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An experimental investigation of managers' escalation errors

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Title:
An experimental investigation of managers' escalation errors
Creator:
Radtke, Robin Rae, 1963- ( Dissertant )
Ajinkya, Bipin B. ( Thesis advisor )
Snowball, Doug ( Reviewer )
Yost, Jeffrey A. ( Reviewer )
Randles, Ronald H. ( Reviewer )
Place of Publication:
Gainesville, Fla.
Publisher:
University of Florida
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Copyright Date:
1992
Language:
English
Physical Description:
vi, 152 leaves ; 29 cm.

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Subjects / Keywords:
Assets ( jstor )
Bidding ( jstor )
Business management ( jstor )
Employee supervision ( jstor )
Financial investments ( jstor )
Information asymmetry ( jstor )
Investment strategies ( jstor )
Lotteries ( jstor )
Return on investment ( jstor )
Signals ( jstor )
Accounting Thesis, Ph.D.
Dissertations, Academic -- UF -- Accounting
Executives -- Pyschology
Managerial accounting
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bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

Notes

Abstract:
An escalation error results when the manager continues with a course of action that has proven to be nonoptimal. These errors can represent costly losses to the firm. The purpose of this study is to investigate the source of managers' escalation errors. It was hypothesized that when the manager commits an escalation error, he may be attempting to conceal his previous incorrect choice and thereby protect his reputation. the protection of the manager's reputation may provide greater benefits to him than the benefits resulting from making a correct choice. A principal-agent model was developed to examine how the reputation value of the manager influences the manager's decisions in an escalation error scenario. Four factors were posited to directly influence escalation behavior: the talent level of the manager, the presence of a monitor, the time at which the manager discovers he has made an escalation error and the difficult level surrounding the manager's decision. The model was modified to fit an experimental setting using student subjects as surrogates for actual managers and supervisors. A manager was expected to escalate when he was untalented, had no monitor present, discovered that he had made an escalation error at a late stage and faced difficult environmental factors. The hypotheses were tested using a logit model. The results for the three independent variables--the manager's talent level, the time at which the manager discovers he has made an escalation error and the difficulty level surrounding the manager's decision--support the predictions. the unexpected effect of the presence of a monitor was not supported. A revised experiment was run with some modifications to re-test the effect of the monitor. The results of these sessions again showed an insignificant effect for the monitor construct. These experimental results may help researchers gain new insight into the escalation error problem in designing further studies.
General Note:
Typescript
General Note:
Vita.
Thesis:
Thesis (Ph. D.)--University of Florida, 1992.
Bibliography:
Includes bibliographical references (leaves 150-151).

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AN EXPERIMENTAL INVESTIGATION OF
MANAGERS' ESCALATION ERRORS

















By

ROBIN RAE RADTKE


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


1992















ACKNOWLEDGEMENTS


I would like to thank the members of my dissertation committee for all of their help: Professor Bipin Ajinkya (Chairman), Professor Doug Snowball, Professor Jeffrey Yost and Professor Ronald Randles. Their guidance and words of wisdom were greatly appreciated throughout this project. Additionally, I am grateful to all the professors from the Fisher School of Accounting and the Department of Finance, Insurance and Real Estate who supplied me with class time to recruit subjects. My experimental assistants, Tom Bristow, Steve Cox, Dominique Marchand, Sean Robb, Sherry Ropp and Sean Williams deserve praise for helping me administer the experiment. The student subjects who participated in the experiment, most of whom were very good natured, are acknowledged for giving their time and effort. Last but not least, I would like to thank my family and friends for their love and support.















TABLE OF CONTENTS

page

ACKNOWLEDGEMENTS . ii

ABSTRACT . v

CHAPTERS

1 INTRODUCTION AND BACKGROUND . 1

Introduction . 1 Motivation for the Research . 3
Overview of Research Method and
organization . 5

2 THE ESCALATION ERROR PROBLEM AND RELATED
STUDIES . 6

Introduction . 6 The Accounting Issue . 7 The Principal-Agent Setting . 9 The Issue of Information Asymmetry . 12 The Formation of the Manager's Reputation . 14 Related Escalation Error Literature . 15 The Present Research . 20

3 DEVELOPMENT OF MODELS AND HYPOTHESES . 22

Introduction . 22 The Principal-Agent Model . 22 The Information System . 27 The Effect of the Manager's Talent Level . 31
Information Asymmetry Effects of the
Monitoring Variable . 33
The Effects of Timing of Information
Disclosure . 36
Effects of the Environment . 37 The Decision Model . 38 Supplementary Analyses . 39


iii









4 RESEARCH DESIGN AND DATA ANALYSIS METHODS . 40

Introduction. 40 Experimental Design . 40
overview . 40 Experimental Task . 41 Subjects . 48 Incentive Structure . 50
Operationalization of Independent
Variables . 51
Measurement of Dependent Variable . 54
Data Analysis Methods . 55
The Decision Model . 55 Predicted Relationships . 57 Supplementary Analyses . 58
Summary . 62

5 RESEARCH RESULTS . 67

Introduction . 67 Results of Hypothesis Testing . 67 Additional Testing . 74
Supplementary Analyses . 74 Manipulation Checks . 81 Results for Revised Experiment . 86
Summary . 92

6 SUMMARY AND CONCLUSIONS . 112

Summary of the Study . 112 Summary and Discussion of Results . 112 Contributions and Implications . 116
Contributions to Managerial Accounting
Research.- - .: . 116
Implications for Understanding
Escalation Errors . 118
Limitations . 120 Directions for Future Research . 120

APPENDICES

A EXPERIMENTAL PROTOCOL . 122

B EXPERIMENTAL INSTRUMENTS . 126

C CALCULATION OF LOGICAL PAYOFFS AND BIDS . 146

REFERENCES . 150

BIOGRAPHICAL SKETCH . 152















Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

AN EXPERIMENTAL INVESTIGATION OF
MANAGERS' ESCALATION ERRORS

By

Robin Rae Radtke

August 1992

Chairman: Bipin B. Ajinkya Major Department: Accounting

An escalation error results when the manager continues with a course of action that has proven to be nonoptimal. These errors can represent costly losses to the firm. The purpose of this study is to investigate the source of managers' escalation errors.

It was hypothesized that when the manager commits an escalation error, he may be attempting to conceal his previous incorrect choice and thereby protect his reputation. The protection of the manager's reputation may provide greater benefits to him than the benefits resulting from making a correct choice. A principal-agent model was developed to examine how the reputation value of the manager influences the manager's decisions in an escalation error scenario. Four factors were posited to directly influence escalation behavior: the talent level of the manager, the








presence of a monitor, the time at which the manager discovers he has made an escalation error and the difficulty level surrounding the manager's decision.

The model was modified to fit an experimental setting

using student subjects as surrogates for actual managers and supervisors. A manager was expected to escalate when he was untalented, had no monitor present, discovered that he had made an escalation error at a late stage and faced difficult environmental factors.

The hypotheses were tested using a logit model. The results for the three independent variables--the manager's talent level, the time at which the manager discovers he has made an escalation error and the difficulty level surrounding the manager's decision--support the predictions. The expected effect of the presence of a monitor was not supported. A revised experiment was run with some modifications to re-test the effect of the monitor. The results of these sessions again showed an insignificant effect for the monitor construct. These experimental results may help researchers gain new insight into the escalation error problem in designing further studies.















CHAPTER 1
INTRODUCTION AND BACKGROUND


Introduction


This dissertation investigates the escalation error

phenomenon. An escalation error results when an individual continues with a course of action that has proven to be nonoptimal for the individual. Behavioral studies constitute the bulk of the previous research in this area.1 These studies have isolated several factors associated with escalation errors. The purpose of this study is to examine the causes of escalation errors from an accounting standpoint.

The causes of escalation errors cannot be identified without a thorough understanding of the escalation error problem. The definition of an escalation error must be applied carefully to each case, as what constitutes nonoptimality, and evidence of nonoptimality, will vary between cases. In general, negative feedback that leads an individual to believe an alternative course of action would produce a more favorable outcome constitutes evidence of nonoptimality. If after receiving this information the

1 See Brockner (1992) for a good review of this literature.










individual continues with the originally chosen course of action, an escalation error is said to exist.

Escalation errors are not unique to any one setting.

The Vietnam War is a prominent example that demonstrates how potentially disastrous an escalation error can be. The following memo emerged in the early stages of the conflict:

The decision you face now is crucial. Once large
numbers of U.S. troops are committed to direct combat, they will begin to take heavy casualties in a war they
are ill-equipped to fight in a non-cooperative if not downright hostile country-side. Once we suffer large
casualties, we will have started a well-nigh
irreversible process. our involvement will be so great
that we cannot--without national humiliation--stop
short of achieving our complete objectives. of the two possibilities, I think humiliation would be more likely
than the achievement of our objectives--even after we
have paid terrible costs. (Memo from George Ball to
President Lyndon Johnson, July, 1965; source: The
Pentagon Papers, 1971. )2

This memo shows the sentiments of Ball and undoubtedly many others. The war continued on for many years, however, even with these strong assertions of the impending horrors ahead. This example shows the depths to which individuals can become entrenched in escalation errors and points out the importance of recognizing negative feedback to avoid an imminent escalation error.

Escalation errors in business may be very costly as well. Many business decisions could lead to escalation errors, but one in particular is the focus of this study.


2 This example is referenced in Staw (1976, 1981) and Staw and Fox (1977) as an important case of an escalation error.










The choice between alternative investment projects, or more specifically the capital budgeting replacement decision, is examined. This scenario provides the opportunity to investigate the escalation error phenomenon in a business setting. By doing so, the determinants which cause a manager to engage in escalation behavior may be isolated and the relationships between these determinants can be investigated to provide additional insight into the problem of escalation errors.


Motivation for the Research


When capital budgeting decisions are made with

inappropriate information, an inefficient allocation of the firm's available capital results which is inconsistent with the business goal of maximizing firm value. Consequently, the issue of determining what causes the manager to make these decision errors is of interest. Empirical evidence suggests that escalation errors also occur in business settings.3 Explanations from the body of psychological

3 DeBondt and Makhija (1988) and Statman and Sepe (1989) are examples of empirical studies. Both of these studies look at share price movements in reaction to the specified events. DeBondt and Makhija test the validity of the sunk cost hypothesis as a basis for committing escalation errors in the context of the U.S. nuclear power program. They examine the market reaction to all plant completions and cancellations (over $50 million) prior to March 1984. Their results are mixed and do not support a 'powerful' sunk cost effect. They point to prudence reviews ordered by U.S. Public Service Commissions, however, as anecdotal evidence of costs incurred and supported by the sunk cost hypothesis. Statman and Sepe examine the reaction











research present many theories for the occurrence of escalation errors. Kanodia, Bushman and Dickhaut (1989) offer an alternative explanation based on economic rationality.

Behavioral studies have tested the hypothesized

psychological factors. Many of these studies have actually used business or investment decision contexts. 4 The current study incorporates factors of interest from a managerial accounting standpoint (agency aspects) and represents an empirical test of the Kanodia, Bushman and Dickhaut (1989) theory. By designing this study with accounting and economic factors specifically in mind, additional insight into understanding escalation errors in terms of accounting can be gained.

The variables of interest in this study that have not been previously investigated (in an experimental study) are the talent level of the manager, the necessity and the impact of information asymmetry, the effect of the timing of information disclosure and the difficulty level surrounding the manager's decision. In an experimental setting each of



of the market to project termination announcements. They find the mean market-adjusted return to be positive. They conclude that on average shareholders consider project termination announcements good news, because managers will no longer throw good money after bad (and are finally abandoning the sunk costs of failed projects).
4 Staw (1976) and Staw and Fox (1977) both use the allocation of research and development funds between two competing divisions as their financial decision case.








5

these variables can be manipulated so a fully crossed design can be established. This allows the impact of each factor to be analyzed. As a result, issues more specifically related to accounting can be investigated in the escalation error scenario.


Overview of Research Method and Organization


The impact of the variables of interest on the

escalation error decision was examined in an experimental study. The true purpose of the study was disguised such that subjects knew only that they were involved in an experiment dealing with investment project choices. Subjects earned lottery tickets based on decisions made in the study. The tickets were entered into a lottery with seven cash prizes.

The chapters are organized as follows. Chapter 2 discusses the escalation error problem and related literature. The research models and hypotheses used in the current study are developed in Chapter 3. Chapter 4 describes the research design used to test the hypotheses. The results of the experiment are described in Chapter 5. Chapter 6 includes a summary of the study and conclusions.














CHAPTER 2
THE ESCALATION ERROR PROBLEM AND RELATED STUDIES Introduction


The discussion in this chapter focuses on the problem of managers committing escalation errors when making investment project or capital budgeting replacement decisions. The accounting issue is discussed first. An analysis of relevant and irrelevant accounting information governs how the manager should choose between alternative capital assets. If the manager allows irrelevant accounting information such as sunk costs to influence his choice, he may commit an escalation error and stray from the optimal choice. The framework for the current study is presented next. This includes a discussion of the principal-agent setting and the related issues of information asymmetry and the formation of the manager's reputation. This framework provides the setting within which a manager can justify an escalation error in terms of a benefit to his reputation value. The chapter then considers the related escalation literature in the accounting and psychology fields. Finally, the link between the adopted framework and the current study is presented.










The Accounting Issue


As discussed in Chapter 1, an escalation error results when an individual continues with a course of action that has proven to be nonoptimal for the individual. The individual of interest from an accounting standpoint in this study is the manager. The manager continually makes important decisions that affect both the well being of the firm and his career. The accounting information the manager has at hand governs his choices. Appropriate use of this information should lead to optimal choices. Misuse of this information may lead to escalation errors. Determining what information the manager should use and how he should use it is the crux of this discussion.

Accounting information for managerial purposes consists of projected and historical data. An old management accounting adage states that only relevant information should be used in the decision making process from the viewpoint of the firm. Information is considered irrelevant when it is equal for all decision alternatives. Thus, historical data from past decisions is considered irrelevant information. Past costs and revenues may be useful in predicting future costs and revenues, but have no direct relevance for future decisions



I See Horngren and Foster (1987) chapter nine for a good discussion of this issue.










One problem facing the manager on a recurring basis is the capital budgeting replacement decision. This decision entails the replacement of an old capital asset (such as a machine) with a new asset. once the decision to replace an old asset has been made, the primary issue becomes what type of new asset to purchase. Many cost figures are available

when considering this problem They include the cost of the new asset, the disposal value of the old asset, the purchase price of the old asset and the book value of the old asset. Only the relevant costs in the above list should be utilized.

If several new assets are being considered for

purchase, then the cost of the replacement asset will vary across decision choices and as such is relevant information. The disposal value, the purchase price and the book value of the old asset do not vary across current decision alternatives and are irrelevant information. The disposal value is equal to the expected proceeds of the sale of the old asset. The purchase price and the book value of the old asset are based on historical information. The purchase price of the old asset is considered a sunk CoSt'3 since

2 Tax considerations inherently play a role in the capital budgeting replacement decision. In the current study tax issues are ignored to make the analysis more tractable.
3 Sunk costs are defined as costs which have been incurred in the past and cannot be changed by future actions. As such, sunk costs are always irrelevant. Individuals, and managers in particular, often fixate on










the cost of the old asset has already been incurred and cannot be recovered (except through productive use of the asset). The book value of the old asset (the purchase price of the old asset less accumulated depreciation) is also irrelevant information since it is a meaningless combination of a historical cost and an arbitrary accumulated expense account. Therefore, once the manager has decided to replace an old asset, the only relevant information he should consider is the cost of the replacement asset . 4,5


The Principal-Agent Setting


From a normative standpoint, the analysis of the

relevance of various pieces of accounting information is always undertaken from the point of view of the firm. When the manager uses the appropriate relevant information, the


sunk costs since they feel responsible for the capital outlay. This fixation can lead to escalation errors when the manager refuses to replace an old asset that has not earned an acceptable return on the initial investment.
4 The other cost figures mentioned may be important in making the decision of when to replace the old asset. They would also be salient in a setting where tax considerations are involved. In the current study however, neither condition is applicable. Once the manager realizes the old asset should be replaced, the decision between replacement assets is the relevant issue in the escalation error scenario.
5 In a broader context, many other relevant items are considered by the manager when making the asset replacement decision. such factors as differences in technologies and production capacities between potential replacement assets are obviously evaluated. Additionally, purchase terms and agreements with different companies may enter into the decision.










firm value is maximized. This analysis leaves no room for the commitment of escalation errors. Empirical evidence suggests, however, that escalation errors do occur in business settings. 6 The proposed framework based upon a principal-agent setting recognizes the opportunity for the occurrence of escalation errors.

In the principal-agent setting, the economic relevance of information may change when agency costs between principals (owners) and agents (managers) are considered. In a strict principal-agent problem, agents attempt to maximize their personal utilities. In the pure context of the business decision, on the other hand, the manager should attempt to maximize the utility of the firm, since the manager is hired as an agent of the firm and takes on the responsibility of acting in the best interests of the firm. In some cases the personal utility of the manager and the utility of the firm may not be maximized by the same course of action. When the manager puts the maximization of his personal utility before that of the firm, a divergence between these two utilities may lead to suboptimal decisions for the firm. Within this context an escalation error is defined as continuing with a course of action that is nonoptimal for the firm, but optimal for the manager.




6 See DeBondt and Makhija (1988) and Statman and Sepe (1989) for examples of empirical studies.








11

The maximization of personal utility on the part of the manager is in accordance with the manager's desire to establish a reputation. This is supported by the proposition of Holmstrom and Ricart i Costa (1986) that the manager's compensation is a function of both the firm value and the manager's reputation value. They assert that some sort of contracting is necessary in order to prevent the manager from fixating solely on his personal utility. They develop a model of the manager's reputation based on learning. Once the manager realizes that his performance is used as a signal of his competence, he tries to influence the evaluation process through his choice of actions. A dilemma arises when the financial value and reputation value of his actions are different. The difference in these values causes the preferences of managers and shareholders (owners) to diverge.

From the point of view of the firm, investment projects should be evaluated on the basis of net present value. 7 Narayanan (1985) posits that managers, on the other hand, often prefer investment projects that pay off quickly, and look to additional measures such as the payback criterion


7 The net present value analysis is the generally
accepted method in capital budgeting. This coincides with the firm's objective to maximize future inflows given a limited amount of resources. Many alternative analysis techniques using cash flows, net incomes, interest rates and time periods also exist. The true value of an investment project to the firm is not disclosed, however, when using these alternative methods of analysis.










for justification. By choosing investment projects with earlier payoffs, managers are attempting to accelerate the enhancement of their human capital value. This behavior could be detrimental to the firm if those projects that have the shortest payback period are not the projects with the highest net present value (given equivalent risk). The manager may then be sacrificing long-term financial benefits of the firm for short-term gains to his reputation value. The escalation error scenario represents a situation where the financial value and reputation value of investment project decisions may be inversely related. If the manager chooses and continues with the nonoptimal project in attempts to receive quicker payoffs, his reputation value may be enhanced, while the firm value decreases in the long run. This study investigates under what circumstances the manager's reputation value is more important to him than the firm value in the escalation error scenario.


The Issue of Information Asymmetry


In a principal-agent setting the issue of information asymmetry is particularly important. Information asymmetry exists when one individual (usually the agent) has more information than another (usually the principal). In this case the agent may be motivated to use his superior information to his benefit, at the expense of the principal. In the current scenario, if the manager has private










information on which to base his asset replacement or investment project decision, he may be able to act nonoptimally from the point of view of the firm without

direct detection.8 Without information asymmetry the incentive for the manager to commit escalation errors disappears. In a world of only public information the interests of the manager and the firm coincide and the manager cannot form a reputation spuriously. consequently, escalation errors may be viewed as a cost of the agency relationship.

Narayanan (1985) shows that only under asymmetric information does the manager have incentives to make suboptimal decisions which are consistent with short-term goals instead of long-term goals. Suboptimal decisions may yield short-term profits, but are not in the best interests of the firm in the long-term. In garnering short-term profits the manager is attempting to bolster his reputation, and therefore his wages. This is an example of a case in which the personal utility of the manager becomes more important to him than the utility of the firm under asymmetric information. Without information asymmetry the agency contract causes the personal utility of the manager and the utility of the firm to coincide. Consequently, for


8 A device such as a monitor, which reports
information to the principal independent of the agent, may be used to reduce the level of information asymmetry between the two parties.










this study, information asymmetry is considered a prerequisite for committing escalation errors.


The Formation of the Manager's Reputation


Since multiple studies suggest the manager may be

concerned about his reputation in an information asymmetric

setting,9 the formation of the manager's reputation is an important issue. Kreps and Wilson (1982) show how imperfect information promotes reputation formation. They posit that uncertainty of players about the payoffs of other players is the simplest type of imperfect information that yields reputation formation in finitely repeated games. This is not an extreme condition, as players' payoffs typically are not common knowledge.

The theory of reputation formation under imperfect

information is tested experimentally by Camerer and Weigelt (1988). They posit that the behavior of players in an experiment can be predicted by a sequential equilibrium model of reputation formation in an incomplete information repeated game. Evidence of reputation-building is found in all of their experimental sessions of an abstracted lending game. They conclude that incomplete information about a player's type (or privately known characteristics) leads




9 See Holmstrom and Ricart i Costa (1986) and Narayanan (1985).








15

other players in the game to form beliefs about the player's type. These beliefs become the player's reputation.10

Imperfect or incomplete information is usually present in a typical principal-agent setting. In the current context, managers invariably have more information available to them than do the owners of the firm. This condition lends itself to reputation formation on the part of the manager and to possible escalation errors if there are apparent gains to the manager's personal utility.


Related Escalation Error Literature


Kanodia, Bushman and Dickhaut (1989) model the

escalation error problem. They posit that the manager may engage in escalation behavior in an attempt to maintain uncertainty about his true management talent. When others can only infer the talent level of the manager from his actions and the resulting consequences, these actions acquire a reputation value." Consequently, the manager will avoid switching to an alternative (superior) course of


10 In the Camerer and Weigelt (1988) experiment some players were borrowers and some were lenders. It was posited that borrowers should be concerned about establishing a good reputation for repayment of funds borrowed in order to secure future amounts. The results showed that the majority of the borrowers did not renege at the first opportunity they had, or as often as predicted. This constitutes evidence of reputation-building behavior.
11 This condition fits the imperfect or incomplete
information criteria of Yreps and Wilson (1982) and Camerer and Weigelt (1988).










action to avoid admitting a past mistake and damaging his reputation as a manager. By maintaining the appearance of good management, the manager's opportunities in a labor market should improve. This theory of the reputation value of the manager is an economic rationale for escalation errors.

Other studies have investigated escalation errors from a behavioral standpoint. These studies do not explain escalation errors in terms of an individual's reputation value, but rather in terms of the psychological effects of various factors on the individual's attempt to rationalize his errant behavior. Empirical evidence suggests that individuals are generally prone to take sunk costs into account and commit escalation errors when they make decisions. Staw (1976) states that individuals seek to maintain or restore the appearance of rationality to their previous behavior. This is known as self-justification theory. The theory posits that individuals cognitively distort negative consequences into a perceived positive outcome. Therefore, an individual who has made a choice that led to negative consequences may continue in that course of action in an attempt to justify his prior behavior or to demonstrate the ultimate rationality of his original course of action. The important factors behind the theory of self-justification in the investment decision are posited to be the level of personal responsibility an individual had











for the decision and the outcomes resulting from the decision. In an experimental setting Staw shows that business school students invest substantially greater amounts of resources when they are personally responsible for previous negative outcomes. He concludes that they are justifying their actions to themselves by attempting to appear competent in previous actions as opposed to future actions.

Staw and Fox (1977) investigate the persistence of the escalation process over time in an experimental setting. They design an experiment in which the commitment of resources to a course of action is the dependent variable and the independent variables are personal responsibility, efficacy of resources and time. They replicate the personal responsibility effect of Staw (1976) using undergraduate business students. Additionally, high-responsibility subjects show the greatest commitment to a course of action immediately following the receipt of negative consequences. Efficacy of resources is also significant. Under high efficacy of resources, subjects invest substantially more resources than under low efficacy of resources. The investment decisions for all subjects, however, are highly unstable over time.

Staw (1981) points out that a cost which is sunk

economically for the firm may not be sunk psychologically for the decision maker. He develops a model of the








18

commitment process and identifies four major determinants of commitment. These are 1) motivation to justify previous decisions, 2) norms for consistency, 3) perceived probability of future outcomes and 4) perceived value of future outcomes. He states that rational and objective decision makers should not be influenced by the first two determinants since they are retrospective instead of prospective determinants. Analogously, in accordance with subjective expected utility models, the maximization of future utility should be the goal of the decision maker.

An experimental study by Staw and Ross (1980) uses both undergraduate students and practicing managers enrolled in an MBA program to assess a case description of an administrator's behavior. The three variables manipulated in the case descriptions are consistency versus experimentation in the administrator's course of action, minimum versus maximum commitment of resources to the course of action and the ultimate success versus failure of the administrator's efforts. Main effects for each of these independent variables are found such that an administrator is rated highest when he follows a consistent course of action, allocates minimum resources and is ultimately successful. Additionally, practicing managers participating in the study view consistency in actions as a much more important characteristic of a successful manager than undergraduate students, even when negative results accrue as










a consequence of previous actions. Continuing with a previously chosen investment apparently benefits the manager more than switching to a superior investment. This suggests that managers consider sunk costs when making capital budgeting decisions and are maximizing their personal utility instead of the utility of the firm.

In summary, the previous behavioral studies have identified personal responsibility as a crucial factor consistent with the commitment of an escalation error. Other factors such as the efficacy of resources, consistency in actions and the ultimate success of the chosen course of action have also been shown to significantly affect the escalation decision. A model of the commitment process has also been presented which coincides with accepted subjective expected utility models.

The accounting study by Kanodia, Bushman and Dickhaut

(1989) provides the primary source for the hypotheses of the current study. Hypotheses concerning the manager's talent level as well as others pertaining to the principal-agent setting are developed. The current study adopts much of the theory of Kanodia, Bushman and Dickhaut (1989) and a limited amount from the body of behavioral literature. This research provides an empirical test of the Kanodia, Bushman and Dickhaut (1989) theory, as well as some extensions.










The Present Research


The purpose of the current study is to integrate the issues discussed in this chapter into a single experiment. The analysis of the accounting issue provides the link between the escalation error phenomenon and accounting. The principal-agent setting and the related issues provide a ready-made framework within which to conduct an analysis. The previous behavioral studies have laid the groundwork for an experimental investigation of the escalation error phenomenon. The current study draws on these bases and the paper by Kanodia, Bushman and Dickhaut (1989).

This study explicitly examines the effect on the

manager's decision to commit an escalation error related to 1) the talent level of the manager, 2) the presence of a monitor of the manager, 3) the time at which the manager discovers he has made an escalation error and 4) the difficulty level surrounding the manager's decision. These four factors have been chosen for the current study because none have been included in the same form in previous studies and all are very important from an accounting standpoint. The importance of personal responsibility to the escalation decision has already been shown in multiple studies (Staw, 1976; Staw and Fox, 1977). Consequently, all subjects in the current study are always personally responsible for all of their decisions. The relevance of consistency in actions is recognized in the experimental instruments and is









21

discussed in detail in the experimental design section of the paper. Therefore, these concepts from the behavioral studies of the escalation phenomenon are not manipulated, but are incorporated in the experimental task as constant factors.















CHAPTER 3
DEVELOPMENT OF MODELS AND HYPOTHESES Introduction


The following discussion first introduces a principalagent model which serves as a structure within which to test the hypotheses. Second, the related information system used in the implementation of the experiment is presented. The development of the research hypotheses is described next. The decision model developed to describe the anticipated effect of each of the independent variables on the manager's decision to commit an escalation error is introduced. The expected effects of each of the independent variables are presented. The chapter concludes with a discussion of some supplementary analyses which are usually associated with this type of experimental study.


The Principal-Agent Model


A principal-agent model is developed as the framework within which the hypotheses of this study are tested. The model is based on Kanodia, Bushman and Dickhaut (1989). Their model includes constructs representing managerial talent, information asymmetry and the time at which the manager discovers he has made an escalation error. The

22










current model is extended and altered to include the constructs of this study.

Kanodia, Bushman and Dickhaut (1989) model an economy consisting of two periods in which escalation behavior versus switching behavior is studied. In this economy, self-employed managers must choose between two projects, A and B, in period one. There are also two possible underlying states of nature, 0 A and 0 B - The project inflows accruing to managers as a result of implementing a project are highly dependent upon a match between the project chosen and the actual underlying state of nature. In stage one of period one, managers receive a private signal which reveals a certain level of information about the underlying state of nature. Each manager then chooses to implement either project A or B. Talented managers have a higher probability of discerning the actual underlying state of nature from the information signal than do untalented managers.

At stage two of period one, each manager receives new information that reveals the true underlying state of nature. Each manager then has the option of continuing to implement the chosen project or switching to the alternative project. The differential project inflows from switching to the correct project always exceed the cost of switching, so that it is always beneficial to switch from the firm's viewpoint. Consequently, the manager will escalate only when he perceives that escalating will provide him with a










higher wage in the impending labor market. At stage three of the first period, project inflows accrue in accordance with the project chosen/actual state of nature combination. In period two, each manager seeks employment. The better a manager's results from his self-employed capacity, the higher the wage he can demand in the job market. Thus, the project inflows that accrued as well as the reputation value of the manager are both involved when the manager seeks employment. These two factors are the basis for analyzing escalation behavior in the current model.

The current model has its basis in the Kanodia, Bushman and Dickhaut (1989) model and is modified to fit an empirical study. Before the beginning of the decision rounds, the state of nature is randomly determined for each manager, but is unknown to the managers. The managers also receive limited information about the two alternative investment projects. The sequence of events in each decision round is

1) managers receive additional discriminating

information to aid them in their investment

project choices,

2) managers make their investment project choices, 3) the random factor drawing takes place for each

manager and

4) investment project wages accrue to the managers.











Managers receive wages, r, which depend upon the

investment project payoff at the end of each decision round.

There are two possible values for the wages, rL and r H I representing low and high wages. The wages are determined by three factors: the manager's choice of investment projects (a), a stochastic realization of the state of nature (s) and an additional stochastic realization of a

random f actor (e) , such that r, (a, s, P) . The manager I s choice is between two investment projects, al and a 21 and is a function of his talent level, t, and the information

signal received, y, such that ai (t, y) . The manager Is talent level impacts his choice, as the talented manager is more likely to make a correct project choice (a choice which leads to a high payoff). An information signal, YE:(yly2). is received in each round by the manager. The signal provides the manager with the probability of each state of nature given the signal. The state of nature is randomly determined between two possible states, sl and s 2 * The random factor, c, is defined as the probability of receiving an inconsistent project payoff. An inconsistent project

payoff occurs when the manager chooses project al. the state of nature is sl and the resulting wage is rL (or a 2 F s 2 and rd. This factor is introduced to allow additional randomness in the model so the manager is not able to discern the true state of nature early on with certainty. The random factor starts relatively high and decreases in








26

magnitude with time. In the later rounds the probability of receiving an inconsistent project payoff becomes minimal.

The last variable of interest to the manager is the cost of switching projects, b. This variable is positive and increases throughout the decision rounds. The cost of switching is a function of the manager's wages and time such that b(rtime). The variable b represents the penalty (the sunk cost) of discovering the true state of nature in the later rounds when the chosen project has begun implementation. Since there are no project costs in the model, the use of b seems to be a plausible way to manifest a sunk cost effect. Therefore, in this context, b is a relevant variable to be considered in the investment project choice problem.

In the principal-agent framework the manager is

considered to be an expected utility maximizer. If the manager is assumed to be risk neutral, maximizing expected utility is equivalent to maximizing wages. Therefore, based upon the previous discussion and definitions, the manager's problem is

maximize tr (a, s, e) - b).
a, b











The Information System


In every principal-agent problem an information system represents the various probabilities associated with the possible combinations of the variables defined in the model. This information system dictates which potential contracts will be optimal. The basic information system adopted for the current study and used in the implementation of the experiment is presented below. The payoff structure can be characterized as


with an c probability of not attaining. The information signal is from an information system of the form


Yi~~sj S 1s j P (y, 1s1) P (Y1 1S2) 2 P (Y21) P (Y21S2)


multiplying P(y,:sj) by P(s~) gives Oij which is











Yi\sj s 1S2

Y 11 012

Y2 21 22


dividing 0i by 0i gives 8(sj |yi), which is


y\Sy S1 S2

Yl 6 (S Y ) 0 (S2 1 Y Y2 1(S1 1 Y2) ( (S22 Y )


where P(yi Is) is the probability of signal yi given that the state is sj, Oij is the joint probability of signal yi and state s , 0i is the marginal probability of signal yi, and 0(sjlyi) is the probability of state sj given signal yi. After receipt of the information signal, the manager's strategy is as follows.

If y1 is observed and al is chosen, then the expected wage

is (s1 y1)(r H(l-e) + rL()) +O(s21)(r(l-) + rH(e)).

If y1 is observed and a2 is chosen, then the expected wage

is O(s, yl)(r(l-H() + rH()) + 0(s221 Y)(rH(l- ) + rL()). If y2 is observed and al is chosen, then the expected wage is O((s 1:y2) (rH(l- ) + rL() ) + (s21y2) (r(l--C) + rH()). If y2 is observed and a2 is chosen, then the expected wage is 0(s 1y2) (rL(-) + rH(F)) + (S21 Y2) (rH(l- ) + rL(C))








29

Let a represent the two possible choices when y, is observed and 0 represent the two possible choices when Y2 is

observed. Given Oi as previously defined, the manager's problem as stated in Equation 1 becomes maximize 101a + 02V (2)
ai (Y)

The variable b is the cost of switching projects.

Presumably, the manager switches whenever he determines that the increase in his expected wages from the alternative project is greater than the cost of switching. Alternatively, if

n
E (U (rIj (a, s, e) - rL (a, s, e) > b (3)


where i = 1 is the round of the switch and n = the last round, then the manager switches.

It is evident that the manager's strategy is to

maximize his utility, or equivalently his wages if the manager is assumed to be risk neutral. To do this he should use all the information signals he receives over the rounds to determine the underlying state of nature. The manager's decision for the first round is

1) estimate the probability of each state of nature

given the signal that has been received and

2) choose the project which has a greater probability

of matching the underlying true state of nature.










After the first round, the manager's decision problem becomes much more complex.

1) If the prior round's payoff was not high, assess

probabilities of possible causes: wrong project

was chosen or an inconsistent project payoff

occurred with probability e. If the prior round's payoff was high, this is evidence (strong but not

conclusive) of a correct project choice.

Alternatively,

P(rH) = (0l + 022) (1-e) + (012 + 021) (e) (4) P(rL) = (011 + 022) (�) + (012 + 021) (I-) (5)

Computing the probability of receiving rH or rL

given the occurrence or non-occurrence of the

random factor e gives


P (F 1 , ) = P : (6)
P (rH)



(012 + 021) (e)
((0i + 022) (1-P) + (012 + 021) ( )



P rL) (011 + 022) (F) (7)
( + 022) (e) + (012 + 021) (1-e)


(0 1+ 02 ( - )
P(l-C1rH) = ((011 +) 022) (1 (8)
0 22) (1-c + (0)12 + 021) (e))












P (1-C :rL) = (012 + 021) (1-F-)(9
( (011 +22) (C) + (0)12+ 0)21) (-) 9


In the current setting,

P(C:rL) > P(l-C:rL) (10)

for the first several rounds. This shows that a

low project payoff is more likely to be due to the random factor than to an incorrect project choice in the early rounds only, as this relationship is

reversed in the later rounds. Throughout the

rounds, however, a high project payoff is most

likely not due to the random factor, as P(e~r H) is

always small. This holds true for any one round

looked at independently.

2) Analyze information from all previous rounds as

well as the current round to determine whether to

remain with the previously chosen project or to

switch to the alternative project.

This model is modified somewhat to fit an experimental setting, but its essence remains unchanged. The hypotheses of the current study are developed within this structure.


The Effect of the Manager's Talent Level


The talent level of the manager is a primary variable of interest. Managerial talent is typically described as the ability to organize and supervise the various inputs









32

needed for investment and production. Kanodia, Bushman and Dickhaut (1989) refine this definition into the idea of "foresight." A manager with superior foresight can anticipate future developments much sooner than a manager who is not farsighted. This equates to the superior ability of the talented manager to discern the true state of nature. Talented managers are more likely to make correct choices in the initial stages of an investment decision and are therefore less likely to face the problem of a superior investment being available later. Occasionally talented managers may make initial incorrect choices and find themselves faced with the question of whether to escalate or switch projects. In this case they should feel less pressure to escalate. Since they are talented and probably already have favorable reputations, they can therefore sustain the adverse effects of admitting a past mistake. Consequently, talented managers make correct project choices a higher percentage of the time than untalented managers.

Untalented managers are unable to discern the true state of nature with certainty until much later than talented managers. Consequently, they learn that the alternative investment should have been chosen initially, and are then faced with the question of whether to switch to the superior investment (assuming that the initially chosen investment is not at the stage of completion and can still be abandoned). Therefore, the untalented manager is more










likely to be faced with the dilemma of whether to escalate with the inferior project or switch to the superior project than the talented manager. Additionally, the untalented manager should feel more pressure to escalate in an attempt to bolster his dubious reputation. Given this, the following hypothesis describes the expected talent effect:

Hi: Escalation errors are made more frequently by
untalented managers than by talented managers.


Information Asymmetry Effects of the Monitoring Variable


Since information asymmetry is considered a

prerequisite for committing escalation errors,' the presence or absence of information asymmetry is not manipulated in this study. Information asymmetry exists between owners and managers throughout the study. When the manager has private information, the possibility for agency problems exists. The agency problem of particular interest in the current study is adverse selection. An adverse selection problem exists when the manager has incentives to hide or misrepresent his private information. When the manager keeps his private information to himself, he is judged only by his observable actions and their results. This allows the manager to choose or continue a course of action which may provide acceptable results, while an alternative superior course of action is available. Since


1 This was noted previously, see Narayanan (1985).










the manager has private information about the superior course of action, he will not switch and therefore admit to a previous incorrect choice without an incentive to do so. Therefore, the manager escalates and is compensated based on his acceptable results while concurrently protecting his reputation. since the owner knows the manager has incentives to act nonoptimally, the question arises as to what measures the owner may take to deter such action. 2

Penno (1983) investigates several issues of information asymmetry in managerial accounting. He specifically addresses the issues of monitoring and reporting/incentive systems. In a reporting/incentive system, the manager reports some of his private information to the owner. The owner then revises the standards by which the manager is evaluated. A major problem with a reporting/incentive system is that the manager may have incentives to distort the reported information. This is possible since the manager privately observes the information. If by distorting the report of his private information the manager can further his own self-interests, he may do so.


2 The possibility of signaling, while considered a solution to the adverse selection issue, does not fit in well with the escalation error problem. Signaling assumes that the individual producing the signal is attempting to honestly convey useful information to the market (see Akerlof, 1970; Spence, 1973). In the escalation error scenario, the manager is trying to conceal his private information. This is contradictory to the premise of signaling and therefore invalidates the use of signaling to reduce information asymmetry in this study.










Participative budgeting is an example of this system.' In contrast, under a monitoring system the manager has no opportunity to distort the information reported to the owner. A monitor is a device which reports information to the owner, independent of the manager. By Penno's definition a monitor is inherently assumed to be noisy or an imperfect signal of the actual information. An essential tradeoff between a reporting/incentive system and a monitor is manipulability versus accuracy.

In the current setting, the availability of a monitor

may act to reduce the level of information asymmetry between the manager and the owner. If the manager knows there is a possibility that additional discriminating information about him will be disclosed via the monitor, he will be less likely to commit an escalation error. This is posited in the following hypothesis:

H 2 The presence of a monitor reduces the incidence of
escalation errors.








3 Participative budgeting requires the employee to
report some of his private information to the employer. The employer then uses the reported information to set a budget by which the employee is evaluated. The value of participative budgeting is in question if the employee misrepresents his private information. Penno (1984) shows that participative budgeting can be strictly valuable if the employer has some control over the private information available to the employee.










The Effects of Timing of Information Disclosure


Time is clearly an important factor in the escalation error problem. Staw and Fox (1977) implement their time construct by having subjects make three consecutive investment decisions. Their study showed an unstable effect of time on the amounts invested. In the current study the manipulation of time is treated quite differently. This manipulation takes the form of the point in time when the manager discovers with certainty that he has made an escalation error. In the Kanodia, Bushman and Dickhaut (1989) model, the manager either discovers the true state of nature immediately or after his initial project choice. The current multi-period model affords the opportunity to vary the point in time when the manager learns the true state of nature. It is posited that the later the manager learns the true state of nature and realizes that he has made an escalation error, the more likely he is to continue with the error.

In the context of the current study, it is posited that this time effect may be due to two factors. First, the longer the firm has undertaken the project, the larger the project's sunk costs.4 Second, the manager's reputation is

4 This assumes that the project's sunk costs
(implementation or construction costs) continue to increase monotonically until the completion of the project. This is usually the case, as even if a large initial outlay is required, some additional costs are typically incurred until the project's completion.










more severely damaged by admitting a mistake made many periods in the past. By doing so, he reveals that he is untalented, and has either been unable to discern that the alternative project is superior or has been hiding his choice of the inferior project. Consequently,

H 3 The later the manager discovers that he has made
an escalation error, the more likely he is to
continue with the error.


Effects of the Environment


The conditions under which the manager makes decisions vary due to uncertainty. Although information signals are often noisy or imperfect, the manager must use them. Additionally, unforeseen economic factors may distort the results from any chosen course of action, giving the manager yet another imperfect feedback signal (project payoffs). The possible combinations or series of signals and payoffs will vary continuously in any given environment. In this study the possible series are divided into difficult series with many occurrences of noisy signals and unforeseen economic factors, easy series with few occurrences and intermediate or moderate series for those in between. The manager is more likely to make an incorrect choice and potentially an escalation error in a more difficult series. Additionally, the more talented manager should always be less likely to commit escalation errors regardless of the series type due to his superior ability to discern the true









38

state of nature (see Hj) This anticipated ef f ect is stated in the following hypothesis:

H 4: Escalation errors are made by both talented and
untalented managers in a difficult series (of
signals and payoffs), by only untalented managers
in a moderate series and by no managers in an
easy series.


The Decision Model


Within the principal-agent framework, the hypotheses of the current study are tested. The proposed model of the manager's escalation error decision is

EE = f(TAL, MON, TIME, DIFF) (11)

where

EE whether an escalation error is committed;

TAL the manager's talent level;

MON the presence (or absence) of a monitor of the
manager;

TIME the timing of information disclosure to the
manager; and

DIFF the difficulty level of the series (of
signals and payoffs) facing the manager.

Within this context, an escalation error is expected when the manager is untalented, has no monitor present, discovers that he has made an escalation error at a late stage and faces a difficult series. Therefore, the expected coefficients for the independent variables are negative for TAL, negative for MON, positive for TIME and positive for DIFF.










Supplementary Analyses


Aside from the four posited hypotheses, additional

exploratory premises are also investigated. These premises pertain to the behavior of the experimental participants. Trends in behavior over time are typically tested in this type of experimental setting (experimental markets). These premises will be developed in Chapter 4, following a detailed explanation of the experiment.

In this chapter, the hypotheses to be tested and the principal-agent model were integrated into the resulting decision model. The experiment designed to test the hypothesized effects is described in Chapter 4.















CHAPTER 4
RESEARCH DESIGN AND DATA ANALYSIS METHODS Introduction


The research method used to test the hypotheses

presented in Chapter 3 is described in this chapter. First, the design of the experiment that was conducted to test the effects of the four independent variables is discussed in detail. Next, the chapter describes the data analysis methods used which take into account the discrete nature of the dependent (and some of the independent) variables. The data analysis section also includes a summary of the predicted results and a discussion of the supplementary analyses.


Experimental Design


overview


The decision model in Chapter 3 was tested in a multiperiod market setting in an attempt to identify those factors responsible for managers' escalation errors. The setting included two types of participants: managers and supervisors. A total of 156 graduate and undergraduate students in the College of Business Administration at the










University of Florida participated in the study. Each subject attended one experimental session. As an incentive to participate, subjects earned lottery tickets based on their decisions made during the experiment. The lottery tickets were entered into a drawing in which seven cash prizes were awarded.

The experiment was run over a two week period and

consisted of a total of 16 sessions. Each session lasted about two hours and could accommodate up to 12 subjects (six manager-supervisor pairs). Some sessions were run with less than 12 subjects. This was done in an attempt to use all willing subjects (some were only available during certain time slots). Precisely, the number of sessions containing each allowable number of subjects was three with 12, three with 11, two with 10, four with 9, three with 8 and one with

7. In all sessions with an uneven number of subjects, the experimental assistant acted as the last supervisor.


Experimental Task


A detailed explanation of the experimental protocol can be found in Appendix A. A more succinct description of each experimental session is presented here. In each session there were four to six manager-supervisor pairs. Subjects acting as managers made investment decisions to maximize their experimental payoffs. Subjects acting as supervisors gave investment decision feedback to managers and bid










competitively for managers, services at the end of each sequence of rounds. Supervisors' wages were based upon the investment project payoffs which resulted from managers' decisions. In those sessions with an uneven number of subjects, the experimental assistant provided the remaining manager with the necessary feedback information, but did not actively bid for managers' services. All bids from this "dummy" supervisor were set equal to $1.50 (the minimum allowable bid).

The steps in each experimental session can be found in Figure 4-1. Managers and supervisors were randomly paired in the first sequence of rounds. For every sequence after the first, managers and supervisors were paired based on the results of the bidding for managers' services. During each sequence, managers and supervisors went through a group of decision rounds in which each manager made an investment project choice based on the information he received. 1,2 The information initially given to managers consisted of the two possible investment projects, the two possible states of nature, the probability of each possible state of nature,

1 Managers chose between two potential investment
projects. They received limited information about the two projects, but were told they should attempt to discern which investment project was favored by the economic conditions in the market. Managers knew that determining which project was superior based on these conditions would maximize project payoffs.
2 If the manager is not attempting to build a reputation, he should be making his project choices consistent with Equation 1 from Chapter 3.











the two possible payoffs and some additional information (see Specific Instructions for Managers in Appendix B).

At the end of the first sequence, project choices for each round, as well as cumulative payoffs for the firm for the first sequence, were made public for all managers. Supervisors were given time to analyze each manager's performance and then bid competitively for managers' services. Each supervisor entered three written bids, one bid apiece for each of three managers. The supervisor entering the highest bid for a particular manager was matched with that manager for the second sequence. If the highest bid for a manager was from a supervisor who had already been matched with another manager, then the supervisor entering the next highest bid was matched with that manager. This process continued until all managers and supervisors were matched. If some managers were not bid upon by supervisors who had not already been matched, then the remaining managers and supervisors were matched by the experimenter at the minimum allowable bid. The more talented managers should have logically commanded the higher bids from the supervisors, since supervisors' wages were based upon the cumulative payoffs resulting from managers' project choices in each sequence. 3

3 The payoff to the firm for each round was either $.50 or $1.00. The cumulative payoff to the firm for the sequence was the total of the payoffs for each round less the cost to switch whenever a switch between projects was made. The cumulative payoffs to the firm each sequence were








44

A time line for the ordering of events in each decision round can be found in Figure 4-2. Managers received an information signal from their supervisors at the beginning of each round. This signal provided them with additional information about the true state of nature. 4 Managers were given time to analyze their information and then made their investment project choices. Supervisors then informed the managers of the project payoffs resulting from their investment project choices. 5 The project payoffs which accrued at the end of each round served as an imperfect signal of whether a correct investment project choice had









divided in the proportions of , to the manager and A, to the supervisor.
4 The signals provided the managers with the
conditional probability of the state of nature given the signal that was received. The probabilities associated with these signals were calculated using the information system presented in Chapter 3. These probabilities were reported to the managers in the Specific Instructions for Managers. The signals were designed such that signal one would be more descriptive than signal two.
5 During every sequence each supervisor acted in
multiple capacities. First, each supervisor acted as an information provider. This can be likened to the supervisor steering the manager in the right direction to make a correct project choice. Next, the supervisor acted in his superior capacity in providing feedback to the manager about his choice. Finally, the supervisor acted in his hiring and firing capacity when deciding which managers he should bid upon. In actuality, most supervisors act in all of these capacities on a regular basis.










been made. This chain of events was repeated until the end

of each sequence. 6

Cost of switching. In each round after the first,

managers chose whether to stay with the current investment

project or switch to the alternative investment project.

Managers had to pay a cost to switch investment projects,

which increased throughout the decision rounds. The cost of

switching was manipulated in a manner which induced managers

to switch investment projects, i.e., the expected benefits

from switching always exceeded the cost of switching.

Managers were explicitly informed that this was the case.

If managers switched projects more than once, the cost of




6 There were six sequences in each experimental
session. Managers and supervisors were led to believe that there would be seven sequences based on their payoff records. The subjects were not informed of the true ending point of the experiment due to what can be termed the finite-period paradox. Luce and Raiffa (1957) address this issue in the context of temporal repetition of the prisoner's dilemma. In the prisoner's dilemma, for any one play of the game, each player does better when he does not cooperate, regardless of what the other player does. The Pareto superior outcome is only achieved, however, if both players cooperate. When the prisoner's dilemma is repeated an infinite number of times, both players gain from a commitment to always cooperate. The problem arises with a large, but not infinite, number of repetitions. In this case, the final stage of a finitely repeated game becomes equivalent to a single-stage game in which cooperation cannot emerge. Inductive reasoning dictates that this scenario repeats for the penultimate stage of the game and so on for all stages back to the first. In this case, ignorance of the ending point of the game is one method to prevent uncooperative behavior. The current case is considered similar enough to the prisoner's dilemma to warrant a similar treatment.








46

switching started at the same initial value and increased as previously noted.

Incentive to escalate. Since many of the experimental variables were designed to discourage escalation behavior (cost of switching and certain monitoring), subtle incentives to promote escalation errors were included in the experimental instructions. 7 These incentives included a suggestion about the importance of consistency in actions and maintaining a smooth stream of earnings for enhancing reputation. Both of these variables are consistent with the commitment of an escalation error. Consistency in actions is maintained when committing an escalation error, since the same project is chosen as has been previously chosen. Additionally, a smooth stream of earnings is maintained, since the project payoff resulting from an escalation error is probably low, as it has been in the past from choosing the nonoptimal project.

Stressing the importance of these two variables is not inappropriate for the manager, as both of these characteristics are considered to enhance the reputation of the manager. Staw and Ross (1980) show that consistency in

7 These incentives were based upon the results of the previous behavioral research on escalation errors. As was noted previously, all subjects were always personally responsible for all investment decisions. Additionally, the importance of consistency in actions was stressed in both the typed experimental instructions as well as the verbal explanations of the experimenter. These two factors were found to be consistent with the commitment of escalation errors in previous studies.










actions is viewed as an integral characteristic of a successful manager. Several studies including Hepworth (1953), Beidleman (1968) and Copeland (1968) note that a great deal of emphasis is placed on the periodic earnings figure. Managers accordingly are apt to feel pressure to maintain the current level of earnings. It is hypothesized that managers may actually try to avoid large variances in the earnings figure from year to year, since variance connotes risk and typically is looked upon unfavorably. Consequently, both consistency in actions and maintaining a smooth stream of earnings are posited to have a favorable effect on the manager's reputation value and are consistent with the manager committing escalation errors.

The experimenter explicitly explained two learning

sequences to the managers before the beginning of the actual experiment. The conditions which would produce comparable payoffs from switching and escalating were examined (e.g., switching payoffs exceeded escalating payoffs by $.50). Managers were informed that supervisors were aware that a switch between projects in a late round signalled an initial incorrect project choice. They were also told that a late round switch may lower supervisors' assessments of the manager's skill level. Consequently, the potential benefit of escalating in terms of increased bids from supervisors in the future was mentioned. It was then left up to each manager to determine whether the potential benefits from








48

escalating exceeded the certain increase in project payoffs from switching.


Subjects


In any experimental study, trade-offs exist between

replicating real world circumstances and the tractability of the experiment. The decisions made by subjects in this experiment are simplified from their real world counterparts. Many factors that would be present in actual business decisions are missing in the experiment. Nevertheless, by isolating the variables of interest, the results of the study can be attributed to the experimental variables.

Business students were targeted as the subject group for the experiment instead of practicing managers for several reasons. These students had the requisite (somewhat uniform) business training to deal with the experimental task, while using practicing managers would have introduced a wide dispersion of experience and knowledge bases. Also, they were readily available to participate in an experimental session on campus. The use of practicing managers would have most likely entailed moving the experiment to the employment sites. Additionally, given the requisite number of subjects, the procurement of this number of practicing managers would have been extremely difficult.











Given these observations, business students were used as experimental subjects.

Subjects were recruited from undergraduate and graduate accounting courses, as well as undergraduate finance courses. The lowest level subjects recruited were second semester juniors and the highest level were final semester master's students. When the students were initially contacted, they were informed that to participate in the experiment they would need to attend one two hour experimental session. They were told that they would earn a chance to win a cash prize in a lottery type drawing as a result of their participation in the experiment (see Appendix B for a copy of the introduction letter). Students who were interested in participating in the experiment chose which experimental session to attend over the two week period of the experiment.

The total number of subjects participating in the experiment was equal to 156. Of the total, 82 subjects acted as managers and 74 subjects acted as supervisors. Descriptive statistics about the subjects can be found in Table 4-1. As shown in the table, the number of male subjects was somewhat greater than the number of female subjects. The average age was 23 and the average GPA was 3.30. The greatest number of subjects were master's level accounting students. Senior level undergraduate accounting students made up the next largest group. Junior level











accounting students and senior level finance students

comprised the only other large groups. The average payoffs

were $28.05 for the managers and $29.60 for the supervisors. The average pre-test score for managers was 9.05, with a

maximum of 12 and minimum of 3. The solicited risk aversion

measure for the managers revealed 70 risk neutral subjects, seven risk averse subjects and three risk seeking

subjects. 8


Incentive Structure


To entice subjects to commit the necessary two hours of their time to participating in the experiment, some

potential reward had to be offered.9 Traditionally, each

subject is paid a flat fee for participating which is

presumably equal to their reservation wage. As the number

8 The risk aversion measure was solicited as a check on an assumption from the principal-agent model of Chapter 3. In the model it is stated that if managers are assumed to be risk neutral, maximizing expected utility is equivalent to maximizing wages. The measure used in this study consisted of one question at the end of the PreExperimental Questionnaire for Managers. This question pertained to an equivalency calculation for a lottery ticket and is admittedly a rough measure. A more detailed measure was not used due to the very tight time constraints of each experimental session. Nevertheless, the solicited measure does show the majority of the subjects to be risk neutral, thus validating the assumption of the principal-agent model.
9 In experimental market studies it is generally
accepted that cash incentives should be offered to subjects in order to induce them to act as expected utility maximizers. Without cash incentives subjects are not motivated to care about their choices (act optimally), or even show up if they are not required to do so for some other reason (such as receiving class credit).










of subjects participating in the experiment becomes large, the total amount required to pay the subjects becomes prohibitively high. Recently Bolle (1990) has shown that under certain circumstances (small decision costs and anonymous choices) a randomized reward structure is equivalent to rewarding each subject. These conditions were met in the current experiment, as there were no decision costs and no identification of subjects (by anything other than their subject numbers) during the experiment. The randomized reward structure for the experiment took the form of a lottery drawing for seven prizes at the completion of all experimental sessions. The seven prizes included one of $200, two of $100 and four of $50. The subjects earned lottery tickets in direct proportion to their cumulative experimental payoffs, which should have induced them to give a good effort during the experimental sessions. The winners of the lottery were identified only by their lottery ticket numbers and had to have the other half of the ticket stub to claim their prizes.


Operationalization of Independent Variables


Manager's talent level. The manager's talent level was measured in the experiment by administering a test to all subjects acting as managers. The test was administered after the learning sequences for managers and before the actual experimental sequences began. The test consisted of










twelve statistical and business questions designed to measure the subject's (manager's) aptitude for the experimental task (see Appendix B for a copy of the PreExperimental Questionnaire for Managers). The designation of each manager's talent level (in terms of his score on the questionnaire) was made by the experimenter after the experimental sessions were completed.

Information asymmetry. The manipulation of the

information asymmetry construct was between-subjects. The two possible states were certain monitoring and no threat of monitoring. Each subject faced only one possible state to enable him to discern the effects of the monitoring condition over the multiple sequences.

When monitoring was present, additional discriminating information about the manager was disclosed to the market (the supervisors) in the form of optimal total payoffs for the sequence. The disclosure of the optimal total payoffs reduced information asymmetry between that particular manager and the market. The difference between the optimal total payoffs and the actual payoffs provided supervisors with important information upon which to revise their estimate of the talent level of the manager. For experimental purposes the monitor was noiseless or a perfect signal of the actual information.

Time. The manipulation of time was within-subjects. Maintaining a constant time condition for each subject was










not considered necessary, since the effect of the time construct was considerably easier for the subjects to understand than the effect of the monitor construct. The time variable was manipulated by informing managers of the superior project for the sequence at different times during the sequence. Two points were chosen within each twelve round sequence. Managers were privately informed of the superior project after either round six or round eight.10 Based on this, an escalation error is defined for experimental purposes as continuing to choose the nonoptimal project in any round after the manager is informed of the superior project.

Environmental factors. The manipulation of the

difficulty level surrounding the manager's decision was also within-subjects.11 This ensured that every manager faced


10 Many factors were involved in choosing to disclose the superior project to the managers after rounds six or eight. During the first half of each sequence managers should attempt to discern the superior project themselves. After this point in time, however, in order to ensure that managers are actually committing escalation errors and are not simply ignorant of which project is superior, the superior project must be disclosed to them. Obviously, many other points in time could be chosen to disclose the superior project, but these two were considered to be sufficient to describe the anticipated effect.
11 The specification of the time and environmental
factors variables as within-subjects is not consistent with the use of the logit model for data analysis. Nevertheless, the true manipulation of these constructs was withinsubjects as each subject faced all possible levels of each construct. The reconciliation of this problem is presented with the discussion of the logit model later in this chapter.










each types of series the same number of times and had the same opportunity to escalate. Each series of information signals, realizations of the random factor c and resulting payoffs was classified as to its level of difficulty. The validity of escalation errors made by managers was ensured by this classification. If this was not done, some escalation errors may have been caused by certain series being more difficult to interpret (to discern the true state of nature for the sequence before it is revealed to the manager) than others. By classifying the randomly generated series into three groups, any spurious results were avoided. The three groups were classified as easy, moderate and difficult. Each group contained one prototype series which fit the predetermined specifications for that group. Measurement of Dependent Variable


The hypotheses discussed in Chapter 3 are based on the dependent variable of whether an escalation error is committed. An escalation error is defined for experimental purposes as continuing to choose the nonoptimal project in any round after the manager is informed of the superior project. This definition is used as the basis for classification of the subjects' choices of investment projects. Their choices after either the sixth or eighth round constitute either an escalation error or a correct project choice. Based on the design of the experiment,











managers should always continue choosing the same project, once they had made their choice between the two projects after round six or eight. 12 Consequently, the dependent variable is dichotomous in nature for each sequence: either an escalation error is committed or no error is committed.


Data Analysis Methods


The Decision Model


The manager's decision model as previously specified is EE = f(TAL, MON, TIME, DIFF) (11)

where

EE whether an escalation error is committed;

TAL the manager's talent level;

MON the presence (or absence) of a monitor of the
manager;

TIME the timing of information disclosure to the
manager; and

DIFF the difficulty level of the series (of
signals and payoffs) facing the manager. The manager's decision model containing the variables specified in hypotheses one through four is tested using the logit model. The logit model is the appropriate model to



12 Subjects knew that there were no gains whatsoever
to be made from switching in any round after round seven or nine (depending on when the superior project was revealed to them). The experimental data showed that subjects understood this fact, as once the subjects had chosen a project after round six or eight they never switched projects again.








56

use when the response variable is binary. The general logit model is defined as


+ p + pC + pD + pE (12)
gikh, = 00 k 1 M

where


gjkl is the logit (logit transformation) corresponding
to the possible levels of the explanatory variables;


00 is the intercept term;

B, C, D and E represent the four explanatory variables;

j, k, 1 and m represent the possible levels of each
explanatory variable; and

H
P represents the effect on the response variable of a
given set 13 of observed values of the explanatory
variable.

In the current case, variable B is the talent level of the manager. This variable has thirteen possible levels in accordance with the possible scores of zero to twelve on the managers' pre-experimental questionnaire. Variable C is the monitoring of the managers. The two possible levels are presence or absence of a monitor. Variable D is the time when the manager learns of the superior project for the sequence. The two possible times are rounds six and eight. Variable E is the difficulty level surrounding the manager's decision. The three possible levels are difficult, moderate



13 See Andersen (1990) for a good discussion of the logit model and its applications.









57

and easy. The response variable to be tested is whether an escalation error was made by the manager (O=no, 1=yes). Each sequence for every manager is treated as an independent observation since each potential combination of the time and difficulty level variables represents a unique problem for the manager. 14


Predicted Relationships


When the manager's talent level is high it is expected that escalation errors will not occur, therefore hypothesis one will be supported by a significant negative coefficient on the talent variable. When a monitor is present it is expected that the manager will not escalate, therefore hypothesis two will be supported by a significant negative coefficient on the monitor variable. When the manager learns of the superior project after round eight, he will be more likely to escalate than if he learned of the superior project after round six. Therefore, hypothesis three will be supported by a significant positive coefficient on the time variable. When the difficulty level of the series is easy the manager will not be expected to escalate, while if


14 This treatment provides a resolution to the problem of these two variables being manipulated within-subjects. If each manager's multiple sequences are treated independently, then these variables actually vary between (instead of within) managers (or sequences). Arguably each manager's multiple sequences are not truly independent, as the managers should have been learning (and making better decisions) as the experiment went on.










the level is difficult the manager will be expected to escalate. Consequently, hypothesis four will be supported by a significant positive coefficient on the difficulty level variable.


Supplementary Analyses


The additional premises to be tested in this study

pertain to the total payoff to the firm for each sequence and the bids of supervisors for managers' services. A trend which can be analyzed in such experiments is whether the market converges to equilibrium. At equilibrium no parties earn excess profits and all parties act rationally to maximize their payoffs. This implies that the parties always make optimal choices to maximize the current period's payoff. This equates to maximizing firm value and not the manager's reputation value. Consequently, in the current case there is no pure equilibrium value as has been defined in previous experimental market studies. 15 This is because the experiment is specifically designed to promote escalation errors (and induce the manager to be concerned

15 In pure experimental market studies based on information economics, convergence to an analytical equilibrium price is typically tested. These markets usually contain as many as 40 periods of bargaining for a hypothetical good. Over this extended time frame, convergence is often observed. The current experiment does not entail such an expansive time frame nor does it qualify as a pure experimental market, since there is no analytical equilibrium price. Nevertheless, trends resembling convergence to equilibrium can be analyzed in the current setting.











about his reputation) by increasing the uncertainty surrounding the manager's decision of which project is optimal, especially in the difficult sequences. Therefore, it is highly illogical to expect the manager to make optimal choices (to maximize the current period's payoff) throughout the experiment. Consequently, an alternative measure of pseudo-equilibrium must be employed.

A value called the logical payoff is used to proxy for the equilibrium value of total payoffs for a sequence. This value is calculated for each type of series by considering all reasonably rational patterns of choices. The logical payoff is simply the expected value (average) of these patterns.

The three premises involving the logical payoffs are Pl) the market should be closer to the logical payoff for the easy sequences than for the moderate and difficult sequences, P 2) the market should move toward the logical payoffs over time (irrespective of series type) and P 3) there should be more dispersion from the logical payoff when an escalation error is made than when no error is made. These three premises are tested using generalized distance measures, i.e., the variance and the average absolute value of the actual payoffs from the logical payoffs. The absolute values are reported in addition to the variances since they are less affected by severely outlying











observations. The variances and absolute values are computed as

n
E (APi - LP i) 2 (13)
VAR i=I
n-1


n
E JAPi - LPi 1 (14)
ABS i=1
n

where
APi actual payoff for observation i;
LPi logical payoff for observation i; and
n total number of observations.

Premise P, pertains to the magnitude of the variances and absolute values in the easy, moderate and difficult sequences. The lowest total measures are anticipated in the easy sequences since there is less uncertainty involved and correct choices should be made the majority of the time. The highest measures are expected in the moderate sequences, due to an interspersion of inconsistent signals and random factors which should cause managers to receive many low payoffs and switch quite often. The dispersion in the difficult sequences should be quite low. The difficult sequences lead managers to believe they are choosing the correct project, when in fact they are choosing the incorrect project. Consequently, managers receive many high payoffs and usually switch only once.

The second premise, P2. concerning the movement of the market toward the logical payoffs over time is tested for









61

the moderate and difficult sequences only. This is because all subjects faced the same moderate and difficult sequence twice, which was not the case for the easy sequence. The premised relationship is expected as the subjects learned more about the market over time. The third premise, P 34' posits that the variances and absolute values should be greater when an escalation error is made than when no error is made. The rationale for this is that more incorrect project choices accompany the commitment of an escalation error, thus increasing the difference between the actual payoff and the logical payoff.

Other relationships that can be explored in this

experiment pertain to the bids from supervisors for the managers' services. 16 The movement of these bids over time and the reaction of supervisors' bids to an escalation error are both relevant issues. Two reference points are used to evaluate these premises about supervisors' bids: the actual average bid and the logical average bid. The logical average bid is calculated based on the logical payoffs discussed previously.

A decreasing trend is expected in the bids over time as supervisors learn and understand the mechanics of the market

16 Each supervisor entered three bids for three
different managers at the end of each sequence (one through six). Only the accepted bids, which were subtracted from the supervisor's payoffs and added to the manager's payoffs with whom the supervisor was paired for the next sequence, were analyzed. The total number of accepted bids was equal to 480; the total number of experimental sequences.











(this was shown to be the case in pilot studies). Additionally, the variance of the bids should also decrease over time, as a bidding strategy is adopted. Two measures of variance based on the actual average and the logical average are used to assess this trend. These variances are calculated as

n
E (BID1 AAB)2 (15)
VARA - i= n-I



n
E (BID1 LAB)2 (16)
VARL -n-i


where
VAR A = variance from actual average bid;
VARL = variance from logical average bid;
BID. actual bid for observation i;
AAB =actual average bid;
LAB =logical average bid; and
n =total number of bids.


Summary


The experiment described in this chapter was

specifically designed to test the hypothesized effects of the independent variables. Consequently, it could be argued that the experiment is devised to promote escalation errors. This is certainly not true, however, since economically (from the viewpoint of the firm) it is never beneficial for subjects to escalate. Furthermore, given the experimental design, escalation behavior is only expected in a very small











percentage of sequences. Specifically, the untalented manager is expected to escalate when no monitor is present, when he is informed of the superior project after the eighth round and when he faces a difficult sequence. Since the experimental design is fully crossed, these circumstances are only met approximately 8% of the time. 17 Thereforel during the majority of the experimental sequences, no escalation errors are expected. This proves that the experiment is certainly not contrived.

The data analysis technique discussed recognizes the dichotomous nature of the dependent variable in the manager's decision model. The supplementary analyses are designed to test trends which are usually examined in this type of experimental setting. The analyses presented in Chapter 5 center around the manager's decision model and incorporate ancillary analyses appropriate for such experiments.












17 The 8% approximation is derived from the following conditions. Approximately half of the managers are untalented and half have no monitor present. If the first sequence (which is easy for all managers) is discounted, then four of the last five sequences are in the eighth round treatment, and two of the last five sequences are difficult. Therefore, 1/2 X 1/2 X 4/5 X 2/5 = 8/100 = 8%.
















TABLE 4-1

Descriptive Statistics of Subjects

Total
Managers Supervisors Sample

Total Number 82 74 156

Sex
Female 34 34 68
Male 47 38 85

Average Age 22 23 23

Average GPA 3.27 3.32 3.30

Classification
3AC 8 12 20
4AC 26 11 37
6AC 1 3 4
7AC 24 31 55
3BA 3 3 6
4BA 13 8 21
7BA 3 3 6
8BA 1 0 1
Other 2 1 3

Average Payoff $28.05 $29.60

Pre-test for Managers
Average Score 9.05 Variance 4.14
Maximum Score 12
Minimum Score 3

Measure of Risk Aversion
Risk Neutral 70
Risk Averse 7
Risk Seeking 3

Classification key: Number equals class year (3=junior, 4=senior, 6=post-baccalaureate, 7 and 8=master's) and AC equals accounting, BA equals business administration.

Note: Any numbers not adding up to the reported total are due to some subjects not filling in all questions asked of them.
















Initial random
pairings bidding


12
roundE


New
pairings based on bidding


New
pairings based on bidding


New
pairings based on bidding


New
pairings based on bidding


Sequence #1 Sequence #2
A


111111111 1
Sequence #6
A


Bidding for
managers services


Bidding for
managers' services


Bidding for
managers' services


Bidding for
managers' services


FIGURE 4-1
Time Line for an Experimental Session

















Supervisors inform managers of project payoffs


Managers receive information signal from supervisors


Managers make investment project choices


FIGURE 4-2
Time Line for a Decision Round















CHAPTER 5
RESEARCH RESULTS


Introduction


The results of the tests of the hypotheses developed in Chapter 3 are described in this chapter. First, the results of the logit analysis for the independent variables is presented. Second, the supplementary analyses related to this type of experimental setting (experimental markets) are described. Manipulation checks of the validity of the experiment are then presented. A summary of the additional sessions run to test the revised operationalization of the information asymmetry construct are reported next. A discussion of the results concludes the chapter.


Results of Hypothesis Testing


The main data analysis centers on the managers'

commitment of escalation errors. As such, the primary data gathered in the experimental setting consist of the sequences in which the managers committed escalation errors. Descriptive data of the frequency of escalation errors for each of the independent variables can be found in Table 5-1. Each panel of the table shows the breakdown of the 33 escalation errors committed during the 480 experimental

67









68

sequences by levels of the independent variable.1 Panel A shows that a greater proportion of escalation errors were committed by managers with talent levels of eight and below (10%=19/184), than were committed by managers with talent levels of nine and above (5%=14/296). A smaller difference is shown in Panel B, as managers escalated 8% (18/222) of the time when a monitor was present and the rate dropped to 6% (15/258) when no monitor was present. Panel C shows a marked difference between the two revelation points with a 2% (3/190) escalation rate after six rounds and a 10% (30/290) rate after eight rounds. The largest differences exist in Panel D where the easy series had a 0% (0/164) escalation rate, the moderate series a 3% (5/158) rate and the difficult series an 18% (28/158) rate. Additional preliminary analysis shows Spearman correlation coefficients between the dependent variable, EE, and the four independent variables of -0.090 (p=0.0246) for TAL, 0.045 (p=0.1615) for MON, 0.169 (p=0.0001) for TIME and 0.285 (p=0.0001) for DIFF. These escalation rates and correlation coefficients are initial indicators of which variables are likely to be significant in the logit analysis.

Hypotheses one through four are tested using the logit model (Equation 12 from Chapter 4), since the dependent


1 The 33 escalation errors committed during the
experiment equates to a 7% escalation rate (33 errors + 480 total experimental sequences). The escalation rate predicted in Chapter 4 was 8%.









69

variable (commitment of an escalation error) is dichotomous
2
in nature (O=no and 1=yes). Hypothesis one states that

escalation errors are made more frequently by untalented

managers than by talented managers. Each subject acting as

a manager was tested before the beginning of the actual

experiment on their knowledge of general business concepts

and probability theory (see Pre-Experimental Questionnaire

for Managers in Appendix B). The scores on this test ranged

from a low score of three to the maximum possible score of

twelve. Therefore, the first independent variable

representing the talent level of the managers has nine

levels. A negative relationship is expected between this

variable and the dependent variable, since a low score

represents an untalented manager who is expected to commit

escalation errors.


2 As mentioned previously, logit analysis assumes each observation to be independent of all others. This is not truly the case in the current experiment, as managers invariably experienced a learning curve throughout the experimental sessions. To mitigate this issue, additional analyses were done with each manager representing one observation. This treatment eliminates the problem of independence of each manager's six sequences. Kendall's TB was computed between the number of escalation errors committed and time, as well as the number of escalation errors and the difficulty level. T-tests of the average T B versus zero showed significant results in both cases. These results are consistent with those of the correlation analysis of the dependent variable from the logit model, EE, and the two independent variables, TIME and DIFF. Additionally, in Chapter 4, the correlation analysis of the manager's descriptive measures showed the number of escalation errors to be negatively correlated with the pretest score representing talent. These tests support the results of the logit analysis.











Hypothesis two states that the presence of a monitor

reduces the incidence of escalation errors. The independent variable for this hypothesis is a simple dichotomous variable where a zero represents the absence of a monitor for the manager throughout the experiment, and a one represents the presence of a monitor. It has been established that a manager should only commit an escalation error when information asymmetry exists. The presence of a monitor reduces the information asymmetry between the manager and the market, while the absence of a monitor widens the asymmetry. Consequently, a negative coefficient is anticipated for this variable, as managers should only commit escalation errors when no monitor is present.

Hypothesis three states that the later the manager discovers that he has made an escalation error, the more likely he is to continue with the error. Again the independent variable representing time is a dichotomous variable. For this hypothesis, the time when the managers were informed of the optimal project was varied across sequences for each manager. A zero for this variable represents the receipt of the information after six decision rounds, while a one indicates after eight decision rounds. Since the manager is expected to escalate when he has chosen the same project consistently over a large time frame, a positive coefficient is anticipated for this variable.








71

Hypothesis four states that escalation errors are made by both talented and untalented managers in a difficult series of signals and payoffs, by only untalented managers in a moderate series and by no managers in an easy series. This hypothesis can be broken down into two parts. The first part fits in with the main analysis and posits that more escalation errors are made in the difficult series than in the moderate series and no errors are made in the easy series. This variable has three levels representing the three types of series. A zero represents the easy series, a one the moderate series and a two the difficult series. since the manager is more likely to escalate in a difficult series, the anticipated relationship is positive.

The results of the logit analysis show that the

hypothesized model fits the actual data (likelihood ratio
3
statistic=43.54 and goodness of fit=0.9998). The fitted logit model is shown in Table 5-2. The p-values associated with the parameter estimates of this model show that hypotheses one, three and four are supported, while hypothesis two is not.

The significant negative coefficient for the talent

variable is -0.235 (p=0.008). This supports the contention that untalented managers make more escalation errors than

3 The 2 likelihood ratio statistic for the logit model follows a X distribution and compares the specified model with the unrestricted model. The X 2 statistic converts to a goodness of fit measure which is analogous to a pseudo-r2 in regression.








72

talented managers. The significant positive coefficient of

1.526 (p=0.011) for the time variable confirms that escalation errors are made more frequently after the same project has been chosen for many periods. The significant positive coefficient for the difficulty level variable is 2.161 (p=0.000). This supports the hypothesis that more escalation errors are made when the uncertainty surrounding the manager's project choices is high. These three results are consistent with the hypothesized effects for the three independent variables.

The lack of significance for hypothesis two

(coefficient=0.064 and p=0.439) is somewhat troubling, however, since information asymmetry is such an important concept in principal-agent problems. The experimental manipulation of the monitor was obviously not salient to the subjects in their choices. Despite this result, managers and supervisors both reported that the monitor did affect their choices in post-experimental questionnaires. The subjects apparently did not fully understand the meaning and purpose of the monitor in the experiment. Changes in the manipulation of the monitor were undertaken for the additional sessions of the experiment (and are discussed later in this chapter).

The second part of hypothesis four asserts that

escalation errors in the difficult series are committed by both talented and untalented managers, while those in the










moderate series are committed only by untalented managers. Of the 28 errors made in the difficult series, 19 were made by untalented managers and nine by talented managers. This supports the contention that both types of managers will escalate in the difficult series. Of the five errors made in the moderate series, two were made by untalented managers and three were made by talented managers. This does not seem to support the contention that only untalented managers will escalate in the moderate series, but is obviously not definitive due to the small sample size. As reported earlier, no errors were made in the easy series. overall, the validity of hypothesis four is supported.

Much descriptive data were gathered from the subjects in the post-experimental questionnaires. Spearman correlation coefficients between these measures are shown in Table 5-3. There are seven significant correlations between the different variables. The manager's score on the "talent" pre-test, administered before the experiment, is correlated with three different variables. 4 The pre-test score is positively correlated with the manager's selfreported GPA. This suggests that the pre-test may be a somewhat accurate measure of the subject's actual talent or intelligence level. There is an apparently spurious


4 Cronbach's a is a measure which reports the
reliability of a test. The a for the manager's talent pretest is 0.62, which is considered an adequate measure of reliability.








74

positive correlation between the pre-test score and the male managers, as no such correlation is expected. A significant negative correlation exists between the pre-test score and the number of escalation errors committed during the experiment. This is additional evidence of the hypothesized negative relationship between the talent variable and the commitment of escalation errors. An anticipated positive correlation exists between the manager's GPA and the experimental payoff. An unexpected positive correlation exists between the number of escalation errors and the manager's GPA. An obvious positive correlation is found between the manager's age and classification. A positive correlation exists between the manager's experimental payoff and the number of escalation errors. This correlation supports the hypothesized positive effect of escalation errors on the manager's reputation. Since the manager actually earned less in a sequence when he escalated, he apparently was able to make up for this discrepancy by garnering higher bids from supervisors in future sequences. The results of the correlation analysis provide support for the main results of the logit analysis.


Additional Testing


Supplementary Analyses


Convergence to equilibrium. The three premises

previously mentioned in Chapter 4 pertaining to the logical









75

payoffs are tested in this section. The calculation of the logical payoffs is shown in Appendix C. Table 5-4 shows the logical payoffs for easy, moderate and difficult sequences when the optimal project is revealed after both six and eight rounds.

The logical payoffs for the two types of easy sequences (see note following table) are inherently the highest of the calculated payoffs. The difficult sequence has a higher logical payoff than the moderate sequence due to several factors. These factors are the result of the difficult series being specifically designed to promote more escalation errors. This was accomplished through a greater incidence of both an inconsistent signal and the random factor in the early rounds of a sequence. This combination induced managers to choose the incorrect project and rewarded them with spuriously high payoffs. Managers were consequently led to believe that they were choosing the correct project, when in fact they were choosing the incorrect project. Therefore, they often continued to choose the incorrect project until the superior project was revealed to them. At this point, the managers normally switched, thus incurring the cost to switch just once. During the moderate sequence, managers were more likely to switch more often since the incidence of inconsistent signals and the random factor was interspersed. This resulted in more low payoffs and higher total switching











costs. Therefore, the combination of inconsistent signals, the random factor and the total switching costs incurred cause the logical payoff for the difficult sequence to be larger than that for the moderate sequence.

Premise P, pertains to the magnitude of the variances and absolute values in the easy, moderate and difficult sequences. The lowest total measures are anticipated in the easy sequences, while the highest are expected in the moderate sequences. Table 5-5 shows the resulting variance and absolute value calculations.

The variances reported suggest no clear-cut trend for either revelation point. The absolute values, on the other hand, display the anticipated relationships. For both revelation points the easy sequences have the lowest absolute values while the moderate sequences have the highest absolute values. As mentioned previously, the absolute values are considered to be better indicators of the true variation in the samples since they are less affected by severe outliers. Z-tests comparing the different absolute values show significant results at a=0.05 or better for all comparisons except the easy versus the difficult after six rounds. 5 Premise Pi is therefore supported by the absolute value calculations.



5 Z-tests on a standardized measure of the absolute value (the absolute value as previously calculated divided by the logical payoff) show comparable results.











The second premise, P 21 posits that the market should move toward the logical payoffs over time. These variances and absolute values are reported in Table 5-6. From this table it is apparent there is no movement toward the logical average in the moderate sequences. The variances and absolute values are very similar when the superior project was revealed after six rounds and actually increase slightly with revelation after eight rounds.

For the difficult sequences, however, there does appear to be movement toward the logical payoffs. In the nonescalation error sample, both variances decrease significantly by over 50% at the second sequences, while the absolute values decrease as well. The entire sample shows similar decreases for revelation after eight rounds. The figures for the non-escalating sample are shown in addition to those for the entire sample since the three escalation errors that occurred during the second sequence under revelation after six rounds accounted for the majority of the entire sample's variance and absolute value at that revelation point. For the non-escalation error sample, Ztests are computed between the absolute values of the first and second sequences at both six and eight rounds. The resulting Z-score after six rounds is insignificant (p=0.1736), but the Z-score after eight rounds is moderately significant (p=0.0793). Since there were more sequences with round eight revelation, this result is encouraging.










The movement toward the logical payoffs for the difficult sequences but not for the moderate sequences is not surprising, given the previous discussion concerning the volatility of payoffs in the moderate sequences. Premise P 2 is thus supported for the difficult sequences (especially for round eight revelation), but not for the moderate sequences.

Premise P 3 posits that the variances and absolute

values should be greater when an escalation error is made than when no error is made. The majority of the escalation errors (28 of 33) were committed in the difficult sequences. Additionally, the majority of these escalation errors (25 of 28) were committed after round eight revelation. Therefore, the expected effect should be the most prominent under these conditions. Table 5-7 presents these variances and absolute values. In the difficult sequences the variances (and absolute values for round six revelation) when an escalation error occurred are greater than those for the non-escalating sequences, as expected. Z-tests on the absolute values show significant results after six rounds, but not after eight. For the few escalation errors made in the moderate sequence (five) the relationship is reversed. The small sample size in this case, however, precludes this result from being significant. Premise P 3 is consequently not supported in the difficult sequences after round eight revelation where the majority of the escalation errors occurred.








79

Bidding strategies. The movement of supervisors' bids over time and the reaction of the bids to an escalation error are two relationships that are investigated. The actual average bid and the logical average bid are used as the two reference points in evaluating these trends. The actual average bid for the 480 experimental sequences is $2.10. The logical average bid, calculated based on the logical payoffs discussed previously, is $2.43 (see Appendix C). The actual average bid is somewhat below the logical average bid partially because the expected payoffs for managers and supervisors were not equal, as is assumed when dividing the logical payoffs between managers and supervisors. Pilot studies showed that supervisors often bid quite low and therefore garnered higher total payoffs. The minimum bid was raised for the actual experiment, but it was still anticipated that supervisors may bid low. This low bidding caused supervisors to earn more during the
6
experiment.

6 When the experiment was initially devised, the division of the firm's total accumulated payoffs between managers and supervisors as well as the boundaries for supervisors' bids were carefully considered. In information economics models the expected payoffs of all participants are equal. In the current experiment the division of total payoffs and the boundaries for supervisors' bids were established in an attempt to equate managers' and supervisors' expected payoffs. Initial pilot tests showed that supervisors often bid low and earned higher experimental payoffs. Since the adopted division of the total payoffs between managers and supervisors seemed logical, the bidding boundaries were manipulated. This manipulation obviously did not fully counteract the observed behavior, since supervisors continued to bid low and earn








80

The optimal strategy for a supervisor in the experiment was to bid low, due to the unequal expected payoffs. Some supervisors identified this strategy quickly, while others did not. Consequently, a decreasing trend is expected in the bids over time. Additionally, the variance of the bids should also decrease over time, as a bidding strategy is adopted. Table 5-8 shows the average bids across the sequences, and the two measures of variance based on the actual average and the logical average. No decreasing trend in either the average bids or the variances is evidenced. After additional analysis of each supervisors, six bids, it is apparent that very few supervisors adopted a discernable bidding strategy. Even though the majority of the subjects acting as supervisors appeared to be giving an earnest effort during the experiment, they were apparently unable to devise a distinct bidding strategy. The reason for this lack of explicit bidding strategies is unknown, since the supervisors' post-experimental questionnaire responses suggest an understanding of the experimental constructs.

The second relationship to be investigated concerning

the supervisors' bids relates to the reaction of the bids to an escalation error. A direct causal relationship cannot be inferred since the supervisors were unable to discern with certainty when an escalation error had been made. Any


more in the actual experiment. In future experimentation, the division of the total payoffs will be manipulated in an attempt to equate experimental payoffs.










observed relationship may be because the commitment of an escalation error lowered the resulting payoff for that sequence. The lower payoff may in turn have caused lower bids from supervisors. This is substantiated by the supervisors' post-experimental questionnaires, where payoffs were found to be an important factor that influenced supervisors' bids. The average bid following an escalation error was $1.97, which is significantly different from the overall average bid of $2.10 at a=0.010. It is difficult to draw conclusions concerning the supervisors' bids, however, since the significant decrease following an escalation error cannot be directly traced to the commitment of the error. Manipulation Checks


To ensure the validity of experimental manipulations, all subjects filled out a post-experimental questionnaire which can be found with the other experimental instruments in Appendix B. Subjects were asked to respond to each question on a five point scale with the options of strongly agree, agree, no opinion, disagree and strongly disagree. The questions were devised such that a response of agreement or disagreement would display the subject's interpretation of the importance of the construct. The constructs were
7
tested using the nonparametric sign test. Consequently,

7 For the sample of escalating managers (sample
size=25), the test statistic B is used in the sign test. The entire sample of managers, the sample of non-escalating








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the salience of a construct is validated by a statistically significant result from the sign test. The analysis of the responses to the questionnaires is in Tables 5-9 through 512. The inferences that can be drawn from this analysis are powerful, particularly those comparing the samples of escalating and non-escalating managers.

Question one dealt with the importance of consistent project choices. Question two examined the importance of project payoffs to the exclusion of all other factors. Question three asked about the importance of a late round switch. Question four dealt with the disclosure of the optimal project payoffs. Question five asked whether the subject felt he was successful in earning the highest payoffs possible during the experiment. Question six asked whether the subject would make the same decisions in the "real world."

For the entire sample of managers, questions one

through four and six are statistically significant with an a of 0.05 or better. Specifically, the majority of the managers (79%) strongly agreed or agreed that consistency in project choices was important. Over half (56%) of the managers strongly disagreed or disagreed that project payoffs were the only factor they should be concerned about.



managers and the sample of supervisors constitute large sample sizes (greater than 30). For these samples, the test
statistic B* is used as the large sample approximation of the sign test.








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The majority (54%) of the managers strongly agreed or agreed that switching projects at a late round in a sequence would have a negative effect on the amounts of bids from supervisors in the future. Almost half (48%) of the managers strongly agreed or agreed that the disclosure or non-disclosure of their optimal total payoffs affected their project choices. over half (53%) strongly agreed or agreed that they would make the same decisions if they were in the "real world." Question five does not show a significant effect. When asked whether they thought they were successful in earning the highest payoffs possible during the experiment, nearly equal percentages replied positively as replied negatively.

A comparison of Tables 5-10 and 5-11 for the samples of escalating and nonescalating managers shows that the means are quite different for many of the questions. For the sample of escalating managers, questions one through three are statistically significant with an a of 0.05 or better. The sample of nonescalating managers shows significance for questions one and six at a of 0.05 or better. More specifically, t-tests comparing the two sample means show significant differences between the two samples for questions two and three (p-value=0.038 for question two and 0.002 for question three). This constitutes strong support for a basic difference between the two samples of managers.










A large difference exists between the two samples for question three. The escalating managers clearly believed that a switch in the later rounds of a sequence would lower the amounts of bids from supervisors in subsequent sequences, while nonescalating managers were not convinced of this point. The difference in results for question two shows that the escalating managers were more convinced that project payoffs were not the only factor that they should be concerned about. These two beliefs led escalating managers to commit escalation errors. These results shown in the post-experimental questionnaires present strong evidence of a critical difference between the samples of escalating and nonescalating managers. Escalating managers clearly believed that late round switches would be detrimental to them in the future sequences and that project payoffs were not the only factor that they should be concerned about.

For the supervisors questions one, two, four and six are statistically significant at a of 0.05 or better. All of these questions are significant for the entire sample of managers as well. Specifically, a large percentage (69%) of the supervisors strongly agreed or agreed that consistency in project choices was an important quality in a manager. For question two, the majority of the supervisors (60%) strongly disagreed or disagreed with the contention that project payoffs were the only factor that they should be concerned about when bidding for managers. The great









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majority (71%) strongly agreed or agreed that the disclosure of the optimal total payoffs for some managers affected their bidding strategy. Almost half (49%) strongly agreed or agreed that they would make the same decisions if they were in the "real world." Questions three and five show insignificant results. For question three, supervisors were almost evenly split on the contention that a late round switch was a signal of a poor manager. Supervisors were also split on question five on whether they were successful in earning the highest payoffs possible during the experiment.

In general the majority of the constructs were proven to be salient in the post-experimental questionnaires. The importance of consistency in project choices, the contention that project payoffs were not the only factor they should be concerned about, the importance of the disclosure of optimal project payoffs and the feeling that subjects would make the same decisions in the "real world" were all proven to be salient for both managers and supervisors. only managers felt that a late round switch would lower the amounts of bids from supervisors in the future. Neither group felt that they were successful in earning the highest payoffs possible during the experiment.










Results for Revised Experiment


A revised experiment consisting of six sessions was run in an attempt to discern whether a stronger manipulation of the information asymmetry (the monitor) construct would prove more salient to the subjects. A total of 55 student subjects from one section of Financial Accounting 2 at the University of Florida participated in the experiment. The students earned class credit for participating (equal to 2% of their final grade) as well as lottery tickets. The cash prizes for the lottery drawing for the revised experiment consisted of two prizes: one of $50 and one of $25.

Subjects. Descriptive statistics about the subjects

for this experiment can be found in Table 5-13. As shown in the table, the numbers of male and female subjects were almost equal. The average age was 23 and the average GPA was 3.21. The vast majority of the subjects were junior and senior level accounting students. The average payoffs were $28.32 for managers and $27.54 for supervisors. The average pre-test score for managers was 8.43, with a maximum of 11 and minimum of 5. The solicited risk aversion measure for the managers revealed 26 risk neutral subjects, two risk averse subjects and no risk seeking subjects. The average age and GPA were similar to those for the subjects for the main experiment (23 and 3.30). The average pre-test score was significantly lower than in the main experiment (9.05). This is possibly due to the lower classification level of










the majority of these subjects compared with the subjects from the main experiment. A larger average payoff was earned by the managers, in contrast to the relationship in the main experiment. This was apparently due to the modifications undertaken for the additional sessions.

As in the main experiment, additional descriptive data were gathered in the post-experimental questionnaires. Spearman correlation coefficients between these measures are shown in Table 5-14. These correlations are quite different from those of the main experiment. The significant positive correlation between the manager's age and classification is the only significant correlation in the same direction as in the main experiment. The three most relevant correlations of the first experiment--a positive correlation between the manager's pre-test score and GPA, a negative correlation between the manager's pre-test score and the number of escalation errors committed and a positive correlation between the manager's experimental payoff and the number of escalation errors--are not present in the revised experiment. The opposite correlations involving the pretest score suggest that this measure was not effective in measuring the subject's aptitude for the experimental task in the revised experiment. The negative correlation between the manager's experimental payoff and the number of escalation errors suggests that the hypothesized reputation effect observed during the main experiment was not present










during the revised experiment. The lack of correlation between these measures suggests that the revised experiment is not entirely comparable to the first.

Modifications. The revised experiment was modified

slightly to promote a greater incidence of escalation errors and to strengthen the information asymmetry manipulation. There was no manipulation of the time variable (the revelation point after either six or eight rounds) in the additional sessions, as all managers were always informed of the superior project after eight rounds. Additionally, managers faced an extra difficult sequence in place of the second easy sequence.

The change in the manipulation of information asymmetry for these sessions was two-fold. First, instead of disclosing the optimal total payoffs (as was done in the main experiment), the superior project itself was disclosed. A second change was also made, since the certain disclosure or non-disclosure of the superior project was a perfect signal of an escalation error (and seemed too blatantly obvious). Instead of certain disclosure or non-disclosure, there was a percentage chance of disclosure for each manager. Half the managers had a 20% chance and half an 80% chance of disclosure. At the end of each sequence managers drew a card from a deck with these probabilities to determine whether the superior project was disclosed. This is considered a stronger manipulation than in the main











experiment. In the main experiment, the commitment of an escalation error by a manager had to be interpreted by a supervisor through a comparison of the optimal total payoffs and the actual payoffs. In the revised experiment, the superior project was actually disclosed. Consequently, if a manager had an 80% chance of disclosure of the superior project he should never have escalated, since there was a very good chance the supervisors would find out that he made an escalation error. With the 20% chance the manager should have felt a very small chance of being "caught." It was hoped that this manipulation would cause managers to only escalate under the 20% chance of disclosure.

Results. There were a total of twelve escalation

errors committed during the six sessions. 8 Exactly half of the errors occurred in each treatment of the information asymmetry construct. The results of the logit analysis are shown in Table 5-15. Again the hypothesized model fits the actual data (likelihood ratio statistic=21.71 and goodness of fit measure=0.9149). An insignificant effect for the

8 The twelve escalation errors committed during the revised experiment equates to a 7% escalation rate (12 errors - 168 total experimental sequences). This rate is equivalent to that of the main experiment. The escalation rate predicted for the revised experiment is 15%--half of the managers are untalented, half have no monitor present and three of the last five sequences are difficult-1/2 X 1/2 X 3/5 = 3/20 = 15%. In the main experiment the actual and predicted escalation rates are almost equal. In this case the actual escalation rate is half of the predicted rate. This observation represents further evidence that the revised experiment is not entirely comparable to the main experiment.








90

monitor was again shown in these experimental sessions. The post-experimental questionnaires for both managers and supervisors showed no significance for question four which deals with the effect of disclosure on the subjects' decisions (the majority of the remaining post-experimental questionnaire responses were similar to those of the main experiment). This is in stark contrast to the significant effect for this question in the post-experimental questionnaires for both managers and supervisors in the main experiment. This lack of results for the monitor variable under the modified manipulation is quite troubling. Apparently the subjects are given too much information about the entire experiment to be able to discern the relevance of each piece. Alternatively, the manipulation of the monitor construct may have been unable to capture the theoretical essence of the variable. Nevertheless, the theory that a monitor should reduce escalation errors cannot be discarded without numerous attempts to prove or disprove the saliency of the monitor construct.

Another troubling result of the revised experiment is

the significant positive coefficient of the talent variable. This relationship was also shown in the correlation analysis. specification testing for outliers shows only one manager in the revised experiment as a severe outlier. 9

9 An outlier is usually considered extreme if its residual is larger than two standard deviations. From a conservative viewpoint, only outliers which had values of










When compared to the main experiment, this manager is the most severe outlier of both samples. When this manager's observations are removed from the logit analysis, the talent variable loses its significance. These results are shown in Table 5-16. This outlier has a great influence on the results of the revised experiment due in part to the small sample size of this experiment (28 managers as compared to 82 in the main experiment). When the data from the main experiment are combined with the data from the revised experiment excluding the outlier, the coefficient on the talent variable reverts to its expected sign and regains its significance. Table 5-17 shows these results, which are similar to those of the main experiment.

Another observation supports the assertion that the

revised experiment was not completely comparable to the main experiment. The subjects in the revised experiment did not have the same incentive system as those of the main experiment. In the main experiment, the subjects were recruited from their classes with the sole incentive of earning lottery tickets and a chance to win a cash prize.



studentized t residuals greater than 3.00 were removed. The identification of the severe outlier from the revised experiment was straightforward and was based on a regression with NEE as the dependent variable and TAL, MON and PAYOFF as the independent variables. only one studentized residual greater than 3.00 was found. The severe outlier's studentized residual was 3.27. When the data from the main experiment was combined with that from the revised experiment, the observed outlier's studentized residual jumped to 4.11.










In the revised experiment, the subjects still had a chance to earn lottery tickets and win a cash prize (although the cash prizes were significantly smaller), but it is believed that the main incentive for these subjects was the class credit they received for participating in the experiment. The credit was given unconditional of the subjects' performance during the experiment, i.e., all students received equal credit. By earning the class credit the subjects were relieved of an out of class assignment. The instructor informed the students that participation in the experiment would probably be "easier" than the completion of the assignment. only three students out of the class of 58 did not participate in the revised experiment. During these experimental sessions, an obvious lack of effort and interest was shown by many subjects (and was noted by the experimental assistant as well as the experimenter). Due to these facts the revised experiment is not considered to be analogous to the main experiment.


Summary


The results of the data analysis suggest that the main experiment was successful in inducing escalation behavior. Three of the four factors posited to influence this behavior were significant: the talent level of the manager, the time when the manager is informed of the superior project and the difficulty level surrounding the manager's decision. The








93

talent factor should be closely related to the commitment of an escalation error. The significance of the talent factor in the experimental study suggests that the pre-experimental questionnaire designed to measure talent was an accurate gauge of the subject's actual aptitude for the experimental task. The time factor is somewhat obvious and its experimental significance supports the hypothesized effect. The manipulation of the difficulty level surrounding the manager's decisions is also logical. It's experimental significance not only supports the predictions, but also provides an internal validity check of the experiment. As mentioned previously, the lack of significance for the monitor manipulation in both the main experiment and the revised experiment is troubling. In general this experiment was successful in identifying which of the posited factors influence the commitment of an escalation error.
















TABLE 5-1
Frequency of Escalation Errors for Independent Variables Panel A: Talent Level Escalation Error

Talent Level Yes No

3 0 4

4 2 4

5 2 10

6 2 22

7 7 65

8 6 60

9 2 72

10 6 80

11 2 80

12 4 50

Panel B: Monitor Variable Escalation Error

Monitor Yes No

Present 18 204

Absent 15 243

Panel C: Time Variable

Revelation Escalation Error

Point Yes No

6 Rounds 3 187

8 Rounds 30 260




Full Text

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AN EXPERIMENTAL INVESTIGATION OF MANAGERS' ESCALATION ERRORS By ROBIN RAE RADTKE A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 1992

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ACKNOWLEDGEMENTS I would like to thank the members of my dissertation committee for all of their help: Professor Bipin Ajinkya (Chairman), Professor Doug Snowball, Professor Jeffrey Yost and Professor Ronald Randles. Their guidance and words of wisdom were greatly appreciated throughout this project. Additionally, I am grateful to all the professors from the Fisher School of Accounting and the Department of Finance, Insurance and Real Estate who supplied me with class time to recruit subjects. My experimental assistants, Tom Bristow, Steve Cox, Dominique Marchand, Sean Robb, Sherry Ropp and Sean Williams deserve praise for helping me administer the experiment. The student subjects who participated in the experiment, most of whom were very good natured, are acknowledged for giving their time and effort. Last but not least, I would like to thank my family and friends for their love and support. ii

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TABLE OF CONTENTS ACKNOWLEDGEMENTS. . . . . . . . . . . . . . . . . . . ii ABSTRACT. . . . . . . . . . . . . . . . . . . . . . . V CHAPTERS 1 INTRODUCTION AND BACKGROUND................... 1 Introduction. . . . . . . . . . . . . . . . 1 Motivation for the Research................. 3 Overview of Research Method and Organization. . . . . . . . . . . . . . . 5 2 THE ESCALATION ERROR PROBLEM AND RELATED STUDIES. . . . . . . . . . . . . . . . . . . 6 Introduction. . . . . . . . . . . . . . . . 6 The Accounting Issue........................ 7 The Principal-Agent Setting................. 9 The Issue of Information Asymmetry.......... 12 The Formation of the Manager's Reputation... 14 Related Escalation Error Literature......... 15 The Present Research........................ 20 3 DEVELOPMENT OF MODELS AND HYPOTHESES.......... 22 Introduction................................ 22 The Principal-Agent Model................... 22 The Information System...................... 27 The Effect of the Manager's Talent Level.... 31 Information Asymmetry Effects of the Monitoring Variable....................... 33 The Effects of Timing of Information Disclosure. . . . . . . . . . . . . . . . 3 6 Effects of the Environment.................. 37 The Decision Model.......................... 38 Supplementary Analyses...................... 39 iii

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4 RESEARCH DESIGN AND DATA ANALYSIS METHODS..... 40 Introduction. . . . . . . . . . . . . . . . 4 O Experimental Design......................... 40 Overview.................................. 40 Experimental Task......................... 41 Subjects. . . . . . . . . . . . . . . . . 4 8 Incentive Structure....................... 50 Operationalization of Independent Variables.......................... . . 51 Measurement of Dependent Variable......... 54 Data Analysis Methods....................... 55 The Decision Model........................ 55 Predicted Relationships................... 57 Supplementary Analyses.................... 58 Summary..................................... 62 5 RESEARCH RESULTS . . . . . . . . . . . . . . . 6 7 Introduction................................ 67 Results of Hypothesis Testing............... 67 Additional Testing.......................... 74 Supplementary Analyses.................... 74 Manipulation Checks....................... 81 Results for Revised Experiment............ 86 Summary..................................... 92 6 SUMMARY AND CONCLUSIONS ....................... 112 Summary of the Study ........................ 112 Summary and Discussion of Results ........... 112 Contributions and Implications .............. 116 Contributions to Managerial Accounting Research. . . . . . . . . . . . . . . . 116 Implications for Understanding Escalation Errors ....................... 118 Limitations................................. 120 Directions for Future Research .............. 120 APPENDICES A EXPERIMENTAL PROTOCOL ......................... 122 B EXPERIMENTAL INSTRUMENTS ...................... 126 C CALCULATION OF LOGICAL PAYOFFS AND BIDS ....... 146 REFERENCES . . . . . . . . . . . . . . . . . . . . . . 15 0 BIOGRAPHICAL SKETCH. . . . . . . . . . . . . . . . . . 152 iv

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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy AN EXPERIMENTAL INVESTIGATION OF MANAGERS' ESCALATION ERRORS By Robin Rae Radtke August 1992 Chairman: Bipin B. Ajinkya Major Department: Accounting An escalation error results when the manager continues with a course of action that has proven to be nonoptimal. These errors can represent costly losses to the firm. The purpose of this study is to investigate the source of managers' escalation errors. It was hypothesized that when the manager commits an escalation error, he may be attempting to conceal his previous incorrect choice and thereby protect his reputation. The protection of the manager's reputation may provide greater benefits to him than the benefits resulting from making a correct choice. A principal-agent model was developed to examine how the reputation value of the manager influences the manager's decisions in an escalation error scenario. Four factors were posited to directly influence escalation behavior: the talent level of the manager, the V

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presence of a monitor, the time at which the manager discovers he has made an escalation error and the difficulty level surrounding the manager's decision. The model was modified to fit an experimental setting using student subjects as surrogates for actual managers and supervisors. A manager was expected to escalate when he was untalented, had no monitor present, discovered that he had made an escalation error at a late stage and faced difficult environmental factors. The hypotheses were tested using a logit model. The results for the three independent variables--the manager's talent level, the time at which the manager discovers he has made an escalation error and the difficulty level surrounding the manager's decision--support the predictions. The expected effect of the presence of a monitor was not supported. A revised experiment was run with some modifications to re-test the effect of the monitor. The results of these sessions again showed an insignificant effect for the monitor construct. These experimental results may help researchers gain new insight into the escalation error problem in designing further studies. vi

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CHAPTER 1 INTRODUCTION AND BACKGROUND Introduction This dissertation investigates the escalation error phenomenon. An escalation error results when an individual continues with a course of action that has proven to be nonoptimal for the individual. Behavioral studies constitute the bulk of the previous research in this area. 1 These studies have isolated several factors associated with escalation errors. The purpose of this study is to examine the causes of escalation errors from an accounting standpoint. The causes of escalation errors cannot be identified without a thorough understanding of the escalation error problem. The definition of an escalation error must be applied carefully to each case, as what constitutes nonoptimality, and evidence of nonoptimality, will vary between cases. In general, negative feedback that leads an individual to believe an alternative course of action would produce a more favorable outcome constitutes evidence of nonoptimality. If after receiving this information the See Brockner (1992) for a good review of this literature. 1

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individual continues with the originally chosen course of action, an escalation error is said to exist. Escalation errors are not unique to any one setting. 2 The Vietnam War is a prominent example that demonstrates how potentially disastrous an escalation error can be. The following memo emerged in the early stages of the conflict: The decision you face now is crucial. Once large numbers of U.S. troops are committed to direct combat, they will begin to take heavy casualties in a war they are ill-equipped to fight in a non-cooperative if not downright hostile country-side. Once we suffer large casualties, we will have started a well-nigh irreversible process. Our involvement will be so great that we cannot--without national humiliation--stop short of achieving our complete objectives. Of the two possibilities, I think humiliation would be more likely than the achievement of our objectives--even after we have paid terrible costs. (Memo from George Ball to President Lyndon Johnson, July, 1965; source: The Pentagon Papers, 1971.) 2 This memo shows the sentiments of Ball and undoubtedly many others. The war continued on for many years, however, even with these strong assertions of the impending horrors ahead. This example shows the depths to which individuals can become entrenched in escalation errors and points out the importance of recognizing negative feedback to avoid an imminent escalation error. Escalation errors in business may be very costly as well. Many business decisions could lead to escalation errors, but one in particular is the focus of this study. 2 This example is referenced in Staw (1976, 1981) and Staw and Fox (1977) as an important case of an escalation error.

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3 The choice between alternative investment projects, or more specifically the capital budgeting replacement decision, is examined. This scenario provides the opportunity to investigate the escalation error phenomenon in a business setting. By doing so, the determinants which cause a manager to engage in escalation behavior may be isolated and the relationships between these determinants can be investigated to provide additional insight into the problem of escalation errors. Motivation for the Research When capital budgeting decisions are made with inappropriate information, an inefficient allocation of the firm's available capital results which is inconsistent with the business goal of maximizing firm value. Consequently, the issue of determining what causes the manager to make these decision errors is of interest. Empirical evidence suggests that escalation errors also occur in business settings. 3 Explanations from the body of psychological 3 DeBondt and Makhija (1988) and Statman and Sepe (1989) are examples of empirical studies. Both of these studies look at share price movements in reaction to the specified events. DeBondt and Makhija test the validity of the sunk cost hypothesis as a basis for committing escalation errors in the context of the U.S. nuclear power program. They examine the market reaction to all plant completions and cancellations (over $50 million) prior to March 1984. Their results are mixed and do not support a 'powerful' sunk cost effect. They point to prudency reviews ordered by U.S. Public Service Commissions, however, as anecdotal evidence of costs incurred and supported by the sunk cost hypothesis. Statman and Sepe examine the reaction

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research present many theories for the occurrence of escalation errors. Kanodia, Bushman and Dickhaut (1989) offer an alternative explanation based on economic rationality. Behavioral studies have tested the hypothesized psychological factors. Many of these studies have actually used business or investment decision contexts. 4 The current study incorporates factors of interest from a managerial accounting standpoint (agency aspects) and represents an empirical test of the Kanodia, Bushman and Dickhaut (1989) theory. By designing this study with accounting and economic factors specifically in mind, additional insight into understanding escalation errors in terms of accounting can be gained. 4 The variables of interest in this study that have not been previously investigated (in an experimental study) are the talent level of the manager, the necessity and the impact of information asymmetry, the effect of the timing of information disclosure and the difficulty level surrounding the manager's decision. In an experimental setting each of of the market to project termination announcements. They find the mean market-adjusted return to be positive. They conclude that on average shareholders consider project termination announcements good news, because managers will no longer throw good money after bad (and are finally abandoning the sunk costs of failed projects). 4 Staw (1976) and staw and Fox (1977) both use the allocation of research and development funds between two competing divisions as their financial decision case.

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5 these variables can be manipulated so a fully crossed design can be established. This allows the impact of each factor to be analyzed. As a result, issues more specifically related to accounting can be investigated in the escalation error scenario. Overview of Research Method and Organization The impact of the variables of interest on the escalation error decision was examined in an experimental study. The true purpose of the study was disguised such that subjects knew only that they were involved in an experiment dealing with investment project choices. Subjects earned lottery tickets based on decisions made in the study. The tickets were entered into a lottery with seven cash prizes. The chapters are organized as follows. Chapter 2 discusses the escalation error problem and related literature. The research models and hypotheses used in the current study are developed in Chapter 3. Chapter 4 describes the research design used to test the hypotheses. The results of the experiment are described in Chapter 5. Chapter 6 includes a summary of the study and conclusions.

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CHAPTER 2 THE ESCALATION ERROR PROBLEM AND RELATED STUDIES Introduction The discussion in this chapter focuses on the problem of managers committing escalation errors when making investment project or capital budgeting replacement decisions. The accounting issue is discussed first. An analysis of relevant and irrelevant accounting information governs how the manager should choose between alternative capital assets. If the manager allows irrelevant accounting information such as sunk costs to influence his choice, he may commit an escalation error and stray from the optimal choice. The framework for the current study is presented next. This includes a discussion of the principal-agent setting and the related issues of information asymmetry and the formation of the manager's reputation. This framework provides the setting within which a manager can justify an escalation error in terms of a benefit to his reputation value. The chapter then considers the related escalation literature in the accounting and psychology fields. Finally, the link between the adopted framework and the current study is presented. 6

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7 The Accounting Issue As discussed in Chapter 1, an escalation error results when an individual continues with a course of action that has proven to be nonoptimal for the individual. The individual of interest from an accounting standpoint in this study is the manager. The manager continually makes important decisions that affect both the well being of the firm and his career. The accounting information the manager has at hand governs his choices. Appropriate use of this information should lead to optimal choices. Misuse of this information may lead to escalation errors. Determining what information the manager should use and how he should use it is the crux of this discussion. Accounting information for managerial purposes consists of projected and historical data. An old management accounting adage states that only relevant information should be used in the decision making process from the viewpoint of the firm. Information is considered irrelevant when it is equal for all decision alternatives. Thus, historical data from past decisions is considered irrelevant information. Past costs and revenues may be useful in predicting future costs and revenues, but have no direct relevance for future decisions. 1 See Horngren and Foster (1987) chapter nine for a good discussion of this issue.

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8 One problem facing the manager on a recurring basis is the capital budgeting replacement decision. This decision entails the replacement of an old capital asset (such as a machine) with a new asset. Once the decision to replace an old asset has been made, the primary issue becomes what type of new asset to purchase. Many cost figures are available when considering this problem. 2 They include the cost of the new asset, the disposal value of the old asset, the purchase price of the old asset and the book value of the old asset. Only the relevant costs in the above list should be utilized. If several new assets are being considered for purchase, then the cost of the replacement asset will vary across decision choices and as such is relevant information. The disposal value, the purchase price and the book value of the old asset do not vary across current decision alternatives and are irrelevant information. The disposal value is equal to the expected proceeds of the sale of the old asset. The purchase price and the book value of the old asset are based on historical information. The purchase price of the old asset is considered a sunk cost, 3 since 2 Tax considerations inherently play a role in the capital budgeting replacement decision. In the current study tax issues are ignored to make the analysis more tractable. 3 Sunk costs are defined as costs which have been incurred in the past and cannot be changed by future actions. As such, sunk costs are always irrelevant. Individuals, and managers in particular, often fixate on

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9 the cost of the old asset has already been incurred and cannot be recovered (except through productive use of the asset). The book value of the old asset (the purchase price of the old asset less accumulated depreciation) is also irrelevant information since it is a meaningless combination of a historical cost and an arbitrary accumulated expense account. Therefore, once the manager has decided to replace an old asset, the only relevant information he should consider is the cost of the replacement asset. 4 5 The Principal-Agent Setting From a normative standpoint, the analysis of the relevance of various pieces of accounting information is always undertaken from the point of view of the firm. When the manager uses the appropriate relevant information, the sunk costs since they feel responsible for the capital outlay. This fixation can lead to escalation errors when the manager refuses to replace an old asset that has not earned an acceptable return on the initial investment. 4 The other cost figures mentioned may be important in making the decision of when to replace the old asset. They would also be salient in a setting where tax considerations are involved. In the current study however, neither condition is applicable. Once the manager realizes the old asset should be replaced, the decision between replacement assets is the relevant issue in the escalation error scenario. 5 In a broader context, many other relevant items are considered by the manager when making the asset replacement decision. Such factors as differences in technologies and production capacities between potential replacement assets are obviously evaluated. Additionally, purchase terms and agreements with different companies may enter into the decision.

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firm value is maximized. This analysis leaves no room for the commitment of escalation errors. Empirical evidence suggests, however, that escalation errors do occur in business settings. 6 The proposed framework based upon a principal-agent setting recognizes the opportunity for the occurrence of escalation errors. 10 In the principal-agent setting, the economic relevance of information may change when agency costs between principals (owners) and agents (managers) are considered. In a strict principal-agent problem, agents attempt to maximize their personal utilities. In the pure context of the business decision, on the other hand, the manager should attempt to maximize the utility of the firm, since the manager is hired as an agent of the firm and takes on the responsibility of acting in the best interests of the firm. In some cases the personal utility of the manager and the utility of the firm may not be maximized by the same course of action. When the manager puts the maximization of his personal utility before that of the firm, a divergence between these two utilities may lead to suboptimal decisions for the firm. Within this context an escalation error is defined as continuing with a course of action that is nonoptimal for the firm, but optimal for the manager. 6 See DeBondt and Makhija (1988) and Statman and Sepe (1989) for examples of empirical studies.

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11 The maximization of personal utility on the part of the manager is in accordance with the manager's desire to establish a reputation. This is supported by the proposition of Holmstrom and Ricart i Costa (1986) that the manager's compensation is a function of both the firm value and the manager's reputation value. They assert that some sort of contracting is necessary in order to prevent the manager from fixating solely on his personal utility. They develop a model of the manager's reputation based on learning. Once the manager realizes that his performance is used as a signal of his competence, he tries to influence the evaluation process through his choice of actions. A dilemma arises when the financial value and reputation value of his actions are different. The difference in these values causes the preferences of managers and shareholders (owners) to diverge. From the point of view of the firm, investment projects should be evaluated on the basis of net present value. 7 Narayanan (1985) posits that managers, on the other hand, often prefer investment projects that pay off quickly, and look to additional measures such as the payback criterion 7 The net present value analysis is the generally accepted method in capital budgeting. This coincides with the firm's objective to maximize future inflows given a limited amount of resources. Many alternative analysis techniques using cash flows, net incomes, interest rates and time periods also exist. The true value of an investment project to the firm is not disclosed, however, when using these alternative methods of analysis.

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12 for justification. By choosing investment projects with earlier payoffs, managers are attempting to accelerate the enhancement of their human capital value. This behavior could be detrimental to the firm if those projects that have the shortest payback period are not the projects with the highest net present value (given equivalent risk). The manager may then be sacrificing long-term financial benefits of the firm for short-term gains to his reputation value. The escalation error scenario represents a situation where the financial value and reputation value of investment project decisions may be inversely related. If the manager chooses and continues with the nonoptimal project in attempts to receive quicker payoffs, his reputation value may be enhanced, while the firm value decreases in the long run. This study investigates under what circumstances the manager's reputation value is more important to him than the firm value in the escalation error scenario. The Issue of Information Asymmetry In a principal-agent setting the issue of information asymmetry is particularly important. Information asymmetry exists when one individual (usually the agent) has more information than another (usually the principal). In this case the agent may be motivated to use his superior information to his benefit, at the expense of the principal. In the current scenario, if the manager has private

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13 information on which to base his asset replacement or investment project decision, he may be able to act nonoptimally from the point of view of the firm without direct detection. 8 Without information asymmetry the incentive for the manager to commit escalation errors disappears. In a world of only public information the interests of the manager and the firm coincide and the manager cannot form a reputation spuriously. Consequently, escalation errors may be viewed as a cost of the agency relationship. Narayanan (1985) shows that only under asymmetric information does the manager have incentives to make suboptimal decisions which are consistent with short-term goals instead of long-term goals. Suboptimal decisions may yield short-term profits, but are not in the best interests of the firm in the long-term. In garnering short-term profits the manager is attempting to bolster his reputation, and therefore his wages. This is an example of a case in which the personal utility of the manager becomes more important to him than the utility of the firm under asymmetric information. Without information asymmetry the agency contract causes the personal utility of the manager and the utility of the firm to coincide. Consequently, for 8 A device such as a monitor, which reports information to the principal independent of the agent, may be used to reduce the level of information asymmetry between the two parties.

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this study, information asymmetry is considered a prerequisite for committing escalation errors. The Formation of the Manager's Reputation 14 Since multiple studies suggest the manager may be concerned about his reputation in an information asymmetric setting, 9 the formation of the manager's reputation is an important issue. Kreps and Wilson (1982) show how imperfect information promotes reputation formation. They posit that uncertainty of players about the payoffs of other players is the simplest type of imperfect information that yields reputation formation in finitely repeated games. This is not an extreme condition, as players' payoffs typically are not common knowledge. The theory of reputation formation under imperfect information is tested experimentally by Camerer and Weigelt (1988). They posit that the behavior of players in an experiment can be predicted by a sequential equilibrium model of reputation formation in an incomplete information repeated game. Evidence of reputation-building is found in all of their experimental sessions of an abstracted lending game. They conclude that incomplete information about a player's type (or privately known characteristics) leads 9 See Holmstrom and Ricart i Costa (1986) and Narayanan (1985).

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15 other players in the game to form beliefs about the player's type. These beliefs become the player's reputation. 10 Imperfect or incomplete information is usually present in a typical principal-agent setting. In the current context, managers invariably have more information available to them than do the owners of the firm. This condition lends itself to reputation formation on the part of the manager and to possible escalation errors if there are apparent gains to the manager's personal utility. Related Escalation Error Literature Kanodia, Bushman and Dickhaut (1989) model the escalation error problem. They posit that the manager may engage in escalation behavior in an attempt to maintain uncertainty about his true management talent. When others can only infer the talent level of the manager from his actions and the resulting consequences, these actions acquire a reputation value. 11 Consequently, the manager will avoid switching to an alternative (superior) course of 10 In the Camerer and Weigelt (1988) experiment some players were borrowers and some were lenders. It was posited that borrowers should be concerned about establishing a good reputation for repayment of funds borrowed in order to secure future amounts. The results showed that the majority of the borrowers did not renege at the first opportunity they had, or as often as predicted. This constitutes evidence of reputation-building behavior. 11 This condition fits the imperfect or incomplete information criteria of Kreps and Wilson (1982) and Camerer and Weigelt (1988).

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16 action to avoid admitting a past mistake and damaging his reputation as a manager. By maintaining the appearance of good management, the manager's opportunities in a labor market should improve. This theory of the reputation value of the manager is an economic rationale for escalation errors. Other studies have investigated escalation errors from a behavioral standpoint. These studies do not explain escalation errors in terms of an individual's reputation value, but rather in terms of the psychological effects of various factors on the individual's attempt to rationalize his errant behavior. Empirical evidence suggests that individuals are generally prone to take sunk costs into account and commit escalation errors when they make decisions. staw (1976) states that individuals seek to maintain or restore the appearance of rationality to their previous behavior. This is known as self-justification theory. The theory posits that individuals cognitively distort negative consequences into a perceived positive outcome. Therefore, an individual who has made a choice that led to negative consequences may continue in that course of action in an attempt to justify his prior behavior or to demonstrate the ultimate rationality of his original course of action. The important factors behind the theory of self-justification in the investment decision are posited to be the level of personal responsibility an individual had

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17 for the decision and the outcomes resulting from the decision. In an experimental setting Staw shows that business school students invest substantially greater amounts of resources when they are personally responsible for previous negative outcomes. He concludes that they are justifying their actions to themselves by attempting to appear competent in previous actions as opposed to future actions. Staw and Fox (1977) investigate the persistence of the escalation process over time in an experimental setting. They design an experiment in which the commitment of resources to a course of action is the dependent variable and the independent variables are personal responsibility, efficacy of resources and time. They replicate the personal responsibility effect of Staw (1976) using undergraduate business students. Additionally, high-responsibility subjects show the greatest commitment to a course of action immediately following the receipt of negative consequences. Efficacy of resources is also significant. Under high efficacy of resources, subjects invest substantially more resources than under low efficacy of resources. The investment decisions for all subjects, however, are highly unstable over time. Staw (1981) points out that a cost which is sunk economically for the firm may not be sunk psychologically for the decision maker. He develops a model of the

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18 commitment process and identifies four major determinants of commitment. These are 1) motivation to justify previous decisions, 2) norms for consistency, 3) perceived probability of future outcomes and 4) perceived value of future outcomes. He states that rational and objective decision makers should not be influenced by the first two determinants since they are retrospective instead of prospective determinants. Analogously, in accordance with subjective expected utility models, the maximization of future utility should be the goal of the decision maker. An experimental study by Staw and Ross (1980) uses both undergraduate students and practicing managers enrolled in an MBA program to assess a case description of an administrator's behavior. The three variables manipulated in the case descriptions are consistency versus experimentation in the administrator's course of action, minimum versus maximum commitment of resources to the course of action and the ultimate success versus failure of the administrator's efforts. Main effects for each of these independent variables are found such that an administrator is rated highest when he follows a consistent course of action, allocates minimum resources and is ultimately successful. Additionally, practicing managers participating in the study view consistency in actions as a much more important characteristic of a successful manager than undergraduate students, even when negative results accrue as

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19 a consequence of previous actions. Continuing with a previously chosen investment apparently benefits the manager more than switching to a superior investment. This suggests that managers consider sunk costs when making capital budgeting decisions and are maximizing their personal utility instead of the utility of the firm. In summary, the previous behavioral studies have identified personal responsibility as a crucial factor consistent with the commitment of an escalation error. Other factors such as the efficacy of resources, consistency in actions and the ultimate success of the chosen course of action have also been shown to significantly affect the escalation decision. A model of the commitment process has also been presented which coincides with accepted subjective expected utility models. The accounting study by Kanodia, Bushman and Dickhaut (1989) provides the primary source for the hypotheses of the current study. Hypotheses concerning the manager's talent level as well as others pertaining to the principal-agent setting are developed. The current study adopts much of the theory of Kanodia, Bushman and Dickhaut (1989) and a limited amount from the body of behavioral literature. This research provides an empirical test of the Kanodia, Bushman and Dickhaut (1989) theory, as well as some extensions.

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20 The Present Research The purpose of the current study is to integrate the issues discussed in this chapter into a single experiment. The analysis of the accounting issue provides the link between the escalation error phenomenon and accounting. The principal-agent setting and the related issues provide a ready-made framework within which to conduct an analysis. The previous behavioral studies have laid the groundwork for an experimental investigation of the escalation error phenomenon. The current study draws on these bases and the paper by Kanodia, Bushman and Dickhaut (1989). This study explicitly examines the effect on the manager's decision to commit an escalation error related to 1) the talent level of the manager, 2) the presence of a monitor of the manager, 3) the time at which the manager discovers he has made an escalation error and 4) the difficulty level surrounding the manager's decision. These four factors have been chosen for the current study because none have been included in the same form in previous studies and all are very important from an accounting standpoint. The importance of personal responsibility to the escalation decision has already been shown in multiple studies (Staw, 1976; Staw and Fox, 1977). Consequently, all subjects in the current study are always personally responsible for all of their decisions. The relevance of consistency in actions is recognized in the experimental instruments and is

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discussed in detail in the experimental design section of the paper. Therefore, these concepts from the behavioral studies of the escalation phenomenon are not manipulated, but are incorporated in the experimental task as constant factors. 21

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CHAPTER 3 DEVELOPMENT OF MODELS AND HYPOTHESES Introduction The following discussion first introduces a principal agent model which serves as a structure within which to test the hypotheses. Second, the related information system used in the implementation of the experiment is presented. The development of the research hypotheses is described next. The decision model developed to describe the anticipated effect of each of the independent variables on the manager's decision to commit an escalation error is introduced. The expected effects of each of the independent variables are presented. The chapter concludes with a discussion of some supplementary analyses which are usually associated with this type of experimental study. The Principal-Agent Model A principal-agent model is developed as the framework within which the hypotheses of this study are tested. The model is based on Kanodia, Bushman and Dickhaut (1989). Their model includes constructs representing managerial talent, information asymmetry and the time at which the manager discovers he has made an escalation error. The 22

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current model is extended and altered to include the constructs of this study. 23 Kanodia, Bushman and Dickhaut (1989) model an economy consisting of two periods in which escalation behavior versus switching behavior is studied. In this economy, self-employed managers must choose between two projects, A and B, in period one. There are also two possible underlying states of nature, 0A and 0 8 The project inflows accruing to managers as a result of implementing a project are highly dependent upon a match between the project chosen and the actual underlying state of nature. In stage one of period one, managers receive a private signal which reveals a certain level of information about the underlying state of nature. Each manager then chooses to implement either project A or B. Talented managers have a higher probability of discerning the actual underlying state of nature from the information signal than do untalented managers. At stage two of period one, each manager receives new information that reveals the true underlying state of nature. Each manager then has the option of continuing to implement the chosen project or switching to the alternative project. The differential project inflows from switching to the correct project always exceed the cost of switching, so that it is always beneficial to switch from the firm's viewpoint. Consequently, the manager will escalate only when he perceives that escalating will provide him with a

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24 higher wage in the impending labor market. At stage three of the first period, project inflows accrue in accordance with the project chosen/actual state of nature combination. In period two, each manager seeks employment. The better a manager's results from his self-employed capacity, the higher the wage he can demand in the job market. Thus, the project inflows that accrued as well as the reputation value of the manager are both involved when the manager seeks employment. These two factors are the basis for analyzing escalation behavior in the current model. The current model has its basis in the Kanodia, Bushman and Dickhaut (1989) model and is modified to fit an empirical study. Before the beginning of the decision rounds, the state of nature is randomly determined for each manager, but is unknown to the managers. The managers also receive limited information about the two alternative investment projects. The sequence of events in each decision round is 1) managers receive additional discriminating information to aid them in their investment project choices, 2) managers make their investment project choices, 3) the random factor drawing takes place for each manager and 4) investment project wages accrue to the managers.

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25 Managers receive wages, r, which depend upon the investment project payoff at the end of each decision round. There are two possible values for the wages, rL and rH, representing low and high wages. The wages are determined by three factors: the manager's choice of investment projects (a), a stochastic realization of the state of nature (s) and an additional stochastic realization of a random factor (e), such that ri(a,s,e). The manager's choice is between two investment projects, a 1 and a 2 and is a function of his talent level, t, and the information signal received, y, such that ai(t,y). The manager's talent level impacts his choice, as the talented manager is more likely to make a correct project choice (a choice which leads to a high payoff). An information signal, yic(y 1 ,y 2 ), is received in each round by the manager. The signal provides the manager with the probability of each state of nature given the signal. The state of nature is randomly determined between two possible states, s 1 and s 2 The random factor, e, is defined as the probability of receiving an inconsistent project payoff. An inconsistent project payoff occurs when the manager chooses project a 1 the state of nature is s 1 and the resulting wage is rL (or a 2 s 2 and rL). This factor is introduced to allow additional randomness in the model so the manager is not able to discern the true state of nature early on with certainty. The random factor starts relatively high and decreases in

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26 magnitude with time. In the later rounds the probability of receiving an inconsistent project payoff becomes minimal. The last variable of interest to the manager is the cost of switching projects, b. This variable is positive and increases throughout the decision rounds. The cost of switching is a function of the manager's wages and time such that b(r,time). The variable b represents the penalty (the sunk cost) of discovering the true state of nature in the later rounds when the chosen project has begun implementation. Since there are no project costs in the model, the use of b seems to be a plausible way to manifest a sunk cost effect. Therefore, in this context, bis a relevant variable to be considered in the investment project choice problem. In the principal-agent framework the manager is considered to be an expected utility maximizer. If the manager is assumed to be risk neutral, maximizing expected utility is equivalent to maximizing wages. Therefore, based upon the previous discussion and definitions, the manager's problem is maximize {r (a, s, e) b}. a,b (1)

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27 The Information System In every principal-agent problem an information system represents the various probabilities associated with the possible combinations of the variables defined in the model. This information system dictates which potential contracts will be optimal. The basic information system adopted for the current study and used in the implementation of the experiment is presented below. The payoff structure can be characterized as EJI s, I sz I a, rH rl a2 rl rH with an e probability of not attaining. The information signal is from an information system of the form 61 s, I sz I Y1 P(y 1 is 1 ) P(y,isz) Y2 P(Yzis,) P (Yz I sz) multiplying P(yi I sj) by P(si) gives eij' which is

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28 61 s, I s2 I Y1 8 ,, 0,2 Y2 02, 8 22 61 s, I s2 I Y, 0(s 1 iy 1 ) 0 ( 5 2 i y 1) Y2 8(s,iy2) 0 ( s2 i Y2) where P(Y;isj) is the probability of signal Y; given that the state is sj, 0ij is the joint probability of signal Y; and state sj, 0; is the marginal probability of signal Y;, and 0(sjiY;) is the probability of state sj given signal Y; After receipt of the information signal, the manager's strategy is as follows. If Y1 is observed and a, is chosen, then the expected wage is 0(s 1 iy 1 ) (rH(l-e) + rL(e)) + 0(s 2 iy 1 ) (rL(l-e) + rH(e)). If y 1 is observed and a 2 is chosen, then the expected wage is 0(s 1 iy 1 ) (rL(l-e) + rH(e)) + 0(s 2 iy 1 ) (rH(l-e) + rL(e)). If y 2 is observed and a 1 lS chosen, then the expected wage is 0(s 1 iy 2 ) (rH(l-e) + rL(e)) + 0(s 2 iy 2 ) (rL(l-e) + rH(e)). If y 2 is observed and a 2 is chosen, then the expected wage is 0(s,iy 2 ) (rL(l-e) + rH(e)) + 0(s 2 iy 2 ) (rH(l-e) + rL(e)).

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29 Let a represent the two possible choices when y 1 is observed and fi represent the two possible choices when y 2 is observed. Given S i as previously defined, the manager's problem as stated in Equation 1 becomes maximize ai ( y ) The variable bis the cost of switching projects. (2) Presumably, the manager switches whenever he determines that the increase in his expected wages from the alternative project is greater than the cost of switching. Alternatively, if n L E(U(rH(a,s,e) rL(a,s,e))) > b i=l where i = 1 is the round of the switch and n = the last round, then the manager switches. (3) It is evident that the manager's strategy is to maximize his utility, or equivalently his wages if the manager is assumed to be risk neutral. To do this he should use all the information signals he receives over the rounds to determine the underlying state of nature. The manager's decision for the first round is 1) estimate the probability of each state of nature given the signal that has been received and 2) choose the project which has a greater probability of matching the underlying true state of nature.

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After the first round, the manager's decision problem becomes much more complex. 30 1) If the prior round's payoff was not high, assess probabilities of possible causes: wrong project was chosen or an inconsistent project payoff occurred with probability e. If the prior round's payoff was high, this is evidence (strong but not conclusive) of a correct project choice. Alternatively, Computing the probability of receiving rH or rL given the occurrence or non-occurrence of the random factor e gives P ( e "'r H) == ----,--== p ( r H) (4) (5) (6) (7) (8)

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31 (9) In the current setting, for the first several rounds. This shows that a low project payoff is more likely to be due to the random factor than to an incorrect project choice in the early rounds only, as this relationship is reversed in the later rounds. Throughout the rounds, however, a high project payoff is most likely not due to the random factor, as P(elrH) is always small. This holds true for any one round looked at independently. 2) Analyze information from all previous rounds as well as the current round to determine whether to remain with the previously chosen project or to switch to the alternative project. This model is modified somewhat to fit an experimental setting, but its essence remains unchanged. The hypotheses of the current study are developed within this structure. The Effect of the Manager's Talent Level The talent level of the manager is a primary variable of interest. Managerial talent is typically described as the ability to organize and supervise the various inputs

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32 needed for investment and production. Kanodia, Bushman and Dickhaut (1989) refine this definition into the idea of "foresight." A manager with superior foresight can anticipate future developments much sooner than a manager who is not farsighted. This equates to the superior ability of the talented manager to discern the true state of nature. Talented managers are more likely to make correct choices in the initial stages of an investment decision and are therefore less likely to face the problem of a superior investment being available later. Occasionally talented managers may make initial incorrect choices and find themselves faced with the question of whether to escalate or switch projects. In this case they should feel less pressure to escalate. Since they are talented and probably already have favorable reputations, they can therefore sustain the adverse effects of admitting a past mistake. Consequently, talented managers make correct project choices a higher percentage of the time than untalented managers. Untalented managers are unable to discern the true state of nature with certainty until much later than talented managers. Consequently, they learn that the alternative investment should have been chosen initially, and are then faced with the question of whether to switch to the superior investment (assuming that the initially chosen investment is not at the stage of completion and can still be abandoned). Therefore, the untalented manager is more

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33 likely to be faced with the dilemma of whether to escalate with the inferior project or switch to the superior project than the talented manager. Additionally, the untalented manager should feel more pressure to escalate in an attempt to bolster his dubious reputation. Given this, the following hypothesis describes the expected talent effect: H 1 : Escalation errors are made more frequently by untalented managers than by talented managers. Information Asymmetry Effects of the Monitoring Variable Since information asymmetry is considered a prerequisite for committing escalation errors, 1 the presence or absence of information asymmetry is not manipulated in this study. Information asymmetry exists between owners and managers throughout the study. When the manager has private information, the possibility for agency problems exists. The agency problem of particular interest in the current study is adverse selection. An adverse selection problem exists when the manager has incentives to hide or misrepresent his private information. When the manager keeps his private information to himself, he is judged only by his observable actions and their results. This allows the manager to choose or continue a course of action which may provide acceptable results, while an alternative superior course of action is available. Since This was noted previously, see Narayanan (1985).

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34 the manager has private information about the superior course of action, he will not switch and therefore admit to a previous incorrect choice without an incentive to do so. Therefore, the manager escalates and is compensated based on his acceptable results while concurrently protecting his reputation. Since the owner knows the manager has incentives to act nonoptimally, the question arises as to what measures the owner may take to deter such action. 2 Penna (1983) investigates several issues of information asymmetry in managerial accounting. He specifically addresses the issues of monitoring and reporting/incentive systems. In a reporting/incentive system, the manager reports some of his private information to the owner. The owner then revises the standards by which the manager is evaluated. A major problem with a reporting/incentive system is that the manager may have incentives to distort the reported information. This is possible since the manager privately observes the information. If by distorting the report of his private information the manager can further his own self-interests, he may do so. 2 The possibility of signaling, while considered a solution to the adverse selection issue, does not fit in well with the escalation error problem. Signaling assumes that the individual producing the signal is attempting to honestly convey useful information to the market (see Akerlof, 1970; Spence, 1973). In the escalation error scenario, the manager is trying to conceal his private information. This is contradictory to the premise of signaling and therefore invalidates the use of signaling to reduce information asymmetry in this study.

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35 Participative budgeting is an example of this system. 3 In contrast, under a monitoring system the manager has no opportunity to distort the information reported to the owner. A monitor is a device which reports information to the owner, independent of the manager. By Penno's definition a monitor is inherently assumed to be noisy or an imperfect signal of the actual information. An essential tradeoff between a reporting/incentive system and a monitor is manipulability versus accuracy. In the current setting, the availability of a monitor may act to reduce the level of information asymmetry between the manager and the owner. If the manager knows there is a possibility that additional discriminating information about him will be disclosed via the monitor, he will be less likely to commit an escalation error. This is posited in the following hypothesis: The presence of a monitor reduces the incidence of escalation errors. 3 Participative budgeting requires the employee to report some of his private information to the employer. The employer then uses the reported information to set a budget by which the employee is evaluated. The value of participative budgeting is in question if the employee misrepresents his private information. Penna (1984) shows that participative budgeting can be strictly valuable if the employer has some control over the private information available to the employee.

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36 The Effects of Timing of Information Disclosure Time is clearly an important factor in the escalation error problem. Staw and Fox (1977) implement their time construct by having subjects make three consecutive investment decisions. Their study showed an unstable effect of time on the amounts invested. In the current study the manipulation of time is treated quite differently. This manipulation takes the form of the point in time when the manager discovers with certainty that he has made an escalation error. In the Kanodia, Bushman and Dickhaut (1989) model, the manager either discovers the true state of nature immediately or after his initial project choice. The current multi-period model affords the opportunity to vary the point in time when the manager learns the true state of nature. It is posited that the later the manager learns the true state of nature and realizes that he has made an escalation error, the more likely he is to continue with the error. In the context of the current study, it is posited that this time effect may be due to two factors. First, the longer the firm has undertaken the project, the larger the project's sunk costs. 4 Second, the manager's reputation is 4 This assumes that the project's sunk costs (implementation or construction costs) continue to increase monotonically until the completion of the project. This is usually the case, as even if a large initial outlay is required, some additional costs are typically incurred until the project's completion.

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more severely damaged by admitting a mistake made many periods in the past. By doing so, he reveals that he is untalented, and has either been unable to discern that the alternative project is superior or has been hiding his choice of the inferior project. Consequently, The later the manager discovers that he has made an escalation error, the more likely he is to continue with the error. Effects of the Environment 37 The conditions under which the manager makes decisions vary due to uncertainty. Although information signals are often noisy or imperfect, the manager must use them. Additionally, unforeseen economic factors may distort the results from any chosen course of action, giving the manager yet another imperfect feedback signal (project payoffs). The possible combinations or series of signals and payoffs will vary continuously in any given environment. In this study the possible series are divided into difficult series with many occurrences of noisy signals and unforeseen economic factors, easy series with few occurrences and intermediate or moderate series for those in between. The manager is more likely to make an incorrect choice and potentially an escalation error in a more difficult series. Additionally, the more talented manager should always be less likely to commit escalation errors regardless of the series type due to his superior ability to discern the true

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38 state of nature (see H 1 ). This anticipated effect is stated in the following hypothesis: Escalation errors are made by both talented and untalented managers in a difficult series (of signals and payoffs), by only untalented managers in a moderate series and by no managers in an easy series. The Decision Model Within the principal-agent framework, the hypotheses of the current study are tested. The proposed model of the manager's escalation error decision is EE= f(TAL, MON, TIME, DIFF) where (11) EE TAL MON = TIME= DIFF = whether an escalation error is committed; the manager's talent level; the presence (or absence) of a monitor of the manager; the timing of information disclosure to the manager; and the difficulty level of the series (of signals and payoffs) facing the manager. Within this context, an escalation error is expected when the manager is untalented, has no monitor present, discovers that he has made an escalation error at a late stage and faces a difficult series. Therefore, the expected coefficients for the independent variables are negative for TAL, negative for MON, positive for TIME and positive for DIFF.

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39 Supplementary Analyses Aside from the four posited hypotheses, additional exploratory premises are also investigated. These premises pertain to the behavior of the experimental participants. Trends in behavior over time are typically tested in this type of experimental setting (experimental markets). These premises will be developed in Chapter 4, following a detailed explanation of the experiment. In this chapter, the hypotheses to be tested and the principal-agent model were integrated into the resulting decision model. The experiment designed to test the hypothesized effects is described in Chapter 4.

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CHAPTER 4 RESEARCH DESIGN AND DATA ANALYSIS METHODS Introduction The research method used to test the hypotheses presented in Chapter 3 is described in this chapter. First, the design of the experiment that was conducted to test the effects of the four independent variables is discussed in detail. Next, the chapter describes the data analysis methods used which take into account the discrete nature of the dependent (and some of the independent) variables. The data analysis section also includes a summary of the predicted results and a discussion of the supplementary analyses. Experimental Design Overview The decision model in Chapter 3 was tested in a multi period market setting in an attempt to identify those factors responsible for managers' escalation errors. The setting included two types of participants: managers and supervisors. A total of 156 graduate and undergraduate students in the College of Business Administration at the 40

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41 University of Florida participated in the study. Each subject attended one experimental session. As an incentive to participate, subjects earned lottery tickets based on their decisions made during the experiment. The lottery tickets were entered into a drawing in which seven cash prizes were awarded. The experiment was run over a two week period and consisted of a total of 16 sessions. Each session lasted about two hours and could accommodate up to 12 subjects (six manager-supervisor pairs). Some sessions were run with less than 12 subjects. This was done in an attempt to use all willing subjects (some were only available during certain time slots). Precisely, the number of sessions containing each allowable number of subjects was three with 12, three with 11, two with 10, four with 9, three with 8 and one with 7. In all sessions with an uneven number of subjects, the experimental assistant acted as the last supervisor. Experimental Task A detailed explanation of the experimental protocol can be found in Appendix A. A more succinct description of each experimental session is presented here. In each session there were four to six manager-supervisor pairs. Subjects acting as managers made investment decisions to maximize their experimental payoffs. Subjects acting as supervisors gave investment decision feedback to managers and bid

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42 competitively for managers' services at the end of each sequence of rounds. Supervisors' wages were based upon the investment project payoffs which resulted from managers' decisions. In those sessions with an uneven number of subjects, the experimental assistant provided the remaining manager with the necessary feedback information, but did not actively bid for managers' services. All bids from this "dummy" supervisor were set equal to $1.50 (the minimum allowable bid) The steps in each experimental session can be found in Figure 4-1. Managers and supervisors were randomly paired in the first sequence of rounds. For every sequence after the first, managers and supervisors were paired based on the results of the bidding for managers' services. During each sequence, managers and supervisors went through a group of decision rounds in which each manager made an investment project choice based on the information he received. 1 2 The information initially given to managers consisted of the two possible investment projects, the two possible states of nature, the probability of each possible state of nature, Managers chose between two potential investment projects. They received limited information about the two projects, but were told they should attempt to discern which investment project was favored by the economic conditions in the market. Managers knew that determining which project was superior based on these conditions would maximize project payoffs. 2 If the manager is not attempting to build a reputation, he should be making his project choices consistent with Equation 1 from Chapter 3.

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the two possible payoffs and some additional information (see Specific Instructions for Managers in Appendix B). 43 At the end of the first sequence, project choices for each round, as well as cumulative payoffs for the firm for the first sequence, were made public for all managers. Supervisors were given time to analyze each manager's performance and then bid competitively for managers' services. Each supervisor entered three written bids, one bid apiece for each of three managers. The supervisor entering the highest bid for a particular manager was matched with that manager for the second sequence. If the highest bid for a manager was from a supervisor who had already been matched with another manager, then the supervisor entering the next highest bid was matched with that manager. This process continued until all managers and supervisors were matched. If some managers were not bid upon by supervisors who had not already been matched, then the remaining managers and supervisors were matched by the experimenter at the minimum allowable bid. The more talented managers should have logically commanded the higher bids from the supervisors, since supervisors' wages were based upon the cumulative payoffs resulting from managers' project choices in each sequence. 3 3 The payoff to the firm for each round was either $.50 or $1.00. The cumulative payoff to the firm for the sequence was the total of the payoffs for each round less the cost to switch whenever a switch between projects was made. The cumulative payoffs to the firm each sequence were

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44 A time line for the ordering of events in each decision round can be found in Figure 4-2. Managers received an information signal from their supervisors at the beginning of each round. This signal provided them with additional information about the true state of nature. 4 Managers were given time to analyze their information and then made their investment project choices. Supervisors then informed the managers of the project payoffs resulting from their investment project choices. 5 The project payoffs which accrued at the end of each round served as an imperfect signal of whether a correct investment project choice had divided in the proportions of to the manager and\ to the supervisor. 4 The signals provided the managers with the conditional probability of the state of nature given the signal that was received. The probabilities associated with these signals were calculated using the information system presented in Chapter 3. These probabilities were reported to the managers in the Specific Instructions for Managers. The signals were designed such that signal one would be more descriptive than signal two. 5 During every sequence each supervisor acted in multiple capacities. First, each supervisor acted as an information provider. This can be likened to the supervisor steering the manager in the right direction to make a correct project choice. Next, the supervisor acted in his superior capacity in providing feedback to the manager about his choice. Finally, the supervisor acted in his hiring and firing capacity when deciding which managers he should bid upon. In actuality, most supervisors act in all of these capacities on a regular basis.

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45 been made. This chain of events was repeated until the end of each sequence. 6 Cost of switching. In each round after the first, managers chose whether to stay with the current investment project or switch to the alternative investment project. Managers had to pay a cost to switch investment projects, which increased throughout the decision rounds. The cost of switching was manipulated in a manner which induced managers to switch investment projects, i.e., the expected benefits from switching always exceeded the cost of switching. Managers were explicitly informed that this was the case. If managers switched projects more than once, the cost of 6 There were six sequences in each experimental session. Managers and supervisors were led to believe that there would be seven sequences based on their payoff records. The subjects were not informed of the true ending point of the experiment due to what can be termed the finite-period paradox. Luce and Raiffa (1957) address this issue in the context of temporal repetition of the prisoner's dilemma. In the prisoner's dilemma, for any one play of the game, each player does better when he does not cooperate, regardless of what the other player does. The Pareto superior outcome is only achieved, however, if both players cooperate. When the prisoner's dilemma is repeated an infinite number of times, both players gain from a commitment to always cooperate. The problem arises with a large, but not infinite, number of repetitions. In this case, the final stage of a finitely repeated game becomes equivalent to a single-stage game in which cooperation cannot emerge. Inductive reasoning dictates that this scenario repeats for the penultimate stage of the game and so on for all stages back to the first. In this case, ignorance of the ending point of the game is one method to prevent uncooperative behavior. The current case is considered similar enough to the prisoner's dilemma to warrant a similar treatment.

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46 switching started at the same initial value and increased as previously noted. Incentive to escalate. Since many of the experimental variables were designed to discourage escalation behavior (cost of switching and certain monitoring), subtle incentives to promote escalation errors were included in the experimental instructions. 7 These incentives included a suggestion about the importance of consistency in actions and maintaining a smooth stream of earnings for enhancing reputation. Both of these variables are consistent with the commitment of an escalation error. Consistency in actions is maintained when committing an escalation error, since the same project is chosen as has been previously chosen. Additionally, a smooth stream of earnings is maintained, since the project payoff resulting from an escalation error is probably low, as it has been in the past from choosing the nonoptimal project. Stressing the importance of these two variables is not inappropriate for the manager, as both of these characteristics are considered to enhance the reputation of the manager. Staw and Ross (1980) show that consistency in 7 These incentives were based upon the results of the previous behavioral research on escalation errors. As was noted previously, all subjects were always personally responsible for all investment decisions. Additionally, the importance of consistency in actions was stressed in both the typed experimental instructions as well as the verbal explanations of the experimenter. These two factors were found to be consistent with the commitment of escalation errors in previous studies.

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47 actions is viewed as an integral characteristic of a successful manager. Several studies including Hepworth (1953), Beidleman (1968) and Copeland (1968) note that a great deal of emphasis is placed on the periodic earnings figure. Managers accordingly are apt to feel pressure to maintain the current level of earnings. It is hypothesized that managers may actually try to avoid large variances in the earnings figure from year to year, since variance connotes risk and typically is looked upon unfavorably. Consequently, both consistency in actions and maintaining a smooth stream of earnings are posited to have a favorable effect on the manager's reputation value and are consistent with the manager committing escalation errors. The experimenter explicitly explained two learning sequences to the managers before the beginning of the actual experiment. The conditions which would produce comparable payoffs from switching and escalating were examined (e.g., switching payoffs exceeded escalating payoffs by $.50). Managers were informed that supervisors were aware that a switch between projects in a late round signalled an initial incorrect project choice. They were also told that a late round switch may lower supervisors' assessments of the manager's skill level. Consequently, the potential benefit of escalating in terms of increased bids from supervisors in the future was mentioned. It was then left up to each manager to determine whether the potential benefits from

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48 escalating exceeded the certain increase in project payoffs from switching. Subjects In any experimental study, trade-offs exist between replicating real world circumstances and the tractability of the experiment. The decisions made by subjects in this experiment are simplified from their real world counterparts. Many factors that would be present in actual business decisions are missing in the experiment. Nevertheless, by isolating the variables of interest, the results of the study can be attributed to the experimental variables. Business students were targeted as the subject group for the experiment instead of practicing managers for several reasons. These students had the requisite (somewhat uniform) business training to deal with the experimental task, while using practicing managers would have introduced a wide dispersion of experience and knowledge bases. Also, they were readily available to participate in an experimental session on campus. The use of practicing managers would have most likely entailed moving the experiment to the employment sites. Additionally, given the requisite number of subjects, the procurement of this number of practicing managers would have been extremely difficult.

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Given these observations, business students were used as experimental subjects. 49 Subjects were recruited from undergraduate and graduate accounting courses, as well as undergraduate finance courses. The lowest level subjects recruited were second semester juniors and the highest level were final semester master's students. When the students were initially contacted, they were informed that to participate in the experiment they would need to attend one two hour experimental session. They were told that they would earn a chance to win a cash prize in a lottery type drawing as a result of their participation in the experiment (see Appendix B for a copy of the introduction letter). Students who were interested in participating in the experiment chose which experimental session to attend over the two week period of the experiment. The total number of subjects participating in the experiment was equal to 156. Of the total, 82 subjects acted as managers and 74 subjects acted as supervisors. Descriptive statistics about the subjects can be found in Table 4-1. As shown in the table, the number of male subjects was somewhat greater than the number of female subjects. The average age was 23 and the average GPA was 3.30. The greatest number of subjects were master's level accounting students. Senior level undergraduate accounting students made up the next largest group. Junior level

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50 accounting students and senior level finance students comprised the only other large groups. The average payoffs were $28.05 for the managers and $29.60 for the supervisors. The average pre-test score for managers was 9.05, with a maximum of 12 and minimum of 3. The solicited risk aversion measure for the managers revealed 70 risk neutral subjects, seven risk averse subjects and three risk seeking subjects. 8 Incentive Structure To entice subjects to commit the necessary two hours of their time to participating in the experiment, some potential reward had to be offered. 9 Traditionally, each subject is paid a flat fee for participating which is presumably equal to their reservation wage As the number 8 The risk aversion measure was solicited as a check on an assumption from the principal-agent model of Chapter 3. In the model it is stated that if managers are assumed to be risk neutral, maximizing expected utility is equivalent to maximizing wages. The measure used in this study consisted of one question at the end of the Pre Experimental Questionnaire for Managers. This question pertained to an equivalency calculation for a lottery ticket and is admittedly a rough measure. A more detailed measure was not used due to the very tight time constraints of each experimental session. Nevertheless, the solicited measure does show the majority of the subjects to be risk neutral, thus validating the assumption of the principal-agent model. 9 In experimental market studies it is generally accepted that cash incentives should be offered to subjects in order to induce them to act as expected utility maximizers. Without cash incentives subjects are not motivated to care about their choices (act optimally), or even show up if they are not required to do so for some other reason (such as receiving class credit).

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51 of subjects participating in the experiment becomes large, the total amount required to pay the subjects becomes prohibitively high. Recently Belle (1990) has shown that under certain circumstances (small decision costs and anonymous choices) a randomized reward structure is equivalent to rewarding each subject. These conditions were met in the current experiment, as there were no decision costs and no identification of subjects (by anything other than their subject numbers) during the experiment. The randomized reward structure for the experiment took the form of a lottery drawing for seven prizes at the completion of all experimental sessions. The seven prizes included one of $200, two of $100 and four of $50. The subjects earned lottery tickets in direct proportion to their cumulative experimental payoffs, which should have induced them to give a good effort during the experimental sessions. The winners of the lottery were identified only by their lottery ticket numbers and had to have the other half of the ticket stub to claim their prizes. Operationalization of Independent Variables Manager's talent level. The manager's talent level was measured in the experiment by administering a test to all subjects acting as managers. The test was administered after the learning sequences for managers and before the actual experimental sequences began. The test consisted of

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52 twelve statistical and business questions designed to measure the subject's (manager's) aptitude for the experimental task (see Appendix B for a copy of the Pre Experimental Questionnaire for Managers). The designation of each manager's talent level (in terms of his score on the questionnaire) was made by the experimenter after the experimental sessions were completed. Information asymmetry. The manipulation of the information asymmetry construct was between-subjects. The two possible states were certain monitoring and no threat of monitoring. Each subject faced only one possible state to enable him to discern the effects of the monitoring condition over the multiple sequences. When monitoring was present, additional discriminating information about the manager was disclosed to the market {the supervisors) in the form of optimal total payoffs for the sequence. The disclosure of the optimal total payoffs reduced information asymmetry between that particular manager and the market. The difference between the optimal total payoffs and the actual payoffs provided supervisors with important information upon which to revise their estimate of the talent level of the manager. For experimental purposes the monitor was noiseless or a perfect signal of the actual information. Time. The manipulation of time was within-subjects. Maintaining a constant time condition for each subject was

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53 not considered necessary, since the effect of the time construct was considerably easier for the subjects to understand than the effect of the monitor construct. The time variable was manipulated by informing managers of the superior project for the sequence at different times during the sequence. Two points were chosen within each twelve round sequence. Managers were privately informed of the superior project after either round six or round eight. 10 Based on this, an escalation error is defined for experimental purposes as continuing to choose the nonoptimal project in any round after the manager is informed of the superior project. Environmental factors. The manipulation of the difficulty level surrounding the manager's decision was also within-subjects. 11 This ensured that every manager faced 10 Many factors were involved in choosing to disclose the superior project to the managers after rounds six or eight. During the first half of each sequence managers should attempt to discern the superior project themselves. After this point in time, however, in order to ensure that managers are actually committing escalation errors and are not simply ignorant of which project is superior, the superior project must be disclosed to them. Obviously, many other points in time could be chosen to disclose the superior project, but these two were considered to be sufficient to describe the anticipated effect. 11 The specification of the time and environmental factors variables as within-subjects is not consistent with the use of the logit model for data analysis. Nevertheless, the true manipulation of these constructs was within subjects as each subject faced all possible levels of each construct. The reconciliation of this problem is presented with the discussion of the legit model later in this chapter.

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54 each types of series the same number of times and had the same opportunity to escalate. Each series of information signals, realizations of the random factor e and resulting payoffs was classified as to its level of difficulty. The validity of escalation errors made by managers was ensured by this classification. If this was not done, some escalation errors may have been caused by certain series being more difficult to interpret (to discern the true state of nature for the sequence before it is revealed to the manager) than others. By classifying the randomly generated series into three groups, any spurious results were avoided. The three groups were classified as easy, moderate and difficult. Each group contained one prototype series which fit the predetermined specifications for that group. Measurement of Dependent Variable The hypotheses discussed in Chapter 3 are based on the dependent variable of whether an escalation error is committed. An escalation error is defined for experimental purposes as continuing to choose the nonoptimal project in any round after the manager is informed of the superior project. This definition is used as the basis for classification of the subjects' choices of investment projects. Their choices after either the sixth or eighth round constitute either an escalation error or a correct project choice. Based on the design of the experiment,

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55 managers should always continue choosing the same project, once they had made their choice between the two projects after round six or eight. 12 Consequently, the dependent variable is dichotomous in nature for each sequence: either an escalation error is committed or no error is committed. Data Analysis Methods The Decision Model The manager's decision model as previously specified is EE= f(TAL, MON, TIME, DIFF) where (11) EE = TAL = MON = TIME= DIFF whether an escalation error is committed; the manager's talent level; the presence (or absence) of a monitor of the manager; the timing of information disclosure to the manager; and the difficulty level of the series (of signals and payoffs) facing the manager. The manager's decision model containing the variables specified in hypotheses one through four is tested using the legit model. The legit model is the appropriate model to 12 Subjects knew that there were no gains whatsoever to be made from switching in any round after round seven or nine (depending on when the superior project was revealed to them). The experimental data showed that subjects understood this fact, as once the subjects had chosen a project after round six or eight they never switched projects again.

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56 use when the response variable is binary. The general logit model is defined as where (12) gjklm is the logit (logit transformation) corresponding to the possible levels of the explanatory variables; Pa is the intercept term; B, C, D and E represent the four explanatory variables; j, k, 1 and m represent the possible levels of each explanatory variable; and p~ represents the effect on the response variable of a given set of observed values of the explanatory variable. 13 In the current case, variable Bis the talent level of the manager. This variable has thirteen possible levels in accordance with the possible scores of zero to twelve on the managers' pre-experimental questionnaire. Variable C is the monitoring of the managers. The two possible levels are presence or absence of a monitor. Variable Dis the time when the manager learns of the superior project for the sequence. The two possible times are rounds six and eight. Variable Eis the difficulty level surrounding the manager's decision. The three possible levels are difficult, moderate 13 See Andersen {1990) for a good discussion of the legit model and its applications.

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57 and easy. The response variable to be tested is whether an escalation error was made by the manager (O=no, l=yes). Each sequence for every manager is treated as an independent observation since each potential combination of the time and difficulty level variables represents a unique problem for the manager. 14 Predicted Relationships When the manager's talent level is high it is expected that escalation errors will not occur, therefore hypothesis one will be supported by a significant negative coefficient on the talent variable. When a monitor is present it is expected that the manager will not escalate, therefore hypothesis two will be supported by a significant negative coefficient on the monitor variable. When the manager learns of the superior project after round eight, he will be more likely to escalate than if he learned of the superior project after round six. Therefore, hypothesis three will be supported by a significant positive coefficient on the time variable. When the difficulty level of the series is easy the manager will not be expected to escalate, while if 14 This treatment provides a resolution to the problem of these two variables being manipulated within-subjects. If each manager's multiple sequences are treated independently, then these variables actually vary between (instead of within) managers (or sequences). Arguably each manager's multiple sequences are not truly independent, as the managers should have been learning (and making better decisions) as the experiment went on.

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the level is difficult the manager will be expected to escalate. Consequently, hypothesis four will be supported by a significant positive coefficient on the difficulty level variable. Supplementary Analyses 58 The additional premises to be tested in this study pertain to the total payoff to the firm for each sequence and the bids of supervisors for managers' services. A trend which can be analyzed in such experiments is whether the market converges to equilibrium. At equilibrium no parties earn excess profits and all parties act rationally to maximize their payoffs. This implies that the parties always make optimal choices to maximize the current period's payoff. This equates to maximizing firm value and not the manager's reputation value. Consequently, in the current case there is no pure equilibrium value as has been defined in previous experimental market studies. 15 This is because the experiment is specifically designed to promote escalation errors (and induce the manager to be concerned 15 In pure experimental market studies based on information economics, convergence to an analytical equilibrium price is typically tested. These markets usually contain as many as 40 periods of bargaining for a hypothetical good. Over this extended time frame, convergence is often observed. The current experiment does not entail such an expansive time frame nor does it qualify as a pure experimental market, since there is no analytical equilibrium price. Nevertheless, trends resembling convergence to equilibrium can be analyzed in the current setting.

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59 about his reputation) by increasing the uncertainty surrounding the manager's decision of which project is optimal, especially in the difficult sequences. Therefore, it is highly illogical to expect the manager to make optimal choices (to maximize the current period's payoff) throughout the experiment. Consequently, an alternative measure of pseudo-equilibrium must be employed. A value called the logical payoff is used to proxy for the equilibrium value of total payoffs for a sequence. This value is calculated for each type of series by considering all reasonably rational patterns of choices. The logical payoff is simply the expected value (average) of these patterns. The three premises involving the logical payoffs are P 1 ) the market should be closer to the logical payoff for the easy sequences than for the moderate and difficult sequences, P 2 ) the market should move toward the logical payoffs over time (irrespective of series type) and P 3 ) there should be more dispersion from the logical payoff when an escalation error is made than when no error is made. These three premises are tested using generalized distance measures, i.e., the variance and the average absolute value of the actual payoffs from the logical payoffs. The absolute values are reported in addition to the variances since they are less affected by severely outlying

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observations. The variances and absolute values are computed as where n L (APi LPJ 2 VAR= i=l n-1 n L IAPi LPil ABS= i=l n = actual payoff for observation i; = logical payoff for observation i; and total number of observations. 60 (13) (14) Premise P 1 pertains to the magnitude of the variances and absolute values in the easy, moderate and difficult sequences. The lowest total measures are anticipated in the easy sequences since there is less uncertainty involved and correct choices should be made the majority of the time. The highest measures are expected in the moderate sequences, due to an interspersion of inconsistent signals and random factors which should cause managers to receive many low payoffs and switch quite often. The dispersion in the difficult sequences should be quite low. The difficult sequences lead managers to believe they are choosing the correct project, when in fact they are choosing the incorrect project. Consequently, managers receive many high payoffs and usually switch only once. The second premise, P 2 concerning the movement of the market toward the logical payoffs over time is tested for

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61 the moderate and difficult sequences only. This is because all subjects faced the same moderate and difficult sequence twice, which was not the case for the easy sequence. The premised relationship is expected as the subjects learned more about the market over time. The third premise, P 3 posits that the variances and absolute values should be greater when an escalation error is made than when no error is made. The rationale for this is that more incorrect project choices accompany the commitment of an escalation error, thus increasing the difference between the actual payoff and the logical payoff. Other relationships that can be explored in this experiment pertain to the bids from supervisors for the managers' services. 16 The movement of these bids over time and the reaction of supervisors' bids to an escalation error are both relevant issues. Two reference points are used to evaluate these premises about supervisors' bids: the actual average bid and the logical average bid. The logical average bid is calculated based on the logical payoffs discussed previously. A decreasing trend is expected in the bids over time as supervisors learn and understand the mechanics of the market 16 Each supervisor entered three bids for three different managers at the end of each sequence (one through six). Only the accepted bids, which were subtracted from the supervisor's payoffs and added to the manager's payoffs with whom the supervisor was paired for the next sequence, were analyzed. The total number of accepted bids was equal to 480; the total number of experimental sequences.

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62 (this was shown to be the case in pilot studies). Additionally, the variance of the bids should also decrease over time, as a bidding strategy is adopted. Two measures of variance based on the actual average and the logical average are used to assess this trend. These variances are calculated as where VARA = VARL = BIDi = AAB = LAB = n = n L (BIDi AAB) 2 i=l VARA= n-1 n L (BIDi LAB) 2 i=l n-1 variance from actual average bid; variance from logical average bid; actual bid for observation i; actual average bid; logical average bid; and total number of bids. Summary (15) (16) The experiment described in this chapter was specifically designed to test the hypothesized effects of the independent variables. Consequently, it could be argued that the experiment is devised to promote escalation errors. This is certainly not true, however, since economically (from the viewpoint of the firm) it is never beneficial for subjects to escalate. Furthermore, given the experimental design, escalation behavior is only expected in a very small

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63 percentage of sequences. Specifically, the untalented manager is expected to escalate when no monitor is present, when he is informed of the superior project after the eighth round and when he faces a difficult sequence. Since the experimental design is fully crossed, these circumstances are only met approximately 8% of the time. 17 Therefore, during the majority of the experimental sequences, no escalation errors are expected. This proves that the experiment is certainly not contrived. The data analysis technique discussed recognizes the dichotomous nature of the dependent variable in the manager's decision model. The supplementary analyses are designed to test trends which are usually examined in this type of experimental setting. The analyses presented in Chapter 5 center around the manager's decision model and incorporate ancillary analyses appropriate for such experiments. 17 The 8% approximation is derived from the following conditions. Approximately half of the managers are untalented and half have no monitor present. If the first sequence (which is easy for all managers) is discounted, then four of the last five sequences are in the eighth round treatment, and two of the last five sequences are difficult. Therefore, 1/2 X 1/2 X 4/5 X 2/5 = 8/100 = 8%.

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TABLE 4-1 Descriptive Statistics of Subjects Managers Total Number 82 Sex Female 34 Male 47 Average Age 22 Average GPA 3.27 Classification 3AC 8 4AC 26 6AC 1 7AC 24 3BA 3 4BA 13 7BA 3 SBA 1 Other 2 Average Payoff $28.05 Pre-test for Managers Average Score 9.05 Variance 4.14 Maximum Score 12 Minimum Score 3 Measure of Risk Aversion Risk Neutral 70 Risk Averse 7 Risk Seeking 3 Supervisors 74 34 38 23 3.32 12 11 3 31 3 8 3 0 1 $29.60 Total Sample 156 68 85 23 3.30 20 37 4 55 6 21 6 1 3 Classification key: Number equals class year (3=junior, 4=senior, 6=post-baccalaureate, 7 and 8=master's) and AC equals accounting, BA equals business administration. Note: Any numbers not adding up to the reported total are due to some subjects not filling in all questions asked of them. 64

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Initial random pairings bidding 12 rounds New pairings based on bidding New pairings based on bidding Sequence #1 Sequence #2 /\ /\ I I I I Bidding for managers' services I I I I Bidding for managers' services FIGURE 4-1 New pairings based on bidding 65 New pairings based on bidding Sequence #6 /\ /\ I I I I Bidding for managers' services I I I I Bidding for managers' services Time Line for an Experimental Session

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Managers receive information signal from supervisors Managers make investment project choices FIGURE 4-2 Supervisors inform managers of project payoffs Time Line for a Decision Round 66

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CHAPTER 5 RESEARCH RESULTS Introduction The results of the tests of the hypotheses developed in Chapter 3 are described in this chapter. First, the results of the legit analysis for the independent variables is presented. Second, the supplementary analyses related to this type of experimental setting (experimental markets) are described. Manipulation checks of the validity of the experiment are then presented. A summary of the additional sessions run to test the revised operationalization of the information asymmetry construct are reported next. A discussion of the results concludes the chapter. Results of Hypothesis Testing The main data analysis centers on the managers' commitment of escalation errors. As such, the primary data gathered in the experimental setting consist of the sequences in which the managers committed escalation errors. Descriptive data of the frequency of escalation errors for each of the independent variables can be found in Table 5-1. Each panel of the table shows the breakdown of the 33 escalation errors committed during the 480 experimental 67

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68 sequences by levels of the independent variable. 1 Panel A shows that a greater proportion of escalation errors were committed by managers with talent levels of eight and below (10%=19/184), than were committed by managers with talent levels of nine and above (5%=14/296). A smaller difference is shown in Panel B, as managers escalated 8% (18/222) of the time when a monitor was present and the rate dropped to 6% (15/258) when no monitor was present. Panel C shows a marked difference between the two revelation points with a 2% (3/190) escalation rate after six rounds and a 10% (30/290) rate after eight rounds. The largest differences exist in Panel D where the easy series had a 0% (0/164) escalation rate, the moderate series a 3% (5/158) rate and the difficult series an 18% (28/158) rate. Additional preliminary analysis shows Spearman correlation coefficients between the dependent variable, EE, and the four independent variables of -0.090 (p=0.0246) for TAL, 0.045 (p=0.1615) for MON, 0.169 (p=0.0001) for TIME and 0.285 (p=0.0001) for DIFF. These escalation rates and correlation coefficients are initial indicators of which variables are likely to be significant in the logit analysis. Hypotheses one through four are tested using the logit model (Equation 12 from Chapter 4), since the dependent The 33 escalation errors committed during the experiment equates to a 7% escalation rate (33 errors 480 total experimental sequences). The escalation rate predicted in Chapter 4 was 8%.

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69 variable (commitment of an escalation error) is dichotomous in nature (O=no and l=yes) 2 Hypothesis one states that escalation errors are made more frequently by untalented managers than by talented managers. Each subject acting as a manager was tested before the beginning of the actual experiment on their knowledge of general business concepts and probability theory (see Pre-Experimental Questionnaire for Managers in Appendix B). The scores on this test ranged from a low score of three to the maximum possible score of twelve. Therefore, the first independent variable representing the talent level of the managers has nine levels. A negative relationship is expected between this variable and the dependent variable, since a low score represents an untalented manager who is expected to commit escalation errors. 2 As mentioned previously, logit analysis assumes each observation to be independent of all others. This is not truly the case in the current experiment, as managers invariably experienced a learning curve throughout the experimental sessions. To mitigate this issue, additional analyses were done with each manager representing one observation. This treatment eliminates the problem of independence of each manager's six sequences. Kendall's r 8 was computed between the number of escalation errors committed and time, as well as the number of escalation errors and the difficulty level. T-tests of the average r 8 versus zero showed significant results in both cases. These results are consistent with those of the correlation analysis of the dependent variable from the logit model, EE, and the two independent variables, TIME and DIFF. Additionally, in Chapter 4, the correlation analysis of the manager's descriptive measures showed the number of escalation errors to be negatively correlated with the pre test score representing talent. These tests support the results of the legit analysis.

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70 Hypothesis two states that the presence of a monitor reduces the incidence of escalation errors. The independent variable for this hypothesis is a simple dichotomous variable where a zero represents the absence of a monitor for the manager throughout the experiment, and a one represents the presence of a monitor. It has been established that a manager should only commit an escalation error when information asymmetry exists. The presence of a monitor reduces the information asymmetry between the manager and the market, while the absence of a monitor widens the asymmetry. Consequently, a negative coefficient is anticipated for this variable, as managers should only commit escalation errors when no monitor is present. Hypothesis three states that the later the manager discovers that he has made an escalation error, the more likely he is to continue with the error. Again the independent variable representing time is a dichotomous variable. For this hypothesis, the time when the managers were informed of the optimal project was varied across sequences for each manager. A zero for this variable represents the receipt of the information after six decision rounds, while a one indicates after eight decision rounds. Since the manager is expected to escalate when he has chosen the same project consistently over a large time frame, a positive coefficient is anticipated for this variable.

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71 Hypothesis four states that escalation errors are made by both talented and untalented managers in a difficult series of signals and payoffs, by only untalented managers in a moderate series and by no managers in an easy series. This hypothesis can be broken down into two parts. The first part fits in with the main analysis and posits that more escalation errors are made in the difficult series than in the moderate series and no errors are made in the easy series. This variable has three levels representing the three types of series. A zero represents the easy series, a one the moderate series and a two the difficult series. Since the manager is more likely to escalate in a difficult series, the anticipated relationship is positive. The results of the logit analysis show that the hypothesized model fits the actual data (likelihood ratio statistic=43.54 and goodness of fit=0.9998) 3 The fitted logit model is shown in Table 5-2. The p-values associated with the parameter estimates of this model show that hypotheses one, three and four are supported, while hypothesis two is not. The significant negative coefficient for the talent variable is -0.235 (p=0.008). This supports the contention that untalented managers make more escalation errors than 3 The likelihood ratio statistic for the logit model follows a x 2 distribution and compares the specified model with the unrestricted model. The x 2 statistic converts to a goodness of fit measure which is analogous to a pseudo-r 2 in regression.

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72 talented managers. The significant positive coefficient of 1.526 (p=0.011) for the time variable confirms that escalation errors are made more frequently after the same project has been chosen for many periods. The significant positive coefficient for the difficulty level variable is 2.161 (p=0.000). This supports the hypothesis that more escalation errors are made when the uncertainty surrounding the manager's project choices is high. These three results are consistent with the hypothesized effects for the three independent variables. The lack of significance for hypothesis two (coefficient=0.064 and p=0.439) is somewhat troubling, however, since information asymmetry is such an important concept in principal-agent problems. The experimental manipulation of the monitor was obviously not salient to the subjects in their choices. Despite this result, managers and supervisors both reported that the monitor did affect their choices in post-experimental questionnaires. The subjects apparently did not fully understand the meaning and purpose of the monitor in the experiment. Changes in the manipulation of the monitor were undertaken for the additional sessions of the experiment (and are discussed later in this chapter). The second part of hypothesis four asserts that escalation errors in the difficult series are committed by both talented and untalented managers, while those in the

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73 moderate series are committed only by untalented managers. Of the 28 errors made in the difficult series, 19 were made by untalented managers and nine by talented managers. This supports the contention that both types of managers will escalate in the difficult series. Of the five errors made in the moderate series, two were made by untalented managers and three were made by talented managers. This does not seem to support the contention that only untalented managers will escalate in the moderate series, but is obviously not definitive due to the small sample size. As reported earlier, no errors were made in the easy series. Overall, the validity of hypothesis four is supported. Much descriptive data were gathered from the subjects in the post-experimental questionnaires. Spearman correlation coefficients between these measures are shown in Table 5-3. There are seven significant correlations between the different variables. The manager's score on the "talent'' pre-test, administered before the experiment, is correlated with three different variables. 4 The pre-test score is positively correlated with the manager's self reported GPA. This suggests that the pre-test may be a somewhat accurate measure of the subject's actual talent or intelligence level. There is an apparently spurious 4 Cronbach's a is a measure which reports the reliability of a test. The a for the manager's talent pre test is 0.62, which is considered an adequate measure of reliability.

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74 positive correlation between the pre-test score and the male managers, as no such correlation is expected. A significant negative correlation exists between the pre-test score and the number of escalation errors committed during the experiment. This is additional evidence of the hypothesized negative relationship between the talent variable and the commitment of escalation errors. An anticipated positive correlation exists between the manager's GPA and the experimental payoff. An unexpected positive correlation exists between the number of escalation errors and the manager's GPA. An obvious positive correlation is found between the manager's age and classification. A positive correlation exists between the manager's experimental payoff and the number of escalation errors. This correlation supports the hypothesized positive effect of escalation errors on the manager's reputation. Since the manager actually earned less in a sequence when he escalated, he apparently was able to make up for this discrepancy by garnering higher bids from supervisors in future sequences. The results of the correlation analysis provide support for the main results of the logit analysis. Additional Testing Supplementary Analyses Convergence to equilibrium. The three premises previously mentioned in Chapter 4 pertaining to the logical

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75 payoffs are tested in this section. The calculation of the logical payoffs is shown in Appendix C. Table 5-4 shows the logical payoffs for easy, moderate and difficult sequences when the optimal project is revealed after both six and eight rounds. The logical payoffs for the two types of easy sequences (see note following table) are inherently the highest of the calculated payoffs. The difficult sequence has a higher logical payoff than the moderate sequence due to several factors. These factors are the result of the difficult series being specifically designed to promote more escalation errors. This was accomplished through a greater incidence of both an inconsistent signal and the random factor in the early rounds of a sequence. This combination induced managers to choose the incorrect project and rewarded them with spuriously high payoffs. Managers were consequently led to believe that they were choosing the correct project, when in fact they were choosing the incorrect project. Therefore, they often continued to choose the incorrect project until the superior project was revealed to them. At this point, the managers normally switched, thus incurring the cost to switch just once. During the moderate sequence, managers were more likely to switch more often since the incidence of inconsistent signals and the random factor was interspersed. This resulted in more low payoffs and higher total switching

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76 costs. Therefore, the combination of inconsistent signals, the random factor and the total switching costs incurred cause the logical payoff for the difficult sequence to be larger than that for the moderate sequence. Premise P 1 pertains to the magnitude of the variances and absolute values in the easy, moderate and difficult sequences. The lowest total measures are anticipated in the easy sequences, while the highest are expected in the moderate sequences. Table 5-5 shows the resulting variance and absolute value calculations. The variances reported suggest no clear-cut trend for either revelation point. The absolute values, on the other hand, display the anticipated relationships. For both revelation points the easy sequences have the lowest absolute values while the moderate sequences have the highest absolute values. As mentioned previously, the absolute values are considered to be better indicators of the true variation in the samples since they are less affected by severe outliers. Z-tests comparing the different absolute values show significant results at a=0.05 or better for all comparisons except the easy versus the difficult after six rounds. 5 Premise P 1 is therefore supported by the absolute value calculations. 5 Z-tests on a standardized measure of the absolute value (the absolute value as previously calculated divided by the logical payoff) show comparable results.

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77 The second premise, P 2 posits that the market should move toward the logical payoffs over time. These variances and absolute values are reported in Table 5-6. From this table it is apparent there is no movement toward the logical average in the moderate sequences. The variances and absolute values are very similar when the superior project was revealed after six rounds and actually increase slightly with revelation after eight rounds. For the difficult sequences, however, there does appear to be movement toward the logical payoffs. In the non escalation error sample, both variances decrease significantly by over 50% at the second sequences, while the absolute values decrease as well. The entire sample shows similar decreases for revelation after eight rounds. The figures for the non-escalating sample are shown in addition to those for the entire sample since the three escalation errors that occurred during the second sequence under revelation after six rounds accounted for the majority of the entire sample's variance and absolute value at that revelation point. For the non-escalation error sample, tests are computed between the absolute values of the first and second sequences at both six and eight rounds. The resulting Z-score after six rounds is insignificant (p=0.1736), but the z-score after eight rounds is moderately significant (p=0.0793). Since there were more sequences with round eight revelation, this result is encouraging.

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78 The movement toward the logical payoffs for the difficult sequences but not for the moderate sequences is not surprising, given the previous discussion concerning the volatility of payoffs in the moderate sequences. Premise P 2 is thus supported for the difficult sequences (especially for round eight revelation), but not for the moderate sequences. Premise P 3 posits that the variances and absolute values should be greater when an escalation error is made than when no error is made. The majority of the escalation errors (28 of 33) were committed in the difficult sequences. Additionally, the majority of these escalation errors (25 of 28) were committed after round eight revelation. Therefore, the expected effect should be the most prominent under these conditions. Table 5-7 presents these variances and absolute values. In the difficult sequences the variances (and absolute values for round six revelation) when an escalation error occurred are greater than those for the non-escalating sequences, as expected. z-tests on the absolute values show significant results after six rounds, but not after eight. For the few escalation errors made in the moderate sequence (five) the relationship is reversed. The small sample size in this case, however, precludes this result from being significant. Premise P 3 is consequently not supported in the difficult sequences after round eight revelation where the majority of the escalation errors occurred.

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79 Bidding strategies. The movement of supervisors' bids over time and the reaction of the bids to an escalation error are two relationships that are investigated. The actual average bid and the logical average bid are used as the two reference points in evaluating these trends. The actual average bid for the 480 experimental sequences is $2.10. The logical average bid, calculated based on the logical payoffs discussed previously, is $2.43 (see Appendix C). The actual average bid is somewhat below the logical average bid partially because the expected payoffs for managers and supervisors were not equal, as is assumed when dividing the logical payoffs between managers and supervisors. Pilot studies showed that supervisors often bid quite low and therefore garnered higher total payoffs. The minimum bid was raised for the actual experiment, but it was still anticipated that supervisors may bid low. This low bidding caused supervisors to earn more during the experiment. 6 6 When the experiment was initially devised, the division of the firm's total accumulated payoffs between managers and supervisors as well as the boundaries for supervisors' bids were carefully considered. In information economics models the expected payoffs of all participants are equal. In the current experiment the division of total payoffs and the boundaries for supervisors' bids were established in an attempt to equate managers' and supervisors' expected payoffs. Initial pilot tests showed that supervisors often bid low and earned higher experimental payoffs. Since the adopted division of the total payoffs between managers and supervisors seemed logical, the bidding boundaries were manipulated. This manipulation obviously did not fully counteract the observed behavior, since supervisors continued to bid low and earn

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80 The optimal strategy for a supervisor in the experiment was to bid low, due to the unequal expected payoffs. Some supervisors identified this strategy quickly, while others did not. Consequently, a decreasing trend is expected in the bids over time. Additionally, the variance of the bids should also decrease over time, as a bidding strategy is adopted. Table 5-8 shows the average bids across the sequences, and the two measures of variance based on the actual average and the logical average. No decreasing trend in either the average bids or the variances is evidenced. After additional analysis of each supervisors' six bids, it is apparent that very few supervisors adopted a discernable bidding strategy. Even though the majority of the subjects acting as supervisors appeared to be giving an earnest effort during the experiment, they were apparently unable to devise a distinct bidding strategy. The reason for this lack of explicit bidding strategies is unknown, since the supervisors' post-experimental questionnaire responses suggest an understanding of the experimental constructs. The second relationship to be investigated concerning the supervisors' bids relates to the reaction of the bids to an escalation error. A direct causal relationship cannot be inferred since the supervisors were unable to discern with certainty when an escalation error had been made. Any more in the actual experiment. In future experimentation, the division of the total payoffs will be manipulated in an attempt to equate experimental payoffs.

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81 observed relationship may be because the commitment of an escalation error lowered the resulting payoff for that sequence. The lower payoff may in turn have caused lower bids from supervisors. This is substantiated by the supervisors' post-experimental questionnaires, where payoffs were found to be an important factor that influenced supervisors' bids. The average bid following an escalation error was $1.97, which is significantly different from the overall average bid of $2.10 at a=0.010. It is difficult to draw conclusions concerning the supervisors' bids, however, since the significant decrease following an escalation error cannot be directly traced to the commitment of the error. Manipulation Checks To ensure the validity of experimental manipulations, all subjects filled out a post-experimental questionnaire which can be found with the other experimental instruments in Appendix B. Subjects were asked to respond to each question on a five point scale with the options of strongly agree, agree, no opinion, disagree and strongly disagree. The questions were devised such that a response of agreement or disagreement would display the subject's interpretation of the importance of the construct. The constructs were tested using the nonparametric sign test. 7 Consequently, 7 For the sample of escalating managers (sample size=25), the test statistic Bis used in the sign test. The entire sample of managers, the sample of non-escalating

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82 the salience of a construct is validated by a statistically significant result from the sign test. The analysis of the responses to the questionnaires is in Tables 5-9 through 512. The inferences that can be drawn from this analysis are powerful, particularly those comparing the samples of escalating and non-escalating managers. Question one dealt with the importance of consistent project choices. Question two examined the importance of project payoffs to the exclusion of all other factors. Question three asked about the importance of a late round switch. Question four dealt with the disclosure of the optimal project payoffs. Question five asked whether the subject felt he was successful in earning the highest payoffs possible during the experiment. Question six asked whether the subject would make the same decisions in the "real world." For the entire sample of managers, questions one through four and six are statistically significant with an a of 0.05 or better. Specifically, the majority of the managers (79%) strongly agreed or agreed that consistency in project choices was important. Over half (56%) of the managers strongly disagreed or disagreed that project payoffs were the only factor they should be concerned about. managers and the sample of supervisors constitute large sample sizes (greater than 30). For these samples, the test statistic a* is used as the large sample approximation of the sign test.

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83 The majority (54%) of the managers strongly agreed or agreed that switching projects at a late round in a sequence would have a negative effect on the amounts of bids from supervisors in the future. Almost half (48%) of the managers strongly agreed or agreed that the disclosure or non-disclosure of their optimal total payoffs affected their project choices. Over half (53%) strongly agreed or agreed that they would make the same decisions if they were in the ''real world." Question five does not show a significant effect. When asked whether they thought they were successful in earning the highest payoffs possible during the experiment, nearly equal percentages replied positively as replied negatively. A comparison of Tables 5-10 and 5-11 for the samples of escalating and nonescalating managers shows that the means are quite different for many of the questions. For the sample of escalating managers, questions one through three are statistically significant with an a of 0.05 or better. The sample of nonescalating managers shows significance for questions one and six at a of 0.05 or better. More specifically, t-tests comparing the two sample means show significant differences between the two samples for questions two and three (p-value=0.038 for question two and 0.002 for question three). This constitutes strong support for a basic difference between the two samples of managers.

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84 A large difference exists between the two samples for question three. The escalating managers clearly believed that a switch in the later rounds of a sequence would lower the amounts of bids from supervisors in subsequent sequences, while nonescalating managers were not convinced of this point. The difference in results for question two shows that the escalating managers were more convinced that project payoffs were not the only factor that they should be concerned about. These two beliefs led escalating managers to commit escalation errors. These results shown in the post-experimental questionnaires present strong evidence of a critical difference between the samples of escalating and nonescalating managers. Escalating managers clearly believed that late round switches would be detrimental to them in the future sequences and that project payoffs were not the only factor that they should be concerned about. For the supervisors questions one, two, four and six are statistically significant at a of 0.05 or better. All of these questions are significant for the entire sample of managers as well. Specifically, a large percentage (69%} of the supervisors strongly agreed or agreed that consistency in project choices was an important quality in a manager. For question two, the majority of the supervisors (60%) strongly disagreed or disagreed with the contention that project payoffs were the only factor that they should be concerned about when bidding for managers. The great

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85 majority (71%) strongly agreed or agreed that the disclosure of the optimal total payoffs for some managers affected their bidding strategy. Almost half (49%) strongly agreed or agreed that they would make the same decisions if they were in the "real world." Questions three and five show insignificant results. For question three, supervisors were almost evenly split on the contention that a late round switch was a signal of a poor manager. Supervisors were also split on question five on whether they were successful in earning the highest payoffs possible during the experiment. In general the majority of the constructs were proven to be salient in the post-experimental questionnaires. The importance of consistency in project choices, the contention that project payoffs were not the only factor they should be concerned about, the importance of the disclosure of optimal project payoffs and the feeling that subjects would make the same decisions in the "real world" were all proven to be salient for both managers and supervisors. Only managers felt that a late round switch would lower the amounts of bids from supervisors in the future. Neither group felt that they were successful in earning the highest payoffs possible during the experiment.

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86 Results for Revised Experiment A revised experiment consisting of six sessions was run in an attempt to discern whether a stronger manipulation of the information asymmetry (the monitor) construct would prove more salient to the subjects. A total of 55 student subjects from one section of Financial Accounting 2 at the University of Florida participated in the experiment. The students earned class credit for participating (equal to 2% of their final grade) as well as lottery tickets. The cash prizes for the lottery drawing for the revised experiment consisted of two prizes: one of $50 and one of $25. Subjects. Descriptive statistics about the subjects for this experiment can be found in Table 5-13. As shown in the table, the numbers of male and female subjects were almost equal. The average age was 23 and the average GPA was 3.21. The vast majority of the subjects were junior and senior level accounting students. The average payoffs were $28.32 for managers and $27.54 for supervisors. The average pre-test score for managers was 8.43, with a maximum of 11 and minimum of 5. The solicited risk aversion measure for the managers revealed 26 risk neutral subjects, two risk averse subjects and no risk seeking subjects. The average age and GPA were similar to those for the subjects for the main experiment (23 and 3.30). The average pre-test score was significantly lower than in the main experiment (9.05). This is possibly due to the lower classification level of

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the majority of these subjects compared with the subjects from the main experiment. A larger average payoff was earned by the managers, in contrast to the relationship in the main experiment. This was apparently due to the modifications undertaken for the additional sessions. 87 As in the main experiment, additional descriptive data were gathered in the post-experimental questionnaires. Spearman correlation coefficients between these measures are shown in Table 5-14. These correlations are quite different from those of the main experiment. The significant positive correlation between the manager's age and classification is the only significant correlation in the same direction as in the main experiment. The three most relevant correlations of the first experiment--a positive correlation between the manager's pre-test score and GPA, a negative correlation between the manager's pre-test score and the number of escalation errors committed and a positive correlation between the manager's experimental payoff and the number of escalation errors--are not present in the revised experiment. The opposite correlations involving the pre test score suggest that this measure was not effective in measuring the subject's aptitude for the experimental task in the revised experiment. The negative correlation between the manager's experimental payoff and the number of escalation errors suggests that the hypothesized reputation effect observed during the main experiment was not present

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88 during the revised experiment. The lack of correlation between these measures suggests that the revised experiment is not entirely comparable to the first. Modifications. The revised experiment was modified slightly to promote a greater incidence of escalation errors and to strengthen the information asymmetry manipulation. There was no manipulation of the time variable (the revelation point after either six or eight rounds) in the additional sessions, as all managers were always informed of the superior project after eight rounds. Additionally, managers faced an extra difficult sequence in place of the second easy sequence. The change in the manipulation of information asymmetry for these sessions was two-fold. First, instead of disclosing the optimal total payoffs (as was done in the main experiment), the superior project itself was disclosed. A second change was also made, since the certain disclosure or non-disclosure of the superior project was a perfect signal of an escalation error (and seemed too blatantly obvious). Instead of certain disclosure or non-disclosure, there was a percentage chance of disclosure for each manager. Half the managers had a 20% chance and half an 80% chance of disclosure. At the end of each sequence managers drew a card from a deck with these probabilities to determine whether the superior project was disclosed. This is considered a stronger manipulation than in the main

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89 experiment. In the main experiment, the commitment of an escalation error by a manager had to be interpreted by a supervisor through a comparison of the optimal total payoffs and the actual payoffs. In the revised experiment, the superior project was actually disclosed. Consequently, if a manager had an 80% chance of disclosure of the superior project he should never have escalated, since there was a very good chance the supervisors would find out that he made an escalation error. With the 20% chance the manager should have felt a very small chance of being "caught." It was hoped that this manipulation would cause managers to only escalate under the 20% chance of disclosure. Results. There were a total of twelve escalation errors committed during the six sessions. 8 Exactly half of the errors occurred in each treatment of the information asymmetry construct. The results of the logit analysis are shown in Table 5-15. Again the hypothesized model fits the actual data (likelihood ratio statistic=21.71 and goodness of fit measure=0.9149). An insignificant effect for the 8 The twelve escalation errors committed during the revised experiment equates to a 7% escalation rate (12 errors+ 168 total experimental sequences). This rate is equivalent to that of the main experiment. The escalation rate predicted for the revised experiment is 15%--half of the managers are untalented, half have no monitor present and three of the last five sequences are difficult--1/2 X 1/2 X 3/5 = 3/20 = 15%. In the main experiment the actual and predicted escalation rates are almost equal. In this case the actual escalation rate is half of the predicted rate. This observation represents further evidence that the revised experiment is not entirely comparable to the main experiment.

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90 monitor was again shown in these experimental sessions. The post-experimental questionnaires for both managers and supervisors showed no significance for question four which deals with the effect of disclosure on the subjects' decisions (the majority of the remaining post-experimental questionnaire responses were similar to those of the main experiment). This is in stark contrast to the significant effect for this question in the post-experimental questionnaires for both managers and supervisors in the main experiment. This lack of results for the monitor variable under the modified manipulation is quite troubling. Apparently the subjects are given too much information about the entire experiment to be able to discern the relevance of each piece. Alternatively, the manipulation of the monitor construct may have been unable to capture the theoretical essence of the variable. Nevertheless, the theory that a monitor should reduce escalation errors cannot be discarded without numerous attempts to prove or disprove the saliency of the monitor construct. Another troubling result of the revised experiment is the significant positive coefficient of the talent variable. This relationship was also shown in the correlation analysis. Specification testing for outliers shows only one manager in the revised experiment as a severe outlier. 9 9 An outlier is usually considered extreme if its residual is larger than two standard deviations. From a conservative viewpoint, only outliers which had values of

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91 When compared to the main experiment, this manager is the most severe outlier of both samples. When this manager's observations are removed from the legit analysis, the talent variable loses its significance. These results are shown in Table 5-16. This outlier has a great influence on the results of the revised experiment due in part to the small sample size of this experiment {28 managers as compared to 82 in the main experiment). When the data from the main experiment are combined with the data from the revised experiment excluding the outlier, the coefficient on the talent variable reverts to its expected sign and regains its significance. Table 5-17 shows these results, which are similar to those of the main experiment. Another observation supports the assertion that the revised experiment was not completely comparable to the main experiment. The subjects in the revised experiment did not have the same incentive system as those of the main experiment. In the main experiment, the subjects were recruited from their classes with the sole incentive of earning lottery tickets and a chance to win a cash prize. studentized t residuals greater than 3.00 were removed. The identification of the severe outlier from the revised experiment was straightforward and was based on a regression with NEE as the dependent variable and TAL, MON and PAYOFF as the independent variables. Only one studentized residual greater than 3.00 was found. The severe outlier's studentized residual was 3.27. When the data from the main experiment was combined with that from the revised experiment, the observed outlier's studentized residual jumped to 4.11.

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92 In the revised experiment, the subjects still had a chance to earn lottery tickets and win a cash prize (although the cash prizes were significantly smaller), but it is believed that the main incentive for these subjects was the class credit they received for participating in the experiment. The credit was given unconditional of the subjects' performance during the experiment, i.e., all students received equal credit. By earning the class credit the subjects were relieved of an out of class assignment. The instructor informed the students that participation in the experiment would probably be "easier" than the completion of the assignment. Only three students out of the class of 58 did not participate in the revised experiment. During these experimental sessions, an obvious lack of effort and interest was shown by many subjects (and was noted by the experimental assistant as well as the experimenter). Due to these facts the revised experiment is not considered to be analogous to the main experiment. Summary The results of the data analysis suggest that the main experiment was successful in inducing escalation behavior. Three of the four factors posited to influence this behavior were significant: the talent level of the manager, the time when the manager is informed of the superior project and the difficulty level surrounding the manager's decision. The

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93 talent factor should be closely related to the commitment of an escalation error. The significance of the talent factor in the experimental study suggests that the pre-experimental questionnaire designed to measure talent was an accurate gauge of the subject's actual aptitude for the experimental task. The time factor is somewhat obvious and its experimental significance supports the hypothesized effect. The manipulation of the difficulty level surrounding the manager's decisions is also logical. It's experimental significance not only supports the predictions, but also provides an internal validity check of the experiment. As mentioned previously, the lack of significance for the monitor manipulation in both the main experiment and the revised experiment is troubling. In general this experiment was successful in identifying which of the posited factors influence the commitment of an escalation error.

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94 TABLE 5-1 Frequency of Escalation Errors for Independent Variables Panel A: Talent Level I II Escalation Error I Talent Level Yes I No 3 0 4 4 2 4 5 2 10 6 2 22 7 7 65 8 6 60 9 2 72 10 6 80 11 2 80 12 4 50 Panel B: Monitor Variable I II Escalation Error I Monitor Yes I No Present I 18 I 204 I Absent 15 243 Panel C: Time Variable Revelation I Escalation Error I Point Yes I No 6 Rounds I 3 I 187 I 8 Rounds 30 260

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TABLE 5-1--continued Panel D: Series Difficulty Level Series Difficulty I Escalation Error Level Yes I No Easy 0 164 Moderate 5 153 Difficult 28 130 95 I

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TABLE 5-2 Logit Analysis of Manager's Decision Model Variable Coefficient Standard t-statistic p-value Mean of Standard Error Variable Deviation of Variable Intercept -5.056 1.261 -4.011 0.000 1.000 0.000 TAL -0.235 0.097 -2.414 0.008 9.058 2.020 MON 0.064 0.414 0.155 0.439 0.463 0.499 TIME 1.526 0.667 2.288 0.011 0.604 0.490 DIFF 2.161 0.477 4.534 0.000 0.988 0.820 Likelihood ratio statistic=43.54, goodness of fit measure=0.9998. Note: All p-values are one-tailed since directional effects were expected.

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TABLE 5-3 Spearman Correlations Between Descriptive Variables for Managers I II TAL I GPA I AGE I CLASS I SEX I PAYOFF I GPA 0.285 ( 0. 005) 1 AGE -0.030 0.169 (0.795) (0.145) CLASS 0.143 -0.084 0.332 (0.101)* (0.460) (0.002)* 1 SEX 0.332 -0.061 -0.025 0.144 (0.003) 1 (0.594) (0.829) (0.199) PAYOFF 0.100 0.206 0.121 0.104 0.101 (0.186)* ( 0. 0 3 4) 1 (0.292) (0.355) (0.367) NEE -0.180 0.242 -0.018 -0.104 -0.067 0.174 (0.053)* 2 (0.016)* 1 (0.878) (0.356) (0.555) (0.059)* 2 TAL = managers' scores on the pre-test administered prior to the experiment GPA = managers' self-reported GPAs AGE = managers' self-reported ages CLASS = managers' self-reported classifications SEX = managers' self-reported sexes PAYOFF = managers' experimental payoffs NEE = number of escalation errors committed by the managers during the experiment *These p-values are one-tailed since a directional effect was expected. All other values are two-tailed. 1 Significant at a=0.05. 2 significant at a=0.10.

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I Series Type I Easy 1 Easy 2 Moderate Difficult 6 TABLE 5-4 Logical Payoffs Revelation Rounds $12.00 $11.50 $ 9.00 $10.00 98 Point 8 Rounds -----$11. 50 $ 7.92 $ 8.875 Note: Calculation of the logical payoffs is shown in Appendix C. 1 The first sequence was an easy series for all subjects and contained no occurrences of the random factor. 2 The second easy series came at either sequence two, three or four for all subjects and was slightly more difficult than the first easy series (the random factor occurred once)

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I TABLE 5-5 Variances and Absolute Values for Each Type of Series Over Both Sequences Series Type I Measure Revelation Point 6 Rounds 8 Rounds Easy 1 Variance 0.966 -----Abs. Value 0.415 Easy 2 Variance 0.857 0.575 Abs. Value 0.430 0.250 Moderate Variance 0.662 1. 468 Abs. Value 0.750 1.053 Difficult Variance 1.067 0.567 Abs. Value 0.482 0.567 99 Note: The moderate and difficult series were faced twice by all subjects. These calculations include both sequences. 1 The first sequence was an easy series for all subjects and contained no occurrences of the random factor. 2 The second easy series came at either sequence two, three or four for all subjects and was slightly more difficult than the first easy series (the random factor occurred once)

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TABLE 5-6 Variances and Absolute Values for Each Sequence Series Type Measure Revelation Point 6 Rounds 8 Rounds 1st 2nd 1st 2nd Sequence Sequence Sequence Sequence Moderate Variance 0.665 0.656 1. 425 1.509 Abs. Value 0.750 0.750 1.031 1. 074 Difficult 1 Variance 0.502 2.196 0.793 0.370 Abs. Value 0.348 0.750 0.639 0.504 Difficul t 2 Variance 0.502 0.227 0.751 0.363 Abs. Value 0.348 0.182 0.649 0.508 Note: Every manager faced the moderate and difficult series twice each. The variances and absolute values for the first and second times the managers faced the moderate and difficult series are reported in this table. 1 These figures include all difficult sequences for the entire sample. 2 These figures include only difficult sequences where an escalation error was not committed. These figures are shown in addition to those for the entire sample since the three escalation errors committed during the second sequence under revelation after six rounds accounted for the majority of the entire sample's variance and absolute value at that revelation point. f-' 0 0

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I TABLE 5-7 Variances and Absolute Values for Escalating Versus Non-escalating Sequences Series Type I Measure Revelation Point 6 Rounds 8 Rounds Difficult Variance 9.417 0.661 Escalating Abs. Value 2.833 0.545 Difficult Variance 0.425 0.542 Non-escalating Abs. Value 0.301 0.573 Moderate Variance -----1. 349 Escalating Abs. Value 0.766 Moderate Variance -----1. 466 Non-escalating Abs. Value 1.059 101 Note: The moderate and difficult series were faced twice by all subjects. These calculations include both sequences.

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102 TABLE 5-8 Average Supervisors' Bids and Variances Around Bids I Sequence # I Average Variance from Variance from Bid Actual Logical Average Average 1 $2.115 0.380 0.479 2 $2.113 0.350 0.450 3 $2.078 0.334 0.457 4 $2.112 0.337 0.438 5 $2.071 0.357 0.486 6 $2.083 0.326 0.446

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103 TABLE 5-9 Post-experimental Questionnaire Responses for Managers Number of Percentage Subjects of Total Mean p Question #1 Strongly Agree 23 Agree 41 No Opinion 6 Disagree 10 Strongly Disagree 1 Question #2 Strongly Agree 7 Agree 20 No Opinion 9 Disagree 39 Strongly Disagree 6 Question #3 Strongly Agree 11 Agree 33 No Opinion 19 Disagree 15 Strongly Disagree 3 Question #4 Strongly Agree 14 Agree 25 No Opinion 19 Disagree 16 Strongly Disagree 7 Question #5 Strongly Agree 3 Agree 32 No Opinion 12 Disagree 30 Strongly Disagree 4 Question #6 Strongly Agree 4 Agree 39 No Opinion 21 Disagree 13 Strongly Disagree 4 28.40 50.62 7.41 12.35 1. 23 8.64 24.69 11.11 48.15 7.41 13.58 40.74 23.46 18.52 3.70 17.28 30.86 23.46 19.75 8.64 3.70 39.51 14.81 37.04 4.94 4.94 48.15 25.93 16.05 4.94 2.07 6.12 0.0000 3.21 2.12 0.0170 2.58 3.30 0.0005 2.72 2.03 0.0212 3.00 0.12 0.4522 2.68 3.36 0.0004 Note: The mean was calculated based on the responses being coded 1 through 5, with strongly agree=l.

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104 TABLE 5-10 Post-experimental Questionnaire Responses for Escalating Managers Number of Percentage Subjects of Total Mean B p Question #1 Strongly Agree 9 36.00 Agree 13 52.00 No Opinion 0 0.00 Disagree 3 12.00 Strongly Disagree 0 0.00 1.88 22 0.0001 Question #2 strongly Agree 0 0.00 Agree 6 24.00 No Opinion 2 8.00 Disagree 15 60.00 Strongly Disagree 2 8.00 3.52 17 0.0173 Question #3 Strongly Agree 6 24.00 Agree 12 48.00 No Opinion 6 24.00 Disagree 1 4.00 Strongly Disagree 0 0.00 2.08 18 0.0000 Question #4 Strongly Agree 5 20.00 Agree 9 36.00 No Opinion 5 20.00 Disagree 6 24.00 Strongly Disagree 0 0.00 2.48 14 0.0577 Question #5 Strongly Agree 0 0.00 Agree 9 36.00 No Opinion 5 20.00 Disagree 9 36.00 Strongly Disagree 2 8.00 3.16 9 0.7483 Question #6 Strongly Agree 1 4.00 Agree 10 40.00 No Opinion 8 32.00 Disagree 6 24.00 Strongly Disagree 0 0.00 2.76 11 0.1662 Note: The mean was calculated based on the responses being coded 1 through 5, with strongly agree=l.

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105 TABLE 5-11 Post-experimental Questionnaire Responses for Non-escalating Managers Question #1 Number of Subjects Strongly Agree 14 Agree 28 No Opinion 6 Disagree 7 Strongly Disagree 1 Question #2 Strongly Agree 7 Agree 14 No Opinion 7 Disagree 24 Strongly Disagree 4 Question #3 Strongly Agree 5 Agree 21 No Opinion 13 Disagree 14 Strongly Disagree 3 Question #4 Strongly Agree 9 Agree 16 No Opinion 14 Disagree 10 Strongly Disagree 7 Question #5 Strongly Agree 3 Agree 23 No Opinion 7 Disagree 21 Strongly Disagree 2 Question #6 Strongly Agree 3 Agree 29 No Opinion 13 Disagree 7 Strongly Disagree 4 Percentage of Total Mean p 25.00 50.00 10.71 12.50 1. 79 12.50 25.00 12.50 42.86 7.14 8.93 37.50 23.21 25.00 5.36 16.07 28.57 25.00 17.86 12.50 5. 36 41.07 12.50 37.50 3.57 5.36 51.79 23.21 12.50 7.14 2.16 4.81 0.0000 3.07 1.00 0.1587 2.80 1.37 0.0853 2.82 1.23 0.1093 2.93 0.43 0.3336 2.64 3.20 0.0007 Note: The mean was calculated based on the responses being coded 1 through 5, with strongly agree~l.

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106 TABLE 5-12 Post-experimental Questionnaire Responses for Supervisors Number of Percentage Subjects of Total Mean B* p Question #1 Strongly Agree 10 13.89 Agree 40 55.56 No Opinion 13 18.06 Disagree 6 8.33 Strongly Disagree 3 4.17 2.33 5.34 0.0000 Question #2 Strongly Agree 6 8.33 Agree 19 26.39 No Opinion 4 5.56 Disagree 40 55.56 Strongly Disagree 3 4.17 3.21 2.18 0.0146 Question #3 Strongly Agree 4 5.56 Agree 24 33.33 No Opinion 14 19.44 Disagree 27 37.50 Strongly Disagree 3 4. 17 3.01 -0.26 0.3974 Question #4 Strongly Agree 16 22.22 Agree 35 48.61 No Opinion 10 13.89 Disagree 8 11.11 Strongly Disagree 3 4.17 2.26 5.08 0.0000 Question #5 Strongly Agree 4 5.56 Agree 21 29.17 No Opinion 14 19.44 Disagree 28 38.89 Strongly Disagree 5 6.94 3.13 -1.05 0.1469 Question #6 Strongly Agree 4 5.56 Agree 31 43.06 No Opinion 19 26.39 Disagree 14 19.44 Strongly Disagree 4 5.56 2.76 2.34 0.0096 Note: The mean was calculated based on the responses being coded 1 through 5, with strongly agree=l.

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TABLE 5-13 Descriptive Statistics for Subjects in the Revised Experiment Managers Total Number 28 Sex Female 15 Male 13 Average Age 23 Average GPA 3.26 Classification 3AC 11 4AC 10 6AC 6 7AC 0 3BA 0 4BA 0 7BA 0 SBA 0 Other 1 Average Payoff $28.32 Pre-test for Managers Average Score 8.43 Variance 2.89 Maximum Score 11 Minimum Score 5 Measure of Risk Aversion Risk Neutral 26 Risk Averse 2 Risk Seeking o Supervisors 27 12 15 22 3.16 11 9 1 0 3 1 0 0 1 $27.54 Total Sample 55 27 28 23 3.21 22 19 7 0 3 1 0 0 2 Classification key: Number equals class year (3=junior, 4=senior, 6=post-baccalaureate, 7 and 8=master's) and AC equals accounting, BA equals business administration. 107 Note: Any numbers not adding up to the reported total are due to some subjects not filling in all questions asked of them.

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I GPA AGE CLASS SEX TABLE 5-14 Spearman Correlations Between Descriptive Variables for Managers for Revised Experiment II TAL I GPA I AGE I CLASS I SEX I PAYOFF -0.161 (0.216)* 0.014 -0.594 (0.947) (0.002) 1 0.189 -0.288 0.681 (0.168)* (0.154) (0.000)* 1 0.068 -0.015 0.021 -0.099 (0.732) (0.940) (0.919) (0.616) PAYOFF -0.106 -0.090 0.165 0.164 0.160 NEE TAL GPA AGE CLASS SEX PAYOFF NEE (0.295)* (0.331)* (0.421) (0.404} (0.417} 0.367 -0.438 0.387 0.352 0.212 0.217 (0.027)* 1 (0.013)* 1 (0.051) 2 (0.066) 2 (0.278) (0.134}* = managers' scores on the pre-test administered prior to the experiment = managers' self-reported GPAs = managers' self-reported ages = managers' self-reported classifications = managers' self-reported sexes = managers' experimental payoffs = number of escalation errors committed by the managers during the I experiment *These p-values are one-tailed since a directional effect was expected. All other values are two-tailed. 1 significant at a=0.05. 2 significant at a=0.10. f-l 0 00

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Variable Intercept TAL MON DIFF TABLE 5-15 Legit Analysis of Manager's Decision Model for Revised Experiment (with all observations) Coefficient standard t-statistic p-value Mean of Error Variable -9.954 2.863 -3.477 0.000 1.000 0.385 0.215 1.790 0.037 8.429 0.315 0.631 0.498 0.309 0.464 2.264 1.011 2.241 0.013 1. 333 Likelihood ratio statistic=21.71, goodness of fit measure=0.9149. standard Deviation of Variable 0.000 1. 704 0.500 0.748 Note: All p-values are one-tailed since directional effects were expected.

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Variable Intercept TAL MON DIFF TABLE 5-16 Legit Analysis of Manager's Decision Model for Revised Experiment (without outlier) Coefficient Standard t-statistic p-value Mean of Error Variable -7.910 2.777 -2.848 0.002 1.000 0.159 0.217 0.732 0.232 8.333 0.888 0.742 1.197 0.116 0.481 1.929 1.005 1. 919 0.027 1. 333 Likelihood ratio statistic=18.80, goodness of fit measure=0.9691. Standard Deviation of Variable 0.000 1.661 0.501 0.748 Note: All p-values are one-tailed since directional effects were expected. t-' t-' 0

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Variable Intercept TAL MON TIME DIFF TABLE 5-17 Logit Analysis of Manager's Decision Model for Combined Samples (without outlier) Coefficient Standard t-statistic p-value Mean of Error Variable -5.627 1.180 -4.767 0.000 1.000 -0.130 0.087 -1.493 0.068 8.875 0.298 0.348 0.857 0.196 0.467 1.033 0.637 1.620 0.053 0.704 1. 985 0.417 4.763 0.000 1. 075 Likelihood ratio statistic=48.06, goodness of fit measure=0.9992. Note: All p-values are one-tailed since directional effects were expected. standard Deviation of Variable 0.000 1. 960 0.499 0.457 0.816

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CHAPTER 6 SUMMARY AND CONCLUSIONS Summary of the Study The hypotheses developed in Chapter 3 predicted that the independent variables; the talent level of the manager, the presence of a monitor of the manager, the time at which the manager discovers he has made an escalation error and the difficulty level surrounding the manager's decision; would impact the manager's decision of whether to commit an escalation error. To test these hypotheses the experiment described in Chapter 4 was conducted. Student subjects participated in the experiment where the true purpose of the study was disguised. Subjects took part in one two hour session over the two week period of the experiment. Lottery tickets were earned by the subjects based on their experimental payoffs. The tickets were entered into a drawing and seven cash prizes were awarded. Summary and Discussion of Results The purpose of this study was to determine under what circumstances managers commit escalation errors. The process of answering this research question was guided by the predictions developed in Chapter 3. The first 112

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113 hypothesis predicted that escalation errors are made more frequently by untalented managers than by talented managers. The expected effect was supported by the contention that the talented manager has a superior ability to see and understand the economic conditions governing his choices. He therefore would make more correct choices and be less likely to commit escalation errors. The logit analysis showed a significant negative effect for the talent measure, i.e., the untalented managers committed more escalation errors. Hypothesis two posited that the presence of a monitor reduces the incidence of escalation errors. In a world of public knowledge, a manager would never knowingly commit an escalation error, since all owners would be aware of the error. As the amount of public knowledge decreases, managers may be enticed to commit escalation errors if they perceive a benefit (such as enhancement of their reputation) from doing so. With a monitor or a device that conveys some of the private information of the manager to the owner, the manager should be less prone to escalation errors. The logit analysis showed an insignificant effect for the monitor variable. A revised experiment was run in an attempt to discern whether a stronger manipulation of the monitor construct would prove salient to the subjects. The results for these sessions also showed an insignificant

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effect for the monitor variable. This construct was obviously not salient to the subjects. 114 The third hypothesis predicted that the later the manager discovers that he has made an escalation error, the more likely he is to continue with the error. This predicted time effect is supported by two contentions. First, the longer the firm has undertaken a project, the larger the project's sunk costs. Second, the manager's reputation is more severely damaged by admitting a mistake made many periods in the past. The logit analysis showed a significant positive effect for the time variable, i.e., the vast majority of escalation errors were committed when the manager discovered he had made an error at a later stage. Hypothesis four provided an internal validity check for the experiment. This hypothesis posited that escalation errors are made by both talented and untalented managers in a difficult series of signals and payoffs, by only untalented managers in a moderate series and by no managers in an easy series. Any spurious escalation errors can be avoided by implementation of this check. The logit analysis showed a significant positive relationship for this measure (as was expected). Premises typically associated with experimental market designs were also tested. Convergence to equilibrium was paralleled by using measures associated with a calculated value called the logical payoff. Three premises involving

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115 the logical payoffs were tested: P 1 ) the market should be closer to the logical payoff for the easy sequences than for the moderate and difficult sequences, P 2 ) the market should move toward the logical payoffs over time and P 3 ) there should be more dispersion from the logical payoff when an escalation error is made than when no error is made. Premise P 1 was supported by the absolute value calculations. Premise P 2 was supported for the difficult sequences, but not for the moderate sequences. Premise P 3 was not supported for the difficult sequences. Additional exploratory relationships pertaining to the bids from supervisors for the managers' services were also investigated. These were 1) a decreasing trend is expected in the bids over time and 2) the average bid following an escalation error should be lower than the overall average bid. The first relationship was not supported, as no apparent bidding trends were displayed by the supervisors. Relationship number two showed significant results, but cannot be confirmed with certainty due to the lack of a direct causal relationship between the commitment of an escalation error and a resulting lower bid. Tests of the validity of experimental manipulations showed that the majority of the constructs were salient to the subjects. Significant differences in the responses to the Post-Experimental Questionnaire for Managers were shown between the samples of escalating and non-escalating

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managers. These differences between the two samples of managers supported the escalation results. 116 The results of this study provide an alternative explanation for the commitment of escalation errors based on economic rationality. Previous behavioral studies have identified factors such as personal responsibility, efficacy of resources, consistency in actions and the ultimate success of the chosen course of action to be consistent with the commitment of escalation errors. While these factors are not explicitly manipulated in the current study, two of the factors--personal responsibility and consistency in actions--are included in the experimental task as controls. The four hypotheses of this study deal with constructs not previously included in experiments investigating escalation errors. As such the results of this study should be looked upon as a first attempt at determining the significance of these constructs in the escalation error problem. Contributions and Implications Contributions to Managerial Accounting Research The contributions of this study are 1) an extension of prior managerial accounting research on the escalation error phenomenon and 2) an explicit examination of the effects of critical accounting factors on the escalation error decision.

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117 The present research extends prior work on the escalation error phenomenon by providing an empirical study which focuses on variables of interest from an accounting standpoint. The contribution of such a study is considerable. First, variables related to the escalation error problem itself and the principal-agent setting are explicitly recognized. These variables are distinctly different from the psychological variables that have been used in previous examinations of the escalation error phenomenon in behavioral studies. The predicted effects of these variables are explored and developed into the constructs used in the experimental study. Second, any effects found in the study can be presented as a first effort at examining whether these variables actually impact the escalation error decision. Determining the factors that influence the escalation error decision is of great interest, since the potentially disastrous effects of escalation errors can be large. While not primary contributions, two methodological enhancements of the study were intended to provide more powerful tests of the hypotheses. First, there were multiple time periods in the experiment. This allowed each subject acting as a manager to face the escalation error decision multiple times. In this setting, the manager can see the effects of either escalating or not escalating and can alter his future behavior based on his interpretation of

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118 these effects, much as in the real world. Second, subjects were given cash incentives. Use of the lottery system allowed the experimenter to provide incentives to a total of over 200 subjects without a prohibitively high cost. Implications for Understanding Escalation Errors Conclusions based on the results of this study are favorable. The results suggest that the talent level of the manager, the time when the manager discovers he has made an escalation error and the difficulty level surrounding the manager's decision affect the manager's decision of whether to commit an escalation error. These results were supported in the experiment, but future research is warranted. The result that a more talented manager is less likely to commit an escalation error is evident and should be embraced in actual practice. Owners obviously attempt to retain only talented managers, but unfortunately this is not always the case. If an untalented manager is hired by the firm, perhaps additional training could transform him into a talented manager. The result for the time factor is also quite apparent, as the later the manager found out he had committed an escalation error, the more likely he was to continue with the error. The implications of this time factor in practice could take the form of more frequent performance reviews. These could afford the opportunity for discovery of escalation errors in their early stages, when

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119 they are more likely to be terminated. The results for the difficulty level are related to the environment surrounding the manager's decision, and as such are beyond the control of all parties involved. The lack of results for the monitor variable is troubling. The strong theoretical significance of a monitor should nevertheless be embraced in actual practice. Empirical evidence suggests that agents will shirk when given the opportunity, at the expense of principals. Consequently, additional monitoring should reduce the incidence of escalation errors. Principals would undoubtedly gain additional information through more frequent performance evaluations (as suggested above). The reported differences between the groups of escalating and nonescalating managers were significant. These differences supported the escalation results and showed dramatically different interpretations of the experimental constructs between the two groups. An attempt to measure these sorts of differences could be made in practice. Personality compilations are already used by many personnel departments. Identifying individuals who are less prone to escalation errors by this sort of testing could be beneficial.

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120 Limitations As discussed in Chapter 4, the use of student subjects restricts the generalizability of the results. The generalization of the results to the population of practicing managers is not appropriate unless some version of this research is actually tested on a subset of practicing managers. In this complex decision task, however, the first step is to examine whether the choices of a homogeneous group of subjects, like students, support the predicted results. In any experimental study trade-offs exist between replicating real world circumstances and the tractability of the experiment. The decisions made by subjects in this experiment are simplified from their real world counterparts. Many factors that would be present in actual business decisions are missing in the experiment. There may be unidentified factors that significantly affect the escalation error decision. The compactness of the current study limits the generalizability of the results. Directions for Future Research Future research efforts could attempt to take a similar version of this experiment to practicing managers. Some experimental measures could be enhanced with a group of practicing managers. The measures of talent and risk aversion could be modified. The talent measure for

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121 practicing managers could consider such factors as years of experience, level of education and profitability of the manager's division. The risk aversion measure could be strengthened by using a more extensive set of questions or by using a different measure altogether. Many types of measurement techniques exist for eliciting risk attitudes. Replication of the results of the current study with practicing managers would validate the conclusions reached. This would bridge one gap with respect to the generalizability of the results of this study.

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APPENDIX A EXPERIMENTAL PROTOCOL This appendix recounts the steps that occurred in each experimental session. When subjects entered the room they were told to sit down in any of the prearranged chairs or desks. The chairs or desks (depending on the room) were arranged in a U shape where ideally in a full session there were two manager supervisor pairs on each side. The subjects faced each other across the table or desks. When all subjects arrived they were thanked for their participation. Each subject was then handed a folder including all experimental papers. The general instructions and market organization were then read aloud. It was pointed out that the managers were sitting on the inside and the supervisors on the outside. This seating was more conducive to the supervisors seeing the displayed information during the experiment. After these three pages were read, the supervisors left the room with the experimental assistant to receive their specific instructions. The managers stayed in the room with the experimenter. The experimental assistant took the supervisors to another empty room down the hall. Once in the room the 122

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123 supervisors were instructed to read over their specific instructions. When they were done, the experimental assistant went over the important points of the instructions and made sure that all supervisors had a good understanding of their role in the experiment. The managers were also instructed to read over their specific instructions. When they were done, the experimenter went over the important points of the instructions with the managers. Two learning sequences were then explicitly explained to the managers. These were included so the managers would have a good understanding of all the factors involved in the experiment before the actual experiment started. This was done to avoid an excessively large learning curve as the experiment went on. The managers were then informed that the first sequence would be an easy series for everyone. After that they were told they would all receive a mixture of easy, moderate and difficult sequences in random order, but they would each receive the same number of each type by the end of the experiment. The managers were then told to work on the pre-experimental questionnaire. They had about seven minutes to complete the questionnaire to the best of their ability before the supervisors returned and the actual experiment started. The supervisors returned to their same seats and all subjects were told to use their folders as a barrier between themselves and the subjects across from them. The

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124 supervisors were then given the first sequence information sheet and the first sequence began. The supervisors read the first round information cue (signal). The managers recorded the cue on their worksheets and made their first round choice. The supervisors then informed the managers of the resulting payoff. Each manager-supervisor pair was checked to make sure they understood what is was they were to do. After either six or eight rounds in each sequence, the managers raised their hands and were given a small sheet of paper containing the superior project for the sequence. The managers recorded the superior project on their worksheets and continued with the sequence. When each manager-supervisor pair was done with the sequence, they were told they would have a few minutes of down time while the experimenter entered their choices into the computer. The managers' project choices for each round, total payoffs for the sequence and in some cases the optimal total payoffs were displayed via the computer on the overhead screen. The supervisors were then told to examine this information and determine which managers they wanted to bid on for the next sequence. The supervisors' bids were then matched with the managers by the experimenter. The managers moved seats to be paired with their new supervisors. The next sequence then began. The subjects were led to believe there would be seven sequences, based on their payoff records. After the bidding

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125 and rematching for the seventh sequence, however, the subjects were informed the experiment was over. The subjects were then told to add up their payoff records and then fill out their post-experimental questionnaires. They were then instructed to put all their sheets back into their folders and bring the folders to the front of the room. The total payoffs were then verified on the computer and lottery tickets were awarded based on a predetermined scale. The subjects tore their lottery tickets in half and deposited one half into the ticket bin. The subjects were then free to leave the experiment.

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APPENDIX B EXPERIMENTAL INSTRUMENTS This appendix contains all instruments used during the experiment. Each instrument is shown in its exact form (some instruments took up fewer pages, as margins were wider) and all are in the same order as the subjects received them. The materials are presented in the following order: (1) introduction letter, (2) general instructions and market organization, (3) specific instructions for managers, (4) specific instructions for supervisors, (5) pre-experimental questionnaire for managers, (6) manager's worksheet, (7) supervisor's information sheet, (8) supervisor's bidding sheet, (9) manager's payoff record, (10) supervisor's payoff record, (11) post-experimental questionnaire for managers and (12) post-experimental questionnaire for supervisors. 126

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127 Dear UF Student: I need volunteers to participate in a study I am conducting for my Ph.D. dissertation here at the University of Florida. I know that as a student, your time is limited and I would appreciate very much your participation in this study. Your participation is voluntary and you may withdraw from the study at any time. If you choose to participate, you would attend one session. Each session will last no longer than two hours. The time of the sessions will be during normal class hours. The sessions will be held in buildings in the Business Administration area between January 27, 1992 and February 7, 1992. Based on the decisions you make in this study, you will earn tickets for a lottery with seven prizes of $200 (1), $100 (2), and $50 (4). The money for the lottery prizes is funded by the Fisher School of Accounting. All tickets will be entered in a drawing that will be held upon conclusion of the study. The range of tickets that can be earned is from 1 to 10. (Everyone gets a chance to win a prize.) This study is about business decisions made by managers and supervisors. You will assume the role of either a manager or a supervisor. As a subject, you will be asked to make decisions similar to those made by actual managers and supervisors in a firm. In order to protect your privacy, no one else will have access to any of your decisions. As the researcher, I will maintain records for the experiment based on your assigned subject number. I will be happy to answer any questions that you have about the study. If you have questions at a later time, you may call my office, 392-1039, or leave a message at the Fisher School of Accounting office, 392-0155. You may also call me at home between 8 A.M. and 8 P.M. at 373-7465. I will be in your classes next week to ask for volunteers to sign up to participate. The conclusions based on the results of this study will be made available to all participants. Sincerely, Robin R. Radtke Ph.D. Student Fisher School of Accounting

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128 GENERAL INSTRUCTIONS This is an experiment in the economics of decision making in the firm. The Fisher School of Accounting has provided funds for conducting this research. If you follow the instructions carefully and make good decisions you will have a good chance to win a large sum of money in a lottery type drawing. The greater the monetary payoffs you earn as a subject during the experiment, the more tickets you will receive for the lottery, thus increasing your chances of winning real cash. In this experiment we are simulating a firm in which some of you are managers and some of you supervise the managers in a series of decisions. Information describing the steps in the experiment is attached to this sheet and will be explained next. Additionally, you will receive information for managers or information for supervisors. These information sheets are identified and numbered. The number is only for data-collecting purposes. If you receive manager's information, you will function only as a manager in the firm. Similarly, if you receive supervisor's information, you will function only as a supervisor in the firm. The information you receive is for your private use. DO NOT REVEAL IT TO ANYONE.

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129 MARKET ORGANIZATION During each sequence there are 12 decision rounds. In the beginning of the first sequence, each manager is randomly paired with one supervisor. The following diagram shows the ordering of events for the experiment: Initial random pairings 12 rounds New pairings based on bidding 12 rounds New pairings based on bidding 12 rounds '---'-----'-----'---'-----'----''--_.___.__--'------'---'-----'-'----'-----'-----'---'-------'----''----.l.----'-----'---'-----'----''----.l.----'----> Sequence# 1 Sequence# 2 /\ I I I I Bidding for Managers' Services Sequence# 3 /\ I I I I Bidding for Managers' Services In each round of a sequence managers are faced with a recurring investment decision. At the beginning of the first round, some additional information (a cue or hint as to the correct investment choice) is given to managers by their supervisors. Supervisors have obtained this additional information from outside consultants in order to aid managers in their investment decisions. Managers then

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130 make their first round choices of investment projects. Supervisors then provide investment project feedback to their managers in the form of project payoffs. Managers then receive their second round information cues from their supervisors and make their second round choices. The following diagram shows the ordering of events for each round: Managers receive information cue from supervisors Managers make investment project choices Supervisors inform managers of project payoffs This process continues until the end of the sequence. At the end of each sequence, supervisors bid for the services of managers. Information pertaining to each manager's performance during the previous sequence is made public for everyone to see. This bidding provides the matching of managers and supervisors for the next sequence. After several sequences the experiment will end. Are there any questions about these instructions at this time? We are now breaking up into two groups (managers and supervisors) to receive specific instructions.

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Manager# SPECIFIC INSTRUCTIONS FOR MANAGERS 131 During each round you are asked to make a decision between two investment projects, A and B, based on the information you receive. In each sequence of rounds the economic conditions in the market favor either project A or B, making the favored project the superior project to choose for that sequence. Choosing the superior project results in higher payoffs to the firm for the sequence. The superior project for each sequence has been randomly determined by the experimenter, but is unknown to you. You do know, however, The probability that project A is superior is 60% for each sequence. The probability that project Bis superior is 40% for each sequence. There are two possible payoffs for each round worth $.50 or $1.00 to the firm. Choosing the superior project increases your chances of receiving the $1.00 payoff each round. At the end of the experiment you receive 25% of your firm's total accumulated payoffs as well as the accumulated bids of the supervisors with whom you are paired over the sequences. Additionally, if you choose the same project in all of the decision rounds, you receive a bonus for being consistent of $.50. There is a probability in each round that even if you choose the superior project, you will receive $.50 instead of $1.00, or if you choose the inferior project, you will receive $1.00 instead of $.50. This probability is representative of economic circumstances beyond your control and decreases as the rounds in each sequence pass. You will receive an information cue, either cue 1 or cue 2, each round. These cues give you better, although not perfect, information as to the superior project. If you receive cue 1, the probability that project A is the superior project is 72% and the probability that project B is the superior project is 28%. If you receive cue 2, the probability that project A is the superior project is 43% and the probability that project Bis the superior project is 57%. You can think of the cue (C) \ superior project (P) combinations as follows:

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132 C\P A B 1 .72 .28 2 .43 .57 Please take note of these information cues as they will help you to discern the superior project for each sequence. Once you have chosen a project, you must pay a cost to switch to the other project. This cost is subtracted from your firm's payoffs in any round in which you switch. This cost increases as you continue to choose one project over the decision rounds. The cost to switch is $.50 in rounds 2 and 3, $.75 in rounds 4 and 5, $1.00 in rounds 6 and 7, $1.25 in rounds 8 and 9, $1.50 in rounds 10 and 11, and $1.75 in round 12. If you switch projects more than once, the cost starts over at $.50 and increases in the same manner. This cost represents the economic losses from beginning to implement a project and then switching to the other project in the future. Switching to the superior project guarantees that the payoffs received in the future rounds from the superior project will be greater than the cost to switch in all rounds except round 12 (the last round). Remember, however, that being consistent throughout a sequence is rewarded in 2 ways: 1) you never have to pay the cost to switch and 2) you receive a bonus payment of $.50 at the end of the sequence. At the end of the first sequence your project choices for each round and total payoffs for the firm for the sequence are made public for everyone to see. At the end of the second sequence and all subsequent sequences some additional information will be disclosed for some of you, but will not be disclosed for others. This additional information consists of your optimal total payoffs for the firm for the sequence. The optimal total payoffs are calculated based on choosing the superior project in every round of the sequence. If the optimal total payoffs are disclosed, supervisors can compare the optimal total payoffs to your actual total payoffs and determine the difference. This will give supervisors another basis upon which to judge your performance. Please pay attention to this fact, as it may affect the way supervisors bid for your services. In your case, the additional information will not be disclosed. Keep in mind that there are many factors to consider when making your investment project choices. While it is important to try to choose the superior project, supervisors will be monitoring your choices, and will make judgements about your skills as a manager based on their observations.

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133 Since they are unaware of which project is superior, they will try to judge your skill at determining the superior project based on your project choices and payoffs. While payoffs are obviously important, a switch from one project to the other very late in a sequence reveals that you discovered the superior project at a late stage in the sequence. This would definitely lower supervisors' assessments of your skill as a manager. Accordingly, this will lower the amount of the bids for your services in the future, which make up a good portion of your experimental payoffs. After either six or eight rounds the superior project for the sequence will be disclosed to you by the experimenter. Please do not show any emotion at this time. Are there any questions about these instructions at this time?

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134 Supervisor# --SPECIFIC INSTRUCTIONS FOR SUPERVISORS During each round you provide your manager with investment project information. You give your manager an information cue for each round and inform him or her of the payoff for each round. The manager's problem each round is to choose from two alternative investment projects, A and B. In each sequence of rounds the economic conditions in the market favor either project A or B. If the manager chooses the favored project, he or she has a higher probability of receiving a high project payoff. Once a manager has chosen a project, he or she must pay a cost to switch to the alternative project in the round of the switch. The manager is aware of the amount of this cost, which is subtracted from the firm's payoffs and increases as one project is chosen in consecutive rounds. Therefore, a switch after one project has been chosen for many decision rounds may significantly decrease the payoffs to the firm for that sequence. In each sequence your experimental payoffs are linked to your manager's investment project payoffs. Specifically, you receive 75% of the accumulated payoffs to the firm for each sequence. At the end of each sequence, the project choices for each round for each manager and the total payoffs to the firm for the sequence are made public for everyone to see. At the end of each sequence subsequent to the first, the optimal total payoffs are disclosed for some managers. The optimal total payoffs are calculated based on choosing the superior project in every round of the sequence. At this time you bid for the services of the managers by entering three secret written bids for three different managers. The supervisor entering the highest bid for a manager is matched with that manager for the next sequence. If all three managers you bid on are matched with other supervisors, you are then matched with one of the remaining managers at the lowest allowable bid. Obviously, you want to be matched with a manager you believe is able to make correct investment project choices. Therefore, it is important to bid competitively for the managers. The amount of your bid for the manager with whom you are matched is subtracted from your experimental earnings. The minimum amount you can bid on any manager is $1.50 and the maximum amount is $3.00.

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135 When bidding for managers, there are several factors that should be considered. After each sequence you receive more information on each manager and you should attempt to keep a tally of this information for each manager for each sequence. This is important since the information cues received by managers will be very easy to interpret in some sequences, and very difficult to interpret in others. This level of difficulty will vary between managers in each sequence, however each manager will encounter the same number of difficult and easy sequences by the end of the experiment. Consequently, consistently high payoffs are probably a signal of a skilled manager, but you may be able to gather more information on each manager by using other observations. You should carefully compare the manager's actual total payoffs for a sequence to the optimal total payoffs, when the optimal total payoffs are disclosed. If the difference between these figures is small, the skill level of the manager is probably high. If the difference is large, the skill level of the manager is probably low. Another observation you should be alert to is a switch from one project to the other in a late round in a sequence. This shows that the manager discovered the superior project very late in the sequence and signals that the skill level of the manager is probably low. This gives you additional information when bidding on the manager in future sequences. Accordingly, you should take these additional factors into account and consider your bidding carefully. Are there any questions about these instructions at this time?

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Manager# PRE-EXPERIMENTAL QUESTIONNAIRE FOR MANAGERS The following questions are based on your knowledge of probability theory and general business concepts. 1. What is the probability of obtaining heads on two consecutive tosses of a fair coin? 136 2. What is the probability that a blue marble is drawn from an urn with 3 blue marbles, 2 red marbles, and 5 green marbles? 3. The greater the risk of a project, the required rate of return. the 4. If you purchase a lottery ticket which has a 40% chance of winning $10 and a 60% chance of winning $5, what is the expected value of the lottery ticket? 5. What is the probability of selecting a heart on the first draw from a standard 52 card deck? 6. If a company has a cost of capital of 10%, those projects with an expected return of at least % -----will be selected for investment. 7. If you have ten balls numbered 1 through 10, what is the probability of selecting a ball with a number greater than 6? 8. What is the probability of rolling a total of 3 with a single toss of 2 balanced dice? 9. The possibility that the actual returns of a project will be different than the forecasted returns of a project represents the ______ of the project. 10. What is the probability of selecting 2 cards of the same suit from a standard 52 card deck? (with replacement) 11. What is the probability of a 1, given the occurrence of an odd number, in a single toss of a balanced die? 12. Which of the following utility functions represents a risk averse individual?(Circle one) Utility= (Wealth) Utility= (Wealth) 2 Utility= (3)*(Wealth)

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137 13. Assume you own a lottery ticket in which your chance of winning $100 is 50% and your chance of winning $0 is 50%. What is the minimum price you would accept in exchange for your lottery ticket?

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MANAGER'S WORKSHEET ROUND 1 Information cue received= Your choice of investment projects Project payoff = ROUND 2 Information cue received= Your choice of investment projects = Project payoff = ROUND 3 Information cue received= Your choice of investment projects = Project payoff ROUND 4 Information cue received= Your choice of investment projects Project payoff = ROUND 5 Information cue received= Your choice of investment projects = Project payoff = ROUND 6 Information cue received= Your choice of investment projects Project payoff Manager# Sequence# 138 --

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ROUND 7 Information cue received= Your choice of investment projects= Project payoff= ROUND 8 Information cue received= Your choice of investment projects= Project payoff= Superior project revealed to be= (Please raise your hand to receive this information.) ROUND 9 Information cue received = Your choice of investment projects = Project payoff = ROUND 10 Information cue received = Your choice of investment projects = Project payoff = ROUND 11 Information cue received = Your choice of investment projects = Project payoff = ROUND 12 Information cue received = Your choice of investment projects = Project payoff = 139

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140 SUPERVISOR FOR MANAGER #1 SEQUENCE ONE Round Cue Payoff 1 2 If project A, payoff is $ 0.50 If project B, payoff is $ 1.00 2 2 If project A, payoff is $ 0.50 If project B, payoff is $ 1.00 3 1 If project A, payoff is $ 0.50 If project B, payoff is $ 1.00 4 2 If project A, payoff is $ 0.50 If project B, payoff is $ 1.00 5 2 If project A, payoff is $ 0.50 If project B, payoff is $ 1.00 6 1 If project A, payoff is $ 0.50 If project B, payoff is $ 1.00 7 2 If project A, payoff is $ 0.50 If project B, payoff is $ 1.00 8 2 If project A, payoff is $ 0.50 If project B, payoff is $ 1.00 9 2 If project A, payoff is $ 0.50 If project B, payoff is $ 1.00 10 1 If project A, payoff is $ 0.50 If project B, payoff is $ 1.00 11 2 If project A, payoff is $ 0.50 If project B, payoff is $ 1.00 12 2 If project A, payoff is $ 0.50 If project B, payoff is $ 1.00 When you have finished the first sequence, please raise your hand.

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Supervisor# Sequence# SUPERVISOR'S BIDDING SHEET Manager Number 141

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Sequence One Payoff +Consistency Bonus Subtotal Sequence Two +Bid +Payoff +Consistency Bonus Subtotal Sequence Three +Bid +Payoff +Consistency Bonus Subtotal Sequence Four +Bid +Payoff +Consistency Bonus Subtotal Sequence Five +Bid +Payoff +Consistency Bonus Subtotal Manager# MANAGER'S PAYOFF RECORD Sequence Six +Bid +Payoff +Consistency Bonus Subtotal Sequence Seven +Bid +Payoff +Consistency Bonus Subtotal Total 142 --

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Sequence One Payoff Subtotal Sequence Two -Bid < > +Payoff Subtotal Sequence Three -Bid < > +Payoff Subtotal Sequence Four -Bid < > +Payoff Subtotal Sequence Five -Bid < > +Payoff Subtotal Sequence Six -Bid < > --+Payoff Subtotal Sequence Seven -Bid +Payoff Subtotal Total < > --143 Supervisor# --SUPERVISOR'S PAYOFF RECORD

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Manager# POST-EXPERIMENTAL QUESTIONNAIRE FOR MANAGERS Your GPA to date: Classification: Please circle one Strongly Agree Agree No Opinion Disagree Strongly Disagree of the following SA A N D SD Age: Sex: F M for each question: 144 1. I felt that being consistent in my project choice was important. SA A N D SD 2. I felt that project payoffs were the only factor that I should be concerned about. SA A N D SD 3. I felt that a switch in the later rounds of a sequence would lower the amounts of bids from supervisors in subsequent sequences. SA A N D SD 4. I felt that the nondisclosure of my optimal total payoffs affected my project choices. SA A N D SD 5. I felt that I was successful in earning the highest payoffs possible during the experiment. SA A N D SD 6. I would make the same decisions if I were in the "real world". SA A N D SD 7. Please write any comments as to problems you had with this experiment (continue on the back of the page if necessary) :

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Supervisor# POST-EXPERIMENTAL QUESTIONNAIRE FOR SUPERVISORS Your GPA to date: Classification: Please circle one Strongly Agree Agree No Opinion Disagree Strongly Disagree Age: Sex: F M of the following for each question: SA A N D SD 1. I felt that consistency in project choices was an important quality in a manager. SA A N D SD 145 2. I felt that project payoffs were the only factor that I should be concerned about when bidding for managers. SA A N D SD 3. I felt that a switch in the later rounds was a signal of a poor manager. SA A N D SD 4. I felt that the disclosure of the optimal total payoffs for some managers affected my bidding strategy. SA A N D SD 5. I felt that I was successful in earning the highest payoffs possible during the experiment. SA A N D SD 6. I would make the same decisions if I were in the "real world". SA A N D SD 7. Please write any comments as to problems you had with this experiment (continue on the back of the page if necessary) :

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APPENDIX C CALCULATION OF LOGICAL PAYOFFS AND BIDS LOGICAL PAYOFFS For the easy sequences, correct project choices should be made throughout the sequences. Therefore, the logical payoff for the first easy sequence is $12.00. The second easy sequence has a logical payoff of $11.50, since the random factor occurs during one round. Moderate For the moderate sequences, all reasonably rational patterns of choices are considered. Revelation Point After 6 Rounds (Superior Project= A) Project Round Cue Choice 1 2 B B B 2 1 B B B 3 2 B B B 4 1 A A B 5 1 A A B 6 2 B A B 7 1 A A A 8 2 A A A 9 1 A A A 10 1 A A A 11 1 A A A 12 1 A A A Total Payoffs: Alternative #1 Alternative #2 Alternative #3 Average= $ 9.00 Payoff 1.00 1.00 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 1.00 0.50 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 = $ 8.25 = $ 8.75 = $ 10.00 146 1.00 0.50 0.50 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Switching Cost 0.75 0.75 0.50 0.50 1.00

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Revelation Point After 8 Rounds (Superior Project= A} Project Switching Round Cue Choice Payoff Cost 1 2 B B B 1.00 1.00 1.00 2 1 B B B 0.50 0.50 0.50 3 2 B B B 0.50 0.50 0.50 4 1 A B B 0.50 1.00 1.00 0.75 5 1 A B B 0.50 1.00 1.00 6 2 B B B 1.00 1.00 1.00 0.50 7 1 B B B 0.50 0.50 0.50 8 2 B B B 0.50 0.50 0.50 9 1 A A B 1.00 1.00 0.50 0.75 1. 25 10 1 A A B 1.00 1.00 0.50 11 1 A A B 1.00 1.00 0.50 12 1 A A B 1.00 1.00 0.50 Total Payoffs: Alternative #1 = $ 7.00 Alternative #2 = $ 8.75 Alternative #3 = $ 8.00 Average= $ 7.92 Difficult For the difficult sequences, all reasonably rational patterns of choices are considered. Revelation Point After 6 Rounds (Superior Project= B) Project switching Round Cue Choice Payoff Cost 1 1 A 1.00 2 1 A 0.50 3 2 A 1.00 4 1 A 1.00 5 2 A 1.00 6 2 A 0.50 7 2 B 1.00 1.00 8 1 B 1.00 9 2 B 1.00 10 2 B 1.00 11 1 B 1.00 12 1 B 1.00 Total Payoffs = $ 10.00 147

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148 Revelation Point After 8 Rounds (Superior Project B) Project switching Round Cue Choice Payoff Cost 1 1 A A 1.00 1.00 2 1 A A 0.50 0.50 3 2 A A 1.00 1.00 4 1 A A 1.00 1.00 5 2 A A 1.00 1.00 6 2 A A 0.50 0.50 7 2 A A 1.00 1.00 8 1 A A 0.50 0.50 9 2 B A 1.00 0.50 1.25 10 2 B A 1.00 0.50 11 1 B A 1.00 0.50 12 1 B A 1.00 0.50 Total Payoffs: Alternative #1 = $ 9.25 Alternative #2 = $ 8.50 Average= $ 8.875 LOGICAL AVERAGE BID The logical average bid is based on the logical average payoff for all experimental sequences. The logical average payoff is based on the number of each type of sequence that actually occurred during the experiment. Therefore, the logical average payoff= where nT m n; LP 1 Given this 1 480 = = = = the total number of experimental sequences; the number of different types of sequences; (17) the number of experimental sequences of type i; and the logical payoff for sequences of type i. equation, the logical average payoff [82 ($12. 00) +82 ($11. 50) +34 ($9. 00) +124 ($7. 92) +42($10.00)+116($8.875)] == $9.72

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149 The logical average payoff is then divided between managers and supervisors in the proportions of and%, respectively. At equilibrium, the payoffs for the managers and supervisors should be equal. The logical average bid required to make the payoffs equal is $2.43. Manager's equilibrium payoff= ($9.72) + $2.43 = $4.86 Supervisor's equilibrium payoff= %($9.72) $2.43 = $4.86

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REFERENCES Akerlof, G. The Market for 'Lemons': Quality Uncertainty and the Market Mechanism. Quarterly Journal of Economics 84 (1970): 488-500. Andersen, E. The Statistical Analysis of Categorical Data, 1st Edition, Springer-Verlag, Berlin, 1990. Beidleman, C. Income Smoothing: The Role of Management. The Accounting Review 73 (1968): 653-667. Belle, F. High Reward Experiments Without High Expenditure for the Experimenter? Journal of Economic Psychology 11 (1990): 157-167. Brockner, J. The Escalation of Commitment to a Failing Course of Action: Toward Theoretical Progress. Academy of Management Review 17 (1992): 39-61. Camerer, C., and K. Weigelt. Experimental Tests of a Sequential Equilibrium Reputation Model. Econometrica 56 (1988): 1-36. Copeland, R. Income Smoothing. Empirical Research in Accounting: Selected studies 6 (1968): 101-121. DeBondt, W., and A. Makhija. Throwing Good Money After Bad? Nuclear Power Plant Investment Decisions and the Relevance of Sunk Costs. Journal of Economic Behavior and Organization 10 (1988): 173-199. Hepworth, S. Review Smoothing Periodic Income. 28 ( 1953) : 32-39. The Accounting Holmstrom, B., and J. Ricart i Costa. Managerial Incentives and Capital Management. Quarterly Journal of Economics 101 (1986): 835-860. Horngren, c., and G. Foster. Cost Accounting: A Managerial Emphasis, 6th Edition, Prentice-Hall, Englewood Cliffs, NJ, 1987. 150

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151 Kanodia, c., R. Bushman, and J. Dickhaut. Escalation Errors and the Sunk Cost Effect: An Explanation Based on Reputation and Information Asymmetries. Journal of Accounting Research 27 (Spring 1989): 59-77. Kreps, D., and R. Wilson. Reputation and Imperfect Information. Journal of Economic Theory 27 (1982): 253-279. Luce, R., and H. Raiffa. Games and Decisions, 1st Edition, John Wiley & Sons, Inc., New York, 1957. Narayanan, M. Results. Managerial Incentives for Short-term Journal of Finance 40 (1985): 1469-1484. Penno, M. Issues of Information Asymmetry in Managerial Accounting. Unpublished Dissertation, Northwestern University, (June 1983). Penno, M. Asymmetry of Pre-Decision Information and Managerial Accounting. Journal of Accounting Research 22 (1984): 177-191. Spence, M. Job Market Signaling. Quarterly Journal of Economics 87 (1973): 355-374. Statman, M., and J. Sepe. Project Termination Announcements and the Market Value of the Firm. Financial Management 18 (Winter 1989): 74-81. Staw, B. Knee Deep in the Big Muddy: A Study of Escalating Commitment to a Chosen Course of Action. Organizational Behavior and Human Performance 16 ( 1976) : 27-44. Staw, B. The Escalation of Commitment To a Course of Action. Academy of Management Review 6 (1981): 577-587. Staw, B., and F. Fox. Escalation: The Determinants of Commitment to a Chosen Course of Action. Human Relations 30 (1977): 431-450. Staw, B., and J. Ross. Commitment in an Experimenting Society: An Experiment on the Attribution of Leadership from Administrative Scenarios. Journal of Applied Psychology 65 (1980): 249-260.

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BIOGRAPHICAL SKETCH Robin Rae Radtke was born in Madison, Wisconsin, on October 16, 1963. She received her B.S. majoring in mathematics from Marquette University in 1984. She was employed as a rate analyst at City Public Service in San Antonio, Texas, in 1984 and 1985. She continued her education at the University of Texas at San Antonio from 1985 to 1987. She entered the Ph.D. program in accounting at the University of Florida in 1987. Radtke completed the Ph.D. program in 1992 and accepted a faculty position at the University of Houston. 152

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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. Bip~kya, Chairman Professor of Accounting I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. ~~-~'Professor of Accounting I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. }),/h)A~f)ri Assistant Professor of Accounting I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. Ro~ 4 ~RKlg~ Professor of Statistics This dissertation was submitted to the Graduate Faculty of the Fisher School of Accounting in the College of Business Administration and to the Graduate School and was accepted as partial fulfillment of the requirements for the degree of Doctor of Philosophy. August 1992 Dean, Graduate School

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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. Bipin B. Ajinkya, Chairman Professor of Accounting I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. Doug Snowball Professor of Accounting I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. Jeffrey A. Yost Assistant Professor of Accounting I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. Ronald H. Randles Professor of Statistics This dissertation was submitted to the Graduate Faculty of the Fisher School of Accounting in the College of Business Administration and to the Graduate School and was accepted as partial fulfillment of the requirements for the degree of Doctor of Philosophy. August 1992 Dean, Graduate School

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UNIVERSITY OF FLORIDA 111I\1111111111111111111111111111111111111111111111111111111111 I 3 1262 08553 5010


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