Citation
Information formats and decision performance

Material Information

Title:
Information formats and decision performance an experimental investigation
Creator:
Amador, José Angel, 1947-
Publication Date:
Language:
English
Physical Description:
x, 100 leaves : ; 28 cm.

Subjects

Subjects / Keywords:
Cost efficiency ( jstor )
Cost estimates ( jstor )
Information storage and retrieval systems ( jstor )
Interval estimators ( jstor )
Mathematical dependent variables ( jstor )
Modeling ( jstor )
Point estimators ( jstor )
Questionnaires ( jstor )
Simulations ( jstor )
Total costs ( jstor )
Decision making ( lcsh )
Dissertations, Academic -- Management -- UF ( lcsh )
Information theory ( lcsh )
Management thesis Ph. D ( lcsh )
Genre:
bibliography ( marcgt )
non-fiction ( marcgt )

Notes

Thesis:
Thesis--University of Florida.
Bibliography:
Bibliography: leaves 66-68.
General Note:
Typescript.
General Note:
Vita.
Statement of Responsibility:
by José A. Amador.

Record Information

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 non-profit 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:
025851858 ( ALEPH )
03403840 ( OCLC )
AAV4043 ( NOTIS )

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Full Text











INFORMATION FORMATS AND DECISION PERFORMANCE:
AN EXPERIMENTAL INVESTIGATION










By


JOSE A. AMADOR


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

1977






























Copyright By

Jose A. Amador

1977































A MagUi y Provi













ACKNOWLEDGEMENTS


I wish to express my gratitude to the University of Puerto Rico

for financing my graduate program; to my dissertation adviser, Dr.

Richard A. Elnicki, for his recurrent insistence and corrections

throughout the course of this work; to my co-adviser, Dr. Jack M.

Feldman, for his invaluable comments and stimulus; to the rest of

my committee, Dr. Christopher B. Barry, Dr. Thom J. Hodgson, and

Dr. Richard R. Jesse, for their help and suggestions at the various

stages of the research; and to my colleague and friend, Jose F. Colon,

for his enlightening suggestions during the early part of the project.

I also want to express special appreciation to my "parents" in

Gainesville, Mr. and Mrs. Bruce Ruiz, for their long, long hours of

companionship and friendship during my stay at the University of

Florida.

Most important, I thank my wife, Magui, for pushing me through

while taking the worst part; my sons, for the time they would have

rather spent with me; and my parents, for their ever present support

and counsel.











TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS ........................................... iv

LIST OF TABLES ....................................... vii

LIST OF FIGURES ............................................ viii

ABSTRACT ........................................... .... ix


CHAPTER 1 RESEARCH BACKGROUND ............................ 1

1.1 Introduction ..................................... 1
1.2 Literature Review ................................. 2
1.2.1 The Minnesota Experiments ..................... 2
1.2.2 The Lucas Model ............................... 7
1.2.3 Other Related Literature ...................... 10
1.3 Organization of the Dissertation .................. 11

CHAPTER 2 THE PROBLEM .................................. 13

2.1 An Information-Decision Problem .................... 13
2.1.1 The Real-Life Problem ......................... 13
2.1.2 The Abstracted Problem ........................ 15
2.2 Information Content ............................... 16
2.3 The "How-Do-We-Present-the-Information?" Problem ... 17
2.3.1 Medium of Transmission ........................ 18
2.3.2 Format of Presentation ........................ 19
2.3.3 Level of Detail .............................. 23
2.4 Number of Decision Entities ....................... 26
2.5 Dependent Variables ............................... 27
2.6 Research Hypotheses ................................ 28
2.7 Basic Functional Model ............................. 31

CHAPTER 3 THE EXPERIMENT ............................... 33

3.1 Method ....................................... 33
3.1.1 Subjects .................................... 33
3.1.2 Design and Analysis ........................... 34
3.2 Experimental Task ................................ 37
3.3 Evaluation of Hypotheses .......................... 40








TABLE OF CONTENTS (continued)


Page


CHAPTER 4 EXPERIMENTAL RESULTS ............................... 44

4.1 Introduction ............................................ 44

4.2 Results .................................................... 44
4.2.1 Effect of Layout on Decision Time ................. 44
4.2.2 Influence of Format on Choice Behavior ............ 46
4.2.3 Joint Effect of Layout and Style on Decision Time .. 47
4.2.4 Effect of Probabilistic Detail on Cost Performance .. 47
4.2.5 Joint Effect of Format and Level of Detail
on Decision Time ................................. 48
4.2.6 Relation Between Number of Decision Entities
and Format ........................................ 49

CHAPTER 5 DISCUSSION OF RESULTS .............................. 51

5.1 Summary of Findings .................................... 51
5.2 The Multivariate Effects .............................. 51
5.3 The Univariate Effects ................................ 57
5.3.1 Effects Related to H1 and H3 ....................... 57
5.3.2 Effects Related to H2 ............................ 58
5.3.3 Effects Related to H5 ............................ 59
5.3.4 Effects Related to H6 ............................ 60

CHAPTER 6 SUMMARY AND POSSIBLE EXTENSIONS ..................... 63


BIBLIOGRAPHY ........................................ ......... 66

APPENDIX A EXPERIMENTAL TREATMENTS ........................... 69

APPENDIX B FORMAT OPINION QUESTIONNAIRE ....................... 78

APPENDIX C COMPUTER SIMULATION PROGRAM ....................... 80

APPENDIX D SUBJECT INSTRUCTIONS ............................. 89

BIOGRAPHICAL SKETCH ............................................. 100















LIST OF TABLES


Page
1.1 SOME STUDIES CONDUCTED UNDER THE CHERVANY
ET AL. FRAMEWORK ..................................... 4

3.1 FACTORIAL DISPLAY AND FACTOR LEVELS .................. 35

3.2 DATA FOR THE EXPERIMENT .............................. 39

3.3 EFFECTS PREDICTED BY THE RESEARCH HYPOTHESES ......... 41

4.1 CELL MEANS FOR THE SIXTEEN EXPERIMENTAL
CONDITIONS ........................................... 45

5.1 MAIN EFFECTS ......................................... 52

5.2 INTERACTION INVOLVING THE NUMBER-OF-DECISION
ENTITIES-VARIABLES ................................... 53

5.3 OTHER INTERACTION EFFECTS ........................... 55














LIST OF FIGURES


1.2 Different formats of presentation .................. 8

2.1 Three forms of presenting the expected
due-dates information ............................. 21

2,2 Graphical style with events in due-date order ....... 22

2.3 Three forms of presenting the interval
estimates information ............................... 25










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


INFORMATION FORMATS AND DECISION PERFORMANCE:
AN EXPERIMENTAL INVESTIGATION

By

Jose A. Amador

June 1977

Chairman: Richard A. Elnicki
Major Department: Management

This study examines some implications of the relationship

between information format and decision performance. A real-life

information-decision problem was abstracted to create a simulated

decision environment in which alternative forms of presenting

information relevant to the problem were manipulated and adminis-

tered to 160 experimental subjects.

Multivariate and univariate analyses of the experimental data

indicated significant differences due to the experimental treatments.

Presentation style tabularr versus graphical) and information layout

(I.D. ordering versus due-date ordering) were found to have separate

and joint effects on decision performance. The style of presentation

had a strong influence on subject choice behavior. The level of

probabilistic information provided (point estimates versus interval

estimates) and the style of presentation had a joint effect on









decision time. Subjects with few decision entities on their reports

felt indifferent toward format, while subjects with many decision

entities indicated clear format preferences.

The implications of the findings for the Management Information

Systems researcher and practitioner are discussed. Suggestions are

given for further research.















CHAPTER 1

RESEARCH BACKGROUND

1.1 Introduction

The last decade has seen a significant increase in the use of

computer-based data systems to support decision making in organizations.

This marriage of computers and organizations has developed into the

rapidly growing field of Management Information Systems (MIS). In

general, MIS refers to the use of computer-based data systems for the

primary purpose of supporting management decisions. Since MIS exist

to support decision making, researchers in the area have suggested

that their effectiveness should be measured in terms of the effectiveness

of the decisions they support. In turn, it has been argued that the

effectiveness of decisions based on information will depend, among other

things, on the accuracy, relevancy, and timeliness of the information.

More recently, it has also been proposed that even when information

is adequate, its effective use can be influenced by the manner in which the

information is presented, in particular, by its format of presentation,

level of detail, and medium of transmission. This line of thought has led

researchers in the area to investigate how the physical form of presenting

the information can influence aspects of decision performance. That






2

relationship, in essence, is the object of this study. In the present

research, the influence of information format on decision performance will

be investigated in the context of a specific information-decision problem.

1.2 Literature Review

The increase in popularity of Management Information Systems

during the last decade has been accompanied by an awareness of the need
for improving the efficiency of the systems designed. The consensus

of the researchers in the area has been that there is a need for a
theory of MIS. Zannetos [31] states that a theory is needed to develop
objective criteria for determining the effectiveness of MIS efforts.

In response to the call for a theory, several research frameworks have
been proposed [10, 16, 21, 22].1

1.2.1 The Minnesota Experiments

The research framework proposed by Chervany et al. [10] has guided

the "Minnesota Experiments," a series of empirical studies that have

been conducted at the Management Information Systems Research Center,
University of Minnesota. The general purpose of these studies has been

to manipulate various MIS variables to investigate their impact on

decision performance. The Chervany et al. framework states that three
categories of variables affect decision performance, P, given a particu-
lar information system. These are the decision environment, DE, the




1These frameworks have not constituted theories, in the formal
sense, but rather pre-theoretical lists of variables.








decision maker, DM, and the characteristics of the information system,

CIS. In functional form,


P = f(DE,DM,CIS)


(1.1)


A number of experiments have been conducted under this framework.

They all appear to have followed Van Horn's [28] suggestion that

laboratory studies provide an effective means for MIS research. In

particular, the experimenters have drawn upon the technique of

"experimental gaming" to create artificial decision-making evironments

within which they have manipulated various aspects of the information

system. In support of this technique, Barret et al. conclude:


Dickson et al.


We have been unable, to date, to generate
evidence which implies that the interpre-
tation of decision performance results in
terms of a treatment variable is likely to
be confounded by the effects of the simu-
lator and/or other aspects of the manage-
ment game context [4, p. 11].

say of experimental gaming:

...we are of the opinion that experimental
gaming, despite its high cost, is an effec-
tive way of investigating this area. The
major problems really are associated with
the measurement of the variables included
in the experiments. Somewhat surprisingly,
problems of subject motivation and
experimental control have been minor
[12, p. 20].


Table 1.1 on pages 4 6 is a summary of some of the most
referenced studies that have been conducted under the Chervany et al.

framework. The variables that were experimentally controlled are shown

in each case before the "given" bar (1) in the functional models along

with a brief description of the measure used for each variable. Other













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7

items in the table are the nature of the simulated decision environ-

ment, the experimental subjects, and a summary of the results.
It is interesting to note that while the form of presenting the
information has been extensively considered in one form or another,

the "layout" or physical arrangement of the information reported has
not been manipulated as an experimental variable in any of the studies

reviewed. Figure 1.2, part A (p. 8) is an example of the type of "form
of presentation" treatment that has been manipulated in the reviewed

literature. Figure 1.2, part B is an example of what is meant here

by information layout. The influence of this variable on decision

performance will play an important role in this study.

1.2.2 The Lucas Model

The model proposed by Lucas [21] includes essentially the same
variables as the Chervany et al. framework, but it also takes into
account the interface between use of the information system and per-
formance. His descriptive model states that performance (P) is a
function of situational, personal, and decision style variables (the

DM group in the Chervany et al. model), the quality of the information
system (the CIS group in the Chervany et al. model) and the analysis
and actions taken by the users (similar to the DE group in the Chervany

et al. model). In addition, his model also states that the performance

of the information system is independently affected by the use of the
system, U. In functional form,


P = f(DE,DM,CIS,U)


(1.2)















Raw Data Treatment

FINISHED GOODS INVENTORY HISTORY


K EE 1 OF MONTH 3


K EEM 2 OF MONTH 3


INVENTORY
LEVELS

Resinoid R-Forced Vitrifid Resinoid R-Forced Vitrifid
HONDAY 0 371 0 0 120 481
TUESDAY 39 102 82 0 153 191
WEDHESDAY 0 0 198 0 202 0
THURSDAY 34 36 299 38 267 0
FRIDAY 71 84 393 79 188 38


Resinoid R-Forced Vitrffid
285 0 58
0 0 0
379 321 0
0 0 0
0 0 0


Resinold R-Forced Vitrifid
354 0 0
423 0 0
144 0 201
0 0 121
0 0 0


Statistically Sunmarized Treatment
FINISHED GOODS INVENlTORY HISTORY
SURIARY STATISTICS CALCULATED
FRO OPERATIONS FOR PERIOD
WEEK 1 OF MONTH 3 THROUGH WEEK 4 OF MONTH 3

Daily Inventory Levels
(End of Day) Stockouts
Resinoid R-Forced Vitrifid Resinoid R-Forced Vttrifid
Mean 23.25 140.80 92.85 Mean 171.30 38.20 123.70
Coef Var 6.28 4.18 7.97 Coef Var 5.63 14.77 7.09
laxmuma 79.00 371.00 481.00 Ma lou 427.00 392.00 484.00
Range 79.00 371.00 481.00 Range 427.00 392.00 484.00

A. Abreviated samples of two "form of presentation" treatments used by
Chervany and Dickson [9, p. 1338].

FINISHED GOODS INVENTORY HISTORY
SUMMRY STATISTICS CALCULATED
FROM OPERATIONS FOR PERIOD
WEEK I OF MONTH 3 THROUGH WEEK 4 OF MONTH 3


Daily Inventory Levels
(End of Gay)
Resinoid
R-Forced
Vitrlfid

Stockouts
Resinoid
R-Forced
Vitrifid


Mean Coef Var Mavitmn


23.25 6.28 79.00 79.00
140.80 4.13 371.00 371.00
92.85 7.97 481.00 481.00



171.30 5.63 427.00 427.00
38.20 14.77 392.00 392.00
123.70 7.09 484.00 484.00


B. A different "layout" for the information in the second report above.


Figure 1.2 Different formats of presentation


STOCKOUTS


MONDAY
TUESDAY
WEDNESDAY
THURSDAY
FRIDAY





9

In a field study [20] with an actual information system and data

from salesmen's performances, Lucas observed relationships among DE,

DM, CIS, and P that are congruent with those observed in the "Minnesota

Experiments." In addition he also noted the following relationship

between performance and information system use:


> 0 when relevant information is
AU provided and used,


AP < 0 when the information provided
AU is irrelevant to the decisions
that must be made.

What he in effect noted is that only those information system designs

that promote effective use of the information will have a positive

effect on performance. One of the indications of his results was that

information structure elements such as the format of presentation, F,

and the level of detail, L, can be determinants of effective use, EU,

given other system characteristics, CIS', and a given set of DM and DE

variables. Although not explicitly stated in his paper, the results

of his study suggest that

EU = f(F,LIDE,DM,CIS') (1.3)

and that P = F(EUIDE,DM,CIS') (1.4)

with AP >
AEU >"

These relationships are inferred from the discussion part of his paper:

One of the most important implications of
the model and results is that different
personal, situational and decision style
variables appear to affect the use of
systems. These findings argue for more








flexible systems to support different
users' needs. For example, the
present sales information system could
be modified to provide different out-
put formats and levels of summarization
[20,p. 918].
Equations 1.3 and 1.4 are combined in the next chapter to produce

a model that will serve as a guide for evaluating a set of propositions

relating information format to decision performance.

1.2.3 Other Related Literature

In addition to the literature referenced above, other related

literature has influenced the formulation of the hypotheses evaluated

in the present study. Two textbooks on MIS, in particular, contain

some interesting but undocumented ideas which have shared in the latter.

Murdick and Ross [23] make such general statements as, "In general,

the format should be established to save the manager's time" [p. 326]

and, "Managers prefer graphic displays, which reduce large amounts of

information into easily understood pictorial form" [p. 263].

The second MIS text which makes similar suggestions is Voich et al.

[29]. They propose:

Format is important because it affects
the ease with which the report can be
read and assimilated. As the complexity
of a report increases, its likelihood
of extent of use falls [29, p. 229].

This writer feels that the authors are saying that more attention

should be given to the format of the report as the number of "entities"

in the report on which decisions are required increases.

Finally, a recent paper by Conrath [8] suggests:


In all the literature on decision making,
and in particular that on statistical de-








cision theory, little if anything has
been said about the form in which the
data should be presented to the de-
cision maker. Perhaps this is because
most theoreticians assume that as long
as the data unambiguously define the
distributions, the format of presenta-
tion should make no difference. This
brings up the question of whether data
can ever be unambiguously presented,
and perhaps more importantly, in whose
eyes? The only answer to the second
question is the user, but he has seldom
been asked [8, p. 878].

Conrath goes on to propose that the format in which probabilistic

data is presented as a basis for choice can influence choice.

The present study centers around the questions raised above.as

they relate to a pragmatic "how-do-we-present-the-information?"

problem.

1.3 Organization of the Dissertation

In Chapter 2, a "real-life" information-decision problem is pre-

sented to provide a setting for the questions investigated in this study.

The nature of the problem is explained in Section 2.1. In Section 2.2,

the information needs of the manager in the problem are considered, and

it is assumed that these needs are relatively well defined and structured.

A number of questions related to the form in which information should be

presented to the manager are raised in Section 2.3. The results of

previous studies are revisited in an effort to provide orientation to

the present information format/decision performance questions. The

criteria used to measure decision performance are defined in Section 2.5,

and a set of research hypotheses relating these criteria to the experi-

mental format variables is presented in Section 2.6. In Section 2.7, a

general model is presented to serve as the guide for the experiment.







The nature and details of the experiment are the subject of

Chapter 3. The methodology is discussed in Section 3.1 and a full

description of the experimental task is given in Section 3.2. Section

3.3 discusses the experimental results that should be observed for the

research hypotheses to be supported, and a table is presented that

shows how each of the hypotheses is to be evaluated from the experi-

mental data.

The statistical results of the experiment are presented in Chapter

4. These are discussed in Chapter 5 from the point of view of their

implications for both the MIS researcher and practitioner. In Chapter

6, suggestions are given for new lines of research.













CHAPTER 2

THE PROBLEM

2.1 An Information-Decision Problem

The information-decision problem that provided the setting for the

current study is presented in this chapter. The situation studied pre-

sents several advantages from the point of view of empirical MIS re-

search. First, the situation is relatively simple, easy to characterize

and to model. Second, the problem points to clearly definable questions

of information structure, an area that has received increased attention

in the recent MIS literature [5, 9, 12, 18, 25, 26]. Finally, the situa-

tion may represent a new area for the application of MIS technology.

2.1.1 The Real-Life Problem

The particular decision situation to be outlined comes from the

field of agriculture and concerns the detection of estrus (heat) in

artificially inseminated dairy herds. The problem is that failure to

detect heat can result in lost breeding opportunities, lower milk pro-

duction, and subsequent capital losses. The following excerpts from the

dairy industry literature illustrate the problem:


Accurate estrus detection is a key to
efficient reproduction and high milk
production. .. Proper detection of
estrus is essential in any planned
breeding program using hand mating,
especially to capitalize on superior
sires available through artificial
insemination [14, p. 248].








...delayed conception means a cow must
stand dry and nonproductive when her
lactation ceases at a maintenance cost
of about $20 per month [19, p. 580].

Approximately 53% of heats are being
missed. Dairymen appear to be losing
twice as many days due to missed heat
periods as due to failure to conceive
2, p. 2473.


The literature includes much advice about methods for heat detec-
tion, most of it having to do with heat recognition in the field.

Even then, it has been suggested that close to 50% of all heats are not

detected [2, 3].

Dairymen using artificial insemination and keeping the appropriate
records have information that can help them in detecting heat [7]. The

information consists of the date of the last service (insemination) of

each cow and data on the average number of days between successive ser-

vices. It has been suggested [27] that a chart with "heat expectancy

dates" could be valuable for detecting heat, as it would enable the

dairyman to concentrate his observations on those cows expected to come
in heat.

The design of such a report motivated initial work on the problem.

A preliminary survey using an experimental report in an actual dairy
operation revealed that rather general agreement existed among the

prospective users as to the desired content of the report and how often



Unpublished; conducted at the dairy farm of Mr. Herman Hernandez,
Isabela, Puerto Rico during January-May, 1976.





15

it should be produced. One issue that remained questionable was the

manner in which the information should be presented in the report. There

were several formats that appeared useful but each seemed to have its

own pros and cons from the point of view of ease of use. The problem

appeared to be sufficiently interesting and important to merit an

experimental evaluation of the various information format alternatives.

The problem discussed in the next section is the abstraction or

prototype designed to Investigate this information structure problem

within a controlled laboratory setting.1 The questions of interest

were widened to include a set of propositions related to a more general

MIS framework and theory. In the problem to be outlined below,-the

term "heat" is replaced with more general terminology.

2.1.2 The Abstracted Problem

Consider an organization that needs to keep records on a number

of random events that occur relatively infrequently but are important

to management. These events represents opportunities for management:

if one occurs and is not detected the organization suffers opportunity

costs.
Management knows that these events occur independently approxi-

mately once every 20 days, and that when they occur they are

"detectable" during a short period of time (approximately 24 hours).



The reasons for taking the research to the laboratory were two-
fold. First, resources were not available for conducting a reasonably
controlled field experiment. Second, the research interests of the
author were shifted from the operational considerations of the problem
to a more general set of research questions more amenable for resolution
in a laboratory setting.








It is assumed that each check made on an event to see whether it is

occurring has a fixed unit cost associated with it, independent of the

number of checks made on the same day. It can, therefore, be uneconom-

ical for management to check on these events too often. Management

is assumed to maintain a computer-based data bank with the following

data on the process:

(1) a three-digit identification (I.D.) number for
each event that is expected to occur during the
next twenty days,

(2) the date of the last observed occurrence of each
event, and

(3) data on past time intervals between successive
occurrences of each event.

It is further assumed that management will use this data to pro-

duce a periodic report to aid them in deciding which events to check

at the beginning of each day.l Their decision problem is relatively

well structured and straight-forward: they would like to detect as many

of these events as possible but face a trade-off between the costs

of "checking" and "missing" the events.

2.2 Information Content

Based on past experience, the managers in charge of checking the

events know that it is not cost-effective to check an event except on

those days when the event is more likely to occur, i.e., the days

around the date figured by adding 20 days to the last observed occur-

rence. They have suggested that a periodic chart with "event



IThey will produce the report; they will rather have the data re-
ported in its worst possible form than no report at all.






17

expectancy dates" would be useful as it would permit management to con-

centrate their checks on those days when each event is expected to occur.

A dichotomy from economic models will help to clarify the type of

report that managers consider appropriate in the problem modeled.

Managerial reports can be descriptive or normative in nature. Purely

descriptive reports, as used here, are those limited to the presenta-

tion of factual information (e.g.: production history reports, financial

reports). Purely normative reports, as used here, explicitly indicate

courses of action to be followed by the user (e.g.: production

schedules). All managerial reports can be placed on this descriptive-

normative scale. A report providing demand forecasts and safety

stock sizes [11] is, for example, more normative than one providing a

detailed sales history but no forecasts. In this study, it is assumed

that managers want more than a descriptive report (for example, one

showing only the dates of the last observed occurrence of each event).

They want a report providing forecasts for the event occurrence dates.

They consider twenty days a reasonable time horizon for the report.

It is assumed that shorter horizons would make the report too costly

to produce and longer horizons would make the forecast data basis too

dated. In conclusion, the report that is assumed to be appropriate

for the problem modeled is a periodic chart containing event I.D.

numbers and "expected due-dates" for those events expected to occur

within the next twenty days.

2.3 The "Hao-Do-We-Present-the-Information?" Problem

The information content needs of management in the problem

characterized above are assumed to be relatively well structured and

defined. The issue that constitutes the main focus of this research







is the question of information structure, i.e., the physical manner in

which the information is presented to the user. Dickson et al. have
suggested three categories of information structure enumerationss

added by the author):


It is naive to assume that information system
requirements do not vary with the type of
decision being formulated. And, it is sub-
optimal to continue developing information
support systems without serious consideration
of (1) the form in which information is pro-
vided, (2) the level of detail incorporated
into ensuing reports, and (3) the media by
which the information in transmitted
[12, p. 3].

The medium of transmission, the format of presentation, and the

level of detail are discussed below in terms of their importance in

the defined decision problem. In each case, arguments are presented

to show why each category was included or excluded as an experimental
variable in the study. The dependent variables measuring decision

performance are then presented, and the questions raised about the
effects of the experimental treatments on performance are presented
as a set of testable hypotheses. In the final section, a functional

model is presented to serve as framework for testing the hypotheses.

2.3.1 Medium of Transmission

Two media are commonly used for reports generated from a
computer-based data bank: paper printout and cathode ray tube (CRT)
display. In the case of a report that is to be produced and released



IWhen reports are generated by a computer, the choice of transmission
medium is usually confined to these two media. Otherwise, the writer is
aware that other more "personalistic" modes of communication are also
available for displaying the information to the user [22]. Only computer







every twenty days, paper would appear to be the more appropriate

medium. A CRT could be a reasonable medium if the time interval be-

tween reports was shorter and if there was a need to reduce paper

clutter. Kozar [8] found that users of CRT's tend to to be unhappy with

the lack of hard copy and that they take significantly more time to

arrive at decisions than hard copy users. The medium of transmission

was not considered a relevant design variable in the present study.

Conventional paper printout was used as the constant medium through-

out the experiment.

2.3.2 Format of Presentation

The format variable has been discussed more extensively in the

MIS literature than the medium variable [5,9,25,26]. The most common

format treatment has been summary versus raw data [9, 25,26]. This

treatment, however, has manipulated the data content more than its

format. Only one study has been concerned with format, if format is

considered to be related to the "style" of presentation.

Style of presentation. Benbasat and Schroeder [5] presented

daily production figures to experimental subjects in one of two styles:

tabular and graphical. The tabular style listed daily production figures

while the graphical style plotted the same daily figures versus time.

Their results indicated that subjects using the graphical reports had

lower costs, with no significant differences in decision time between

the two groups.1 These results suggest that the graphical format might



generated reports are considered here, however, mainly because of
the lack of resources for experimenting with other media.
IMurdick and Ross [23,p. 263] state that managers prefer graphical
displays, although they do not support their contention.








be a more appropriate style of presentation for the time-staged infor-

mation in our problem. Specifically, if the reports are to consist

only of event I.D. numbers and expected due-dates, the question of

interest is whether the formats shown in Figure 2.1 (p. 21) can in-

fluence aspects of decision performance. As discussed in section 2.5,

decision performance will be measured in this study in terms of time

performance (the time devoted to making the "check" decisions) and

cost performance (the total cost of checking and missing the events).

A prior, it would seem logical to expect the formats in Figure

2.1 to influence, if anything, time performance. The dates reported

are future dates and the information is going to be used chronologically.

Consequently, the time dimension added by the graphical style should

be helpful in that it orders the events chronologically from left

to right on the x-axis In part C of Figure 2.1, for example, it is

seen that event "032" is expected to occur first (May 26), then event

"146" (May 28), and so on.

Information layout. A chronological ordering of the events can

also be achieved with the tabular style by arranging the events in

order of expected due-dates, as in part B of Figure 2.1. It is assumed,

however, that the ordering of events by ascending I.D. numbers is a

desirable condition in these reports because management frequently

needs to make quick reference to the due-dates of particular events.

The quickest way to make these references is when the events are arranged




If these reports were intended for chinese managers, an attempt
would be made to present the information from right to left.








A. Tabular style
with I.D. layout
EVENT EXPECTED DUE-DATE
!DENT. (MONTH-DAY)

004 6-07
009 5-29
017 6-11
024 6-04
032 5-26
038 6-04
051 5-31
070 6-01
076 6-10
078 5-30
082 6-03
035 6-10
097 6-05
110 6-11
121 6-05
128 6-01
142 6-09
146 5-28
155 6-12
163 6-09
168 6-07
171 5-31
173 6-06
177 6-06
186 5-31


B. Tabular style
with due-date layout
EVENT EXPECTED DUE-DATE
IDENT. (MOnTH-0AY)

032 5-26
146 5-28
003 5-29
078 5-30
171 5-31
051 5-31
186 5-31
070 6-01
128 6-01
082 6-03
024 6-04
038 6-04
121 6-05
097 6-05
173 6-06
177 6-06
168 6-07
004 6-07
163 6-09
142 6-09
085 6-10
076 6-10
110 6-11
017 6-11
155 6-12


C. Graphical style with I.D. layout

EXPECTED DUE-DATES

EVENT MAY JUNE EVENT
IDENT. 25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13 IDENT.

004 H 004
009 H 009
017 H 017
024 H 024
032 H 032
038 H 038
051 H 051
970 H 070
076 H 076
078 H 078
082 H 082
085 H 085
097 H 097
110 H 110
121 H 121
128 H 128
142 H 142
146 H 146
155 H 155
163 H 163
163 H 168
171 H 171
173 H 173
177 H 177
186 H 186

25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13


Figure 2.1 Three forms of presenting the expected due-dates information








in ascending I.D. number order, especially when there are a large

number of events to be referenced. One solution to this dual need is

to produce two reports: one in order of expected due-dates to support

the daily checking of decisions, and another in ascending I.D. order for

quick references. But, if an experiment revealed no significant

difference in performance between the users of the graphical I.D.

ordered reports and the users of the due-date ordered reports, the im-

plication would be that there is no need for both reports. The graphical

report would provide the two desired features. Another explanation

for that result could be that the graphical style in this case has

a "calendar" resemblance and therefore presents a more familiar .

picture to the user than a listing of numbers. If this were the case,

a graphical report that presented the events in due-date order might

also influence performance. Figure 2.2 below shows such a report.



EXPECTED DUE-DATES
------------------------------------------------~----
EVENT MAY JUNE EVENT
IOENT. 25 26 27 28 29 30 31 01 02 03 04 05 05 07 08 09 10 1112 13 10ENT.
------------------------------------------------------
032 H 032
146 H 146
009 H 009
078 H 078
171 H 171
051 H 051
185 H 186
070 H 070
128 H 128
082 H 082
024 H 024
038 H 038
121 H 121
097 H 097
173 H 173
177 H 177
168 H 168
004 H 004
163 H 163
142 H 142
085 H OE5
076 H 076
110 H 110
017 H 017
155 H 155
25 26 27 28 29 30 31 01 02 03 0z 05 06 07 08 09 10 11 12 13

Figure 2.2 Graphical style with events in due-date order








The only difference between the arrangement above and that in part C

of Figure 2.1 is the "layout" of the information in the report. The

term "layout" will be used here to refer strictly to the order in

which the information is arranged in the report. Two layout schemes

are considered: I.D. number ordering and expected due-date ordering.

Questions of interest. The format alternatives considered above

appear to have pros and cons from the point of view of the ease with

which the report can be used. The format variables layout and style

will be experimentally manipulated in an attempt to address the following

questions:

(1) Can information layout by itself affect decision
performance?

(2) Can information layout interact with presentation
style to enhance or reduce separate performance
effects of either layout or style?

2.3.3 Level of Detail

Given the probabilistic nature of our data, a wide range of levels of

detail can be provided in the "expected due-dates" reports. These could

rangefrom point estimates to complete probability distributions of the

event occurrence times. On this subject, Conrath [8] proposes that

decision makers are not likely to think in terms of probability distribu-

tions, and that they prefer to think in terms of, and use, point estimates.

His argument would suggest the use of point estimate forecasts as one level

of detail in this study. The question would remain, however, whether the users
in this decision context could benefit from additional information about the






24
probability distributions from which these estimates are drawn. This

additional information could be presented, for instance, in the form

of percentiles of the distribution (e.g.: the days lying above the

fifth percentile and below the ninety-fifth percentile of the distri-

bution), The latter would have the advantage of incorporating infor-

mation about the variability of the event occurrence times and, there-
fore, the risk involved in making the check decisions,

In the problem modeled, it is assumed that there is enough data
available on past intervals between events to permit estimates of the

mean, ii, and standard deviation, si, for each event i. Using this data,

and assuming normal and stable distributions, the intervals ii 2si

were used as interval estimates for the days during which each event i
is more likely to occur. In the case of the tabular style, the reports

with such "95% confidence intervals" could appear as in parts A and B of
Figure 2.3 (p.25). The issue of format takes new importance now

since it is possible that the graphical style (part C of Figure 2.3)

may have properties that make the checking choices easier for the user.
Specifically, the level of probabilistic detail (point estimates or

interval estimates) may interact with the style of presentation

tabularr or graphical) to affect the ability of the user to process
and effectively use the information.

Questions of interest. The two levels of probabilistic informa-
tion described above, point estimates and interval estimates, will be

experimentally manipulated in connection with the format variables to
address the following questions:







A. Tabular style
with I.D. layout
EVENT 95% CCGFIDErNCE InTERVAL
IDENT. (FIRST DAY, LAST bAY)
-------------------------------
004 6-06 6-09
009 5-27 5-31
017 6-10 6-12
024 6-01 6-07
032 5-25 5-28
038 6-02 6-05
051 5-29 6-03
070 5-31, 6-02
076 6-09 6-12
078 5-27 6-02
082 6-01 6-05
085 6-09 6-14
097 6-04 6-07
110 6-10 6-12
121 6-03 6-07
128 5-30 6-04
142 6-06 6-12
146 5-26 5-30
155 6-11 6-13
163 6-07 6-1
168 6-04 6-10
171 5-28 ,6-03
173 6-05 6-07
177 6-05 6-08
186 5-29 6-03


8. Tabular style
with due-date layout
EVE!F S55 CC;FIDE.'CE INrTERVAL
IO;DE. (FIRST CAY, LAST DAY)

.032 5-25 ,5-28
146 5-26 5-30
009 5-27 5-31
C78 5-27 6-02
171 5-28 6-03
051 5-29 6-03
186 5-29 6-03
128 5-30 6-04
070 5-31 6-02
082 6-01 6-05
024 6-01 6-07
036 6-02 6-06
121 6-03 6-07
168 6-04 6-10
097 6-04 6-07
173 6-05 6 -07
177 6-05 6-08
004 6-06 6-09
142 6-06 6-12
163 6-07 6-12
085 6-09 6-14
076 6-09 6-12
110 6-10 6-12
017 6-10 6-12
155 6-11 6-13


C. Graphical style with I.D. layout

95% CONFIDENCE INTERVALS

EVENT MAY JUNE EVENT
IDENT. 25 26 27 28 29 30 31 01 02 03 04 05 05 07 08 09 10 11 12 13 IDENT.


HHH


H H H H


H


H H H






H H H H


H H

H


H H H H 004
H H 009
H H H 017
H H H H H H H 024
032
H H H H H 038
SH H H H 051
H H H 070
H H K H 076
H H H H 078
H H H H H 082
H H H H H 085
H H H H 097
H H H 110
H H H H H 121
H H H H H H 128
H I H H H H H 142
H 146
H H H 155
H H H H H 163
H H H H H H H 163
H H H H 171
H H H 173
H H H H 177
H H II H H 185


25 26 27 28 29 30 31 01 02 03 04 05 06 07


08 09 10 11 12 13


Figure 2.3 Three forms of presenting the interval estimates information







(1) Can users of probabilistic data make effective
use of information beyond point estimates?

(2) Can the format in which probabilistic data is
presented affect choice behavior?

(3) Can the level of probabilistic information in-
teract with format to affect user performance?

2.4 Number of Decision Entities

An important consideration is now introduced: the influence that

any report format will have is very likely to be related to what is

called here the "number of decision entities" on the report. The

term "decision entities" will be used to refer to the separate pieces

of information present on a report and on which decisions are required.

In the real problem this study is based on, there is little doubt that

the report users would be indifferent about format if their reports con-

tained information on only two or three events. This is not expected

to be the case, however, if the reports contain information on 200

events.1 The "number of of decision entities" was included as an experi-

mental variable to empirically test the assertion that while report

users may feel indifferent about format when small amounts of infor-

mation must be processed, they will move toward preferred formats,

and their performance will be more sensitive to format as the amount

of information they must process increases.2



A manager dealing with more than 200 decision events told the
writer he "could care less" about format if he did not have so many
events to look after.
2"Amount of information," as used here, must not be confused with
"information overload," a condition where the decision maker is given
too much, unnecessary information [1, 9, 15].








2.5 Dependent Variables

Time performance. Since time is a valuable managerial commodity,

decision time is commonly used as a decision performance criterion

[5,9,18,26]. Although not supported by any studies, Murdick and Ross
contend that "... format should be established to save the manager's

time" [23, p. 326]. Decision time will be used here as a proxy for the

value of managerial time to the organization. It will be measured by

the total time that the decision maker devotes to making the. checking

decisions. It is expected that this measure will be correlated with

the cost measure, although the cost measure will not include the cost

of managerial time to avoid double counting. Chervany and Dickson [9]

found that some decision makers will take longer to arrive at their

decisions but will make lower cost decisions. The possible correla-

tion between the time and cost measures will be taken into account

through the use of multivariate statistical procedures (viz., MANOVA).

Cost performance. Cost is also commonly used as a performance
criteria when decision effectiveness is discussed [5,9,18,26]. Cost

performance will be measured here by the total of the "checking" and

"missing" costs. For each check made, the decision maker will in-

cur a fixed dollar cost. The checking cost, then, will be given by

the product of the total number of checks made times the fixed cost

per check. For each event that is not detected, the decision maker
will incur a fixed opportunity cost. The cost of missing is then

calculated as the product of the total number of misses times the fixed

cost per miss.

Choice behavior. Choice behavior was also included as a criterion

variable to test Conrath's [ 8] contention that the format in which






28
probabilistic data is presented influences the choice behavior of the

user. It was assumed this could be the case with the tabular versus

graphical formats in the current study. The graphical format appears

to "bring out" more vividly the information, especially in the case

of the interval estimates (see Figure 2.3, p. 25). The measure used

for choice behavior was the number of checks performed by the decision

maker, disregarding which were successful and which were not.

2.6 Research Hypotheses

The questions raised above are now presented as six testable
hypotheses. Three of the hypotheses relate to format, two to the

level of detail, and one to the number of decision entities in the

report. The hypotheses relating to format are presented first.

1. The layout or physical order of the information in a (HI)
report can reduce decision time. In this case, it is
expected that the users of the due-date ordered reports
will have shorter decision times than the users of the
I.D. ordered reports.

This hypothesis was not found to have been considered in the
MIS literature, either in field or laboratory work. There are many

ways in which the same information can be arranged in a report. Even

though the "best" way may usually be considered "apparent" or the

issue simply "unimportant," this may not always be the case.

2. The format in which probabilistic data is presented as a (H2)
basis for choice can influence choice. In this case, it
is expected that the graphical report users will choose
to make more checks than the users of the tabular reports.
This hypothesis was suggested by Conrath [ 8] but not statis-
tically demonstrated in his paper. He states:

Apparently format has the characteristic
that it can focus one's attention on one







dimension of the choice space, and that
dimension becomes paramount in the de-
cision process. ...Whether the attention
focusing attributes of data format are
the keys to the influence that format has
on choice is a question not yet resolved.
But the question would appear to be
sufficiently important that it should no
longer be ignored [8, p. 880].

The format variable that is expected to have "attention focusing at-

tributes" in this case is the style variable (graphical versus tabular).

As such, the style factor will be the one analyzed in the evaluation

of this hypothesis.

3. Report layout and style can interact to enhance or reduce (H3)
the decision time effects of a particular layout or style."
In this case, it is expected that the users of the I.D.
ordered reports in graphical style will have shorter de-
cision times than the users of the same layout in tabular
style.

The objective in testing this proposition is to demonstrate the

existence of information format characteristics that may have joint

effects on decision performance. Here, the combination of the I.D.

ordering layout with the graphical style is expected to reduce the

long decision times associated with the absence of the convenient due-

date ordering.

The next two hypotheses relate to the level of detail of the

probabilistic information provided.

4. Users of probabilistic data can make effective use of (H4)
information beyond point estimates. In this case, it
is expected that the interval estimates users will make
more cost-effective decisions than the point estimates
users.

The interest in this hypothesis is twofold. First, its evaluation

should give an indication as to whether the users of this type of







report can make effective use of interval estimates. In the real

problem this study is based on, it is expected that interval estimates

can be useful. Second, this proposition provides a setting for testing

Conrath's [8] contention that decision makers are not likely to think

in terms of probability measures other than point estimates.

5. The time to process and effectively use probabilistic (H5)
information is related to the format in which the in-
formation is presented. In this case, it is expected
that the users receiving the interval estimates in the
graphical style will have shorter decision times than
those receiving the interval estimates in the tabular
style.
The difference between HI and H5 is that HI refers to the direct

(main effect) influence of layout on performance while H5 refers to
the interaction between a format variable (style) and the level of
probabilistic information provided (point estimates or interval

estimates). The purpose in testing this hypothesis is to show that

different levels of probabilistic information will be more easily

processed and used with different formats of presentation.

The hypothesis relating the number of decision entities to

format preference is:

6. Report users will move from format indifference to for- (H6)
mat preference and their performance will be more sensitive
to format as the number of decision entities on their
report increases. In this case, no significant format
opinion differences are expected among users of reports
with few decision entities in them, with the opposite
expected among users of reports containing many decision
entities. Differences in performance are also expected
to be larger among the users of the reports with many
decision entities.

The objective in testing this proposition is to demonstrate the
existence of a "number of decision entities" variable that should be






31

considered in MIS design. This variable will occur in most situations

where the number of physical phenomena on which decisions are required

is variable. A procurement manager, for instance, may be indifferent

about the format of his inventory status reports if he must place orders

for only five items. He would probably be concerned about format (and

his performance would be more influenced by format) if he has 250 items

on which to place orders.

2.7 Basic Functional Model

The following model presents, in equation form, the relationships

to be analyzed.

Given equations 1.3 and 1.4,

EU = f(F,LIDE,DM,CIS') (1.3)

P = f(EUIDE,DM,CIS') (1.4)

it follows that

P = f(F,LIDE,DM,CIS') (2.1)

where, P = decision performance

F = format of presentation

L = level of detail

DE = decision environment characteristics

DM = decision maker characteristics

CIS' = other characteristics of the information system.

In words, equation 2.1 states that the format of presentation

and the level of detail are determinants of decision performance






32

given a particular decision environment, decision maker, and other

characteristics of the information system. In the next chapter, a

table is presented that shows each of the research hypotheses expressed

as a variant of this basic model.














CHAPTER 3

THE EXPERIMENT

3.1 Method

This chapter presents the details of the experiment that was con-

ducted to evaluate the decision performance effects of four factors:

information layout, style of presentation, level of detail, and the

number of decision entities on the report. The material has been

arranged as follows. Section 3.1.1 discusses the nature of the experi-

mental subjects. The methods used for collecting and analyzing the

experimental data are presented in Section 3.1.2. A full description

of the experimental task is given in Section 3.2. Finally, the experi-

mental results that should be expected for the hypotheses to be backed

up are discussed in Section 3.3.

3.1.1 Subjects

One-hundred sixty subjects participated in the experiment. The

subjects were undergraduate students in Business Administration who had

completed the first semester of introductory statistics at the Univer-

sity of Puerto Rico, Mayaguez Campus. They were invited to participate

through announcements placed on bulletin boards and read in classrooms.

No monetary incentives were offered but the rate of volunteering was

high: an initial "sign-up" list yielded more than 200 subjects.






34

Ten subjects were randomly assigned to each of the sixteen (four
factors each at two levels) experimental conditions. The assignments

were made with a random number generator that uniformly distributed

the numbers 1 through 160 among the sixteen conditions until a

schedule was formed with ten numbers assigned to each condition. As

subjects arrived to participate, their order of arrival was checked

against the schedule to determine their condition assignment. It

was felt that this assignment scheme avoided the problem of making

individual subject "appointments" at the same time that it provided

a means for stratifying the assignment of the experimental conditions

through the three months it took to complete the study.

3.1.2 Design and Analysis

Table 3.1 (p. 35) is a summary of the 24 factorial experimental
design. The two levels for the number-of-decision-entities variable

were achieved by dividing the experimental subjects into two groups.

One group was assigned to an experimental condition where only five

events of interest had to be checked, referred to below as the "few

decision entities" condition. The five events were selected to form

a stratified representation of the twenty-five events that would take

place in the "many decision entities" condition. User preference for

particular formats was measured with a questionnaire administered to the

subjects at the end of the simulation runs (see Appendix B, p. 78).

In this study, no significant differences in format preference ratings
are expected between the various format combinations among subjects

assigned to the few-decision-entities group. Significant opinion

differences, however, are expected among the ratings of the subjects

























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36
assigned to the many-decision-entities group. Differences in decision
performance are also expected to be larger for the many decision

entities group.

Each of the sixteen resulting cells contained observations of 10
subjects' decision time, total checks made, total cost, and the five

ratings to the format opinion questionnaire. The data was analyzed

using Multivariate Analysis of Variance (MANOVA)2 MANOVA procedures

have the advantage of considering correlation among the dependent vari-

ables [6]. Peter et al. [24], suggest that the technique should be
used, as opposed to the univariate ANOVA, whenever there is reason to

believe that multiple dependent varialbes might be correlated. 'Winer

[30, p. 232] points out that by considering possibly correlated depen-
dent variables in a series of independent univariate tests, one fails
to obtain information about the total effect of the experimental treat-
ments on all the criteria simultaneously. In the case of experimental

MIS research, a close correlation has been suggested between time and
cost, two of the criteria most frequently considered in the literature.

In none of the reviewed literature, however, was MANOVA used.

In the current study, separate ANOVA's will be conducted on
each of the dependent variables after overall significance is obtained



IDecision time was rounded to the nearest minute and did not in-
clude the time devoted to the post-experimental questionnaire. The
subjects were clocked as soon as their last "check" decision was made.
2BMD12V Multivariate Analysis of Variance and Covariance, Health
Sciences Computing Facility, Department of Biomathematics, School of
Medicine, University of California, Los Angeles, 1976, p. 751.





37

by MANOVA. This procedure is necessary in order to evaluate directional

hypotheses relating to specific dependent variables. Further investi-

gation into the directions of obtained differences will be conducted

using Scheffe's posthoc test for comparisons between means [17, pp.
483-486].


3.2 Experimental Task

The experimental subjects acted as the decision makers in the

problem described in Chapter 2 A computer simulation of the decision

environment was created which modelled the essential features1 of the

decision problem. Those features are:

1. The decision maker wants to detect a number of events that
occur at random with normally distributed intervals between
successive occurrences.

2. He has data on the random events and wants to use it to make
cost-effective decisions on when to check each event.

3. He incurs a fixed opportunity cost for each event that occurs
and goes undetected.

4. He incurs a fixed cost for each "check" that he makes on an event.

5. His objective is to minimize the total combined costs of
"checking" and "missing" the events.

These features were incorporated into the simulation model as
follows:

1. A hypothetical data set for the means, x., and standard
deviations, s., for the between occurrences interval of each
event i was uied to generate "actual" occurrence



Van Horn recommends that a good guide in developing an
effective prototype is "to restrict the prototype content to the
minimum set of features that are directly relevant to the problem
modeled" [28, p. 179]. His advice was followed here.







dates for a number of hypothetical events.1 Table 3.2 (p.39)
shows that data in two parts: the data used for generating
the 25 events in the "many decision entities" condition, and
the data used for generating the 5 events in the "few
decision entities" condition. The "generator"'was validated
to verify that the events were generated according to a normal
distribution with the means and standard deviations indicated
in Table 3.2.

2. Based on the data on Table 3.2, forecasts for the event
occurrence dates were prepared and presented in report form.
These were the experimental reports (conditions) administered
to the subjects to help them in making their daily check
decisions. Several of these reports have already have been
shown in Chapter 2. The rest are shown in Appendix A, p. 69.
3. The subjects were told that undetected events at the end
of the simulation would cost them $5 each. These will be
counted and multiplied by $5 to determine their total "missing"
cost.

4. Subjects were also told that each and every check made during
the run would be charged at $1 per check. These would be
counted at the end of the run to determine their total
"checking" cost.

5. Finally, subjects were instructed that their objective in the
game was to minimize their total cost figured as the sum of
their "checking" and "missing cost. Subjects were told that
their run time was also being measured, but were given no
time limit or other time pressures.

Subjects interacted with the simulator through typewriter-type

computer terminals in deciding which events to check for at each of

twenty decision points (days). At each decision point, the subject

chose the I.D. numbers of the events he wanted to check and entered

them for processing by the simulator. The simulator reported whether

or not the events checked occurred on that day, i.e., whether the

checks were successful or not.



Another Van Horn guide followed here in developing the present
prototype is "...to replace large actual data bases with small, care-
fully stratified representations." [28, p. 179].
2This and subsequent dollar figures were also simulated. No
monetary incentive scheme was used to make subjects do "their best."










TABLE 3.2

DATA FOR THE EXPERIMENT


Event Date of Last Number of Days between Occurrences
I.n. Occurrence Mean Standard Deviation
____


A. Data for the many-decision-entities condition


B. Data for the few-decision-entities condition


Event Date of Last Number of Days between Occurrences
1.0. Occurrence Mean Standard Deviation
(;j) (sj)










Uniform instructions were administered to all subjects regarding

the general nature of the experiment and their participation (see

Appendix D, P. 89). Special care was given to insure that subjects

understood their objective in the game. A familiarization session

consisting of five decision points (days) was conducted to acquaint

subjects with their decision environment. These sessions were con-

sidered to be long enough to check subject "learning" effects during

the experimental runs. In no case did data collection begin until

subjects indicated that they felt comfortable with the procedure and

ready to start.l

Three remote terminals were used for conducting the runs. Each

was on line with a master program that maintained a file with the results

of each run. The file included the three performance criteria and the

scores of the post-experimental questionnaire.

3.3 Evaluation of Hypotheses

Table 3.3 (p. 41) shows the main effects and interactions that

should be observed for the hypotheses to be supported. Each of these
effects is discussed next.

Hypothesis 1. Information layout (factor A in table 3.1) should

affect decision time such that the subjects with due-date ordered reports

should have shorter decision times than those with I.D. ordered reports,

i.e., A2 < Al: TIME. The due-date order should be more convenient

given the chronological way in which the information is going to be used.



The average familiarization session took 15.2 minutes.




















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42

Hypothesis 2. The total number of chekcs made should be affected

by the style treatment (factor B). It is expected that the number of

checks made by the graphical style users will be larger than that
made by the tabular style users, i.e., B2 > Bl: CHECKS, since the

graphical style appears to illustrate the "choice space" more clearly,
thus inviting more check decisions.

Hypothesis 3. Decision times of subjects receiving some com-

bination of layout and style (A,B) should be significantly different

from decision times of subjects receiving some other combination.

Specifically it is expected that subjects receiving I.D. ordered

reports and the graphical style will have shorter decision times than

those receiving the I.D. ordered report but not the graphical style,

i.e., A12 < AB1 : TIME. The graphical style should reduce the

need for the time-convenient due-date ordering.

Hypothesis 4. Subjects receiving the interval estimates should

perform better than those receiving only point estimates, i.e., it is
expected that t2 < el : COST. The interval estimate subjects will
have more information on the random nature of the events.

Hypothesis 5. Decision times of subjects receiving some combina-
tion of style and level of detail (B,C) should be significantly

different from decision times of subjects receiving some other com-
bination. In particular, it is expected that the subjects receiving
interval estimates in the graphical style will have shorter decision

times than those receiving the interval estimates in the tabular
style, i.e., B2-2 < BIC2 : TIME. The graphical style should make

it easier for users to process the interval estimates information.





43

Hypothesis 6, Differences in format opinion should be observed
among the various layout and style treatments administered to the
many-decision-entities subjects (D1). Differences in both time and
cost performance should also be observed among this group. This
will indicate that report users have preference differences for
format and their performance is more sensitive to format when the
number of decision entities on their report is large. Non-significant
differences should result among the same layout and style combinations
administered to the few-decision-entities subjects (D2). In general,
it is expected that the opinion ratings will average higher for the
few decision entities group, i.e., 62 > B1: RATINGS. Differences

i, A2D1, BID B2DI and AIB1D1 A1BD are expected to be
significant for cost, time and the opinion ratings. The same com-
parisons with D2 instead of D1 are not expected to be significant
(the few-decision-entities case). The experimental results are pre-
sented in the next chapter.














CHAPTER 4

EXPERIMENTAL RESULTS

4.1 Introduction

The statistical results of the experiment are presented in this

chapter. Table 4.1 (p. 45) shows the cell means obtained for the three

performance variables and the five format opinion questions. The

results revealed significant differences among treatment means to

support five of the six research hypotheses.

4.2 Results

4.2.1 Effect of Layout on Decision Time

The first hypothesis, that information layout can reduce decision

time, was supported. As Table 4.1 shows, decision time was shorter for

the subjects receiving the due-date ordered reports, A2, than for

those receiving the I.D. ordered reports, A, (13.8 versus 16.4 minutes).

A multivariate test on the three performance variables showed a sig-

nificant layout main effect (F = 7.41, p < .00001).1 A univariate

test on the decision time variable also revealed a significant dif-

ference between the two layout groups (A2 < A1, F = 13.35, p < .003).
A Scheffe post-hoc test revealed an even stronger relationship when



1All multivariate F's presented here are based on 3 and 142 degrees
of freedom. All the univariate F's are based on I and 144 degree of
freedom.
















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46
only the many-decision-entities subjects, DI, were considered. With-
in this group, the subjects receiving the due-date ordered reports

had significantly shorter decision times (A21, < AID1, F = 26.76,
p < .00001). No significant interactions were observed within the
few-decision-entities group. In all the tests, decision time was

significantly shorter for the subjects receiving the due-date
ordered reports, thus supporting the hypothesis.

4.2.2 Influence of Format on Choice Behavior

The second hypothesis, that the format in which probabilistic
information is presented can influence choice behavior, was supported.
As Table 4.1 illustrates, the total number of checks made was higher
for the subjects using the graphical style, B2, as opposed to the
tabular style, Bl. A multivariate test revealed a weak interaction
between style and number of decision entities (F = 2.60, p < .06).
Univariate tests on the number of checks variable showed a weaker style
main effect (B2 < B1, F = 3.38, p < .07) and a stronger style and
number of decision entities interaction (F = 4.75, p < .03). With-
in the many-decision-entities group, a Scheffe test revealed that
the average number of checks was significantly higher for the
graphical report users (73.5 versus 66.0, F = 8.07, p < .005). No
significant differences were found in the number of checks made within
the few-decision-entities group, and a comparison of the differences
in the number of checks between the two styles subjects for the DI
and D2 groups was highly significant ([B2D1 BlDI]<[B2D2 BD2],
F = 131.83, p < .00001). Presuming that the total number of checks
made, regardless of success, was a reasonable measure of choice






47
behavior in this problem, the results support Conrath's [8] contention
that presentation format influences choice behavior.

4.2.3 Joint Effect of Layout and Style Decision Time

The third hypothesis, that information layout and style can inter-

act to reduce decision time, was supported. Table 4.1 shows that,

within the many decision entities group, subjects using the I.D.

ordered reports, Al, had shorter decision times when they also re-

ceived the graphical style, B2. A multivariate test revealed a mar-
ginal interaction between style and number of decision entities

(F = 2.43, p < .08). Univariate tests on the decision time variable
showed a stronger ABD interaction (F = 6.20, p < .02). A Scheffe

test revealed that the significance was due to the shorter decision
times of the subjects receiving the I.D. ordered reports in graphical

style ( 1A, < A1B1Dl, F = 7.01, p < .009). The fact that a
significant layout and style interaction was observed only within the

many-decision-entities group also supports H6: that performancel

becomes more sensitive to format as the number of decision entities

on the report increases. This is also demonstrated by the fact that,

within the few-decision-entities group, both the layout main effect

(F 1 0, p 1 1) and the interaction between layout and style (F = .78,

p > .35) were not significant.

4.2.4 Effect of Probabilistic Detail on Cost Performance

The fourth hypothesis, that users of probabilistic data can make
cost-effective use of information beyond point estimates, was not


Time performance in this case.






48
supported. Subjects receiving the interval estimates treatment, C2,

had lower costs than those receiving the point estimates, C1, but
the difference was not significant. The univariate level-of-detail

main effect, with cost as the dependent variable, had F = .34, and

the Scheffe test on C1D, 12 was also non-significant (F = 2.42,
p > .10). Contrary to the author's expectation, these results do not

systematically support the hypothesis, though the directions are as

predicted, nor do they support Conrath's [8] argument that decision

makers do better with point estimates than with other probability
measures.

4.2.5 Joint Effect of Format and Level of Detail on Decision Time

Support of the fifth hypothesis was weak. The hypothesis is that
the format in which probabilistic data is presented interacts with the
level of detail to influence the time required to process and use the

information. The multivariate level-of-detail and style interaction
was not significant (F = 1.19, p > .25). There was a significant
univariate interaction between style, level of detail, and the number

of decision entities on the report (F = 3.70, p 4 .05). In particular,
the many-decision-entities subjects, D1, receiving interval estimates,

C2, in graphical style, B2, had significantly shorter decision times
than those receiving the same level of detail but in tabular style

(B2C2D1 < BIC2DI, F = 4.95, p < .03). This result suggests that
certain formats may be better for reporting certain levels of prob-
abilistic detail, but the absence of a significant multivariate
effect makes the inference rather weak.








4.2.6 Relation between Number of Decision Entities and Format

The sixth hypothesis, that report users' preference for and

sensitivity to format is related to the number of decision entities

on their reports, was supported. Table 4.1 shows that subjects with-

in the many-decision-entities group gave significantly different

ratings to the various layout and style combinations. Two of the five

opinion questions were used to verify that the subjects understood

and systematically answered the post-experimental questionnaire

(see Appendix B, p. 78). The validation consisted of checking that
the ratings for these two questions were consistent with performance
of subjects receiving the particular treatments mentioned in the

questions:

(1) Question asked the subjects to rate the order of
the information in the reports. Table 4.1 shows
that the subjects receiving the due-date ordered
reports gave significantly higher ratings to this
item than those receiving the I.D. ordered reports
(A2 > Al, F = 17.33, p < .00004).

(2) Question 4 asked the subjects to rate the level of
probabilistic detail given. Table 4.1 illustrates
that the subjects receiving the interval estimates
consistently gave higher ratings to.this item than
those receiving the point estimates treatment
(C2 > Cl' F = 18.86, p < .00002).

In the case of Question 1, the many-decision-entities subjects

gave significantly higher ratings to the due-date ordered reports
(A21 > A1D1, F = 19.08, p < .12). In Question 5, where the subjects
were asked to give an over-all rating for the format of their reports,
there was a significant difference in ratings between the many and

few-decision-entities groups (D2 > Dl F = 5.23, p < .03).






50

With regard to the relationship between the number of decision

entities and the sensitivity of performance to format, the discussion

of the first five hypotheses has shown that the performance of the

many-decision-entities subjects was more sensitive to format than

that of the few-decision-entities subjects. In all the comparisons

the differences in performance were larger among the many-decision-

entities subjects than among the few-decision-entities subjects.














CHAPTER 5

DISCUSSION OF RESULTS

5.1 Summary of Findings

Tables 5.1, 5.2, and 5.3 (pp. 52- 55) present a summary of the

experimental results that had a significance level of p <.10 or better.

The results have been grouped into main effects, interaction effects

involving the number-of-decision-entities variable, and other interaction

effects. In each case the actual significance figure has been given so

that the reader can make his own judgement on the significance of each

result. The hypotheses relating to each result are also shown in the

right margin, along with a line reference number, to facilitate the dis-

cussion in the following sections.

The results are discussed first for the multivariate (MANOVA) effects.

These do not relate to any hypothesis in particular, since the hypotheses

have been stated in terms of the effect of the experimental treatments on

specific criterion variables. They contain, however, important infor-

mation about the total effect of the treatments on decision performance

in general. The univariate effects are discussed next as they relate to

each hypothesis. In each case, the implications for both the MIS re-

searcher and practitioner are discussed.

5.2 The Multivariate Effects

Information layout (I.D. versus due-date ordering) was found to




















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56

affect decision performance in general (5.1.1). Multivariate analysis

is called for here since the performance criteria measured (decision

time, cost, and number of checks made) are not independent. In the pre-

sent study, the simultaneous effect of layout on the three performance

criteria gives more value to the observed univariate effect on decision

time. If Hypothesis 1 (p.28) had read "information layout can influence

decision performance," it would have been equally supported. The fact

that the effect observed was stronger among the many-decision-entities

subjects (5.2.1) also lends support to Hypothesis 6 (p. 30),

The style of presentation tabularr versus graphical) was also seen

to have a significant total effect on decision performance (5.2.3).

Although the univariate effect of style on cost was not significant, the

graphical style users within the many-decision-entities group had higher

costs than the tabular style users ($104.73 versus $100.95, p = .258).

This result contradicts Benbasat and Schroeder's [5] results, who

observed that subjects with graphical reports had lower costs than those

with tabular listings. Neither result, however, is significant (theirs

had p = .148). This indicates that there is still no basis for predicting

the effect of presentation style on cost performance. Either result

could have been due to chance alone.

In terms of future MIS research, it is suggested here that MANOVA

should be used in the analysis of experimental data. Winer [30, p. 232]

explains that whenever there is reason to believe that dependent variables



The number in parenthesis is the reference to the related result in
the tables.






57
are correlated, these should be considered simultaneously to obtain infor-

mation about the "total" effect of the experimental variables. In the case

of experimental MIS research, there is reason to believe that commonly

used criteria, such as cost performance and decision time, are correlated

[5, 9].
From the point of view of the MIS practitioner, these multivariate

results point to one conclusion: the format in which information is pre-

sented can influence decision performance. The multivariate separate

and joint effects of the two format variables in this study, layout and

style, support this view. The specific directions of these effects are

discussed next.

5.3 The Univariate Effects

5.3.1 Effects Related to HI and H3

The influence of information layout on decision time was found to be

strong (5.1.2), as predicted in Hypothesis 1 (p. 28). The shorter deci-

sion times for the subjects with due-date as opposed to I.D. ordered

reports were expected, since the due-date ordering was more convenient in

the present problem. This hypothesis, however, was evaluated for two

reasons. The first was to demonstrate the importance of arranging the

information in a manner consistent with the way information is used. This

seemingly obvious observation appears to have been ignored in many reports

this author has had to use. The second reason was to prepare a basis

for Hypothesis 3 (p.29). There, it is proposed that long decision times

due to an inconvenient information layout can be reduced by introducing

a second format element, namely, the graphical style. The time dimension

added by the graphical style had the effect of reducing the need for the






58
due-date ordering, while still maintaining a desirable feature of the

report (the I.D. ordering of the events). This is evidenced by the re-

sult in Table 5.2, line 6.

These results have other implications, besides supporting HI and

H3. For the MIS researcher, they suggest the need for more investigation

on the layout variable. It might be revealing example, to look at

information layout schemes for information on events that are less time-

dependent in nature than the ones studied here.l Maintenance data on

some mechanical process, for example, could provide a setting for an in-

teresting and practical experiment.

To the MIS practitioner, and in particular to the person in charge
of designing information formats, the results emphasize the importance

of reporting information in a manner consistent with the way recipients

use it. Also, the observed interaction between layout and style suggests

that practitioners should be on the alert for joint effects among format

elements that can work to their advantage in enhancing the readability

of the report.

5.3.2 Effects Related to H2

Perhaps most striking was the result that subjects with graphical re-
ports chose to make substantially more "checks" than subjects with tabular

reports (5.1.3). This supports Hypothesis 2 (p. 28), namely, that the for-

mat in which probabilistic information is presented can influence choice.

From a research point of view, a question that remains to be answered is



All events in this world are probably time dependent, but their
occurrence may be more dependent on time for some types (e.g., biological)
than for others (e.g., electrical components).






59
whether the observed effect was related to the short duration of the

experiment, or whether the effect would have continued even if the sub-

jects had been given enough time to get fully acquainted with their re-

port style. In either case, the result observed here is an important

finding since many real-life managerial reports have short-term use, are
"one-shot-non-recurrent" reports, and, very frequently, contain informa-

tion of a probabilistic nature. Ergo, the information analyst that must

report probabilistic information as a basis for decisions appears to have

a delicate problem at hand: if the format in which he presents the in-

formation is going to bias the choice of the decision maker, he will

surely want that bias to be in the "correct" direction. This point is

also related to the issue of normative versus descriptive reports, and is

a point that should be further investigated elsewhere.

5.3.3 Effects Related to H5

A result closely related to the preceding discussion provided

support for Hypothesis 5 (p. 30; 5.2.7). Within the many-decision entities

group, the subjects with interval estimates had shorter decision times

when the information was given to them in graphical as opposed to tabular

style (21.4 versus 24.6 minutes, p = .026). Point estimates users, how-

ever, did not experience the same benefits in moving from the tabular

to the graphical style. Their average decision time, in fact, was higher

with the graphical style than with the tabular style (22.1 versus 20.7

minutes, p = .32). These results suggest that different levels of

probabilistic information may be more appropriately reported using dif-

ferent presentation formats. In the present experiment, subjects with

the tabular style did as good or better than-subjects with the graphical








style when only point estimates were reported. The compact tabular for-

mat was inadequate, however, for processing the interval estimates in-

formation.

5.3.4 Effects Related to H6

All the effects that have been discussed thus far were found to be

more marked within the many-decision-entities group (5.2.2, 5.2.4, 5.2.6,

5.2.7). This supports one of the propositions in Hypothesis 6, (p. 30),

that user performance becomes more sensitive to report format as the

number of decision entities on the report increases. In the present ex-

periment, the subjects with five decision entities on their reports had

so little information to process that whether it was given in I.D. order,

due-date order, tabular style or graphical style did not make much

difference on their performance. Evidence of this is that, within the

five decision events group, there were no significant differences in

performance for any of the performance measures. The only effect that

approached significance in that group was an interaction between style

and level of detail with cost as the dependent variable (p < .10).

The other proposition in H6, that report users will move from in-

difference to preference for particular formats as the number of decision

entities increases, was also supported. The subjects in the few-decision-

entities group gave more or less constant high ratings to the various

format characteristics of their reports (5.2.8, 5.2.9). Within that

group, there were no significant differences in the ratings for the order

of the information in the reports (layout). For the over-all format

rating, only one difference was significant. Interval estimate subjects

gave significantly higher ratings than point estimate subjcets (4.25

versus 3.80, p = .044). Among the subjects with twenty-five decision






61
entities, the story was quite different (5.2.8, 5.2.9, 5.3.3, 5.3.4).

There were significant differences between their ratings in the various

layout and style conditions.

For the MIS practitioner, these results suggest that they should give

careful attention to report format, especially when the report must grow.

The results obtained here give meaning to Voich, et al.'s statement,

"As the complexity of a report increases, its likelihood of extent of use

falls" [28, p. 229]. When preparing a report for a procurement manager,

for example, a standard layout by major classes of items, code number,

etc., may be appropriate if the number of items that must be ordered each

time, and their frequencies of ordering, are small. If the number of

orders that must be placed were to increase considerably, it may be to

the manager's advantage to have the layout of his report revised. A more

favorable layout in that case could be, for example, to have the items

arranged according to the frequency with which they are ordered.

For format revisions or similar actions to occur, the channels of

communication between the information analyst and user must first be im-

proved. At the present, there appears to be a "tail versus dog" problem

between information users and providers when it comes to seemingly un-

important matters, such as designing a format for a report. Voich et al.

state:

Report formats are often not tailored
precisely to users' needs. One reason
for this is the programming costs asso-
ciated with special arrangements of in-
formation, especially if several dif-
ferent users each request a unique for-
mat. A second reason for finding formats
not tailored exactly to user's needs is
that report designs are often based on






62

the system analysts' or programmers' pre-
ferences for programming ease [28, p. 229].

It would appear that better communication channels between the

analyst and the user should, at least, help to alleviate the second

reason noted above.












CHAPTER 6

SUMMARY AND POSSIBLE EXTENSIONS

The present study has considered some of the implications of the

relationship between information format and decision performance. A

specific information-decision problem was abstracted to create a simu-

lated decision environment within which alternative forms of presenting

information relevant to the problem were experimentally manipulated.

Six hypotheses were tested in relation to the effects of the information

format treatments on subject performance. The experimental data supported

five of the six hypotheses. As is always the case with empirical re-

search, however, a number of questions can be raised in connection with

the observed results. Some questions result from inquiring into the

limitations of the present study. Others follow logically from the re-

sults.

Among the limitations, there is the problem of having used student

subjects as surrogates for managers [13]. The actual managers in the

real-life problem modeled could have served as subjects in a field study.

This, of course, may bring about other complications, in particular,

problems of experimental control. Van Horn states:

The unifying theme of field tests is sad
stories. In every one, operational con-
siderations (understandably) dominate
test conditions. As soon as a conflict
arises, the test yields. Even if a test
proceeds to completion, endless arguments
arise over interpretation of the results
[28, p. 175].









Going to the field also implies having to deal with uncooperative

mother nature, as opposed to pre-chosen probability distributions for

the events of interest. Notwithstanding this dismal picture, a field

experiment should be useful. By establishing the external validity of

the present study with respect to the subject population, decision-

making conditions, and other areas of interest, its benefit for the

actual population can be established and considered in the design of

a field study. A practical field study could be, for example, one in

which a more normative report is provided to the manager facing the

heat detection problem. Such a report could be based on some optimal

decision rule indicating to the dairyman the days in which he should

check for heat in particular cows. The decision rule would have to be

based on some cost estimates (costs of "checking," "missing," and

breeding after a successful "check"), and on some probability estimates

(probability of detecting heat on given "checks," probability of a

successful breeding once heat is detected, etc.).

Although growing fast, "experimental work on MIS is still in its

infancy" [5, p. 17]. Many promising areas have not been investigated.

One line of research that follows from the present results is the re-

lationship between the number of decision entities in the report and

the sensitivity of performance to format variables. Various number-

of-decision-entities levels could be manipulated in a parametric study
to investigate such questions as, "Can a report user adapt to an

increasing number of decision entities in his report without a rapid

deterioration in his performance?" To investigate this question

the same subjects would have to be given increasing numbers of decision

entities, and this could bring up problems of subject "learning." If

properly controlled, however, an experiment along these lines could






65


shed light into such questions as, "At what point would it have been

appropriate to have a format revision?"

Another area that appears to need more consideration is the analysis

of interaction effects among information structure characteristics [5, 12].

In this study, an interaction was found between the level of detail and

format, suggesting that different levels of detail may be more easily

processed with different styles of presentation. The validity of findings

such as this one should be further investigated in other decision contexts.

Finally, research relating empirical MIS findings to current trends in the
theory of human information processing may be useful in providing a better

understanding of the results observed.














BIBLIOGRAPHY

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2. Barr, Harry L., "Influence of Estrus Detection on Days Open in
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3. Barr, Harry L., "Breed at Forty Days to Reduce Days Open,"
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4. Barret,'M. J., N. L. Chervany and G. W. Dickson, "On Some Aspects
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5. Benbasat, I. and R. Schroeder, "An Experimental Investigation of
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6. Bock, R. D. and E. A. Haggard, "The Use of Multivariate Analysis
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Handbook of Measurement and Assessment in Behavioral Sciences,
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7. Conlin, B. J., "Use of Records in Managing for Good Lactational
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9. Chervany, N. L. and G. W. Dickson, "An Experimental Evaluation of
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10. Chervany, N. L., G. W. Dickson and K. A. Kozar, "An Experimental
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71-12, MIS Research Center, University of Minnesota, 1972.








11. Dancer, Robert E.,"An Empirical Evaluation of Constant and Adaptive
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May, 1975.

13. Fleming, J. E., "Managers as Subjects in Business Decisions Re-
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14. Foote, R. H., "Estrus Detection and Estrus Detection Aids,"
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16. Gory, G. A. and M. S. Morton, "A Framework for Management Infor-
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17. Hays, W. L., Statistics for Psychologists, Holt, Rinehart and
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18. Kozar, K. A., Decision Making in a Simualted Environment: A
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19. Louca, A. and J. E. Legates, "Production Losses in Dairy Cattle Due
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68

26. Senn, J. A. and G. W. Dickson, "Information System Structure
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1973.













APPENDIX A

EXPERIMENTAL TREATMENTS

The sixteen reports that constituted the experimental treat-

ments are shown here in the same size they were administered to the

subjects. The only difference between these reports and those used

by the subjects is that the latter had horizontal green lines across

them to facilitate their use. Each report is labeled with the
"condition" numbers used in Table 3.1 (p. 35).

















EVENT EXPECTED DUE-DATE
IDENT. (MONTH-DAY)

004 6-07
009 5-29
017 6-11
024 6-04
032 5-26
038 6-04
051 5-31
070 6-01
076 6-10
078 5-30
082 6-03
085 6-10
097 6-05
110 6-11
121 6-05
128 6-01
142 6-09
146 5-28
155 6-12
163 6-09
168 6-07
171 5-31
173 6-06
177 6-06
186 5-31
Condition 1


EVENT EXPECTED DUE-DATE
IDENT. (MONTH-DAY)
----------------------..----
032 5-26
146 5-28
009 5-29
078 5-30
171 5-31
051 5-31
186 5-31
070 6-01
128 6-01
082 6-03
024 6-04
038 6-04
121 6-05
097 6-05
173 6-06
177 6-06
168 6-07
004 6-07
163 6-09
142 6-09
085 6-10
076 6-10
110 6-11
017 6-11
155 6-12

Condition 2

















EVENT 95% CONFIDENCE INTERVAL
IDENT. (FIRST DAY, LAST DAY)

004 6-06 6-09
009 5-27 5-31
017 6-10 6-12
024 6-01 6-07
032 5-25 5-28
038 6-02 6-06
051 5-29 6-03
070 5-31 6-02
076 6-09 ,6-12
078 5-27 6-02
082 6-01, 6-05
085 6-09 ,6-14
097 6-04 6-07
110 6-10 6-12
121 6-03 6-07
128 5-30 6-04
142 6-06 6-12
146 5-26 5-30
155 6-11 6-13
163 6-07 6-12
168 6-04 6-10
171 5-28 6-03
173 6-05 6-07
177 6-05 6-08
186 5-29 6-03

Condition 3


EVENT 95% CONFIDENCE INTERVAL
IDENT. (FIRST DAY, LAST DAY)

032 5-25 5-28
146 5-26 5-30
009 5-27 ,5-31
078 5-27 6-02
171 5-28 6-03
051 5-29 6-03
186 5-29 6-03
128 5-30 6-04
070 5-31 6-02
082 6-01 6-05
024 6-01 6-07
038 6-02 6-06
121 6-03 6-07
168 6-04 6-10
097 6-04 6-07
173 6-05 6-07
177 6-05 6-08
004 6-06 6-09
142 6-06 ,6-12
163 6-07 6-12
085 6-09 6-14
076 6-09 6-12
110 6-10 6-12
017 6-10 6-12
155 6-11 6-13

Condition 4


















EXPECTED DUE-DATES
.........................................................................
EVENT MAY JUNE EVENT
IDENT. 25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13 IDENT.
-------------------004 H 004--
009 H 009
009 H 009
017 H 017
024 H 024
032 H 032
038 H 038
051 H 051
070 H 070
076 H 076
078 H 078
082 H .082
085 H 085
097 H 097
110 H 110
121 H 121
128 H 128
142 H 142
146 H 146
155 H 155
163 H 163
168 H 168
171 H 171
173 H 173
177 H 177
186 H 186
-------------25 26 27 28 29 30 31 01 02 03 04 6-------------
25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13


Condition 5
















EXPECTED DUE-DATES
- ----------------- ----------- ------------------------
EVENT MAY JUNE EVENT
IDENT. 25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13 IDENT.

032 H 032
146 H 146
009 H 009
078 H 078
171 H 171
051 H 051
186 H 186
070 H 070
128 H 128
082 H 082
024 H 024
038 H 038
121 H 121
097 H 097
173 H 173
177 H 177
168 H 168
004 H 004
163 H 163
142 H 142
085 H 085
076 H 076
110 H 110
017 H 017
155 H 155
-------------25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13
25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13


Condition 6
















95% CONFIDENCE INTERVALS
----------------------------------------------------------


EVENT MAY
IDENT. 25 26 27 28 29 30 31


JUNE
01 02 03


04 05 06 07 08 09 10 11 12


EVENT
13 IDENT.


H H H H H


H H


H H H H H H H


H H
H H H H H H
H H H


H H H H H




HH

HHHHH


HH H


H H
H H H H H

H H H H

H H H H H
HHHHH
HHHH

HHHHH
HHHH
HH


HHHHHHH


HHHHHH


H H 004
009
H H H 017
024
032
038
051
070
H H H H 076
078
082
H H H H H 085
097
H H H 110
121
128
H H H H H 142
146
H H H 155
H H H H H 163
H H H 168
171
173
H 177
186


25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13
25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13


---- ---- --- ---- --- ---- --- ---- ---- --- ---- --- ---- --- ---- ---


H H H H


Condition 7















95% CONFIDENCE INTERVALS
---------------------------------------------------------
EVENT MAY JUNE EVENT
IDENT. 25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13 IDENT.

032 H H H H 032
146 H H H H H 146
009 H H H H H 009
078 H H H H H H H 078
171 H H H H H H H 171
051 H H H H H H 051
186 H H H H H H 186
128 H H H H H H 128
070 H H H 070
082 H H H H H 082
024 H H H H H H H 024
038 H H H H H 038
121 H H H H H 121
097 H H H H 097
168 H H H H H H H 168
177 H H H H 177
173 H H H 173
004 H H H H 004
142 H H H H H H H 142
163 H H H H H H 163
076 H H H H 076
085 H H H H H 085
110 H H H 110
017 H H H 017
155 H H H 155
25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13


Condition 8














EVENT EXPECTED DUE-DATE
IDENT. (MONTH-DAY)

009 5-29
032 5-26
128 6-01
155 6-12
168 6-07
Condition 9




EVENT 95% CONFIDENCE INTERVAL
IDENT. (FIRST DAY, LAST DAY)
--------- ------ ------ ------
009 5-27 5-31
032 5-25 5-28
128 5-30 6-04
155 6-11 6-13
168 6-04 6-10
Condition 11 -


EVENT EXPECTED DUE-DATE
IDENT. (MONTH-DAY)

032 5-26
009 5-29
128 6-01
168 6-07
155 6-12
Condition 10




EVENT 95% CONFIDENCE INTERVAL
IDENT. (FIRST DAY, LAST DAY)

032 5-25 5-28
009 5-27 5-31
128 5-30 6-04
168 6-04 6-10
155 6-11 6-13
Condition 12


EXPECTED DUE-DATES

EVENT MAY JUNE EVENT
IDENT. 25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13 IDENT.

009 H 009
032 H 032
128 H 128
155 H 155
168 H 168

25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13


Condition 13













EXPECTED DUE-DATES

EVENT MAY JUNE EVENT
IDENT. 25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13 IDENT.
--------------------------------------------------------------
032 H 032
009 H 009
128 H 128
168 H 168
155 H 155
------------------------------------------------
25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13

Condition 14



95% CONFIDENCE INTERVALS
----.--.--..-------------........------------ ------
EVENT MAY JUNE EVENT
IDENT. 25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13 IDENT.
------------------------------------------------
009 H H H H H 009
032 H H H H 032
128 H H H H H H 128
155 H H H 155
168 H H H H H H H 168
------------------------------------------------
25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13

Condition 15



95% CONFIDENCE INTERVALS
.............................................----------------------......
EVENT MAY JUNE EVENT
IDENT. 25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13 IDENT.
.............................................----------------------......
032 H H H H 032
009 H H H H H 009
128 H H H H H H 128
168 H H H H H H H 168
155 H H H 155
--25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13
25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13


Condition 16














APPENDIX B

FORMAT OPINION QUESTIONNAIRE

The post-experimental questionnaire is presented here in an

English version of the actual questions (shown toward the end of the

program in Appendix C). A connotative rather than literal translation

has been attempted to give the non-Spanish reader a more accurate

representation of the content.










Please answer the following questions by entering a 1, 2, 3, 4, or 5, and

pressing the "return" key. An entry close to 1 will indicate "very

little" and an entry close to 5 will indicate "very much," as follows:


VERY LITTLE : 1 : 2 : 3 : 4 : 5 : VERY MUCH

1. How appropriate did you considered the order of the infor-
mation in the report, given the type of decisions that had
to be made?

2. How appropriate did you considered the format of the report
at the time of making the daily decisions?

3. How appropriate did you considered the format of the report
at the time of recording the feedback on events checked
and detected?

4. How appropriate did you considered the detail of the prob-
abilistic information on the possible date of occurrence
of each event?

5. All factors considered, how appropriate did you considered
the format of your report?














APPENDIX C
COMPUTER SIMULATION PROGRAM

A.1 General

This program created the simulated decision environment for the
experimental runs. The program was written in BASIC, Version 17,

Digital Equipment Corporation System 10. It was run from a type-

writer terminal, model DEC 33 TELETYPE, on line with a PDP 10.

A.2 Input

The fixed input to the program was the data of Table 3.2, (p. 39)

arranged as follows:

E$(J) M D I S
004 5 18 20.5 1.00
009 5 9 20.0 1.25
017 5 22 20.0 0.75
024 5 15 20.0 1.75
032 5 6 20.5 1.00
038 5 15 20.0 1.25
051 5 11 20.5 1.50
070 5 12 20.0 0.75
076 5 21 20.5 1.00
078 5 10 20.0 1.75
082 5 14 20.0 1.25
085 5 21 20.5 1.50
097 5 16 20.5 1.00
110 5 22 20.0 0.75
121 5 16 20.0 1.25
128 5 12 20.5 1.50
142 5 20 20.0 1.75
146 5 8 20.0 1.25
155 5 23 20.0 0.75
163 5 20 20.5 1.50








E$(J)

168
171
173
177
186

where E&(J)

M

D

I

S


= event I.D. number

= month of last occurrence

= day of last occurrence

= mean interval between occurrences

= standard deviation of interval between occurrences


All other input to the program was manually entered by the subjects.

It consisted of the I.D. numbers of the events they checked on each

successive day, and the answers to the post-experimental questionnaire.

A.3 Output

A sample of the beginning of a run is illustrated in Figure A.1

(p. 82). It shows an identification of the experimental report used in

the run, the initial time clocked after the subject indicated he was

ready to start,* and some of the outcomes of the early part of the run.

Figure A.2 (p. 83) is a sample from the last part of the experiment.

It shows some of the last decisions made by the subject, the final

decision time clocked (26 minutes and 3 seconds in the sample shown), the

summarized results of the run, and the post-experimental questionnaire

with the subject's answers. The last two lines seen are part of the

file updated with the results of each run. The program is on pp. 84-88.


*A pre-experimental familiarization session had already been con-
ducted.








82











TI ? !.Y' !l;O3C Zr':: .,I) :.lsJ37 'h-.1 2!".:C 167*r7? -I1 S' Cs:!476E




?-- -- -- -

CC7:.52 032 DETECTrEL :!1 ;


T"lDAY Ir 5 --!

CPEC 3 ?
?Z32 1.6
C'C'LDI 032 146 =T1.-r: 1'3:JE



Fiu "e IC op t- i7n
COEt;s ?
?032
..CRiC i 0",2 :=TEC.TE,: O32



TCrCE5 T


4EKCKE: 009 14E Vi~E>EiL InO:IE





Figure A.1 Sample output at the beginning of a run









83



'LC"F7 : IT I I TTIT:
e r:r":.0 JII 1DI in2 11 I.; PC-14765











?******* : **7 *8 ,"- l',, P:0'LT. 7 '% "G. T .! ..7 i PHZ $ *6






TTAl. CVST 3 ;I4

ACT'UAL E1S4T ATES

*!E2ET T 3. D.TE

034 fe-
S 09 ..-.3














009 23



038 ; -4
2730 1 -721









076 ;-12
07 -30
0o2 f-2
385 6-10
07 E-5

121 c-5
t42 C-2




'." *-.3
t4f 6r-


763 6-!

171 S-9
172 6-6
177 6-6
1t6 5-3,

V 0ISEL EST 0 zFS.ET. DE n R'*TiOq PAA DVr Fil?7!CA. 71.2 LA IOfT'OA7IOit
-V CL.'.VT3 A EVE7T') CMTEIJ1A'S Y DTECTAO2S ES CT.ELC.7..- LI.. VE7 HEC0
UT3 7PI'22 ". IT73 '.


TAlft DE C3"TE TA'. LAS SIt': :2;E3 PR.I5CL'TA1 5 TC:l'1"E:C0 'U I*r''**3'
'4* ** *' 7?D 7i'1: I LA T- CL'A *FETU' t :.' ?ZSP7 ESTA CRE.CA DE I
VI'DIC2.'A *P'73 APROPi7?1 "i Y 2.ESPL'STA CE.OC Dr 1:rICAi'c A 'I 'Y
.1P'IPIA"7*- O"io S*SLt :


PI)C7 3?. P"7.1 :; I : 3 i 2 a1 E ; t y .1 '.l" 1 t433



1. CC.! 407?-i.' l T 7 ic'4' i .C2TEI 2 L M rC J 7. L A .F'r"TrA't11-t
:' 1.:. 2?." .T ri i3: L' TIP. .0 212f1'T- S 3IE 51 1L31S T01A0 *'2





Figure '"2.- T'', 'STam ''32 op at17" 32thT. 32n7f 7u
t t2TO 2 TOW 2 2!.

7" Z :L F t r ?,)







C 1 LL' I. L -.' '.1 '... C.71p7 '.%1 .1- C P l.1 7:' ***





Figure A.2 Sample output at the end of a run











00005 REM ******************************************************
00010 REM *** INTERACTIVE SIMULATION OF THE EVENT CHECKING PROBLEM ***
00015 REM ************* *************************************
00020 DIM A$(25),E$(25)
00030 FILES A1%,A2%,A3%,A4%,A5%,AO%,DATA,TALYA$
00035 DO=25
00040 PRINT "SO,R";
00050 INPUT SO,R
00060 IF R =8 THEN 70
00062 00=5
00064 FOR J=1 TO 25.
00066 INPUT #7, NO,E$(J),M,D,I,S
00068 NEXT J
00070 C1=1
00075 PRINT
00080 C2=5
00090 PRINT
00100 PRINT "SUBJECT IS USING REPORT ";STR$(R)+"."
00105 PRINT
00110 FOR J=1 TO DO
00125 RO=0
00130 INPUT #7, NO,E$(J),M,D,I,S
00140 S$=S$+E$(J)
00210 RANDOMIZE
00220 FOR N=1 TO 12
00230 RO=RO+RND
00240 NEXT N
00250 Y=S*(RO-6) + I
00260 X=INT(Y+.5)
00280 IF (D+X) 31 GO TO 310
00290 D=D+X
00300 GO TO 330
00310 D=D+X-31
00320 M=M+1
00330 A$(J) = STR$(M)+"-"+STR$(D)
00340 NEXT J
00350 PRINT
00360 M=5
00370 D=24
00380 PRINT "Ti";
00381 INPUT Tl$
00389 PRINT
00390 FOR I=1 TO 20
00400 D=D+1
00410 IF D 32 GO TO 440
00420 D=l
00430 M=M+1
00440 D$=STR$(M)+"-"+STR$(D)
00442 PRINT
00450 PRINT "TODAY IS ";D$
00460 PRINT "-------------










00470 PRINT "CHECKS ?"
00480 INPUT E$
00482 IF E$=" THEN 630
00485 E$=E$+"AAAA."
00490 L=INSTR(E$,".")/4-1
00502 IF ABS(L-INT(L)) = 0 THEN 510
00504 GOSUB 2000
00506 GO TO 480
00510 FOR K=1 TO L
00520 Y=4*K-3
00525 Z=4*K-1
00530 K$=MID$(E$,Y,Z-Y+1)
00550 J=(INSTR(S$,K$)+2)/3
00560 C$=C$+E$(J)+"
00570 C=C+1
00580 IF A$(J) D$ THEN 610
00590 0$=0$+E$(J)+" "
00600 0=0+1
00610 NEXT K
00620 IF L =1 THEN 660
00630 PRINT "CHECKED: NONE"
00640 PRINT
00650 GO TO 730
00660 PRINT "CHECKED: ";C$;
00670 IF LEN(0$) 1 THEN 690
00680 0$="NONE"
00690 PRINT DETECTED: ";0$
00700 PRINT
00710 C$=" "
00720 0$=" "
00730 NEXT I
00735 PRINT "***********"
00740 PRINT "******...FAVOR DE LLAMAR AL PROF. AMADOR....*******
00751 INPUT T2$
00752 IF LEN(T2$) 1 THEN 754
00753 T2$="O"+T2$
00754 PRINT
00756 PRINT
00760 PRINT "******* SUMMARY OF RESULTS FOR THE RUN *******"
00770 PRINT
00780 PRINT C;"CHECKS @ $";STR$(C1)+"/"+"CHECK = $";STR$(C*C1)
00782 C8$=STR$(C)
00783 IF LEN(C8$) 2 THEN 790
00784 IF LEN(C8$) 1 THEN 788
00785 C8$="00"+C8$
00786 GO TO 790
00788 C8$="0"+C8$
00790 PRINT DO-O;"MISSES @ $";STR$(C2)+"/"+"MISS = $";STR$((DO-0)*C2)
00795 PRINT
00800 PRINT "TOTAL COST = $"+STR$(C*C1+(DO-O)*C2)
00802 T8$=STR$(C*C1+(DO-0)*C2)










00804 IF LEN(T8$) 2 THEN 870
00806 T8$="O"+T8$
00870 PRINT
00880 PRINT "ACTUAL EVENT DATES"
00890 PRINT "----------------
00900 PRINT "EVENT ID. DATE"
00910 PRINT "--------- -----
00920 FOR J=l TO DO
00930 PRINT TAB(3);E$(J);TAB(11);A$(J)
00940 NEXT J
00950 PRINT
00960 PRINT "REVISE ESTE RESUME DE RESULTADOS PARA VERIFICAR"
00965 PRINT "QUE LA INFORMATION EN CUANTO A EVENTS COTEJADOS"
00970 PRINT "Y DETECTADOS ES CORRECT. UNA VEZ HECHO ESTO,"
00975 PRINT "OPRIMA 'RETURN'."
01010 INPUT R9$
01020 PRINT
01030 PRINT "FAVOR DE CONTESTAR LAS SIGUIENTES PREGUNTAS ESCRIBIENDO UN"
01040 PRINT "1, 2, 3, 4, 0 5, Y OPRIMIENDO LA TECLA 'RETURN'. UNA"
01045 PRINT "RESPUESTA CERCA DE 1 INDICARA POCOO APROPRIADO'; UNA"
01050 PRINT "RESPUESTA CERCA DE 5 INDICARA 'MUY APROPRIADO, COMO SIGUE:"
01070 PRINT
01080 PRINT
01090 PRINT POCO APROPRIADO : 1 : 2 : 3 : 4 : 5 : MUY APROPRIADO"
01100 PRINT --
01110 PRINT
01120 PRINT
01130 PRINT "1. CUAN APROPRIADO ENCONTRO USTED EL ORDEN DE LA INFORMATION"
01140 PRINT EN EL REPORT PARA EL TIPO DE DECISIONS QUE SE"
01145 PRINT DEBIAN TOMAR?"
01150 INPUT Al
01152 IF Al 5 THEN 1154
01153 IF Al =1 THEN 1160
01154 GOSUB 2000
01156 60 TO 1150
01160 SET :1, R; :2, R; :3, R; :4, R; :5, R; :6, R
01162 INPUT :6, QO
01164 QO=Q0+1
01170 INPUT :1, Cl
01180 C1=C1+A1
01210 PRINT
01220 PRINT "2. CUAN APROPRIADO FUE EL FORMAT DE PRESENTATION DEL REPORT"
01230 PRINT AL MOMENT DE TOMAR LAS DECISIONS DIARIAS?"
01240 INPUT A2
01242 IF A2 5 THEN 1244
01243 IF A2 =1 THEN 1250
01244 GOSUB 2000
01246 GO TO 1240
01250 INPUT :2, C2
01260 C2=C2+A2
01290 PRINT










01300 PRINT "3. CUAN APROPRIADO FUE EL FORMAT DEL REPORT AL MOMENT"
01305 PRINT DE ANOTAR LA INFORMATION SOBRE LOS EVENTS COTEJADOS"
01310 PRINT Y DETECTADOS?"
01320 INPUT A3
01322 IF A3 5 THEN 1324
01323 IF A3 =1 THEN 1330
01324 GOSUB 2000
01326 GO TO 1320
01330 INPUT :3, C3
01340 C3=C3+A3
01370 PRINT
01380 PRINT "4. CUAN APROPRIADO FUE EL DETALLE DE LA INFORMATION"
01385 PRINT PROBABILISTICA SOBRE LA POSSIBLE FECHA DE OCURRENCIA"
01390 PRINT DE CADA EVENTO"
01400 INPUT A4
01402 IF A4 5 THEN 1404
01403 IF A4 =1 THEN 1410
01404 GOSUB 2000
01406 GO TO 1400
01410 INPUT :4, C4
01420 C4=C4+A4
01450 PRINT
01460 PRINT "5. CONSIDERANDO TODOS LOS FACTORS, COMO DE APROPRIADO"
01465 PRINT ENCONTRO USED EL FORMAT DE ESTE REPORTE"
01480 INPUT A5
01482 IF A5 5 THEN 1484
01483 IF A5 =1 THEN 1490
01484 GOSUB 2000
01486 GO TO 1480
01490 INPUT :5, C5
01500 C5=C5+A5
01530 PRINT
01540 PRINT "........HEMOS CONCLUIDO EL EXPERIMENT ......
01545 PRINT ...... GRACIAS POR SU COOPERACION......"
01550 R8$=STR$(R)
01552 IF LEN(R8$) 1 THEN 1560
01554 R8$="O"+R8$
01560 Q8$=" "+STR$(A1)+" "+STR$(A2)+" "+STR$(A3)+" "+STR$(A4)+" "+STR$(A5)
01561 SO$=STR$(SO)
01562 IF LEN(SO$) 2 THEN 1570
01563 IF LEN(SO$) 1 THEN 1566
01564 SO$="00"+SO$
01565 GO TO 1570
01566 SO$="0"+SO$
01570 TO$=SO$+" "+R8$+" "+T2$+" "+C8$+" "+T8$+Q8$
01600 SET :1,R; :2,R; :3,R; :4,R; :5,R; :6,R
01610 WRITE :1,C1
01620 WRITE :2,C2
01630 WRITE :3,C3
01640 WRITE :4,C4
01650 WRITE :5,C5





88




01660 WRITE :6,QO
01670 PRINT C1;C2;C3;C4;C5;Q0
01672 PRINT
01674 PRINT TO$
01680 SET :8,SO
01685 WRITE :8,TO$
01690 GO TO 2050
02000 PRINT
02005 PRINT "? INPUT DATA NOT IN CORRECT FORM--PLEASE RETYPE"
02010 RETURN
02050 END














APPENDIX D

SUBJECT INSTRUCTIONS

The written instructions given to the experimental subjects are

shown here in their original version (in Spanish) and in an English

version. Again, an effort has been made to present a connotative

rather than literal translation so the non-Spanish reader can have

a more accurate picture of their content. The statement of consent

that was signed by each subject is also included.







Instrucciones

Introducci6n

Con este experiment se quiere medir la efectividad de un informe
gerencial. El Informe estudiado ha sido disefiado para ayudar a un gerente
a "detectar" una series de events de interest que han de ocurrir en el future
pr6ximo. La raz6n que amerlta el uso de un informed en este caso es que
estos events de interes ocurren al azar solamente durante el termino de un
dia y luego no vuelven a ocurrir hasta despues de aproximadamente 20 dlas.
Es convenient para la gerencla "detectar" el dfa en que estos events ocu-
rren ya que se incurre en un cost de oportunidad cada vez que uno de estos
events sucede y pasa sin ser detectado (la proxima oportunidad de observer
el event no vuelve a ocurrir hasta despues de aproximadamente 20 dras). El
informed estudiado es preparado en base a estadIsticas pasadas y consiste
precisamente de las fechas mas probables de ocurrencia para cada uno de
una serie de estos events. El format general de este informed es como
sigue:

Identiflcaci6n del Posible Fecha de
Evento Ocurrencia











En este experiment se quiere medir el efecto del format de presenta-
ci6n de este informed sobre el uso efectivo que se le ha de dar al mismo.
Usted recibird urn de various formatos experimentales de este informed y du-
rante un period simulado de 20 dfas used utilizard dicho informed para
tratar de "detectar" una series de events que segfn su informe han sido
Identificados como que han de ocurrir durante esos 20 dfas.

Reglas de la Simulacion

Durante el experiment usted jugara el papel de un gerente que debe
decidir a diario cuantos y cuales eventso" cotejar para ver si estan
"ocurriendo" en ese dia o no. Las caracterfsticas dcl problema que usted
debera mantener en mente son las siguientes:

1. Usted recibira un informed experimental con los eventso"
que deben ser "cotejados" durante los pr6ximos 20 dfas.
Estos events estaran idenrificados al lado izquierdo del




Full Text
56
affect decision performance in general (5.1.1).^ Multivariate analysis
is called for here since the performance criteria measured (decision
time, cost, and number of checks made) are not independent. In the pre
sent study, the simultaneous effect of layout on the three performance
criteria gives more value to the observed univariate effect on decision
time. If Hypothesis 1 (p.28) had read "information layout can influence
decision performance," it would have been equally supported. The fact
that the effect observed was stronger among the many-decision-entities
subjects (5.2.1) also lends support to Hypothesis 6 (p. 30),
The style of presentation (tabular versus graphical) was also seen
to have a significant total effect on decision performance (5.2.3).
Although the univariate effect of style on cost was not significant, the
graphical style users within the many-decision-entities group had higher
costs than the tabular style users ($104.73 versus $100.95, p = .258).
This result contradicts Benbasat and Schroeder's [5] results, who
observed that subjects with graphical reports had lower costs than those
with tabular listings. Neither result, however, is significant (theirs
had p = .148). This indicates that there is still no basis for predicting
the effect of presentation style on cost performance. Either result
could have been due to chance alone.
In terms of future MIS research, it is suggested here that MANOVA
should be used in the analysis of experimental data. Winer [30, p. 232]
explains that whenever there is reason to believe that dependent variables
The number in parenthesis is the reference to the related result in
the tables.


7
items in the table are the nature of the simulated decision environ
ment, the experimental stubjects, and a summary of the results.
It is interesting to note that while the form of presenting the
information has been extensively considered in one form or another,
the "layout" or physical arrangement of the information reported has
not been manipulated as an experimental variable in any of the studies
reviewed. Figure 1.2, part A (p. 8) is an example of the type of "form
of presentation" treatment that has been manipulated in the reviewed
literature. Figure 1.2, part B is an example of what is meant here
by information layout. The influence of this variable on decision
performance will play an important role in this study.
1.2.2 The Lucas Model
The model proposed by Lucas [21] includes essentially the same
variables as the Chervany et al. framework, but it also takes into
account the interface between use of the information system and per
formance. His descriptive model states that performance (P) is a
function of situational, personal, and decision style variables (the
DM group in the Chervany et al. model), the quality of the information
system (the CIS group in the Chervany et al. model) and the analysis
and actions taken by the users (similar to the DE group in the Chervany
et al. model). In addition, his model also states that the performance
of the information system is independently affected by the use of the
system, U. In functional form,
P = f(DE,DM,CIS,U)
(1.2)


60
style when only point estimates were reported. The compact tabular for
mat was inadequate, however, for processing the interval estimates in
formation.
5.3.4 Effects Related to H6
All the effects that have been discussed thus far were found to be
more marked within the many-decision-entities group (5.2.2, 5.2.4, 5.2.6,
5.2.7). This supports one of the propositions in Hypothesis 6, (p. 30),
that user performance becomes more sensitive to report format as the
number of decision entities on the report increases. In the present ex
periment, the subjects with five decision entities on their reports had
so little information to process that whether it was given in I.D. order3
due-date order, tabular style or graphical style did not make much
difference on their performance. Evidence of this is that, within the
five decision events group, there were no significant differences in
performance for any of the performance measures. The only effect that
approached significance in that group was an interaction between style
and level of detail with cost as the dependent variable (p < .10).
The other proposition in H6, that report users will move from in
difference to preference for particular formats as the number of decision
entities increases, was also supported. The subjects in the few-decision-
entities group gave more or less constant high ratings to the various
format characteristics of their reports (5.2.8, 5.2.9). Within that
group, there were no significant differences in the ratings for the order
of the information in the reports (layout). For the over-all format
rating, only one difference was significant. Interval estimate subjects
gave significantly higher ratings than point estimate subjcets (4.25
versus 3.80, p = .044). Among the subjects with twenty-five decision


15
it should be produced. One issue that remained questionable was the
manner in which the information should be presented in the report. There
were several formats that appeared useful but each seemed to have its
own pros and cons from the point of view of ease of use. The problem
appeared to be sufficiently interesting and important to merit an
experimental evaluation of the various information format alternatives.
The problem discussed in the next section is the abstraction or
prototype designed to investigate this information structure problem
within a controlled laboratory setting.^ The questions of interest
were widened to include a set of propositions related to a more general
MIS framework and theory. In the problem to be outlined below,-the
term "heat" is replaced with more general terminology.
2.1.2 The Abstracted Problem
Consider an organization that needs to keep records on a number
of random events that occur relatively infrequently but are important
to management. These events represents opportunities for management:
if one occurs and is not detected the organization suffers opportunity
costs.
Management knows that these events occur independently approxi
mately once every 20 days, and that when they occur they are
"detectable" during a short period of time (approximately 24 hours).
The reasons for taking the research to the laboratory were two
fold. First, resources were not available for conducting a reasonably
controlled field experiment. Second, the research interests of the
author were shifted from the operational considerations of the problem
to a more general set of research questions more amenable for resolution
in a laboratory setting.


40
Uniform instructions were administered to all subjects regarding
the general nature of the experiment and their participation (see
Appendix D, P. 89). Special care was given to insure that subjects
understood their objective in the game. A familiarization session
consisting of five decision points (days) was conducted to acquaint
subjects with their decision environment. These sessions were con
sidered to be long enough to check subject "learning11 effects during
the experimental runs. In no case did data collection begin until
subjects indicated that they felt comfortable with the procedure and
ready to startJ
Three remote terminals were used for conducting the runs. Each
was on line with a master program that mantained a file with the results
of each run. The file included the three performance criteria and the
scores of the post-experimental questionnaire.
3.3 Evaluation of Hypotheses
Table 3.3 (p. 41) shows the main effects and interactions that
should be observed for the hypotheses to be supported. Each of these
effects is discussed next.
Hypothesis 1. Information layout (factor A in table 3.1) should
affect decision time such that the subjects with due-date ordered reports
should have shorter decision times than those with I.D. ordered reports,
i.e., 2 < A-j: TIME. The due-date order should be more convenient
given the chronological way in which the information is going to be used.
1
The average familiarization session took 15.2 minutes.


11
cisin theory, little if anything has
been said about the form in which the
data should be presented to the de
cision maker. Perhaps this is because
most theoreticians assume that as long
as the data unambiguously define the
distributions, the format of presenta
tion should make no difference. This
brings up the question of whether data
can ever be unambiguously presented,
and perhaps more importantly, in whose
eyes? The only answer to the second
question is the user, but he has seldom
been asked [8, p. 878].
Conrath goes on to propose that the format in which probabilistic
data is presented as a basis for choice can influence choice.
The present study centers around the questions raised above.as
they relate to a pragmatic "how-do-we-present-the-information?"
problem.
1.3 Organization of the Dissertation
In Chapter 2, a "real-life" information-decision problem is pre
sented to provide a setting for the questions investigated in this study.
The nature of the problem is explained in Section 2.1. In Section 2.2,
the information needs of the manager in the problem are considered, and
it is assumed that these needs are relatively well defined and structured.
A number of questions related to the form in which information should be
presented to the manager are raised in Section 2.3. The results of
previous studies are revisited in an effort to provide orientation to
the present information format/decision performance questions. The
criteria used to measure decision performance are defined in Section 2.5,
and a set of research hypotheses relating these criteria to the experi
mental format variables is presented in Section 2.6. In Section 2.7, a
general model is presented to serve as the guide for the experiment.


39
TABLE 3.2
DATA FOR THE EXPERIMENT
Event Date of Last Number of Days between Occurrences
Z.O. Occurrence Mean Standard Deviation
(*)) (s,)
004
5-18
20.5
1.00
009
5-9
20.0
1.25
017
5-22
20.0
0.75
024
5-15
20.0
1.75
032
5-6
20.5
1.00
038
5-15
20.0
1.25
051
5-11
20.5
1.50
070
5-12
20.0
0.75
076
5-21
20.5
1.00
078
5-10
20.0
1.75
082
5-14
20.0
1.25
085
5-21
20.5
1.50
097
5-16
20.5
1.00
110
5-22
20.0
0.75
121
5-16
20.0
1.25
128
5-12
20.5
1.50
142
5-20
20.0
1.75
146
5-8
20.0
1.25
155
5-23
20.0
0.75
163
5-20
20.5
1.50
168
5-18
20.0
1.75
171
5-11
20.0
1.75
173
5-17
20.0
0.75
177
5-17
20.5
1.00
186
5-11
20.5
1.50
A. Data for
the many-decision-entities
condition
Event
I.D.
Date of Last
Occurrence
Number of Days
Mean
Oil
between Occurrences
Standard Deviation
009
5-9
20.0
1.25
032
5-6
20.5
1.00
128
5-12
20.5
1.50
155
5-23
20.0
0.75
168
5-18
20.0
1.75
B. Data for the few-decision-entities condition


31
considered in MIS design. This variable will occur in most situations
where the number of physical phenomena on which decisions are required
is variable. A procurement manager, for instance, may be indifferent
about the format of his inventory status reports if he must place orders
for only five items. He would probably be concerned about format (and
his performance would be more influenced by format) if he has 250 items
on which to place orders.
2.7 Basic Functional Model
The following model presents, in equation form, the relationships
to be analyzed.
Given equations 1.3 and 1.4,
EU = f(F,L|DE,DM,CIS)
P = f(EU|DE,DM,CIS')
(1.3)
(1.4)
it follows that
P = f(F,L|DE,DM,CIS')
(2.1)
where, P = decision performance
F format of presentation
L = level of detail
DE = decision environment characteristics
DM = decision maker characteristics
CIS = other characteristics of the information system.
In words, equation 2.1 states that the format of presentation
and the level of detail are determinants of decision performance


49
4.2.6 Relation between Number of Decision Entities and Format
The sixth hypothesis, that report users' preference for and
sensitivity to format is related to the number of decision entities
on their reports, was supported. Table 4.1 shows that subjects with
in the many-decisi on-entities group gave significantly different
ratings to the various layout and style combinations. Two of the five
opinion questions were used to verify that the subjects understood
and systematically answered the post-experimental questionnaire
{see Appendix B, p. 78). The validation consisted of checking that
the ratings for these two questions were consistent with performance
of subjects receiving the particular treatments mentioned in the
questions:
(1) Question 1 asked the subjects to rate the order of
the information in the reports. Table 4.1 shows
that the subjects receiving the due-date ordered
reports gave significantly higher ratings to this
item than those receiving the I.D. ordered reports
(A2 > Ap F = 17.33, p < .00004).
(2) Question 4 asked the subjects to rate the level of
probabilistic detail given. Table 4.1 illustrates
that the subjects receiving the interval estimates
consistently gave higher ratings to this item than
those receiving the point estimates treatment
(C2 > Cr F = 18.86, p < .00002).
In the case of Question 1, the many-decisi on-entities subjects
gave significantly higher ratings to the due-date ordered reports
(A2D-| > A^D-j, F = 19.08, p < .12). In Question 5, where the subjects
were asked to give an over-all rating for the format of their reports,
there was a significant difference in ratings between the many and
few-deci si on-entities groups (D2 > D-j, F = 5.23, p < .03).


14
...delayed conception means a cow must
stand dry and nonproductive when her
lactation ceases at a maintenance cost
of about $20 per month [19, p. 580].
Approximately 53% of heats are being
missed. ,Dairymen appear to be losing
twice as many days due to missed heat
periods as due to failure to conceive
[2, p. 247].
The literature includes much advice about methods for heat detec
tion, most of it having to do with heat recognition in the field.
Even then, it has been suggested that close to 50% of all heats are not
detected [2, 3].
Dairymen using artificial insemination and keeping the appropriate
records have information that can help them in detecting heat [7]. The
information consists of the date of the last service (insemination) of
each cow and data on the average number of days between successive ser
vices. It has been suggested [27] that a chart with "heat expectancy
dates" could be valuable for detecting heat, as it would enable the
dairyman to concentrate his observations on those cows expected to come
in heat.
The design of such a report motivated initial work on the problem.
A preliminary survey^ using an experimental report in an actual dairy
operation revealed that rather general agreement existed among the
prospective users as to the desired content of the report and how often
^Unpublished; conducted at the dairy farm of Mr. Herman Hernandez,
Isabela, Puerto Rico during January-May, 1976.


20
be a more appropriate style of presentation for the time-staged infor
mation in our problem. Specifically, if the reports are to consist
only of event I.D. numbers and expected due-dates, the question of
interest is whether the formats shown in Figure 2.1 (p. 21) can in
fluence aspects of decision performance. As discussed in section 2.5,
decision performance will be measured in this study in terms of time
performance (the time devoted to making the "check" decisions) and
cost performance (the total cost of checking and missing the events).
A priori, it would seem logical to expect the formats in Figure
2.1 to influence, if anything, time performance. The dates reported
are future dates and the information is going to be used chronologically.
Consequently, the time dimension added by the graphical style should
be helpful in that it orders the events chronologically from left
to right on the x~axis^. In part C of Figure 2.1, for example, it is
seen that event "032" is expected to occur first (May 26), then event
"146" (May 28), and so on.
Information layout. A chronological ordering of the events can
also be achieved with the tabular style by arranging the events in
order of expected due-dates, as in part B of Figure 2.1. It is assumed,
however, that the ordering of events by ascending I.D. numbers is a
desirable condition in these reports because management frequently
needs to make quick reference to the due-dates of particular events.
The quickest way to make these references is when the events are arranged
^If these reports were intended for Chinese managers, an attempt
would be made to present the information from right to left.


67
11. Dancer, Robert E., "An Empirical Evaluation of Constant and Adaptive
Computer Forecasting Models for Inventory Control," Decision
Sciences, 8, pp. 228-238, 1977.
12. Dickson, G. W., J. A. Senn and N. L. Chervany, "Research in Manage
ment Information-Decision Systems: The Minnesota Experiments,"
Working Paper 75-08, MIS Research Center, University of Minnesota,
May, 1975.
13. Fleming, J. E., "Managers as Subjects in Business Decisions Re
search," Academy of Management Journal, 12, pp. 59-66, 1969.
14. Foote, R. H., "Estrus Detection and Estrus Detection Aids,"
Journal of Dairy Science, 58, pp. 248-256, 1975.
15. Gehrlein, W. V. and P. C. Fishburn, "Information Overload in
Mechanical Processes," Management Science, 23, pp. 391-398, 1976.
16. Gory, G. A. and M. S. Morton, "A Framework for Management Infor
mation Systems," Sloan Management Review, 13, pp. 55-70, 1971.
17. Hays, W. L., Statistics for Psychologists, Holt, Rinehart and
Winston, Inc., New York, New York, 1963.
18. Kozar, K. A., Decision Making in a Simualted Environment: A
Comparative Analysis of Computer Data Display Media, Ph.D. Thesis,
University of Minnesota, 1972.
19. Louca, A. and J. E. Legates, "Production Losses in Dairy Cattle Due
to Days Open," Journal of Dairy Science, 51, pp. 573-583, 1968.
20. Lucas, Henry C., "Performance and the Use of an Information System,"
Management Science, 21, pp. 908-919, 1975.
21. Lucas, Henry C., "A Descriptive Model of Information Systems in
the Context of the Organization," Data Base, 5^ pp. 27-36, 1973.
22. Mason, R. 0. and I. I. Mitroff, "A Program for Research on Manage
ment Information Systems," Management Science, 19, pp. 475-487, 1973.
23. Murdick, R. G. and J. E. Ross, Information Systems for Modern
Management, Prentice-Hall, Inc., Englewood Cliffs, New Jersey, New
Jersey, 1975.
24. Peter, J. P., M. J. Ryan and R. E. Hughes, A MAN0VA Approach to
Disentangling Correlated Dependent Variables in Organizational Re
search," Academy of Management Journal, 18, pp. 904-911, 1975.
25. Schroeder, R. G. and I. Benbasat, "An Experimental Evaluation of the
Relationship of Uncertainty in the Environment to Information Used
by Decision Makers," Decision Sciences, 6, pp. 556-567, 1975.


82
Figure
srirjrc? ir treno t 3.
TI ?,VSt :1S¡3C .O "L*l37 UKt£2 D".::C 67*r? T 5V
Tl ?D/VjT:
; I:
TS
t. 2 *
CFtEC.S ?
T; 32
Cf£CKiO:
032
ta pay i r
s Cf
o r£ ?
TOSS l<*6
C'ec::ld
033
Tnrr.v rr
5*37
Offers ?
? 032
CHLriFr;
0:3
TT o ft y !£
f. o r?
"TFcrrrt rio :e
3i~i.trir: \>3:je
rrTECTrts ota
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TOO* U
DECIIIi 00- U
trf k7EH:
A.l Sample output at the beginning of a run


24
probability distributions from which these estimates are drawn. This
additional information could be presented, for instance, in the form
of percentiles of the distribution (e.g.: the days lying above the
fifth percentile and below the ninety-fifth percentile of the distri
bution), The latter would have the advantage of incorporating infor
mation about the variability of the event occurrence times and, there
fore, the risk involved in making the check decisions,
In the problem modeled, it is assumed that there is enough data
available on past intervals between events to permit estimates of the
mean, and standard deviation, s., for each event i. Using this data,
and assuming normal and stable distributions, the intervals 2s.j
were used as interval estimates for the days during which each event i
is more likely to occur. In the case of the tabular style, the reports
with such "95% confidence intervals" could appear as in parts A and B of
Figure 2.3 (p.25). The issue of format takes new importance now
since it is possible that the graphical style (part C of Figure 2.3)
may have properties that make the checking choices easier for the user.
Specifically, the level of probabilistic detail (point estimates or
interval estimates) may interact with the style of presentation
(tabular or graphical) to affect the ability of the user to process
and effectively use the information.
Questions of interest. The two levels of probabilistic informa
tion described above, point estimates and interval estimates, will be
experimentally manipulated in connection with the format variables to
address the following questions:


TABLE 1.1
SOME STUDIES CONDUCTED UNDER THE CHERVANY ET AL. FRAMEWORK
Experimenter
Decision Environment/
Basic
Experimental
[Reference]
Experimental Subjects
Model
Variables
Measure
Results
Chervany
Production-Inventory/
P-f(CISlOE.DM)
P
Cost, time
and
Graduate Business
confidence 1n
Subjects receiving the
Dickson
C9]
students
the decisions
sunsrory dita treatments
performed better cost-
wisn but took longer to
CIS
Supinary vs.
make decisions. Subjects
raw data re
receiving the raw data
ports
treatments had more con
fidence in their decisions.
Schroeder
Production-Inventory/
P
System utiliza
The frequency of informa
and
Undergraduate Business
P-f(OE.CISlDM)
tion, decision
tion use was not affected
Benbasat
students
confidence
by environmental variability.
The low variability groups
[253
OE
Variability In
preferred less detailed re**
the decision en
ports. Ho decision confi
vironment at 3
dence effects were estab
levels: low,
medium, high
lished.
CIS
Summary vs. de
tail reports


TABLE 1.1 (continued)
Experimenter
Decision Environment/
Basic
Experimental
Results
[Reference]
Experimental Subjects
Model
Variables
Measure
Senn
Purchasing Decisions/
P-f(DM,CI$jO£)
Cost, time,
No significant relation
and
Purchasing Managers
P
confidence,
was observed between
Dickson
number of re
organization size and the
ports requested
performance measures. The
C263
DM
Decision makers
display medio had a signi
ficant effect on the number
from large and
of reports requested. The
small organiza
subjects receiving hard copy
tions
reports requested more in
formation than those using
CIS
Paper reports
the CRT medium. There was
vs. CRT, sum
mary vs. detail
reports
no significant difference
in cost performance between
users of summary and detail
information.
Kozar
Production-Inventory/
Cost, time,
CRT users took significantly
[18]
Graduate Business
P*f(CI$|DE,DM)
P
confidence,
longer time to make decisions
students
CIS
In the decisions
They felt the device was use
ful as a filing organizer
*
CRT vs, hard
but were unhappy with the
copy report*
lack of hard copy. Subjects
receiving the hard ccpy re
ports had lower costs. There
was no difference in decision
confidence between the two
arras.


TABLE 4.1
CELL MEANS FOR THE SIXTEEN EXPERIMENTAL CONDITIONS
Many decision entities
(Dx)
Few decision entities

Tabular styl
Graphical style
Tabular style
Graphical style
V
PF.b
IE
PE
IE
PE
EE
PE
IE
lCl>
t^)
(c^
(C1J
ID c
DD
ID
DD
ID
DD
ID
DD
ID
DD
ID
DD
ID
CD
ID
DD
Dependent Variables
U\)

V
(Aj)
(V
{A1)
<*2>
<*2)
(Aj)
(A^
V
1. Decision Time
23.8
17.6
29,5
19.6
23.0
21.2
22.8
20.0
7.7
7.6
7.0
8.4
8.5
7.1
8.7
8.8
2. Nutter of Checks
69. S
62.6
65.8
66.1
73.4
75.5
66.8
76.2
18,4
20.7
16.2
20.0
16.2
17.6
18.9
20.1
3. Total Cost
103.3
105.1
92.3
103.1
100.4
111.5
98.8
108.2
22.4
26.2
19.3
25.5
13.2
17.6
21.9
24.1
4, Layout3
2.9
3.8
1.7
4.5
3.2
3.6
3.5
4.0
3.2
3.5
3.3
4.2
3.6
4.0
4.1
4.1
5. Format (1)
3.5
3.3
2,6
4.0
3.2
3.9
3.2
3.1
3.4
3.2
4.2
3.8
3.2
3,9
4.4
4.1
6. Format (2)
3.8
3.7
3.4
4.6
3.8
4.0
4.1
3.0
4.0
3.8
3.0
4.5
3.5
4.4
4.6
4.1
7. Level of Detail
3,4
3.7
4.1
4.3
3.0
3.0
3.9
4.2
3,6
3.7
4.3
3.9
3.3
4,2
4.7
4.5
8. Over-all Format
3.8
3.6
2.8
4.5
3.5
3.6
3.5
3.9
3.7
3.9
4.3
4.2
3.4
4.2
| 4.3
4.2
Notes,
* I
a Variables numbered 4 through 8 are the mean ratings for the format opinion questionnaire. These ratings
are based cn a five-choice scale: 1, 2, 3, 4, 5, with 1 indicating "very little" and 5 indicating "very
much" liking,
h
PE point estilen tes; IE interval estimates. cn
*
c ID I.D. ordered reports; DD due-date ordered reports*


43
Hypothesis 6, Differences in format opinion should be observed
among the various layout and style treatments administered to the
many-decision-entities subjects (D-j). Differences in both time and
cost performance should also be observed among this group. This
will indicate that report users have preference differences for
format and their performance is more sensitive to format when the
number of decision entities on their report is large. Non-significant
differences should result among the same layout and style combinations
administered to the few-decisi on-entities subjects (D^)- In general,
it is expected that the opinion ratings will average higher for the
few decision entities group, i.e. 62 > D^: RATINGS. Differences
A^Dj AgD-j B^D-j B^D.| and ^B^D-j A^D-j are expected to be
significant for cost, time and the opinion ratings. The same com
parisons with D2 instead of D^ are not expected to be significant
(the few-decision-entities case). The experimental results are pre
sented in the next chapter.


32
given a particular decision environment, decision maker, and other
characteristics of the information system. In the next chapter, a
table is presented that shows each of the research hypotheses expressed
as a variant of this basic model.


TABLE 5.1
Independent Variable
Information Layout
Information Layout
Style of
Presentation
Number of
Decision Entities
Information Layout
Number of
Decision Entities
MAIN EFFECTS
Dependent Variable
Level of
Significance
Results
Related
Hypotheses
Line
No.
Decision Time,
Cost, and Number
of Checks Made
.00001
Information layout had a simul
taneous effect on all three
performance criteria.

5.1.1
Decision Time
.00026
Subjects with due-date ordered
reports had shorter decision
times than subjects with I.D.
ordered reports.
HI
5.1.2
Number of
Checks Made
.066
Subjects with graphical reports
made more checks than subjects
with tabular reports.
H2
5.1.3
Layout Rating
.060
Subjects with few decision events
rated their report layout higher
than subjects with many events.
H6
5.1.4
Layout Rating
.00003
Subjects with due-date ordered
reports rated their layout higher
than subjects with I.D. ordered
reports.
H6
5.1.5
Level of
Detail Rating
.060
Subjects with few decision events
rated the detail of their reports
higher than subjects with many
decision events.
H6
5.1.6


9
In a field study [20] with an actual information system and data
from salesmens performances, Lucas observed relationships among DE,
DM, CIS, and P that are congruent with those observed in the "Minnesota
Experiments." In addition he also noted the following relationship
between performance and information system use:
when relevant information is
provided and used,
when the information provided
is irrelevant to the decisions
that must be made.
What he in effect noted is that only those information system designs
that promote effective use of the information will have a positive
effect on performance. One of the indications of his results was that
information structure elements such as the format of presentation, F,
and the level of detail, L, can be determinants of effective use, EU,
given other system characteristics, CIS', and a given set of DM and DE
variables. Although not explicitly stated in his paper, the results
of his study suggest that
EU = f(F,L|DE,DM,CIS) (1.3)
and that P = F(EU|DE,DM,CIS') (1.4)
with AP n
AEU U
These relationships are inferred from the discussion part of his paper:
One of the most important implications of
the model and results is that different
personal, situational and decision style
variables appear to affect the use of
systems. These findings argue for more
AP
AU
> 0
AP
AU
< 0


3
decision maker, DM, and the characteristics of the information system,
CIS. In functional form,
P = f(DE,DM,CIS)
(i.i)
A number of experiments have been conducted under this framework.
They all appear to have followed Van Horn's [28] suggestion that
laboratory studies provide an effective means for MIS research. In
particular, the experimenters have drawn upon the technique of
"experimental gaming" to create artificial decision-making evironments
within which they have manipulated various aspects of the information
system. In support of this technique, Barret et al. conclude:
We have been unable, to date, to generate
evidence which implies that the interpre
tation of decision performance results in
terms of a treatment variable is likely to
be confounded by the effects of the simu
lator and/or other aspects of the manage
ment game context [4, p. 11].
Dickson et al. say of experimental gaming:
...we are of the opinion that experimental
gaming, despite its high cost, is an effec
tive way of investigating this area. The
major problems really are associated with
the measurement of the variables included
in the experiments. Somewhat surprisingly,
problems of subject motivation and
experimental control have been minor
[12, p. 20].
Table 1.1 on pages 4-6 is a summary of some of the most
referenced studies that have been conducted under the Chervany et al.
framework. The variables that were experimentally controlled are shown
in each case before the "given" bar (|) in the functional models along
with a brief description of the measure used for each variable. Other


12
The nature and details of the experiment are the subject of
Chapter 3. The methodology is discussed in Section 3.1 and a full
description of the experimental task is given in Section 3.2. Section
3.3 discusses the experimental results that should be observed for the
research hypotheses to be supported, and a table is presented that
shows how each of the hypotheses is to be evaluated from the experi
mental data.
The statistical results of the experiment are presented in Chapter
4. These are discussed in Chapter 5 from the point of view of their
implications for both the MIS researcher and practitioner. In Chapter
6, suggestions are given for new lines of research.


APPENDIX D
SUBJECT INSTRUCTIONS
The written instructions given to the experimental subjects are
shown here in their original version (in Spanish) and in an English
version. Again, an effort has been made to present a connotative
rather than literal translation so the non-Spanish reader can have
a more accurate picture of their content. The statement of consent
that was signed by each subject is also included.
89


TABLE 1.1 (continued)
Experimental
Decision Environment/
Basic
Experimental
[Reference]
Experimental Subjects
Model
Variables
Measure
Results
Benbasat
Production-Inventory/
P
Cost, time.
Subjects using graphs had
and
Students enrolled In
number of
lower costs than subjects
Schroeder
an operations manage-
P*f(DMtCi$|DE)
reports
using tabular information.
C5]
ment course
requested
Subjects with graphical
DM
Decision
reports requested less re
ports than those with
making
tabular reports. Low
style: low
or high
analytic subjects who
neither had graphical re
analytic;
ports nor decision aids
knowledge of
had the laryest costs. Low
functional
analytic subjects who had a
I
area: low
or high
low functional knowledge of
the area requested the
largest number of reports.
CIS
Graphs vs.
There were no significant
tabular in
differences In time perfor
formation;
mance between the users of
decision
the graphical and tabular
aids: pro
vided or
not; num
ber of reports
available;
reports.
. .
necessary vs.
overload groups ,


19
every twenty days, paper would appear to be the more appropriate
medium. A CRT could be a reasonable medium if the time interval be
tween reports was shorter and if there was a need to reduce paper
clutter. Kozar [18] found that users of CRTs tend to to be unhappy with
the lack of hard copy and that they take significantly more time to
arrive at decisions than hard copy users. The medium of transmission
was not considered a relevant design variable in the present study.
Conventional paper printout was used as the constant medium through
out the experiment.
2.3.2 Format of Presentation
The format variable has been discussed more extensively in the
MIS literature than the medium variable [5,9,25,26]. The most common
format treatment has been summary versus raw data [9, 25,26]. This
treatment, however, has manipulated the data content more than its
format. Only one study has been concerned with format, if format is
considered to be related to the "style" of presentation.
Style of presentation. Benbasat and Schroeder [5] presented
daily production figures to experimental subjects in one of two styles:
tabular and graphical. The tabular style listed daily production figures
while the graphical style plotted the same daily figures versus time.
Their results indicated that subjects using the graphical reports had
lower costs, with no significant differences in decision time between
the two groups.^ These results suggest that the graphical format might
generated reports are considered here, however, mainly because of
the lack of resources for experimenting with other media.
^Murdick and Ross [23,p. 263] state that managers prefer graphical
displays, although they do not support their contention.


CHAPTER 2
THE PROBLEM
2.1 An Information-Decision Problem
The information-decision problem that provided the setting for the
current study is presented in this chapter. The situation studied pre
sents several advantages from the point of view of empirical MIS re
search. First, the situation is relatively simple, easy to characterize
and to model. Second, the problem points to clearly definable questions
of information structure, an area that has received increased attention
in the recent MIS literature [5, 9, 12, 18, 25, 26]. Finally, the situa
tion may represent a new area for the application of MIS technology.
2.1.1 The Real-Life Problem
The particular decision situation to be outlined comes from the
field of agriculture and concerns the detection of estrus (heat) in
artificially inseminated dairy herds. The problem is that failure to
detect heat can result in lost breeding opportunities, lower.milk pro
duction, and subsequent capital losses. The following excerpts from the
dairy industry literature illustrate the problem:
Accurate estrus detection is a key to
efficient reproduction and high milk
production. . .Proper detection of
estrus is essential in any planned
breeding program using hand mating,
especially to capitalize on superior
sires available through artificial
insemination [14, p. 248].
13


decision time. Subjects with few decision entities on their reports
felt indifferent toward format, while subjects with many decision
entities indicated clear format preferences.
The implications of the findings for the Management Information
Systems researcher and practitioner are discussed. Suggestions are
given for further research.
x


INFORMATION FORMATS AND DECISION PERFORMANCE:
AN EXPERIMENTAL INVESTIGATION
By
JOSE A. AMADOR
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

Copyright By
José A. Amador
1977

A Magüi y Provi

ACKNOWLEDGEMENTS
I wish to express my gratitude to the University of Puerto Rico
for financing my graduate program; to my dissertation adviser, Dr.
Richard A. Elnicki, for his recurrent insistance and corrections
throughout the course of this work; to my co-adviser, Dr. Jack M.
Feldman, for his invaluable comments and stimulus; to the rest of
my committee, Dr. Christopher B. Barry, Dr. Thom 0. Hodgson, and
Dr. Richard R. Jesse, for their help and suggestions at the various
stages of the research; and to my colleague and friend, Jose F. Colon
for his enlightening suggestions during the early part of the project
I also want to express special appreciation to my "parents" in
Gainesville, Mr. and Mrs. Bruce Ruiz, for their long, long hours of
companionship and friendship during my stay at the University of
Florida.
Most important, I thank my wife, Magui, for pushing me through
while taking the worst part; my sons, for the time they would have
rather spent with me; and my parents, for their ever present support
and counsel.

TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS
LIST OF TABLES
LIST OF FIGURES
ABSTRACT
CHAPTER 1 RESEARCH BACKGROUND
1.1 Introduction
1.2 Literature Review
1.2.1 The Minnesota Experiments
1.2.2 The Lucas Model
1.2.3 Other Related Literature
1.3 Organization of the Dissertation
CHAPTER 2 THE PROBLEM
2.1 An Information-Decision Problem
2.1.1 The Real-Life Problem
2.1.2 The Abstracted Problem
2.2 Information Content
2.3 The "How-Do-We-Present-the-Information?" Problem
2.3.1 Medium of Transmission
2.3.2 Format of Presentation
2.3.3 Level of Detail
2.4 Number of Decision Entities
2.5 Dependent Variables
2.6 Research Hypotheses
2.7 Basic Functional Model
13
13
15
16
17
18
19
23
26
27
28
31
CHAPTER 3 THE EXPERIMENT
33
3.1 Method
3.1.1 Subjects
3.1.2 Design and Analysis
3.2 Experimental Task ....
3.3 Evaluation of Hypotheses
33
33
34
37
40
v

TABLE OF CONTENTS (continued)
Page
CHAPTER 4 EXPERIMENTAL RESULTS 44
4.1 Introduction 44
4.2 Results 44
4.2.1 Effect of Layout on Decision Time 44
4.2.2 Influence of Format on Choice Behavior 46
4.2.3 Joint Effect of Layout and Style on Decision Time .. 47
4.2.4 Effect of Probabilistic Detail on Cost Performance .. 47
4.2.5 Joint Effect of Format and Level of Detail
on Decision Time 48
4.2.6 Relation Between Number of Decision Entities
and Format 49
CHAPTER 5 DISCUSSION OF RESULTS 51
5.1 Summary of Findings 51
5.2 The Multivariate Effects 51
5.3 The Univariate Effects 57
5.3.1 Effects Related to HI and H3 57
5.3.2 Effects Related to H2 58
5.3.3 Effects Related to H5 59
5.3.4 Effects Related to H6 60
CHAPTER 6 SUMMARY AND POSSIBLE EXTENSIONS 63
BIBLIOGRAPHY 66
APPENDIX A EXPERIMENTAL TREATMENTS 69
APPENDIX B FORMAT OPINION QUESTIONNAIRE 78
APPENDIX C COMPUTER SIMULATION PROGRAM 80
APPENDIX D SUBJECT INSTRUCTIONS 89
BIOGRAPHICAL SKETCH 100
vi

LIST OF TABLES
Page
1.1 SOME STUDIES CONDUCTED UNDER THE CHERVANY
ETjAL. FRAMEWORK 4
3.1 FACTORIAL DISPLAY AND FACTOR LEVELS 35
3.2 DATA FOR THE EXPERIMENT 39
3.3 EFFECTS PREDICTED BY THE RESEARCH HYPOTHESES 41
4.1 CELL MEANS FOR THE SIXTEEN EXPERIMENTAL
CONDITIONS 45
5.1 MAIN EFFECTS 52
5.2 INTERACTION INVOLVING THE NUMBER-OF-DECISION
ENTITIES-VARIABLES 53
5.3 OTHER INTERACTION EFFECTS 55

LIST OF FIGURES
Page
1,2Different formats of presentation 8
2.1 Three forms of presenting the expected
due-dates information .. 21
2.2 Graphical style with events in due-date order 22
2.3 Three forms of presenting the interval
estimates information 25
viii

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
INFORMATION FORMATS AND DECISION PERFORMANCE:
AN EXPERIMENTAL INVESTIGATION
By
Jose A. Amador
June 1977
Chairman: Richard A. Elnicki
Major Department: Management
This study examines some implications of the relationship
between information format and decision performance. A real-life
information-decision problem was abstracted to create a simulated
decision environment in which alternative forms of presenting
information relevant to the problem were manipulated and adminis¬
tered to 160 experimental subjects.
Multivariate and univariate analyses of the experimental data
indicated significant differences due to the experimental treatments.
Presentation style (tabular versus graphical) and information layout
(I.D. ordering versus due-date ordering) were found to have separate
and joint effects on decision performance. The style of presentation
had a strong influence on subject choice behavior. The level of
probabilistic information provided (point estimates versus interval
estimates) and the style of presentation had a joint effect on
IX

decision time. Subjects with few decision entities on their reports
felt indifferent toward format, while subjects with many decision
entities indicated clear format preferences.
The implications of the findings for the Management Information
Systems researcher and practitioner are discussed. Suggestions are
given for further research.
x

CHAPTER 1
RESEARCH BACKGROUND
1.1 Introduction
The last decade has seen a significant increase in the use of
computer-based data systems to support decision making in organizations.
This marriage of computers and organizations has developed into the
rapidly growing field of Management Information Systems (MIS). In
general, MIS refers to the use of computer-based data systems for the
primary purpose of supporting management decisions. Since MIS exist
to support decision making, researchers in the area have suggested
that their effectiveness should be measured in terms of the effectiveness
of the decisions they support. In turn, it has been argued that the
effectiveness of decisions based on information will depend, among other
things, on the accuracy, relevancy, and timeliness of the information.
More recently, it has also been proposed that even when information
is adequate, its effective use can be influenced by the manner in which the
information is presented, in particular, by its format of presentation,
level of detail, and medium of transmission. This line of thought has led
researchers in the area to investigate how the physical form of presenting
the information can influence aspects of decision performance. That
1

2
relationship, in essence, is the object of this study. In the present
research, the influence of information format on decision performance will
be investigated in the context of a specific information-decision problem.
1.2 Literature Review
The increase in popularity of Management Information Systems
during the last decade has been accompanied by an awareness of the need
for improving the efficiency of the systems designed. The consensus
of the researchers in the area has been that there is a need for a
theory of MIS. Zannetos [31] states that a theory is needed to develop
objective criteria for determining the effectiveness of MIS efforts.
In response to the call for a theory, several research frameworks have
been proposed [10, 16, 21, 22] J
1.2.1 The Minnesota Experiments
The research framework proposed by Chervany et al. [10] has guided
the "Minnesota Experiments," a series of empirical studies that have
been conducted at the Management Information Systems Research Center,
University of Minnesota. The general purpose of these studies has been
to manipulate various MIS variables to investigate their impact on
decision performance. The Chervany et al. framework states that three
categories of variables affect decision performance, P, given a particu¬
lar information system. These are the decision environment, DE, the
^These frameworks have not constituted theories, in the formal
sense, but rather pre-theoretical lists of variables.

3
decision maker, DM, and the characteristics of the information system,
CIS. In functional form,
P = f(DE,DM,CIS)
(1.1)
A number of experiments have been conducted under this framework.
They all appear to have followed Van Horn's [28] suggestion that
laboratory studies provide an effective means for MIS research. In
particular, the experimenters have drawn upon the technique of
"experimental gaming" to create artificial decision-making evironments
within which they have manipulated various aspects of the information
system. In support of this technique, Barret et alâ–  conclude:
We have been unable, to date, to generate
evidence which implies that the interpre¬
tation of decision performance results in
terms of a treatment variable is likely to
be confounded by the effects of the simu¬
lator and/or other aspects of the manage¬
ment game context [4, p. 11].
Dickson et alâ–  say of experimental gaming:
...we are of the opinion that experimental
gaming, despite its high cost, is an effec¬
tive way of investigating this area. The
major problems really are associated with
the measurement of the variables included
in the experiments. Somewhat surprisingly,
problems of subject motivation and
experimental control have been minor
[12, p. 20].
Table 1.1 on pages 4-6 is a summary of some of the most
referenced studies that have been conducted under the Chervany et al.
framework. The variables that were experimentally controlled are shown
in each case before the "given" bar (|) in the functional models along
with a brief description of the measure used for each variable. Other

TABLE 1.1
SOME STUDIES CONDUCTED UNDER THE CHERVANY ETAL. FRAMEWORK
Experimenter
Decision Environment/
Basic
Experimental
[Reference]
Experimental Subjects
Model
Variables
Measure
Results
Chervany
Production-Inventory/
P-f(cisIde.dm)
P
Cost, time
and
Graduate Business
confidence 1n
Subjects receiving the
Dickson
C9]
students
the decisions
summery data treatments
performed better cost-
CIS
Summary vs.
wise but took longer to
make decisions. Subjects
raw data re-
receiving the raw data
ports
treatments had more con¬
fidence in their decisions.
Schroeder
Production-Inventory/
p
System utiliza-
The frequency of informa-
and
Undergraduate Business
P*f(OE,CIS|DM)
tiun, decision
t1on use was not affected
Benbaset
C25]
students
confidence
by environmental variability.
The low variability nroups
DE
Variability In
preferred less derailed re-
the decision en-
ports. No decision confí-
vlroranent at 3
dence effects were estab-
levels: low,
medium, high
11shed.
CIS
Summary vs. de¬
tail reports

TABLE 1.1 (continued)
Experimenter Decision Environment/
[Reference] Experimental Subjects
Basic
Model
Experimental
Variables
Measure
Results
Senn
and
Dickson
C263
Purchasing Decisions/
Purchasing Managers
P-f(DM.CISlDE)
P
DM
CIS
Cost, time,
confidence,
number of re¬
ports requested
Decision makers
from large and
small organiza¬
tions
Paper reports
vs. CRT, sum¬
mary vs. detail
reports
No significant relation
was observed between
organization size and the
performance measures. The
display media had a signi¬
ficant effect on the number
of reports requested. The
subjects receiving hard copy
reports requested more in¬
formation than those using
the CRT medium. There was
no significant difference
1n cost performance between
users of summary and detail
information.
Kozar
OBJ
Productlon-Inventory/
Graduate Business
students
P-f(CIS|DE,DM)
P
CIS
Cost, time,
confidence,
in the decisions
CRT vs. hard
copy reports
CRT users took significantly
longer time to make decisions.
They felt the device was use¬
ful as a filing organizer
but were unhappy with the
lack of hard copy. Subjects
receiving the hard copy re¬
ports had lower costs. There
was no difference 1n decision
confidence between the two
groups.

TABLE 1.1 (continued)
Experimental
Decision Environment/
Basic
Experimental
[Reference]
Experimental Subjects
Model
Variables
Measure
Results
Benbasat
Production-Inventory/
P
Cost, time.
Subjects using graphs had
and
Students enrolled In
number of
lower costs than subjects
Schroeder
an operations manage-
P-f(DM,CI5|BE)
reports
using tabular information.
C5J
ment course
requested
Subjects with graphical
DM
Decision-
reports requested less re¬
ports than those with
making
tabular reports, low
style: low
analytic subjects who
or hiqh
neither had graphical re-
analytic;
ports nor decision aids
knowledge of
had the largest costs, low
functional
analytic subjects who had a
area: low
low functional knowledge of
or high
the area requested the
largest number of reports.
CIS
Graphs vs.
There were no significant
tabular in-
differences 1n time perfor-
formation;
manee between the users of
decision
the graphical and tabular
aids: pro¬
vided or
not; num-
reports.
ber of reports
available;
necessary vs.
overload groups

7
items in the table are the nature of the simulated decision environ¬
ment, the experimental stubjects, and a summary of the results.
It is interesting to note that while the form of presenting the
information has been extensively considered in one form or another,
the "layout" or physical arrangement of the information reported has
not been manipulated as an experimental variable in any of the studies
reviewed. Figure 1.2, part A (p. 8) is an example of the type of "form
of presentation" treatment that has been manipulated in the reviewed
literature. Figure 1.2, part B is an example of what is meant here
by information layout. The influence of this variable on decision
performance will play an important role in this study.
1.2.2 The Lucas Model
The model proposed by Lucas [21] includes essentially the same
variables as the Chervany et al. framework, but it also takes into
account the interface between use of the information system and per¬
formance. His descriptive model states that performance (P) is a
function of situational, personal, and decision style variables (the
DM group in the Chervany et al. model), the quality of the information
system (the CIS group in the Chervany et al. model) and the analysis
and actions taken by the users (similar to the DE group in the Chervany
et al. model). In addition, his model also states that the performance
of the information system is independently affected by the use of the
system, U. In functional form,
P = f(DE,DM,CIS,U)
(1.2)

8
Raw Data Treatment
FINISHED GOODS INVENTORY HISTORY
MEEK 1 OF MONTH 3 WEEK 2 OF MONTH 3
INVENTORY
LEVELS
Resinoid
R-Forced
Vitrifid
Resinoid
R-Forced
Vitrifid
MONDAY
0
371
0
0
120
481
TUESDAv
39
102
82
0
153
191
WEDNESDAY
0
0
193
0
202
0
THURSDAY
34
36
259
38
267
0
FRIDAY
71
84
393
79
188
38
STOCKOUTS
Resinoid
R-Forced
Vitrifid
Resinoid
R-Forced
Vitrifid
MONDAY
235
0
58
354
0
0
TUESDAY
0
0
0
423
0
0
WEDNESDAY
379
321
0
144
0
201
THURSDAY
0
0
0
0
0
121
FRIDAY
0
0
0
0
0
0
Statistically Summarized Treatment
FINISHED GOODS INVENTORY HISTORY
SUMMARY STATISTICS CALCULATED
FROM OPERATIONS FOR PERIOD
WEEK 1 OF MONTH 3 THROUGH WEEK 4 OF MONTH 3
A.
Daily Inventory Levels
(End of Day)
Resinoid R-Forced Vitrifid
Stockouts
Resinoid R-Forced Vitrifid
Mean 23.25
Coef Var 6.2S
Maximum 79.CO
Range 79.00
140.80 92.85
4.18 7.97
371.00 431.00
371.00 481.00
Mean 171.30
Coef Var 5.63
Maximum 427.00
Range 427.CO
38.20 123.70
14.77 7.09
392.00 434.00
392.00 434.00
Abreviated samples of two "form of presentation" treatments used by
Chervany and Dickson [9, p. 1338].
FINISHED GOODS INVENTORY HISTORY
SUMMARY STATISTICS CALCULATED
FROM OPERATIONS FOR PERIOD
WEEK 1 OF MONTH 3 THROUGH KEEK 4 OF MONTH 3
Mean
Coef Var
Maximum
Ranqe
Daily Inventory Levels
(End of Day)
Resinoid
23.25
6.28
79. CO
79.00
R-Forcod
140.80
4.13
371.00
371.00
Vitrifid
92.85
7.97
481.00
481.00
Stockouts
Resinoid
171.30
5.63
427.00
427.00
R-Forced
38.20
14.77
392.00
352.00
Vitrifid
123.70
7.C9
484.00
434.00
B. A different "layout" for the information in the second report above.
Figure 1.2 Different formats of presentation

9
In a field study [20] with an actual information system and data
from salesmen's performances, Lucas observed relationships among DE,
DU, CIS, and P that are congruent with those observed in the "Minnesota
Experiments." In addition he also noted the following relationship
between performance and information system use:
AP
ttt > 0 when relevant information is
provided and used,
¿P < o when the information provided
AU is irrelevant to the decisions
that must be made.
What he in effect noted is that only those information system designs
that promote effective use of the information will have a positive
effect on performance. One of the indications of his results was that
information structure elements such as the format of presentation, F,
and the level of detail, L, can be determinants of effective use, EU,
given other system characteristics, CIS', and a given set of DM and DE
variables. Although not explicitly stated in his paper, the results
of his study suggest that
EU = f(F,L|DE,DM,CIS1) (1.3)
and that P = F(EU|DE,DM,CIS') (1.4)
with AP n
AEU u>
These relationships are inferred from the discussion part of his paper:
One of the most important implications of
the model and results is that different
personal, situational and decision style
variables appear to affect the use of
systems. These findings argue for more

10
flexible systems to support different
users' needs. For example, the
present sales information system could
be modified to provide different out¬
put formats and levels of summarization
[20, p. 918].
Equations 1.3 and 1.4 are combined in the next chapter to produce
a model that will serve as a guide for evaluating a set of propositions
relating information format to decision performance.
1.2.3 Other Related Literature
In addition to the literature referenced above, other related
literature has influenced the formulation of the hypotheses evaluated
in the present study. Two textbooks on MIS, in particular, contain
some interesting but undocumented ideas which have shared in the latter.
Murdick and Ross [23] make such general statements as, "In general,
the format should be established to save the manager's time" [p. 326]
and, "Managers prefer graphic displays, which reduce large amounts of
information into easily understood pictorial form" [p.263].
The second MIS text which makes similar suggestions is Voich et al.
[29], They propose:
Format is important because it affects
the ease with which the report can be
read and assimilated. As the complexity
of a report increases, its likelihood
of extent of use falls [29, p. 229].
This writer feels that the authors are saying that more attention
should be given to the format of the report as the number of "entities"
in the report on which decisions are required increases.
Finally, a recent paper by Conrath [8] suggests:
In all the literature on decision making,
and in particular that on statistical de-

11
cisión theory, little if anything has
been said about the form in which the
data should be presented to the de¬
cision maker. Perhaps this is because
most theoreticians assume that as long
as the data unambiguously define the
distributions, the format of presenta¬
tion should make no difference. This
brings up the question of whether data
can ever be unambiguously presented,
and perhaps more importantly, in whose
eyes? The only answer to the second
question is the user, but he has seldom
been asked [8, p. 878].
Conrath goes on to propose that the format in which probabilistic
data is presented as a basis for choice can influence choice.
The present study centers around the questions raised above-as
they relate to a pragmatic "how-do-we-present-the-information?"
problem.
1.3 Organization of the Dissertation
In Chapter 2, a "real-life" information-decision problem is pre¬
sented to provide a setting for the questions investigated in this study.
The nature of the problem is explained in Section 2.1. In Section 2.2,
the information needs of the manager in the problem are considered, and
it is assumed that these needs are relatively well defined and structured.
A number of questions related to the form in which information should be
presented to the manager are raised in Section 2.3. The results of
previous studies are revisited in an effort to provide orientation to
the present information format/decision performance questions. The
criteria used to measure decision performance are defined in Section 2.5,
and a set of research hypotheses relating these criteria to the experi¬
mental format variables is presented in Section 2.6. In Section 2.7, a
general model is presented to serve as the guide for the experiment.

12
The nature and details of the experiment are the subject of
Chapter 3. The methodology is discussed in Section 3.1 and a full
description of the experimental task is given in Section 3.2. Section
3.3 discusses the experimental results that should be observed for the
research hypotheses to be supported, and a table is presented that
shows how each of the hypotheses is to be evaluated from the experi¬
mental data.
The statistical results of the experiment are presented in Chapter
4. These are discussed in Chapter 5 from the point of view of their
implications for both the MIS researcher and practitioner. In Chapter
6, suggestions are given for new lines of research.

CHAPTER 2
THE PROBLEM
2.1 An Information-Decision Problem
The information-decision problem that provided the setting for the
current study is presented in this chapter. The situation studied pre¬
sents several advantages from the point of view of empirical MIS re¬
search. First, the situation is relatively simple, easy to characterize
and to model. Second, the problem points to clearly definable questions
of information structure, an area that has received increased attention
in the recent MIS literature [5, 9, 12, 18, 25, 26]. Finally, the situa¬
tion may represent a new area for the application of MIS technology.
2.1.1 The Real-Life Problem
The particular decision situation to be outlined comes from the
field of agriculture and concerns the detection of estrus (heat) in
artificially inseminated dairy herds. The problem is that failure to
detect heat can result in lost breeding opportunities, lower.milk pro¬
duction, and subsequent capital losses. The following excerpts from the
dairy industry literature illustrate the problem:
Accurate estrus detection is a key to
efficient reproduction and high milk
production. . . .Proper detection of
estrus is essential in any planned
breeding program using hand mating,
especially to capitalize on superior
sires available through artificial
insemination [14, p. 248].
13

14
...delayed conception means a cow must
stand dry and nonproductive when her
lactation ceases at a maintenance cost
of about $20 per month [19, p. 580].
Approximately 53% of heats are being
missed. . . .Dairymen appear to be losing
twice as many days due to missed heat
periods as due to failure to conceive
[2, p. 247].
The literature includes much advice about methods for heat detec¬
tion, most of it having to do with heat recognition in the field.
Even then, it has been suggested that close to 50% of all heats are not
detected [2, 3].
Dairymen using artificial insemination and keeping the appropriate
records have information that can help them in detecting heat [7]. The
information consists of the date of the last service (insemination) of
each cow and data on the average number of days between successive ser¬
vices. It has been suggested [27] that a chart with "heat expectancy
dates" could be valuable for detecting heat, as it would enable the
dairyman to concentrate his observations on those cows expected to come
in heat.
The design of such a report motivated initial work on the problem.
A preliminary survey^ using an experimental report in an actual dairy
operation revealed that rather general agreement existed among the
prospective users as to the desired content of the report and how often
^Unpublished; conducted at the dairy farm of Mr. Herman Hernandez,
Isabela, Puerto Rico during January-May, 1976.

15
it should be produced. One issue that remained questionable was the
manner in which the information should be presented in the report. There
were several formats that appeared useful but each seemed to have its
own pros and cons from the point of view of ease of use. The problem
appeared to be sufficiently interesting and important to merit an
experimental evaluation of the various information format alternatives.
The problem discussed in the next section is the abstraction or
prototype designed to investigate this information structure problem
within a controlled laboratory setting.^ The questions of interest
were widened to include a set of propositions related to a more general
MIS framework and theory. In the problem to be outlined below,-the
term "heat" is replaced with more general terminology.
2.1.2 The Abstracted Problem
Consider an organization that needs to keep records on a number
of random events that occur relatively infrequently but are important
to management. These events represents opportunities for management:
if one occurs and is not detected the organization suffers opportunity
costs.
Management knows that these events occur independently approxi¬
mately once every 20 days, and that when they occur they are
"detectable" during a short period of time (approximately 24 hours).
The reasons for taking the research to the laboratory were two¬
fold. First, resources were not available for conducting a reasonably
controlled field experiment. Second, the research interests of the
author were shifted from the operational considerations of the problem
to a more general set of research questions more amenable for resolution
in a laboratory setting.

16
It is assumed that each check made on an event to see whether it is
occurring has a fixed unit cost associated with it, independent of the
number of checks made on the same day. It can, therefore, be uneconom¬
ical for management to check on these events too often. Management
is assumed to maintain a computer-based data bank with the following
data on the process:
(1) a three-digit identification (I.D.) number for
each event that is expected to occur during the
next twenty days,
(2) the date of the last observed occurrence of each
event, and
(3) data on past time intervals between successive
occurrences of each event.
It is further assumed that management will use this data to pro¬
duce a periodic report to aid them in deciding which events to check
at the beginning of each dayJ Their decision problem is relatively
well structured and straight-forward: they would like to detect as many
of these events as possible but face a trade-off between the costs
of "checking" and "missing" the events.
2.2 Information Content
Based on past experience, the managers in charge of checking the
events know that it is not cost-effective to check an event except on
those days when the event is more likely to occur, i.e., the days
around the date figured by adding 20 days to the last observed occur¬
rence. They have suggested that a periodic chart with "event
^They will produce the report; they will rather have the data re¬
ported in its worst possible form than no report at all.

17
expectancy dates" would be useful as it would permit management to con¬
centrate their checks on those days when each event is expected to occur.
A dichotomy from economic models will help to clarify the type of
report that managers consider appropriate in the problem modeled.
Managerial reports can be descriptive or normative in nature. Purely
descriptive reports, as used here, are those limited to the presenta¬
tion of factual information (e.g.: production history reports, financial
reports). Purely normative reports, as used here, explicitly indicate
courses of action to be followed by the user (e.g.: production
schedules). All managerial reports can be placed on this descriptive-
normative scale. A report providing demand forecasts and safety
stock sizes [11] is, for example, more normative than one providing a
detailed sales history but no forecasts. In this study, it is assumed
that managers want more than a descriptive report (for example, one
showing only the dates of the last observed occurrence of each event).
They want a report providing forecasts for the event occurrence dates.
They consider twenty days a reasonable time horizon for the report.
It is assumed that shorter horizons would make the report too costly
to produce and longer horizons would make the forecast data basis too
dated. In conclusion, the report that is assumed to be appropriate
for the problem modeled is a periodic chart containing event I.D.
numbers and "expected due-dates" for those events expected to occur
within the next twenty days.
2.3 The "How-Do-Me-Present-the-Information?" Problem
The information content needs of management in the problem
characterized above are assumed to be relatively well structured and
defined. The issue that constitutes the main focus of this research

18
is the question of information structure, i.e., the physical manner in
which the information is presented to the user. Dickson et al. have
suggested three categories of information structure (enumerations
added by the author):
It is naive to assume that information system
requirements do not vary with the type of
decision being formulated. And, it is sub-
optimal to continue developing information
support systems without serious consideration
of (1) the form in which information is pro¬
vided, (2) the level of detail incorporated
into ensuing reports, and (3) the media by
which the information in transmitted
[12, p. 3],
The medium of transmission, the format of presentation, and the
level of detail are discussed below in terms of their importance in
the defined decision nroblem. In each case, arguments are presented
to show why each category was included or excluded as an experimental
variable in the study. The dependent variables measurina decision
performance are then presented, and the questions raised about the
effects of the experimental treatments on performance are presented
as a set of testable hypotheses. In the final section, a functional
model is presented to serve as framework for testing the hypotheses.
2.3.1 Medium of Transmission
Two media are commonly used for reports generated from a
computer-based data bank: paper printout and cathode ray tube (CRT)
display.^ In the case of a report that is to be produced and released
When reports are generated by a computer, the choice of transmission
medium is usually confined to these two media. Otherwise, the writer is
aware that other more "personalistic" modes of communication are also
available for displaying the information to the user [22], Only computer

19
every twenty days, paper would appear to be the more appropriate
medium. A CRT could be a reasonable medium if the time interval be¬
tween reports was shorter and if there was a need to reduce paper
clutter. Kozar [18] found that users of CRT's tend to to be unhappy with
the lack of hard copy and that they take significantly more time to
arrive at decisions than hard copy users. The medium of transmission
was not considered a relevant design variable in the present study.
Conventional paper printout was used as the constant medium through¬
out the experiment.
2.3.2 Format of Presentation
The format variable has been discussed more extensively in the
MIS literature than the medium variable [5,9,25,26]. The most common
format treatment has been summary versus raw data [9, 25,26], This
treatment, however, has manipulated the data content more than its
format. Only one study has been concerned with format, if format is
considered to be related to the "style" of presentation.
Style of presentation. Benbasat and Schroeder [5] presented
daily production figures to experimental subjects in one of two styles:
tabular and graphical. The tabular style listed daily production figures
while the graphical style plotted the same daily figures versus time.
Their results indicated that subjects using the graphical reports had
lower costs, with no significant differences in decision time between
the two groups.^ These results suggest that the graphical format might
generated reports are considered here, however, mainly because of
the lack of resources for experimenting with other media.
^Murdick and Ross [23,p. 263] state that managers prefer graphical
displays, although they do not support their contention.

20
be a more appropriate style of presentation for the time-staged infor¬
mation in our problem. Specifically, if the reports are to consist
only of event 1.0. numbers and expected due-dates, the question of
interest is whether the formats shown in Figure 2.1 (p. 21) can in¬
fluence aspects of decision performance. As discussed in section 2.5,
decision performance will be measured in this study in terms of time
performance (the time devoted to making the "check" decisions) and
cost performance (the total cost of checking and missing the events).
A priori, it would seem logical to expect the formats in Figure
2.1 to influence, if anything, time performance. The dates reported
are future dates and the information is going to be used chronologically.
Consequently, the time dimension added by the graphical style should
be helpful in that it orders the events chronologically from left
to right on the x-axis^. In part C of Figure 2.1, for example, it is
seen that event "032" is expected to occur first (May 26), then event
"146" (May 28), and so on.
Information layout. A chronological ordering of the events can
also be achieved with the tabular style by arranging the events in
order of expected due-dates, as in part B of Figure 2.1. It is assumed,
however, that the ordering of events by ascending I.D. numbers is a
desirable condition in these reports because management frequently
needs to make quick reference to the due-dates of particular events.
The quickest way to make these references is when the events are arranged
'If these reports were intended for Chinese managers, an attempt
would be made to present the information from right to left.

21
. Tabular style
B. Tabular style
with
I.D. layout
with due-date layout
EVENT
EXPECTED DUE-DATE
EVENT
EXPECTED DUE-DATE
IDENT.
(MONTH-DAY)
I0EN7.
(MONTH-BAY)
004
6-07
032
5-26
009
5-29
146
5-28
017
6-11
0C9
5-29
024
6-04
073
5-30
032
5-26
171
5-31
038
6-C4
051
5-31
051
5-31
185
5-31
070
6-01
070
6-01
076
6-10
128
6-01
078
5-30
08?
6-C3
052
6-03
024
6-04
035
6-10
038
6-04
097
6-05
121
6-05
110
6-11
097
6-05
121
6-05
173
6-06
123
6-01
177
6-05
142
6-09
168
6-07
146
5-28
004
6-07
155
6-12
163
6-09
163
6-09
142
6-09
168
6-07
085
6-10
171
5-31
076
6-10
173
6-05
no
6-11
177
6-06
017
6-11
186
5-31
155
6-12
C. Graphical style with I.D. layout
EXPECTED DUE-DATES
EVENT
IDENT.
MAY
25 26 27
23 29
JUNE
30 31 01 02 03
in
o
nf-
o
06
07 08 09
10 11
12 13
EVENT
IDENT.
004
H
004
009
H
009
017
H
017
C24
H
024
032
H
G32
028
H
C38
051
H
051
070
H
070
076
H
076
0:3
H
078
082
H
082
035
H
035
097
H
097
110
H
no
121
H
121
128
H
128
142
H
142
146
H
146
155
H
155
163
H
163
163
H
168
171
H
171
173
H
173
177
H
177
186
H
186
25 26 27 28 29 30 31 01 02 03 04 05 06 07 03 09 10 11 12 13
Figure 2.1 Three forms of presenting the expected due-dates information

22
in ascending I.D. number order, especially when there are a large
number of events to be referenced. One solution to this dual need is
to produce two reports: one in order of expected due-dates to support
the daily checking of decisions, and another in ascending I.D. order for
quick references. But, if an experiment revealed no significant
difference in performance between the users of the graphical I.D.
ordered reports and the users of the due-date ordered reports, the im¬
plication would be that there is no need for both reports. The graphical
report would provide the two desired features. Another explanation
for that result could be that the graphical style in this case has
a "calendar" resemblance and therefore presents a more familiar .
picture to the user than a listing of numbers. If this were the case,
a graphical report that presented the events in due-date order might
also influence performance. Figure 2.2 below shov/s such a report.
EXPECTED DUE-DATES
EVENT MAY JUNE EVENT
IDENT. 25 26 27 28 29 30 31 01 02 03 04 05 C5 07 08 OS 10 11 12 13 10ENT.
032
146
009
078
171
051
186
070
128
082
024
038
121
097
173
177
168
004
163
142
085
076
110
017
155
032
146
009
078
171
051
186
070
128
082
024
038
121
097
173
177
168
004
163
142
OE5
076
110
017
155
25 26 27 28 29 30 31 01 02 03 04 05 06 07 C8 C9 10 11 12 13
Figure 2.2 Graphical style with events in due-date order

23
The only difference between the arrangement above and that in part C
of Figure 2.1 is the "layout" of the information in the report. The
term "layout" will be used here to refer strictly to the order in
which the information is arranged in the report. Two layout schemes
are considered: I.D. number ordering and expected due-date ordering.
Questions of interest. The format alternatives considered above
appear to have pros and cons from the point of view of the ease with
which the report can be used. The format variables 1 a.yout and style
will be experimentally manipulated in an attempt to address the following
questions:
(1) Can information layout by itself affect decision
performance?
(2) Can information layout interact with presentation
style to enhance or reduce separate performance
effects of either layout or style?
2.3.3 Level of Detail
Given the probabilistic nature of our data, a wide range of levels of
detail can be provided in the "expected due-dates" reports. These could
rangefrom point estimates to complete probability distributions of the
event occurrence times. On this subject, Conrath [8] proposes that
decision makers are not likely to think in terms of probability distribu¬
tions, and that they prefer to think in terms of, and use, point estimates.
His argument would suggest the use of point estimate forecasts as one level
of detail in this study. The question would remain, however, whether the users
in this decision context could benefit from additional information about the

24
probability distributions from which these estimates are drawn. This
additional information could be presented, for instance, in the form
of percentiles of the distribution (e.g.: the days lying above the
fifth percentile and below the ninety-fifth percentile of the distri¬
bution), The latter would have the advantage of incorporating infor¬
mation about the variability of the event occurrence times and, there¬
fore, the risk involved in making the check decisions,
In the problem modeled, it is assumed that there is enough data
available on past intervals between events to permit estimates of the
mean, x-, and standard deviation, s., for each event i. Using this data,
and assuming normal and stable distributions, the intervals ± 2s.j
were used as interval estimates for the days during which each event i
is more likely to occur. In the case of the tabular style, the reports
with such "95”/ confidence intervals" could appear as in parts A and B of
Figure 2.3 (p.25). The issue of format takes new importance now
since it is possible that the graphical style (part C of Figure 2.3)
may have properties that make the checking choices easier for the user.
Specifically, the level of probabilistic detail (point estimates or
interval estimates) may interact with the style of presentation
(tabular or graphical) to affect the ability of the user to process
and effectively use the information.
Questions of interest. The two levels of probabilistic informa¬
tion described above, point estimates and interval estimates, will be
experimentally manipulated in connection with the format variables to
address the following questions:

25
A. Tabular style B. Tabular style
with I.D. layout with due-date layout
EVENT
IOENT.
S5S CCÍ.FIDENÍ
(FIRST DAY.
:e interval
, LAST DAY)
Ev'EN'f
IOENT.
S5Í CONFIDE?
(FIRST CAT
;CE INTERVAL
r, LAST DAY)
004
6-06 ,
6-09
032
5-25 ,
, 5-23
009
5-27 ,
5-31
146
5-26 ,
, 5-30
017
6-10 ,
6-12
009
5-27 ,
, 5-31
024
6-01 ,
6-07
C73
5-27 ,
, 6-02
032
5-25 ,
5-23
17!
5-28 ,
, 6-03
038
6-02 ,
6-05
051
5-29 ,
, 6-03
051
5-29 f
6-03
1C6
5-29 ,
, 6-03
070
5-31 ,
6-02
128
5-30 ,
, 6-04
076
6-09 ,
6-12
070
5-31 ,
, 6-02
073
5-27 ,
6-02
082
6-01 ,
, 6-05
032
6-01 ,
6-05
024
6-01 ,
, 6-07
035
6-09 ,
6-14
038
6-02 ,
, 6-06
097
6-04 ,
6-07
121
6-03 .
> 6-07
110
6-10 .
6-12
168
• 6-04 ,
, 6-10
121
6-03 ,
6-07
C97
6-04 ,
, 6-07
128
5-30 ,
6-04
173
6-05 ,
, 6-07
142
6-06 ,
6-12
177
6-05 ,
, 6-03
146
5-26 ,
5-30
004
6-C5 .
, 6-03
155
6-11 ,
6-13
142
6-06 ,
, 6-12
163
6-07 ,
6-12
163
6-07 ,
, 6-12
163
6-04 ,
6-10
085
6-09
, 6-14
171
5-28 f
6-03
076
6-09
, 6-12
173
6-05 ,
6-07
110
6-10
, 6-12
177
6-05 ,
6-03
017
6-10
, 6-12
186
5-29 ,
6-03
155
6-11
, 6-13
C. Graphical style with I.D. layout
S5t CONFIDENCE INTERVALS
EVENT MAY JUNE EVENT
IDENT.
25
26
?7
28
Ol 1
C\J 1
30
31
01
02
03
04
05
05
07
03
09
10
11
12
13
IDENT.
004
H
H
K
H
GC4
009
H
H
H
H
H
009
017
H
H
H
017
024
H
H
H
H
H
H
H
024
032
H
H
H
H
032
C38
H
H
H
H
H
038
051
H
H
H
H
H
H
051
070
H
H
H
07C
075
H
H
H
H
076
078
H
H
H
H
H
H
H
078
082
H
H
H
H
H
082
085
H
H
H
H
H
085
097
H
H
H
H
097
110
H
H
H
110
121
H
H
II
H
H
12 i
128
H
H
H
H
H
H
123
142
H
H
H
H
H
H
H
142
146
H
H
h
H
H
143
155
H
H
K
155
163
H
H
H
H
H
K
163
168
H
H
H
H
H
H
H
163
171
H
H
H
H
. H
H
H
171
173
H
H
H
173
177
H
H
H
H
177
186
H
H
H
ti
H
H
185
25
26
27
28
29
30
31
01
02
03
04
05
06
07
08
09
10
11
12
13
igure 2.3 Three forms of presenting the interval estimates information

26
(1) Can users of probabilistic data make effective
use of information beyond point estimates?
(2) Can the format in which probabilistic data is
presented affect choice behavior?
(3) Can the level of probabilistic information in¬
teract with format to affect user performance?
2.4 Number of Decision Entities
An important consideration is now introduced: the influence that
any report format will have is very likely to be related to what is
called here the "number of decision entities" on the report. The
term "decision entities" will be used to refer to the separate pieces
of information present on a report and on which decisions are required.
In the real problem this study is based on, there is little doubt that
the report users would be indifferent about format if their reports con¬
tained information on only two or three events. This is not expected
to be the case, however, if the reports contain information on 200
events.1 The "number of of decision entities" was included as an experi¬
mental variable to empirically test the assertion that while report
users may feel indifferent about format when small amounts of infor¬
mation must be processed, they will move toward preferred formats,
and their performance will be more sensitive to format as the amount
2
of information they must process increases.
A manager dealing with more than 200 decision events told the
writer he "could care less" about format if he did not have so many
events to look after.
2
"Amount of information," as used here, must not be confused with
"information overload," a condition where the decision maker is given
too much, unnecessary information [1, 9, 15].

27
2.5 Dependent Variables
Time performance. Since time is a valuable managerial commodity,
decision time is commonly used as a decision performance criterion
[5,9,18,26]. Although not supported by any studies, Murdick and Ross
contend that "... format should be established to save the manager's
time" [23, p. 326]. Decision time will be used here as a proxy for the
value of managerial time to the organization. It will be measured by
the total time that the decision maker devotes to making the checking
decisions. It is expected that this measure will be correlated with
the cost measure, although the cost measure will not include the cost
of managerial time to avoid double counting. Chervany and Dickson [9]
found that some decision makers will take longer to arrive at their
decisions but will make lower cost decisions. The possible correla¬
tion between the time and cost measures will be taken into account
through the use of multivariate statistical procedures (viz., MANOVA).
Cost performance. Cost is also commonly used as a performance
criteria when decision effectiveness is discussed [5,9,18,26]. Cost
performance will be measured here by the total of the "checking" and
"missing" costs. For each check made, the decision maker will in¬
cur a fixed dollar cost. The checking cost, then, will be given by
the product of the total number of checks made times the fixed cost
per check. For each event that is not detected, the decision maker
will incur a fixed opportunity cost. The cost of missing is then
calculated as the product of the total number of misses times the fixed
cost per miss.
Choice behavior. Choice behavior was also included as a criterion
variable to test Conrath's [8] contention that the format in which

28
probabilistic data is presented influences the choice behavior of the
user. It was assumed this could be the case with the tabular versus
graphical formats in the current study. The graphical format appears
to "bring out" more vividly the information, especially in the case
of the interval estimates (see Figure 2.3, p. 25). The measure used
for choice behavior was the number of checks performed by the decision
maker, disregarding which were successful and which were not.
2.6 Research Hypotheses
The questions raised above are now presented as six testable
hypotheses. Three of the hypotheses relate to format, two to the
level of detail, and one to the number of decision entities in the
report. The hypotheses relating to format are presented first.
1. The layout or physical order of the information in a (HI)
report can reduce decision time. In this case, it is
expected that the users of the due-date ordered reports
will have shorter decision times than the users of the
I.D. ordered reports.
This hypothesis was not found to have been considered in the
MIS literature, either in field or laboratory work. There are many
ways in which the same information can be arranged in a report. Even
though the "best" way may usually be considered "apparent" or the
issue simply "unimportant," this may not always be the case.
2. The format in which probabilistic data is presented as a (H2)
basis for choice can influence choice. In this case, it
is expected that the graphical report users will choose
to make more checks than the users of the tabular reports.
This hypothesis was suggested by Conrath [8] but not statis¬
tically demonstrated in his paper. He states:
Apparently format has the characteristic
that it can focus one's attention on one

29
dimension of the choice space, and that
dimension becomes paramount in the de¬
cision process. ...Whether the attention
focusing attributes of data format are
the keys to the influence that format has
on choice is a question not yet resolved.
But the question would appear to be
sufficiently important that it should no
longer be ignored [8, p. 880].
The format variable that is expected to have "attention focusing at¬
tributes" in this case is the style variable (graphical versus tabular).
As such, the style factor will be the one analyzed in the evaluation
of this hypothesis.
3. Report layout and style can interact to enhance or reduce (H3)
the decision time effects of a particular layout or style.'
In this case, it is expected that the users of the I.D.
ordered reports in graphical style will have shorter de¬
cision times than the users of the same layout in tabular
style.
The objective in testing this proposition is to demonstrate the
existence of information format characteristics that may have joint
effects on decision performance. Here, the combination of the I.D.
ordering layout with the graphical style is expected to reduce the
long decision times associated with the absence of the convenient due-
date ordering.
The next two hypotheses relate to the level of detail of the
probabilistic information provided.
4. Users of probabilistic data can make effective use of (H4)
information beyond point estimates. In this case, it
is expected that the interval estimates users will make
more cost-effective decisions than the point estimates
users.
The interest in this hypothesis is twofold. First, its evaluation
should give an indication as to whether the users of this type of

30
report can make effective use of interval estimates. In the real
problem this study is based on, it is expected that interval estimates
can be useful. Second, this proposition provides a setting for testing
Conrath's [8] contention that decision makers are not likely to think
in terms of probability measures other than point estimates.
5. The time to process and effectively use probabilistic (H5)
information is related to the format in which the in¬
formation is presented. In this case, it is expected
that the users receiving the interval estimates in the
graphical style will have shorter decision times than
those receiving the interval estimates in the tabular
style.
The difference between HI and H5 is that HI refers to the direct
(main effect) influence of layout on performance while H5 refers to
the interaction between a format variable (style) and the level of
probabilistic information provided (point estimates or interval
estimates). The purpose in testing this hypothesis is to show that
different levels of probabilistic information will be more easily
processed and used with different formats of presentation.
The hypothesis relating the number of decision entities to
format preference is:
6. Report users will move from format indifference to for- (H6)
mat preference and their performance will be more sensitive
to format as the number of decision entities on their
report increases. In this case, no significant format
opinion differences are expected among users of reports
with few decision entities in them, with the opposite
expected among users of reports containing many decision
entities. Differences in performance are also expected
to be larger among the users of the reports with many
decision entities.
The objective in testing this proposition is to demonstrate the
existence of a "number of decision entities" variable that should be

31
considered in MIS design. This variable will occur in most situations
where the number of physical phenomena on which decisions are required
is variable. A procurement manager, for instance, may be indifferent
about the format of his inventory status reports if he must place orders
for only five items. He would probably be concerned about format (and
his performance would be more influenced by format) if he has 250 items
on which to place orders.
2.7 Basic Functional Model
The following model presents, in equation form, the relationships
to be analyzed.
Given equations 1.3 and 1.4,
EU = f(F,L|DE,DM,CIS')
P = f(EU|DE,DM,CIS')
0-3)
0-4)
it follows that
P = f(F,L|DE,DM,CIS1)
(2.1)
where, P = decision performance
F = format of presentation
L = level of detail
DE = decision environment characteristics
DM = decision maker characteristics
CIS' = other characteristics of the information system.
In words, equation 2.1 states that the format of presentation
and the level of detail are determinants of decision performance

32
given a particular decision environment, decision maker, and other
characteristics of the information system. In the next chapter, a
table is presented that shows each of the research hypotheses expressed
as a variant of this basic model.

CHAPTER 3
THE EXPERIMENT
3.1 Method
This chapter presents the details of the experiment that was con¬
ducted to evaluate the decision performance effects of four factors:
information layout, style of presentation, level of detail, and the
number of decision entities on the report. The material has beeh
arranged as follows. Section 3.1.1 discusses the nature of the experi¬
mental subjects. The methods used for collecting and analyzing the
experimental data are presented in Section 3.1.2. A full description
of the experimental task is given in Section 3.2. Finally, the experi¬
mental results that should be expected for the hypotheses to be backed
up are discussed in Section 3.3.
3.1.1 Subjects
One-hundred sixty subjects participated in the experiment. The
subjects were undergraduate students in Business Administration who had
completed the first semester of introductory statistics at the Univer¬
sity of Puerto Rico, Mayaguez Campus. They were invited to participate
through announcements placed on bulletin boards and read in classrooms.
No monetary incentives were offered but the rate of volunteering was
high: an initial "sign-up" list yielded more than 200 subjects.
33

34
Ten subjects were randomly assigned to each of the sixteen (four
factors each at two levels) experimental conditions. The assignments
were made with a random number generator that uniformly distributed
the numbers 1 through 160 among the sixteen conditions until a
schedule was formed with ten numbers assigned to each condition. As
subjects arrived to participate, their order of arrival was checked
against the schedule to determine their condition assignment. It
was felt that this assignment scheme avoided the problem of making
individual subject "appointments" at the same time that it provided
a means for stratifying the assignment of the experimental conditions
through the three months it took to complete the study.
3.1.2 Design and Analysis
Table 3.1 (p. 35) is a summary of the 2^ factorial experimental
design. The two levels for the number-of-decision-entities variable
were achieved by dividing the experimental subjects into two groups.
One group was assigned to an experimental condition where only five
events of interest had to be checked, referred to below as the "few
decision entities" condition. The five events were selected to form
a stratified representation of the twenty-five events that would take
place in the "many decision entities" condition. User preference for
particular formats was measured with a questionnaire administered to the
subjects at the end of the simulation runs (see Appendix B, p. 78).
In this study, no significant differences in format preference ratings
are expected between the various format combinations among subjects
assigned to the few-decision-entities group. Significant opinion
differences, however, are expected among the ratings of the subjects

TABLE 3.1
FACTORIAL DISPLAY AND FACTOR LEVELS
A1
A2
B1
«2
B1
B2
C1
C2
C1
C2
C1
C2
C1
C2
D1
D2
D1
°2
°1
D2
D1
D2
°1
D2
°2
D1
D2
D1
D2
c
o
Zj
â– 5
c
o
I
Condition 9 ¡
Condition 3
Condition 11
Condition 5
Condition 13
Condition 7 j
Condition 15
Condition 2
Condition 10
Condition 4
Condition 12
Condition 6
j Condition 14
Condition 3 |
Condition 16
A. Factorial Display
. ..Factor
Identification
Level 1
level 2
A
Information Layout
Order by I.D.
Order by Due-date
B
Style of Presentation
Tabular Style
Graphical Style
C
Level of Detail
Point Estimates
Interval Estimates
D
Decision Entitles
Twenty-five
Five
u>
cn
B. Factor levels

36
assigned to the inany-decision-entities group. Differences in decision
performance are also expected to be larger for the many decision
entities group.
Each of the sixteen resulting cells contained observations of 10
subjects' decision time, total checks made, total cost, and the five
ratings to the format opinion questionnaire.^ The data was analyzed
o
using Multivariate Analysis of Variance (MANOVA). MANOVA procedures
have the advantage of considering correlation among the dependent vari¬
ables [6]. Peter et al. [24], suggest that the technique should be
used, as opposed to the univariate ANOVA, whenever there is reason to
believe that multiple dependent varialbes might be correlated. Winer
[30, p. 232] points out that by considering possibly correlated depen¬
dent variables in a series of independent univariate tests, one fails
to obtain information about the total effect of the experimental treat¬
ments on all the criteria simultaneously. In the case of experimental
MIS research, a close correlation has been suggested between time and
cost, two of the criteria most frequently considered in the literature.
In none of the reviewed literature, however, was MANOVA used.
In the current study, separate ANOVA's will be conducted on
each of the dependent variables after overall significance is obtained
Decision time was rounded to the nearest minute and did not in¬
clude the time devoted to the post-experimental questionnaire. The
subjects were clocked as soon as their last "check" decision was made.
2
BMD12V - Multivariate Analysis of Variance and Covariance, Health
Sciences Computing Facility, Department of Biomathematics, School of
Medicine, University of California, Los Angeles, 1976, p. 751.

37
by MANOVA. This procedure is necessary in order to evaluate directional
hypotheses relating to specific dependent variables. Further investi¬
gation into the directions of obtained differences will be conducted
using Scheffe's posthoc test for comparisons between means [17, pp.
483-486].
3.2 Experimental Task
The experimental subjects acted as the decision makers in the
problem described in Chapter 2 . A computer simulation of the decision
environment was created which modelled the essential features^ of the
decision problem. Those features are:
1. The decision maker wants to detect a number of events that
occur at random with normally distributed intervals between
successive occurrences.
2. He has data on the random events and wants to use it to make
cost-effective decisions on when to check each event.
3. He incurs a fixed opportunity cost for each event that occurs
and goes undetected.
4. He incurs a fixed cost for each "check" that he makes on an event.
5. His objective is to minimize the total combined costs of
"checking" and "missing" the events.
These features were incorporated into the simulation model as
follows:
1. A hypothetical data set for the means, x., and standard
deviations, s,, for the between occurrences interval of each
event i was used to generate "actual" occurrence
Van Horn recommends that a good guide in developing an
effective prototype is "to restrict the prototype content to the
minimum set of features that are directly relevant to the problem
modeled" [28, p. 179]. His advice was followed here.

38
dates for a number of hypothetical events. Table 3.2 (p.39)
shows that data in two parts: the data used for generating
the 25 events in the "many decision entities" condition, and
the data used for generating the 5 events in the "few
decision entities" condition. The "generator" was validated
to verify that the events were generated according to a normal
distribution with the means and standard deviations indicated
in Table 3.2.
2. Based on the data on Table 3.2, forecasts for the event
occurrence dates were prepared and presented in report form.
These were the experimental reports (conditions) administered
to the subjects to help them in making their daily check
decisions. Several of these reports have already have been
shown in Chapter 2. The rest are shown in Appendix A, p. 69.
3. The subjects were told that undetected events at the end
of the simulation would cost them $5 each. These will be
counted and multiplied by $5 to determine their total "missing"
cost.
4. Subjects were also told that each and every check made during
the run would be charged at $1 per check. These would be
counted at the end of the run to determine their total
"checking" cost.
5. Finally, subjects were instructed that their objective in the
game was to minimize their total cost figured as the sum of
their "checking" and "missing" cost. Subjects were told that
their run time was also being measured, but were given no
time limit or other time pressures.
Subjects interacted with the simulator through typewriter-type
computer terminals in deciding which events to check for at each of
twenty decision points (days). At each decision point, the subject
chose the I.D. numbers of the events he wanted to check and entered
them for processing by the simulator. The simulator reported whether
or not the events checked occurred on that day, i.e., whether the
checks were successful or not.
Another Van Horn guide followed here in developing the present
prototype is "...to replace large actual data bases with small, care¬
fully stratified representations." [28, p. 179].
2
This and subsequent dollar figures were also simulated. No
monetary incentive scheme was used to make subjects do "their best."

39
TABLE 3.2
DATA FOR THE EXPERIMENT
Event
I.D.
Date of Last
Occurrence
Number of
Mean
(*,)
Days between Occurrences
Standard Deviation
(s,)
004
5-18
20.5
1.00
009
5-9
20.0
1.25
017
5-22
20.0
0.75
024
5-15
20.0
1.75
032
5-6
20.5
1.00
038
5-15
20.0
1.25
051
5-11
20.5
1.50
070
5-12
20.0
0.75
076
5-21
20.5
1.00
078
5-10
20.0
1.75
082
5-14
20.0
1.25
085
5-21
20.5
1.50
097
5-16
20.5
1.00
110
5-22
20.0
0.75
121
5-16
20.0
1.25
128
5-12
20.5
1.50
142
5-20
20.0
1.75
146
5-8
20.0
1.25
155
5-23
20.0
0.75
163
5-20
20.5
1.50
168
5-18
20.0
1.75
171
5-11
20.0
1.75
173
5-17
20.0
0.75
177
5-17
20.5
1.00
186
5-11
20.5
1.50
A. Data for the
many-decision-
entities condition
Event
1.0.
Date of Last
Occurrence
Number of Days between Occurrences
Mean Standard Deviation
(*,) (s,)
009
5-9
20.0
1.25
032
5-6
20.5
1.00
123
5-12
20.5
1.50
155
5-23
20.0
0.75
168
5-18
20.0
1.75
B. Data for the few-decision-entities condition

40
Uniform instructions were administered to all subjects regarding
the general nature of the experiment and their participation (see
Appendix D, P. 89). Special care was given to insure that subjects
understood their objective in the game. A familiarization session
consisting of five decision points (days) was conducted to acquaint
subjects with their decision environment. These sessions were con¬
sidered to be long enough to check subject "learning" effects during
the experimental runs. In no case did data collection begin until
subjects indicated that they felt comfortable with the procedure and
ready to start J
Three remote terminals were used for conducting the runs. Each
was on line with a master program that mantained a file with the results
of each run. The file included the three performance criteria and the
scores of the post-experimental questionnaire.
3.3 Evaluation of Hypotheses
Table 3.3 (p. 41) shows the main effects and interactions that
should be observed for the hypotheses to be supported. Each of these
effects is discussed next.
Hypothesis 1. Information layout (factor A in table 3.1) should
affect decision time such that the subjects with due-date ordered reports
should have shorter decision times than those with I.D. ordered reports,
i.e., ñ2 < : TIME. The due-date order should be more convenient
given the chronological way in which the information is going to be used.
1
The average familiarization session took 15.2 minutes.

TABLE 3.3
EFFECTS
PREDICTED BY
THE RESEARCH
HYPOTHESES
Variant of Model of
Experimental
Hypothesis
Section 2.7, p. 31
Variables
Measure
Effect
Main Effects
hi
P â–  f(F|L,DE.DM,CI$*)
P
Decision tin*
Subjects with due-date ordered
reports should have shorter
F
I.D. ordered vs.
decision times than subjects
due-date ordered
reports
with I.D. ordered reports.
H2
P « f(FIL.DE.DM.CIS*)
P
Number of Checks
Subjects with the graphical style
should make more checks than
F
Tabular vs.
graphical reports
subjects with the tabular style.
H4
f - f(L|F,DE,DM,CIS')
P
Total cost
Subjects with Interval estimates
should have lower costs than
L
Point estimates vs.
Interval estimates
subjects with point estimates.
Interactions
H3
f - f(F1L,D£,DH,C1S1)
P
Decision time
Subjects with I.D. ordered reports
in craphical style should have
F
I.D. vs. due-date
shorter decision times than those
ordered reports,
with the I.D. ordered reports 1n
tabular vs.
graphical reports
tabular style.
H5
P - f(F,L|DE,DM,CIS')
P
Decision time
Subjects with Interval estimates 1n
graphical style should have shorter
F
Tabular vs.
decision times than those with inter-
graphical reports
val estimates in tabular style.
L
Point vs. Interval
estimates
H6
P - f(F,DE|L,DM,CIS')
P
Decision time.
The time and cost performance of
total cost
subjects 1n the many-dec1sion-ent1t1es
group should be more sensitive to
F
I.D. vs. due-date
format than that of the subjects in
ordered reports,
tabular vs.
graphical reports
the few-dedsion-entities group.
DE
Many vs. few
decision entitles

42
Hypothesis 2. The total number of chekcs made should be affected
by the style treatment (factor B). It is expected that the number of
checks made by the graphical style users will be larger than that
made by the tabular style users, i.e., B2 > : CHECKS, since the
graphical style appears to illustrate the "choice space" more clearly,
thus inviting more check decisions.
Hypothesis 3. Decision times of subjects receiving some com¬
bination of 1ayout and style (A,B) should be significantly different
from decision times of subjects receiving some other combination.
Specifically it is expected that subjects receiving I.D. ordered
reports and the graphical style will have shorter decision times than
those receiving the I.D. ordered report but not the graphical style,
i.e., A^2 < A-|B.| : TIME. The graphical style should reduce the
need for the time-convenient due-date ordering.
Hypothesis 4. Subjects receiving the interval estimates should
perform better than those receiving only point estimates, i.e., it is
expected that C2 < C-j : COST. The interval estimate subjects will
have more information on the random nature of the events.
Hypothesis 5. Decision times of subjects receiving some combina¬
tion of style and level of detail (B,C) should be significantly
different from decision times of subjects receiving some other com¬
bination. In particular, it is expected that the subjects receiving
interval estimates in the graphical style will have shorter decision
times than those receiving the interval estimates in the tabular
style, i.e., B^C^ < biC2 : The graphical style should make
it easier for users to process the interval estimates information.

43
Hypothesis 6, Differences in format opinion should be observed
among the various layout and style treatments administered to the
many-decision-entities subjects (D^). Differences in both time and
cost performance should also be observed among this group. This
will indicate that report users have preference differences for
format and their performance is more sensitive to format when the
number of decision entities on their report is large. Non-significant
differences should result among the same layout and style combinations
administered to the few-decisi on-entities subjects (D^). In general,
it is expected that the opinion ratings will average higher for the
few decision entities group, i.e., D^ > D^: RATINGS. Differences
A-|Dj - A2D^ B^D^ - B^D^ and A^B^D^ - A^B^D^ are expected to be
significant for cost, time and the opinion ratings. The same com¬
parisons with D^ instead of D-j are not expected to be significant
(the few-decision-entities case). The experimental results are pre¬
sented in the next chapter.

CHAPTER 4
EXPERIMENTAL RESULTS
4.1 Introduction
The statistical results of the experiment are presented in this
chapter. Table 4.1 (p. 45) shows the cell means obtained for the three
performance variables and the five format opinion questions. The
results revealed significant differences among treatment means to
support five of the six research hypotheses.
4.2 Results
4.2.1 Effect of Layout on Decision Time
The first hypothesis, that information layout can reduce decision
time, was supported. As Table 4.1 shows, decision time was shorter for
the subjects receiving the due-date ordered reports, A£, than for
those receiving the I.D. ordered reports, A-| (13.8 versus 16.4 minutes).
A multivariate test on the three performance variables showed a sig¬
nificant layout main effect (F = 7.41, p < .00001J.1 A univariate
test on the decision time variable also revealed a significant dif¬
ference between the two layout groups (&£ < A^, F = 13.35, p < .003).
A Scheffe post-hoc test revealed an even stronger relationship when
All multivariate F's presented here are based on 3 and 142 degrees
of freedom. All the univariate F's are based on 1 and 144 degree of
freedom.
44

TABLE 4.1
CELL MEANS FOR THE SIXTEEN EXPERIMENTAL CONDITIONS
Many decision entities
®i>
Few decision entities
(d2)
Tabular styl
e
Graphical style
Tabular style
Graphical style
(Bi>
(b2)
®x>

PEb
IE
PE
IE
PE
IE
PE
IE

i
(C^)
(C2)
(ci>

(Cl)
(C2)
IDC
DD
ID
DD
ID
DD
ID
DD
ID
DD
ID
DD
ID
CD
ID
DD
Dependent Variables
(A2}
(A-,)
0^)
(*2>
0^)
<«2>
(A^
(ty
(A2>
(i^)
(Sj)
(^1
1. Decision Time
23.8
17.6
29.5
19.6
23.0
21.2
22.8
20.0
7.7
7.6
7.0
8.4
8.5
7.1
8.7
8.8
2. Number of Checks
69.8
62.6
65.8
66.1
73.4
75.5
68.8
76.2
18.4
20.7
16.2
20.0
16.2
17.6
18.9
20.1
3. Total Cost
103.3
105.1
92.3
103.1
100.4
111.5
98.8
108.2
22.4
26.2
19.3
25.5
18.2
17.6
21.9
24.1
4, Layout3
2.9
3.8
1.7
4.5
3.2
3.6
3.5
4.0
3.2
3.5
3.3
4.2
3.6
4.0
4.1
4.1
5. Format (1)
3.5
3.3
2.6
4.0
3.2
3.9
3.2
3.1
3.4
3.2
4.2
3.8
3.2
3.9
4.4
4.1
6. Format (2)
3.8
3.7
3.4
4.6
3.8
4.0
4.1
3.8
4.0
3.8
3.0
4.5
3.5
4.4
4.6
4.1
7. Level of Detail
3.4
3.7
4.1
4.3
3.0
3.0
3.9
4.2
3.6
3.7
4.3
3.9
3.3
4.2
4.7
4.5
8. Over-all Format
3.8
3.6
2.8
4.5
3.5
3.6
3.5
3.9
3.7
3.9
4.3
4.2
3.4
4.2
4.3
4.2
Notes.
a Variables nunbered 4 through 8 are the mean ratings for the format opinion questionnaire. These ratings
are based on a five-choice scale: 1, 2, 3, 4, 5, with 1 indicating "very little" and 5 indicating "very
much" liking.
k PE - point estimates; IE - interval estimates. cn
c ID - I.D. ordered reports; DD - due-date ordered reports.

46
only the many-deci si on-entities subjects, D-j, were considered. With¬
in this group, the subjects receiving the due-date ordered reports
had significantly shorter decision times (A^D^ < A-jD-j, F = 26.76,
p < .00001). No significant interactions were observed within the
few-decisi on-entities group. In all the tests, decision time was
significantly shorter for the subjects receiving the due-date
ordered reports, thus supporting the hypothesis.
4.2.2 Influence of Format on Choice Behavior
The second hypothesis, that the format in which probabilistic
information is presented can influence choice behavior, was supported.
As Table 4.1 illustrates, the total number of checks made was higher
for the subjects using the graphical style, B,,, as opposed to the
tabular style, . A multivariate test revealed a weak interaction
between style and number of decision entities (F = 2.60, p < .06).
Univariate tests on the number of checks variable showed a weaker style
main effect (§2 < B-j, F = 3.38, p < .07) and a stronger style and
number of decision entities interaction (F = 4.75, p < .03). With¬
in the many-decisi on-entities group, a Scheffé test revealed that
the average number of checks was significantly higher for the
graphical report users (73.5 versus 66.0, F = 8.07, p < .005). No
significant differences were found in the number of checks made within
the few-decision-entities group, and a comparison of the differences
in the number of checks between the two styles subjects for the D-j
and D2 groups was highly significant ([B2D1 - B1D^]<[B2D2 - B^],
F = 131.83, p < .00001). Presuming that the total number of checks
made, regardless of success, was a reasonable measure of choice

47
behavior in this problem, the results support Conrath's [8] contention
that presentation format influences choice behavior.
4.2.3 Joint Effect of Layout and Style Decision Time
The third hypothesis, that information layout and style can inter¬
act to reduce decision time, was supported. Table 4.1 shows that,
within the many decision entities group, subjects using the I.D.
ordered reports, A-j, had shorter decision times when they also re¬
ceived the graphical style, E^- A multivariate test revealed a mar¬
ginal interaction between style and number of decision entities
(F = 2.43, p < .08). Univariate tests on the decision time variable
showed a stronger ABD interaction (F = 6.20, p < .02). A Scheffe
test revealed that the significance was due to the shorter decision
times of the subjects receiving the I.D. ordered reports in graphical
style {A"jTyí] < A^B-|D^, F = 7.01, p < .009). The fact that a
significant layout and style interaction was observed only within the
many-decision-entities group also supports H6: that performance^
becomes more sensitive to format as the number of decision entities
on the report increases. This is also demonstrated by the fact that,
within the few-decision-entities group, both the layout main effect
(F = 0, p = 1) and the interaction between layout and style (F = .78,
p > .35) were not significant.
4.2.4 Effect of Probabilistic Detail on Cost Performance
The fourth hypothesis, that users of probabilistic data can make
cost-effective use of information beyond point estimates, was not
^Time performance in this case.

48
supported. Subjects receiving the interval estimates treatment, C2,
had lower costs than those receiving the point estimates, C-j, but
the difference was not significant. The univariate level-of-detail
main effect, with cost as the dependent variable, had F = .34, and
the Scheffe test on was also non-significant (F = 2.42,
p > .10). Contrary to the author's expectation, these results do not
systematically support the hypothesis, though the directions are as
predicted, nor do they support Conrath's [8] argument that decision
makers do better with point estimates than with other probability
measures.
4.2.5 Joint Effect of Format and Level of Detail on Decision Time
Support of the fifth hypothesis was weak. The hypothesis is that
the format in which probabilistic data is presented interacts with the
level of detail to influence the time required to process and use the
information. The multivariate level-of-detail and style interaction
was not significant (F = 1.19, p > .25). There was a significant
univariate interaction between style, level of detail, and the number
of decision entities on the report (F = 3.70, p = .05). In particular,
the many-decision-entities subjects, , receiving interval estimates,
C2, in graphical style, B2, had significantly shorter decision times
than those receiving the same level of detail but in tabular style
(B2C2D-| < B^C2Dp F = 4.95, p < .03). This result suggests that
certain formats may be better for reporting certain levels of prob¬
abilistic detail, but the absence of a significant multivariate
effect makes the inference rather weak.

49
4.2.6 Relation between Number of Decision Entities and Format
The sixth hypothesis, that report users' preference for and
sensitivity to format is related to the number of decision entities
on their reports, was supported. Table 4.1 shows that subjects with¬
in the many-decisi on-entities group gave significantly different
ratings to the various layout and style combinations. Two of the five
opinion questions were used to verify that the subjects understood
and systematically answered the post-experimental questionnaire
(see Appendix B, p. 78). The validation consisted of checking that
the ratings for these two questions were consistent with performance
of subjects receiving the particular treatments mentioned in the
questions:
(1) Question 1 asked the subjects to rate the order of
the information in the reports. Table 4.1 shows
that the subjects receiving the due-date ordered
reports gave significantly higher ratings to this
item than those receiving the I.D. ordered reports
(Á2 > A1, F = 17.33, p < .00004).
(2) Question 4 asked the subjects to rate the level of
probabilistic detail given. Table 4.1 illustrates
that the subjects receiving the interval estimates
consistently gave higher ratings to this item than
those receiving the point estimates treatment
(C2 > C1, F = 18.86, p < .00002).
In the case of Question 1, the many-decision-entities subjects
gave significantly higher ratings to the due-date ordered reports
(A^D-| > A^D-j , F = 19.08, p < .12). In Question 5, where the subjects
were asked to give an over-all rating for the format of their reports,
there was a significant difference in ratings between the many and
few-decision-entities groups (D,, > D-j, F = 5.23, p < .03).

50
With regard to the relationship between the number of decision
entities and the sensitivity of performance to format, the discussion
of the first five hypotheses has shown that the performance of the
many-decision-entities subjects was more sensitive to format than
that of the few-decision-entities subjects. In all the comparisons
the differences in performance were larger among the many-decision-
entities subjects than among the few-decision-entities subjects.

CHAPTER 5
DISCUSSION OF RESULTS
5.1 Summary of Findings
Tables 5.1, 5.2, and 5.3 (pp. 52 - 55) present a summary of the
experimental results that had a significance level of p <.10 or better.
The results have been grouped into main effects, interaction effects
involving the number-of-decision-entities variable, and other interaction
effects. In each case the actual significance figure has been given so
that the reader can make his own judgement on the significance of each
result. The hypotheses relating to each result are also shown in the
right margin, along with a line reference number, to facilitate the dis¬
cussion in the following sections.
The results are discussed first for the multivariate (MANOVA) effects.
These do not relate to any hypothesis in particular, since the hypotheses
have been stated in terms of the effect of the experimental treatments on
specific criterion variables. They contain, however, important infor¬
mation about the total effect of the treatments on decision performance
in general. The univariate effects are discussed next as they relate to
each hypothesis. In each case, the implications for both the MIS re¬
searcher and practitioner are discussed.
5.2 The Multivariate Effects
Information layout (I.D. versus due-date ordering) was found to
51

TABLE 5.1
MAIN EFFECTS
Independent Variable
Dependent Variable
Level of
Significance
Results
Related
Hypotheses
Line
No.
Information Layout
Decision Time,
Cost, and Number
of Checks Made
.00001
Information layout had a simul¬
taneous effect on all three
performance criteria.
-
5.1.1
Information Layout
Decision Time
.00026
Subjects v/ith due-date ordered
reports had shorter decision
times than subjects with I.D.
ordered reports.
HI
5.1.2
Style of
Presentation
Number of
Checks Made
.066
Subjects with graphical reports
made more checks than subjects
with tabular reports.
H2
5.1.3
Number of
Decision Entities
Layout Rating
.060
Subjects with few decision events
rated their report layout higher
than subjects with many events.
H6
5.1.4
Information Layout
Layout Rating
.00003
Subjects with due-date ordered
reports rated their layout higher
than subjects with I.D. ordered
reports.
H6
5.1.5
Number of
Decision Entities
Level of
Detail Rating
.060
Subjects with few decision events
rated the detail of their reports
higher than subjects with many
decision events.
H6
5.1.6

TABLE 5.2
INTERACTION INVOLVING THE NUMBER-OF-DECISION ENTITIES-VARIABLES
Other
Independent Variables
Dependent Variable
Level of
Significance
Results
Related
Hypotheses
Information Layout
Decision Time,
Cost, and Number
of Checks Made
.0015
The effect of layout on general
decision performance was stronger
among the many decision entities
group.
-
Information Layout
Decision Time
.00001
Subjects in the many-decision-
entities group experienced larger
decision time reductions when
they were given the due-date
order layout.
H1.H6
Style of
Presentation
Decision Time,
Cost, and Number
of Checks Made
.062
Style and number of decision
entitles had a joint effect on
the three performance criteria
taken simultaneously.
Style of
Presentation
Number of
Checks Made
.029
The effect of style on number of
checks made was stronger among
the many decision entities group.
H2.H6
Information Layout
and Style
of Presentation
Decision Time,
Cost, and Number
of Checks Made
.074
Layout and style had a joint total
effect on decision performance.
The effect was stronger among
the many decision entities group.
-
Line
No.
5.2.1
5.2.2
5.2.3
5.2.4
5.2.5
CJI
u>

TABLE 5.2 (continued)
Other Level of
Independent Variables Dependent Variable Significance
Results
Related
Hypotheses
Information Layout Decision Time .013
and Style
of Presentation
Subjects with many decision events H3.H6
and I.D. ordered reports had
shorter decision, times when they
were also given the graphical style.
The effect was not observed among
the few-decision-entities group.
Style of Presentation Decision Time .055
and Level o.f Detail
Many-dec1sion-entities subjects H5,H6
receiving interval estimates in
graphical style had shorter times
than those receiving the same
level of detail but in tabular
style. The effect was not observed
among the few-dedsion-entities
group.
Information Layout Layout Rating .044 The many-decision-entities group H6
gave the highest rating to the
due-date layout. No significant
differences in ratings were
observed among the few-decision-
entities group.
Style of
Presentation
Level of
Detail Rating
the detail of their reports
higher than subjects with the
graphical style. No signifi¬
cant differences in ratings were
observed among the few-decision-
entities group.
.060
Subjects with many decision
entities in tabular style rated
H6
Line
No.
5.2.6
5.2.7
5.2.8
5.2.9
ai
-F*

TABLE 5.3
OTHER INTERACTION EFFECTS
Independent Variable
Dependent Variable
Level of
Significance
Results
Related
Hypotheses
Line
No.
Information Layout
and Style
of Presentation
Layout Rating
.016
Subjects with the I.D. layout
rated it very low, except in
the case of those that also
had the graphical style.
H6
5.3.1
Information Layout,
Style of Presentation,
and Level of Detail
Layout Rating
.060.
Ratings for the I.D. layout
were specially lower from
the point estimate subjects.
The difference was not sig¬
nificant for those subjects
who also received the graphical
style.
H6
5.3.2
Information Layout,
Style of Presentation,
and Level of Detail
Format (1,2)
Rating
.039,.004
Interval estimate subjects with
the I.D. layout had low ratings,
except in the case of those that
also received the graphical style.
H6
5.3.3
Information Layout,
Style of Presentation,
and Level of Detail
Over-all Format
Rating
.093
Same effect as above. The effect
was more marked among the many-
decision-entities group.
H6
5.3.4
cn
<_n

56
affect decision performance in general (5.1.1).' Multivariate analysis
is called for here since the performance criteria measured (decision
time, cost, and number of checks made) are not independent. In the pre¬
sent study, the simultaneous effect of layout on the three performance
criteria gives more value to the observed univariate effect on decision
time. If Hypothesis 1 (p.28) had read "information layout can influence
decision performance," it would have been equally supported. The fact
that the effect observed was stronger among the many-decisi on-entities
subjects (5.2.1) also lends support to Hypothesis 6 (p. 30),
The style of presentation (tabular versus graphical) was also seen
to have a significant total effect on decision performance (5.2.3).
Although the univariate effect of style on cost was not significant, the
graphical style users within the many-decision-entities group had higher
costs than the tabular style users ($104.73 versus $100.95, p = .258).
This result contradicts Benbasat and Schroeder's [5] results, who
observed that subjects with graphical reports had lower costs than those
with tabular listings. Neither result, however, is significant (theirs
had p = .148). This indicates that there is still no basis for predicting
the effect of presentation style on cost performance. Either result
could have been due to chance alone.
In terms of future MIS research, it is suggested here that MANOVA
should be used in the analysis of experimental data. Winer [30, p. 232]
explains that whenever there is reason to believe that dependent variables
The number in parenthesis is the reference to the related result in
the tables.

57
are correlated, these should be considered simultaneously to obtain infor¬
mation about the "total" effect of the experimental variables. In the case
of experimental MIS research, there is reason to believe that commonly
used criteria, such as cost performance and decision time, are correlated
[5, 9].
From the point of view of the MIS practitioner, these multivariate
results point to one conclusion: the format in which information is pre¬
sented can influence decision performance. The multivariate separate
and joint effects of the two format variables in this study, layout and
style, support this view. The specific directions of these effects are
discussed next.
5.3 The Univariate Effects
5.3.1 Effects Related to HI and H3
The influence of information layout on decision time was found to be
strong (5.1.2), as predicted in Hypothesis 1 (p. 28). The shorter deci¬
sion times for the subjects with due-date as opposed to I.D. ordered
reports were expected, since the due-date ordering was more convenient in
the present problem. This hypothesis, however, was evaluated for tv/o
reasons. The first was to demonstrate the importance of arranging the
information in a manner consistent with the way information is used. This
seemingly obvious observation appears to have been ignored in many reports
this author has had to use. The second reason was to prepare a basis
for Hypothesis 3 (p.29). There, it is proposed that long decision times
due to an inconvenient information layout can be reduced by introducing
a second format element, namely, the graphical style. The time dimension
added by the graphical style had the effect of reducing the need for the

58
due-date ordering, while still maintaining a desirable feature of the
report (the I.D. ordering of the events). This is evidenced by the re¬
sult in Table 5.2, line 6.
These results have other implications, besides supporting HI and
H3. For the HIS researcher, they suggest the need for more investigation
on the layout variable. It might be revealing example, to look at
information layout schemes for information on events that are less time-
dependent in nature than the ones studied here.^ Maintenance data on
some mechanical process, for example, could provide a setting for an in¬
teresting and practical experiment.
To the MIS practitioner, and in particular to the person in charge
of designing information formats, the results emphasize the importance
of reporting information in a manner consistent with the way recipients
use it. Also, the observed interaction between layout and style suggests
that practitioners should be on the alert for joint effects among format
elements that can work to their advantage in enhancing the readability
of the report.
5.3.2 Effects Related to H2
Perhaps most striking was the result that subjects with graphical re¬
ports chose to make substantially more "checks" than subjects with tabular
reports (5.1.3). This supports Hypothesis 2 (p. 28), namely, that the for¬
mat in which probabilistic information is presented can influence choice.
From a research point of view, a question that remains to be answered is
All events in this world are probably time dependent, but their
occurrence may be more dependent on time for some types (e.g., biological)
than for others (e.g., electrical components).

59
whether the observed effect was related to the short duration of the
experiment, or whether the effect would have continued even if the sub¬
jects had been given enough time to get fully acquainted with their re¬
port style. In either case, the result observed here is an important
finding since many real-life managerial reports have short-term use, are
"one-shot-non-recurrent" reports, and, very frequently, contain informa¬
tion of a probabilistic nature. Ergo, the information analyst that must
report probabilistic information as a basis for decisions appears to have
a delicate problem at hand: if the format in which he presents the in¬
formation is going to bias the choice of the decision maker, he will
surely want that bias to be in the "correct" direction. This point is
also related to the issue of normative versus descriptive reports, and is
a point that should be further investigated elsewhere.
5.3.3 Effects Related to H5
A result closely related to the preceding discussion provided
support for Hypothesis 5 (p. 30; 5.2.7). Within the many-decision entities
group, the subjects with interval estimates had shorter decision times
when the information was given to them in graphical as opposed to tabular
style (21.4 versus 24.6 minutes, p = .026). Point estimates users, how¬
ever, did not experience the same benefits in moving from the tabular
to the graphical style. Their average decision time, in fact, was higher
with the graphical style than with the tabular style (22.1 versus 20.7
minutes, p = .32). These results suggest that different levels of
probabilistic information may be more appropriately reported using dif¬
ferent presentation formats. In the present experiment, subjects with
the tabular style did as good or better than subjects with the graphical

60
style when only point estimates were reported. The compact tabular for¬
mat was inadequate, however, for processing the interval estimates in¬
formation.
5.3.4 Effects Related to H6
All the effects that have been discussed thus far were found to be
more marked within the many-decision-entities group (5.2.2, 5.2.4, 5.2.6,
5.2.7). This supports one of the propositions in Hypothesis 6, (p. 30),
that user performance becomes more sensitive to report format as the
number of decision entities on the report increases. In the present ex¬
periment, the subjects with five decision entities on their reports had
so little information to process that whether it was given in I.D. order,
due-date order, tabular style or graphical style did not make much
difference on their performance. Evidence of this is that, within the
five decision events group, there were no significant differences in
performance for any of the performance measures. The only effect that
approached significance in that group was an interaction between style
and level of detail with cost as the dependent variable (p < .10).
The other proposition in H6, that report users will move from in¬
difference to preference for particular formats as the number of decision
entities increases, was also supported. The subjects in the few-decision-
entities group gave more or less constant high ratings to the various
format characteristics of their reports (5.2.8, 5.2.9). Within that
group, there were no significant differences in the ratings for the order
of the information in the reports (layout). For the over-all format
rating, only one difference was significant. Interval estimate subjects
gave significantly higher ratings than point estimate subjcets (4.25
versus 3.80, p = .044). Among the subjects with twenty-five decision

61
entities, the story was quite different (5.2.8, 5.2.9, 5.3.3, 5.3.4).
There were significant differences between their ratings in the various
layout and style conditions.
For the MIS practitioner, these results suggest that they should give
careful attention to report format, especially when the report must grow.
The results obtained here give meaning to Voich, et al.'s statement,
"As the complexity of a report increases, its likelihood of extent of use
falls" [28, p. 229]. When preparing a report for a procurement manager,
for example, a standard layout by major classes of items, code number,
etc., may be appropriate if the number of items that must be ordered each
time, and their frequencies of ordering, are small. If the number of
orders that must be placed were to increase considerably, it may be to
the manager's advantage to have the layout of his report revised. A more
favorable layout in that case could be, for example, to have the items
arranged according to the frequency with which they are ordered.
For format revisions or similar actions to occur, the channels of
communication between the information analyst and user must first be im¬
proved. At the present, there appears to be a "tail versus dog" problem
between information users and providers when it comes to seemingly un¬
important matters, such as designing a format for a report. Voich et alâ– 
state:
Report formats are often not tailored
precisely to users' needs. One reason
for this is the programming costs asso¬
ciated with special arrangements of in¬
formation, especially if several dif¬
ferent users each request a unique for¬
mat. A second reason for finding formats
not tailored exactly to user's needs is
that report designs are often based on

62
the system analysts' or programmers' pre¬
ferences for programming ease [28, p. 229].
It would appear that better communication channels between the
analyst and the user should, at least, help to alleviate the second
reason noted above.

CHAPTER 6
SUMMARY AND POSSIBLE EXTENSIONS
The present study has considered some of the implications of the
relationship between information format and decision performance. A
specific information-decision problem was abstracted to create a simu¬
lated decision environment within which alternative forms of presenting
information relevant to the problem were experimentally manipulated.
Six hypotheses were tested in relation to the effects of the information
format treatments on subject performance. The experimental data supported
five of the six hypotheses. As is always the case with empirical re¬
search, however, a number of questions can be raised in connection with
the observed results. Some questions result from inquiring into the
limitations of the present study. Others follow logically from the re¬
sults.
Among the limitations, there is the problem of having used student
subjects as surrogates for managers [13]. The actual managers in the
real-life problem modeled could have served as subjects in a field study.
This, of course, may bring about other complications, in particular,
problems of experimental control. Van Horn states:
The unifying theme of field tests is sad
stories. In every one, operational con¬
siderations (understandably) dominate
test conditions. As soon as a conflict
arises, the test yields. Even if a test
proceeds to completion, endless arguments
arise over interpretation of the results
[28, p. 175],
63

64
Going to the field also implies having to deal with uncooperative
mother nature, as opposed to pre-chosen probability distributions for
the events of interest. Notwithstanding this dismal picture, a field
experiment should be useful. By establishing the external validity of
the present study with respect to the subject population, decision¬
making conditions, and other areas of interest, its benefit for the
actual population can be established and considered in the design of
a field study. A practical field study could be, for example, one in
which a more normative report is provided to the manager facing the
heat detection problem. Such a report could be based on some optimal
decision rule indicating to the dairyman the days in which he should
check for heat in particular cows. The decision rule would have to be
based on some cost estimates (costs of "checking," "missing," and
breeding after a succesful "check"), and on some probability estimates
(probability of detecting heat on given "checks," probability of a
succesful breeding once heat is detected, etc.).
Although growing fast, "experimental work on MIS is still in its
infancy" [5, p. 17]. Many promising areas have not been investigated.
One line of research that follows from the present results is the re¬
lationship between the number of decision entities in the report and
the sensitivity of performance to format variables. Various number-
of-decision-entities levels could be manipulated in a parametric study
to investigate such questions as, "Can a report user adapt to an
increasing number of decision entities in his report without a rapid
deterioration in his performance?" To investigate this question
the same subjects would have to be given increasing numbers of decision
entities, and this could bring up problems of subject "learning." If
properly controlled, however, an experiment along these lines could

65
shed light into such questions as, "At what point would it have been
appropriate to have a format revision?"
Another area that appears to need more consideration is the analysis
of interaction effects among information structure characteristics [5, 12].
In this study, an interaction was found between the level of detail and
format, suggesting that different levels of detail may be more easily
processed with different styles of presentation. The validity of findings
such as this one should be further investigated in other decision contexts.
Finally, research relating empirical MIS findings to current trends in the
theory of human information processing may be useful in providing a better
understanding of the results observed.

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Hoard's Dairyman, 120, p. 1115, 1975.
4. Barret, M. J., N. L. Chervany and G. W. Dickson, "On Some Aspects
of the Validity of an Experimental Simulator in MIS Research,"
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5. Benbasat, I. and R. Schroeder, "An Experimental Investigation of
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6. Bock, R. D. and E. A. Haggard, "The Use of Multivariate Analysis
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71-12, MIS Research Center, University of Minnesota, 1972.
66

67
11. Dancer, Robert E., "An Empirical Evaluation of Constant and Adaptive
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12. Dickson, G. W., J. A. Senn and N. L. Chervany, "Research in Manage¬
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APPENDIX A
EXPERIMENTAL TREATMENTS
The sixteen reports that constituted the experimental treat¬
ments are shown here in the same size they were administered to the
subjects. The only difference between these reports and those used
by the subjects is that the latter had horizontal green lines across
them to facilitate their use. Each report is labeled with the
"condition" numbers used in Table 3.1 (p. 35).
69

70
'ENT
IENT.
EXPECTED DUE-DATE
(MONTH-DAY)
EVENT
IDENT.
EXPECTED Dl
(MONTH-I
004
6-07
032
5-26
009
5-29
146
5-28
017
6-11
009
5-29
024
6-04
078
5-30
032
5-26
171
5-31
038
6-04
051
5-31
051
5-31
186
5-31
070
6-01
070
6-01
076
6-10
128
6-01
078
5-30
082
6-03
082
6-03
024
6-04
085
6-10
038
6-04
097
6-05
121
6-05
no
6-11
097
6-05
121
6-05
173
6-06
128
6-01
177
6-06
142
6-09
168
6-07
146
5-28
004
6-07
155
6-12
163
6-09
163
6-09
142
6-09
168
6-07
085
6-10
171
5-31
076
6-10
173
6-06
110
6-11
177
6-06
017
6-11
186
5-31
155
6-12
Condition 1
Condition 2

71
EVENT 95% CONFIDENCE INTERVAL EVENT 95% CONFIDENCE INTERVAL
IDENT.
(FIRST DAY, LAST DAY)
IDENT.
(FIRST DAY
, LAST
004
6-06 , 6-09
032
5-25
5-28
009
5-27 , 5-31
146
5-26
5-30
017
6-10 , 6-12
009
5-27
5-31
024
6-01 , 6-07
078
5-27
6-02
032
5-25 , 5-28
171
5-28
6-03
038
6-02 , 6-06
051
5-29
6-03
051
5-29 , 6-03
186
5-29
6-03
070
5-31 , 6-02
128
5-30
6-04
076
6-09 , 6-12
070
5-31
6-02
078
5-27 , 6-02
082
6-01
6-05
082
6-01 , 6-05
024
6-01
6-07
085
6-09 , 6-14
038
6-02
6-06
097
6-04 , 6-07
121
6-03
6-07
110
6-10 , 6-12
168
6-04
6-10
121
6-03 , 6-07
097
6-04
6-07
128
5-30 , 6-04
173
6-05
6-07
142
6-06 , 6-12
177
6-05
6-08
146
5-26 , 5-30
004
6-06
6-09
155
6-11 , 6-13
142
6-06
6-12
163
6-07 , 6-12
163
6-07
6-12
168
6-04 , 6-10
085
6-09
6-14
171
5-28 , 6-03
076
6-09
6-12
173
6-05 , 6-07
110
6-10
6-12
177
6-05 , 6-08
017
6-10
6-12
186
5-29 , 6-03
155
6-11
9
6-13
Condition 3
Condition 4

72
EXPECTED DUE-DATES
EVENT MAY JUNE EVENT
IDENT. 25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13 IDENT.
004
009
017
024
032
038
051
070
076
078
082
085
097
110
121
128
142
146
155
163
168
171
173
177
186
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13
004
009
017
024
032
038
051
070
076
078
082
085
097
110
121
128
142
146
155
163
168
171
173
177
186
Condition 5

73
EXPECTED DUE-DATES
EVENT MAY JUNE EVENT
IDENT. 25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13 IDENT.
032
146
009
078
171
051
186
070
128
082
024
038
121
097
173
177
168
004
163
142
085
076
110
017
155
032
146
009
078
H 171
H 051
H 186
H 070
H 128
H 082
H 024
H 038
H 121
H 097
H 173
H 177
H 168
H 004
H 163
H 142
H 085
H 076
H 110
H 017
H 155
25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13
Condition 6

74
95% CONFIDENCE INTERVALS
EVENT MAY JUNE EVENT
IDENT. 25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13 IDENT.
004
009
017
024
032
038
051
070
076
078
082
085
097
110
121
128
142
146
155
163
168
171
173
177
186
H H H H H
H H
H H H
H H
H H H H
H
H H H H H
H H H H H
H H H H
H H H H
H H H
H H H
H H H H H
H H
H H H
H H H H H
H
H
H
H
H
H
H
H
H
H H H H H H H
H H H H H H
H
H H H
H H H
H H H
H H
H H
H H H
H H H
H H
H H H H
H
H H H H
H H H
H
H
H
H H
H
H
H H
H
004
009
017
024
032
038
051
070
076
078
082
085
097
110
121
128
142
146
155
163
168
171
173
177
186
25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13
Condition 7

75
95% CONFIDENCE INTERVALS
EVENT
IDENT.
MAY
25
26
27
28 29
30
31
JUNE
01 02
03
04
05
06
07
08
09
10
11
12
13
EVENT
IDENT.
032
H
H
H
H
032
146
H
H
H
H
H
146
009
H
H
H
H
H
009
078
H
H
H
H
H
H
H
078
171
H
H
H
H
H
H
H
171
051
H
H
H
H
H
H
051
186
H
H
H
H
H
H
186
128
H
H
H
H
H
H
128
070
H
H
H
070
082
H
H
H
H
H
082
024
H
H
H
H
H
H
H
024
038
H
H
H
H
H
038
121
H
H
H
H
H
121
097
H
H
H
H
097
168
H
H
H
H
H
H
H
168
177
H
H
H
H
177
173
H
H
H
173
004
H
H
H
H
004
142
H
H
H
H
H
H
H
142
163
H
H
H
H
H
H
163
076
H
H
H
H
076
085
H
H
H
H
H
085
110
H
H
H
110
017
H
H
H
017
155
H
H
H
155
25
26
27
28 29
30
31
01
02
03
04
05
06
07
08
09
10
11
12
13
Condition 8

76
EVENT
IDENT.
EXPECTED DUE-DATE
(MONTH-DAY)
EVENT
IDENT.
EXPECTED DUE-DATE
(MONTH-DAY)
009
5-29
032
5-26
032
5-26
009
5-29
128
6-01
128
6-01
155
6-12
168
6-07
168
6-07
155
6-12
Condition 9 ~ Condition 10
EVENT 95% CONFIDENCE INTERVAL
IDENT. (FIRST DAY, LAST DAY)
009
5-27
, 5-31
032
5-25
, 5-28
128
5-30
, 6-04
155
6-11
, 6-13
168
6-04
, 6-10
Condition 11
EVENT 95% CONFIDENCE INTERVAL
IDENT. (FIRST DAY, LAST DAY)
032
5-25 .
, 5-28
009
5-27 ,
, 5-31
128
5-30 .
, 6-04
168
6-04 .
, 6-10
155
6-11 .
, 6-13
Condition 12
EXPECTED DUE-DATES
EVENT
MAY
JUNE
EVENT
IDENT.
25 26 27
28 29 30
31 01 02 03 04 05
06 07 08 09
10 11 12
13 IDENT.
009
H
009
032
H
032
128
H
128
155
H
155
168
H
168
25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13
Condition 13

77
EXPECTED DUE-DATES
EVENT
MAY
JUNE
EVENT
IDENT.
25
26
27
28
29
30
31
01 02
03
04
05
06
07
08
09
10
11
12
13
IDENT.
032
H
032
009
H
009
128
H
128
168
H
168
155
H
155
25
26
27
28
29
30
31
01 02
03
04
05
06
07
08
09
10
11
12
13
Conditi
on
14
95% CONFIDENCE
INTERVALS
EVENT
MAY
JUNE
EVENT
IDENT.
25
26
27
28
29
30
31
01 02
03
04
05
06
07
08
09
10
11
12
13
IDENT.
009
H
H
H
H
H
009
032
H
H
H
H
032
128
H
H
H H
H
H
128
155
H
H
H
155
168
H
H
H
H
H
H
H
168
25
26
27
28
29
30
31
01 02
03
04
05
06
07
08
09
10
11
12
13
Condition
15
95% CONFIDENCE
INTERVALS
EVENT
MAY
JUNE
EVENT
IDENT.
25
26
27
28 29
30
31 01 02 03
04 05
06
07
08
09
10
11
12
13
IDENT.
032
H
H
H
H
032
009
H
H
H
H
H
009
128
H
H H H H
H
128
168
H H
H
H
H
H
H
168
155
H
H
H
155
25
26
27
28
29
30
31 01 02 03
04 05
06
07
08
09
10
11
12
13
Condition 16

APPENDIX B
FORMAT OPINION QUESTIONNAIRE
The post-experimental questionnaire is presented here in an
English version of the actual questions (shown toward the end of the
program in Appendix C). A connotative rather than literal translation
has been attempted to give the non-Spanish reader a more accurate
representation of the content.
78

79
Please answer the following questions by entering a 1, 2, 3, 4, or 5, and
pressing the "return" key. An entry close to 1 will indicate "very
little" and an entry close to 5 will indicate "very much," as follows:
VERY LITTLE : 1 : 2 : 3 : 4 : 5 : VERY MUCH
1. How appropriate did you considered the order of the infor¬
mation in the report, given the type of decisions that had
to be made?
2. How appropriate did you considered the format of the report
at the time of making the daily decisions?
3. How appropriate did you considered the format of the report
at the time of recording the feedback on events checked
and detected?
4. How appropriate did you considered the detail of the prob¬
abilistic information on the possible date of occurrence
of each event?
5. All factors considered, how appropriate did you considered
the format of your report?

APPENDIX C
COMPUTER SIMULATION PROGRAM
A.l General
This program created the simulated decision environment for the
experimental runs. The program was written in BASIC, Version 17,
Digital Equipment Corporation System 10. It was run from a type¬
writer terminal, model DEC 33 TELETYPE, on line with a PDP 10.
A.2 Input
The fixed input to the program was the data of Table 3.2, (p. 39)
arranged as follows:
E$(J)
M
D
1
S
004
5
18
20.5
1.00
009
5
9
20.0
1.25
017
5
22
20.0
0.75
024
5
15
20.0
1.75
032
5
6
20.5
1.00
038
5
15
20.0
1.25
051
5
11
20.5
1.50
070
5
12
20.0
0.75
076
5
21
20.5
1.00
078
5
10
20.0
1.75
082
5
14
20.0
1.25
085
5
21
20.5
1.50
097
5
16
20.5
1.00
110
5
22
20.0
0.75
121
5
16
20.0
1.25
128
5
12
20.5
1.50
142
5
20
20.0
1.75
146
5
8
20.0
1.25
155
5
23
20.0
0.75
163
5
20
20.5
1.50
80

81
E$(J)
M
D
I
S
168
171
173
177
186
5
5
5
5
5
18
11
17
17
11
20.0
20.0
20.0
20.5
20.5
1.75
1.75
0.75
1.00
1.50
where E&(J) = event I.D. number
M = month of last occurrence
D = day of last occurrence
I = mean interval between occurrences
S = standard deviation of interval between occurrences
All other input to the program was manually entered by the subjects,
It consisted of the I.D. numbers of the events they checked on each
successive day, and the answers to the post-experimental questionnaire.
A.3 Output
A sample of the beginning of a run is illustrated in Figure A.l
(p. 82). It shows an identification of the experimental report used in
the run, the initial time clocked after the subject indicated he was
ready to start,* and some of the outcomes of the early part of the run.
Figure A.2 (p. 83) is a sample from the last part of the experiment.
It shows some of the last decisions made by the subject, the final
decision time clocked (26 minutes and 3 seconds in the sample shown), the
summarized results of the run, and the post-experimental questionnaire
with the subject's answers. The last two lines seen are part of the
file updated with the results of each run. The program is on pp. 84-88.
*A pre-experimental familiarization session had already been con¬
ducted.

r.irc: id re ns
T1
: 13.I.3C r.r-i: £.03 :.Eslo7 2ADIC 71 S',’ >C*«!4765
crirc'is ?
7 032
CHSpesst 032 detected? :io:e
TODAY Ir £-££
checyd ?
7 032 i4d
C'ECYED» 032 !«£• DETESTED: M3 ME
TODAY ID £-27
C4ECYS ?
7032
CHCCYED: or. 2 DETECTED; 032
TODAY ID f-2S
O’ECYD ?
7305 146
CHECKED: 00’y Uí DrTEvDEEj NOME
Figure A.l Sample output at the beginning of a run

83
T'-io” rs
chord ?
vm r.r*. i:j mo jus
eiuivFrj -nr üoc. in uc i?í írtrc-TíS; no
today ir c-n
C’lC'.Z ?
TOPS I £5
cveched: s\s iet x.-e'Ticti-j iff
..pavor D£ llaiar. ,-.l r-Ror. .viador....**■-**„«
:lC:Cj RCIr l.-JC PS:3 "P.sQ RftStC ISP»2í? TI .¿V PC:
M765
•#■+**.*** siwary of results for the p.ci **»***•»
5? CHECKS 3 SI/CHEC: - S£9
7 MISSES 8 JV'ISS = S3£
TOTAL COST = S94
ACTUAL EVENT DATES
EVENT ID. DOTE
oda e-7
009 £-33
017 5-9
024 £-4
332 £-27
233 £-4
0S1 6-1
CTO £-31
076 6-12
07 S £-30
0?2 6-2
065 6-10
097 6-5
110 6-12
121 £-5
123 6-2
M2 6-6
1 if. £-29
ÍES 6- 3
1Ó3 6-! 0
153 S-*
17 1 £-29
17 C 6-6
177 6-6
1S6 £-31
REVISE ESTE r:FSi.'lE* DE RESULTADOS PATA VEnIFICAr: OLE LA INFORMACION
31 CUANTO A EVENTOS COTEJADOS V DETECTADOS ES CO-íPfcCTA. L"JA VEZ HECHO
ESTO» OPRIMA ' RETURN* .
FAVO?. DE CONTESTADLAS SI EL I ENTES PRESUNTAS ESC?: VI TICO UN 'l*.*2*.*3*.
M* 0 .c. y OPRIMIENDO LA T"CLA * FE TUR."'. LIA RESPUESTA CERCA DE 1 I *
INDICARA 'POCO APP.O PRIADO * V USA RESPUESTA CERCA DE * r* INDICADA 'MUY
APROPRIADO*. COMO S’OLE:
POCO APROPRIADO : 1 ; 2 s 2 s « : £ : ’ll'Y APRO PRIADO
1. CU VI APRO PRIADO EN CON m USTED r.L ORDEN í'L LA INFORMACION
r.-j EL REPORTE: PARA LL TIPO DI DECISIONES 2U£ LE CEDIAN TOMAR ’2
2. CUVJ APR.OPRI.V'? R’T EL FT.R1 ATO 7r *»?.ESrmCÍO« DEL REPORTE
al rrrrro rr triar l*; dec trióme.1 diarias ?d
ó. CVVJ .'•'-opp.l.'.r-' -■■■ EL ! '".ni'.] CE!. P.IPO 'Tl .*.L -TO-E-fTO DE. ANOTAR
La I IROR1ACI0 1 se:“•£ LO. FABÍTIIS COTEJADOS Y DíTíCTADOS 74
•u a- . .' o:n >1.1 fl ri talle cr la imfor iocion prodariliftica
SOJAS LA PEDIDLE Di::: . - E DCl'F Í.ITICl A SI C\ DA DVPJT1 ? :•
s Co-u-1 servido t nr lo - -•••cto:::. con rs A~.nPRi.ViW lncontho usted
LL FORV.--A LF r.-Tt RE??*Tr ?3
..... ?T *«• C-a It'LVlD.A 1L H-r-.ITViro... c:-. VCIA3 POR sr COOPERACION! I ♦
£ ? ? : O ■» 3
152 03 :•£■ ,)‘ ? O?/, R J * * 2
Figure A.2 Sample output at the end of a run

84
00005 REM ************************************************************
00010 REM *** INTERACTIVE SIMULATION OF THE EVENT CHECKING PROBLEM ***
00015 REM ************************************************************
00020 DIM A$(25),E$(25)
00030 FILES A13!,A2%,A3%,A4%,A5%,A0%, DATA, TAL YA$
00035 D0=25
00040 PRINT "SO,R";
00050 INPUT SO,R
00060 IF R =8 THEN 70
00062 D0=5
00064 FOR J=1 TO 25.
00066 INPUT #7, NO,E$(U),M,D,I,S
00068 NEXT J
C0070 Cl=l
00075 PRINT
00080 C2=5
00090 PRINT
00100 PRINT "SUBJECT IS USING REPORT ";STR$(R)+"."
00105 PRINT
00110 FOR J=1 TO DO
00125 R0=0
00130 INPUT #7, NO,E$(J),M,D,I,S
00140 S$=S$+E$(J)
00210 RANDOMIZE
00220 FOR N=1 TO 12
00230 RO=RO+RND
00240 NEXT N
00250 Y=S*(R0-6) + I
00260 X=INT(Y+.5)
00280 IF (D+X) 31 GO TO 310
00290 D=D+X
00300 GO TO 330
00310 D=D+X-31
00320 M=M+1
00330 A$(0) = STR$(M)+"-“+STR$(D)
00340 NEXT J
00350 PRINT
00360 M=5
00370 D=24
00380 PRINT "Tl";
00381 INPUT Tl$
00389 PRINT
00390 FOR 1=1 TO 20
00400 D=D+1
00410 IF D 32 GO TO 440
00420 D=1
00430 M=M+1
00440 D$=STR$(M)+"-"+STR$(D)
00442 PRINT
00450 PRINT "TODAY IS ";D$
00460 PRINT " "

85
00470 PRINT "CHECKS ?"
60480 INPUT E$
00482 IF E$=" " THEN 630
00485 E$=E$+"AAAA."
00490 L=INSTR(E$,".")/4-l
00502 IF ABS(L-IMT(L)) = 0 THEN 510
00504 GOSUB 2000
00506 GO TO 480
00510 FOR K=1 TO L
00520 Y=4*K-3
00525 Z=4*K-1
00530 K$=MID$(E$,Y,Z-Y+1)
00550 J=(INSTR(S$,K$)+2)/3
00560 C$=C$+E$(J)+" "
00570 C=C+1
00580 IF A$(J) D$ THEN 610
00590 0$=0$+E$(J)+“ "
00600 0=0+1
00610 NEXT K
00620 IF L =1 THEN 660
00630 PRINT "CHECKED: NONE"
00640 PRINT
00650 GO TO 730
00660 PRINT "CHECKED: ";C$;
00670 IF LEN(0$) 1 THEN 690
00680 0$="N0NE"
00690 PRINT " DETECTED: ";0$
00700 PRINT
00710 C$=" "
00720 0$=" "
00730 NEXT I
00735 PRINT "***********"
00740 PRINT "******...FAVOR DE LLAMAR AL PROF. AMADOR....*******"
00751 INPUT T2$
00752 IF LEN(T2$) 1 THEN 754
00753 T2$="0"+T2$
00754 PRINT
00756 PRINT
00760 PRINT "******** SUMMARY OF RESULTS FOR THE RUN ********"
00770 PRINT
00780 PRINT C;"CHECKS @ $";STR$(Cl)+"/"+"CHECK = $";STR$(C*C1)
00782 C8$=STR$(C)
00783 IF LEN(C8$) 2 THEN 790
00784 IF LEN(C8$) 1 THEN 788
00785 C8$="00"+C8$
00786 GO TO 790
00788 C8$="0"+C8$
00790 PRINT DO-O;"MISSES 0 $";STR$(C2)+"/"+"MISS = $";STR$((D0-O)*C2)
00795 PRINT
00800 PRINT "TOTAL COST = $"+STR$(C*Cl+(D0-0)*C2)
00802 T8$=STR$(C*C1+(D0-0)*C2)

86
00804
00806
00870
00880
00890
00900
00910
00920
00930
00940
00950
00960
00965
00970
00975
01010
01020
01030
01040
01045
01050
01070
01080
01090
01100
orno
01120
01130
01140
01145
01150
01152
01153
01154
01156
01160
01162
01164
01170
01180
01210
01220
01230
01240
01242
01243
01244
01246
01250
01260
01290
IF LEN(T8$) 2 THEN 870
T8$="0"+T8$
PRINT
PRINT "ACTUAL EVENT DATES"
PRINT "
PRINT "EVENT ID. DATE"
PRINT " "
FOR 0=1 TO DO
PRINT TAB(3);E$(J);TAB(11);A$(J)
NEXT J
PRINT
PRINT "REVISE ESTE RESUMEN DE RESULTADOS PARA VERIFICAR"
PRINT "QUE LA INFORMACION EN CUANTO A EVENTOS COTEJADOS"
PRINT "Y DETECTADOS ES CORRECTA. UNA VEZ HECHO ESTO,"
PRINT "OPRIMA 'RETURN'."
INPUT R9$
PRINT
PRINT "FAVOR DE CONTESTAR LAS SIGUIENTES PREGUNTAS ESCRIBIENDO UN"
PRINT "1, 2, 3, 4, O 5, Y OPRIMIENDO LA TECLA 'RETURN'. UNA"
PRINT "RESPUESTA CERCA DE 1 INDICARA 'POCO APROPRIADO'; UNA"
PRINT "RESPUESTA CERCA DE 5 INDICARA 'MUY APROPRIADO, COMO SIGUE:1'
PRINT
PRINT
PRINT " POCO APROPRIADO : 1 : 2 : 3 : 4 : 5 : MUY APROPRIADO"
PRINT " "
PRINT
PRINT
PRINT "1. CUAN APROPRIADO ENCONTRO USTED EL ORDEN DE LA INFORMACION"
PRINT " EN EL REPORTE PARA EL TIPO DE DECISIONES QUE SE"
PRINT " DEBIAN TOMAR?"
INPUT Al
IF Al 5 THEN 1154
IF Al =1 THEN 1160
GOSUB 2000
GO TO 1150
SET :1, R; :2, R; :3, R; :4, R; :5, R; :6, R
INPUT :6, QO
Q0=Q0+1
INPUT :1, Cl
C1=C1+A1
PRINT
PRINT "2. CUAN APROPRIADO FUE EL FORMATO DE PRESENTACION DEL REPORTE
PRINT " AL MOMENTO DE TOMAR LAS DECISIONES DIARIAS?"
INPUT A2
IF A2 5 THEN 1244
IF A2 =1 THEN 1250
GOSUB 2000
GO TO 1240
INPUT :2, C2
C2=C2+A2
PRINT

87
01300 PRINT "3. CUAN APR0PRIAD0 FUE EL FORMATO DEL REPORTE AL MOMENTO"
01305 PRINT " OE ANOTAR LA INFORMACION SOBRE LOS EVENTOS COTEJADOS"
01310 PRINT " Y DETECTADOS?"
01320 INPUT A3
01322 IF A3 5 THEN 1324
01323 IF A3 =1 THEN 1330
01324 GOSUB 2000
01326 GO TO 1320
01330 INPUT :3, C3
01340 C3=C3+A3
01370 PRINT
01380 PRINT "4. CUAN APROPRIADO FUE EL DETALLE DE LA INFORMACION"
01385 PRINT " PROBABILISTICA SOBRE LA POSIBLE FECHA DE OCURRENCIA"
01390 PRINT " DE CADA EVENTO?"
01400 INPUT A4
01402 IF A4 5 THEN 1404
01403 IF A4 =1 THEN 1410
01404 GOSUB 2000
01406 GO TO 1400
01410 INPUT :4, C4
01420 C4=C4+A4
01450 PRINT
01460 PRINT "5. CONSIDERANDO TODOS LOS FACTORES, COMO DE APROPRIADO"
01465 PRINT " ENCONTRO USTED EL FORMATO DE ESTE REPORTE?"
01480 INPUT A5
01482 IF A5 5 THEN 1484
01483 IF A5 =1 THEN 1490
01484 GOSUB 2000
01486 GO TO 1480
01490 INPUT :5, C5
01500 C5=C5+A5
01530 PRINT
01540 PRINT " HEMOS CONCLUIDO EL EXPERIMENTO "
01545 PRINT " GRACIAS POR SU COOPERACION "
01550 R8$=STR$(R)
01552 IF LEN(R8$) 1 THEN 1560
01554 R8$="0"+R8$
01560 Q8$=" "+STR$(A1)+" "+STR$(A2)+" "+STR$(A3)+" "+STR$(A4)+" "+STR$(A5)
01561 SO$=STR$(SO)
01562 IF LEN(S0$) 2 THEN 1570
01563 IF LEN(SO$) 1 THEN 1566
01564 S0$="00"+S0$
01565 GO TO 1570
01566 S0$="0"+S0$
01570 TO$=SO$+" "+R8$+" "+T2$+" "+C8$+" "+T8$+Q8$
01600 SET :1,R; :2,R; :3,R; :4,R; :5,R; :6,R
01610 WRITE :1,C1
01620 WRITE :2,C2
01630 WRITE :3,C3
01640 WRITE :4,C4
01650 WRITE :5,C5

01660 WRITE :6,Q0
01670 PRINT C1;C2;C3;C4;C5;Q0
01672 PRINT
01674 PRINT T0$
01680 SET :8,S0
01685 WRITE :8,T0$
01690 GO TO 2050
02000 PRINT
02005 PRINT "? INPUT DATA NOT IN CORRECT FORM—PLEASE RETYPE"
02010 RETURN
02050 END

APPENDIX D
SUBJECT INSTRUCTIONS
The written instructions given to the experimental subjects are
shown here in their original version (in Spanish) and in an English
version. Again, an effort has been made to present a connotative
rather than literal translation so the non-Spanish reader can have
a more accurate picture of their content. The statement of consent
that was signed by each subject is also included.
89

Instrucciones
Introducción
Con este experimento se quiere medir la efectividad de un informe
gerencial. El informe estudiado ha sido diseñado para ayudar a un gerente
a "detectar" una serie de eventos de interés que han de ocurrir en el futuro
próximo. La razón que amerita el uso de un informe en este caso es que
estos eventos de interés ocurren al azar solamente durante el término de un
dfa y luego no vuelven a ocurrir hasta después de aproximadamente 20 días.
Es conveniente para la gerencia "detectar" el dfa en que estos eventos ocu¬
rren ya que se incurre en un costo de oportunidad cada vez que uno de estos
eventos sucede y pasa sin ser detectado (la próxima oportunidad de observar
el evento no vuelve a ocurrir hasta después de aproximadamente 20 días). El
informe estudiado es preparado en base a estadísticas pasadas y consiste
precisamente de las fechas más probables de ocurrencia para cada uno de
una serie de estos eventos. El formato general de este informe es como
sigue:
En este experimento se quiere medir el efecto del formato de presenta¬
ción de este informe sobre el uso efectivo que se le ha de dar al mismo.
Usted recibirá uno de varios formatos experimentales de este informe y du¬
rante un periodo simulado de 20 días usted utilizará dicho informe para
tratar de "detectar" una serie de eventos que segén su informe han sido
identificados como que han de ocurrir durante esos 20 días.
Reglas de la Simulación
Durante el experimento usted jugará el papel de un gerente que debe
decidir a diario cuántos y cuáles "eventos " cotejar para ver si están
"ocurriendo" en ese día o no. Las características del problema que usted
deberá mantener en mente son las siguientes:
1. Usted recibirá un informe experimental con los "eventos"
que deben ser “cotejados" durante los próximos 20 días.
Estos eventos estarán identificados al lado izquierdo del

91
Informe con un número de identificación de tres (3) dígitos.
Al lado derecho del número de identificación de cada evento
usted hallará información sobre la posible fecha de ocurren¬
cia de ese evento.
2. Los eventos han de ocurrir durante el perfodo de 20 días
comprendido entre mayo 25 y junio 13.
3. Cada evento ha de ocurrir en solo uno de esos veinte días
y solamente puede ser "detectado" si es "cotejado" en ese
día.
4. Cada "coteio" que se hace sobre un evento le cuesta a la
5. Cada evento que sucede y pasa sin ser detectado le cuesta
a la gerencia $5.00.
6. El objetivo suyo como gerente es conseguir un balance entre
el costo de "cotejar" y el costo de "no detectar" los eventos
de manera que se minimize la suma de estos dos costos.
Uso del Simulador
Sus decisiones "diarias" serán comunicadas a un simulador. Esto se
hará por medio del teletipo, el cual le pedirá a usted que le informe qué
eventos desea cotejar en cada uno de los 20 días de la simulación. A su
vez, el simulador lo mantendrá a usted informado sobre cuáles de los eventos
"cotejados " son "detectados ". El simulador funciona como sigue:
1. Al principio del experimento, el teletipo escribirá:
TODAY IS 5-25 (mayo 25)
Inmediatamente, el simulador le indicará que está listo para
recibir sus instrucciones sobre qué eventos cotejar ese día
escribiendo:
CHECKS ?
Usted deberá entonces escribir el número de identificación
para cada evento que desea cotejar ese día, dejando un
espacio entre cada evento y oprimiendo "return" cuando haya
entrado el último evento. Los 3 dígitos del número de identi¬
ficación deben ser escritos para cada evento que desea cote¬
jar. De otra manera el simulador escribirá el mensaje:
? INPUT DATA NCT IN CORRECT FORM - PLEASE RETYPE

2.
92
Inmediatamente después que usted escribe los eventos que
desea cotejar, el simulador determinará cuáles de los even¬
tos cotejados ocurrieron ese día y por lo tanto fueron detec¬
tados . El simulador entonces escribirá los números de iden¬
tificación de los eventos cotejados y detectados como en los
ejemplos que siguen:
TODAY IS: 6-4
CHECKS ?
? 121 097 168
CHECKED: 121 097 168 DETECTED: 168
(Eáa información deberé ser anotada para evitar cometer el error de volver a
cotejar el "168")
TODAY XS: 6-5
CHECKS ? (No escribió todos los dígitos del "097”)
? 97 177
? INPUT DATA NOT IN CORRECT FORM—PLEASE RETYPE
? 097 177
CHECKED: 097 177 DETECTED: NONE
TODAY IS : 6-6
CHECKS ? ,
•) (no deseaba cotejar ese día y
CHECKED: NONE oprimió "return")
3. La información dada por el simulador en cuanto a los eventos
que son detectados deberá ser anotada para evitar cometer el
error de volver a cotejar un evento que ya ha sido detectado.
Estas anotaciones se deberán hacer en el mismo informe
provisto.

93
4.Al final del último día (junio 13) el simulador escribiré un
resumen de su ejecutoria, incluyendo una lista de las fechas
en que realmente sucedieron cada uno de los eventos como
en el ejemplo que sigue:
******** SUMMARY OF RESULTS FOR THE RUN ********
54 CHECKS 0 $l/CHECK
6 MISSES @ $5/MISS =
TOTAL COST
= $84
ACTUAL EVENT DATES
EVENT ID.
DATE
004
6-6
009
5-28
168
6-4
171
5-31
173
6-5
177
6-4
186
6-1.
5. Usted deberé revisar este resumen para verificar que la infor¬
mación er. cuanto a eventos cotejados y detectados es correcta.
Una vez hecho esto deberá oprimir la tecla "return" para que
el simulador prosiga.
6. El simulador entonces procederá a escribir una serie de pre¬
guntas que usted deberá contestar siguiendo las instrucciones
por el teletipo. Una vez contestadas las preguntas habremos
concluido el experimento.

94
DECLARACION DE CONSENTIMIENTO
He leído y entiendo el procedimiento experimental descrito
arriba. Deseo participar en dicho procedimiento, entendiendo que
estoy libre de retirar mi consentimiento y participación en el mismo
en cualquier momento.si as? lo deseare.
Firmas
Sujeco
Testigo
José Amador
Investigador Principal
Colegio de Administración de Empresas
Recinto Universitario de Mayaguez
Mayaguez, Puerto Rico

95
Instructions
Introduction
The purpose of this experiment is to measure the effectiveness of
a managerial report. The report is intended to aid a manager in "de¬
tecting" a series of events that are due to occur in the near future
and are of interest to him. These events occur at random approximately
every 20 days, and when they do occur, they are "detectable" only during
one day. It is to the manager's advantage to detect these events when
they occur, as there is an opportunity cost associated with each event
that occurs and goes undected (it will not occur again until about 20
days). The reports under study have been prepared on the basis of past
event occurrence data and consist of estimates of the next occurrence
dates for a number of such events. The general format of the report
studied is as follows:
Event
I.D.
Expected Occurrence
Date

96
The idea behind the experiment is to measure the effort that variants
of this format might have on its effective use. You will receive one of
various experimental formats for this report, and, during a period of 20
simulated days, you will use the report to help you "detect" a number of
events-which according to the report are due to occur during those days.
Game Ruies
During the experiment you will play the role of the manager in charge
of deciding how many and which events to check for on each successive day.
The rules you will want to keep in mind when making your "check" decisions
are the following:
1. You will receive a reoort indicating the events that you
want to "check" during the next 20 days. Each event will
be identified with a three-digit number on the left margin
of your report. To the right of these numbers you will find
information about the estimated occurrence date of each
event.
2. All events on the report will occur during the 20-day
period between May 25 and June 13.
3. Each event will occur on only one of these 20 days, and
can only be "detected" if "checked" on that day.
4. Each "check" made on an event will cost management $1.00
5. Each event that occurs and is not detected will cost manage¬
ment $5.00.
6. Your objective as manager is to compromise between the
cost of "checking" and the cost of "missing" so as to
minimize the sum of these two costs.
Use of the Simulator
Your daily check decisions will be input to a computer simulator. This
will be done via a teletype terminal, which will ask you to enter the I.D.
number of the events you want to check on each day. In turn, the simulator
will inform you which of the events you "check" are "detected," as follows:

97
1. The simulator starts by printing:
TODAY_IS__5;25 (May 25)
Immediately, the simulator will ask for your day's check decision:
CHECKS?
You will then enter the three-digit I.D. number for each event you
want to check on that day, leaving a space between each number entered
and pressing the "return" key after the last number is entered. All 3
digits must be entered for each event checked. Otherwise, the simulator
will print:
? INPUT DATA NOT IN CORRECT FORM—PLEASE RETYPE
2. Immediately after entering the events to be checked, the simulator
will determine which of the events checked occurred on that day
and thus, were detected. The simulator will then proceed to
print the I.D. number of the events checked and detected as in the
examples that follow:
TODAY IS: 6-4
CHECKS?
? 121 097 168
CHECKED: 121 097 168 DETECTED: 168
(The feedback from the simulator should be recorded to avoid the
mistake of checking "168" again).
TODAY IS 6-5
CHECKS?
? 97 177 (Did not use all 3 digits on "097")
? INPUT DATA NOT IN CORRECT FORM—PLEASE RETYPE
? 097 177
CHECKED: 097 177 DETECTED: NONE

98
3. The feedback from the simulator on events checked and detected
should be recorded to avoid checking an event that has already
been detected. All writing should be done on the report pro¬
vided.
4. After the last day of the run (June 13) the simulator will
proceed to print a summary of your performance, including
a list showing the actual dates on which each event occurred,
as follows:
********SUMMARY OF RESULTS FOR THE RUN********
54 CHECKS 0 $1/CHECK = $54
6 MISSES 0 Í5/MISS = $30
TOTAL COST = $84
ACiyALJVENIJAIES
EVENIJD. .DATE.
004 6-6
009 5-28
168 6-4
171 5-31
173 6-5
177 6-4
186 6-1
5. You should then revise this summary to verify that the information
on checks and misses is correct. Once done this, you will press
"return."
6. The simulator will then proceed to print a short questionnaire that
you must answer following the instructions it will also print.
Once done this, we will have concluded the experiment.

99
STATEMENT OF CONSENT
I have read and understand the instructions to the experimental
procedure described above. I wish to participate in the experiment,
understanding that I am free to withdraw my consent and participation
at any time if I so desired.
Signatures
Subject
Witness
José Amador
Principal Investigator
College of Business Administration
University of Puerto Rico, Mayaguez
Mayaguez, Puerto Rico

BIOGRAPHICAL SKETCH
José A. Amador was born December 23, 1947, in Aguadilla, Puerto Rico.
He received his elementary and secondary education at Colegio San Carlos,
Aguadilla, and graduated from San Carlos High School in May, 1965. He
attended the University of Puerto Rico, Mayaguez Campus, from 1965 to
1969 and received a Bachelor's degree with a major in Business Adminis¬
tration in May, 1969.
In September 1970, José Amador was granted an appointment and license
from the University of Puerto Rico to undertake graduate studies. He
entered the graduate program in Business Administration at the University
of Florida, and in August, 1971, received a Master of Business Administration
degree. In August, 1974, he received a Master of Science degree from the
University of Florida with a major in Industrial and Systems Engineering.
José Amador is presently an Assistant Professor in the College of
Business Administration at the University of Puerto Rico. He is a member
of Phi Eta Mu fraternity, Beta Gamma Sigma honorary business society, and
the American Institute for Decision Sciences. He is married to the former
Maria L. Lopez of Aguadilla, Puerto Rico, and is the father of two sons,
José Angel and Juan Carlos.
100

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. Elnicki, Chairman
Associate Professor of Management
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.
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.
Associate Professor of Management

I certify that I have read this study and that in my opinion it
confroms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Philosophy.
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.
This dissertation was submitted to the Graduate Faculty of the Department
of Management in the College of Business Administration and to the Graduate
Council, and was accepted as partial fulfillment of the requirements for
the degree of Doctor of Philosophy.
June 1977

UNIVERSITY OF FLORIDA



61
entities, the story was quite different (5.2.8, 5.2.9, 5.3.3, 5.3.4).
There were significant differences between their ratings in the various
layout and style conditions.
For the MIS practitioner, these results suggest that they should give
careful attention to report format, especially when the report must grow.
The results obtained here give meaning to Voich, et al.'s statement,
As the complexity of a report increases, its likelihood of extent of use
falls" [28, p. 229]. When preparing a report for a procurement manager,
for example, a standard layout by major classes of items, code number,
etc., may be appropriate if the number of items that must be ordered each
time, and their frequencies of ordering, are small. If the number of
orders that must be placed were to increase considerably, it may be to
the manager's advantage to have the layout of his report revised. A more
favorable layout in that case could be, for example, to have the items
arranged according to the frequency with which they are ordered.
For format revisions or similar actions to occur, the channels of
communication between the information analyst and user must first be im
proved. At the present, there appears to be a "tail versus dog" problem
between information users and providers when it comes to seemingly un
important matters, such as designing a format for a report. Voich et al.
state:
Report formats are often not tailored
precisely to users' needs. One reason
for this is the programming costs asso
ciated with special arrangements of in
formation, especially if several dif
ferent users each request a unique for
mat. A second reason for finding formats
not tailored exactly to user's needs is
that report designs are often based on


95
Instructions
Introduction
The purpose of this experiment is to measure the effectiveness of
a managerial report. The report is intended to aid a manager in "de
tecting" a series of events that are due to occur in the near future
and are of interest to him. These events occur at random approximately
every 20 days, and when they do occur, they are "detectable" only during
one day. It is to the manager's advantage to detect these events when
they occur, as there is an opportunity cost associated with each event
that occurs and goes undected (it will not occur again until about 20
days). The reports under study have been prepared on the basis of past
event occurrence data and consist of estimates of the next occurrence
dates for a number of such events. The general format of the report
studied is as follows:
Event
I.D.
Expected Occurrence
Date


TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS iv
LIST OF TABLES vii
LIST OF FIGURES viii
ABSTRACT ix
CHAPTER 1 RESEARCH BACKGROUND
1.1 Introduction
1.2 Literature Review
1.2.1 The Minnesota Experiments
1.2.2 The Lucas Model
1.2.3 Other Related Literature
1.3 Organization of the Dissertation
CHAPTER 2 THE PROBLEM
2.1 An Information-Decision Problem
2.1.1 The Real-Life Problem
2.1.2 The Abstracted Problem
2.2 Information Content
2.3 The "How-Do-We-Present-the-Information?" Problem
2.3.1 Medium of Transmission
2.3.2 Format of Presentation
2.3.3 Level of Detail
2.4 Number of Decision Entities
2.5 Dependent Variables
2.6 Research Hypotheses
2.7 Basic Functional Model
13
13
15
16
17
18
19
23
26
27
28
31
CHAPTER 3 THE EXPERIMENT
33
3.1 Method
3.1.1 Subjects
3.1.2 Design and Analysis
3.2 Experimental Task ....
3.3 Evaluation of Hypotheses
33
33
34
37
40
v


17
expectancy dates" would be useful as it would permit management to con
centrate their checks on those days when each event is expected to occur.
A dichotomy from economic models will help to clarify the type of
report that managers consider appropriate in the problem modeled.
Managerial reports can be descriptive or normative in nature. Purely
descriptive reports, as used here, are those limited to the presenta
tion of factual information (e.g.: production history reports, financial
reports). Purely normative reports, as used here, explicitly indicate
courses of action to be followed by the user (e.g.: production
schedules). All managerial reports can be placed on this descriptive-
normative scale. A report providing demand forecasts and safety
stock sizes [11] is, for example, more normative than one providing a
detailed sales history but no forecasts. In this study, it is assumed
that managers want more than a descriptive report (for example, one
showing only the dates of the last observed occurrence of each event).
They want a report providing forecasts for the event occurrence dates.
They consider twenty days a reasonable time horizon for the report.
It is assumed that shorter horizons would make the report too costly
to produce and longer horizons would make the forecast data basis too
dated. In conclusion, the report that is assumed to be appropriate
for the problem modeled is a periodic chart containing event I.D.
numbers and "expected due-dates" for those events expected to occur
within the next twenty days.
2.3 The "How-Do-Wa-Present-the-Information?" Problem
The information content needs of management in the problem
characterized above are assumed to be relatively well structured and
defined. The issue that constitutes the main focus of this research


70
EVENT
IDENT.
EXPECTED DUE-DATE
(MONTH-DAY)
EVENT
IDENT.
EXPECTED DUE-DATE
(MONTH-DAY)
004
6-07
032
5-26
009
5-29
146
5-28
017
6-11
009
5-29
024
6-04
078
5-30
032
5-26
171
5-31
038
6-04
051
5-31
051
5-31
186
5-31
070
6-01
070
6-01
076
6-10
128
6-01
078
5-30
082
6-03
082
6-03
024
6-04
085
6-10
038
6-04
097
6-05
121
6-05
110
6-11
097
6-05
121
6-05
173
6-06
128
6-01
177
6-06
142
6-09
168
6-07
146
5-28
004
6-07
155
6-12
163
6-09
163
6-09
142
6-09
168
6-07
085
6-10
171
5-31
076
6-10
173
6-06
110
6-11
177
6-06
017
6-11
186
5-31
155
6-12
Condition 1
Condition 2


46
only the many-decisi on-entities subjects, D-j, were considered. With
in this group, the subjects receiving the due-date ordered reports
had significantly shorter decision times (A^ < A-j D-j, F = 26.76,
p < .00001). No significant interactions were observed within the
few-decisi on-entities group. In all the tests, decision time was
significantly shorter for the subjects receiving the due-date
ordered reports, thus supporting the hypothesis.
4.2.2 Influence of Format on Choice Behavior
The second hypothesis, that the format in which probabilistic
information is presented can influence choice behavior, was supported.
As Table 4.1 illustrates, the total number of checks made was higher
for the subjects using the graphical style, as opposed to the
tabular style, A multivariate test revealed a weak interaction
between style and number of decision entities (F = 2.60, p < .06).
Univariate tests on the number of checks variable showed a weaker style
main effect (§2 < B-j, F = 3.38, p < .07) and a stronger style and
number of decision entities interaction (F = 4.75, p < .03). With
in the many-decision-entities group, a Scheffe test revealed that
the average number of checks was significantly higher for the
graphical report users (73.5 versus 66.0, F = 8.07, p < .005). No
significant differences were found in the number of checks made within
the few-decision-entities group, and a comparison of the differences
in the number of checks between the two styles subjects for the D-j
and D2 groups was highly significant ([B2D1 B1]<[B2D2 B-^],
F = 131.83, p < .00001). Presuming that the total number of checks
made, regardless of success, was a reasonable measure of choice


TABLE OF CONTENTS (continued)
Page
CHAPTER 4 EXPERIMENTAL RESULTS 44
4.1 Introduction 44
4.2 Results 44
4.2.1 Effect of Layout on Decision Time 44
4.2.2 Influence of Format on Choice Behavior 46
4.2.3 Joint Effect of Layout and Style on Decision Time .. 47
4.2.4 Effect of Probabilistic Detail on Cost Performance .. 47
4.2.5 Joint Effect of Format and Level of Detail
on Decision Time 48
4.2.6 Relation Between Number of Decision Entities
and Format 49
CHAPTER 5 DISCUSSION OF RESULTS 51
5.1 Summary of Findings 51
5.2 The Multivariate Effects 51
5.3 The Univariate Effects 57
5.3.1 Effects Related to HI and H3 57
5.3.2 Effects Related to H2 58
5.3.3 Effects Related to H5 59
5.3.4 Effects Related to H6 60
CHAPTER 6 SUMMARY AND POSSIBLE EXTENSIONS 63
BIBLIOGRAPHY 66
APPENDIX A EXPERIMENTAL TREATMENTS 69
APPENDIX B FORMAT OPINION QUESTIONNAIRE 78
APPENDIX C COMPUTER SIMULATION PROGRAM 80
APPENDIX D SUBJECT INSTRUCTIONS 89
BIOGRAPHICAL SKETCH 100
vi


96
The idea behind the experiment is to measure the effort that variants
of this format might have on its effective use. You will receive one of
various experimental formats for this report, and, during a period of 20
simulated days, you will use the report to help you detect" a number of
events.which according to the report are due to occur during those days.
Game Rules
During the experiment you will play the role of the manager in charge
of deciding how many and which events to check for on each successive day.
The rules you will want to keep in mind when making your "check" decisions
are the following:
1. You will receive a report indicating the events that you
want to "check" during the next 20 days. Each event will
be identified with a three-digit number on the left margin
of your report. To the right of these numbers you will find
information about the estimated occurrence date of each
event.
2. All events on the report will occur during the 20-day
period between May 25 and June 13.
3. Each event will occur on only one of these 20 days, and
can only be "detected" if "checked" on that day.
4. Each "check" made on an event will cost management $1.00
5. Each event that occurs and is not detected will cost manage
ment $5.00.
6. Your objective as manager is to compromise between the
cost of "checking" and the cost of "missing" so as to
minimize the sum of these two costs.
Use of the Simulator
Your daily check decisions will be input to a computer simulator. This
will be done via a teletype terminal, which will ask you to enter the I.D.
number of the events you want to check on each day. In turn, the simulator
will inform you which of the events you "check" are "detected," as follows:


18
is the question of information structure, i.e., the physical manner in
which the information is presented to the user. Dickson et al. have
suggested three categories of information structure (enumerations
added by the author):
It is naive to assume that information system
requirements do not vary with the type of
decision being formulated. And, it is sub-
optimal to continue developing information
support systems without serious consideration
of (1) the form in which information is pro
vided, (2) the level of detail incorporated
into ensuing reports, and (3) the media by
which the information in transmitted
[12, p. 3].
The medium of transmission, the format of presentation, and the
level of detail are discussed below in terms of their importance in
the defined decision oroblem. In each case, arguments are presented
to show why each category was included or excluded as an experimental
variable in the study, The dependent variables measuring decision
performance are then presented, and the questions raised about the
effects of the experimental treatments on performance are presented
as a set of testable hypotheses. In the final section, a functional
model is presented to serve as framework for testing the hypotheses.
2.3.1 Medium of Transmission
Two media are commonly used for reports generated from a
computer-based data bank: paper printout and cathode ray tube (CRT)
display.^ In the case of a report that is to be produced and released
When reports are generated by a computer, the choice of transmission
medium is usually confined to these two media. Otherwise, the writer is
aware that other more "personalistic" modes of communication are also
available for displaying the information to the user [22]. Only computer


whether the observed effect was related to the short duration of the
experiment, or whether the effect would have continued even if the sub
jects had been given enough time to get fully acquainted with their re
port style. In either case, the result observed here is an important
finding since many real-life managerial reports have short-term use, are
"one-shot-non-recurrent" reports, and, very frequently, contain informa
tion of a probabilistic nature. Ergo, the information analyst that must
report probabilistic information as a basis for decisions appears to have
a delicate problem at hand: if the format in which he presents the in
formation is going to bias the choice of the decision maker, he will
surely want that bias to be in the "correct" direction. This point is
also related to the issue of normative versus descriptive reports, and is
a point that should be further investigated elsewhere.
5.3.3 Effects Related to H5
A result closely related to the preceding discussion provided
support for Hypothesis 5 (p. 30; 5.2.7). Within the many-decision entities
group, the subjects with interval estimates had shorter decision times
when the information was given to them in graphical as opposed to tabular
style (21.4 versus 24.6 minutes, p = .026). Point estimates users, how
ever, did not experience the same benefits in moving from the tabular
to the graphical style. Their average decision time, in fact, was higher
with the graphical style than with the tabular style (22.1 versus 20.7
minutes, p = .32). These results suggest that different levels of
probabilistic information may be more appropriately reported using dif
ferent presentation formats. In the present experiment, subjects with
the tabular style did as good or better than subjects with the graphical


74
95% CONFIDENCE INTERVALS
EVENT MAY JUNE EVENT
IDENT. 25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13 IDENT.
004 H H H H 004
009 H H H H H 009
017 H H H 017
024 H H H H H H H 024
032 H H H H 032
038 H H H H H 038
051 H H H H H H 051
070 H H H 070
076 H H H H 076
078 H H H H H H H 078
082 H H H H H 082
085 H H H H H 085
097 H H H H 097
110 H H H 110
121 H H H H H 121
128 H H H H H H 128
142 H H H H H H H 142
146 H H H H H 146
155 H H H 155
163 H H H H H H 163
168 H H H H H H H 168
171 H H H H H H H 171
173 H H H 173
177 H H H H 177
186 H H H H H H 186
25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13
Condition 7


73
EXPECTED DUE-DATES
EVENT MAY JUNE EVENT
IDENT. 25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13 IDENT.
032
H
032
146
H
146
009
H
009
078
H
078
171
H
171
051
H
051
186
H
186
070
H
070
128
H
128
082
H
082
024
H
024
038
H
038
121
H
121
097
H
097
173
H
173
177
H
177
168
H
168
004
H
004
163
H
163
142
H
142
085
H
085
076
H
076
110
H
110
017
H
017
155
H
155
25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13
Condition 6


36
assigned to the many-decision-entities group. Differences in decision
performance are also expected to be larger for the many decision
entities group.
Each of the sixteen resulting cells contained observations of 10
subjects' decision time, total checks made, total cost, and the five
ratings to the format opinion questionnaire.^ The data was analyzed
using Multivariate Analysis of Variance (MANOVA). MANOVA procedures
have the advantage of considering correlation among the dependent vari
ables [6]. Peter et al. [24], suggest that the technique should be
used, as opposed to the univariate ANOVA, whenever there is reason to
believe that multiple dependent varialbes might be correlated. Winer
[30, p. 232] points out that by considering possibly correlated depen
dent variables in a series of independent univariate tests, one fails
to obtain information about the total effect of the experimental treat
ments on all the criteria simultaneously. In the case of experimental
MIS research, a close correlation has been suggested between time and
cost, two of the criteria most frequently considered in the literature.
In none of the reviewed literature, however, was MANOVA used.
In the current study, separate ANOVA's will be conducted on
each of the dependent variables after overall significance is obtained
Decision time was rounded to the nearest minute and did not in
clude the time devoted to the post-experimental questionnaire. The
subjects were clocked as soon as their last "check" decision was made.
2
BMD12V Multivariate Analysis of Variance and Covariance, Health
Sciences Computing Facility, Department of Biomathematics, School of
Medicine, University of California, Los Angeles, 1976, p. 751.


98
3. The feedback from the simulator on events checked and detected
should be recorded to avoid checking an event that has already
been detected. All writing should be done on the report pro
vided.
4. After the last day of the run (June 13) the simulator will
proceed to print a summary of your performance, including
a list showing the actual dates on which each event occurred,
as follows:
********SUMMARY OF RESULTS FOR THE RUN********
54 CHECKS @ $1/CHECK = $54
6 MISSES 0 J5/MISS $30
TOTAL COST = $84
ACiyAL_EVENJ_pATES
EVENTJD. _DATE_
004 6-6
009 5-28
168 6-4
171 5-31
173 6-5
177 6-4
186 6-1
5. You should then revise this summary to verify that the information
on checks and misses is correct. Once done this, you will press
"return."
6. The simulator will then proceed to print a short questionnaire that
you must answer following the instructions it will also print.
Once done this, we will have concluded the experiment.


A Magi y Provi


TABLE 5.2 (continued)
Other Level of
Independent Variables Dependent Variable Significance
Results
Related
Hypotheses
Information Layout Decision Time .013
and Style
of Presentation
Subjects with many decision events H3,H6
and I.D. ordered reports had
shorter decision times when they
were also given the graphical style.
The effect was not observed among
the few-decision-entities group.
Style of Presentation Decision Time .055
and Level o.f Detail
Many-decision-entities subjects H5,H6
receiving interval estimates in
graphical style had shorter times
than those receiving the same
level of detail but in tabular
style. The effect was not observed
among the few-decisi on-entities
group.
Information Layout Layout Rating
.044
The many-riecision-ent1ties group H6
gave the highest rating to the
due-date layout. No significant
differences in ratings were
observed among the few-decisi on-
entities group.
Style of
Presentation
Level of .060
Detail Rating
Subjects with many decision H6
entities in tabular style rated
the detail of their reports
higher than subjects with the
graphical style. No signifi
cant differences 1n ratings were
observed among the few-decisi on-
entities group.


26
(1) Can users of probabilistic data make effective
use of information beyond point estimates?
(2) Can the format in which probabilistic data is
presented affect choice behavior?
(3) Can the level of probabilistic information in
teract with format to affect user performance?
2.4 Number of Decision Entities
An important consideration is now introduced: the influence that
any report format will have is very likely to be related to what is
called here the "number of decision entities" on the report. The
term "decision entities" will be used to refer to the separate pieces
of information present on a report and on which decisions are required.
In the real problem this study is based on, there is little doubt that
the report users would be indifferent about format if their reports con
tained information on only two or three events. This is not expected
to be the case, however, if the reports contain information on 200
events.^ The "number of of decision entities" was included as an experi
mental variable to empirically test the assertion that while report
users may feel indifferent about format when small amounts of infor
mation must be processed, they will move toward preferred formats,
and their performance will be more sensitive to format as the amount
2
of information they must process increases.
^A manager dealing with more than 200 decision events told the
writer he "could care less" about format if he did not have so many
events to look after.
2
"Amount of information," as used here, must not be confused with
"information overload," a condition where the decision maker is given
too much, unnecessary information [1, 9, 15].


99
STATEMENT OF CONSENT
I have read and understand the instructions to the experimental
procedure described above. I wish to participate in the experiment,
understanding that I am free to withdraw my consent and participation
at any time if I so desired.
Signatures
Subject
Witness
Jos Amador
Principal Investigator
College of Business Administration
University of Puerto Rico, Mayaguez
Mayaguez, Puerto Rico


77
EXPECTED DUE-DATES
EVENT
MAY
JUNE
EVENT
IDENT.
25 26 27
28 29
30
31 01 02 03 04 05
06 07 08 09
10 11 12
13 IDENT.
032
H
032
009
H
009
128
H
128
168
H
168
155
H
155
25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13
Condition 14
95% CONFIDENCE INTERVALS
EVENT
MAY
JUNE
EVENT
IDENT.
25 26
27
28
29
30
31
01 02
03
04
05
06
07
08
09
10
11
12
13
IDENT.
009
H
H
H
H
H
009
032
H H
H
H
032
128
H
H
H H
H
H
128
155
H
H
H
155
168
H
H
H
H
H
H
H
168
25 26
27
28
29
30
31
01 02
03
04
05
06
07
08
09
10
11
12
13
Condition 15
95% CONFIDENCE INTERVALS
EVENT
MAY
JUNE
EVENT
IDENT.
25
26
27
28
29
30
31
01
02
03
04
05
06
07
08
09
10
11
12
13
IDENT.
032
H
H
H
H
032
009
H
H
H
H
H
009
128
H
H
H
H
H
H
128
168
H
H
H
H
H
H
H
168
155
H
H
H
155
25
26
27
28
29
30
31
01
02
03
04
05
06
07
08
09
10
11
12
13
Condition 16


APPENDIX B
FORMAT OPINION QUESTIONNAIRE
The post-experimental questionnaire is presented here in an
English version of the actual questions (shown toward the end of the
program in Appendix C). A connotative rather than literal translation
has been attempted to give the non-Spanish reader a more accurate
representation of the content.
78


79
Please answer the following questions by entering a 1, 2, 3, 4, or 5, and
pressing the "return" key. An entry close to 1 will indicate "very
little" and an entry close to 5 will indicate "very much," as follows:
VERY LITTLE : 1 : 2 : 3 : 4 : 5 : VERY MUCH
1- How appropriate did you considered the order of the infor
mation in the report, given the type of decisions that had
to be made?
2. How appropriate did you considered the format of the report
at the time of making the daily decisions?
3. How appropriate did you considered the format of the report
at the time of recording the feedback on events checked
and detected?
4. How appropriate did you considered the detail of the prob
abilistic information on the possible date of occurrence
of each event?
5. All factors considered, how appropriate did you considered
the format of your report?


30
report can make effective use of interval estimates. In the real
problem this study is based on, it is expected that interval estimates
can be useful. Second, this proposition provides a setting for testing
Conrath's [8] contention that decision makers are not likely to think
in terms of probability measures other than point estimates,
5. The time to process and effectively use probabilistic (H5)
information is related to the format in which the in
formation is presented. In this case, it is expected
that the users receiving the interval estimates in the
graphical style will have shorter decision times than
those receiving the interval estimates in the tabular
style.
The difference between HI and H5 is that HI refers to the direct
(main effect) influence of layout on performance while H5 refers to
the interaction between a format variable (style) and the level of
probabilistic information provided (point estimates or interval
estimates). The purpose in testing this hypothesis is to show that
different levels of probabilistic information will be more easily
processed and used with different formats of presentation.
The hypothesis relating the number of decision entities to
format preference is:
6. Report users will move from format indifference to for- (H6)
mat preference and their performance will be more sensitive
to format as the number of decision entities on their
report increases. In this case, no significant format
opinion differences are expected among users of reports
with few decision entities in them, with the opposite
expected among users of reports containing many decision
entities. Differences in performance are also expected
to be larger among the users of the reports with many
decision entities.
The objective in testing this proposition is to demonstrate the
existence of a "number of decision entities" variable that should be


48
supported. Subjects receiving the interval estimates treatment, C2>
had lower costs than those receiving the point estimates, C-|, but
the difference was not significant. The univariate level-of-detail
main effect, with cost as the dependent variable, had F .34, and
the Scheffe test on was also non-significant (F = 2.42,
p > .10). Contrary to the author's expectation, these results do not
systematically support the hypothesis, though the directions are as
predicted, nor do they support Conrath's [8] argument that decision
makers do better with point estimates than with other probability
measures.
4.2.5 Joint Effect of Format and Level of Detail on Decision Time
Support of the fifth hypothesis was weak. The hypothesis is that
the format in which probabilistic data is presented interacts with the
level of detail to influence the time required to process and use the
information. The multivariate level-of-detail and style interaction
was not significant (F = 1.19, p > .25). There was a significant
univariate interaction between style, level of detail, and the number
of decision entities on the report (F = 3.70, p = .05). In particular,
the many-decision-entities subjects, D-j, receiving interval estimates,
C2, in graphical style, B2> had significantly shorter decision times
than those receiving the same level of detail but in tabular style
(B2C2D-j < B^C2Dp F = 4.95, p < .03). This result suggests that
certain formats may be better for reporting certain levels of prob
abilistic detail, but the absence of a significant multivariate
effect makes the inference rather weak.


TABLE 3.1
FACTORIAL DISPLAY AND FACTOR LEVELS
-Factor
Identification
Level 1
level 2
A
Information Layout
Order by I.D.
Order by Due-date
B
Style of Presentation
Tabular Style
Graphical Style
C
Level of Detail
Point Estimates
Interval Estimates
D
Decision Entitles
Twenty-five
Five
B. Factor Levels
u>
cn


22
in ascending I.D. number order, especially when there are a large
number of events to be referenced. One solution to this dual need is
to produce two reports: one in order of expected due-dates to support
the daily checking of decisions, and another in ascending I.D. order for
quick references. But, if an experiment revealed no significant
difference in performance between the users of the graphical I.D.
ordered reports and the users of the due-date ordered reports, the im
plication would be that there is no need for both reports. The graphical
report would provide the two desired features. Another explanation
for that result could be that the graphical style in this case has
a "calendar" resemblance and therefore presents a more familiar .
picture to the user than a listing of numbers. If this were the case,
a graphical report that presented the events in due-date order might
also influence performance. Figure 2.2 below shows such a report.
EXPECTED DUE-DATES
EVENT
KAY
JUNE
EVENT
IDENT.
25 26 27 28 25 30 31 01 02 03 04 05 C5 07 OS OS
10 11 12
13 IDENT.
032
H
032
146
H
146
009
K
009
078
H
078
171
H
171
051
H
051
186
H
186
070
H
070
128
H
128
082
H
082
024
N
024
038
H
038
121
H
121
037
K
097
173
H
173
177
K
177
168
H
168
004
H
004
163
H
163
142
H
142
085
H
085
076
H
076
no
H
110
017
K
017
155
K
155
25 26 27 23 29 30 31 01 C2 05 0* 05 06 07 C8 C3
10 11 12
13
Figure 2.2 Graphical style with events in due-date order


CHAPTER 1
RESEARCH BACKGROUND
1.1 Introduction
The last decade has seen a significant increase in the use of
computer-based data systems to support decision making in organizations.
This marriage of computers and organizations has developed into the
rapidly growing field of Management Information Systems (MIS). In
genera1, MIS refers to the use of computer-based data systems for the
primary purpose of supporting management decisions. Since MIS exist
to support decision making, researchers in the area have suggested
that their effectiveness should be measured in terms of the effectiveness
of the decisions they support. In turn, it has been argued that the
effectiveness of decisions based on information will depend, among other
things, on the accuracy, relevancy, and timeliness of the information.
More recently, it has also been proposed that even when information
is adequate, its effective use can be influenced by the manner in which the
information is presented, in particular, by its format of presentation,
level of detail, and medium of transmission. This line of thought has led
researchers in the area to investigate how the physical form of presenting
the information can influence aspects of decision performance. That
1


2.
92
Inmediatamente despus que usted escribe los eventos que
desea cotejar, el simulador determinar cules de los even
tos cotejados ocurrieron ese da y por lo tanto fueron detec
tados El simulador entonces escribir los nmeros de iden
tificacin de los eventos cotejados y detectados como en los
ejemplos que siguen:
TODAY IS: 6-4
CHECKS ?
? 121 097 168
CHECKED: 121 097 168 DETECTED: 168
(Ea Informacin deber ser anotada para evitar cometer el error de volver a
cotejar el "168")
TODAY IS: 6-5
CHECKS ? (No escribi todos los dgitos del "097")
? 97 177
? INPUT DATA NOT IN CORRECT FORMPLEASE RETYPE
? 097 177
CHECKED: 097 177 DETECTED: NONE
TODAY IS : 6-6
CHECKS ? ,
9 (no deseaba cotejar ese da y
CHECKED: NONE oprimi "return")
3. La informacin dada por el simulador en cuanto a los eventos
que son detectados deber ser anotada para evitar cometer el
error de volver a cotejar un evento que ya ha sido detectado.
Estas anotaciones se debern hacer en el mismo informe
provisto.


APPENDIX A
EXPERIMENTAL TREATMENTS
The sixteen reports that constituted the experimental treat
ments are shown here in the same size they were administered to the
subjects. The only difference between these reports and those used
by the subjects is that the latter had horizontal green lines across
them to facilitate their use. Each report is labeled with the
"condition" numbers used in Table 3.1 (p. 35).
69


47
behavior in this problem, the results support Conrath's [8] contention
that presentation format influences choice behavior.
4.2.3 Joint Effect of Layout and Style Decision Time
The third hypothesis, that information layout and style can inter
act to reduce decision time, was supported. Table 4.1 shows that,
within the many decision entities group, subjects using the I.D.
ordered reports, had shorter decision times when they also re
ceived the graphical style, B2- A multivariate test revealed a mar
ginal interaction between style and number of decision entities
(F = 2.43, p < .08). Univariate tests on the decision time variable
showed a stronger ABD interaction (F = 6.20, p < .02). A Scheffe
test revealed that the significance was due to the shorter decision
times of the subjects receiving the I.D. ordered reports in graphical
style (A1 B2D-j < A^B^D^, F 7.01, p < .009). The fact that a
significant layout and style interaction was observed only within the
many-decision-entities group also supports H6: that performance^
becomes more sensitive to format as the number of decision entities
on the report increases. This is also demonstrated by the fact that,
within the few-decision-entities group, both the layout main effect
(F = 0, p = 1) and the interaction between layout and style (F = .78,
p > .35) were not significant.
4.2.4 Effect of Probabilistic Detail on Cost Performance
The fourth hypothesis, that users of probabilistic data can make
cost-effective use of information beyond point estimates, was not
1
Time performance in this case.


CHAPTER 5
DISCUSSION OF RESULTS
5.1 Summary of Findings
Tables 5.1, 5.2, and 5.3 (pp. 52 55) present a summary of the
experimental results that had a significance level of p <.10 or better.
The results have been grouped into main effects, interaction effects
involving the number-of-decisi on-entities variable, and other interaction
effects. In each case the actual significance figure has been given so
that the reader can make his own judgement on the significance of each
result. The hypotheses relating to each result are also shown in the
right margin, along with a line reference number, to facilitate the dis
cussion in the following sections.
The results are discussed first for the multivariate (MANOVA) effects.
These do not relate to any hypothesis in particular, since the hypotheses
have been stated in terms of the effect of the experimental treatments on
specific criterion variables. They contain, however, important infor
mation about the total effect of the treatments on decision performance
in general. The univariate effects are discussed next as they relate to
each hypothesis. In each case, the implications for both the MIS re
searcher and practitioner are discussed.
5.2 The Multivariate Effects
Information layout (I.D. versus due-date ordering) was found to
51


90
Instrucciones
Introduccin
Con este experimento se quiere medir la efectividad de un informe
gerencia!. El informe estudiado ha sido diseado para ayudar a un gerente
a "detectar" una serie de eventos de inters que han de ocurrir en el futuro
prximo. La razn que amerita el uso de un informe en este cas es que
estos eventos de inters ocurren al azar solamente durante el trmino de un
dfa y luego no vuelven a ocurrir hasta despus de aproximadamente 20 das.
Es conveniente para la gerencia "detectar" el da en que estos eventos ocu
rren ya que se incurre en un costo de oportunidad cada vez que uno de estos
eventos sucede y pasa sin ser detectado (la prxima oportunidad de observar
el evento no vuelve a ocurrir hasta despus de aproximadamente 20 das). El
informe estudiado es preparado en bass a estadsticas pasadas y consiste
precisamente de las fechas ms probables de ocurrencia para cada uno de
una serie de estos eventos. El formato general de este informe es como
sigue:
En este experimento se quiere medir el efecto del formato de presenta
cin de este informe sobre el uso efectivo que se le ha de dar al mismo.
Usted recibir uno de varios formatos experimentales de este informe y du
rante un periodo simulado de 20 das usted utilizar dicho informe para
tratar de "detectar" una serie de eventos que segCm su informe han sido
identificados como que han de ocurrir durante esos 20 das.
Reglas de la Simulacin
Durante el experimento usted jugar el papel de un gerente que debe
decidir a diario cuntos y cules "eventos" cotejar para ver si estn
"ocurriendo" en ese dfa o no. Las caractersticas del problema que usted
deber mantener en mente son las siguientes:
1. Usted recibir un informe experimental con los "eventos"
que deben ser "cotejados" durante los prximos 20 das.
Estos eventos estarn identificados al lado izquierdo del


94
DECLARACION DE CONSENTIMIENTO
He ledo y entiendo el procedimiento experimental descrito
arriba* Deseo participar en dicho procedimiento, entendiendo que
estoy libre de retirar mi consentimiento y participacin en el mismo
en cualquier momento-si as lo deseare.
Firmas
Sujeto
Testigo
Jos Amador
Investigador Principal
Colegio de Administracin de Empresas
Recinto Universitario de Mayaguez
Mayaguez, Puerto Rico


INFORMATION FORMATS AND DECISION PERFORMANCE:
AN EXPERIMENTAL INVESTIGATION
By
JOSE A. AMADOR
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


83
Figure
r- >:-
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r*t T i** e 11 * i er c i i i 'i'" (ft
V L A t > i i aJ i I / I * W I
t:ttt t;r:
T'r -t-v T ~ / t '
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C*LCm'Z ?
?0r5 J :
DrLcrrEDi
" V t ic.
VdT 1 *-
ii 4 * *
* +
****#. .
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TT^Vt :C
:Cj rv
g.
VS1 */,Y
53 CH7XK
3 S 31/
7 rilS£l3
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T = 5 PA
rv
A# V _*
LJT DAT
r;sr;T ic.
DATE
03*
f-7
00*
£-30
017
S-9
02^
6-4
332
5-27
^ n
to m O
e-4
051
6-1
C70
5-31
076
6- 1 2
07 5:
a£-30
0B2
6-2
V V
6-10
0^7
6 5
1 1 0
6-12
121
£ £
125
6-2
142
6-6
J m6
f-20
; r c
6- .3
1 63
6- 0
155
$-* '
171
£-29
172
6-6
177
6-6
S6
£- 3 i
i' r .
t - W '
I c c
-
La". *,'j_ r-r.oF* .v*ad?
i.;c r-so ?.iQ rmstt iipse? ti,:v
-.zrULTC FOT Th-r P-LI +****-
; ; s ij?
*te
.TEVIS ES?r r.F-i-'Iil DE SESYLT-VM- PATA VERIFICA?; TLF LA IMFOTIA?ID
S CVAVT3 A EVENTOS TEJADOS Y DETECTADOS ES CVPkCTA. t*JA VEZ HECHO
13 TO OP-iri* 1 SET,;*J*.

FAVDP. DE CO*TTCT.'T LAS SIDt: ESTES PP.SSOtTAS ESC'TI'I PICO L'V |*,,2*,*3,a
a a y o.",rr:r,iD!> la -_cla HETLT7; *. va respuesta cettca de i
riDICAHA 'POSO APiOPPlA-A* Y 'AL-, RESPUESTA CECA CE Vi' IDDI CATA *IfY
APEO P.T I ASO j COHO S 3L E:
POCO AFP.OPPlADO : 1 : O s 2 : u : S : 'iVf APEOPIADO
i. cvvr apeo pe iat'' roc'll esto ru onr'-i ce la iveataciov
n l P.EPor.T. pata ll tip? di decidid-es. ele sr lidiad to tap 7 2
ri ¡A* \-vOA A # I
\ t .** * *-1 I
* a f r^T <*** r
*1 **_-,!+ i t
IL FT- L1 "iT"' T T ^EFSr ,TICJO" DEL PIPO PTE
L .: 11CI: I.rFF LIARAS ? D
ivv - t jon. aipt-ti *.l -To^i-fT? cr. a-jota.~
La lirotlACIP! ;n L-''- KVL'TOJ. Ti-JAD?. Y ITCTALDS ?A
ci i Av,niv;*A'jo >iL Ti. :t?ll: cr la mft:. r.c roo p.-oca 'ihftxca
* : t-T" 1 r > i;hti
^ t 1 *H L I l n ( 1 i i
*
£ >7 3 2 ¡* y; 70 7 r *\ f l %
.. t tiO'y 1 jT. - .
'.cm:.;;, C7M n 7-\o;>::i,u.o uste-
t r
' * *
V'
TI
r.i -1f! t -
- % m * M
i
K r i Ci \cj i7 c or .\c:'M!
|f-2 03 75 t* * -0/1 7 ^ 1
A.2 Sample output at the end of a run


64
Going to the field also implies having to deal with uncooperative
mother nature, as opposed to pre-chosen probability distributions for
the events of interest. Notwithstanding this dismal picture, a field
experiment should be useful. By establishing the external validity of
the present study with respect to the subject population, decision
making conditions, and other areas of interest, its benefit for the
actual population can be established and considered in the design of
a field study. A practical field study could be, for example, one in
which a more normative report is provided to the manager facing the
heat detection problem. Such a report could be based on some optimal
decision rule indicating to the dairyman the days in which he should
check for heat in particular cows. The decision rule would have to be
based on some cost estimates (costs of "checking," "missing," and
breeding after a succesful "check"), and on some probability estimates
(probability of detecting heat on given "checks," probability of a
succesful breeding once heat is detected, etc.).
Although growing fast, "experimental work on MIS is still in its
infancy" [5, p. 17]. Many promising areas have not been investigated.
One line of research that follows from the present results is the re
lationship between the number of decision entities in the report and
the sensitivity of performance to format variables. Various number-
of-decision-entities levels could be manipulated in a parametric study
to investigate such questions as, "Can a report user adapt to an
increasing number of decision entities in his report without a rapid
deterioration in his performance?" To investigate this question
the same subjects would have to be given increasing numbers of decision
entities, and this could bring up problems of subject "learning." If
properly controlled, however, an experiment along these lines could


10
flexible systems to support different
users' needs. For example, the
present sales information system could
be modified to provide different out
put formats and levels of summarization
[20, p. 918].
Equations 1.3 and 1.4 are combined in the next chapter to produce
a model that will serve as a guide for evaluating a set of propositions
relating information format to decision performance.
1.2.3 Other Related Literature
In addition to the literature referenced above, other related
literature has influenced the formulation of the hypotheses evaluated
in the present study. Two textbooks on MIS, in particular, contain
some interesting but undocumented ideas which have shared in the latter.
Murdick and Ross [23] make such general statements as, "In general,
the format should be established to save the manager's time" [p. 326]
and, "Managers prefer graphic displays, which reduce large amounts of
information into easily understood pictorial form" [p.263].
The second MIS text which makes similar suggestions is Voich et al.
[29]. They propose:
Format is important because it affects
the ease with which the report can be
read and assimilated. As the complexity
of a report increases, its likelihood
of extent of use falls [29, p. 229].
This writer feels that the authors are saying that more attention
should be given to the format of the report as the number of "entities"
in the report on which decisions are required increases.
Finally, a recent paper by Conrath [8] suggests:
In all the literature on decision making,
and in particular that on statistical de-


27
2.5 Dependent Variables
Time performance. Since time is a valuable managerial commodity,
decision time is commonly used as a decision performance criterion
[5,9,18,26], Although not supported by any studies, Murdick and Ross
contend that "... format should be established to save the manager's
time" [23, p. 326]. Decision time will be used here as a proxy for the
value of managerial time to the organization. It will be measured by
the total time that the decision maker devotes to making the. checking
decisions. It is expected that this measure will be correlated with
the cost measure, although the cost measure will not include the cost
of managerial time to avoid double counting. Chervany and Dickson [9]
found that some decision makers will take longer to arrive at their
decisions but will make lower cost decisions. The possible correla
tion between the time and cost measures will be taken into account
through the use of multivariate statistical procedures (viz., MANOVA).
Cost performance. Cost is also commonly used as a performance
criteria when decision effectiveness is discussed [5,9,18,26]. Cost
performance will be measured here by the total of the "checking" and
"missing" costs. For each check made, the decision maker will in
cur a fixed dollar cost. The checking cost, then, will be given by
the product of the total number of checks made times the fixed cost
per check. For each event that is not detected, the decision maker
will incur a fixed opportunity cost. The cost of missing is then
calculated as the product of the total number of misses times the fixed
cost per miss.
Choice behavior. Choice behavior was also included as a criterion
variable to test Conrath's [8] contention that the format in which


68
26. Senn, J. A. and G. W. Dickson, "Information System Structure
and Purchasing Decision Effectiveness," Journal of Purchasing
and Materials Management, 10, No. 3, pp. 52-64, 1974.
27. Twenty-one Day Reproductive Calendar, Agricultural Extension
Service, Iowa State University, Ames, Iowa, 1970.
28. Van Horn, R. L., "Empirical Studies of Management Information
Systems," Data Base, _5, pp. 172-180, 1973.
29. Voich, D., H. J. Mottice and W. A. Shrode, Information Systems
for Operations and Management, South-Western Publishing Co.,
Cincinnati, Ohio, 1975.
30. Winer, B. J., Statistical Principles in Experimental Design,
McGraw-Hill Book Co., New York, New York, 1971.
31. Zannetos, Z. S., Discussion Comments for "An Overview of
Management Information Systems," Data Base, 5, pp. 13-14,
1973.


50
With regard to the relationship between the number of decision
entities and the sensitivity of performance to format, the discussion
of the first five hypotheses has shown that the performance of the
many-decisi on-entities subjects was more sensitive to format than
that of the few-decisi on-entities subjects. In all the comparisons
the differences in performance were larger among the many-decision-
entities subjects than among the few-decision-entities subjects.


75
9555 CONFIDENCE INTERVALS
EVENT MAY JUNE EVENT
IDENT.
25
26
27
28
29
30
31
01
02
03
04
05
06
07
08
09
10
11
12
13
IDENT.
032
H
H
H
H
032
146
H
H
H
H
H
146
009
H
H
H
H
H
009
078
H
H
H
H
H
H
H
078
171
H
H
H
H
H
H
H
171
051
H
H
H
H
H
H
051
186
H
H
H
H
H
H
186
128
H
H
H
H
H
H
128
070
H
H
H
070
082
H
H
H
H
H
082
024
H
H
H
H
H
H
H
024
038
H
H
H
H
H
038
121
H
H
H
H
H
121
097
H
H
H
H
097
168
H
H
H
H
H
H
H
168
177
H
H
H
H
177
173
H
H
H
173
004
H
H
H
H
004
142
H
H
H
H
H
H
H
142
163
H
H
H
H
H
H
163
076
H
H
H
H
076
085
H
H
H
H
H
085
110
H
H
H
110
017
H
H
H
017
155
H
H
H
155
25
26
27
28
29
30
31
01
02
03
04
05
06
07
08
09
10
11
12
13
Condition 8


16
It is assumed that each check made on an event to see whether it is
occurring has a fixed unit cost associated with it, independent of the
number of checks made on the same day. It can, therefore, be uneconom
ical for management to check on these events too often. Management
is assumed to maintain a computer-based data bank with the following
data on the process:
(1) a three-digit identification (I.D.) number for
each event that is expected to occur during the
next twenty days,
(2) the date of the last observed occurrence of each
event, and
(3) data on past time intervals between successive
occurrences of each event.
It is further assumed that management will use this data to pro
duce a periodic report to aid them in deciding which events to check
at the beginning of each dayJ Their decision problem is relatively
well structured and straight-forward: they would like to detect as many
of these events as possible but face a trade-off between the costs
of "checking" and "missing" the events.
2.2 Information Content
Based on past experience, the managers in charge of checking the
events know that it is not cost-effective to check an event except on
those days when the event is more likely to occur, i.e., the days
around the date figured by adding 20 days to the last observed occur
rence. They have suggested that a periodic chart with "event
Vhey will produce the report; they will rather have the data re
ported in its worst possible form than no report at all.


I certify that I have read this study and that in my opinion it
confroms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Philosophy.
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.
This dissertation was submitted to the Graduate Faculty of the Department
of Management in the College of Business Administration and to the Graduate
Council, and was accepted as partial fulfillment of the requirements for
the degree of Doctor of Philosophy.
June 1977


81
mi
M
D
x
S
168
5
18
20.0
1.75
171
5
11
20.0
1.75
173
5
17
20.0
0.75
177
5
17
20.5
1.00
186
5
11
20.5
1.50
where
E&(J) = event I.D. number
M = month of last occurrence
D = day of last occurrence
I = mean interval between occurrences
S standard deviation of interval between occurrences
All other input to the program was manually entered by the subjects.
It consisted of the I.D. numbers of the events they checked on each
successive day, and the answers to the post-experimental questionnaire.
A.3 Output
A sample of the beginning of a run is illustrated in Figure A.l
(p. 82). It shows an identification of the experimental report used in
the run, the initial time clocked after the subject indicated he was
ready to start,* and some of the outcomes of the early part of the run.
Figure A.2 (p. 83) is a sample from the last part of the experiment.
It shows some of the last decisions made by the subject, the final
decision time clocked (26 minutes and 3 seconds in the sample shown), the
summarized results of the run, and the post-experimental questionnaire
with the subject's answers. The last two lines seen are part of the
file updated with the results of each run. The program is on pp. 84-88.
*A pre-experimental familiarization session had already been con
ducted.


the system analysts or programmers' pre
ferences for programming ease [28, p. 229],
It would appear that better communication channels between the
analyst and the user should, at least, help to alleviate the second
reason noted above.


58
due-date ordering, while still maintaining a desirable feature of the
report (the I.D. ordering of the events). This is evidenced by the re
sult in Table 5.2, line 6.
These results have other implications, besides supporting HI and
H3. For the MIS researcher, they suggest the need for more investigation
on the layout variable. It might be revealing example, to look at
information layout schemes for information on events that are less time-
dependent in nature than the ones studied hereJ Maintenance data on
some mechanical process, for example, could provide a setting for an in
teresting and practical experiment.
To the MIS practitioner, and in particular to the person in charge
of designing information formats, the results emphasize the importance
of reporting information in a manner consistent with the way recipients
use it. Also, the observed interaction between layout and style suggests
that practitioners should be on the alert for joint effects among format
elements that can work to their advantage in enhancing the readability
of the report.
5.3.2 Effects Related to H2
Perhaps most striking was the result that subjects with graphical re
ports chose to make substantially more 'checks" than subjects with tabular
reports (5.1.3). This supports Hypothesis 2 (p. 28), namely, that the for
mat in which probabilistic information is presented can influence choice.
From a research point of view, a question that remains to be answered is
All events in this world are probably time dependent, but their
occurrence may be more dependent on time for some types (e.g., biological)
than for others (e.g., electrical components).


97
1. The simulator starts by printing:
T0DAY_IS_J:25 (May 25)
Immediately, the simulator will ask for your day's check decision:
CHECKS?
You will then enter the three-digit I.D. number for each event you
want to check on that day, leaving a space between each number entered
and pressing the "return" key after the last number is entered. All 3
digits must be entered for each event checked. Otherwise, the simulator
will print:
? INPUT DATA NOT IN CORRECT FORM-PLEASE RETYPE
2. Immediately after entering the events to be checked, the simulator
will determine which of the events checked occurred on that day
and thus, were detected. The simulator will then proceed to
print the I.D. number of the events checked and detected as in the
examples that follow:
TODAY IS: 6-4
CHECKS?
? 121 097 168
CHECKED: 121 097 168 DETECTED: 168
(The feedback from the simulator should be recorded to avoid the
mistake of checking "168" again).
TODAY IS 6-5
CHECKS?
? 97 177 (Did not use all 3 digits on "097")
? INPUT DATA NOT IN CORRECT FORMPLEASE RETYPE
? 097 177
CHECKED: 097 177 DETECTED: NONE


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.
Associate Professor of Management
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.
Christopner B. Barry//
of~Mar
Associate Professor
lanagement
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.
//wzf mi,
JatR M. Feldman
dissociate Professor of Management


42
Hypothesis 2. The total number of chekcs made should be affected
by the style treatment (factor B). It is expected that the number of
checks made by the graphical style users will be larger than that
made by the tabular style users, i.e., §2 > B^: CHECKS, since the
graphical style appears to illustrate the "choice space" more clearly,
thus inviting more check decisions.
Hypothesis 3. Decision times of subjects receiving some com
bination of layout and sty!e (A,B) should be significantly different
from decision times of subjects receiving some other combination.
Specifically it is expected that subjects receiving I.D. ordered
reports and the graphical style will have shorter decision times than
those receiving the I.D. ordered report but not the graphical style,
i.e., A^B2 < : TIME. The graphical style should reduce the
need for the time-convenient due-date ordering.
Hypothesis 4. Subjects receiving the interval estimates should
perform better than those receiving only point estimates, i.e., it is
expected that C2 < C-| : COST. The interval estimate subjects will
have more information on the random nature of the events.
Hypothesis 5. Decision times of subjects receiving some combina
tion of style and level of detail (B,C) should be significantly
different from decision times of subjects receiving some other com
bination. In particular, it is expected that the subjects receiving
interval estimates in the graphical style will have shorter decision
times than those receiving the interval estimates in the tabular
style, i.e., B^C2 < B]C2 : TIME. The graphical style should make
it easier for users to process the interval estimates information.


BIBLIOGRAPHY
1. Ackoff, Russell L., "Management Misinformation Systems," Manage
ment Science, 14, pp. 147-156, 1967.
2. Barr, Harry L., "Influence of Estrus Detection on Days Open in
Dairy Herds," Journal of Dairy Science, 58, pp. 246-247, 1975.
3. Barr, Harry L., "Breed at Forty Days to Reduce Days Open,"
Hoard's Dairyman, 120, p. 1115, 1975.
4. Barret,M. J., N. L. Chervany and G. W. Dickson, "On Some Aspects
of the Validity of an Experimental Simulator in MIS Research,"
Working Paper 72-02, MIS Research Center, University of Min
nesota, January, 1973.
5. Benbasat, I. and R. Schroeder, "An Experimental Investigation of
Some MIS Design Variables," Working Paper 75-01, MIS Research
Center, University of Minnesota, September, 1974.
6. Bock, R. D. and E. A. Haggard, "The Use of Multivariate Analysis
of Variance in Behavioral Research," in D. K. Whitla (Ed.),
Handbook of Measurement and Assessment in Behavioral Sciences,
Addison-Wesley Publishing Co., Reading, Massachusetts, pp. 100-142,
1968.
7. Conlin, B. J., "Use of Records in Managing for Good Lactational
and Reproductive Performance," Journal of Dairy Science, 51, pp.
377-385, 1974.
8. Conrath, David W., "From Statistical Decision Theory to Practice:
Some Problems with the Transition," Management Science, 19, pp.
873-883, 1973.
9. Chervany, N. L. and G. W. Dickson, "An Experimental Evaluation of
Information Overload in a Production Environment," Management
Science, 20, pp. 1335-1344, 1974.
10.Chervany, N. L., G. W. Dickson and K. A, Kozar, "An Experimental
Gaming Framework for Investigating the Influence of Management
Information Systems on Decision Effectiveness," Working Paper
71-12, MIS Research Center, University of Minnesota, 1972.
66


LIST OF FIGURES
Page
1.2Different formats of presentation 8
2.1 Three forms of presenting the expected
due-dates information .. 21
2.2 Graphical style with events in due-date order 22
2.3 Three forms of presenting the interval
estimates information 25
viii


LIST OF TABLES
Page
1.1 SOME STUDIES CONDUCTED UNDER THE CHERVANY
ET AL. FRAMEWORK 4
3.1 FACTORIAL DISPLAY AND FACTOR LEVELS 35
3.2 DATA FOR THE EXPERIMENT 39
3.3 EFFECTS PREDICTED BY THE RESEARCH HYPOTHESES 41
4.1 CELL MEANS FOR THE SIXTEEN EXPERIMENTAL
CONDITIONS 45
5.1 MAIN EFFECTS 52
5.2 INTERACTION INVOLVING THE NUMBER-OF-DECISION
ENTITIES-VARIABLES 53
5.3 OTHER INTERACTION EFFECTS 55
vi i


71
EVENT
IDENT.
95% CONFIDENCE INTERVAL
(FIRST DAY, LAST DAY)
EVENT
IDENT
004
6-06
9
6-09
032
009
5-27
9
5-31
146
017
6-10
9
6-12
009
024
6-01
9
6-07
078
032
5-25
9
5-28
171
038
6-02
9
6-06
051
051
5-29
9
6-03
186
070
5-31
9
6-02
128
076
6-09
9
6-12
070
078
5-27
9
6-02
082
082
6-01
9
6-05
024
085
6-09
9
6-14
038
097
6-04
9
6-07
121
110
6-10
9
6-12
168
121
6-03
9
6-07
097
128
5-30
9
6-04
173
142
6-06
9
6-12
177
146
5-26
9
5-30
004
155
6-11
9
6-13
142
163
6-07
9
6-12
163
168
6-04
9
6-10
085
171
5-28
9
6-03
076
173
6-05
9
6-07
110
177
6-05
9
6-08
017
186
5-29
9
6-03
155
95% CONFIDENCE INTERVAL
(FIRST DAY, LAST DAY)
5-25 5-28
5-26 5-30
5-27 5-31
5-27 6-02
5-28 6-03
5-29 6-03
5-29 6-03
5-30 6-04
5-31 6-02
6-01 6-05
6-01 6-07
6-02 6-06
6-03 6-07
6-04 6-10
6-04 6-07
6-05 6-07
6-05 6-08
6-06 6-09
6-06 6-12
6-07 6-12
6-09 6-14
6-09 6-12
6-10 6-12
6-10 6-12
6-11 6-13
Condition 3
Condition 4


TABLE 5.2
INTERACTION INVOLVING THE NUMBER-OF
Other
Level of
Independent Variables
Dependent Variable
Significance
Information Layout
Decision Time,
Cost, and Number
of Checks Made
.0015
Information Layout
Decision Time
.00001
Style of
Decision Time,
.062
Presentation
Cost, and Number
of Checks Made
Style of
Number of
.029
esentatior.
Checks Made
Information Layout
Decision Time,
.074
and Style
Cost, and Number
of Presentation
of Checks Made
DECISION ENTITIES-VARIABLES
Related Line
Results Hypotheses No.
The effect of layout on general 5.2.1
decision performance was stronger
among the many decision entities
group.
Subjects in the many-decision- H1.H6 5.2.2
entities group experienced larger
decision time reductions when
they were given the due-date
order layout.
Style and number of decision 5.2.3
entitles had a joint effect on
the three performance criteria
taken simultaneously.
The effect of style on number of H2,H6 5.2.4
checks made was stronger among
the many decision entities group.
Layout and style had a joint total 5.2.5
effect on decision performance.
The effect was stronger among
the many decision entities group.
U1
CO


84
00005 REM ************************************************************
00010 REM *** INTERACTIVE SIMULATION OF THE EVENT CHECKING PROBLEM ***
00015 REM ************************************************************
00020 DIM A$(25),E$(25)
00030 FILES A1%,A22!,A3%,A4%,A5%,A0%,DATA,TALYA$
00035 D0=25
00040 PRINT "SO,R";
00050 INPUT SO,R
00060 IF R =8 THEN 70
00062 D0=5
00064 FOR J=1 TO 25.
00066 INPUT #7, NO,E$(J),M,D,I,S
00063 NEXT 0
C0070 Cl=l
00075 PRINT
00080 C2=5
00090 PRINT
00100 PRINT "SUBJECT IS USING REPORT ";STR$(R)+"."
00105 PRINT
00110 FOR J=1 TO DO
00125 R0=0
00130 INPUT #7, NO,E$(J),M,D,I,S
00140 S$=S$+E$(J)
00210 RANDOMIZE
00220 FOR N=1 TO 12
00230 RO=RO+RND
00240 NEXT N
00250 Y=S*(R0-6) + I
00260 X=INT(Y+.5)
00280 IF (D+X) 31 GO TO 310
00290 D=D+X
00300 GO TO 330
00310 D=D+X-31
00320 M=M+1
00330 A$(J) = STR$(M)+"-"+STR$(D)
00340 NEXT J
00350 PRINT
00360 M=5
00370 D=24
00380 PRINT "Tl";
00381 INPUT Tl$
00389 PRINT
00390 FOR 1=1 TO 20
00400 D=D+1
00410 IF D 32 GO TO 440
00420 D=1
00430 M=M+1
00440 D$=STR$(M)+"-"+STR$(D)
00442 PRINT
00450 PRINT "TODAY IS ";D$
00460 PRINT 11


86
00804
00806
00870
00880
00890
00900
00910
00920
00930
00940
00950
00960
00965
00970
00975
01010
01020
01030
01040
01045
01050
01070
01080
01090
01100
OHIO
01120
01130
01140
01145
01150
01152
01153
01154
01156
01160
01162
01164
01170
01180
01210
01220
01230
01240
01242
01243
01244
01246
01250
01260
01290
IF LEN(T8$) 2 THEN 870
T8$="0"+T8$
PRINT
PRINT "ACTUAL EVENT DATES"
PRINT "
PRINT "EVENT ID. DATE"
PRINT "
FOR J=1 TO DO
PRINT TAB(3);E$(J);TAB(11);A$(J)
NEXT J
PRINT
PRINT "REVISE ESTE RESUMEN DE RESULTADOS PARA VERIFICAR"
PRINT "QUE LA INFORMACION EN CUANTO A EVENTOS COTEJADOS"
PRINT "Y DETECTADOS ES CORRECTA. UNA VEZ HECHO ESTO,"
PRINT "OPRIMA RETURN'."
INPUT R9$
PRINT
PRINT "FAVOR DE CONTESTAR LAS SIGUIENTES PREGUNTAS ESCRIBIENDO UN"
PRINT "1, 2, 3, 4, O 5, Y OPRIMIENDO LA TECLA 'RETURN'. UNA"
PRINT "RESPUESTA CERCA DE 1 INDICARA 'POCO APROPRIADO'; UNA"
PRINT "RESPUESTA CERCA DE 5 INDICARA 'MUY APROPRIADO, COMO SIGUE:"
PRINT
PRINT
PRINT POCO APROPRIADO : 1 : 2 : 3 : 4 : 5 : MUY APROPRIADO"
PRINT "
PRINT
PRINT
PRINT "1. CUAN APROPRIADO ENCONTRO USTED EL ORDEN DE LA INFORMACION"
PRINT EN EL REPORTE PARA EL TIPO DE DECISIONES QUE SE"
PRINT DEBIAN TOMAR?"
INPUT Al
IF Al 5 THEN 1154
IF Al =1 THEN 1160
GOSUB 2000
GO TO 1150
SET :1, R; :2, R; :3, R; :4, R; :5, R; :6, R
INPUT :6, QO
Q0=Q0+1
INPUT :1, Cl
C1=C1+A1
PRINT
PRINT "2. CUAN APROPRIADO FUE EL FORMATO DE PRESENTACION DEL REPORTE"
PRINT AL MOMENTO DE TOMAR LAS DECISIONES DIARIAS?"
INPUT A2
IF A2 5 THEN 1244
IF A2 =1 THEN 1250
GOSUB 2000
GO TO 1240
INPUT :2, C2
C2=C2+A2
PRINT


BIOGRAPHICAL SKETCH
Jose A. Amador was born December 23, 1947, in Aguadilla, Puerto Rico.
He received his elementary and secondary education at Colegio San Carlos,
Aguadilla, and graduated from San Carlos High School in May, 1965. He
attended the University of Puerto Rico, Mayaguez Campus, from 1965 to
1969 and received a Bachelor's degree with a major in Business Adminis
tration in May, 1969.
In September 1970, Jos Amador was granted an appointment and license
from the University of Puerto Rico to undertake graduate studies. He
entered the graduate program in Business Administration at the University
of Florida, and in August, 1971, received a Master of Business Administration
degree. In August, 1974, he received a Master of Science degree from the
University of Florida with a major in Industrial and Systems Engineering.
Jos Amador is presently an Assistant Professor in the College of
Business Administration at the University of Puerto Rico. He is a member
of Phi Eta Mu fraternity, Beta Gamma Sigma honorary business society, and
the American Institute for Decision Sciences. He is married to the former
Maria L. Lopez of Aguadilla, Puerto Rico, and is the father of two sons,
Jos Angel and Juan Carlos.
100


38
dates for a number of hypothetical events.^ Table 3.2 (P-39)
shows that data in two parts: the data used for generating
the 25 events in the "many decision entities" condition, and
the data used for generating the 5 events in the "few
decision entities" condition. The "generator" was validated
to verify that the events were generated according to a normal
distribution with the means and standard deviations indicated
in Table 3.2.
2. Based on the data on Table 3.2, forecasts for the event
occurrence dates were prepared and presented in report form.
These were the experimental reports (conditions) administered
to the subjects to help them in making their daily check
decisions. Several of these reports have already have been
shown in Chapter 2 The rest are shown in Appendix A, p. 69.
3. The subjects v/ere told that undetected events at the end
of the simulation would cost them $5 each. These will be
counted and multiplied by $5 to determine their total "missing"
cost.
4. Subjects were also told that each and every check made during
the run would be charged at $1 per check. These would be
counted at the end of the run to determine their total
"checking" cost.
5. Finally, subjects were instructed that their objective in the
game was to minimize their total cost figured as the sum of
their "checking" and "missing" cost. Subjects v/ere told that
their run time v/as also being measured, but were given no
time limit or other time pressures.
Subjects interacted with the simulator through typewriter-type
computer terminals in deciding which events to check for at each of
twenty decision points (days). At each decision point, the subject
chose the I.D. numbers of the events he wanted to check and entered
them for processing by the simulator. The simulator reported whether
or not the events checked occurred on that day, i.e., whether the
checks were successful or not.
Another Van Horn guide followed here in developing the present
prototype is "...to replace large actual data bases with small, care
fully stratified representations." [28, p. 179].
2
This and subsequent dollar figures were also simulated. No
monetary incentive scheme was used to make subjects do "their best."


34
Ten subjects were randomly assigned to each of the sixteen (four
factors each at two levels) experimental conditions. The assignments
were made with a random number generator that uniformly distributed
the numbers 1 through 160 among the sixteen conditions until a
schedule was formed with ten numbers assigned to each condition. As
subjects arrived to participate, their order of arrival was checked
against the schedule to determine their condition assignment. It
was felt that this assignment scheme avoided the problem of making
individual subject "appointments" at the same time that it provided
a means for stratifying the assignment of the experimental conditions
through the three months it took to complete the study.
3.1.2 Design and Analysis
Table 3.1 (p. 35) is a summary of the 2^ factorial experimental
design. The two levels for the number-of-decision-entities variable
were achieved by dividing the experimental subjects into two groups.
One group was assigned to an experimental condition where only five
events of interest had to be checked, referred to below as the "few
decision entities" condition. The five events were selected to form
a stratified representation of the twenty-five events that would take
place in the "many decision entities" condition. User preference for
particular formats was measured with a questionnaire administered to the
subjects at the end of the simulation runs (see Appendix B, p. 78).
In this study, no significant differences in format preference ratings
are expected between the various format combinations among subjects
assigned to the few-decision-entities group. Significant opinion
differences, however, are expected among the ratings of the subjects


2
relationship, in essence, is the object of this study. In the present
research, the influence of information format on decision performance will
be investigated in the context of a specific information-decision problem.
1.2 Literature Review
The increase in popularity of Management Information Systems
during the last decade has been accompanied by an awareness of the need
for improving the efficiency of the systems designed. The consensus
of the researchers in the area has been that there is a need for a
theory of MIS. Zannetos [311 states that a theory is needed to develop
objective criteria for determining the effectiveness of MIS efforts.
In response to the call for a theory, several research frameworks have
been proposed [10, 16, 21, 22].1
1.2.1 The Minnesota Experiments
The research framework proposed by Chervany et al. [10] has guided
the "Minnesota Experiments," a series of empirical studies that have
been conducted at the Management Information Systems Research Center,
University of Minnesota. The general purpose of these studies has been
to manipulate various MIS variables to investigate their impact on
decision performance. The Chervany et al. framework states that three
categories of variables affect decision performance, P, given a particu
lar information system. These are the decision environment, DE, the
^These frameworks have not constituted theories, in the formal
sense, but rather pre-theoretical lists of variables.


37
by MANOVA. This procedure is necessary in order to evaluate directional
hypotheses relating to specific dependent variables. Further investi
gation into the directions of obtained differences will be conducted
using Scheffe's posthoc test for comparisons between means [17, pp.
483-486].
3.2 Experimental Task
The experimental subjects acted as the decision makers in the
problem described in Chapter 2 A computer simulation of the decision
environment was created which modelled the essential features^ of the
decision problem. Those features are:
1. The decision maker wants to detect a number of events that
occur at random with normally distributed intervals between
successive occurrences.
2. He has data on the random events and wants to use it to make
cost-effective decisions on when to check each event.
3. He incurs a fixed opportunity cost for each event that occurs
and goes undetected.
4. He incurs a fixed cost for each "check" that he makes on an event.
5. His objective is to minimize the total combined costs of
"checking" and "missing" the events.
These features were incorporated into the simulation model as
follows:
1. A hypothetical data set for the means, x., and standard
deviations, s^, for the between occurrences interval of each
event i was used to generate "actual" occurrence
^Van Horn recommends that a good guide in developing an
effective prototype is "to restrict the prototype content to the
minimum set of features that are directly relevant to the problem
modeled" [28, p. 179]. His advice was followed here.


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INGEST IEID E3HTV0955_EV7JBG INGEST_TIME 2012-09-24T14:33:18Z PACKAGE AA00011840_00001
AGREEMENT_INFO ACCOUNT UF PROJECT UFDC
FILES


APPENDIX C
COMPUTER SIMULATION PROGRAM
A.l General
This program created the simulated decision environment for the
experimental runs. The program was written in BASIC, Version 17,
Digital Equipment Corporation System 10. It was run from a type
writer terminal, model DEC 33 TELETYPE, on line with a PDP 10.
A.2 Input
The fixed input to the program was the data of Table 3.2, (p. 39)
arranged as follows:
EM
M
D
i
S
004
5
18
20.5
1.00
009
5
9
20.0
1.25
017
5
22
20.0
0.75
024
5
15
20.0
1.75
032
5
6
20.5
1.00
038
5
15
20.0
1.25
051
5
11
20.5
1.50
070
5
12
20.0
0.75
076
5
21
20.5
1.00
078
5
10
20.0
1.75
082
5
14
20.0
1.25
085
5
21
20.5
1.50
097
5
16
20.5
1.00
no
5
22
20.0
0.75
121
5
16
20.0
1.25
128
5
12
20.5
1.50
142
5
20
20.0
1.75
146
5
8
20.0
1.25
155
5
23
20.0
0.75
163
5
20
20.5
1.50
80


ACKNOWLEDGEMENTS
I wish to express my gratitude to the University of Puerto Rico
for financing my graduate program; to my dissertation adviser, Dr.
Richard A. Elnicki, for his recurrent insistance and corrections
throughout the course of this work; to my co-adviser, Dr. Jack M.
Feldman, for his invaluable comments and stimulus; to the rest of
my committee, Dr. Christopher B. Barry, Dr. Thom J. Hodgson, and
Dr. Richard R. Jesse, for their help and suggestions at the various
stages of the research; and to my colleague and friend, Jose F. Colon,
for his enlightening suggestions during the early part of the project.
I also want to express special appreciation to my "parents'1 in
Gainesville, Mr. and Mrs. Bruce Ruiz, for their long, long hours of
companionship and friendship during my stay at the University of
Florida.
Most important, I thank my wife, Magui, for pushing me through
while taking the worst part; my sons, for the time they would have
rather spent with me; and my parents, for their ever present support
and counsel.
iv


57
are correlated, these should be considered simultaneously to obtain infor
mation about the "total effect of the experimental variables. In the case
of experimental MIS research, there is reason to believe that commonly
used criteria, such as cost performance and decision time, are correlated
[5, 9].
From the point of view of the MIS practitioner, these multivariate
results point to one conclusion: the format in which information is pre
sented can influence decision performance. The multivariate separate
and joint effects of the two format variables in this study, layout and
style, support this view. The specific directions of these effects are
discussed next.
5.3 The Univariate Effects
5-3.1 Effects Related to HI and H3
The influence of information layout on decision time was found to be
strong (5.1.2), as predicted in Hypothesis 1 (p. 28). The shorter deci
sion times for the subjects with due-date as opposed to I.D. ordered
reports v/ere expected, since the due-date ordering was more convenient in
the present problem. This hypothesis, however, was evaluated for two
reasons. The first was to demonstrate the importance of arranging the
information in a manner consistent with the way information is used. This
seemingly obvious observation appears to have been ignored in many reports
this author has had to use. The second reason was to prepare a basis
for Hypothesis 3 (p.29). There, it is proposed that long decision times
due to an inconvenient information layout can be reduced by introducing
a second format element, namely, the graphical style. The time dimension
added by the graphical style had the effect of reducing the need for the


76
EVENT
IDENT.
EXPECTED DUE-DATE
(MONTH-DAY)
009
5-29
032
5-26
128
6-01
155
6-12
168
6-07
Condition 9
EVENT
IDENT.
EXPECTED DUE-DATE
(MONTH-DAY)
032
5-26
009
5-29
128
6-01
168
6-07
155
6-12
Condition 10
EVENT 95% CONFIDENCE INTERVAL
IDENT. (FIRST DAY, LAST DAY)
009
5-27
, 5-31
032
5-25
, 5-28
128
5-30
, 6-04
155
6-11
, 6-13
168
6-04
, 6-10
Condition 11
EVENT 95% CONFIDENCE INTERVAL
IDENT. (FIRST DAY, LAST DAY)
032
5-25
, 5-28
009
5-27
, 5-31
128
5-30
, 6-04
168
6-04
, 6-10
155
6-11
, 6-13
Condition 12
EXPECTED DUE-DATES
EVENT
MAY
JUNE
EVENT
IDENT.
25 26 27
28 29
30
31 01 02 03 04 05
06 07 08 09
10 11 12
13 IDENT.
009
H
009
032
H
032
128
H
128
155
H
155
168
H
168
25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13
Condition 13


23
The only difference between the arrangement above and that in part C
of Figure 2.1 is the "layout" of the information in the report. The
term "layout" will be used here to refer strictly to the order in
which the information is arranged in the report. Two layout schemes
are considered: I.D. number ordering and expected due-date ordering.
Questions of interest. The format alternatives considered above
appear to have pros and cons from the point of view of the ease with
which the report can be used. The format variables layout and style
will be experimentally manipulated in an attempt to address the following
questions:
(1) Can information layout by itself affect decision
performance?
(2) Can information layout interact with presentation
style to enhance or reduce separate performance
effects of either layout or style?
2.3.3 Level of Detail
Given the probabilistic nature of our data, a wide range of levels of
detail can be provided in the "expected due-dates" reports. These could
range from point estimates to complete probability distributions of the
event occurrence times. On this subject, Conrath [8] proposes that
decision makers are not likely to think in terms of probability distribu
tions, and that they prefer to think in terms of, and use, point estimates.
His argument would suggest the use of point estimate forecasts as one level
of detail in this study. The question would remain, however, whether the users
in this decision context could benefit from additional information about the


8
Raw Data Treatment
FINISHED GOODS INVENTORY HISTORY
WEEK 1 OF MONTH 3 WEEK 2 OF MONTH 3
INVENTORY
LEVELS
Resinoid
R-Forced
Vitrifid
Resinoid
R-Forced
Vitrifid
MONDAY
0
371
0
0
120
481
TUESDAV
39
102
82
0
153
191
WEDNESDAY
0
0
198
0
202
0
THURSDAY
34
36
259
38
267
0
FRIDAY
71
84
393
79
138
38
STOCKOUTS
Resinoid
R-Forced
Vitrifid
Resinoid
R-Forced
Vitrifid
MONDAY
235
0
58
354
0
0
TUESDAY
0
0
0
423
0
0
WEDNESDAY
379
321
0
144
0
201
THURSDAY
0
0
0
0
0
121
FRIDAY
0
0
0
0
0
0
Statistically Summarized Treatment
FINISHED GOODS INVENTORY HISTORY
SUMMARY STATISTICS CALCULATED
FF.OM OPERATIONS FOR PERIOD
WEEK 1 OF MONTH 3 THROUGH WEEK 4 OF MONTH 3
Daily Inventory Levels
(End of Day) Stockouts
Resinoid R-
-Forced
Vitrifid
Resinoid
R-Force Vitrifid
Mean
23.25
140.30
92.85
Mean
171.30
38.20
123.70
Coef Var
6.28
4.18
7.97
Coef Var
5.63
14.77
7.09
Maximum
79. CO
371.00
431.00
Maximum
427.00
392.00
434.00
Range
79.00
371.00
481.00
Range
427.CO
392.00
434.00
breviated
samples
of two
"form
of presentation"
treatments
used by
Chervany and Dickson [9, p. 1338].
FINISHED GOODS INVENTORY HISTORY
SUMMARY STATISTICS CALCULATED
FROM OPERATIONS FOR PERIOD
WEEK I OF MONTH 3 THROUGH WEEK 4 OF MONTH 3
Mean
Coef Var
Maximum
Ranqe
Daily Inventory Levels
(End of Day)
Resinoid
23.25
6.28
79.CO
79.00
R-Forced
140.80
4.13
371.00
371.00
Vitrifid
92.85
7.97
431.00
481.00
Stockouts
Resinoid
171.30
5.63
427.00
427.00
R-Forced
38.20
14.77
392.00
392.00
Vitrifid
123.70
7.C9
484.00
434.00
B. A different "layout" for the information in the second report above.
Figure 1.2 Different formats of presentation


29
dimension of the choice space, and that
dimension becomes paramount in the de
cision process. ...Whether the attention
focusing attributes of data format are
the keys to the influence that format has
on choice is a question not yet resolved.
But the question would appear to be
sufficiently important that it should no
longer be ignored [8, p. 880].
The format variable that is expected to have "attention focusing at
tributes" in this case is the style variable (graphical versus tabular).
As such, the style factor will be the one analyzed in the evaluation
of this hypothesis.
3. Report layout and style can interact to enhance or reduce (H3)
the decision time effects of a particular layout or style.'
In this case, it is expected that the users of the I.D.
ordered reports in graphical style will have shorter de
cision times than the users of the same layout in tabular
style.
The objective in testing this proposition is to demonstrate the
existence of information format characteristics that may have joint
effects on decision performance. Here, the combination of the I.D.
ordering layout with the graphical style is expected to reduce the
long decision times associated with the absence of the convenient due-
date ordering.
The next two hypotheses relate to the level of detail of the
probabilistic information provided.
4. Users of probabilistic data can make effective use of (H4)
information beyond point estimates. In this case, it
is expected that the interval estimates users will make
more cost-effective decisions than the point estimates
users.
The interest in this hypothesis is twofold. First, its evaluation
should give an indication as to whether the users of this type of


UNIVERSITY OF FLORIDA
3 1262 08554 7171


CHAPTER 3
THE EXPERIMENT
3.1 Method
This chapter presents the details of the experiment that was con
ducted to evaluate the decision performance effects of four factors:
information layout, style of presentation, level of detail, and the
number of decision entities on the report. The material has bee'n
arranged as follows. Section 3.1.1 discusses the nature of the experi
mental subjects. The methods used for collecting and analyzing the
experimental data are presented in Section 3.1.2. A full description
of the experimental task is given in Section 3.2. Finally, the experi
mental results that should be expected for the hypotheses to be backed
up are discussed in Section 3.3.
3.1.1 Subjects
One-hundred sixty subjects participated in the experiment. The
subjects were undergraduate students in Business Administration who had
completed the first semester of introductory statistics at the Univer
sity of Puerto Rico, Mayaguez Campus. They were invited to participate
through announcements placed on bulletin boards and read in classrooms.
No monetary incentives were offered but the rate of volunteering was
high: an initial Usign-up" list yielded more than 200 subjects.
33


72
EXPECTED DUE-DATES
EVENT MAY JUNE EVENT
IDENT. 25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13 IDENT.
004 H 004
009 H 009
017 H 017
024 H 024
032 H 032
038 H 038
051 H 051
070 H 070
076 H 076
078 H 078
082 H 082
085 H 085
097 H 097
110 H 110
121 H 121
128 H 128
142 H 142
146 H 146
155 H 155
163 H 163
168 H 168
171 H 171
173 H 173
177 H 177
186 H 186
25 26 27 28 29 30 31 01 02 03 04 05 06 07 08 09 10 11 12 13
Condition 5


85
00470 PRINT "CHECKS ?"
60480 INPUT E$
00482 IF E$=" THEN 630
00485 E$=E$+"AAAA."
00490 L=INSTR(E$,".")/4-l
00502 IF ABS(L-IMT(L)) = 0 THEN 510
00504 GOSUB 2000
00506 GO TO 480
00510 FOR K=1 TO L
00520 Y=4*K-3
00525 Z=4*K-1
00530 K$=MID$(E$,Y,Z-Y+1)
00550 J=(IHSTR(S$,K$)+2)/3
00560 C$=C$+E$(J)+" "
00570 C=C+1
00580 IF A$(J) D$ THEN 610
00590 0$=0$+E$(J)+" "
00600 0=0+1
00610 NEXT K
00620 IF L =1 THEN 660
00630 PRINT "CHECKED: NONE"
00640 PRINT
00650 GO TO 730
00660 PRINT "CHECKED: ;C$;
00670 IF LEN(0$) 1 THEN 690
00680 0$="N0NE"
00690 PRINT DETECTED: ";0$
00700 PRINT
00710 C$=" "
00720 0$=" "
00730 NEXT I
00735 PRINT "***********"
00740 PRINT "******...FAVOR DE LLAMAR AL PROF. AMADOR....*******"
00751 INPUT T2$
00752 IF LEN(T2$) 1 THEN 754
00753 T2$="0"+T2$
00754 PRINT
00756 PRINT
00760 PRINT "******** SUMMARY OF RESULTS FOR THE RUN ********"
00770 PRINT
00780 PRINT C;"CHECKS @ $";STR$(Cl)+"/"+"CHECK = $";STR$(C*C1)
00782 C8$=STR$(C)
00783 IF LEM(C8$) 2 THEN 790
00784 IF LEN(C8$) 1 THEN 788
00785 C8$="00"+C8$
00786 GO TO 790
00788 C8$="0+C8$
00790 PRINT DO-O;"MISSES 0 $";STR$(C2)+"/"+"MISS = $";STR$((D0-0)*C2)
00795 PRINT
00800 PRINT "TOTAL COST = $"+STR$(C*Cl+(D0-0)*C2)
00802 T8$=STR$(C*C1+(DO-O)*C2)


CHAPTER 4
EXPERIMENTAL RESULTS
4.1 Introduction
The statistical results of the experiment are presented in this
chapter. Table 4.1 (p. 45) shows the cell means obtained for the three
performance variables and the five format opinion questions. The
results revealed significant differences among treatment means to
support five of the six research hypotheses.
4.2 Results
4.2.1 Effect of Layout on Decision Time
The first hypothesis, that information layout can reduce decision
time, was supported. As Table 4.1 shows, decision time was shorter for
the subjects receiving the due-date ordered reports, A^, than for
those receiving the I.D. ordered reports, A^ (13.8 versus 16.4 minutes).
A multivariate test on the three performance variables showed a sig
nificant layout main effect (F = 7.41, p < .00001).^ A univariate
test on the decision time variable also revealed a significant dif
ference between the two layout groups < A^, F = 13.35, p < .003).
A Scheffe post-hoc test revealed an even stronger relationship when
All multivariate F's presented here are based on 3 and 142 degrees
of freedom. All the univariate F's are based on 1 and 144 degree of
freedom.
44


21
A. Tabular style B. Tabular style
with I.D. layout with due-date layout
EVENT
1DENT.
EXPECTED DUE-DATE
(MONTH-DAY)
EVENT
IDENT.
EXPECTED DU:
(MQNTH-W
004
6-07
032
5-26
009
5-29
146
5-28
CI7
6-11
009
5-29
024
6-04
078
5-30
032
5-26
171
5-31
033
6-C4
051
5-31
051
5-31
185
5-31
070
6-01
070
6-01
076
6-10
128
6-01
073
5-30
082
6-C3
062
6-03
024
6-04
035
6-10
038
6-04
097
6-05
121
6-05
110
6-11
097
6-05
121
6-05
173
6-06
123
6-01
177
6-05
142
6-09
168
6-07
146
5-28
004
6-07
155
6-12
163
6-09
163
6-09
142
6-09
168
6-07
085
6-10
171
5-31
076
6-10
173
6-06
no
6-11
177
6-06
017
6-11
1B6
5-31
155
6-12
C. Graphical
style with I.D.
layout
EXPECTED DUE-DATES
EVENT
IDENT.
MAY JUNE
25 26 27 23 29 30 31 01 02 03 04 05 06 07 08 09
10 11 12 13
EVENT
1DENT.
004
H
004
009
H
009
017
H
017
C24
H
024
032
H
C32
038
H
C38
051
H
051
970
H
070
076
H
076
073
H
078
082
H
082
035
H
035
097
H
097
110
H
no
121
H
121
128
H
128
142
H
142
146
H
146
155
H
155
163
H
163
163
H
168
171
H
171
173
H
173
177
H
177
186
H
186
25 26 27 28 29 30 31 01 02 03 C4 05 06 07 03 09 1C 11 12 13
Figure 2.1 Three forms of presenting the expected due-dates information


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
INFORMATION FORMATS AND DECISION PERFORMANCE:
AN EXPERIMENTAL INVESTIGATION
By
Jose A. Amador
June 1977
Chairman: Richard A. Elnicki
Major Department: Management
This study examines some implications of the relationship
between information format and decision performance. A real-life
information-decision problem was abstracted to create a simulated
decision environment in which alternative forms of presenting
information relevant to the problem were manipulated and adminis
tered to 160 experimental subjects.
Multivariate and univariate analyses of the experimental data
indicated significant differences due to the experimental treatments.
Presentation style (tabular versus graphical) and information layout
(I.D. ordering versus due-date ordering) were found to have separate
and joint effects on decision performance. The style of presentation
had a strong influence on subject choice behavior. The level of
probabilistic information provided (point estimates versus interval
estimates) and the style of presentation had a joint effect on
IX


TABLE 5.3
Independent Variable
Dependent Variable
OTHER INTERACTION
Level of
Significance
¡ EFFECTS
Results
Related
Hypotheses
Line
No.
Information Layout
and Style
of Presentation
Layout Rating
.016
Subjects with the I.D. layout
rated it very low, except in
the case of those that also
had the graphical style.
H6
5.3.1
Information Layout,
Style of Presentation,
and Level of Detail
Layout Rating
060.
*
Ratings for the I.D. layout
were specially lower from
the point estimate subjects.
The difference was not sig
nificant for those subjects
who also received the graphical
style.
H6
5.3,2
Information Layout,
Style of Presentation,
and Level cf Detail
Format (1,2)
Rating
039,.004
Interval estimate subjects with
the I.D. layout had low ratings,
except in the case of those that
also received the graphical style.
H6
5.3.3
Information Layout,
Style of Presentation,
and Level of Detail
Over-all Format
Rating
.093
Same effect as above. The effect
was more marked among the many-
cecisi on-entities group.
H6
5.3.4


Copyright By
Jose A. Amador
1977


25
A. Tabular style
with I.D. layout
B. Tabular style
with due-date layout
EVENT 95% CONFIDENCE INTERVAL EVENT 95% CONFIDENCE INTERVAL
IDE.NT.
(FIRST DAY, LAST DAY)
IDEN7.
(FIRST CAY, LAST
004
6-06 6-09
.032
5-25 5-23
009
5-27 5-31
146
5-25 5-30
017
6-10 6-12
003
5-27 5-31
024
6-01 6-07
C73
5-27 6-02
03?
5-25 5-23
17!
5-28 6-03
038
6-02 6-05
051
5-29 6-03
051
5-29 6-03
1C6
5-29 6-03
070
5-31 6-02
128
5-30 6-04
076
6-09 6-12
070
5-31 6-02
073
5-27 6-02
082
6-01 6-05
032
6-01 6-05
024
6-01 6-07
035
6-09 6-14
038
6-02 6-06
097
6-04 6-07
121
6-03 6-07
110
6-10 6-12
168
6-04 -10
121
6-03 6-07
097
6-04 6-07
128
5-30 6-04
173
6-05 6-07
142
6-06 6-12
177
6-05 6-03
146
5-26 5-30
004
6-C5 6-03
155
6-11 6-13
142
6-06 6-12
163
6-07 6-12
163
6-07 6-12
163
6-04 6-10
085
6-09 6-14
171
5-28 6-03
07c
6-09 6-12
173
6-05 6-07
110
6-10 6-12
177
6-05 6-03
017
6-10 6-12
186
5-29 6-03
155
6-11 6-13
C. Graphical style with I.D. layout
S5% CONFIDENCE INTERVALS
EVENT MAY JUNE EVENT
IDENT.
25
26
?7
28
29
30
31
01
02
03
04
05
Go
07
03
09
10
11
12
13
IDENT.
004
H
H
K
H
GC4
009
H
H
H
H
H
009
017
H
H
H
017
024
H
H
H
H
H
H
H
024
032
K
H
H
H
032
C38
H
H
H
K
H
038
051
H
H
H
H
H
H
051
070
H
H
H
07C
076
H
H
H
H
076
078
H
H
H
H
H
H
K
078
082
M
H
H
H
H
082
085
H
H
H
H
H
085
097
H
H
H
H
097
110
K
H
H
no
121
H
H
II
H
H
121
128
H
H
H
H
H
H
123
142
H
H
H
H
H
H
H
142
146
H
H
h
H
H
145
155
H
H
H
155
163
H
H
H
H
K
H
163
168
H
H
H
H
H
H
H
163
171
H
H
H
K
, H
H
H
171
173
H
H
H
173
177
H
H
H
11
177
186
H
H
H
11
H
H
185
25
26
27
?S
29
30
31
01
02
03
04
05
06
07
08
C9
10
11
12
13
Figure 2.3 Three forms of presenting the interval estimates information


87
01300 PRINT "3. CUAN APR0PRIAD0 FUE EL FORMATO DEL REPORTE AL MOMENTO"
01305 PRINT DE ANOTAR LA INFORMACION SOBRE LOS EVENTOS COTEJADOS"
01310 PRINT Y DETECTADOS?"
01320 INPUT A3
01322 IF A3 5 THEN 1324
01323 IF A3 =1 THEN 1330
01324 GOSUB 2000
01326 GO TO 1320
01330 INPUT :3, C3
01340 C3=C3+A3
01370 PRINT
01380 PRINT "4. CUAN APROPRIADO FUE EL DETALLE DE LA INFORMACION"
01385 PRINT PROBABILISTICA SOBRE LA POSIBLE FECHA DE OCURRENCIA"
01390 PRINT DE CADA EVENTO?"
01400 INPUT A4
01402 IF A4 5 THEN 1404
01403 IF A4 =1 THEN 1410
01404 GOSUB 2000
01406 GO TO 1400
01410 INPUT :4, C4
01420 C4=C4+A4
01450 PRINT
01460 PRINT "5. CONSIDERANDO TODOS LOS FACTORES, COMO DE APROPRIADO"
01465 PRINT ENCONTRO USTED EL FORMATO DE ESTE REPORTE?"
01480 INPUT A5
01482 IF A5 5 THEN 1434
01483 IF A5 =1 THEN 1490
01484 GOSUB 2000
01486 GO TO 1480
01490 INPUT :5, C5
01500 C5=C5+A5
01530 PRINT
01540 PRINT HEMOS CONCLUIDO EL EXPERIMENTO "
01545 PRINT GRACIAS POR SU COOPERACION "
01550 R8$=STR$(R)
01552 IF LEN(R8$) 1 THEN 1560
01554 R8$="0"+R8$
01560 Q8$=" "+STR$(A1)+" "+STR$(A2)+" "+STR$(A3)+" "+STR$(A4)+" "+STR$(A5)
01561 S0$=STR$(S0)
01562 IF LEN(SO$) 2 THEN 1570
01563 IF LEN(S0$) 1 THEN 1566
01564 S0$="00"+S0$
01565 GO TO 1570
01566 S0$="0"+S0$
01570 TO$=SO$+" "+R8$+" "+T2$+" "+C8$+ "+T8$+Q8$
01600 SET :1,R; :2,R; :3,R; :4,R; :5,R; :6,R
01610 WRITE :1,C1
01620 WRITE :2,C2
01630 WRITE :3,C3
01640 WRITE :4,C4
01650 WRITE :5,C5


28
probabilistic data is presented influences the choice behavior of the
user. It was assumed this could be the case with the tabular versus
graphical formats in the current study. The graphical format appears
to "bring out" more vividly the information, especially in the case
of the interval estimates (see Figure 2.3, p. 25). The measure used
for choice behavior was the number of checks performed by the decision
maker, disregarding which were successful and which were not.
2.6 Research Hypotheses
The questions raised above are now presented as six testable
hypotheses. Three of the hypotheses relate to format, two to the
level of detail, and one to the number of decision entities in the
report. The hypotheses relating to format are presented first.
1. The layout or physical order of the information in a (HI)
report can reduce decision time. In this case, it is
expected that the users of the due-date ordered reports
will have shorter decision times than the users of the
I.D. ordered reports.
This hypothesis was not found to have been considered in the
MIS literature, either in field or laboratory work. There are many
ways in which the same information can be arranged in a report. Even
though the "best" way may usually be considered "apparent" or the
issue simply "unimportant," this may not alv/ays be the case.
2. The format in which probabilistic data is presented as a (H2)
basis for choice can influence choice. In this case, it
is expected that the graphical report users will choose
to make more checks than the users of the tabular reports.
This hypothesis was suggested by Conrath [8] but not statis
tically demonstrated in his paper. He states:
Apparently format has the characteristic
that it can focus one's attention on one


01660 WRITE :6,Q0
01670 PRINT C1;C2;C3;C4;C5;Q0
01672 PRINT
01674 PRINT T0$
01680 SET :8,S0
01685 WRITE :8,T0$
01690 GO TO 2050
02000 PRINT
02005 PRINT "? INPUT DATA NOT IN CORRECT FORMPLEASE RETYPE"
02010 RETURN
02050 END


91
informe con un nmero de identificacin de tres (3) dgitos.
Ai lado derecho del nmero de identificacin de cada evento
usted hallar informacin sobre la posible fecha de ocurren
cia de ese evento.
2. Los eventos han de ocurrir durante el perfodo de 20 das
comprendido entre mayo 25 y junio 13.
3. Cada evento ha de ocurrir en solo uno de esos veinte das
y solamente puede ser "detectado" si es "cotejado" en ese
da.
4. Cada "coteio" gue se hace sobre un evento le cuesta a la
5. Cada evento que sucede y pasa sin ser detectado le cuesta
a la gerencia $5.00.
6. £1 objetivo suyo como gerente es conseguir un balance entre
el costo de "cotejar" y el costo de "no detectar" los eventos
de manera gue se minimize la suma de estos dos costos.
Uso del Simulador
Sus decisiones "diarias" sern comunicadas a un simulador. Esto se
har por medio del teletipo, el cual le pedir a usted que le informe qu
eventos desea cotejar en cada uno de los 20 das de la simulacin. A su
vez, el simulador lo mantendr a usted informado sobre cules de los eventos
"cotejados" son "detectados". El simulador funciona como sigue:
1. Al principio del experimento, el teletipo escribir:
TODAY IS 5-25 (mayo 25)
Inmediatamente, el simulador le indicar que est listo para
recibir sus instrucciones sobre qu eventos cotejar ese da
escribiendo:
CHECKS ?
Usted deber entonces escribir el nmero de identificacin
para cada evento que desea cotejar ese da, dejando un
espacio entre cada evento y oprimiendo "return" cuando haya
entrado el ltimo evento. Los 3 dgitos del nmero de identi
ficacin deben ser escritos para cada evento gue desea cote
jar. De otra manera el simulador escribir el mensaje:
? INPUT DATA NOT IN CORRECT FORM PLEASE RETYPE


CHAPTER 6
SUMMARY AND POSSIBLE EXTENSIONS
The present study has considered some of the implications of the
relationship between information format and decision performance. A
specific information-decision problem was abstracted to create a simu
lated decision environment within which alternative forms of presenting
information relevant to the problem were experimentally manipulated.
Six hypotheses were tested in relation to the effects of the information
format treatments on subject performance. The experimental data supported
five of the six hypotheses. As is always the case with empirical re
search, however, a number of questions can be raised in connection with
the observed results. Some questions result from inquiring into the
limitations of the present study. Others follow logically from the re
sults.
Among the limitations, there is the problem of having used student
subjects as surrogates for managers [13]. The actual managers in the
real-life problem modeled could have served as subjects in a field study.
This, of course, may bring about other complications, in particular,
problems of experimental control. Van Horn states:
The unifying theme of field tests is sad
stories. In every one, operational con
siderations (understandably) dominate
test conditions. As soon as a conflict
arises, the test yields. Even if a test
proceeds to completion, endless arguments
arise over interpretation of the results
[28, p. 175].
63


65
shed light into such questions as, "At what point would it have been
appropriate to have a format revision?"
Another area that appears to need more consideration is the analysis
of interaction effects among information structure characteristics [5, 12],
In this study, an interaction was found between the level of detail and
format, suggesting that different levels of detail may be more easily
processed with different styles of presentation. The validity of findings
such as this one should be further investigated in other decision contexts.
Finally, research relating empirical MIS findings to current trends in the
theory of human information processing may be useful in providing a better
understanding of the results observed.


93
4. Al final del ltimo da (junio 13) el simulador escribir un
resumen de su ejecutoria, incluyendo una lista de las fechas
en que realmente sucedieron cada uno de los eventos como
en el ejemplo que sigue:
******** SUMMARY OF RESULTS FOR THE RUN. ********
54 CHECKS @ $l/CiECK = $54
6 MISSES @ $5/MlSS = $30
TOTAL COST = $84
ACTUAL EVENT DATES
EVENT ID. DATE
004 6-6
009 5-28


168 6-4
171 5-31
173 6-5
177 6-4
186 6-1.
5. Usted deber revisar este resumen para verificar que la infor
macin en cuanto a eventos cotejados y detectados es correcta.
Una vez hecho esto deber oprimir la tecla "return" para que
el simulador prosiga.
6. El simulador entonces proceder a escribir una serie de pre
guntas que usted deber contestar siguiendo las instrucciones
por el teletipo. Una vez contestadas las preguntas habremos
concluido el experimento.


TABLE 3.3
EFFECTS
PREDICTED BY
THE RESEARCH
HYPOTHESES
Variant of Model of
Experimental
Hypothesis
Section 2.7, p. 31
Variables
Measure
Effect
Main Effects
Kl
P f(F|l.DE.OM,CIS*)
P
Decision time
Subjects with due-date ordered
reports should have shorter
F
I.D. ordered vs.
decision times than subjects
due-date ordered
reports
with I.D. ordered reports.
H2
P f(FIL.DE.DM.CIS*)
P
Number of Checks
Subjects with the graphical style
should make more checks than
F
Tabular vs.
graphical reports
subjects with the tabular style.
H4
P f{L|FfDE,DM,CI5')
P
Total cost
Subjects with Interval estimates
should have lower costs than
L
Point estimates vs.
Interval estimates
Subjects with point estimates.
Interactions
H3
P f(FiL.DE.DM.ClS*)
P
Decision time
Subjects with I.D. ordered reports
in graphical style should have
F
1.0. vs. due-date
shorter decision times than those
ordered reports,
with the I.D. ordered reports In
tabular vs.
graphical reports
tabular style.
H5
P f(F,UOE,DM,CIS')
P
Decision time
Subjects with Interval estimates In
graphical style should have shorter
F
Tabular vs,
decision times than those with inter
graphical reports
val estimates in tabular style.
*
L
Point vs. interval
estimates
H6
P f(F,DC|l,DM,CIS*)
P
Decision time,
The time and cost performance of
total cost
subjects 1n the many-declslon-entltles
group should be more sensitive to
F
I.D. vs. due-date
ordered reports,
tabular vs.
graphical reports
format than that of the subjects 1n
the few-decision-entities group.
DE
Many vs. few
decision entities