the two f(G.Cl) measures is much smaller than
the difference between the two f(G^C2) measures. The differences are
0.00376 and 0.03286, respectively. This would indicate that the
distribution variable has a significant effect only when given the
C2 cost level. Such q result would explain the larger but not signifi
cant effect of the SI distribution level on the f(S ) measures.
Other implications which can be derived from these relative
relations involve relationships between f(GP.Â¡) and f(GN.j) measures.
Two such relationships appear to be of possible significance. Both
involve the ratio f(GP^)/f(GN.) (i.e., the effect of additional errors
relative to the effect of additional correct decisions). The results
of the first relationship are as follows:
f(GPi/GNiC1) = 1.20480
f(GPi/GNiÂ¡C2) = 1.38337
The difference between these measures is large compared to the standard
error of the estimates (the difference divided by the standard error is
0.17857/0.07621 = 2.34313). Consequently, the GP^/GN^ ratio is ex
pected to be larger given the C2 cost level than given the Cl cost
level.
The results of the second relationship are as follows:
f(GPi/GNiSl,Cl) = 1.19908
f(GPi/GNiSl,C2) = 1.48206
f(GP./GNiS2,Cl) = 1.21057
71
f(G.)
Distribution
Variable
FIGURE 4
SIMULATED COST BY DISTRIBUTION VARIABLE INTERACTION ON f(G.Â¡)
72
f(GPi/GN1Â¡S2,C2) = 1.28465
The difference between the two f(GPj/GNÂ¡ ]SI) is much smaller than the
difference between the two f(GPÂ¡/GNj S2). This would indicate the
presence of a distribution by cost interaction in which the effect of
the cost variable is significant only under the SI distribution level.
A graphical representation of this interaction is presented in Figure 5.
A final implication derived from these relative relations in
volves the homogeneity of variance for the f(G^) given the different
levels of the cost variable. The variance for the f(G^Cl) is equal to
5.26 x 106 for this simulation, and the variance for the f(Gj [C2) is
equal to 6.06 x 10"4 for this simulation. Using the F test for equal
variances, F=115.24 (3 and 3 d.f.) which indicates the variances are
not equal. Consequently, the variance of (Gj  Cl) is expected to be less
than the variance of (GÂ¡C2).
Given the above simulation the effects of the independent vari
ables on the GÂ¡ variable can be hypothesized as follows:
H4.1 a2(Gj  Cl) < a2(Gj C2).
The Gj of those subjects within the Cl cost level will have
significantly smaller variance than will the GÂ¡ of those subjects
within the C2 cost level.
H4.2a (GilCl) < (OfC2).
Those subjects within the Cl cost level will have a significantly
smaller mean Gj than will those subjects within the C2 level.
H4.2b (GÂ¡iS2) < (GiSl).
Those subjects within the S2 distribution level will have smaller
mean Gj than will those subjects within the SI level. Signifi
cance is not predicted due to the interaction effect with the
cost variable.
73
Distribution
Variable
FIGURE 5
SIMULATED COST BY DISTRIBUTION VARIABLE INTERACTION ON f(GPi/GNi)
74
H4.2c (Gj  II) = (Gi 112).
The mean Gj of those subjects within the II information level
and within the 12 information level will be equal.
H4.3 (GjlSl.Cl) (GiS2,Cl) < (Gi]Sl,C2) (G.jS2,C2) .
There will be a significant interaction of the distribution and
cost variables in which the distribution variable will have no
significant effect given the Cl cost level but will have a
significant effect given the C2 cost level.
H4.4a (GPj/GN.Â¡  Cl) < (GPÂ¡/GNÂ¡  C2).
Those subjects within the Cl cost level will have a significantly
smaller mean GPj/GNj than will those subjects within the C2 level.
H4.4 (GPj/GNj C2,S2) (GPj/GNj  Cl,S2) <
(GPj/GNj Â¡C2,S1) (GPi/GNi 1 C1,S1) .
There will be a significant interaction of the distribution and
cost variables in which the cost variable will have no signifi
cant effect given the S2 distribution level but will have a
significant effect given the SI level.
Individual longrun decision efficiency and the training phase
Since performance within the experiment is assumed to be a
function of the training adjustment process, it would be expected that
the subjects within the PP classification of training adjustment
pattern would have a lower average experiment GÂ¡ measure than those
subjects within the MM classification. Furthermore, the mean expected
Gj measure for the mixed classifications (PM and MP) would be expected
to fall between those of the nonmixed classifications. The mixed
75
classifications have one positive adjustment which would improve per
formance above that of subjects who make only negative adjustments. On
the other hand, the mixed classifications have one negative adjustment
which would decrease performance below that of subjects who make only
positive adjustments.
The following hypotheses predict the effects of the training
adjustment pattern on the Gi variable:
H4.5 (G^PP) < (G^ MM).
Those subjects within the PP classification of training adjust
ment pattern will have a significantly smaller mean G measure
than those subjects within the MM classification.
H4.6 (GjPP) < (G^MP) = PM) < (G^MM).
Those subjects within the mixed classifications of training
adjustment pattern will have mean G^s that fall between those of
the nonmixed classifications. The mean G^s of the mixed classi
fications are not expected to be significantly different.
CHAPTER IV
THE EXPERIMENT
Experimental Environment
The decision situation studied within this research was standard
cost variance investigation decision making at the operational manage
ment level. The experimental setting was the simulated environment of
a manufacturing company, and the subjects were requested to assume the
role of an operational manager of an assembly department within this
environment. The assembly department assembled a single product, a
metal folding chair. Subjects were presented with a series of standard
cost variance reports for the department and were asked to decide for
each report whether the underlying physical process should be investi
gated to correct an outofcontrol situation. Each series of variance
reports were crosssectional in nature: i.e., all reports within a
series were assumed to have occurred simultaneously and were independent
of each other.
The physical process within the simulated environment was a
laborpaced process (Barefield, 1972): i.e., the operating efficiency
of the department was determined completely by the labor efficiency of
the workers. The labor efficiency standard (stated in terms of time
per unit assembled) was based on engineering estimates that allowed for
unavoidable labor inefficiencies and reasonable variation in worker
76
77
performance (i.e., the standards were currently attainable). The sub
jects were instructed to accept the labor efficiency standard as fair
in terms of control and performance goals.
The physical labor process of the department could be in one of
two mutually exclusive states of nature: either incontrol or outof
control. The overall labor process consisted of many individual
physical labor operations: the expected aggregate of these procedures
was represented by the overall labor efficiency standard. The variance
reports, however, included only the aggregate standard and labor
efficiency variance. The labor process was defined to be incontrol
when all of the individual physical procedures were being performed as
expected. The labor process was defined to be outofcontrol when one
or more of these procedures was not being performed as expected.
The task of the subject was to decide whether to investigate each
labor efficiency variance for its underlying causes. The purpose of
investigation was to facilitate correction of those individual pro
cedures which were not operating as expected. Tv/o assumptions were pro
vided to aid the subject's decision making process. First, if they
decided to investigate a variance and the labor process turned out to
be outofcontrol, the process would be returned to the original
incontrol state with certainty. Second, if they decided not to in
vestigate a variance and the labor process was outofcontrol, the process
would remain outofcontrol with certainty.
Various costs were associated with the variance investigation
decision. These costs depended upon the subject's decision (either in
vestigate or do not investigate) and upon the actual state of the labor
process (either incontrol or outofcontrol). Estimates of these
78
costs were presented to the subject on the face of each variance report
using the following format:
If Your
Investigation
Decision Is
And If The
Assembly Line
State Is
Then
Your
Costs
Are
Investigation
Production
Total
Yes
Incontrol
$
X
$
0.00
$ X
Yes
Outofcontrol
$
Y
$
0.00
$ Y
No
Incontrol
$
0.00
$
0.00
$ 0.00
No
Outofcontrol
$
0.00
$
Z
$ z
The letter X represented the estimated investigation cost when the
assembly line was incontrol. The cost was related to the size of the
labor efficiency variance: the more negative (unfavorable) the variance
the larger the cost. The letter Y represented the estimated investi
gation cost when the assembly line was outofcontrol. The cost was
related to the size of the labor efficiency variance: the more negative
(unfavorable) the variance the smaller the cost. Given any particular
labor efficiency variance, the investigation cost when outofcontrol
(Y) was always larger than the investigation cost when incontrol (X).
This was because the investigation cost included correction costs when
the assembly line was outofcontrol. The letter Z represented the
estimated marginal production cost of operating the next period in the
outofcontrol state. The cost was constant for all variance reports.
The subjects were told that their immediate supervisor, the pro
duct section manager, would evaluate their control performance in terms
of their minimization of both investigation and production costs above
the expected standard (labeled the total investigation decision cost).
A cash bonus was promised to the subjects, the size of the bonus being
contingent upon the extent to which they minimized their total investi
gation decisions cost. The measure of a subject's control performance,
labeled TIDC^^, was determined by summing the total investigation
79
decision costs incurred by the subject over the series of variance re
ports and dividing this sum by the sum of the total investigation de
cision costs incurred by an optimal model over the same series of vari
ance reports. The subject's cash bonus function, constant for all sub
jects, was inversely related to this measure. As TIDCmin approached
one, the payoff approached the maximum: as TIDCmin became larger than
one, the payoff approached the minimum.
Subjects
The subjects were 86 senior year undergraduate and master's level
graduate students enrolled in the business college at the University of
Florida. The subjects participated in the experiment during a two week
period; 47 participated in the first week and 39 participated in the
second week. A total of 92 subjects initially volunteered to participate
but six subjects failed to complete the experiment. The 86 subjects
who completed the experiment consisted of 63 males and 23 females.
Three subject selection criteria were applied: 1) the subject
must have completed an intermediatelevel managerial accounting course,
2) the subject must have completed an introductorylevel statistics
course, and 3) the subject must have earned an overall grade point
average (GPA) of at least 2.0 on a 4.0 scale.
Subjects initially were contacted within senior level and graduate
accounting classes. The contact was made by the experimenter giving a
brief oral presentation followed by passing signup sheets around each
class (a copy of the oral presentation is presented in Appendix B). To
motivate volunteering, the presentation focused on the student's pro
fessional responsibilities as future accountants and on the monetary
80
benefits that would accrue to those who volunteered. The subjects were
told that they would be given a cash payment for participating in the
experiment and that the amount of a subject's payment would depend upon
his performance. The minimum payment was set at $2.00 and the maximum
at $10.00.
Experimental Materials
The experimental materials included a background information book
let, variance investigation decision stimuli, heuristics questionnaire,
and motivations questionnaire.
Background Information
A background information booklet (see Appendix C) was designed
to provide the subjects with a common experimental environment. The
booklet provided the subject with general company information, general
product information, general manufacturing process information, and
specific assembly department information. The specific assembly depart
ment information included information concerning the employees, the
physical process, the accounting control system, the subject's task as
the operational manager, and the subject's performance evaluation as
the operational manager.
Variance Investigation Decisions
Various information constant over all decision trials within a
treatment condition was presented on a separate page prior to the start
81
of the decision trials. The extent of such information depended upon the
treatment condition but could include information such as the prior
probabilities of both states, the means of both states, the standard
deviations of both states, the shape of the distributions of both states,
the range of past labor efficiency variances, and the range of past
investigation costs. Copies of the prior information pages for two
specific treatment conditions are presented in Appendix D.
Each variance investigation decision trial consisted of the pre
sentation of a labor efficiency variance report and a subject's re
sponse to two questions. The questions were 1) would you investi
gate this reported variance, and 2) how strongly do you feel about your
decision? During the training phase each decision trial was followed
by feedback concerning the actual state of the assembly line and the
actual costs incurred for each possible decision given the actual state.
Decision trials were presented in booklets of 33 trials (each trial
included the report with questions followed by the feedback). Within
the experimental phase decision trials were presented in booklets of
50 trials (each trial included only the report with questions). In
both the training and the experimental phases, answer sheets were pro
vided for the subject to record his responses.
An example of a labor efficiency variance report with the set of
questions is presented in Figure 6. The format of the report and
questions was constant for all treatment conditions: the only variation
related to the distribution of the actual minutes incurred per chair
and the labor efficiency variance, and to the magnitude and structure
of the costs of investigation.
82
AMSECO
Metal Folding
Chair Assembly Department
Labor Efficiency
Variance Report
For Job 5247
Standard Minutes
Actual Minutes Labor
1 Efficiency
Total Chai
Allowed Per Chair
Incurred Per
Chair Variance Per Chair
Produced
36.0
44.0
8.0
200
The Costs Associated With Investigation Are:
If Your
And If The
Then
Your Costs
Are
Investigation
Assembly Line
kkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
Decision Is
State Is
Investigation
Production
Total
4rkkk kkkkkk
kkkkkkkkkkkkkk
A*********
kkkkkkk
kkkkkkkkkkkk
Yes
InControl
$ 28.33
$ 0.00
$ 28.33
Yes
0ut0fControl
$ 90.00
$ 0.00
$ 90.00
No
InControl
$ O.CO
$ 0.00
$ 0.00
No
0u0fControl
S 0.00
$ 175.00
$ 175.00
Please answer the following questions placing your answers on the answer sheet:
A. Would you investigate this reported variance /circle the appropriate
response on the answer sheet/
NO
YES
B. How strongly do you feel about your decision /select a number between
0 and 100 which indicates the strength of your feeling and place this
number on the answer sheet/
0 10
20
30
40 50 60
70
80
90
100
*
*
k k k
k
*
k
kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
k k
k
k k k
k
k
k
k
k
k
Uncertain
Reasonably
Almost
Certain
Certain
FIGURE 6
VARIANCE INVESTIGATION DECISION TRIAL
83
Elicitation of Heuristics
The elicitation of each subject's heuristics was accomplished using
a predominately openended questionnaire (see Appendix E). The objec
tives of this questionnaire were 1) to determine the strategy the sub
ject used in answering the variance investigation questions within the
experimental phase, 2) to determine, where appropriate, the exact values
of any numerical rules used by the subject, 3) to determine whether the
subject thought he used his strategy consistently within the experimental
phase, 4) to determine the method or approach used by the subject in
forming his strategy within the training phase, and 5) to determine the
subjective importance attached to each information item presented to
the subject.
Elicitation of Subject Motivations
Subject motivations were elicited using a motivation questionnaire
(see Appendix F) developed by Snowball and Brown (1977). The question
naire is a ten item Likerttype scale which has submeasures for both
intrinsic and extrinsic motivation.
Experimental Procedures
Experimental procedures included assignment of subjects to
treatment conditions, adminstration of a training phase, administration
of an experimental phase, and final debriefing.
84
Assignment of Subjects to Treatment Conditions
Since each of the 86 subjects was assigned to one of eight groups,
randomization per se can not be relied upon to control for individual
attribute differences between groups. An alternative is to block the
randomization process onindi vidual attribute dimensions assumed to
significantly affect the subject's information processing within the
task required by the experiment.
The literature relating to individual attributes has not pro
duced conclusive results concerning individual attributes effect on
decision processes. The most comprehensive study is that of Taylor and
Dunnette (1974) who analyzed the effect of 16 decision maker attributes
upon eight measures of predecisional, decisionpoint, and post decisional
processes within a personnel decision simulation experiment. Of con
siderable interest is that the relevant decision maker attributes
accounted for a generally small portion of the total decision variance
within the various processes (the range was from 8 percent to 33 percent).
The decision maker attribute with the greatest predictive capacity was
intelligence. In two of the predecisional processes (diagonosticity
and information processing rate) and in two of the decisionpoint and
post decisional processes (information retention and decision accuracy),
intelligence accounted for from onehalf to almost all of the variance
explained by individual characteristics.
In the present study, the randomization process of assigning
subjects to treatment conditions was blocked on individual intelligence.
Ideally, individual intelligence should be measured using some validated
instrument (e.g., the Wesman Personnel Classification Test or the
85
Wechsler Adult Intelligence Scale). Due to resource limitations, however,
subject grade point average (GPA) was used as a surrogate for such a
measure. A median GPA was identified, and those subjects with a GPA
above the median were categorized as above average intelligence and
those subjects with a GPA below the median were categorized as average
intelligence. Each subject within an intelligence category then was
assigned randomly to one of the treatment conditions with the restric
tion that each intelligence group contributed an equal number of sub
jects to each condition.1 Upon assignment to a treatment condition each
subject received the background information booklet.
Training Phase
%
Each subject received training within the treatment condition to
*
which he was assigned. Training was conducted in groups of two subjects
within a 50 minute session administered by either the experimenter or
by an experimental assistant.2 Subjects were assigned randomly to the
experimental assistants subject to two restrictions. The restrictions
were that the experimental assistant was not currently the subject's
teacher for an academic course and that the subject's available time
coincided with that of the experimental assistant. Training of all
!The assignment process involved two procedures. First, the assignment
to the cost variable levels was by the week in which the subject par
ticipated in the experiment. Those subjects who participated during
the first week were assigned to the Cl cost level, and those subjects
who participated during the second week were assigned to the C2 cost
level. Second, the assignment to the information variable and the
distribution variable levels was by random selection based upon a ran
dom number table.
2The experimental assistants were not paid. The author believes, how
ever, that this is an exception to the aphorism that price reflects
value.
86
subjects (within each week) was completed over two contiguous days. The
training phase consisted of 99 decision trials with feedback. The
experimental materials used in the training phase were similar to those
used in the experimental phase. The decision trials with feedback were
presented in three booklets of 33 trials and the subject was provided
an answer sheet on which to record his responses. Additional perfor
mance feedback was given at the completion of each book of 33 decision
trials. This performance feedback was the subjects's TIDC^^ for that
particular book.
Experimental Phase
The experimental session lasted one hour and was administered by
the experimenter. The experimental phase (within each week) was com
pleted over two contiguous days immediately following the training phase.
The experimental session consisted of three parts. The first
part was the presentation of 100 decision trials. The decision trials
were presented in two booklets of 50 trials each and the subject re
corded his responses on a separate answer sheet. The subjects were
allowed twenty minutes to complete the 100 decision trials. The second
part of the experimental phase was the elicitation of the subject's
heuristics. This elicitation was accomplished using the openended
questionnaire (see Appendix E). The third part of the experimental
phase was the administration of the motivation questionnaire (see
Appendix F).
87
Final Debriefing
Each subject's final performance measure (TIDCmin) for the
variance investigation decisions part of the experimental phase was
presented individually at a later date. At this time his cash pay
ment was determined, he was debriefed as to the purpose of the experi
ment, and any questions were answered. Additional data were collected
from those subjects not responding completely to the heuristic elicita
tion questionnaire.
CHAPTER V
ANALYSES AND RESULTS
Summary of Results
A summary of the analyses and results contained in this chapter
is presented in Table 4. The objective of this summary is to provide
a brief statement of each hypothesis together with the results of analy
ses for that hypothesis. Such a summary will facilitate the presenta
tion of the analyses and results and will serve as a reference for the
discussion and conclusions contained in the next chapter.
General Method of Analysis
Although several methods of analysis are employed, the most
prevalent method is analysis of variance using the model comparison
procedure (Appelbaum and Cramer, 1974; Lewis and Keren, 1977). This
method of analysis is employed due to the nonorthogonality of the data
structure. The problem of nonorthogonality arises in this instance as
a result of nonequal cell frequencies.
The model comparison procedure involves fitting a linear model
allowing for certain effects and then comparing the obtained fit to
that of a linear model which omits one or more of the effects. The
objective is to find the simplest model that adequately fits the data.
The procedure begins with the complete or full model (which allows for
88
TABLE 4
SUMMARY OF ANALYSES AND RESULTS
Hypotheses
Analysis
Method(s)
Test
Statistic(s)
Results
1.1a (TDVi1 SI)=38.5
Twotailed Z test
Z=0.9912
(n.s.)
The mean initial decision anchor of
those subjects within the SI distribu
tion level was not significantly
different from 38,5
1.1b (TDViS2)=40.5
Twotailed Z test
Z=1.5681
(p<.10)
The mean initial decision anchor of
those subjects within the S2 distribu
tion level was significantly less than
40.5
1.2a (TDV.Il)=(TDVi
112)
Model comparison
Twotailed Z test
F= 1.0944
Z=0.7007
(n.s.)
(n.s.)
The mean initial decision anchor of
those subjects within the reduced infor
mation level was not significantly dif
ferent from that of those subjects with
in the expanded information level.
1.2b (TDViCl)=(TDV.
1 C2)
Model comparison
Twotailed Z test
F=3.3931
Z=1.4362
(p<.10)
(p<.10)
The mean initial decision anchor of
those subjects within the Cl cost level
was significantly smaller than that of
those subjects within the C2 cost level.
1.3a (RAill)=(RA.l2)
Model comparison
Twotailed Z test
F=0.0903
Z=0.4432
(n.s.)
(n.s.)
The mean relative decision anchor ad
justment of those subjects within the
reduced information level was not sig
nificantly different from that of those
subjects within the expanded information
level.
Hypotheses
Analysis
Method(s)
1.3b (MiSl) = (MiS2)
Model comparison
Twotailed Z test
1.3c (MiCl) = (MiC2)
Model comparison
Twotailed Z test
2.1a (cH Â¡Sl)<(dT S2)
Model comparison
Onetailed Z test
2.1b (34  Il) = (cT'. 112)
Model comparison
Twotailed Z test
2.1c (dt Cl) = (d;. C2)
Model comparison
Twotailed Z test
ABLE 4 Continued
Test
Statistic(s) Results
F=1.7308 (n.s.)
The mean relative decision anchor ad
Z=1.3054 (n.s.)
justment of those subjects within the
SI distribution level was not signifi
cantly different from that of those
subjects within the S2 distribution level
F=0.1735 (n.s.)
The mean relative decision anchor ad
Z=0.4865 (n.s.)
justment of those subjects within the
Cl cost level was not significantly dif
ferent from that of those subjects with
in the C2 cost level.
F=46.0985 (p<.01)
The mean decision model sensitivity of
those subjects within the SI distribu
Z=6.1144 (p<.01)
tion level was significantly smaller than
that of those subjects within the S2
distribution level.
F=0.5883 (n.s.)
The mean decision model sensitivity of
Z=0.4588 (n.s.)
those subjects within the reduced infor
mation level was not significantly dif
ferent from that of those subjects with
in the expanded information level.
F0.2638 (n.s.)
The mean decision model sensitivity of
Z=0.3167 (n.s.)
those subjects within the Cl cost level
was not significantly different from
that of those subjects within the C2
cost level.
TABLE 4 Continued
Hypotheses
Analysis
Method(s)
Test
Statistic(s)
Results
2.2a (DNAj  Il) = (DNAi
112)
Model comparison
Twotailed Z test
F=0.0066 (n.s.)
ZO.OOIO (n.s.)
The mean adjusted relative decision
model sensitivity of those subjects
within the reduced information level was
not significantly different from that of
those subjects within the expanded
information level.
2.2b (DNAj SI) = (DNAj
IS2)
Model comparison
Twotailed Z test
F=0.8368 (n.s.)
Z=0.4662 (n.s.)
The mean adjusted relative decision
model sensitivity of those subjects
within the SI distribution level was not
significantly different from that of
those subjects within the S2 distribu
tion level.
2.2c (DNAj  Cl) = (DNAÂ¡
C2)
Model comparison
Twotailed Z test
F=15.3114 (p<.01)
Z=3.6362 (p<.01)
The mean adjusted relative decision
model sensitivity of those subjects
within the Cl cost level was signifi
cantly larger than that of those sub
jects within the C2 cost level.
2.3a (DNAÂ¡DNj S1) <
(DNAÂ¡ DNÂ¡ S2)
Model comparison
Onetailed Z test
F=6.0130 (p<.05)
Z=2.2737 (pc.05)
The mean multiple decision anchor effect
on relative decision model sensitivity
of those subjects within the SI distribu
tion was significantly larger than that
of those subjects within the S2 dis
tribution level.
TABLE 4 Continued
2.3b
3.1a
3.1b
3.1c
Hypotheses
Analysis
Method(s)
Test
Statistic(s)
Results
(DNAjDNj  Cl) <
(DNAiDNiC2)
Model comparison
Onetailed Z test
F=0.6618 (n.s.)
Z=0.8642 (n.s.)
The mean multiple decision anchor effect
on relative decision model sensitivity
of those subjects within the Cl cost
level was not significantly different
from that of those subjects within the
C2 cost level.
(BNAiIl)=(BNAi
12)
Model comparison
Twotailed Z test
F=2.6738 (n.s.)
Z=0.7891 (n.s.)
The mean adjusted relative decision
criteria of those subjects within the
reduced information level was not sig
nificantly different from that of those
subjects within the expanded information
level.
(BNAiSl)=(BNAi
S2)
Model comparison
Twotailed Z test
F=1.9398 (n.s.)
Z=0.7048 (n.s.)
The mean adjusted relative decision
criteria of those subjects within the SI
distribution level was not significantly
different from that of those subjects
within the S2 distribution level.
(BNAiCl)>(BNAi
C2)
Model comparison
Onetailed Z test
F=107.4386 (p<.01)
Z=10.6306 (p<.01)
The mean adjusted relative decision
criteria of those subjects within the Cl
cost level was significantly larger than
that of those subjects within the C2
cost level.
TABLE 4 Continued
Analysis Test
Hypotheses Method(s) Statistic(s) Resul ts
3.2 a2(BNA.Cl) <
F test of equal
F9.36 (p
K.01)
The adjusted relative decision criteria
variances
variance of those subjects within the Cl
cost level was significantly larger than
that of those subjects within the C2
cost level.
3.3a (MCiIl) = (BCiI2)
Model comparison
F=0.9075
(n.s.)
The mean decision criteria conservatism
of those subjects within the reduced
Twotailed Z test
Z=0.6802
(n.s.)
information level was not significantly
different from that of those subjects
within the expanded information level.
3.3b (BNCiSl)=(BNC.S2)
Model comparison
F=0.3377
(n.s.)
The mean decision criteria conservatism
of those subjects within the SI distribu
Twotailed Z test
Z=0.3295
(n.s.)
tion level was not significantly differ
ent from that of those subjects within
the S2 distribution level.
3.3c (BNCiCl)<(BNCiC2)
Model comparison
F=6.6104
(p<.05)
The mean decision criteria conservatism
of those subjects within the Cl cost
Onetailed Z test
Z=2.5598
(pc.05)
level was significantly larger than that
of those subjects within the C2 cost
level.
3.4a (BNAiBN.Sl) <
Model comparison
F=0.0924
(n.s.)
The mean multiple decision anchor effect
on relative decision criteria of those
(BNAiBNiS2)
Onetailed Z test
Z=0.3892
(n.s.)
subjects within the SI distribution level
was not significantly different from that
of those subjects within the S2 distribu
tion level.
CO
Hypotheses
Analysis
Method(s)
3.4b (BNAÂ¡BN,1Cl) <
Model comparison
(BNAiBN1C2)
Onetailed Z test
4.1 a2(GiCl)
F test of equal
variances
4.2a (GiCl)<(GiC2)
Sources F value
Onetailed Z test
4.2b (Gj1S2)<(GiSI)
Sources F value
Onetailed Z test
4.2c (GiIl)=(GiI2)
Model comparison
Twotailed Z test
4 Continued
Test
Statistic(s)
F=11.4812 (pc.Ol)
Z=3.7090 (p<.01)
F=2.43 (p<.01)
F=2.56 (n.s.)
Z=2.442 (p<.01)
F=0.51 (n.s.)
Z=1.7452 (p<.05)
F=1.0488 (n.s.)
Z=0.4528 (n.s.)
Results
The mean multiple decision anchor effect
on relative decision criteria of those
subjects within the Cl cost level was
significantly larger than that of those
subjects within the C2 cost level.
The relative net decision costs variance
of those subjects within the Cl cost
level was significantly smaller than that
of those subjects within the C2 cost level.
The mean relative net decision costs of
those subjects within the C2 cost level
was larger than that of those subjects
within the Cl cost level.
The mean relative net decision costs of
those subjects within the SI distribution
level was larger than that of those sub
jects within the S2 distribution level.
The mean relative net decision costs of
those subjects within the reduced infor
mation level was not significantly dif
ferent than that of those subjects with
in the expanded information level.
TABLE 4 Continued
Hypotheses
Analysis Test
Method(s) Statistic(s)
4.3 [(G^S1,C1)(GÂ¡S2,C1)] Model comparison F=7.7124 (pc.Ol)
range test
4.4a (GPi/GNj  Cl) <
Sources F value F=0.35 (n.s.)
(GP Â¡/GN  C2)
Onetailed Z test Z=2.0198 (p<.01)
4.4b [ (GP j/GN j  C2, S2) 
Model comparison F=8.7438 (pc.Ol)
(GPi/GNiCl,S2)J
Duncan's multiple (a=.01)
<[(GPj/GNj C2,S1)
range test
(GPj/GNj  Cl, SI)]
4.5 (GjPP)<(GjMM) Onetailed t test
t=l.303 (p<.10)
Results
There was a significant cost by distribu
tion interaction on the relative net
decision costs in which the C2 cost level
had a greater effect than the Cl cost
level while the distribution levels had
little effect.
The mean relative additional decision
costs and relative additional decision
savings ratio of those subjects within
the C2 cost level was larger than that
of those subjects within the Cl cost
level.
There was a significant cost by distribu
tion interaction on the relative addi
tional decision costs and relative addi
tional decision savings ratio in which
the cost levels had a significant effect
only when given the SI distribution level.
The mean relative net decision costs of
those subjects within the constantly
decreasing training session performances
category was significantly smaller than
that of those subjects within the con
stantly increasing training session per
formances category.
TABLE 4 Continued
Hypotheses
4.6 (GiPP)<(GiMP)=
(Gj  PM)<(G^11 MM)
Analysis Test
Method(s) Statistic(s)
Results
The mean relative net decision costs of
those subjects within the mixed training
session performances categories were less
than that of those subjects within the
constantly increasing training session
performances category and greater than
that of those subjects within the con
stantly decreasing training session
performances category.
LO
G">
97
all effects) and eliminates effects starting with the highest order
interactions. The model comparisons utilize the general linear model
theory (the method of least squares) as described by Scheffe" (1959)
and the F test as described by Lewis and Keren (1977) is used to test
the fit of the various models. Defining two models as ft and w, the
F test is:
d.f.(ft) SSe(w) SSe(ft)
F =
d.f. (ajft) SSe(ft)
where w is the more restricted model and the degrees of freedom associ
ated with the numerator and the denominator of the F statistic are
d.f.(coft) and d.f. (ft), respectively.
Given a dependent variable, the model comparison procedure is
used to find the simplest model which adequately fits the data. After
the simplest (or reduced) model is found the sources of this model are
presented with their corresponding F values. These source F values are
the same as model comparison F tests where the comparison models are the
reduced model and the reduced model without the corresponding source.
Within this research the source F values are also the same as the Type IV
sum of squares (Barr et al., 1976).
Unless otherwise specified, the a levels for the rejection of the
null hypothesis are pc.10 for main effects and p<.05 for interactions.
The selection of these a values is largely arbitrary: the values reflect
the preliminary nature of this research. Also, unless otherwise speci
fied, all 2way interactions are tested against the without 3way
interaction model, employing the assumption that the 3way interaction
effect is equal to zero within the subject population. All the individual
attributes are retained in each model, employing the assumption that
98
these effects are not necessarily equal to zero within the subject
population.
The parameters used by the various models as independent variables
are defined as follows:
U = the overall mean of the dependent variable,
oij= the effect of level j of the information variable,
8^= the effect of level k of the distribution variable,
y= the effect of level 1 of the cost variable,
ag. ccy.., By,, agy. = the various interactions of the above
J K J I KI J KI
three variables,
ip = the effect of individual i's DN measure,
i
E, = the effect of individual i's intrinsic motivation measure,
i
t = the effect of individual i's extrinsic (nonmonetary)
i
motivation measure,
# *
\^= the effect of individual i's extrinsic (monetary) motivation
measure, and
5.= the effect of individual i's GPA.
i
Training Phase Analyses and Results
The following sections report the analyses and results of the
training phase. The sections include the initial decision anchors and
the decision anchor adjustment.
Initial Decision Anchors
Hypotheses 1.1 and 1.2 relate to an assumption made in the con
ceptual development and simulation: i.e., that subjects would use a
point of central tendency (between the means of the appropriate states
99
of nature) as their initial decision anchor. The simulation arbitrarily
employed the geometric intersection of the appropriate state distribu
tion curves as the expected initial decision anchor. These expected
points are conditional upon the distribution variable: i.e., the
intersection point is 38.25 actual minutes incurred given the SI distribu
tion level and is 40.5 actual minutes incurred given the S2 distribution
level. The cutoff points employed within the first training session
(TDV.s) were used as an estimate of the subjects' initial decision
anchors.
The method of analysis was the model comparison procedure where
the dependent variable is the TDV^ measure and the independent variables
are the three situation variables (information, distribution, and cost)
with their interactions, and the GPA variable. The full TDV^ model is:
TDV1 = V + Oj + Bk + Y, + a6jk + aYj, + 6Ykl + ctSyjkl + Si
The model comparison procedure results for this analysis are
presented in Table 5, and the F values associated with the sources of
the reduced model are presented in Table 6. The results indicate that
the reduced model contains the distribution variable and the cost vari
able main effects. The distribution variable main effect is significant
at the p<.01 level and the cost variable main effect is significant at
the p<.10 level. The means, variances, and sample sizes of the TDV^
measure given the levels of these two independent variables are included
in Table 6. Using the F test of equal variances, the variances between
the levels of the distribution variable differ significantly (F=2.82,
pc.01), with the S2 level having the greater variance. The variances
between the levels of the cost variable do not differ significantly
(F=l.12, n.s.). Using the Z test of equal means, the means of the levels
100
TABLE 5
MODEL COMPARISON PROCEDURE RESULTS
FOR THE TDVj
DEPENDENT
VARIABLE
Model
SSe
d.f.(F)
F
Ful 1d
91.6549
8,77
a
8.19
Without 3way interaction
93.1248
1,77
1.23
Without distribution x cost
interaction
93.1426
1,78
0.01
Without information x distribu
tion interaction
93.1463
1,78
0.02
Without information x cost
interaction
93.7287
1,78
0.51
e
Without information
95.0374
1,81
1.09
e
Without distribution
164.6998
1,81
61.27a
. e
Without cost
97.6986
1,81
3.39C
a
p<.01
b
pc.05
Cp<.10
d
The F value associated with the full model is the test of the full
model and not a model comparison test.
e
The test of this model assumes the effects of those interactions
that include this variable are equal to zero.
101
TABLE 6
PARAMETER VALUES ASSOCIATED WITH THE REDUCED TDVj MODEL
F Values
Associated With the
Sources of the Reduced Model
Source
d.f.
F
Distribution
1,81
60.843
Cost
1,81
b
3.59
GPA
1,81
0.02
Means and
Variances Associated
With the Significant Sources
Variable Level
N
Mean
Variance
SI
44
38.368
0.62548
S2
42
40.179
1.76465
Cl
47
39.053
1.86776
C2
39
39.492
2.09599
a
p<. 01
b
p<. 10
102
of the distribution variable differ significantly (Z=7.6346, p<.01
twotailed), with the S2 level having the larger mean. Differences in
the means of the levels of the cost variable are also significant
(Z=1.4362, p<.10 twotailed), with the C2 level having the larger mean.
Hypothesis 1.1a predicted that (TDV^S1) would not differ signifi
cantly from 38.25 actual minutes incurred. A singlesample Z test was
employed to test this hypothesis, and the difference was found to be not
significant (Z=0.9912, n.s. twotailed). Hypothesis 1.1b predicted
that (TWi S2) would not differ significantly from 40.5 actual minutes
incurred. A singlesample Z test indicated that the difference was
significant (Z=1.5681, p<.10 twotailed). Hypothesis 1.2a predicted
that (TDV.j  II) would not differ significantly from (TDV^]I2). The Z
test used to test this hypothesis indicated no significant difference
(Z=0.7007, n.s. twotailed). Hypothesis 1.2b predicted that (TDV^Cl)
would not differ significantly from (TDV.Â¡C2). This hypothesis was
tested by the above model comparison procedure and related Z test, and
the results indicated that the difference was significant (p<.10).
Decision Anchor Adjustment
Hypothesis 1.3 relates to an assumption made in the training phase
simulation: i.e., that subjects would have equal relative decision
anchor adjustment between the levels of the various experiment conditions.
The simulation arbitrarily used an adjustment of onehalf the linear
distance between the appropriate expected initial decision anchor and
the appropriate optimal model decision cutoff value.
The method of analysis was the model comparison procedure where
the dependent variable is the RA^ measure and the independent variables
103
are the three situation variables (with their interactions) and the
GP/l variable. The full RAj model is:
RAj = ii + a, + ^ + Y, + aeJk + otyj, + BYkl + aBYjkl + 6,
The model comparison procedure results for this analysis are
presented in Table 7. The results indicate that the reduced model con
taines only the p parameter: i.e., none of the situation variables had
a significant effect on an individual's relative decision anchor adjust
ment. Using the F test of equal variances, the variances between the
levels of the distribution variable and between the levels of the cost
variable were found to differ significantly (F=5.22, p<.01; F=6.23,
pc.01). The S2 distribution level and the Cl cost level had the greater
variances.
Hypothesis 1.3a predicted that (RAI1) would not differ signifi
cantly from (RA^I2). A Z test indicated that the difference was not
significant (Z=0.4432, n.s. twotailed). Hypothesis 1.3b predicted
that (RA.JS1) would not differ significantly from (RA^S2). The
application of the Z test revealed no significant difference (Z=1.3052,
n.s. twotailed). Hypothesis 1.3c predicted that (RA.Â¡C1) would not
differ significantly from (RAC2). A Z test was employed to test this
hypothesis: again, the difference was found to be not significant
(Z=0.4865, n.s. twotailed).
Table 8 presents the actual patterns of the TDVi and EDV.Â¡ measures
given the various levels of the distribution and cost variables. Given
that the TDV.Â¡ measures have incorporated some initial adjustment (during
the first training session), the expected TDVi and EDVi patterns are
1) (DV1C1,S1) < (TViCl,Sl) < 38.25 < (TDViC2,Sl) < (EDViC2,S1),
and 2) (EDV.C1,S2) < (TDVi Cl ,S2) < 40.5 < (TDVi C2,S2) < (EDVjC2,S2).
104
TABLE 7
MODEL COMPARISON PROCEDURE RESULTS FOR THE RAj DEPENDENT VARIABLE
Model
SSe
d.f.(F)
F
a
Full
67.8716
8,73
0.58
Without 3way interaction
67.8852
1,73
0.01
Without distribution x cost
interaction
67.8852
1,74
0.00
Without information x distribu
tion interaction
67.8889
1,74
0.00
Without information x cost
interaction
68.2633
1,74
0.41
b
Without information
68.3470
1,77
0.09
b
Without distribution
69.8016
1,77
1.73
b
Without cost
68.4208
1,77
0.17
a
The F value associated with the full
model is the
test of
the full
model and not a model comparison test.
I
The test of this model assumes the effects of those interactions
that include this variable are equal to zero.
TABLE 8
OBSERVED MEAN
EDV.jS AND
TDV.jS GIVEN THE
VARIOUS LEVELS OF THE
DISTRIBUTION AND COST VARIABLES
Distribution
Cl Cost
Variable Level
Expected Initial
C2 Cost
Variable Level
Variable
DV
TV
Decision Anchor
TDV
EDV
SI
37.83
38.16
38.25
38.62
39.01
S2
39.62
39.98
40.50
40.41
42.43
o
en
106
The expected pattern given the SI distribution level was obtained. How
ever, the expected pattern given the S2 distribution level was not con
sistent with respect to the expected initial decision anchor (40.5).
Individual Decision Model Sensitivity
The following sections report the analyses and results of the
individual decision model sensitivity variables. The variables include
the d.! dependent variable, the relative decision model sensitivity, and
the relationships between intrinsic motivation and decision model
sensitivity.
The dj Dependent Variable
Hypothesis 2.1 relates to the fit of the subjects' responses to
the TSD model. The dependent variable d.! should have little relation
with the information and cost situation variables, but should have a
relation with the distribution variable. The relation with the dis
tribution variable is derived from the optimal model decision sensi
tivity measure, d'. Within the SI level of the distribution variable
the theoretical value of d^ is 1.5, and within the SI level of the dis
tribution variable the theoretical value of d.1 is 1.8.
k
The method of analysis was the model comparison procedure where
the dependent variable is the d.! measure and the independent variables
are the three situation variables (with their interactions), the three
motivation factors, and the GPA. variable. The full d! model is:
i i
di + j + Bk + Y, + aBjk + otYj, + BYkl + aByjkl + C, + T,
107
The model comparison procedure results for this analysis are
presented in Table 9 and the F values associated with the sources of
the reduced model are presented in Table 10. The results indicate that
the dÂ¡ reduced model contains only the distribution variable and the
intrinsic motivation factor. Both of these parameters are significant
at the p<.01 level. The means, variances, and sample sizes of the d^'
measure given the levels of the distribution variable are included in
Table 10. Applying the F test of equal variances, the variances be
tween the levels of this variable do not differ significantly (F=1.27,
n.s.). However, the Z test of equal means indicates that the means of
the levels of this variable differ siqnificantly (Z=6.1144, d<.01
twotailed).
Hypothesis 2.1a predicted that (d.! S1) would be siqnificantly
less than (d! S2). This hypothesis was tested by the model comparison
procedure and Z test presented above, and the difference was found to
be siqnificant (p<.01), with the SI level having the smaller mean.
Hypothesis 2.1b predicted that (d.! I1) would not differ significantly
from (d! I2). This hypothesis was tested using a Z test, and the
difference was found to be not siqnificant (Z=0.4588, n.s. twotailed).
Hypothesis 2.1c predicted that (d! C1) would not differ significantly
from (dl C2). A Z test indicated that the difference was not
significant (Z=0.3167, n.s. twotailed).
Relative Decision Model Sensitivity
Hypotheses 2.2 and 2.3 relate to the adjusted relative deviation
of the individual's decision model sensitivity from the optimal model
sensitivity. The adjustment is concerned with the elimination of the
108
TABLE 9
MODEL COMPARISON PROCEDURE RESULTS
FOR THE d'.
i
DEPENDENT
VARIABLE
Model
SSe
d.f.(F)
F
Full5
5.8767
11,74
4.66a
Without 3way interaction
5.8926
1,74
0.20
Without distribution x cost
interaction
5.9032
1,75
0.13
Without information x distribu
tion interaction
5.8947
1,75
0.03
Without information x cost
interaction
5.8969
1,75
0.05
Without information0
5.9950
1,78
0.59
Without distribution0
9.4035
1,78
46.10a
Without cost0
5.9304
1,78
0.26
V.oi
bThe F value associated with the full
model is the
test of
the full
model and not a model comparison test,
c
The test of this model assumes the effects of those interactions
that include this variable are equal to zero.
109
TABLE 10
PARAMETER VALUES ASSOCIATED WITH THE REDUCED d! MODEL
i
F Values Associated With the
Sources of the Reduced d1 Model
Source
d.f.
F
Distribution
1,80
46.52a
Intrinsic motivation
1,80
10.33a
Extrinsic (nonmonetary)
motivation
1,80
1.80
Extrinsic (monetary)
motivation
1,80
0.06
GPA
1,80
0.20
Means and Variances Associated
With the Significant Sources
Variable Level N
Mean
Variance
SI 44
1.2325
0.09165
S2 42
1.6093
0.07200
p<.01
no
effects of multiple cutoff values on individual decision model sensi
tivity. Assuming random subject assignment to experimental situations,
the Dfij and DNA. variables should be independent of the effects of the
d' absolute magnitude differences (within the distribution variable).
The remaining effects are 1) the relative variable response range
within the DNA^ measure, and 2) the relative variable response range
and multiple cutoff value usage within the DN.Â¡ measure.
The methods of analysis were the model comparison procedures where
the dependent variables a"e the DN.Â¡, DNA^ and DNA^DN measures. The
independent variables are the three situation variables (with their
interactions), the three motivation factors, and the GPA.Â¡ variable.
The three full models are
DNi
DNAi u + j + 8k + ^ + 6jk + Tj! + + a6Yjk] + 5,
DNADN + t. + X. +6.
ill
The three model comparison procedure results are presented in
Table 11 and the F values associated with the sources of the reduced
models are presented in Table 12. The results indicate that the reduced
DN.. model contains the distribution variable, the cost variable, and
the intrinsic motivation factor. Both the distribution variable main
effect and the motivation factor are significant at the p<.01 level,
and the cost variable is significant at the p<.05 level. The reduced
DNA. model contains the cost variable and the intrinsic motivation
factor. The cost variable main effect is significant at the p<.01
level and the motivation factor is significant at the pc.10 level.
The reduced DNA..DN.. model contains the distribution variable and the
intrinsic motivation factor. The distribution variable main effect is
TABLE 11
MODEL COMPARISON PROCEDURE RESULTS FOR THE DNi, DNAi, AND DNAiDMi DEPENDENT VARIABLES
DNi
DNAi
DNAiDMi
Model
SSe
d.f.(FT~
F
SSe
d.TTUT
F
SSe
d.f.(F)
F
Ful 1d
2.1852
11,74
4.66a
1.1753
11,74
2.03b
1.4200
11,74
0.97
Without 3way interaction
2.2031
1,74
0.61
1.1758
1,74
0.03
1.4327
1,74
0.66
Without distribution x
cost interaction
2.2236
1,75
0.70
1.2105
1,75
2.22
1.4345
1,75
0.10
Without information x dis
tribution interaction
2.2032
1,75
0.00
1.1783
1,75
0.16
1.4343
1,75
0.09
Without information x
cost interaction
2.2123
1,75
0.31
1.1758
1,75
0.00
1.4412
1,75
0.45
. e
Without information
2.2324
1,78
0.00
1.2140
1,78
0.01
1.4453
1,78
0.01
. 6
Without distribution
2.4330
1,78
7.01a
1.2269
1,78
0.84
1.5566
1,78
6.01b
. e
Without cost
2.3748
1,78
4.98b
1.4522
1,78
15.31a
1.4575
1,78
0.66
V.Ol bp<.05 Cp<.10
^The F value associated with the full model is the test of the full model and not a model comparison test,
e
The test of this model assumes the effects of those interactions that include this variable are equal to
zero.
TABLE 12
PARAMETER VALUES ASSOCIATED WITH THE REDUCED DNj, DNAj, AND DNAjDNj MODELS
Source
F Values Associated
With
the Sources of the Reducec
1 Models
DNi
DNi
DNAi
DNAi
d.f.
F
d.f.
F
d.f.
F
Distribution
1,79
7.10a
1,80
6.17 b
Cost
1,79
5.05b
1,80
15.52 a
Intrinsic motivation
1,79
8.64a
1,80
3.55C
1,80
3.59C
Extrinsic (nonmonetary) motivation 1,79
1.68
1,80
1.38
1,80
0.24
Extrinsic (monetary) motivation 1,79
0.04
1,80
0.07
1,80
0.01
GPA
1,79
0.02
1,80
0.14
1,80
0.27
Means and Variances
Associated With
the Siqnificant
Variables
DNi
DNAi
DNAiDNi
Variable Level N
Mean Variance
N
Mean
Variance
N
Mean
Variance
SI 44
0.8052 0.0391
44
0.9193
0.0183
44
0.1141
0.0249
S2 42
0.8845 0.0240
42
0.9329
0.0180
42
0.0484
0.0113
Cl 47
0.8789 0.0276
47
0.9726
0.0091
47
0.0938
0.0197
C2 39
0.8017 0.0369
39
0.8696
0.0234
39
0.0679
0.0185
9P<.01 bp<.05 Cp<.10
113
significant at the pc.05 level, and the motivation factor is significant
at the p<.10 level.
Means, variances, and sample sizes of the three dependent variables
given the levels of the significant situation variables are included in
Table 12. Using the F test of equal variances, the following variances
were found to differ significantly: 1) within the DN.Â¡ variable between
the levels of the distribution variable (F=1.63, p<.10), 2) within the
DNA.j variable between the levels of the cost variable (F=2.57, pc.01),
and 3) within the DNA^DN^ variable between the levels of the distribu
tion variable (F=2.20, pc.Ol). The Z test of equal means indicated that
the means of the variable levels differ significantly (pc.Ol) for each
of the situation variables found to be significant by the above sources
F values (see Table 12).
Hypothesis 2.2a predicted that (DNAjll) would not differ signifi
cantly from (DNA.jI2). A Z test v/as employed to test this hypothesis
and the difference was found to be not significant (Z=0.0010, n.s.
twotailed). Hypothesis 2.2b predicted that (DNAjSl) would not differ
significantly from (DAijS2). Again, a Z test indicated that the
difference was not significant (Z=0.4662, n.s. twotailed). Hypothesis
2.2c predicted that (DNA^Â¡Cl) would not differ significantly from
(DNA^C2). The results of the model comparison procedure and Z test
indicated that the difference was significant at the pc.Ol level, with
the Cl level having the greater mean.
Hypothesis 2.3a predicted that (DNADN^ jSl) would be significantly
smaller than (DNAÂ¡DN.Â¡ S2). Although the results of the model comparison
procedure and Z test indicated that there was a significant difference
between the means, the S2 distribution level had the smaller mean.
114
Hypothesis 2.3b predicted that (DNADNCl) would be significantly
smaller than (DNADNC2). A Z test was employed to test this hypothesis
and the difference was found to be not significant (Z=0.8642, n.s.
onetailed).
Intrinsic Motivation and Decision Model Sensitivity
The model comparison procedure indicated that the intrinsic moti
vation factor had a significant effect on each of the four individual
decision model sensitivity dependent variables.1 The analysis of these
effects requires additional tests. A productmoment correlation was
computed for the intrinsic motivation factor with each of the four
dependent variables. The results of these correlations are presented
in Table* 13. The weakness of these correlations can be expected given
the relatively low explained variance associated with the various in
trinsic motivation measure sums of squares.
The positive association between intrinsic motivation and the d'
i
measure indicate that as the d1 measure increased in size (approached
i
the d1 measure or used a single cutoff value) the subjects' intrinsic
k
motivation measures increase. This result was confirmed by the posi
tive association between the motivation factor and the DN. measure.
As either the variable response range decreases or the use of multiple
cutoff values decreases (both are associated with increases in the DN
measure) the subjects' intrinsic motivation measures increase. The
substantial drop in the level of significance for the association be
tween the intrinsic motivation and the DNA^ measures would suggest that
^ee the last section in this chapter for a definition and for additional
analyses concerning the intrinsic motivation factor.
115
TABLE 13
CORRELATION COEFFICIENTS FOR INTRINSIC MOTIVATION WITH
INDIVIDUAL DECISION MODEL SENSITIVITY VARIABLES
Model
Intrinsic Motivation
d!
i
0.2045 (.059)
DNj
0.2549 (.018)
DNAÂ¡
0.1724 (.113)
DNAjDNÂ¡
0.1672 (.124)
Note: The numbers within the parentheses are the exact alevels
associated with the rejection of the null hypothesis that the
correlation coefficient is equal to zero.
116
the association between the motivation factor and individual decision
model sensitivity is only partially a result of the variable response
range (the remaining effect within the DNA^ measure). This drop in the
significance level could be explained by the negative association (albeit
an equally low significance level) between the motivation factor and
the DNA^DN^ measure. As the use of multiple cutoff values decreases
(DNA^DN^ decreases) the subjects' intrinsic motivation measures
increase.
Individual Decision Criteria
The following sections report the analyses and results of the
individual decision criteria variables. The variables include the
relative decision criteria, the relative decision criteria conservatism,
and the relationships between individual attributes and decision criteria.
Relative Decision Criteria
Hypotheses 3.1 and 3.2 relate to the adjusted relative individual
decision criteria. The adjustment concerns the elimination of the
effects of multiple cutoff values on individual decision criteria. The
variables which affect the adjusted relative decision criteria should be
those related to the individual's selection of a cutoff value. Of the
three situation variables the one most related to this selection is
the cost variable.
The method of analysis was the model comparison procedure where
the dependent variables are the BN.Â¡, BNA^, and BNABN measures. The
independent variables are the three situation variables (with their
117
interactions), the three motivation factors, and the GPA^ variable. The
three full models are:
 p + aj + Bk + Yi + 0Sjk + ctYj, + eykl + aSYjkl + Â£j
+ Ti + xi + 6i
The three model comparison procedure results are presented in
Table 14, and the F values associated with the sources of the reduced
models are presented in Table 15. The results indicate that the reduced
BN.j model contains the distribution variable, the cost variable, and
the extrinsic (monetary) motivation factor. Both the cost variable main
effect and the motivation factor are significant at the p<.01 level,
and the distribution variable main effect is significant at the p<.10
level. The reduced BNA model contains the cost variable and the
extrinsic (monetary) motivation factor, and both are significant at the
p<.01 level. The reduced BNABN.Â¡ model contains the cost variable and
the GPA.j variable. The cost variable main effect is significant at the
pc.01 level, and the GPA.Â¡ variable is significant at the p<.10 level.
The means, variances, and sample sizes for the dependent variables
given the levels of the significant situation variables are included in
Table 15. The F test of equal variances indicated that the following
variances differ significantly: 1) within the BN variable between the
levels of the distribution variable (F=1.51, p<.10), 2) within the BN
variable between the levels of the cost variable (F=6.11, p<.01),
3) within the BNA^ variable between the levels of the cost variable
(F=9.36, p<.01), and 4) within the BNA BN^ variable between the levels
of the cost variable (F=39.74, p<.01). Using a Z test of equal means,
the means of the various levels were found to differ significantly
BNAi
BNA,BN,
TABLE 14
MODEL COMPARISON PROCEDURE RESULTS FOR THE BN j, BNA j, AND BNAjBN j DEPENDENT VARIABLES
BN i
BNAi
BNAiBNi
Model
SSe
d.f.(F)
F
SSe
d.f.(F)
F
SSe
d.f.(F)
F
Ful 1 d
26.857
11,74
12.35 9
40.102
11,74
10.76a
8.299
11,74
2.47b
Without 3way interaction
27.387
1,74
1.46
40.107
1,74
0.01
8.716
1,74
3.72C
Without distribution x
cost interaction
27.443
1,75
0.15
40.226
1,75
0.23
8.727
1,75
0.09
Without information x dis
tribution interaction
27.491
1,75
0.29
40.222
1,75
0.22
9.137
1,75
3.62C
Without information x
cost interaction
27.471
1,75
0.23
40.114
1,75
0.02
8.773
1,75
0.49
Without information6
28.415
1,78
2.23
41.707
1,78
2.61
9.258
1,78
0.43
Without distribution6
28.840
1,78
3.43C
41.359
1,78
1.94
9.217
1,78
0.09
g
Without cost
70.389
1,78
120.73a
95.941
1,78
107.44a
10.562
1,78
11.48a
3p<.01 bp<.05 Cp<.10
^The F value associated with the full model is the test of the full model and not a model comparison test,
e
The test of this model assumes the effects of those interactions that include this variable are equal to
zero.
TABLE 15
PARAMETER VALUES ASSOCIATED WITH THE REDUCED BNj, BNAÂ¡, AND BNAÂ¡BNÂ¡ MODELS
F Values Associated With the Sources of the Reduced Models
Source
BN i
BNAi
BNA
iBNi
d. f.
F
d.f.
F
d.f.
F
Distribution
1,79
3.29
Cost
1,79
120.57a
1,80
105.60a
1,80
a
11.91
Intrinsic motivation
1,79
0.33
1,80
0.26
1,80
2.00
Extrinsic (noni
nonetary) motivation 1,79
0.02
1,80
0.08
1,80
0.01
Extrinsic (monetary) motivation
1,79
10.16a
1,80
a
7.24
1,80
0.17
GPA
1,79
0.49
1,80
2.11
1,80
c
3.86
Means and Variances
Associated Witt
i the Significant
Variables
BN i
BNAi
BNAiBNi
Variable Level
N
Mean
Variance
N
Mean
Variance
N
Mean
Variance
SI
44
1.0458
0.71977
44
1.1873
0.93408
44
0.0968
0.06872
S2
42
1.2272
1.08552
42
1.3572
1.54782
42
0.1280
0.20420
Cl
47
1.7777
0.63707
47
2.0046
0.96602
47
0.2269
0.21259
C2
39
0.3591
0.10422
39
0.3853
0.10326
39
0.0263
0.00535
p<.01
p<.05
p<. 10
120
(px.01) for each of the situation variables found to be significant by
the above sources F values. The sole exception was the distribution
variable within the BN^ measure. The means of the distribution levels
within the BHj variable do not differ significantly (Z=0.8830, n.s.
twotailed).
Hypothesis 3.1a predicted that (BNA^jll) would not differ signifi
cantly from (BNA112). A Z test indicated that the difference was not
significant (Z=0.7891, n.s. twotailed). Hypothesis 3.1b predicted that
(BNA.SI) would not differ significantly from (BNA^S2). Again, a
Z test showed the difference to be not significant (Z=0.7048, n.s.
twotailed). Hypothesis 3.1c predicted that (BNACl) would be signifi
cantly larger than (BNAC2). The model comparison procedure and Z test
indicated a significant difference at the p<.01 level, with the Cl level
having the larger mean. Hypothesis 3.2 predicted that the variance of
(BNA. Cl) would be significantly smaller than the variance of (BNA1C2).
The F test of equal variances resulted in a finding of a significant
difference at the p<.01 level, with the C2 level having the smaller
variance.
Hypothesis 3.4a predicted that (BNA^BN^S1) would be signifi
cantly smaller than (BNABN.Â¡ S2). A Z test of equal means indicated
that the difference was not significant (Z=0.3892, n.s. onetailed):
however, the obtained direction is the same as the predicted direction
of this effect. Hypothesis 3.4b predicted that (BNAiBNiÂ¡C1) would be
significantly smaller than (BNA BN .jC2). The model comparison pro
cedure and Z test indicated that the difference was significant at the
p<01 level, with the C2 level having the smaller mean.
121
Relative Decision Criteria Conservatism
Hypothesis 3.3 relates to the effect of conservatism on the
relative individual decision criteria. The hypothesis proposes that
the greater the difference between the individual's initial decision
anchor and the optimal cutoff value, the higher the level of conser
vatism. The situation variable most related to such a difference is
the cost variable.
The method of analysis was the model comparison procedure where
the dependent variable is the BNCÂ¡ measure and the independent vari
ables are the three situation variables (with their interactions), the
three motivation factors, and the GPAj variable. The full BNCj model
is:
BNCi = p + aj + ek + Y, + aSjk + Yji + BYk) + aSYjkl + Ej
+ t, + X, + ,
The model comparison procedure results are presented in Table
16, and the F values associated with the sources of the reduced BNCi
model are presented in Table 17. The results indicate that the re
duced BNCn model contains the cost variable, the extrinsic (monetary)
motivation factor, and the GPA.Â¡ variable. Both the cost variable
main effect and the GPA^ variable are significant at the p<.05 level.
The extrinsic (monetary) motivation factor is significant at the
p<.01 level.
The means, variances, and sample sizes of the BNC^ measure given
the levels of the cost variable are included in Table 17. Using the
F test of equal variances, the variances of the BNC^ given the levels
of the cost variable were found to differ significantly (F=9.36, p<.01).
122
TABLE 16
MODEL COMPARISON PROCEDURE RESULTS FOR THE BNCj DEPENDENT VARIABLE
Model
SSe
d.f.(F)
F
b
Full
38.249
11,74
a
2.35
Without 3way interaction
38.510
1,74
0.51
Without distribution x cost
interaction
39.291
1,75
1.52
Without information x distribu
tion interaction
38.543
1,75
0.06
Without information x cost
interaction
38.624
1,75
0.22
c
Without information
39.895
1,78
0.91
c
Without distribution
39.607
1,78
0.34
c
Without cost
42.778
1,78
a
6.61
a
p<.05
b
The F value associated with the full
model is the
test of
the full
model and not a model comparison test.
The test of this model assumes the effects of those interactions
that include this variable are equal to zero.
123
TABLE 17
PARAMETER VALUES ASSOCIATED WITH THE REDUCED BNCÂ¡ MODEL
F Values Associated With the
Sources of the Reduced Model
Source
d.f.
F
Cost
1,80
6.88b
Intrinsic motivation
1,80
2.61
Extrinsic (nonmonetary)
motivation
1,80
0.04
Extrinsic (monetary)
motivation
1,80
7.79a
GPA
1,80
4.90b
Means and Variances
Associated With the Significant Sources
Variable Level N
Mean
Variance
Cl
47
1.0046
C2
39
0.6147
0.96602
V.Ol
bp<.05
0.10326
124
The Z test of equal means indicated that the means of the cost levels
differ significantly (Z=2.5598, p<.05 twotailed), with the C2 level
having the smaller mean.
Hypothesis 3.3a predicted that (BNC^I1) would not differ signifi
cantly from (BNC.Â¡ 112). A Z test indicated that the difference was not
significant (Z=0.6802, n.s. twotailed). Hypothesis 3.3b predicted that
(BNC^  SI) would not differ significantly from (BNC.jS2). Again, a Z
test indicated that the difference was not significant (Z=0.3295, n.s.
twotailed). Hypothesis 3.3c predicted that (BNC^Cl) would be signifi
cantly smaller than (BNC1C2). The model comparison procedure and Z
test indicated that the difference was significant at the p<.05 level,
with the C2 level having the smaller mean.
%
Individual Attributes and Decision Criteria

The model comparison procedure results indicated that the
extrinsic (monetary) motivation factor and the GPA^ variable had signifi
cant effects upon the various individual decision criteria variables.2
The analysis of these effects also required additional tests. A product
moment correlation was computed for the motivation factor and the GPA.Â¡
variable with each of the decision criteria dependent variables. The
results of these correlations are presented in Table 18.
The negative association between extrinsic (monetary) motivation
and the BN^ variable indicates that as the BNj measure increases the
subjects' extrinsic (monetary) motivation measures decrease. The BN^
2See the last sections in this chapter for definitions and for additional
analyses concerning the extrinsic (monetary) motivation factor and the
GPA.j variable.
125
TABLE 18
CORRELATION COEFFICIENTS FOR EXTRINSIC (MONETARY) MOTIVATION AND
SUBJECT GPA WITH INDIVIDUAL DECISION CRITERIA VARIABLES
Model
Extrinsic (Monetary)
Motivation
Subject GPA
BNÂ¡
0.1975 (.068)
0.0468 (.669)
BNAj
0.1916 (.077)
0.1000 (.360)
BNAjBNj
0.0564 (.606)
0.1896 (.080)
BNCj
0.2951 (.006)
0.2346 (.030)
Note: The numbers within the parentheses are the exact alevels
associated with the rejection of the null hypothesis that the
correlation coefficient is equal to zero.
126
measure approaches infinity as the distance between the frj measure and
the 3^ measure increases. A similar association exists between the
motivation factor and the BNAj measure (the BNAj measure eliminates the
effects of multiple cutoff values). The BNA^BN^ variable, which
measures the effect of multiple cutoff values upon individual decision
criteria, has no significant association with the motivation factor. The
BNC.J variable, which measures the effect of conservatism upon individual
decision criteria, has a stronger negative association with the moti
vation factor. As the level of conservatism increases (BNC.Â¡ increases)
subjects' extrinsic (monetary) motivation measures decrease.
The negative association between the GPA.Â¡ variable and the BNA^BN^
variable indicates that as the BNABN.Â¡ variable increases the subjects'
GPA.Â¡s decrease (the BNA^BN^ measure increases as multiple cutoff value
usage increases). The negative association between the GPA.Â¡ variable
and the BNC.Â¡ variable indicates that as the subjects' GPA^s decrease
the level of conservatism increases.
Individual LongRun Decision Efficiency
The following sections report the analyses and results of the
individual longrun decision efficiency variables. The variables
include the relative decision costs, the relationships between individual
attributes and longrun decision efficiency, and the relationships be
tween longrun decision efficiency and training phase performance.
Relative Decision Costs
Hypotheses 4.1, 4.2, and 4.3 relate to the decision costs incurred
by the individuals relative to the decision costs incurred by the optimal
127
models. Both the stated objective function and subject payoff function
involved the minimization of the decision costs. A subject's longrun
decision efficiency was measured in terms of his minimization of his
decision costs relative to those of an optimal model.
The method of analysis was the model comparison procedure where
the dependent variables are the GP.Â¡, and GN.Â¡ measures. The in
dependent variables are the three situation variables (with their
interactions), the DN.Â¡ variable, the three motivation factors, and the
GPA.j variable. Ideally, both the DN.Â¡ and BN^ variables should be
included in the model: however, the BN.Â¡ variable had significantly
greater association with the other independent variables than did the
DN.j variable (the R2 for the full BN.Â¡ model was 0.6474, whereas the
R2 for the full DN^ model was 0.2192). Inclusion of the BN.Â¡ variable
would produce a significant problem of multi colinearity. The three
full models are:
Gi
GPi = y + j + Bk + Y1 + a6jk + fjl + 6Ykl + a6Yjkl + *1 +
GN + t. + X. +6.
1 ill
The three model comparison procedure results are presented in
Table 19, and the F values associated with the sources of the reduced
models are presented in Table 20. The results indicate that the re
duced G. model contains the distribution by cost interaction and the
DN. variable. Both the interaction and the DN. variable are siqnifi
l i a
cant at the p<.01 level. The reduced GP.. model contains the distribu
tion by cost interaction, the DN. variable, the intrinsic motivation
factor, the extrinsic (monetary) motivation factor, and the GPA.
variable. The interaction, extrinsic motivation factor, and GPA.
l
variable are significant at the p<.05 level. The interaction,
TABLE 19
MODEL COMPARISON PROCEDURE RESULTS FOR THE GÂ¡, GPÂ¡, AND GNÂ¡ DEPENDENT VARIABLES
Gi
GPi
GNi
Model
SSe
d. f. ( F)
F
SSe
dTfTFT
F
SSe
error
F
c
Full
0.0809
12,73
12.08a
0.1824
12,73
a
7.46
0.0349
12,73
a
2.67
Without 3way interaction
0.0811
1,73
0.17
0.1824
1,73
0.00
0.0352
1,73
0.52
Without distribution x
cost interaction
0.0895
1,74
a
7.71
0.1953
1,74
b
5.21
0.0356
1,74
0.96
Without information x dis
tribution interaction
0.0812
1,74
0.09
0.1824
1,74
0.01
0.0352
1,74
0.06
Without information x
cost interaction
0.0814
1,74
0.26
0.1826
1,74
0.07
0.0352
1,74
0.03
d
Without information
0.0826
1,76
1.05
0.1835
1,76
0.36
0.0357
1,77
0.05
d
Without distribution
0.0360
1,77
0.71
d
Without cost
0.0369
1,77
2.60
a b
p<.01 p<.05
c
The F value associated with the full model is the test of the full model and not a model comparison test,
d
The test of this model assumes the effects of those interactions that include this variable are equal to
zero.
TABLE 20
PARAMETER VALUES ASSOCIATED WITH THE REDUCED G.Â¡, GPi, AND GNj MODELS
F Values
Associated
! With the
Sources of
the Reduced
Models
Source
Gi
GPi
GNi
d.f.
F
d.f.
F
d.f.
F
Distribution
1,70
0.51
1,70
0.77
Cost
1,70
2.56
1,70
0.11
Distribution x cost
1,70
7.92a
1,70
5.39b
DNi
1,70
88.76a
1,70
46.39a
1,80
0.90
Intrinsic motivation
1,70
2.40
1,70
3.95C
1,80
5.00b
Extrinsic (nonmonetary) motivation 1,70
0.00
1,70
0.01
1,80
0.05
Extrinsic (monetary) motivation
1,70
1.71
1,70
4.33b
1,80
7.59a
GPA
1,70
2.39
1,70
5.93b
1,80
9.82a
Means and
Variances
Associated
With the
Significant
Variables
... Gi
GP
j
Variable Level
N
Mean
Variance
N
Mean
Variance
si,
Cl
24
0.0504
0.00229
24
0.0987
0.00456
SI,
C2
20
0.0921
0.00371
20
0.1372
0.00538
S2,
Cl
23
0.0430
0.00097
23
0.0886
0.00297
S2,
C2
19
0.0575
0.00375
19
0.0934
0.00576
3p<.01
V.05
Cp<.10
ro
CO
130
extrinsic motivation factor, and GPA^ variable are significant at
the p<.05 level. The DN^ variable is significant at the p<.01 level
and the intrinsic motivation factor is significant at the p<.10 level.
The reduced GN.Â¡ model contains the intrinsic motivation factor, the
extrinsic (monetary) motivation factor, and the GPA^ variable. Both the
extrinsic motivation factor and the GPA.Â¡ variable are significant at the
pc.Ol level and the intrinsic motivation factor is significant at the
p<.05 level.
The means, variances, and sample sizes for the distribution by
cost interaction within the G^ and GP^ models are included in Table 20.
Figure 7 presents a graphic representation of both distribution by
cost interactions. Bartlett's test for homogeneity of variances in
dicted that the variances within the G^ model distribution by cost
interaction are significantly heterogeneous (x2=l0.66, p<.05 with
3 d.f.). The variances within the GP model distribution by cost inter
action are considered homogeneous (x2=2.52, p<.25 with 3 d.f.).
Duncan's multiple range test of equal means indicated that the means
within the G^ model distribution by cost interaction form two groups:
the means within a group do not differ significantly (p=.01) but a
significant difference (p=.01) exists for the means between groups.
These groups are 1) (G.S1,C2) and (G.Â¡ S2,C2), and 2) S2,C1),
(G\j SI,C1), and (G.S2,C2).
Hypothesis 4.1 predicted that the variance of (G^C1) would be
significantly smaller tha n the variance of (G.jC2). Using the F test
of equal variances, the variances differ significantly (F=2.43, pc.Ol),
with (G^ Cl) having the smaller variance. Hypothesis 4.3 predicted a
significant distribution by cost interaction within the G model in
FIGURE 7
DISTRIBUTION BY COST VARIABLE INTERACTION WITHIN
G1 AND GPi MODELS
132
0.03 1 J
SI S2
Distribution
Variable
0.13
0.12
0.11
GPi 0.10
0.09
0.08
0.07
C2
Cl
h
SI S2
Distribution
Variable
Cost
Variable
Cost
Variable
133
which the (Gj S1 ,C1 )(G.Â¡ S2,C1) would be smaller than the (G^ [SI ,C2)
(Gj S2,C2). The results of the model comparison procedure indicated a
significant distribution by cost interaction (pc.Ol), with the
I SI ,C1)(Gj Â¡S2,C1) equal to 0.0074 and the (Gj S1 ,C2)(Gi S2,C2)
equal to 0.0346. Hypothesis 4.2a predicted that (G^C1) would be
significantly smaller than (G^C2). A Z test of equal means indicated
that the means differ significantly (Z=2.4420, p<.01 onetailed), with
the Cl level having the smaller mean. The lack of significance for
the cost variable main effect within the model comparison procedure
could be the result of the distribution by cost interaction. There
exists substantially less difference between the levels of the cost
variable when given S2 distribution level than when given the SI dis
tribution level. Hypothesis 4.2b predicted that (G.jS2) would be
smaller than (G^S1). A Z test of equal means indicated that the means
differ significantly (Z=1.7452, p<.05 onetailed), with the S2 level
having the smaller mean. The lack of significance within the model
comparison procedure for the distribution main effect was predicted by
hypothesis 4.2b. Hypothesis 4.2c predicted that (G^II) would not
differ significantly from (G^ j12). Using a Z test of equal means, the
difference was found to be not significant (Z=0.4528, n.s. twotailed).
Hypotheses 4.4a and 4.4b relate to the ratio of the individual's
relative additional decision costs to the individual's relative addi
tional decision savings. The method of analysis was the model compari
son procedure where the dependent variable is the GP^/GN^ variable, and
the independent variables are the same as for the full G^ model. The
full GP.Â¡/GN.Â¡ model is:
Gpi/GN1 = + j + Bk + Y1 + aSjk + aYjl + 8Yk1 + a8Yjkl + +1 +
+ t + X. + 5.
1 l l
134
The model comparison procedure results are presented in Table 21,
and the F values associated with the sources of the reduced model are
presented in Table 22. The results indicate that the reduced GPÂ¡/GMÂ¡
model contains the distribution by cost interaction and the DMj variable,
both significant at the p<01 level.
The means, variances, and sample sizes of the distribution by
cost interaction are included in Table 22. Figure 8 presents a graphic
representation of the distribution by cost interaction. Bartlett's test
of homogeneity of variances indicated that the variances within the
distribution by cost interaction are homogeneous (x2=1.068, p<.25 with
3 d.f.). Duncan's multiple range test of equal means indicated that
the means within the distribution by cost interaction form two over
lapping groups: the means within a group do not differ significantly
(p=.01), whereas the means between groups do differ significantly (p=.01).
These groups are 1) (GPi/GNiC2,S1), (GPi/GNiC2,S2), and
(GPi/GNiC1,S2); and 2) (GPi/GNiC1 ,S1), (GPi/GNiC1.52), and
(GPi/GNiC2,S2).
Hypothesis 4.4b predicted a significant distribution by cost
variable interaction in which the (GP^/GN^ C2,S2)(GPi/GN11Cl ,S2) would
be smaller than (GPi/GNiC2,Sl)(GPi/GNiC1 ,S1). The results of the
model comparison procedure indicated a significant distribution by cost
interaction (p<.01). The (GPV/GN^ C2,S2)(GP^/GN1 C1 ,S2) was equal to
0.0618, and the (GPi/GNiC2,S1)(GPi/GNiC1,S1) was equal to 1.1086.
Hypothesis 4.4a predicted that (GP^/GN^Cl) would be significantly
smaller than (GP^/GNjÂ¡C2). Using a Z test of equal means, the difference
between the means was found to be significant (Z=2.0109, p<.01
onetailed), with the Cl level having the smaller mean. The lack of
135
TABLE 21
MODEL COMPARISON PROCEDURE RESULTS FOR THE GPi/GN1 DEPENDENT VARIABLE
Model
SSe
d.f.(F)
F
Ful 1b
66.265
12,66
6.91a
Without 3way interaction
67.538
1,66
1.27
Without distribution x cost
interaction
76.352
1,67
8.74a
Without information x distribution
interaction
67.607
1,67
0.07
Without information x cost
interaction
69.242
1,67
1.69
C
Without information
70.493
1,69
1.17
ap<.01
bfhe F value associated with the full
model is the
test of
the full
model and not a model comparison test,
c
The test of this model assumes the effects of those interactions
that include this variable are equal to zero.
136
TABLE 22
PARAMETER VALUES ASSOCIATED WITH THE REDUCED GPi/GNi MODEL
F Values Associated With the Sources of the Reduced Model
Source
d.f.
F
Distribution
1,70
0.64
Cost
1,70
0.35
Distribution
1,70
8.85a
DNj
1,70
59.01a
Intrinsic motivation
1,70
0.11
Extrinsic (nonmonetary) motivation
1,70
0.54
Extrinsic (monetary) motivation
1,70
0.09
GPA
1,70
2.54
Means and Variances Associated With
the Siqnif
icant Sources
Variable Level
N
Mean
Variance
SI, Cl
24
2.0189
1.42035
SI, C2
19
3.1275
2.12453
S2, Cl
19
2.3372
2.09340
S2, C2
17
2.3990
1.71284
ap<.01
137
GPi/GNi
Variable
FIGURE 8
DISTRIBUTION BY COST INTERACTION WITHIN THE GPi/GNi MODEL
138
significance for the cost variable main effect within the model com
parison procedure could be the result of the distribution by cost
interaction. The cost variable has a significant effect only when given
the SI distribution level.
Individual Attributes and LongRun Decision Efficiency
The model comparison procedure results indicated that the DN
variable, the intrinsic motivation factor, the extrinsic (monetary)
motivation factor, and the GPA variable had significant effects upon
various individual longrun decision efficiency measures.3 The
analysis of these effects required additional tests. A productmoment
correlation was computed for each of these individual attributes with
each of the dependent variables. The results of these correlations
are presented in Table 23.
The negative association between the variable and the DN
variable indicates that as the relative net decision costs increase the
subjects' variable response ranges increase (DN increases). The
same association exists between the GP.Â¡ variable and the DN.Â¡ variable:
as relative additional decision costs increase the subjects' variable
response ranges increase. The positive association between the GN^
variable and the DN.Â¡ variable indicates that as the relative additional
decision savings increase the subjects' variable response ranges decrease
(DN.j decreases). The GP^/GN^ variable and the DN^ variable were
associated negatively: as the relative additional decision costs
3See the last sections in this chapter for definitions and additional
analyses concerning these individual attributes.
139
TABLE 23
CORRELATION COEFFICIENTS FOR INDIVIDUAL ATTRIBUTES WITH
LONGRUN DECISION EFFICIENCY
Individual Attribute
G
GP
GN
GP /GN
DN
0.7616
(.001)
0.6528
(.001)
0.1868
(.085)
0.6613
(.001)
Intrinsic motivation
0.2854
(.008)
0.3044
(.004)
0.2399
(.026)
0.1758
(.121)
Extrinsic (monetary)
motivation
0.1071
(.327)
0.1887
(.081)
0.3018
(.005)
0.0185
(.871)
GPA
0.1737
(.110)
0.2526
(.019)
0.3377
(.002)
0.0861
(.450)
Note: The numbers within the parentheses are the exact levels
associated with the rejection of the null hypothesis that the
correlation coefficient is equal to zero.
140
increase at a rate greater than the relative additional decision savings
the subjects' variable response ranges increase.
The negative association between the G.Â¡ variable and the intrinsic
motivation factor indicates that as the relative net decision costs
increase the subjects' intrinsic motivation measures decrease. The same
association exists between the GP.Â¡ variable and the intrinsic motivation
factor: as the relative additional decision costs increase the subjects'
intrinsic motivation measures decrease. The GN.Â¡ variable and the in
trinsic motivation factor were associated positively: as the relative
additional decision savings increase the subjects' intrinsic motivation
measures increase.
Similar associations exist for the extrinsic (monetary) motivation
factor. Thus, as the relative additional decision costs increase (GP^
increases) the subjects' extrinsic (monetary) motivation measures de
crease. Conversely, as the relative additional decision savings in
crease (GN.Â¡ increases) the subjects' extrinsic (monetary) motivation
measures increase.
The negative association between the GP^ variable and the GPA
variable indicates that as subjects' GPAs decrease relative additional
decision costs increase. Likewise, the positive association between the
GN.j variable and the GPA variable indicates that as subjects' GPAs in
crease relative additional decision savings increase.
LongRun Decision Efficiency and Training Phase Performance
Hypothesis 4.5 predicted that those subjects in the PP classi
fication of training adjustment direction would have a signficantly
smaller mean G.Â¡ than those subjects in the MM classification. A ttest
141
was used to test this hypothesis. The (GLÂ¡PP) was 0.0536 and the
(GjMM) was 0.0780. The F test for equal variances indicated that
the variances were equal (F=1.6378, n.s.). The ttest for the
hypothesis of equal means indicated that the hypothesis could be
rejected at the p<.10 level (t=l.303 with 41 d.f.).
Hypothesis 4.6 predicted that those subjects in the mixed
classifications (PM and MP) of training adjustment direction would
have mean measures between the nonmixed classifications. The (G.Â¡PM)
was 0.0603 and the (G^(MP) was 0.0577. Since the hypothesis did not
predict significance (and because of the results of hypothesis 4.5),
these means were not tested statistically. The directional prediction
of hypothesis 4.6 was supported.
Individual Attributes
The following sections report the analyses and results of two
individual attributes: subject grade point average and subject moti
vations. The objective of both analyses was the determination of the
significance of any betweengroup variance of these attributes.
Subject Grade Point Average
Grade point averages (GPAs) of subjects were analyzed to determine
whether this individual attribute varied between groups of subjects. A
systematic variance of this attribute between groups could bias the
interpretation of the effects of the independent variables on particu
lar observation variables. Two methods of analysis were used to test
the association of GPA with the independent variables: Duncan's
multiple range test and Bartlett's test of homogeneity of variance.
142
Duncan's multiple range test was used to determine if the sub
jects' mean GPAs differed between the eight independent variables con
ditions. Given a completely betweensubject assignment these eight
groups represented the results of the experimental sampling plan. The
results indicate that there were not significant differences (p=.01)
between the mean GPAs of these eight groups. Bartlett's test of
homogeneity of variance was used to test the variances of the GPAs for
these eight groups. The results indicate that the hypothesis of homo
geneity of variance could not be rejected (x2=7.6906 with 7 d.f., p<.50).
Overall, the results indicate that betweengroup variance should
not be affected significantly by differences between subjects' GPAs.
This does not indicate, however, that withingroup variance will not be
affected by the differences between GPAs. The possible effects of
withingroup GPA variance were presented above.
Subject Motivations
The objective underlying the analysis of subject motivations was
similar to that relating to subject GPAs: i.e., the analysis was
performed to determine whether motivations varied between groups of
subjects. The initial stage of this analysis consisted of reducing the
ten motivation scale items into several motivation factors using the
technique of factor analysis. The subjects' responses to the ten
motivation scale items were factor analyzed using a varimax rotation for
a four factor solution. The four factor solution was chosen on the
basis of prior beliefs concerning the general dimensions (or sources)
of subject motivations: intrinsic motivation, extrinsic nonmonetary
motivation, extrinsic monetary motivation, and general motivation not
143
clearly associated with any one of the previous sources. The rotated
factor pattern for the ten scale items is presented in Table 24.
Analyzing the eigenvalues of these factors, the sharpest break (or
decline in slope) occurred between the third and fourth factor. For
this reason only the first three factors are included within the
subject motivation analysis. The first three factors account for 62.1
percent of the variability in the motivation scale items.
The first motivation factor had heavy positive loadings on the
scale items "enjoy making the decisions," "satisfied with performance,"
and "enjoy overall experience of participating." The first motivation
factor was interpreted as basically measuring intrinsic motivation
stimulated by enjoyment of the experiment and the required task.
The second motivation factor had heavy positive loadings on the
scale items "desire to perform well," "desire to cooperate with the
experimenter," and "desire to contribute to research knowledge." This
factor was interpreted as basically measuring extrinsic motivation
stimulated by nonmonetary sources.
The third motivation factor had heavy positive loadings on the
scale items "try less hard if money rewards halved" and "money rewards
caused you to try harder than without them." The third motivation
factor was interpreted as basically measuring extrinsic motivation
stimulated by monetary sources.
Using an orthogonal transformation matrix the individual scale
items were transformed into individual factor scores for each of the
first three motivation factors. Two methods of analysis were used to
test the association of subject motivation with the independent
variables: Duncan's multiple range test and Bartlett's test of homo
geneity of variance.
144
. TABLE 24
VARIMAX ROTATED MOTIVATION FACTOR PATTERN AMD DESCRIPTION OF
MOTIVATION SCALE ITEMS
Scale Item
Factor 1
Factor 2
Factor 3
Factor 4
1
0.1399
0.2336
0.2112
0.7551
2
0.2610
0.7485
0.0327
0.0503
3
0.8287
0.2336
0.0712
0.0359
4
0.8001
0.1721
0.2190
0.0836
5
0.0305
0.0197
0.9037
0.1436
6
0.1229
0.2524
0.0876
0.8044
7
0.0231
0.8198
0.0104
0.0174
8
0.0814
0.1075
0.8789
0.0385
9
0.7324
0.3174
0.0963
0.3140
10
0.0148
0.7586
0.0848
0.0441
Eigenvalue
2.6975
1.8552
1.6548
0.9528
Cum Eigenvalue
Portion
0.2700
0.4550
0.6210
0.7160
Description
of Motivation
Scale Items
1 Number of
times willing
to participate
in the future.
2 Desire to perform well in undertaking a challenging task.
3 Enjoy making the decisions.
4 Satisfied with your performance.
5 Would have tried less hard if money rewards had been halved.
6 Feel tense during the experiment.
7 Desire to cooperate with the experimenter.
8 Money rewards caused you to try harder than without them.
9 Enjoy the overall experience of participation.
10 Desire to contribute to research knowledge.
145
Duncan's multiple range test was used to determine if the subjects'
motivations differed between the eight independent variables conditions.
The results indicate that there were no significant differences (p=.01)
in the mean motivation factors between the eight groups. Bartlett's
test of homogeneity of variance was used to test the variances of the
three motivation factors for these eight groups. The results indicate
that the hypothesis of homogeneity of variance could not be rejected
for the second and third motivation factors (x2=10.9095 with 7 d.f.,
p<.25; x2=l.796 with 7 d.f., pc.95). The hypothesis of homogeneity
of variance could be rejected at the pc.10 level for the first moti
vation factor (x2=l2.7829 with 7 d.f.).
Overall, the results indicate that betweengroup variance should
not be affected significantly by differences between the subjects'
motivations. Again, the analysis does not indicate that withingroup
variance will be unaffected by the differences between subjects'
motivations: these possible effects were presented above.
CHAPTER VI
DISCUSSION OF RESULTS
Conceptual Development Revisited
A brief restatement of the conceptual development presented in
Chapter III will serve as a basis for discussion of the results that
were reported in Chapter V. It was posited that an individual's
longrun decision efficiency would be affected by 1) the structure
of the particular decision situation, 2) the contents of the available
information set, 3) the individual's efficiency in processing the
available information, and 4) the individual's ability to expand the
available information through experience with the particular situation.
A general heuristic was expected to systematically account for a por
tion of the decision behavior of the subjects. A heuristic, within
this context, refers to a learned set of rules or principles which are
utilized by individuals in making the particular decisions required of
them. Since a heuristic is a learned response to some decision task
stimuli, individual characteristics can have considerable effect upon
the specific form of the heuristic. The conceptual development, how
ever, proposed that specific decision task stimuli would give rise to
general forms of heuristics which would be (at least partially) in
dependent of individual characteristies.
The decision task utilized in this research required the discrim
ination between the uncertain existence of two states of nature
146
147
(incontrol and outofcontrol) for a given production labor process.
A random variable produced by the accountant provided evidence con
cerning state existance at particular points in time. This random
variable (the labor efficiency variance) had certain statistical
properties within the states of nature: the decision maker either
had or did not have knowledge of these particular properties. The
decision task was complicated by the existence of unequal values (costs)
associated with the various possible decision outcomes.
The form of the general heuristic proposed to describe decision
making behavior was that of anchoring and adjustment. Anchoring and
adjustment first requires the selection of an initial decision value
(anchor) from the continuum of possible decision values that are
located within the domain of the random variable (the labor efficiency
variance in the case of the present study). Once an initial decision
anchor is selected by the decision maker, training and experience
(learning) will lead to adjustments in the value of the anchor. Such
adjustments will be motivated primarily by feedback of various decision
performance measures. Should a particular decision performance measure
indicate less than satisfactory decision performance, the decision
maker will be motivated to adjust his decision anchor in an attempt to
improve decision performance measures in the future. The concept of
less than satisfactory decision performance assumes that the decision
maker has internalized the objective function from which the decision
performance measure was derived. Within this research the decision
performance objective function had a homomorphic relation with the
decision maker's payoff function.
148
Overall Results
The overall results generally are supportive of the conceptual
development. The major deviations from the hypotheses derived from the
conceptual development and the simulations are summarized in this
section. Plausible explanations, consistent with the observed results,
are developed for those situations where the results deviated from the
hypotheses. These explanations, ex post in nature, have not been tested
by this research: they are offered as potential modifications to the
existing conceptual development and can be tested by future research.
First, within situations where the state distributions had wider
separation and the adjustment process involved divergence from the
standard, subjects' initial decision anchors were biased toward the
standard. The extent of this bias was not large and as the adjustment
process continued the bias was reduced substantially relative to the
adjustment bias of subjects within the opposite situations.
Second, variable response ranges were larger within situations
where the adjustment process involved divergence from the standard, and
decision criteria conservatism was larger within situations where the
adjustment process involved convergence toward the standard. Although
these effects were contrary to those predicted, the concept of a sub
jective adjustment limit provides a plausible explanation. The concept
proposes that subjects will perceive the standard as a subjective
adjustment limit and will be hesitant to approach the standard. This
subjective limit will have an effect where the situation involves
adjustment toward the standard. If this phenomenon does exist it would
affect both the variable response range and the decision criteria con
servatism in the same manner as the obtained results.
149
Third, the obtained effects of the situation variables on the
relative decision costs were weaker than those predicted. The pre
dicted effects were based, in part, upon the assumption of equal
learning efficiency between the levels of the various situation vari
ables. However, as indicated earlier the obtained learning efficien
cies were not equal between various situation variable levels. A
plausible explanation for the weaker obtained effects of the situation
variables on the relative decision costs utilizes the unequal learning
efficiency results. That is, the incorporation of unequal learning
efficiencies within the prediction of the effects of the situation
variables on the relative decision costs will produce expected effects
with strengths similar to the obtained effects.
Finally, multiple decision anchor usage had greater effects upon
individual decision model sensitivity within situations where the state
distributions were closer together and had smaller variance. It had
greater effects on individual decision criteria within situations where
the decision costs favored making investigation decisions for smaller
(in absolute value) labor efficiency variances. Both of these results
are contrary to those predicted, and no explanations are offered.
Overall, the variable which had the largest impact on the relative
decision costs was the subjects' decision criteria conservatism. The
mean DNA^ measure over all subjects was 0.926. Considering that a
value of one would indicate variable response ranges were not used, the
subjects' overall decision model sensitiviy was approximately that of
the optimal model. The mean BNC^ over all subjects was 0.828. This
indicated that the average distance between the subjects' decision
criteria and the optimal model's decision criteria was 82.8 percent
of that of the optimal model. This was supported by the mean RA.Â¡ over
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all subjects: the overall mean RA was 0.641 which indicated that the
subjects adjusted their decision anchors only 35.9 percent (1RA^) of
the distance between their initial decision anchors and the optimal
model's decision value. However, even given this level of overall deci
sion criteria conservatism, the mean relative decision costs (G.Â¡) over
all subjects was 0.06 (the average subject's total decision costs were
6.0 percent greater than the optimal model's total decision costs).
The deviation of the G.Â¡ measure from a value of zero has been
attributed to the following factors: 1) variable response ranges,
2) multiple decision anchors, and 3) decision criteria conservatism.
Although each factor was affected differently by various situation
variables and individual attributes, the overall effects of these
factors on the relative decision costs indicate their relative import
ance. The overall effect of variable response ranges was shown to be
negligible. Ignoring the variable response range effects, the overall
relative decision efficiency measure (G) can be adjusted into sub
measures which are affected by only one of the two remaining factors.
Eliminating the effects of multiple decision anchors, the adjusted
Gj measure was 0.045. This indicated that of the total deviation of
the subjects' decision costs from those of the optimal models, approxi
mately 75 percent was due to individual decision criteria conservatism
and approximately 25 percent was due to multiple decision anchors.
Discussion of Results
The following discussion of results is organized according to
decision processes and dependent variables. The discussion includes
decision anchor adjustment, individual decision model sensitivity,
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individual decision criteria conservatism, and individual longrun
decision efficiency.
Decision Anchor Selection and Adjustment
The first aspect of the anchoring and adjustment heuristic is the
selection of the initial decision anchor. The formation of the hypo
theses concerned with this aspect was based upon the assumptions that
1) individuals (within a given level of distribution variable) would
tend to use a common initial decision anchor, and 2) the location of this
common anchor would be related to a measure of central tendency between
the two states of nature. This central tendency was operationalized as
the geometric intersection of the state distribution curves (the inter
section was conditional upon the level of the distribution variable).
The results indicated that the expected point of central tendency
was obtained for those subjects within the SI distribution level, but
that the expected point was statistically larger than that obtained
for those subjects within the S2 distribution level. The state means
were farther apart within the S2 distribution level than within the SI
level. As a consequence, the intersection of the state distribution
curves was located farther from the standard (the mean of the incontrol
state) within the S2 distribution level than within the SI level. An
initial subjective bias provides a plausible explanation for the
estimate obtained within the S2 level. This bias would take the form of
a tendency to place greater weight on the standard as the intersection
of the distribution curves became larger (i.e., shifted to a higher
point on the actual minutes incurred axis).
152
An effect which differed from expectations was that the mean
initial decision anchor given the C2 cost level was significantly
larger than the mean initial decision anchor given the Cl level
(equality was predicted). A bias of the estimates of the initial de
cision anchors may explain this difference. It will be recalled that
the estimates had incorporated some initial adjustment (during the
first training session). Those subjects within the C2 cost level were
expected to adjust their initial decision anchors such that the value
(actual minutes incurred) increased: those subjects within the Cl cost
level were expected to adjust in the opposite direction. Initial ad
justment in the appropriate directions would produce the obtained cost
variable effect on the estimates of the initial decision anchors.
The second aspect of the anchoring and adjustment heuristic is
the adjustment process. The adjustment process was examined using a
measure of the individual's relative decision anchor adjustment. This
observation variable, RA, was the linear distance ever which the
individual adjusted his decision anchor (the distance between his final
decision anchor and the optimal decision value) relative to the linear
distance over which the individual should have adjusted his decision
anchor (the distance between his initial decision anchor and the
optimal decision value). The results of analysis indicated that the
RA.j measure did not differ significantly between the levels of the
various situation variables. This was consistent with the predictions
from the training phase simulation. A major limitation of the measure
is that it is confined to the relative linear movement along the random
variable decision axis. As such the RAj measure is not sensitive to
the learning of various situation parameters and variables (e.g., the
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relative frequencies of the two states and the relative decision error
costs), nor is it sensitive to the effects of various individual attri
butes (e.g., the individual's variable response range). Possible
effects of the situation variables on these aspects of the adjustment
process are examined in greater detail ( with measures of greater
sensitivity) in later sections relating to the individual decision
criteria and the individual decision model sensitivity.
Individual Decision Model Sensitivity
Individual decision model sensitivity was measured employing a
parameter of the TSD model, d'. This parameter measures the decision
sensitivity of the individual's model relative to a theoretical sensi
tivity. The factors which can affect an individual's decision model
sensitivity are 1) the use of a variable response range, and 2) the
use of multiple decision anchors. To isolate'these factors three
decision sensitivity measures were developed: 1) the DNj measure
(which is affected by both factors), 2) the DNAj measure (which is
affected by only the variable response range), and 3) the DNAjDNj
measure (which is affected by only the multiple decision anchor).
The variable response range (the DNAj measure) was affected
primarily by the cost variable. That is, those subjects within the C2
cost level demonstrated larger variable response ranges than did those
subjects within the Cl cost level. The locations of the optimal de
cision values and the subjects' final decision anchors were closer to
the standard for the Cl cost level than for the C2 cost level (see
Figure 2, Chapter III). The subjective adjustment limit concept offered
154
earlier may explain the difference in effect of the cost variable on
the DNA.: measure. When the adjustment process involved convergence
toward the standard the subjects may have perceived the standard as a
limit to their adjustment process, a limit which they could have been
reluctant to approach. When the adjustment process involved divergence
from the standard the subjects did not have an objective value to per
ceive as a limit to their adjustment process. Whether a subject's
adjustment process involved convergence toward or divergence from the
standard depended upon the location of his initial decision anchor
relative to the optimal decision value. This factor was conditional
upon the cost variable. Given the Cl level the subject's initial
decision anchor was greater than the optimal decision value and the
adjustment process involved convergence toward the standard. Given
the C2 level the opposite held: i.e., the subject's initial decision
anchor was less than the optimal decision value and the adjustment
process involved divergence from the standard. As subjects' adjustments
within the Cl level converged toward the standard the subjective
limit of the standard could have acted as an intervening variable which
reduced the relative magnitude of the variable response range. As
subjects' adjustments within the C2 cost level diverged from the stan
dard no such subjective adjustment limit existed: therefore, the
relative magnitude of the variable response range could have increased.
The higher (statistical) moments of the DNA^ measure given the
levels of the cost variable were consistent with this concept of a
subjective adjustment limit. Such a limit should have had the effect
of reducing the variance of this measure. The test of the variances
indicated that (DNA^JCl) had a significantly smaller variance than
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(DNA.jC2). The subjective limit also should have had the effect of
skewing the measure away from those values which indicated larger
variable response ranges. The third moment (as expressed by the co
efficient of skewness) of the DNA measure indicated that 1) the
skewness of the (DNA^C1) distribution was positive (skewed away from
values which indicated larger variable response ranges), and 2) the
skewness of the (DNAC2) distribution was negative (skewed toward
values which indicated larger variable response ranges).
The multiple decision anchor variable (the DNA^DN^ measure) was
affected primarily by the distribution variable. Those subjects within
the SI distribution level employed multiple decision anchors to a
larger extent than did those subjects within the S2 level. The hypo
theses posited a relation between multiple decision anchors and the
%
distance separating the lower tail of the incontrol state from^the
subject's final decision anchor. It was expected that the larger this
distance (found within the C2 cost level and the S2 distribution level)
the greater would have been the subject's propensity to have perceived
a second outofcontrol state below the existing incontrol state
(and used multiple decision anchors). However, the obtained results
do not support this proposed relation.
The observed associations between the individual decision model
sensitivity measures and intrinsic motivation have intuitive inter
pretations. The intrinsic motivation factor measured experimental
task and experimental environment enjoyment as modified by decision
performance satisfaction. As the subjects use of multiple decision
anchors and variable response ranges increased, their intrinsic moti
vation appeared to decrease. Since the use of multiple decision
156
anchors adversely affects decision performance (as reported by the feed
back measure used in this research), their increased use would affect
intrinsic motivation through the performance satisfaction modifier.
Greater use of multiple decision anchors would result in poorer decision
performance feedback measures: these measures, in turn, could lower
the subject's decision performance satisfaction and thereby lower his
intrinsic motivation. As the variable response range increases in
relative size the number of decisions falling within this range increases.
This requires more guesswork or more complicated decision rules on the
part of the subjects, thereby increasing the difficulty (or the uncer
tainty) of the decision task. This increased task difficulty could
affect the subject's enjoyment of the decision task and thus lower his
intrinsic motivation.
Individual Decision Criteria
Individual decision criteria were measured using a parameter of
the TSD model, 6. This parameter measures the decision criteria used
by the individual independent of his decision model sensitivity. The
factors which can affect an individual's decision criteria are 1) the
individual's efficiency in processing (or learning) the effects of the
states' relative frequencies and the relative decision error costs,
and 2) the use of multiple decision anchors. To isolate these factors
several decision criteria measures were developed: 1) the BN measure
(which is affected by both factors), 2) the BNA.Â¡ and BNC^ measures
(which are affected by only the individual's information processing
efficiency), and 3) the BNA^BM^ measure (which is affected by multiple
decision anchors).
157
Those subjects within the Cl cost level produced significantly
larger mean BNA.Â¡ measures than did those subjects within the C2 level.
However, the BNA measure can not be interpreted strictly as a measure
of the efficiency of individual information processing: rather, it is
a function of such a measure, the BNC^ measure. This is due to
characteristics of the data structure and the decision task. The 3,
parameter is related monotonically to the random variable decision
axis (actual minutes incurred). Consequently, for those subjects with
in the Cl cost level the 3.Â¡ measure associated with the initial decision
anchor is larger than the Bk measure, and the expected adjustment pro
cess has the effect of reducing the 3 measure. For those subjects
within the C2 cost level the 3.Â¡ measure associated with the initial
decision anchor is smaller than the 3^ measure, and the expected ad
justment process has the effect of increasing the 3^ measure. There
fore, where BNA is defined as the ratio of the final 3 measure and
the 3^ measure, a value greater than one is produced for those subjects
within the Cl cost level and a value less than one is produced for those
subjects within the C2 cost level. The BNC^ measure eliminates this
systematic relation with the value of one.
The efficiency of information processing (learning) was affected
primarily by the cost variable. Those subjects within the Cl cost
level were significantly more conservative (less efficient) in pro
cessing the information than were those subjects within the C2 cost
level. These results were the reverse of those predicted by the hypo
theses. The subjective limit concept may again be introduced as an
explanation. As subjects' adjustments within the Cl cost level con
verged toward the standard the subjective adjustment limit of the
158
standard could have acted as an intervening variable which increased
the level of relative conservatism (reduced the level of relative infor
mation processing efficiency). As subjects' adjustments within the C2
cost level diverged from the standard no such subjective adjustment
limit existed, thus the level of relative conservatism could have de
creased.
The higher (statistical) moments of the BNC.Â¡ measure given the
levels of the cost variable were consistent with this concept of a
subjective adjustment limit. The difference in variances of the
BNC.j measure was due to the difference in the ranges of the measure.
The range of (BNC^C1) was 4.67 and the range of (BNC.Â¡C2) was 1.57.
The subjective adjustment limit should have had the effect of skewing
the measure away from those values which indicated lower levels of
conservatism. The third moment (as expressed by the coefficient of
skewness) of the BNC^ measure indicated that 1) the skewness of the
(BNC^. Â¡C1) distribution was positive (skewed away from values which
indicated lower levels of conservatism), and 2) the skewness of the
(BNC^C2) distribution was negative (skewed toward values which in
dicated lower levels of conservatism).
The multiple decision anchor (the BNABN^ measure) was affected
primarly by the cost variable. Those subjects within the Cl cost
level employed multiple decision anchors to a larger extent than did
those subjects within the C2 cost level. The hypotheses posited a
relation between multiple decision anchors and the distance separating
the lower tail of the incontrol state from the subject's final de
cision anchor. This relation was similar to that proposed in the
DNA^DN^ measure. However, the results did not support the hypotheses
159
and thus parallel those obtained with the DNA^DN^ measure. Both the
DNA^DN^ and the BNA^BN^ measures obtained results that indicated
greater multiple decision anchor usage when the subjects' initial de
cision anchors were closer to the standard (the SI distribution level
within the DNA^DN^ measure and the Cl cost level within the BNA^BN^
measure). No plausible explanation for these results is readily
apparent.
The obtained associations between the individual decision criteria
measures and the individual attributes of extrinsic (monetary) moti
vation and subject GPA have intuitive interpretation. As both the
extrinsic (monetary) motivation measures and the subjects' GPAs de
creased the level of conservatism increased. Furthermore, as the sub
jects' GPAs decreased the use of multiple decision anchors increased.
Of the two individual process variables (decision model sensitivity
and decision criteria), the decision criteria had the more signifi
cant affect on a subject's performance feedback measure and, in turn,
on the level of his monetary rewards. However, since motivation
pretests were not administered,causal relationships (i.e., decreased
motivation led to increased conservatism, or increased conservatism led
to decreased motivation) can not be claimed.
Individual LongRun Decision Efficiency
The overall efficiency of an individual's decision making within
the experiment was measured using the G.Â¡ variable. The G variable was
the individual's net additional decision costs relative to the optimal
model's total decision costs. The G^ variable was disaggregated into
two measures the total additional individual decision costs relative
160
to the optimal model's total decision costs (GPj) and the total
additional individual decision savings relative to the optimal model's
total decision costs (GNÂ¡). The hypotheses concerning these variables
were derived using a simulation which assumed a set of initial decision
anchors and assumed equal learning efficiency (in terms of relative
linear adjustments along the random variable axis). Given these
assumptions, specific variations in the decision situation were ex
pected to have specific effects on a subject's relative decision costs
(see Table 4 in Chapter V for a summary of these expected effects and
the obtained results). All of these expected decision situation effects
were supported, at least in part, by the obtained results. However,
the results of the individual decision model sensitivity and the indi
vidual decision criteria analyses indicated that learning efficiency
was not equal between the levels of the situation variables.
The support obtained for the hypotheses concerning decision
efficiency variables would suggest that the unequal learning efficiency
did not have a significant effect on the relative decision costs. The
majority of the unequal learning efficiency occurred between the levels
of the cost variable (the most significant effect, decision criteria
conservatism, was greater within the Cl cost level than within the C2
level). A closer examination of both the simulated and the obtained
cost variable effects on the GÂ¡ variable indicated that the obtained
effects were not as strong as the simulated effects (note that (GjC2)/
(GilCl) equalled 1.61 whereas f(GjC2)/f(GjCl) equalled 3.27). The
simulated effects may be adjusted for unequal learning efficiency by
assuming the relative decision costs within the Cl cost level would be
increased by a factor of two (relative to the C2 cost level). After
161
this adjustment the simulated cost variable effects would have the same
strength as the obtained effects (i.e., f (Gj  C2 )/2 f (G^  Cl) would equal
1.60).
The simulated and obtained distribution by cost variable inter
actions involving the relative decision costs had similar relationships
between their strengths: the effects of the obtained interaction were
not as strong as the effects of the simulated interaction (note that
[(G.Â¡ S1,C2)(G.Â¡ S2,C2)] /[(^ S1 ,C1 )(Gi S2,C1)] equalled 3.15 whereas
[f(GiSl,C2)f(Gi S2,C2)] / [fÂ¡SI,C1)f(Oj S2,C1)] equalled 8.74).
Again, if the simulated effects are adjusted for unequal learning
efficiency, then the simulated distribution by cost variable inter
actions have a strength similar to that of the obtained effects (i.e.,
[f(Gn. Sl,C2)f(Gi Â¡S2,C2)] / [2f (G^1 SI ,C1 )2*f (G.Â¡ S2,C1)] would equal
4.37).
The simulated and obtained distribution by cost variable inter
action within the GP^/GN^ measure had opposite relationships between
their strength: the effects of the obtained interaction were stronger
than the effects of the simulated interaction (note that [(GP^/GN^C2,S1)
(GPi/GNiC1,S1)] / [(GPi/GNiC2,S2)(GPi/GNi C1 ,S2)] equalled 17.94
whereas [f(GPi/GNiC2,S1)f(GPi/GMiC1,S1)] / [f(GP1/GNiC2,S2)
f(GP^/GN^jCl,S2)] equalled 3.82). To explain the difference in effect
strengths requires reference to two variables: multiple decision an
chors and unequal learning efficiency. If the GP.Â¡/GN.Â¡ measures are
adjusted for the effects of multiple decision anchors the strenqth of
the obtained interaction effect is 2.89 (using the above interaction
strength ratio). This reverses the relationships between the effect
strengths: i.e., the obtained interaction strength becomes weaker
162
than the predicted strength. The effects of unequal learning efficiency
were 1) an expansion of the interval between the final decision an
chor and the optimal decision value for those subjects with greater
decision criteria conservatism, and 2) the reduction of the interval
between the final decision anchor and the optimal decision value for
those subjects with less decision criteria conservatism. As the in
terval associated with those subjects within the Cl cost level expanded
(this level had greater decision criteria conservatism), both the
(GPÂ¡Â¡C1) and the (GNj Cl) became larger. However, the GPÂ¡ measure
became larger at a greater rate than did the GNÂ¡ measure (3GPÂ¡/9GNÂ¡>l).
As the interval associated with those subjects within the C2 cost level
contracted (this level had less decision criteria conservatism), both
the (GPjC2) and the (GNj C2) became smaller. However, the GPÂ¡ measure
became smaller at a greater rate than did the GN.Â¡ measure (3GP1/3GN1>1).
Consequently, the ratio (GP^/GN^Cl) increased as the level of conser
vatism increased, and the ratio (GP^/GNJC2) decreased as the level of
conservatism decreased. Incorporating these effects of unequal learning
efficiency into the simulated distribution by cost variable interaction
lowered the predicted strength of such an interaction.
The observed associations between the individual longrun decision
efficiency measures and the individual attributes may be interpreted
with some degree of intuitive satisfaction. As the subjects' variable
response ranges increased the relative decision costs increased (either
as a result of increased additional decision costs or decreased addi
tional decision savings). Furthermore, as the relative decision costs
increased both the subjects' intrinsic and extrinsic (monetary) moti
vation measures decreased. A similar association existed between the
163
subjects' GPAs and the relative decision costs: as the subjects' GPAs
increased either additional decision costs decreased or additional de
cision savings increased. It would be reasonable to assume that both
the DN. measure and the subject GPA are modifying variables on a sub
ject's decision cost performance. Since pretests were not administered
it was not possible to state the specific relations between the moti
vation factors and a subject's relative decision cost performance.
Li mitations
The major limitations of this study involve two general aspects;
the subjects employed within the experiment and the experimental
environment itself. Although these aspects are discussed separately,
in many instances the limitations they impose overlap one another.
These limitations affect primarily the external validity of this study.
There are several possible limitations involving the experimental
environment. First, the precision of subject performance feedback
during the training phase could be a limitation. The results obtained
in this study might be modified substantially if such accurate feedback
was not employed. The lack of decision performance difference between
the levels of the available information variable could be a direct
result of this feedback: i.e., the accuracy of the performance feedback
and its relationship with the optimal model may have replaced the need
for such additional statistical information.
Second, the background of the subjects in relation to the ex
perimental task is a possible limitation. Although the subjects re
ceived training in the experimental task, the primary source of their
knowledge concerning standard cost variance investigation could come
164
from the college classroom. Consequently, if they were not taught
(within the classroom) that situations exist in which investigation
decision values are located relatively close to the standard, then
greater decision criteria conservatism within these situations could
be the result of the lack of such knowledge. This suggests the
possibility of an availability bias (Tversky and Kahneman, 1973).
However, to the extent a manager must learn from his own experiences,
such a bias could exist in the real world.
A final limitation (discussed within this section) involves the
selection of the levels of the situation variables. Several situation
variables were set at a single level, while others were manipulated at
multiple levels. The important point is that only specific combina
tions of variable levels were studied within this research whereas an
infinite number of combinations are possible. Different variable levels
and different variable manipulations would create a difference in the
experimental environment which could produce results other than those
obtained in this study.
These experimental environment limitations do not mean, however,
that the concepts proposed within this study have not passed a meaning
ful empirical test. The objectives of experiments involving theory
or concept testing, and the relations between the nature created in the
experiment and the nature existing in the world at large are discussed
by Zigler (1963):
What the experimenter is saying is that if such and such holds
in the real world because of the principles expounded in the
particular theory under investigation, then such and such should
hold in the world which the experimenter has created. This
transatability is what gives theoretical import to experiments
which involve phenomena which, taken in isolation, not only
appear picayune but seem to have little relationship with what
one observes in nature, (pp. 3534).
165
Finding the proposed concepts to hold within the experimental world
does not validate the claim that they hold within the real world. On
the other hand, finding the proposed concepts not to hold within the
experimental world would cast serious doubt on whether they hold in the
real world. Positive experimental results are a form of negative
assurance: the concepts have passed an initial empirical test and
remain (albeit somewhat scarred in most instances) potential candidates
for explaining real world phenomena.
The major limitation involving the subjects employed within this
research is that they were college students, predominantly accounting
majors. The obtained results and the conclusions based upon these
results are not generalizable beyond some unspecified population of
which the subjects are representative.
The selection of the type of subject to be employed within an
experiment is not independent of the experimental environment. Within
this research principal interest lies in the behavior of operational
managers within real world environments. However, given the selection
of the laboratory experimentation method (which implies the use of a
surrogate experimental environment), the selection of the type of
subject depends upon the answer to the question, "Which subject is a
better surrogate for a manager in a real world environment, a know
ledgeable student or the manager himself?" There are, of course, no
clear answers to this question. However, several aspects of the
situation favor the use of a knowledgeable student. First, the high
degree of abstraction within the experimental environment could have
made the task appear trivial to a manager and could have elicited
behavior not consistent with the principal situation. Second, the
156
experimental environment is closer to that of a testtaking situation
which is a more natural environment for a student than for a manager.
Implications For Accounting
Two general implications of this research for accounting are
discussed. These implications are the value of additional information,
and the general standard setting process.
Value of Additional Information
The manipulation of the information variable involved the quan
tity of information contained in the available information set, specifi
cally the presence or absence of various distribution information items.
The results indicated that the information variable did not have a
significant effect upon the individual relative decision costs. The
mean relative decision cost of subjects within the reduced information
level did not differ significantly from that of subjects within the
expanded information level. An implication of this result concerns the
net benefit (for the company) of providing the additional information
within the expanded information level. The additional information
within an actual environment is not costless. Consequently, if such
information is to be provided, other things being equal, the net
benefit of such an action should be positive. In this context, the net
benefit would be the marginal decision cost savings resulting from
improved manager variance investigation decisions (given the additional
information) less the costs associated with the production and dis
semination of the additional information. Within this research the lack
167
of a significant information variable effect implies that the net
benefit of providing additional information may not be positive.
However, it should be noted that the lack of an information variable
effect could be the result of either 1) that the subjects within the
reduced information level were able to estimate (with relative
efficiency) the missing information as a result of their experiences
with the decision task, or 2) that the subjects within the expanded
information level did not utilize the additional information efficiently.
General Standard Setting Process
Another implication of this research concerns the general standard
setting process. Within decision situations involving either con
vergent or divergent adjustment decision criteria,conservatism was de
fined as a less than complete adjustment toward the optimal decision
value. Convergent adjustment involved individual decision value
adjustment toward the standard, and divergent adjustment involved in
dividual decision value adjustment away from the standard. The stan
dard used within this research can be conceived of as a type of de
cision behavior magnet. Over certain ranges of its field (the rele
vant decision axis) the standard attracts variance investigation be
havior, and over other ranges of its field it repels variance investi
gation decision behavior. Decision criteria conservatism when the
adjustment process diverges from the standard could be the result of
the attraction (or pull) of the standard that restrains the individual
from making a complete (divergent) adjustment to the optimal decision
value. Decision criteria conservatism when the adjustment process
converges to the standard could be the result of the repelling force
168
(or push) of the standard, subjectively limiting a complete (convergent)
adjustment to the optimal decision value. The decision behavior magnet
view represents a generalization of the subjective adjustment limit
concept. The subjective limit concept as presented earlier dealt with
decision criteria conservatism involving only convergent adjustment.
The question of whether decision makers could learn (either through
training or experience) to reduce the decision biases implied by the
decision behavior magnet concept remains unanswered. An alternative
approach, however, involves the standard setting process itself.
Previous research involving the nature of standards (e.g., strict
standards, currently attainable standards, lax standards) has primarily
dealt with the standard's motivational affects. Other things being
equal, the nature of the standard may affect the decision behavior
magnet biases.. Within situations requiring adjustment, lax standards
(which have values greater than the mean of the incontrol state) may
reduce substantially divergent decision criteria conservatism. Within
situations requiring convergent adjustment, strict standards (which
have values less than the mean of the incontrol state) may reduce
substantially convergent decision criteria conservatism.
Future Research
The implications of this study for future accounting research
take two general forms: modification of the existing conceptual
development and generalizations of the modified conceptual development.
Modifications of the existing conceptual development would involve
incorporating and testing the ex post explanations offered for certain
159
variance investigation decision behavior observed within this research.
Generalization of the modified conceptual development would involve
extensions aimed at reducing the limitations of the existing study.
The primary ex post concept developed within this research is
the decision behavior magnet view (incorporating the concept of sub
jective limits). Future research could directly test this concept.
Such research could involve variance investigation decision situations
in which the standard was not the same value as the mean of the
incontrol state of nature. If this ex post concept is valid, then
subjects within situations involving convergent adjustment and a
strict standard should have less decision criteria conservatism than
subjects within situations involving convergent adjustment and a lax
standard. Similarly, subjects within situations involving divergent
adjustment and a lax standard should have less decision criteria
conservatism than subjects within situations involving divergent ad
justment and a strict standard. Additional research could test
whether the biases implied by the decision behavior magnet concept can
be reduced or eliminated by extended training within the particular
decision situation.
Generalizations of the conceptual development would require
extensions concerning both the experimental subjects and the experimental
environment. Extensions concerning the experimental subjects could
involve the use of nonstudents, perferably actual operational managers.
Such extensions could include managers within familiar experimental
environments and within nonfamiliar experimental environments. Be
yond extending the decision criteria conservatism effects, the central
question would be whether differential information processing
170
efficiency (decision criteria conservatism) is a characteristic of only
training (learning) situations or remains a characteristic of
welltrained (nonlearning) situations.
Extensions involving the experimental environment could take
several forms. First, they may relate to the levels of the situation
variables. Only certain situation variables were studied within this
research: extensions could study the affects of manipulating different
situation variables at different levels (e.g., more than two states of
nature, nonnormal distributions, variable relative frequencies, vari
able decision cost functions, multiple standard cost variances).
Second, extensions could be made in relation to the experimental
task itself. Within this research the decision task was of a
crosssectional nature: i.e., each decision trial was independent of
the previous decision trials. An extension could involve a decision
task of a timeseries nature: i.e., where each decision trial would
be dependent on the previous decision trials. Such a decision task
extension would involve sequential decision making, where the physical
process is initially in some state of nature and over a series of
decision trials (variance reports) the process may or may not move to
another state of nature. The task of the decision maker would be to
detect the point in time in which the physical process shifts to an
other state.
The third and final extension discussed within this section con
cerns the nature of the experimental decisions. Within this research
the decisions involved the detection of state of nature for the purpose
of physical process control within cost minimization constraints. Be
yond the operational control level, standard variance reports can be
171
utilized as performance measures for the appropriate operational managers
and for the physical systems under their control. The nature of de
cisions of this type, although generally aggregated over longer time
periods, is that of adequate performance verses inadequate performance
rather than that of investigation verses noninvestigation. Within the
current research, performance decisions were assumed implicitly within
the performance feedback measures given the subjects during the train
ing phase. An extension would involve the actual formation of such
decisions and, in turn, their effects upon operational control decisions.
APPENDIX A
THEORY OF SIGNAL DETECTION DERIVATIONS
Relationship of Conditional Probability Matrix and Bayes1 Theorem
The unconditional probabilities of the four event outcomes can be
stated in terms of the conditional probabilities and the prior proba
bilities of the sn and n distributions: 1) the probability of a hit,
P(sn,Y) equals P(Ysn)P(sn); 2) the probability of a miss, P(sn,N)
equals P(N sn)P(sn); 3) the probability of a false alarm, P(n,Y) equals
P(Yn)P(n); and 4) the probability of a correct rejection, P(n,N)
equals P(Nn)P(n).
Egan (1975, pp. 1213) demonstrates the relationship between the
singleinterval procedure and Bayes1 theorem. The posterior probability
of the event sn, given the discrete observation x, can be represented
as P(snx). Since the two events sn and n are exhaustive the addition
of P(snx) and P(njx) equals one. The posterior probability of the
event sn can be stated as P(sn,x) which equals:
P(xsn)P(sn) = P(snx)P(x).
Rearranging the terms of this equality and expanding the P(x):
n/ i P(xsn)P(sn)
P(sn x)= iÂ¡!
P(x]sn)P(sn) + P(x[n)P(n)
This equation is a simple expression of Bayes' theorem. It can be ex
tended to the odds form, the posterior odds equals the prior odds times
the likelihood ratio of the observation x:
P(sn[x) P(sn) P(xsn)
P(nx) P(n) P(xn)
172
Egan (1975) further demonstrates that the posterior probability is
strictly monotone with the likelihood ratio of x, L(x); that is, when
L(x2) > LCXj) then P(snx2) > P(snx1).
173
Maximization of Expected Value Objective Function
The decision function for maximizing the expected value decision
goal is:
E(V) = P(Y!sn)P(sn)Vsn)Y + P(Yn)P(n)VnjY
+ P(Nn)P(n)Vn>N + P(Nsn)P(sn)VsnjN
Given an observation x, the subject has two decision functions:
1) E(Vx,Y), the expected value given the observation and the
subject responds "Yes," and
2) E(Vx,N), the expected value give the observation and the
subject responds "No."
These two decision functions can be expressed as the sum of the values
associated with each decision weighted by the appropriate posterior
probability:
E(Vx,Y) = P(snx)VsrljY + P(nx)VnjY, and
E(V j x,N) = P(nx)VnjN + P(snx)VsnjN.
The subject should say "Yes" only if E(Vx,Y) > E(Vx,N). If the
righthand side of the above two equations are used in this inequality,
then the subject should say "Yes" only when:
P(snx)Vsn,y + P(nx)VnY > P(nx)VnjN + P(snx)Vsn>N
Rearranging the terms and expressing the posterior odds in terms of the
prior odds and the likelihood ratio of x results in the decision rule
for saying "Yes" in terms of the likelihood ratio of the observation:
P(x1sn) P(n) Vn,N Vn,Y
P(x n) > P(sn) 'vsn>Y VsM
APPENDIX B
ORAL PRESENTATION TO ELICITE VOLUNTEER SUBJECTS
I assume that most, if not all, of you intend to become account
ants. You should realize that accounting is more than an occupation,
it is a profession. As a member of a profession you have certain re
sponsibilities beyond those encountered in other occupations. One of
these responsibilities is the readiness to help expand the state of the
art, the level of knowledge, of the profession. Today you have an
opportunity to meet this responsibility. As part of my Ph.D. require
ments I am conducting an experiment which examines the impact upon
decision makers of certain accounting produced information. I am asking
that you volunteer to participate as subjects in this experiment.
The accounting information under study is the standard cost vari
ance report and, as a subject, you would be asked to assume the role of
an operations manager and to make certain decisions based upon standard
cost variance reports. After volunteering, greater detail concerning
the role and the required decisions will be provided in a short booklet
and in a training session.
Benefits to volunteering as a subject, beyond meeting part of your
professional responsibilities, include:
1) A chance to expand your knowledge of standard cost variance
reports and their utilization from the point of view of a
line manager.
174
175
2) A chance to earn up to $10.00 as payment for participating in
the experiment.
3) A chance to gain firsthand experience with an accounting
research project.
Your participation in the experiment would not involve a great
sacrifice of your time. The entire experiment requires only two
oneperiod sessions. The times of these sessions are flexible to meet
the requirements of your personal schedule. The first oneperiod
session, a training session, would be scheduled sometime on Monday or
Tuesday, April 10th and 11th. The second oneperiod session, the actual
experiment, would be scheduled sometime on Wednesday or Thursday,
April 12th and 13th.
I can assure you that your responses within the experiment will
be held strictly confidential. Only aggregate or anomymous results
will be made pub!ic.
I will pass around a signup sheet; I would appreciate it if
everyone would print their name and indicate with a checkmark whether
you are willing to participate. If you are willing to participate,
fill in your phone number and indicate some times that I can contact
you to schedule your participation.
I would like to thank you in advance for your time. The overall
success of this study is dependent upon a positive response from most
of you in this room. I am confident that as professionals you will
exceed my expectations.
Does anyone have any general questions?
APPENDIX C
BACKGROUND INFORMATION BOOKLET
The following represents background information concerning the role
that you will be asked to assume in the business experiment for which
you have volunteered to participate. Please read this information
several times, familarizing yourself with the general aspects of this
role.
More detailed information together with specific instructions will be
presented at the inception of the actual experiment. You may use this
booklet when you participate in the experiment.
PLEASE DO NOT DISCUSS THIS EXPERIMENT WITH OTHER PEOPLE. If you have
questions contact Cl if Brown (Bryan 214E or telephone 20155).
My Training Session is:
Date:
Time:
Room:
My Experiment Session is:
Date: Time:
Room:
176
177
You will be asked to assume the role of an assembly department
operational control manager and within this role you will be asked to
make certain decisions based upon information that will be presented to
you. The specific nature of these decisions will become clear as you
read this background information booklet.
General Company Information
Assume you are employed by the American Seating Company (AMSECO).
AMSECO manufactures and sells public seating and institutional furniture
to education, amusement, transportation and health care markets. The
company is one of the largest suppliers in each of its markets. Select
ed AMSECO financial statement items for the previous calendar year
include:
Net Sales
Cost of Sales
Net Income
Total Assets
Net Assets
$ 84,000,000
$ 67,250,000
$ 1,550,000
$ 60,465,000
$ 33,947,000
General Product Information
The assembly department of which you are the operational control
manager is engaged in the manufacturing of a metal folding chair. Since
the major market for the folding chair is the education market the chair
must meet stricter specifications than the typical homeuse variety.
The chair is produced in three styles, the only difference between the
styles is the color; grey, brown, and black. The production of the
folding chair averages 1,000 chairs per week; this production accounts
for approximately 1.5 percent of AMSECO's net sales.
Metal Folding Chair Manufacturing Process
The folding chair is manufactured within three operational depart
ments using a sequential production system. The three departments are
fabrication, finishing, and assembly; you are the operational control
manager of the metal folding chair assembly department.
The fabrication department manufactures the parts required to make
the folding chair and transfers them to the finishing department in
large identical unit batches. The finishing department paints and fin
ishes the metal parts and groups them into kits, each kit containing
the parts necessary to assemble one folding chair. The kits are trans
fered to the assembly department in 200 kit batches. The assembly
department assembles the folding chair kits in 200 chair joborders,
packages each assembled chair, and transfers them to the product inven
tory department.
Metal Folding Chair Assembly Department
Employees
The assembly department employs one operational control manager
(yourself), one assembly supervisor, fifteen fulltime assembly workers,
and two packagers. Two quality control inspectors work within the
178
assembly department; however, the costs associated with the inspectors
are not charged directly to the assembly department but rather to a
central product engineering and quality control department.
The assembly supervisor does not actually participate in the
physical assembly procedures but rather supervises the assembly workers,
the kit input to the assembly department, the packagers, and the
finished chair output to the product inventory department.
The operational control manager (yourself) has overall responsi
bility for assembly department product scheduling, physical assembly
process and procedures, and operational efficiency.
Physical Process
Five years ago the folding chair assembly was accomplished using
an assemblyline type process in which each assembly worker performed
a specific, repetitive task. An experiment at that time, however,
demonstrated that greater worker efficiency could be obtained if each
worker was responsible for the complete assembly of a chair. The
result was that the average time required to assemble a chair decreased
substantially. Consequently, the current assembly process consists
of each assembly worker assembling a complete chair from a kit. The
single largest drawback of this method is that the assembly line goes
outofcontrol much more often than when the previous assembly method
was utilized. It is believed, however, that the net benefits of the
current method outweights this drawback.
The folding chair assembly is accomplished following a predeter
mined assembly sequence. After assembling a folding chair the worker
places it on a conveyor belt which takes it to the quality control
inspectors. The onsight quality control process consists of a visual
inspection of the chair's finish and a manual inspection of the chair's
operation. A chair which passes this inspection is folded and placed
on a conveyor belt which takes it to the packagers. A chair which
does not pass this inspection is placed in a rework area with a form
indicating the reason for inspection failure. Periodically the central
quality control department randomly selects a sample of finished chairs
and performs additional structural tests. The packagers box each folded
chair in a corregated cardboard carton, stamping the carton with the
color of the chair.
Accounting Control System
AMSECO utilizes a joborder costing and control system employing
standard costs. The folding chair is manufactured and controlled in
200 chair lots. The assembly department is charged only with material
usage and labor efficiency variances, not with material price and wage
rate variances. In the past the material usage variance has been
negligable and the operating performance of the assembly department has
been completely determined by the labor efficiency of the workers.
Engineering estimates and historical statistical studies have been
used to set the labor efficiency standards within the assembly depart
ment. These labor efficiency standards are reasonably attainable and
allow for unavoidable labor inefficiencies and reasonable variation in
worker performance. As the operational control manager you should
accept the labor efficiency standards in terms of fair control and
performance goals.
179
The
ment are:
per chair labor efficiency standards for the assembly depart
Process
Standard Time Allowed
Assembly
Packaging
Total
The variance report contains
variance is reported only in
33
3
minutes
minutes
36 minutes
only the labor efficiency variance and this
aggregate form (i.e., the variance is not
broken into the assembly and packaging processes). The accounting
system is computerized and the variance report is in computer output
format.
The physical labor process of the assembly department can be in one
of two states; either incontrol or outofcontrol. The overall labor
process is made up of many individual physical labor procedures; the
expected aggregate of these procedures is represented by the labor
efficiency standard of 36 minutes per chair. The department is defined
to be incontrol when all of these physical procedures are performed as
expected. The department is defined to be outofcontrol when one or
more of these physical labor procedures are not performed as expected.
Since the overall labor process is made up of many individual labor
procedures it is possible for the department to be outofcontrol while
at the same time the reported labor efficiency variance is small. This
would occur when some of the individual procedures are performed at
higher than normal efficiency while other procedures are performed at
subnormal (outofcontrol) efficiency. It is also possible for the
department to be incontrol while at the same time the reported labor
efficiency variance is large. This would occur when most of the indivd
ual procedures are performed at slightly below normal efficiency. These
examples represent two extreme situations; the probability of occurance
for either of them is low when compared to the probabilities of occur
ance for situations that fall inbetween the two extreme examples.
Your Task as the Operational Control Manager
Based upon the variance report you must decide whether to investi
gate the particular labor efficiency variance for its underlying causes.
The purpose of investigation is to facilitate correcting those indivdual
labor procedures which are not operating as expected. The following
assumptions will aid your decision making:
1) If you decide to investigate the variance and the assembly
department turns out to be outofcontrol, the department will
be returned to the original incontrol state with certainty.
2) If you decide not to investigate the variance and the assembly
department turns out to be outofcontrol, the department will
remain outofcontrol with certainty.
There are certain marginal costs associated with the variance
investigation decision. These costs depend upon your decision (either
investigate or do not investigate) and upon the actual state of the
assembly department (either incontrol or outofcontrol). These mar
ginal costs have the following values and relationships:
180
If Your
Investigation
Decision Is
And If The
Assembly Line
State Is
Then
Your
Costs
Are
Investigation
Production
Total
Investigate
Incontrol
$
X
5
0.00
$ X
Investigate
Outofcontrol
$
Y
$
0.00
$ Y
Not Investigate
Incontrol
$
0.00
$
0.00
$ 0.00
Not Investigate
Outofcontrol
$
0.00
$ 175.00
$ 175.00
X represents the variable investigation cost when the assembly
line is incontrol. This investigation cost is related to the size of
the labor efficiency variance; the more negative (unfavorable) the
variance the larger this cost.
Y represents the variable investigation cost when the assembly
line is outofcontrol. This investigation cost also is related to the
size of the labor efficiency variance; the more negative (unfavorable)
the variance the smaller this cost. For any given labor efficiency
variance the investigation cost if the assembly line is outofcontrol,
Y, is always larger than the investigation cost if the assembly line is
incontrol, X. This is due to the investigation cost including the cost
of correcting the outofcontrol labor procedure when the assembly line
is outofcontrol.
The $175.00 cost is the expected marginal production cost of
operating next period in the outofcontrol state. This cost is fixed
for all labor efficiency variance reports.
Your Performance Measure as the Operational Control Manager
The metal folding chair section manager (your immediate supervisor)
evaluates your control performance in terms of the minimization of both
investigation costs and production costs above the expected standard
(the total of these costs is called the Total Investigation Decisions
Cost). The extent to which your investigation decisions for a specified
period of time minimizes the Total Investigation Decision Cost is
determined as follows:
Extent of Total Investigation
Decisions Cost Minimization =
Total Investigation Decisions Cost of your decisions
Optimal Total Investigation Decisions Cost
The optimal Total Investigation Decisions Cost is determined by the
section manager at the end of the specified period of time. It i_s_
possible for your Total Investigation Decisions Cost to equal the optimal
Total Investigation Decisions Cost. AMSECO pays a cash bonus based on
this measure of the extent of Total Investigation Decisions Cost
minimization. The closer this measure is to one or less than one the
larger the cash bonus.
The cash bonus for 100 labor efficiency variance reports is as
fol 1ows:
Extent of Total Investi
gation Decisions Cost
Minimization
1.000
1.001 to 1.010
1.011 to 1.020
Cash Bonus
$ 10.00
9.00
8.00
181
Extent of Total Investi
gation Decisions Cost
Minimization Cash Bonus
1.021
to
1.030
$ 7.00
1.031
to
1.040
6.00
1.041
to
1.050
5.00
1.051
to
1.100
4.00
1.011
to
1.150
3.00
1.151
2.00
Again, I rely on your not discussing this experiment with other people.
APPENDIX D
PRIOR INFORMATION SHEETS
Experiment Condition (I1,S2,C2)
The following information is based upon past experience and past obser
vations of the assembly line. This information may be helpful to you
in making your decisions.
The portion of time the assembly line was:
Incontrol 60% Outofcontrol 40%
The actual minutes per chair for both states are assumed to be
normally distributed.
The most favorable labor efficiency variance was:
10.0 minutes per chair (26.0 actual minutes incurred per chair)
The most unfavorable labor efficiency variance was:
19.0 minutes per chair (55.0 actual minutes incurred per chair)
The maximum investigation cost when the assembly line state was:
Incontrol $ 98.75 Outofcontrol $ 154.17
The minimum investigation cost when the assembly line state was:
Incontrol $ 62.50 0utofcontro $ 142.08
Experiment Condition (I2,S2,C2)
The following information is based upon past experience and past obser
vations of the assembly line. This information may be helpful to you
in making your decisions.
182
183
The two assembly line state means, in minutes per chair, are:
Incontrol 36.0 Outofcontrol 45.0
The two assembly line state standard deviations, in minutes per chair:
Incontorl 5.0 Outofcontrol 5.0
The portion of time the assembly line was:
Incontrol 60% Outofcontrol 40%
The actual minutes per chair for both states are normally distributed.
The most favorable labor efficiency variance was:
10.0 minutes per chair (26.0 actual minutes incurred per chair)
The most unfavorable labor efficiency variance was:
19.0 minutes per chair (55.0 actual minutes incurred per chair)
The maximum investigation cost when the assembly line state was:
Incontrol $ 98.75 Outofcontrol $ 154.17
The minimum investigation cost when the assembly line state was:
Incontrol $ 62.50 Outofcontrol $ 142.08
APPENDIX E
SUBJECT HEURISTIC ELICITATION QUESTIONNAIRE
Please answer the following questions in the spaces provided. This is
not a test; there are no right or wrong answers. It is important that
you try to answer each question honestly. If you need additional space
use the back of this questionnaire.
1. What rule or set of rules did you use to make your investigation
decisions in the last 50 labor efficiency variance reports (the
second block)? Please include any numerical values that were part
of this rule or set of rules.
2. Do you feel you used the same rule or set of rules as you gave in
question one to make your variance investigation decisions in the
first 50 labor efficiency variance reports (the first block)? If
not the same, how does the rule or set of rules used in the first
50 reports differ from what you gave in question one? Please in
clude any numerical values used as part of the rules or sets of
rules that were different.
3.Rate the following items of information in terms of their importance
to you in making your variance investigation decisions (do not rate
the 'other' item unless you have specified some other information).
Circle the appropriate response.
A.The background information booklet.
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k
*
Not Slightly
Important Important
Moderately Very
Important Important
Extremely
Important
B. The information based upon past experience and past observation
which was presented in the front of each experiment booklet.
k'kk'kk'kkk'kk'kkkkic'k'k'kkkk'kk'k'kkk'k'k'k'k'k'kk'kkkkk'k'kk'k'k'k'kk'k'k
* * *
Not Slightly Moderately Very Extremely
Important Important Important Important Important
C. The standard minutes allowed per chair.
**************************************************
k
k
*
*
*
Not Slightly
Important Important
Moderately Very
Important Important
Extremely
Important
184
185
D. The actual minutes incurred per chair.
*************************************************
*****
Not Slightly Moderately Very Extremely
Important Important Important Important Important
E. The labor efficiency variance per chair.
*************************************************
*****
Not Slightly Moderately Very Extremely
Important Important Important Important Important
F. The total chairs produced.
*************************************************
Not Slightly Moderately Very Extremely
Important Important Important Important Important
G.The costs associated with investigation decisions.
k'k'k'kk'k'k'k'k'k'kk'k'kirk'k'k'k'k'k'k'k'k'k'k'k'k'k'k'k'k'k'kk'k'k'k'kic'k'k'k'k'k'k'k'k'k
* * *
Not Slightly Moderately Very Extremely
Important Important Important Important Important
H.Other (please specify) .
*************************************************
*****
Not Slightly Moderately Very Extremely
Important Important Important Important Important
4. Recalling the training phase which you completed at an earlier time,
what rule or set of rules did you use to make your variance investi
gation decisions in the first 33 variance reports? Please include
any numerical values that were part of this rule or set of rules.
5. How did you initially form the rule or set of rules you gave in
question four? What lead you to use this particular rule or set of
rules?
6. If the rule or set of rules you gave in question four is different
from the rule or set of rules you gave in question one, what caused
you to make the change?
7. What other information items, if presented, do you feel would im
prove you variance investigation decisions?
8. Were your decisions influenced by discussions with other partici
pants in this experiment?
APPENDIX F
SUBJECT MOTIVATION QUESTIONNAIRE
Please respond to the following questions by circling the appropriate
response. This is not a test; there are no right or wrong answers. It
is important that you try to answer each question honestly.
1. Suppose this same experiment was to be repeated on a number of future
occasions. How many more times would you be prepared to participate
in the experiment under circumstances similar to the present?
kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
*
*
* *
*
No 1
2
3
4 5
More
More More
More
More
More More
Than 5
Times Time
Ti mes
Times
Times Times
Times
Did your desire to perform well
in undertaking a challenging
task
cause you to try
very hard?
******************************* ******************************
k
* *
*
Definitely Yes
Probably
Don t
Probably No
Definitely
Yes
Yes
Know
No
No
Did you enjoy making the decisions requi
red in the experiment?
***************************************** ********************
* *
k
*
* *
*
Definitely No
Probably
Don' t
Probably Yes
Definitely
No
No
Know
Yes
Yes
Are you satisfied
with your performance
in the experiment?
*************************************************************
k k
*
*
k k
*
Definitely Yes
Probably
Don't
Probably No
Definitely
Yes
Yes
Know
No
No
Do you think you
would have tried less hard if the money rewards had
been halved?
*************************************************************
* *
*
*
* *
*
Definitely No
Probably
Don't
Probably Yes
Definitely
No
No
Know
Yes
Yes
186
187
6. Did you feel tense during the experiment?
kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
k k k k k k k
Definitely Yes Probably Don't Probably No Definitely
Yes Yes Know No No
7. Did your desire to cooperate with the experimenter cause you to try
very hard?
*************************************************************
*******
Definitely No Probably Don't Probably Yes Definitely
No No Know Yes Yes
8. Do you think the money rewards caused you to try harder than you
would have without them?
kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
k k k k k k k
Definitely Yes Probably Don't Probably No Definitely
Yes Yes Know No No
9. Did you enjoy the overall experience of participating in the
experiment?
kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
k k k k k k k
Definitely No Probably Don't Probably Yes Definitely
No No Know Yes Yes
10.Did your desire to contribute to research knowledge cause you to try
very hard?
kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
k k k k k k k
Definitely Yes Probably Don't Probably No Definitely
Yes Yes Know No No
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BIOGRAPHICAL SKETCH
Clifton Edward Brown was born on May 17, 1947,in Kansas City,
Kansas. In June 1965 he graduated from St. Petersburg High School,
St. Petersburg, Florida. Mr. Brown enlisted in the United States Air
Force from 1965 through 1968, and was honorably separated with the
rank of Sergeant. He was married in December 1968 to Sandra Cornell.
Mr. Brown attended the University of South Florida from which
he graduated in June 1971 with a Bachelor of Arts degree majoring in
accounting. From graduation until 1974 Mr. Brown was employed as an
industrial accountant, during which time he held positions of con
troller and vicepresident of finance. From September 1974 until
the present Mr. Brown has attended the University of Florida where
he has studied for a Doctor of Philosophy degree in business admin
istration (accounting).
While attending the University of Florida, Mr. Brown was the
recipent of the Price Waterhouse Foundation Grant, the American
Accounting Association Fellowship Grant, and the Haskin and Sells
Fellowship Grant. Mr. Brown was elected by the faculty as the
university representative to the 1975 American Accounting Association
Doctoral Consortium.
193
I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Philosophy.
A. Rashad Abdelffhali k, Chairman
Professor of Accounting
I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Philosophy.
Gary L. irHstrum
Associate Professor of Accounting
I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Philosophy.
Dougla<Â£^ T. STiowLalT1^"
Assistant Professor of Accounting
I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Philosophy.
Richard A. Griggs ^ //
Assistant Professor of Psychology
This dissertation was submitted to the Graduate Faculty of the Department
of Accounting in the College of Business Administration and to the
Graduate Council, and was accepted as partial fulfillment of the require
ments for the degree of Doctor of Philosophy.
August 1978
Dean, Graduate School
47
P(xout) P(in) V1r>N Vin>Y
Equation 1
where x = the actual minutes incurred per chair;
out = the state of outofcontrol;
in
= the state of incontrol;
Vin,N = ^e cost associated with not investigating an incontrol
variance;
VÂ¡nsy = the cost associated with investigating an incontrol
variance;
V0Ut,Y = the cost associated with investigating an outofcontrol
variance; and
Vout,N = the cost associated with not investigating and
outofcontrol variance.
The manipulation of the cost variable produces a constant cost ratio
at each of the two levels of this variable. When the cost variable
level is Cl the constant cost ratio is equal to onethird, and when the
cost variable level is C2 the constant cost ratio is equal to three.
The optimal model investigation decision rule expressed in equation 1
can be restate as:
LRq > PR.j CR Equation 2
where LRQ = the likelihood odds of the reported labor efficiency
variance being outofcontrol (P(xout)/P(xin));
PR.j = the prior odds of the incontrol state (P(in)/P(out)); and
CR = the constant cost ratio associated with the appropriate
level of the cost variable.
Assuming that both states of nature are normally distributed with equal
variance the .optimal model investigation decision rule expressed in
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INGEST IEID EPUS0L2VE_2XQ0O6 INGEST_TIME 20141013T21:10:20Z PACKAGE AA00025728_00001
AGREEMENT_INFO ACCOUNT UF PROJECT UFDC
FILES
142
Duncan's multiple range test was used to determine if the sub
jects' mean GPAs differed between the eight independent variables con
ditions. Given a completely betweensubject assignment these eight
groups represented the results of the experimental sampling plan. The
results indicate that there were not significant differences (p=.01)
between the mean GPAs of these eight groups. Bartlett's test of
homogeneity of variance was used to test the variances of the GPAs for
these eight groups. The results indicate that the hypothesis of homo
geneity of variance could not be rejected (x2=7.6906 with 7 d.f., p<.50).
Overall, the results indicate that betweengroup variance should
not be affected significantly by differences between subjects' GPAs.
This does not indicate, however, that withingroup variance will not be
affected by the differences between GPAs. The possible effects of
withingroup GPA variance were presented above.
Subject Motivations
The objective underlying the analysis of subject motivations was
similar to that relating to subject GPAs: i.e., the analysis was
performed to determine whether motivations varied between groups of
subjects. The initial stage of this analysis consisted of reducing the
ten motivation scale items into several motivation factors using the
technique of factor analysis. The subjects' responses to the ten
motivation scale items were factor analyzed using a varimax rotation for
a four factor solution. The four factor solution was chosen on the
basis of prior beliefs concerning the general dimensions (or sources)
of subject motivations: intrinsic motivation, extrinsic nonmonetary
motivation, extrinsic monetary motivation, and general motivation not
145
Duncan's multiple range test was used to determine if the subjects'
motivations differed between the eight independent variables conditions.
The results indicate that there were no significant differences (p=.01)
in the mean motivation factors between the eight groups. Bartlett's
test of homogeneity of variance was used to test the variances of the
three motivation factors for these eight groups. The results indicate
that the hypothesis of homogeneity of variance could not be rejected
for the second and third motivation factors (x2=10.9095 with 7 d.f.,
p<.25; x2=l.796 with 7 d.f., pc.95). The hypothesis of homogeneity
of variance could be rejected at the pc.10 level for the first moti
vation factor (x2=l2.7829 with 7 d.f.).
Overall, the results indicate that betweengroup variance should
not be affected significantly by differences between the subjects'
motivations. Again, the analysis does not indicate that withingroup
variance will be unaffected by the differences between subjects'
motivations: these possible effects were presented above.
153
relative frequencies of the two states and the relative decision error
costs), nor is it sensitive to the effects of various individual attri
butes (e.g., the individual's variable response range). Possible
effects of the situation variables on these aspects of the adjustment
process are examined in greater detail ( with measures of greater
sensitivity) in later sections relating to the individual decision
criteria and the individual decision model sensitivity.
Individual Decision Model Sensitivity
Individual decision model sensitivity was measured employing a
parameter of the TSD model, d'. This parameter measures the decision
sensitivity of the individual's model relative to a theoretical sensi
tivity. The factors which can affect an individual's decision model
sensitivity are 1) the use of a variable response range, and 2) the
use of multiple decision anchors. To isolate'these factors three
decision sensitivity measures were developed: 1) the DNj measure
(which is affected by both factors), 2) the DNAj measure (which is
affected by only the variable response range), and 3) the DNAjDNj
measure (which is affected by only the multiple decision anchor).
The variable response range (the DNAj measure) was affected
primarily by the cost variable. That is, those subjects within the C2
cost level demonstrated larger variable response ranges than did those
subjects within the Cl cost level. The locations of the optimal de
cision values and the subjects' final decision anchors were closer to
the standard for the Cl cost level than for the C2 cost level (see
Figure 2, Chapter III). The subjective adjustment limit concept offered
114
Hypothesis 2.3b predicted that (DNADNCl) would be significantly
smaller than (DNADNC2). A Z test was employed to test this hypothesis
and the difference was found to be not significant (Z=0.8642, n.s.
onetailed).
Intrinsic Motivation and Decision Model Sensitivity
The model comparison procedure indicated that the intrinsic moti
vation factor had a significant effect on each of the four individual
decision model sensitivity dependent variables.1 The analysis of these
effects requires additional tests. A productmoment correlation was
computed for the intrinsic motivation factor with each of the four
dependent variables. The results of these correlations are presented
in Table* 13. The weakness of these correlations can be expected given
the relatively low explained variance associated with the various in
trinsic motivation measure sums of squares.
The positive association between intrinsic motivation and the d'
i
measure indicate that as the d1 measure increased in size (approached
i
the d1 measure or used a single cutoff value) the subjects' intrinsic
k
motivation measures increase. This result was confirmed by the posi
tive association between the motivation factor and the DN. measure.
As either the variable response range decreases or the use of multiple
cutoff values decreases (both are associated with increases in the DN
measure) the subjects' intrinsic motivation measures increase. The
substantial drop in the level of significance for the association be
tween the intrinsic motivation and the DNA^ measures would suggest that
^ee the last section in this chapter for a definition and for additional
analyses concerning the intrinsic motivation factor.
123
TABLE 17
PARAMETER VALUES ASSOCIATED WITH THE REDUCED BNCÂ¡ MODEL
F Values Associated With the
Sources of the Reduced Model
Source
d.f.
F
Cost
1,80
6.88b
Intrinsic motivation
1,80
2.61
Extrinsic (nonmonetary)
motivation
1,80
0.04
Extrinsic (monetary)
motivation
1,80
7.79a
GPA
1,80
4.90b
Means and Variances
Associated With the Significant Sources
Variable Level N
Mean
Variance
Cl
47
1.0046
C2
39
0.6147
0.96602
V.Ol
bp<.05
0.10326
156
experimental environment is closer to that of a testtaking situation
which is a more natural environment for a student than for a manager.
Implications For Accounting
Two general implications of this research for accounting are
discussed. These implications are the value of additional information,
and the general standard setting process.
Value of Additional Information
The manipulation of the information variable involved the quan
tity of information contained in the available information set, specifi
cally the presence or absence of various distribution information items.
The results indicated that the information variable did not have a
significant effect upon the individual relative decision costs. The
mean relative decision cost of subjects within the reduced information
level did not differ significantly from that of subjects within the
expanded information level. An implication of this result concerns the
net benefit (for the company) of providing the additional information
within the expanded information level. The additional information
within an actual environment is not costless. Consequently, if such
information is to be provided, other things being equal, the net
benefit of such an action should be positive. In this context, the net
benefit would be the marginal decision cost savings resulting from
improved manager variance investigation decisions (given the additional
information) less the costs associated with the production and dis
semination of the additional information. Within this research the lack
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TABLE 4 Continued
Hypotheses
Analysis Test
Method(s) Statistic(s)
4.3 [(G^S1,C1)(GÂ¡S2,C1)] Model comparison F=7.7124 (pc.Ol)
range test
4.4a (GPi/GNj  Cl) <
Sources F value F=0.35 (n.s.)
(GP Â¡/GN  C2)
Onetailed Z test Z=2.0198 (p<.01)
4.4b [ (GP j/GN j  C2, S2) 
Model comparison F=8.7438 (pc.Ol)
(GPi/GNiCl,S2)J
Duncan's multiple (a=.01)
<[(GPj/GNj C2,S1)
range test
(GPj/GNj  Cl, SI)]
4.5 (GjPP)<(GjMM) Onetailed t test
t=l.303 (p<.10)
Results
There was a significant cost by distribu
tion interaction on the relative net
decision costs in which the C2 cost level
had a greater effect than the Cl cost
level while the distribution levels had
little effect.
The mean relative additional decision
costs and relative additional decision
savings ratio of those subjects within
the C2 cost level was larger than that
of those subjects within the Cl cost
level.
There was a significant cost by distribu
tion interaction on the relative addi
tional decision costs and relative addi
tional decision savings ratio in which
the cost levels had a significant effect
only when given the SI distribution level.
The mean relative net decision costs of
those subjects within the constantly
decreasing training session performances
category was significantly smaller than
that of those subjects within the con
stantly increasing training session per
formances category.
5
deciding on the information set to be presented within the cost variance
report have been advocated. First, the accountant can determine those
models which managers use in making variance investigation decisions and
provide an information set which would permit the implementation of
these models. Problems with this individual model approach are 1) the
possibility exists that different individual models use a wide range of
different information sets (which are not costless), and 2) the indi
vidual models may not be optimal (i.e., they could be inefficient with
respect to other decision models). The individual model approach cen
ters on the psychological question of what the manager is doing (or
would do) with the available (or additional) information. Second, the
accountant could create a normative model of the variance investigation
decision and provide the information required by that model. Problems
with the normative model approach are 1) the information provided may
not optimize the individual's investigation decisions (which may con
tinue to be made using the individual's model), and 2) the costs
associated with operationalization of the normative model may exceed
the benefits (cost savings as a result of more optimal investigation
decisions and greater congruence of manager goals with overall organi
zation goals) offered by the model. The normative model approach
centers on the analytical question of what the manager should be doing
(or should do) with the available (or additional) information.
A substantial portion of the accounting variance investigation
literature has focused upon the second approach the normative model
approach.4 However, rarely has attention been given to how the manager
4This literature is reviewed within Chapter II.
152
An effect which differed from expectations was that the mean
initial decision anchor given the C2 cost level was significantly
larger than the mean initial decision anchor given the Cl level
(equality was predicted). A bias of the estimates of the initial de
cision anchors may explain this difference. It will be recalled that
the estimates had incorporated some initial adjustment (during the
first training session). Those subjects within the C2 cost level were
expected to adjust their initial decision anchors such that the value
(actual minutes incurred) increased: those subjects within the Cl cost
level were expected to adjust in the opposite direction. Initial ad
justment in the appropriate directions would produce the obtained cost
variable effect on the estimates of the initial decision anchors.
The second aspect of the anchoring and adjustment heuristic is
the adjustment process. The adjustment process was examined using a
measure of the individual's relative decision anchor adjustment. This
observation variable, RA, was the linear distance ever which the
individual adjusted his decision anchor (the distance between his final
decision anchor and the optimal decision value) relative to the linear
distance over which the individual should have adjusted his decision
anchor (the distance between his initial decision anchor and the
optimal decision value). The results of analysis indicated that the
RA.j measure did not differ significantly between the levels of the
various situation variables. This was consistent with the predictions
from the training phase simulation. A major limitation of the measure
is that it is confined to the relative linear movement along the random
variable decision axis. As such the RAj measure is not sensitive to
the learning of various situation parameters and variables (e.g., the
4
labeled a heuristic. Within this context, heuristic refers to the
learned set of rules or principles that are utilized by the individual
in making the particular decisions required of him. The manager's
specific heuristic can be affected substantially by his individual
characteristics (e.g., intelligence, cognitive complexity and style,
decision process sensitivity, motivations, etc.). However, a general
heuristic, labeled anchoring and adjustment (Tversky and Kahnemar,
1974), is expected to describe the general form of the manager's vari
ance investigation decision process.3
The above examination of the manager's variance investigation
decision process indicates that the variables which can affect both
the process and the results of the process include 1) the structure
of the decision situation, 2) the contents of the available information
set, 3) the manager's information processing efficiency, and 4) the
manager's learning efficiency (from his experiences with the controlled
process). The manager's knowledge concerning the structure of the
decision situation may be limited by the contents of the available
information set and by his information processing and learning effici
encies. The accountant, to a large extent, has control over the con
tents of the available information set.
The Research Objectives
Many problems in accounting reduce to one of choosing among
alternative information sets that could be provided to a decision maker
(American Accounting Association, 1972). Two basic approaches to
3The anchoring and adjustment heuristic is described in greater detail
within Chapter II.
170
efficiency (decision criteria conservatism) is a characteristic of only
training (learning) situations or remains a characteristic of
welltrained (nonlearning) situations.
Extensions involving the experimental environment could take
several forms. First, they may relate to the levels of the situation
variables. Only certain situation variables were studied within this
research: extensions could study the affects of manipulating different
situation variables at different levels (e.g., more than two states of
nature, nonnormal distributions, variable relative frequencies, vari
able decision cost functions, multiple standard cost variances).
Second, extensions could be made in relation to the experimental
task itself. Within this research the decision task was of a
crosssectional nature: i.e., each decision trial was independent of
the previous decision trials. An extension could involve a decision
task of a timeseries nature: i.e., where each decision trial would
be dependent on the previous decision trials. Such a decision task
extension would involve sequential decision making, where the physical
process is initially in some state of nature and over a series of
decision trials (variance reports) the process may or may not move to
another state of nature. The task of the decision maker would be to
detect the point in time in which the physical process shifts to an
other state.
The third and final extension discussed within this section con
cerns the nature of the experimental decisions. Within this research
the decisions involved the detection of state of nature for the purpose
of physical process control within cost minimization constraints. Be
yond the operational control level, standard variance reports can be
CHAPTER VI
DISCUSSION OF RESULTS
Conceptual Development Revisited
A brief restatement of the conceptual development presented in
Chapter III will serve as a basis for discussion of the results that
were reported in Chapter V. It was posited that an individual's
longrun decision efficiency would be affected by 1) the structure
of the particular decision situation, 2) the contents of the available
information set, 3) the individual's efficiency in processing the
available information, and 4) the individual's ability to expand the
available information through experience with the particular situation.
A general heuristic was expected to systematically account for a por
tion of the decision behavior of the subjects. A heuristic, within
this context, refers to a learned set of rules or principles which are
utilized by individuals in making the particular decisions required of
them. Since a heuristic is a learned response to some decision task
stimuli, individual characteristics can have considerable effect upon
the specific form of the heuristic. The conceptual development, how
ever, proposed that specific decision task stimuli would give rise to
general forms of heuristics which would be (at least partially) in
dependent of individual characteristies.
The decision task utilized in this research required the discrim
ination between the uncertain existence of two states of nature
146
42
BNC.J approaches either positive infinity or a value of one as the extent
of conservatism increases, approaches a value of zero as the extent of
conservatism decreases, and approaches either a value of negative one
or negative infinity as the extent of nonconservatism increases.
Initial and final decision anchor variables
The relative relationships between an individual's initial and
final decision anchors can be measured in terms of the relative linear
distance between these two decision anchors. A measure of the relative
adjustment for individual i, labeled RA^, is conditional on the direc
tion of adjustment along the relevant decision axis. This direction
of adjustment is in turn conditional on the level of the cost variable.
Given the Cl cost level the initial decision anchor is greater than the
optimal model cutoff value, and given the C2 cost level the initial
decision anchor is less than the optimal model cutoff value. The RA.
measure is defined as:
RAi = (EDVi 0DVk) / (TDVi 0DVk) ,given the Cl cost level;
RA.. = (0DVk EDV.Â¡) / (0DVk TDV.Â¡) ,given the C2 cost level,
where EDV^ = individual i's final (experiment) decision anchor;
TDV. = individual i's initial (training) decision anchor; and
0DVk = optimal model k's final (experiment) cutoff value.
The RA.j measure has the following relationships: 1) if no
adjustment occurs between the training and the experiment phases the
measure will equal a value of one, 2) if the adjustment is in the wrong
direction (away from the optimal model's experiment cutoff value) the
measure will be greater than a value of one, 3) if the adjustment is in
120
(px.01) for each of the situation variables found to be significant by
the above sources F values. The sole exception was the distribution
variable within the BN^ measure. The means of the distribution levels
within the BHj variable do not differ significantly (Z=0.8830, n.s.
twotailed).
Hypothesis 3.1a predicted that (BNA^jll) would not differ signifi
cantly from (BNA112). A Z test indicated that the difference was not
significant (Z=0.7891, n.s. twotailed). Hypothesis 3.1b predicted that
(BNA.SI) would not differ significantly from (BNA^S2). Again, a
Z test showed the difference to be not significant (Z=0.7048, n.s.
twotailed). Hypothesis 3.1c predicted that (BNACl) would be signifi
cantly larger than (BNAC2). The model comparison procedure and Z test
indicated a significant difference at the p<.01 level, with the Cl level
having the larger mean. Hypothesis 3.2 predicted that the variance of
(BNA. Cl) would be significantly smaller than the variance of (BNA1C2).
The F test of equal variances resulted in a finding of a significant
difference at the p<.01 level, with the C2 level having the smaller
variance.
Hypothesis 3.4a predicted that (BNA^BN^S1) would be signifi
cantly smaller than (BNABN.Â¡ S2). A Z test of equal means indicated
that the difference was not significant (Z=0.3892, n.s. onetailed):
however, the obtained direction is the same as the predicted direction
of this effect. Hypothesis 3.4b predicted that (BNAiBNiÂ¡C1) would be
significantly smaller than (BNA BN .jC2). The model comparison pro
cedure and Z test indicated that the difference was significant at the
p<01 level, with the C2 level having the smaller mean.
84
Assignment of Subjects to Treatment Conditions
Since each of the 86 subjects was assigned to one of eight groups,
randomization per se can not be relied upon to control for individual
attribute differences between groups. An alternative is to block the
randomization process onindi vidual attribute dimensions assumed to
significantly affect the subject's information processing within the
task required by the experiment.
The literature relating to individual attributes has not pro
duced conclusive results concerning individual attributes effect on
decision processes. The most comprehensive study is that of Taylor and
Dunnette (1974) who analyzed the effect of 16 decision maker attributes
upon eight measures of predecisional, decisionpoint, and post decisional
processes within a personnel decision simulation experiment. Of con
siderable interest is that the relevant decision maker attributes
accounted for a generally small portion of the total decision variance
within the various processes (the range was from 8 percent to 33 percent).
The decision maker attribute with the greatest predictive capacity was
intelligence. In two of the predecisional processes (diagonosticity
and information processing rate) and in two of the decisionpoint and
post decisional processes (information retention and decision accuracy),
intelligence accounted for from onehalf to almost all of the variance
explained by individual characteristics.
In the present study, the randomization process of assigning
subjects to treatment conditions was blocked on individual intelligence.
Ideally, individual intelligence should be measured using some validated
instrument (e.g., the Wesman Personnel Classification Test or the
CHAPTER I
INTRODUCTION AND OBJECTIVES
Business organizations generate internal accounting reports for
use by managers for purposes of evaluation and decision making.1 One
important evaluation process and decision task of management is that of
standard cost variance analysis and investigation. Based in part upon
standard cost variance reports generated by the internal accounting
system, managers estimate the likelihoods that various production pro
cesses remain under control and decide whether to investigate particu
lar variances. The present study concerns the effects of selected
variables on standard cost variance investigation decision making by
operational control managers. This chapter discusses general aspects
of the standard cost variance investigation decision process and the
variables which may affect the process and presents the research
objectives of the study.
Variance Investigation Decision Processes
An important factor in the evaluation and use of standard cost
variance reports is the manager's perception of the validity of the
xThe American Accounting Association 1966 Statement of Basic Accounting
Theory states that "the objective of accounting for internal use is to
provide information to persons within an organization that enables them
to make informed judgements and effective decisions which further the
organization's goals" (p. 38).
1
156
anchors adversely affects decision performance (as reported by the feed
back measure used in this research), their increased use would affect
intrinsic motivation through the performance satisfaction modifier.
Greater use of multiple decision anchors would result in poorer decision
performance feedback measures: these measures, in turn, could lower
the subject's decision performance satisfaction and thereby lower his
intrinsic motivation. As the variable response range increases in
relative size the number of decisions falling within this range increases.
This requires more guesswork or more complicated decision rules on the
part of the subjects, thereby increasing the difficulty (or the uncer
tainty) of the decision task. This increased task difficulty could
affect the subject's enjoyment of the decision task and thus lower his
intrinsic motivation.
Individual Decision Criteria
Individual decision criteria were measured using a parameter of
the TSD model, 6. This parameter measures the decision criteria used
by the individual independent of his decision model sensitivity. The
factors which can affect an individual's decision criteria are 1) the
individual's efficiency in processing (or learning) the effects of the
states' relative frequencies and the relative decision error costs,
and 2) the use of multiple decision anchors. To isolate these factors
several decision criteria measures were developed: 1) the BN measure
(which is affected by both factors), 2) the BNA.Â¡ and BNC^ measures
(which are affected by only the individual's information processing
efficiency), and 3) the BNA^BM^ measure (which is affected by multiple
decision anchors).
80
benefits that would accrue to those who volunteered. The subjects were
told that they would be given a cash payment for participating in the
experiment and that the amount of a subject's payment would depend upon
his performance. The minimum payment was set at $2.00 and the maximum
at $10.00.
Experimental Materials
The experimental materials included a background information book
let, variance investigation decision stimuli, heuristics questionnaire,
and motivations questionnaire.
Background Information
A background information booklet (see Appendix C) was designed
to provide the subjects with a common experimental environment. The
booklet provided the subject with general company information, general
product information, general manufacturing process information, and
specific assembly department information. The specific assembly depart
ment information included information concerning the employees, the
physical process, the accounting control system, the subject's task as
the operational manager, and the subject's performance evaluation as
the operational manager.
Variance Investigation Decisions
Various information constant over all decision trials within a
treatment condition was presented on a separate page prior to the start
124
The Z test of equal means indicated that the means of the cost levels
differ significantly (Z=2.5598, p<.05 twotailed), with the C2 level
having the smaller mean.
Hypothesis 3.3a predicted that (BNC^I1) would not differ signifi
cantly from (BNC.Â¡ 112). A Z test indicated that the difference was not
significant (Z=0.6802, n.s. twotailed). Hypothesis 3.3b predicted that
(BNC^  SI) would not differ significantly from (BNC.jS2). Again, a Z
test indicated that the difference was not significant (Z=0.3295, n.s.
twotailed). Hypothesis 3.3c predicted that (BNC^Cl) would be signifi
cantly smaller than (BNC1C2). The model comparison procedure and Z
test indicated that the difference was significant at the p<.05 level,
with the C2 level having the smaller mean.
%
Individual Attributes and Decision Criteria

The model comparison procedure results indicated that the
extrinsic (monetary) motivation factor and the GPA^ variable had signifi
cant effects upon the various individual decision criteria variables.2
The analysis of these effects also required additional tests. A product
moment correlation was computed for the motivation factor and the GPA.Â¡
variable with each of the decision criteria dependent variables. The
results of these correlations are presented in Table 18.
The negative association between extrinsic (monetary) motivation
and the BN^ variable indicates that as the BNj measure increases the
subjects' extrinsic (monetary) motivation measures decrease. The BN^
2See the last sections in this chapter for definitions and for additional
analyses concerning the extrinsic (monetary) motivation factor and the
GPA.j variable.
36
1) the information variable, 2) the distribution variable, and 3) the
cost variable. As indicated previously, each is varied across two levels.
The individual process variable types, measured on a continuous scale,
include the following: 1) individual decision model sensitivity, 2)
individual decision criteria, and 3) relative initial and final decision
anchor relationships. The major dependent variable is the individual
longrun decision efficiency (in terms of incurred costs). The
following discussion describes each of the variables or variable types
as they are employed in this study.
The information variable
The first level, labeled Jl_, is derived from the set of infor
mation assumed to come from individual experience with the physical
system. It includes the following items: 1) the historically derived
portion of time in which the process has been found to fall in each of
the two states, 2) the assumption that the random variable of interest
(actual minutes incurred per chair or its associated standard vari
ance) is normally distributed for both states, 3) the lowest observed
value of the random variable, 4) the highest observed value of the
random variable, 5) the maximum costs associated with each state, and
6) the minimum costs associated with each state.
The second level of the information variable, labeled 12^, includes
additional distributional information. In addition to the six items
contained in the II information set, the following two items are
included: 1) the mean of the random variable within each state, and
2) the standard deviation of the random variable within each state.
79
decision costs incurred by the subject over the series of variance re
ports and dividing this sum by the sum of the total investigation de
cision costs incurred by an optimal model over the same series of vari
ance reports. The subject's cash bonus function, constant for all sub
jects, was inversely related to this measure. As TIDCmin approached
one, the payoff approached the maximum: as TIDCmin became larger than
one, the payoff approached the minimum.
Subjects
The subjects were 86 senior year undergraduate and master's level
graduate students enrolled in the business college at the University of
Florida. The subjects participated in the experiment during a two week
period; 47 participated in the first week and 39 participated in the
second week. A total of 92 subjects initially volunteered to participate
but six subjects failed to complete the experiment. The 86 subjects
who completed the experiment consisted of 63 males and 23 females.
Three subject selection criteria were applied: 1) the subject
must have completed an intermediatelevel managerial accounting course,
2) the subject must have completed an introductorylevel statistics
course, and 3) the subject must have earned an overall grade point
average (GPA) of at least 2.0 on a 4.0 scale.
Subjects initially were contacted within senior level and graduate
accounting classes. The contact was made by the experimenter giving a
brief oral presentation followed by passing signup sheets around each
class (a copy of the oral presentation is presented in Appendix B). To
motivate volunteering, the presentation focused on the student's pro
fessional responsibilities as future accountants and on the monetary
150
all subjects: the overall mean RA was 0.641 which indicated that the
subjects adjusted their decision anchors only 35.9 percent (1RA^) of
the distance between their initial decision anchors and the optimal
model's decision value. However, even given this level of overall deci
sion criteria conservatism, the mean relative decision costs (G.Â¡) over
all subjects was 0.06 (the average subject's total decision costs were
6.0 percent greater than the optimal model's total decision costs).
The deviation of the G.Â¡ measure from a value of zero has been
attributed to the following factors: 1) variable response ranges,
2) multiple decision anchors, and 3) decision criteria conservatism.
Although each factor was affected differently by various situation
variables and individual attributes, the overall effects of these
factors on the relative decision costs indicate their relative import
ance. The overall effect of variable response ranges was shown to be
negligible. Ignoring the variable response range effects, the overall
relative decision efficiency measure (G) can be adjusted into sub
measures which are affected by only one of the two remaining factors.
Eliminating the effects of multiple decision anchors, the adjusted
Gj measure was 0.045. This indicated that of the total deviation of
the subjects' decision costs from those of the optimal models, approxi
mately 75 percent was due to individual decision criteria conservatism
and approximately 25 percent was due to multiple decision anchors.
Discussion of Results
The following discussion of results is organized according to
decision processes and dependent variables. The discussion includes
decision anchor adjustment, individual decision model sensitivity,
163
subjects' GPAs and the relative decision costs: as the subjects' GPAs
increased either additional decision costs decreased or additional de
cision savings increased. It would be reasonable to assume that both
the DN. measure and the subject GPA are modifying variables on a sub
ject's decision cost performance. Since pretests were not administered
it was not possible to state the specific relations between the moti
vation factors and a subject's relative decision cost performance.
Li mitations
The major limitations of this study involve two general aspects;
the subjects employed within the experiment and the experimental
environment itself. Although these aspects are discussed separately,
in many instances the limitations they impose overlap one another.
These limitations affect primarily the external validity of this study.
There are several possible limitations involving the experimental
environment. First, the precision of subject performance feedback
during the training phase could be a limitation. The results obtained
in this study might be modified substantially if such accurate feedback
was not employed. The lack of decision performance difference between
the levels of the available information variable could be a direct
result of this feedback: i.e., the accuracy of the performance feedback
and its relationship with the optimal model may have replaced the need
for such additional statistical information.
Second, the background of the subjects in relation to the ex
perimental task is a possible limitation. Although the subjects re
ceived training in the experimental task, the primary source of their
knowledge concerning standard cost variance investigation could come
9
A conceptual framework of managerial decisions that synthesizes
these dimensions is presented by Gorry and Morton (1971). Simon's
dimension is modified by replacing the terms "programmed" and "non
programmed" with the terms "structured" and "unstructured" and adding
a third category labeled semistructured. Gorry and Morton's conceptual
framework is used in this research as a model that will permit the
identification of a standard cost system within the overall managerial
decision framework.
Management Task Planning and Control
Standard cost systems present information sets to managers to
support various decisions concerning task planning and control.
Demski (1967) provides an excellent conceptual discussion of the manage
ment task planning and control process, and his approach (with some
modification) is adopted in this research.
Figure 1 presents a model of the management task planning and
control process. Environmental information, largely external to the
standard cost system, facilitates the planning of overall goals and
policies within the constraints imposed by the environment. Overall
objective control feedback 1) provides evidence for the ccntinuted
validity of overall assumptions made in forming the organization ob
jectives, and 2) facilitates strategic planning evaluation of organi
zation performance.
Both the environmental variables and the overall task control
feedback of the management control activity 1) provide evidence for
the continued validity of the overall task assumptions made in forming
165
Finding the proposed concepts to hold within the experimental world
does not validate the claim that they hold within the real world. On
the other hand, finding the proposed concepts not to hold within the
experimental world would cast serious doubt on whether they hold in the
real world. Positive experimental results are a form of negative
assurance: the concepts have passed an initial empirical test and
remain (albeit somewhat scarred in most instances) potential candidates
for explaining real world phenomena.
The major limitation involving the subjects employed within this
research is that they were college students, predominantly accounting
majors. The obtained results and the conclusions based upon these
results are not generalizable beyond some unspecified population of
which the subjects are representative.
The selection of the type of subject to be employed within an
experiment is not independent of the experimental environment. Within
this research principal interest lies in the behavior of operational
managers within real world environments. However, given the selection
of the laboratory experimentation method (which implies the use of a
surrogate experimental environment), the selection of the type of
subject depends upon the answer to the question, "Which subject is a
better surrogate for a manager in a real world environment, a know
ledgeable student or the manager himself?" There are, of course, no
clear answers to this question. However, several aspects of the
situation favor the use of a knowledgeable student. First, the high
degree of abstraction within the experimental environment could have
made the task appear trivial to a manager and could have elicited
behavior not consistent with the principal situation. Second, the
APPENDIX B. ORAL PRESENTATION TO ELICITE VOLUNTEER SUBJECTS .... 174
APPENDIX C. BACKGROUND INFORMATION BOOKLET 176
APPENDIX D. PRIOR INFORMATION SHEETS 182
APPENDIX E. SUBJECT HEURISTIC ELICITATION QUESTIONNAIRE 184
APPENDIX F. SUBJECT MOTIVATION QUESTIONNAIRE 186
BIBLIOGRAPHY 188
BIOGRAPHICAL SKETCH 193
126
measure approaches infinity as the distance between the frj measure and
the 3^ measure increases. A similar association exists between the
motivation factor and the BNAj measure (the BNAj measure eliminates the
effects of multiple cutoff values). The BNA^BN^ variable, which
measures the effect of multiple cutoff values upon individual decision
criteria, has no significant association with the motivation factor. The
BNC.J variable, which measures the effect of conservatism upon individual
decision criteria, has a stronger negative association with the moti
vation factor. As the level of conservatism increases (BNC.Â¡ increases)
subjects' extrinsic (monetary) motivation measures decrease.
The negative association between the GPA.Â¡ variable and the BNA^BN^
variable indicates that as the BNABN.Â¡ variable increases the subjects'
GPA.Â¡s decrease (the BNA^BN^ measure increases as multiple cutoff value
usage increases). The negative association between the GPA.Â¡ variable
and the BNC.Â¡ variable indicates that as the subjects' GPA^s decrease
the level of conservatism increases.
Individual LongRun Decision Efficiency
The following sections report the analyses and results of the
individual longrun decision efficiency variables. The variables
include the relative decision costs, the relationships between individual
attributes and longrun decision efficiency, and the relationships be
tween longrun decision efficiency and training phase performance.
Relative Decision Costs
Hypotheses 4.1, 4.2, and 4.3 relate to the decision costs incurred
by the individuals relative to the decision costs incurred by the optimal
158
standard could have acted as an intervening variable which increased
the level of relative conservatism (reduced the level of relative infor
mation processing efficiency). As subjects' adjustments within the C2
cost level diverged from the standard no such subjective adjustment
limit existed, thus the level of relative conservatism could have de
creased.
The higher (statistical) moments of the BNC.Â¡ measure given the
levels of the cost variable were consistent with this concept of a
subjective adjustment limit. The difference in variances of the
BNC.j measure was due to the difference in the ranges of the measure.
The range of (BNC^C1) was 4.67 and the range of (BNC.Â¡C2) was 1.57.
The subjective adjustment limit should have had the effect of skewing
the measure away from those values which indicated lower levels of
conservatism. The third moment (as expressed by the coefficient of
skewness) of the BNC^ measure indicated that 1) the skewness of the
(BNC^. Â¡C1) distribution was positive (skewed away from values which
indicated lower levels of conservatism), and 2) the skewness of the
(BNC^C2) distribution was negative (skewed toward values which in
dicated lower levels of conservatism).
The multiple decision anchor (the BNABN^ measure) was affected
primarly by the cost variable. Those subjects within the Cl cost
level employed multiple decision anchors to a larger extent than did
those subjects within the C2 cost level. The hypotheses posited a
relation between multiple decision anchors and the distance separating
the lower tail of the incontrol state from the subject's final de
cision anchor. This relation was similar to that proposed in the
DNA^DN^ measure. However, the results did not support the hypotheses
164
from the college classroom. Consequently, if they were not taught
(within the classroom) that situations exist in which investigation
decision values are located relatively close to the standard, then
greater decision criteria conservatism within these situations could
be the result of the lack of such knowledge. This suggests the
possibility of an availability bias (Tversky and Kahneman, 1973).
However, to the extent a manager must learn from his own experiences,
such a bias could exist in the real world.
A final limitation (discussed within this section) involves the
selection of the levels of the situation variables. Several situation
variables were set at a single level, while others were manipulated at
multiple levels. The important point is that only specific combina
tions of variable levels were studied within this research whereas an
infinite number of combinations are possible. Different variable levels
and different variable manipulations would create a difference in the
experimental environment which could produce results other than those
obtained in this study.
These experimental environment limitations do not mean, however,
that the concepts proposed within this study have not passed a meaning
ful empirical test. The objectives of experiments involving theory
or concept testing, and the relations between the nature created in the
experiment and the nature existing in the world at large are discussed
by Zigler (1963):
What the experimenter is saying is that if such and such holds
in the real world because of the principles expounded in the
particular theory under investigation, then such and such should
hold in the world which the experimenter has created. This
transatability is what gives theoretical import to experiments
which involve phenomena which, taken in isolation, not only
appear picayune but seem to have little relationship with what
one observes in nature, (pp. 3534).
82
AMSECO
Metal Folding
Chair Assembly Department
Labor Efficiency
Variance Report
For Job 5247
Standard Minutes
Actual Minutes Labor
1 Efficiency
Total Chai
Allowed Per Chair
Incurred Per
Chair Variance Per Chair
Produced
36.0
44.0
8.0
200
The Costs Associated With Investigation Are:
If Your
And If The
Then
Your Costs
Are
Investigation
Assembly Line
kkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
Decision Is
State Is
Investigation
Production
Total
4rkkk kkkkkk
kkkkkkkkkkkkkk
A*********
kkkkkkk
kkkkkkkkkkkk
Yes
InControl
$ 28.33
$ 0.00
$ 28.33
Yes
0ut0fControl
$ 90.00
$ 0.00
$ 90.00
No
InControl
$ O.CO
$ 0.00
$ 0.00
No
0u0fControl
S 0.00
$ 175.00
$ 175.00
Please answer the following questions placing your answers on the answer sheet:
A. Would you investigate this reported variance /circle the appropriate
response on the answer sheet/
NO
YES
B. How strongly do you feel about your decision /select a number between
0 and 100 which indicates the strength of your feeling and place this
number on the answer sheet/
0 10
20
30
40 50 60
70
80
90
100
*
*
k k k
k
*
k
kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk
k k
k
k k k
k
k
k
k
k
k
Uncertain
Reasonably
Almost
Certain
Certain
FIGURE 6
VARIANCE INVESTIGATION DECISION TRIAL
27
possible decision outcomes. The parameters required to fit the optimal
model include 1) the relative frequencies of the two states of nature,
2) the mean of each state, 3) the statistical variance of each state,
and 4) the costs associated with each of the two possible decisions in
combination with each of the two possible states of nature. Since the
individual's decisions are to be evaluated by comparisons with the out
puts of the optimal model, it would seem reasonable that the information
available to the optimal model be the same as the information available
to the individual. Consequently, for decision situations in which some
of the parameters required by the optimal model are not contained in the
available information set the optimal model must make estimates of the
missing parameters.
Individual Ability to Expand the Information Set
The individual's ability to expand the available information set
over time refers to his ability to learn from his experiences with the
states of nature. Such learning can occur through improved estimates
of unknown items of distributional information and through modifications
of information processing strategies to incorporate state relationships
which were unknown or undetected previously.
The general form of the expected individual information processing
strategy is described by the heuristic of anchoring and adjustment
(Tversky and Kahneman, 1974).2 A natural starting point, or anchor, is
used as the first approximation for the decision. This anchor is then
2A heuristic, within this context, refers to a learned set of rules or
principles which are utilized by an individual in making the particular
decisions required of him.
The measure approaches either a value of zero or positive infinity as
the result of several factors: 1) the individual does not process
properly the effects of the relative costs of the two types of decision
errors, 2) the individual does not process properly the effects of the
relative frequencies of the two states, and 3) the individual uses more
than one cutoff value.
Using the same subjective analysis as that used in adjusting the
DN measure, the BN^ measure can be adjusted for the effects of multipl
cutoff values. Defining B^ to be the individual's criteria measure
with the effects of multiple cutoff values eliminated:
BNAi B* / Bk
The difference BNA^ BN^ approaches a value of zero as the effects of
multiple cutoff values decrease and becomes zero when the individual
uses a single cutoff value.
Another measure derived from the BN measure can be considered
a measure of individual conservatism. In this study, conservatism
refers to incomplete adjustment from an initial decision anchor towards
the optimal cutoff value. Nonconservatism refers to a more that
complete adjustment from the initial decision anchor past the optimal
cutoff value. The extent of conservatism is measured by the relative
distance between the individual's cutoff value and the optimal model's
cutoff value. Since the measure is dependent on the direction of ad
justment it is conditional upon the level of the cost variable. Using
BNC.Â¡ to denote tne extent of an individual's conservatism:
BNC.Â¡ = (3? B<) / B^ given the Cl cost level; and
BNC.j = (b^ Bj) / B^ given the C2 cost level.
APPENDIX B
ORAL PRESENTATION TO ELICITE VOLUNTEER SUBJECTS
I assume that most, if not all, of you intend to become account
ants. You should realize that accounting is more than an occupation,
it is a profession. As a member of a profession you have certain re
sponsibilities beyond those encountered in other occupations. One of
these responsibilities is the readiness to help expand the state of the
art, the level of knowledge, of the profession. Today you have an
opportunity to meet this responsibility. As part of my Ph.D. require
ments I am conducting an experiment which examines the impact upon
decision makers of certain accounting produced information. I am asking
that you volunteer to participate as subjects in this experiment.
The accounting information under study is the standard cost vari
ance report and, as a subject, you would be asked to assume the role of
an operations manager and to make certain decisions based upon standard
cost variance reports. After volunteering, greater detail concerning
the role and the required decisions will be provided in a short booklet
and in a training session.
Benefits to volunteering as a subject, beyond meeting part of your
professional responsibilities, include:
1) A chance to expand your knowledge of standard cost variance
reports and their utilization from the point of view of a
line manager.
174
60
between the DNA.Â¡ and DN^ measures. The two possible explanations in
clude the distribution variable and the cost variable.
Theoretically, the more extreme the optimal cutoff value relative
to a central point between the distributions of the two states, the
higher should be the individual conservatism. The decision situations
with the most extreme optimal cutoff values are those within the C2
cost level; the situations with the least extreme optimal cutoff values
are those within the Cl cost level. Consequently, the BNCj measure
should be larger within the C2 cost level than within the Cl cost level.
The simulation and assumptions used in developing the training
phase hypotheses can be extended to enable formation of hypotheses con
cerning the individual decision criteria. The earlier simulation de
rived the following final decision cutoff values: 1) 37.555 actual
minutes incurred for the Cl cost level given the 12 information level
and the SI distribution level, and 2) 39.755 actual minutes incurred
for the C2 cost level given the 12 information level and the SI dis
tribution level. Given these cutoff values a f(e^) measure can be
calculated directly (the measure is used rather than the 3. measure
due to the assumption of a single cutoff value). The functional
notation, f( ), is employed to indicate these estimates are based upon
a simulation rather than upon empirical observation. For this simu
lation the f(3?)s are as follows:
f(B*Cl,Sl,I2) = (zout) / <Â¡>(zin) = 0.70818
f(B*C2,Sl,I2) = (zQut) / *(zin) = 2.11774
where ( ) indicates the value of the normal density function at the
point in parentheses. Given the f(B^) measures the f(BNA^) measures
can be calculated. For this simulation the f(BNA^)s are as follows:
no
effects of multiple cutoff values on individual decision model sensi
tivity. Assuming random subject assignment to experimental situations,
the Dfij and DNA. variables should be independent of the effects of the
d' absolute magnitude differences (within the distribution variable).
The remaining effects are 1) the relative variable response range
within the DNA^ measure, and 2) the relative variable response range
and multiple cutoff value usage within the DN.Â¡ measure.
The methods of analysis were the model comparison procedures where
the dependent variables a"e the DN.Â¡, DNA^ and DNA^DN measures. The
independent variables are the three situation variables (with their
interactions), the three motivation factors, and the GPA.Â¡ variable.
The three full models are
DNi
DNAi u + j + 8k + ^ + 6jk + Tj! + + a6Yjk] + 5,
DNADN + t. + X. +6.
ill
The three model comparison procedure results are presented in
Table 11 and the F values associated with the sources of the reduced
models are presented in Table 12. The results indicate that the reduced
DN.. model contains the distribution variable, the cost variable, and
the intrinsic motivation factor. Both the distribution variable main
effect and the motivation factor are significant at the p<.01 level,
and the cost variable is significant at the p<.05 level. The reduced
DNA. model contains the cost variable and the intrinsic motivation
factor. The cost variable main effect is significant at the p<.01
level and the motivation factor is significant at the pc.10 level.
The reduced DNA..DN.. model contains the distribution variable and the
intrinsic motivation factor. The distribution variable main effect is
191
Simon, H. A. The New Science of Management Decision. New York: Harper
and Row, Inc., 1966.
Slovic, P. "From Shakepeare to Simon: Speculations and Some Evidence
About Man's Ability to Process Information." Oregon Research
Institute Bulletin, 12 (1972), 129.
, and Lichtenstein, S. "Comparison of Bayesian and Regression
Approaches to the Study of Information Processing in Judgement."
Organizational Behavior and Human Performance, 6 (1971),
649744.
Snowball, D., and Brown, C. "DecisionMaking Involving Sequential
Events: Some Effects of Disaggregated Data and Dispositions
Toward Risk." Unpublished manuscript, University of Florida, 1977.
Swets, J. A. "Signal Detection in Medical Diagonis." In Computer
Diagnosis and Diagnostic Methods, J. A. Jacquez (Ed.).
Springfield, IL: C. C. Thomas, 1972.
Swieringa, R. J.; Gibbins, M.; Larsson, L.; and Sweeney, J. L. "Ex
periments in the Heuristics of Human Information Processing."
Journal of Accounting Research: Supplement on Studies on
Human Information Processing In Accounting, 14 (1976), 15987.
Taylor, R. N., and Dunnette, M. D. "Relative Contribution of Decision
Maker Attributes to Decision Processes." Organizational
Behavior and Human Performance, 12 (1974), 28698.
Tversky, A., and Kahneman, D. "Belief in the Law of Small Numbers."
Psychological Bulletin, 76 (1971), 10510.
. "Availability: A Heuristic for Judging Frequency and
Probabi1ity." Cognitive Psychology, 5 (1973), 20732.
. "Judgement Under Uncertainty: Heuristics and Biases."
Science, 185 (1974), 112431. ~
. "Causal Schemata in Judegments Under Uncertainty." In
Progress in Social Psychology, M. Fishbein (Ed.). Hillsdale,
N.J.: Lawrence Erlbaum Associates, 1977.
Ulehla, Z. J.; Canges, L; Wackwitz, F. "Signal Detectability Theory
Applied to Conceptual Discrimination." Psychonomic Science,
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. "Integration of Conceptual Information." Psychonomic
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Watson, C. S. "Psychophysics." In Handbook of General Psychology,
B. B. Wolman (Ed.). New York: PrenticeHall, Inc., 1973.
78
costs were presented to the subject on the face of each variance report
using the following format:
If Your
Investigation
Decision Is
And If The
Assembly Line
State Is
Then
Your
Costs
Are
Investigation
Production
Total
Yes
Incontrol
$
X
$
0.00
$ X
Yes
Outofcontrol
$
Y
$
0.00
$ Y
No
Incontrol
$
0.00
$
0.00
$ 0.00
No
Outofcontrol
$
0.00
$
Z
$ z
The letter X represented the estimated investigation cost when the
assembly line was incontrol. The cost was related to the size of the
labor efficiency variance: the more negative (unfavorable) the variance
the larger the cost. The letter Y represented the estimated investi
gation cost when the assembly line was outofcontrol. The cost was
related to the size of the labor efficiency variance: the more negative
(unfavorable) the variance the smaller the cost. Given any particular
labor efficiency variance, the investigation cost when outofcontrol
(Y) was always larger than the investigation cost when incontrol (X).
This was because the investigation cost included correction costs when
the assembly line was outofcontrol. The letter Z represented the
estimated marginal production cost of operating the next period in the
outofcontrol state. The cost was constant for all variance reports.
The subjects were told that their immediate supervisor, the pro
duct section manager, would evaluate their control performance in terms
of their minimization of both investigation and production costs above
the expected standard (labeled the total investigation decision cost).
A cash bonus was promised to the subjects, the size of the bonus being
contingent upon the extent to which they minimized their total investi
gation decisions cost. The measure of a subject's control performance,
labeled TIDC^^, was determined by summing the total investigation
136
TABLE 22
PARAMETER VALUES ASSOCIATED WITH THE REDUCED GPi/GNi MODEL
F Values Associated With the Sources of the Reduced Model
Source
d.f.
F
Distribution
1,70
0.64
Cost
1,70
0.35
Distribution
1,70
8.85a
DNj
1,70
59.01a
Intrinsic motivation
1,70
0.11
Extrinsic (nonmonetary) motivation
1,70
0.54
Extrinsic (monetary) motivation
1,70
0.09
GPA
1,70
2.54
Means and Variances Associated With
the Siqnif
icant Sources
Variable Level
N
Mean
Variance
SI, Cl
24
2.0189
1.42035
SI, C2
19
3.1275
2.12453
S2, Cl
19
2.3372
2.09340
S2, C2
17
2.3990
1.71284
ap<.01
70
f(G.Â¡Sl,C2) = 0.06471
f(GiS2,Cl) = 0.01287
f(GiS2,C2) = 0.03185
A graphic representation of this interaction is presented in Figure 4.
The difference between the two f(G.Cl) measures is much smaller than
the difference between the two f(G^C2) measures. The differences are
0.00376 and 0.03286, respectively. This would indicate that the
distribution variable has a significant effect only when given the
C2 cost level. Such q result would explain the larger but not signifi
cant effect of the SI distribution level on the f(S ) measures.
Other implications which can be derived from these relative
relations involve relationships between f(GP.Â¡) and f(GN.j) measures.
Two such relationships appear to be of possible significance. Both
involve the ratio f(GP^)/f(GN.) (i.e., the effect of additional errors
relative to the effect of additional correct decisions). The results
of the first relationship are as follows:
f(GPi/GNiC1) = 1.20480
f(GPi/GNiÂ¡C2) = 1.38337
The difference between these measures is large compared to the standard
error of the estimates (the difference divided by the standard error is
0.17857/0.07621 = 2.34313). Consequently, the GP^/GN^ ratio is ex
pected to be larger given the C2 cost level than given the Cl cost
level.
The results of the second relationship are as follows:
f(GPi/GNiSl,Cl) = 1.19908
f(GPi/GNiSl,C2) = 1.48206
f(GP./GNiS2,Cl) = 1.21057
44
SMC= the sum of optimal model k's investigation decisions costs
over all decisions (m in number).
The G. measure can be classified into one of three submeasures
' J
where the classification is dependent upon the algebraic sign of G.Â¡j.
The classifications are 1) G > 0 the individual's investigation
decision cost for decision j is greater than that of the optimal model,
2) G^j = 0 the individual's investigation decision cost for decision
j is equal to that of the optimal model, and 3) G < 0 the indi
* J
vidual's investigation decision cost for decision j is less than that
of the optimal model.
Summing those Gs with identical algebraic signs, the summations
' 0
are defined as follows:
GP.j = the sum of those Gs with a positive algebraic sign;
GN^ = the sum of those Gs with a negative algebraic sign; and
GZ.j = the sum of those Gs with a value of zero.
It is easily shown that the following relation holds:
m
E G.. = GP, + GN, + GZ,
j=l J
where m equals the total number of decisions made by individual i. Since
the GZ.Â¡ summation equals zero, the sum of the G over all j = l,m
decisions, denoted G, is:
G = GP + GN.
i i i
Conceptually, GP^ is the relative additional cost of those de
cisions made by individual i which are greater in value than that of
the optimal model. The GN^ is the relative savings of those decisions
made by individual i which are less in value than that of the optimal
model.
69
0.0089/0.01808 = 0.49226). Therefore, a significant effect due to the
information variable would not be expected.
Averaging over all conditionals except the distribution variable
gives the following results:
fiG^Sl) = 0.04152
f(G.S2) = 0.02235
Although the difference between these measures is much larger than that
above, this difference is only slightly larger than the standard error
of the estimates (the difference divided by the standard error is
0.01832/0.01658 = 1.10495). The direction of the f(G^) differences
would be expected to be as predicted by the simulation but may not be
significant.
Averaging over all conditionals except the cost variable gives
the following results:
f(G.Cl) = 0.01475
f(GiC2) = 0.04827
The difference between these measures is large compared to the
standard error of the estimates (the difference divided by the standard
error is 0.03353/0.00951 = 3.5258). Consequently, a significant effect
would be expected for the cost variable.
These relative relations can be averaged over all but pairs of
conditionals. Such measures could indicate the presence of interaction
effects of the independent variables. The results of averaging the
pairwise conditionals indicate only one interaction of possible
significance the distribution by cost interaction. The f(G^) measures
conditional to these variables are as follows:
ftf^ISl.Cl) = 0.01663
General Research Methodology 33
General Experimental Environment and Task 34
Operationalization of Variables 35
Hypothesis Formation 46
Simulation of Optimal Model Performances 46
Simulation of Investigation Decision Performances 49
CHAPTER IV. THE EXPERIMENT 76
Experimental Environment 76
Subjects 79
Experimental Materials 80
Background Information 80
Variance Investigation Decisions 80
Elicitation of Heuristics 83
%
Elicitation of Subject Motivations . . 83
Experimental Procedures 83
Assignment of Subjects to Treatment Conditions 84
Training Phase 85
Experimental Phase 86
Final Debriefing 87
CHAPTER V. ANALYSIS AND RESULTS 88
Summary of Results 88
General Method of Analysis 88
Training Phase Analysis and Results 98
Initial Decision Anchors 98
Decision Anchor Adjustment 102
Individual Decision Model Sensitivity 106
The dj Dependent Variable 106
v
Overall
Objective
Control
Strategic
Planning
Overall
Objective
Planning
Overal1
Task
Control
Management
Control
Overall and
Specific Task
Planning
Specific
Task
Control
V
Operational
Control
Specific Task
Structuring
Source: Modified from Demski, 1967.
Environment
Output
FIGURE 1
MANAGEMENT TASK PLANNING AND CONTROL PROCESS
75
classifications have one positive adjustment which would improve per
formance above that of subjects who make only negative adjustments. On
the other hand, the mixed classifications have one negative adjustment
which would decrease performance below that of subjects who make only
positive adjustments.
The following hypotheses predict the effects of the training
adjustment pattern on the Gi variable:
H4.5 (G^PP) < (G^ MM).
Those subjects within the PP classification of training adjust
ment pattern will have a significantly smaller mean G measure
than those subjects within the MM classification.
H4.6 (GjPP) < (G^MP) = PM) < (G^MM).
Those subjects within the mixed classifications of training
adjustment pattern will have mean G^s that fall between those of
the nonmixed classifications. The mean G^s of the mixed classi
fications are not expected to be significantly different.
30
The statistical structure. The effects of the statistical struc
ture of the decision situation are studied by manipulating a distribu
tional information variable. This involves the specification of two
levels of statistical relationship among the two states. Since the
difficulty of the discrimination task increases as the area of dis
tributional overlap between the two states increases, one level of the
distribution variable will have a greater area of overlap than will
the other level. This independent variable is labeled the distribution
variable. All other distributional information items, if presented,
will be controlled as constants.
The cost structure. The effects of the cost structure of the
possible decision outcomes are studied by manipulating a singular in
formation variable. The manipulation of this variable involves the
specification of two levels of decision outcome relationships (in terms
of incurred costs). This independent variable is labeled the cost
variable. Since the importance of the discrimination task increases as
the costs associated with decision outcomes that involve decision errors
become unequal, the different levels of the cost variable will be
associated with different decision error costs. One level of the cost
variable is structured in favor of more variance investigations and the
other level of the cost variable is structured in favor of fewer vari
ance investigations. The only other singular information variable, the
actual results of the physical process and its cost variance, is the
primary experimental stimulus. This information item will be a random
variable whose distributions, given either of the two states of nature,
will be normally shaped with parameter values defined by the appropriate
level of the distribution variable.
144
. TABLE 24
VARIMAX ROTATED MOTIVATION FACTOR PATTERN AMD DESCRIPTION OF
MOTIVATION SCALE ITEMS
Scale Item
Factor 1
Factor 2
Factor 3
Factor 4
1
0.1399
0.2336
0.2112
0.7551
2
0.2610
0.7485
0.0327
0.0503
3
0.8287
0.2336
0.0712
0.0359
4
0.8001
0.1721
0.2190
0.0836
5
0.0305
0.0197
0.9037
0.1436
6
0.1229
0.2524
0.0876
0.8044
7
0.0231
0.8198
0.0104
0.0174
8
0.0814
0.1075
0.8789
0.0385
9
0.7324
0.3174
0.0963
0.3140
10
0.0148
0.7586
0.0848
0.0441
Eigenvalue
2.6975
1.8552
1.6548
0.9528
Cum Eigenvalue
Portion
0.2700
0.4550
0.6210
0.7160
Description
of Motivation
Scale Items
1 Number of
times willing
to participate
in the future.
2 Desire to perform well in undertaking a challenging task.
3 Enjoy making the decisions.
4 Satisfied with your performance.
5 Would have tried less hard if money rewards had been halved.
6 Feel tense during the experiment.
7 Desire to cooperate with the experimenter.
8 Money rewards caused you to try harder than without them.
9 Enjoy the overall experience of participation.
10 Desire to contribute to research knowledge.
52
a subject will select an initial anchor as a first decision approxi
mation and will adjust this decision anchor toward an optimally correct
point as he learns from his experiences with the states of nature.
However, the individual's adjustment process will not be sufficient:
i.e., he will tend to approach the optimally correct point but will not
adjust completely to that point.
The second assumption is that the location of the initial de
cision anchor on the actual minutes incurred continuum (axis) will be
conditional upon the level of the distribution variable. The reason
for this is that the initial decision anchor is expected to be located
at a central point between the means of the two states. Since the
mean of the outofcontrol distribution shifts with the level of the
distribution variable, the central point between the means of the two
states also shifts with the level of the distribution variable. The
initial decision anchor used in the simulation of subjective investi
gation decision performances is the geometric intersection point of the
two states' distribution curves. This particular initial decision
anchor is used because it is the exact central point between the means
of the two states. However, it is selected for operational purposes
and is not necessary to the conceptual development. Any point of
central tendency would be satisfactory.
The final assumption is that the subjects' adjustment process
will be approximately equal over the various treatment conditions. The
adjustment process is defined as a linear movement along the actual
minutes incurred axis from the initial decision anchor towards the
appropriate optimal model decision cutoff. The magnitude of the linear
movement used in the simulation of subjective investigation decision
24
the specific values which these variables may assume, the structure of
the particular decision situation can affect both the difficulty and
the importance of the individual's discrimination among the states. In
general, as the number of possible states increases and as the area of
distributional overlap of these states increases the discrimination task
becomes more difficult. Furthermore, the discrimination task becomes
more important (in terms of incurred costs) as the relative frequencies
of the states become equal and as the costs associated with the possible
decision outcomes which involve decision errors (an incorrect decision
given the existence of a specific state) become unequal.
The general situation employed in this research, selected for
its relevance to the accounting discipline, is the cost variance
investigation decision. In this situation two states of nature are
possible: 1) incontrol (the underlying physical process described by
the standard cost variance report is functioning as planned), and 2)
outofcontrol (the underlying physical process described by the stan
dard cost variance report is not functioning as planned). In reality,
the possible states of nature may be located on a continuum whose end
points are the states of incontrol and outofcontrol. Between these
two end points are any number of states that take the general form of
partially outofcontrol or moving outofcontrol. In the present
research the possible states of nature are confined to the two end
points. The relative frequencies of the two states are controlled as
constants with their values being close to equal. The statistical
relationships among the two states and the relationships between the
various decision outcomes are manipulated as independent variables.
49
information level the available information set contains statistical
estimates of all the parameters required to fit the optimal model.
However, within the II information level the available information
set does not contain all the required parameter estimates. Therefore,
within the II information level the optimal model must use the training
sessions to estimate the missing parameters. The parameter estimates,
both given and estimated, are presented in Table 2.
The second criterion is that the labor efficiency variance re
ports presented to the subjects in the experiment be the same reports
used in the simulation of optimal model performances. The appropriate
parameter estimates presented in Table 2 were used in the optimal
model decision rule expressed by equation 2 to simulate optimal model
investigation decisions. Table 3 presents some performance results
of this optimal model simulation.
Simulation of Subjective Investigation Decision Performances
This section discusses assumptions derived from the conceptual
development that form the basis for the simulation of subjective investi
gation decision performances. It also presents the results of this
simulation and the derivation of the experimental hypotheses.
Assumptions of simulation
The first assumption employed for the simulation of subjective
investigation decision performances is that the subjects will behave
as if they use the anchoring and adjustment heuristic during the
training phase. The anchoring and adjustment heuristic proposes that
171
utilized as performance measures for the appropriate operational managers
and for the physical systems under their control. The nature of de
cisions of this type, although generally aggregated over longer time
periods, is that of adequate performance verses inadequate performance
rather than that of investigation verses noninvestigation. Within the
current research, performance decisions were assumed implicitly within
the performance feedback measures given the subjects during the train
ing phase. An extension would involve the actual formation of such
decisions and, in turn, their effects upon operational control decisions.
This dissertation was submitted to the Graduate Faculty of the Department
of Accounting in the College of Business Administration and to the
Graduate Council, and was accepted as partial fulfillment of the require
ments for the degree of Doctor of Philosophy.
August 1978
Dean, Graduate School
161
this adjustment the simulated cost variable effects would have the same
strength as the obtained effects (i.e., f (Gj  C2 )/2 f (G^  Cl) would equal
1.60).
The simulated and obtained distribution by cost variable inter
actions involving the relative decision costs had similar relationships
between their strengths: the effects of the obtained interaction were
not as strong as the effects of the simulated interaction (note that
[(G.Â¡ S1,C2)(G.Â¡ S2,C2)] /[(^ S1 ,C1 )(Gi S2,C1)] equalled 3.15 whereas
[f(GiSl,C2)f(Gi S2,C2)] / [fÂ¡SI,C1)f(Oj S2,C1)] equalled 8.74).
Again, if the simulated effects are adjusted for unequal learning
efficiency, then the simulated distribution by cost variable inter
actions have a strength similar to that of the obtained effects (i.e.,
[f(Gn. Sl,C2)f(Gi Â¡S2,C2)] / [2f (G^1 SI ,C1 )2*f (G.Â¡ S2,C1)] would equal
4.37).
The simulated and obtained distribution by cost variable inter
action within the GP^/GN^ measure had opposite relationships between
their strength: the effects of the obtained interaction were stronger
than the effects of the simulated interaction (note that [(GP^/GN^C2,S1)
(GPi/GNiC1,S1)] / [(GPi/GNiC2,S2)(GPi/GNi C1 ,S2)] equalled 17.94
whereas [f(GPi/GNiC2,S1)f(GPi/GMiC1,S1)] / [f(GP1/GNiC2,S2)
f(GP^/GN^jCl,S2)] equalled 3.82). To explain the difference in effect
strengths requires reference to two variables: multiple decision an
chors and unequal learning efficiency. If the GP.Â¡/GN.Â¡ measures are
adjusted for the effects of multiple decision anchors the strenqth of
the obtained interaction effect is 2.89 (using the above interaction
strength ratio). This reverses the relationships between the effect
strengths: i.e., the obtained interaction strength becomes weaker
162
than the predicted strength. The effects of unequal learning efficiency
were 1) an expansion of the interval between the final decision an
chor and the optimal decision value for those subjects with greater
decision criteria conservatism, and 2) the reduction of the interval
between the final decision anchor and the optimal decision value for
those subjects with less decision criteria conservatism. As the in
terval associated with those subjects within the Cl cost level expanded
(this level had greater decision criteria conservatism), both the
(GPÂ¡Â¡C1) and the (GNj Cl) became larger. However, the GPÂ¡ measure
became larger at a greater rate than did the GNÂ¡ measure (3GPÂ¡/9GNÂ¡>l).
As the interval associated with those subjects within the C2 cost level
contracted (this level had less decision criteria conservatism), both
the (GPjC2) and the (GNj C2) became smaller. However, the GPÂ¡ measure
became smaller at a greater rate than did the GN.Â¡ measure (3GP1/3GN1>1).
Consequently, the ratio (GP^/GN^Cl) increased as the level of conser
vatism increased, and the ratio (GP^/GNJC2) decreased as the level of
conservatism decreased. Incorporating these effects of unequal learning
efficiency into the simulated distribution by cost variable interaction
lowered the predicted strength of such an interaction.
The observed associations between the individual longrun decision
efficiency measures and the individual attributes may be interpreted
with some degree of intuitive satisfaction. As the subjects' variable
response ranges increased the relative decision costs increased (either
as a result of increased additional decision costs or decreased addi
tional decision savings). Furthermore, as the relative decision costs
increased both the subjects' intrinsic and extrinsic (monetary) moti
vation measures decreased. A similar association existed between the
55
adjustment process will be approximately equal over the various con
ditions. The location of the initial decision anchor can be investigated
by examining the relationship between the individual's initial cutoff
value (in the first training session) and the intersection point of
the appropriate two distribution curves (the TDV.Â¡ measure). These
intersection points are conditional upon the level of the distribution
variable. Given the SI distribution level the intersection point is
38.25 actual minutes incurred, and given the S2 distribution level the
intersection point is 40.5 actual minutes incurred (see Figure 2 for a
graphical presentation of these points). The expected relationship is
that the mean initial decision anchor (TDV.) of the individuals, given
a level of the distribution variable, will not be significantly differ
ent from the intersection point of the appropriate two distribution
curves.
The assumption of approximately equal adjustment over the various
conditions can be investigated by examining the relationship between
the individual's adjustment over the complete experiment and the total
adjustment that would be required to reach the optimal model's decision
cutoff value. A measure of the relative adjustment for individual i,
RA.j, is conditional upon the direction of adjustment along the relevant
decision axis (i.e., the actual minutes incurred per chair produced).
The expected relationship is that the mean RA^ will not differ signifi
cantly between the levels of the various conditions.
Given the above simulation and discussion the following hypo
theses can be derived:
HI.la (TDV.S1) = 38.25.
The mean initial decision anchor given the SI distribution level
58
the use of multiple cutoff values, the average difference between DNA.Â¡
and DN.Â¡ should differ between the two levels of the distribution vari
able. A second possible explanation involves the cost variable. As
the individual's final cutoff value increases, the absolute distance
between the individual's final cutoff value and the lowest values of
the random variable of interest increases. In turn, as this distance
increases subjects again may be more inclined to perceive that a sec
ond outofcontrol distribution overlaps the lower range of the
incontrol state. If such perceptions underly the use of multiple
cutoff values, the average difference between DNA and DN.Â¡ should
differ between the two levels of the cost variable.
Based on the above discussion the effects of the independent
variables on the individual decision model sensitivity measures can
be hypothesized as follows:
H2.la (di SI) < (di S2).
Those subjects within the SI distribution level will have
significantly smaller mean djs then those subjects within the
S2 level.
H2.lb (di Â¡II) = (di Â¡12).
Those subjects within the II information level and within the
12 information level will have equal mean d^s.
H2.1c (di 1 Cl) = (di C2).
Those subjects within the Cl cost level and within the C2 cost
level will have equal mean dis.
H2.2a (DNAilll) = (DAj ] 12).
Those subjects within the II information level and within the 12
information level will have equal mean DNAjS.
Egan (1975) further demonstrates that the posterior probability is
strictly monotone with the likelihood ratio of x, L(x); that is, when
L(x2) > LCXj) then P(snx2) > P(snx1).
173
Maximization of Expected Value Objective Function
The decision function for maximizing the expected value decision
goal is:
E(V) = P(Y!sn)P(sn)Vsn)Y + P(Yn)P(n)VnjY
+ P(Nn)P(n)Vn>N + P(Nsn)P(sn)VsnjN
Given an observation x, the subject has two decision functions:
1) E(Vx,Y), the expected value given the observation and the
subject responds "Yes," and
2) E(Vx,N), the expected value give the observation and the
subject responds "No."
These two decision functions can be expressed as the sum of the values
associated with each decision weighted by the appropriate posterior
probability:
E(Vx,Y) = P(snx)VsrljY + P(nx)VnjY, and
E(V j x,N) = P(nx)VnjN + P(snx)VsnjN.
The subject should say "Yes" only if E(Vx,Y) > E(Vx,N). If the
righthand side of the above two equations are used in this inequality,
then the subject should say "Yes" only when:
P(snx)Vsn,y + P(nx)VnY > P(nx)VnjN + P(snx)Vsn>N
Rearranging the terms and expressing the posterior odds in terms of the
prior odds and the likelihood ratio of x results in the decision rule
for saying "Yes" in terms of the likelihood ratio of the observation:
P(x1sn) P(n) Vn,N Vn,Y
P(x n) > P(sn) 'vsn>Y VsM
ACKNOWLEDGEMENTS
I am grateful to my supervisory committee; Rashad Abdelkhalik,
Douglas Snowball, Gary Hoi strum, and Richard Griggs. Without their
valuable contributions this dissertation would have had far more
obscurities.
I am indebted to my experimental assistants: Doug Snowball,
Gary Hoi strum, Ron Teichman, Ed Bailey, John Wragge, Robert Thompson,
Nancy Hetsko, Andy Judd, and Michael Gift (even though he was not on
time). Without their help the experimental procedures used within
this study would not have been possible.
Finally, my greatest debt is to my wife, Sandy. Without her
encouragement and support this dissertation never would have been done.
151
individual decision criteria conservatism, and individual longrun
decision efficiency.
Decision Anchor Selection and Adjustment
The first aspect of the anchoring and adjustment heuristic is the
selection of the initial decision anchor. The formation of the hypo
theses concerned with this aspect was based upon the assumptions that
1) individuals (within a given level of distribution variable) would
tend to use a common initial decision anchor, and 2) the location of this
common anchor would be related to a measure of central tendency between
the two states of nature. This central tendency was operationalized as
the geometric intersection of the state distribution curves (the inter
section was conditional upon the level of the distribution variable).
The results indicated that the expected point of central tendency
was obtained for those subjects within the SI distribution level, but
that the expected point was statistically larger than that obtained
for those subjects within the S2 distribution level. The state means
were farther apart within the S2 distribution level than within the SI
level. As a consequence, the intersection of the state distribution
curves was located farther from the standard (the mean of the incontrol
state) within the S2 distribution level than within the SI level. An
initial subjective bias provides a plausible explanation for the
estimate obtained within the S2 level. This bias would take the form of
a tendency to place greater weight on the standard as the intersection
of the distribution curves became larger (i.e., shifted to a higher
point on the actual minutes incurred axis).
134
The model comparison procedure results are presented in Table 21,
and the F values associated with the sources of the reduced model are
presented in Table 22. The results indicate that the reduced GPÂ¡/GMÂ¡
model contains the distribution by cost interaction and the DMj variable,
both significant at the p<01 level.
The means, variances, and sample sizes of the distribution by
cost interaction are included in Table 22. Figure 8 presents a graphic
representation of the distribution by cost interaction. Bartlett's test
of homogeneity of variances indicated that the variances within the
distribution by cost interaction are homogeneous (x2=1.068, p<.25 with
3 d.f.). Duncan's multiple range test of equal means indicated that
the means within the distribution by cost interaction form two over
lapping groups: the means within a group do not differ significantly
(p=.01), whereas the means between groups do differ significantly (p=.01).
These groups are 1) (GPi/GNiC2,S1), (GPi/GNiC2,S2), and
(GPi/GNiC1,S2); and 2) (GPi/GNiC1 ,S1), (GPi/GNiC1.52), and
(GPi/GNiC2,S2).
Hypothesis 4.4b predicted a significant distribution by cost
variable interaction in which the (GP^/GN^ C2,S2)(GPi/GN11Cl ,S2) would
be smaller than (GPi/GNiC2,Sl)(GPi/GNiC1 ,S1). The results of the
model comparison procedure indicated a significant distribution by cost
interaction (p<.01). The (GPV/GN^ C2,S2)(GP^/GN1 C1 ,S2) was equal to
0.0618, and the (GPi/GNiC2,S1)(GPi/GNiC1,S1) was equal to 1.1086.
Hypothesis 4.4a predicted that (GP^/GN^Cl) would be significantly
smaller than (GP^/GNjÂ¡C2). Using a Z test of equal means, the difference
between the means was found to be significant (Z=2.0109, p<.01
onetailed), with the Cl level having the smaller mean. The lack of
185
D. The actual minutes incurred per chair.
*************************************************
*****
Not Slightly Moderately Very Extremely
Important Important Important Important Important
E. The labor efficiency variance per chair.
*************************************************
*****
Not Slightly Moderately Very Extremely
Important Important Important Important Important
F. The total chairs produced.
*************************************************
Not Slightly Moderately Very Extremely
Important Important Important Important Important
G.The costs associated with investigation decisions.
k'k'k'kk'k'k'k'k'k'kk'k'kirk'k'k'k'k'k'k'k'k'k'k'k'k'k'k'k'k'k'kk'k'k'k'kic'k'k'k'k'k'k'k'k'k
* * *
Not Slightly Moderately Very Extremely
Important Important Important Important Important
H.Other (please specify) .
*************************************************
*****
Not Slightly Moderately Very Extremely
Important Important Important Important Important
4. Recalling the training phase which you completed at an earlier time,
what rule or set of rules did you use to make your variance investi
gation decisions in the first 33 variance reports? Please include
any numerical values that were part of this rule or set of rules.
5. How did you initially form the rule or set of rules you gave in
question four? What lead you to use this particular rule or set of
rules?
6. If the rule or set of rules you gave in question four is different
from the rule or set of rules you gave in question one, what caused
you to make the change?
7. What other information items, if presented, do you feel would im
prove you variance investigation decisions?
8. Were your decisions influenced by discussions with other partici
pants in this experiment?
Hypotheses
Analysis
Method(s)
1.3b (MiSl) = (MiS2)
Model comparison
Twotailed Z test
1.3c (MiCl) = (MiC2)
Model comparison
Twotailed Z test
2.1a (cH Â¡Sl)<(dT S2)
Model comparison
Onetailed Z test
2.1b (34  Il) = (cT'. 112)
Model comparison
Twotailed Z test
2.1c (dt Cl) = (d;. C2)
Model comparison
Twotailed Z test
ABLE 4 Continued
Test
Statistic(s) Results
F=1.7308 (n.s.)
The mean relative decision anchor ad
Z=1.3054 (n.s.)
justment of those subjects within the
SI distribution level was not signifi
cantly different from that of those
subjects within the S2 distribution level
F=0.1735 (n.s.)
The mean relative decision anchor ad
Z=0.4865 (n.s.)
justment of those subjects within the
Cl cost level was not significantly dif
ferent from that of those subjects with
in the C2 cost level.
F=46.0985 (p<.01)
The mean decision model sensitivity of
those subjects within the SI distribu
Z=6.1144 (p<.01)
tion level was significantly smaller than
that of those subjects within the S2
distribution level.
F=0.5883 (n.s.)
The mean decision model sensitivity of
Z=0.4588 (n.s.)
those subjects within the reduced infor
mation level was not significantly dif
ferent from that of those subjects with
in the expanded information level.
F0.2638 (n.s.)
The mean decision model sensitivity of
Z=0.3167 (n.s.)
those subjects within the Cl cost level
was not significantly different from
that of those subjects within the C2
cost level.
48
equation 2 can be restated in the following mathematical terms:
exp (x y2)2/2cr2
: r > PRi CR
exp (x yj^cr2
where x = actual minutes incurred per chair;
Vj = the mean of the incontrol state;
y2 = the mean of the outofcontrcl state;
a = the common standard deviation of both states; and
exp = the notation for the exponential funcition (e).
If the above equation was changed to an equality and solved for x the
result would be the optimal cutoff value in terms of actual minutes
incurred. This solution takes the general form:
G2(ln(PRÂ¡) + ln(CR))
x = + 0.50(y2 + y,) Equation 3
y2 yi
where x = the optimal cutoff value in terms of actual minutes incurred;
and
In = the notation for the natural logarithm function (loge).
With the decision rule in the form of equation 3 the parameters required
to fit the optimal model become identifiable. These parameters include
the mean of both states of nature, the common variance of the two
states, the prior odds of the incontrol state, and the appropriate
value of the cost ratio.
With knowledge of the parameters required to fit the optimal
model the criteria employed for the simulation of optimal model per
formances can be discussed. The first criterion is that information
available to the optimal model be the same as the information avail
able to the individual. The information available to the individual
is manipulated by the levels of the information variable. Within the 12
83
Elicitation of Heuristics
The elicitation of each subject's heuristics was accomplished using
a predominately openended questionnaire (see Appendix E). The objec
tives of this questionnaire were 1) to determine the strategy the sub
ject used in answering the variance investigation questions within the
experimental phase, 2) to determine, where appropriate, the exact values
of any numerical rules used by the subject, 3) to determine whether the
subject thought he used his strategy consistently within the experimental
phase, 4) to determine the method or approach used by the subject in
forming his strategy within the training phase, and 5) to determine the
subjective importance attached to each information item presented to
the subject.
Elicitation of Subject Motivations
Subject motivations were elicited using a motivation questionnaire
(see Appendix F) developed by Snowball and Brown (1977). The question
naire is a ten item Likerttype scale which has submeasures for both
intrinsic and extrinsic motivation.
Experimental Procedures
Experimental procedures included assignment of subjects to
treatment conditions, adminstration of a training phase, administration
of an experimental phase, and final debriefing.
TABLE 3
RESULTS OF THE SIMULATION OF OPTIMAL MODEL PERFORMANCES GIVEN THE VARIOUS CONDITIONS
k Information
Distribution Cost
P(investigate
outofcontrol)
P( investigate
incontrol)
Optimal Cutoff
Value (actual
minutes incurred)
dk
6k
1
11
SI
Cl
0.875
0.383
36.90
1.447
0.539
2
11
SI
C2
0.400
0.033
41.29
1.581
5.206
3
11
S2
Cl
0.875
0.300
38.70
1.674
0.592
4
11
S2
C2
0.425
0.017
45.88
1.939
9.453
5
12
SI
Cl
0.900
0.400
36.86
1.535
0.454
6
12
SI
C2
0.400
0.033
41.26
1.581
5.206
7
12
S2
Cl
0.900
0.300
38.57
1.806
0.504
8
12
S2
C2
0.525
0.033
44.68
1.897
5.364
cn
45
The anchoring and adjustment process may occur over three training
sessions and one experiment session. Using the first training session
as an estimate of the initial decision anchor and the experiment
session as an estimate of the final decision anchor, there remains two
training sessions within which the adjustment process itself can be
analyzed. In particular, the effects of the pattern of adjustments
within the training sessions are expected to influence significantly
the magnitude of an individual's experiment Gj measure. The adjustment
process can be measured in the training sessions by computing the
individual's Gj measure at the completion of each session. The
measures, labeled TG^, TG.Â¡0, and TG.Â¡3, are defined in the same manner
as the G. measure discussed above (with the exception that they are
computed from the appropriate training session data instead of the
experiment session data). The differences between these training
measures reflect the effects of the individual's adjustments in the
training sessions. These differences are defined as follows:
DTGii = TG11 TGi2
DTGi2 = TGi2 TGi3
where, DTGjj = the difference between the GÂ¡ measure for the jth and
the jth + 1 training sessions of individual i; and
TG^j = the G.j measure for the jth training session of indi
vidual i.
Negative DTGjjvalues indicate an increase in the TGjj measure between
training sessions, and positive DTGjj values indicate the reverse.
The effects of the directions of these adjustments should be
related to the effects of the final anchor on the individual's Gj
measure (in the experiment). The effects of the adjustment measures
109
TABLE 10
PARAMETER VALUES ASSOCIATED WITH THE REDUCED d! MODEL
i
F Values Associated With the
Sources of the Reduced d1 Model
Source
d.f.
F
Distribution
1,80
46.52a
Intrinsic motivation
1,80
10.33a
Extrinsic (nonmonetary)
motivation
1,80
1.80
Extrinsic (monetary)
motivation
1,80
0.06
GPA
1,80
0.20
Means and Variances Associated
With the Significant Sources
Variable Level N
Mean
Variance
SI 44
1.2325
0.09165
S2 42
1.6093
0.07200
p<.01
53
performances is 50 percent of the distance between the initial decision
anchor and the appropriate optimal decision value. The 50 percent
adjustment value, selected for operational purposes, is completely
arbitrary and is not necessary to the conceptual development. The only
restriction is that it be less than 100 percent. Furthermore, the
general assumption of equal adjustment over the various conditions is
not necessary to the conceptual development: the objective of this
general assumption is to facilitate a simple operationalization of the
simulation.
Training phase simulation and hypotheses formation
The first stage of the simulation involves simulating the
anchoring and adjustment process in the training sessions for each
combination of independent variables. A simulated subject sample size
of one is used for each condition (resulting in a total simulated sub
ject sample of eight). The results of this first stage simulation are
presented in graphical form in Figure 2. This figure depicts the
simulated initial decision anchors (points A), the simulated final
decision anchors (points B), and the simulated optimal cutoff values
(points C) for each of the eight combinations of independent variables.
The assumptions used in simulating the training phase can be
investigated using the results of the experimental training phase. The
assumptions are i) that the subjects will behave as if they used the
anchoring and adjustment heuristic, 2) that the initial decision anchor
will be located centrally between the means of the two states and will
be dependent on the level of the distribution variable, and 3) that the
99
of nature) as their initial decision anchor. The simulation arbitrarily
employed the geometric intersection of the appropriate state distribu
tion curves as the expected initial decision anchor. These expected
points are conditional upon the distribution variable: i.e., the
intersection point is 38.25 actual minutes incurred given the SI distribu
tion level and is 40.5 actual minutes incurred given the S2 distribution
level. The cutoff points employed within the first training session
(TDV.s) were used as an estimate of the subjects' initial decision
anchors.
The method of analysis was the model comparison procedure where
the dependent variable is the TDV^ measure and the independent variables
are the three situation variables (information, distribution, and cost)
with their interactions, and the GPA variable. The full TDV^ model is:
TDV1 = V + Oj + Bk + Y, + a6jk + aYj, + 6Ykl + ctSyjkl + Si
The model comparison procedure results for this analysis are
presented in Table 5, and the F values associated with the sources of
the reduced model are presented in Table 6. The results indicate that
the reduced model contains the distribution variable and the cost vari
able main effects. The distribution variable main effect is significant
at the p<.01 level and the cost variable main effect is significant at
the p<.10 level. The means, variances, and sample sizes of the TDV^
measure given the levels of these two independent variables are included
in Table 6. Using the F test of equal variances, the variances between
the levels of the distribution variable differ significantly (F=2.82,
pc.01), with the S2 level having the greater variance. The variances
between the levels of the cost variable do not differ significantly
(F=l.12, n.s.). Using the Z test of equal means, the means of the levels
Variables that can affect the manager's variance investigation
decision process include 1) structure of the decision situation, 2)
contents of the available information set, 3) manager's information
processing efficiency, and 4) manager's learning efficiency. The
structure of the decision situation depends upon number possible states
of control, relative frequencies of the states, various statistical
relationships among the states, and various relationships between the
decision/state outcomes. The contents of the available information set
include information known by the manager prior to his decision. The
manager's information processing efficiency relates to the particular
strategies employed in combining and weighting various items of infor
mation. The manager's learning efficiency refers to the manager's
ability to learn from his experiences with the controlled process. Both
information processing and learning efficiency are the results of the
manager's particular decision strategies. These decision strategies
were expected to adapt a general form, referred to as anchoring and ad
justment. A natural starting point is used as a first approximation for
the investigation decision rule and is then adjusted as the manager
learns from his experiences.
Two experimental methods were used to derive and test implications
of the conceptual framework. Simulation techniques were used to opera
tionalize hypotheses and a laboratory experiment was used to test these
hypotheses.
The experimental environment was that of an assembly department
within a simulated manufacturing company. The assembly department as
sembled a single product and performance of this department was deter
mined completely by the assembly workers' labor efficiency. Student
IX
12
not statistically significant. Whether a variance resulting from a
change in the physical system, labeled a Type 2 variance, requires an
operational control response depends on whether the underlying cause of
the change is a temporary rather than a permanent phenomena. A tem
porary or controllable variance, labeled a Type 2a variance, is one
that operational control can correct in the future (i.e., the physical
system can be returned to the previous incontrol condition). A per
manent or noncontrollable variance, labeled a Type 2b variance, is one
that operational control can not correct in the future.
If the manager decides that the variance is of Type 1 no action
is required. If, however, the manager decides that the variance is of
Type 2 he must then decide if the variance sh  