Advances in Accounting Behavioral Research, Volume 8 (Advances in Accounting Behhavioral Research)

ADVANCES IN ACCOUNTING BEHAVIORAL RESEARCH

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ADVANCES IN ACCOUNTING BEHAVIORAL RESEARCH Series Editor: Vicky Arnold Volumes 1–4: Series Editor: James E. Hunton Volumes 5–7: Series Editor: Vicky Arnold

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ADVANCES IN ACCOUNTING BEHAVIORAL RESEARCH VOLUME 8

ADVANCES IN ACCOUNTING BEHAVIORAL RESEARCH EDITED BY

VICKY ARNOLD Department of Accounting, School of Business, University of Connecticut, USA and Department of Accounting and Business Information Systems, University of Melbourne, Australia Associate Editors:

B. DOUGLAS CLINTON Northern Illinois University, USA

PETER LUCKETT University of New South Wales, Australia

ROBIN ROBERTS University of Central Florida, USA

CHRIS WOLFE Texas A&M University, USA

SALLY WRIGHT University of Massachusetts Boston, USA

2005

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CONTENTS LIST OF CONTRIBUTORS

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REVIEWER ACKNOWLEDGEMENTS

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EDITORIAL POLICY AND SUBMISSION GUIDELINES

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BELIEF REVISION IN ACCOUNTING: A LITERATURE REVIEW OF THE BELIEFADJUSTMENT MODEL Jennifer Kahle, Robert Pinsker and Robin Pennington AUDITOR CALIBRATION IN THE REVIEW PROCESS Noel Harding, Sally Hughes and Ken T. Trotman LINGUISTIC DELIVERY STYLE, CLIENT CREDIBILITY, AND AUDITOR JUDGMENT Christie L. Comunale, Thomas R. Sexton and Terry L. Sincich CLIENT INQUIRY VIA ELECTRONIC COMMUNICATION MEDIA: DOES THE MEDIUM MATTER? Anna No¨teberg and James E. Hunton ROLE MORALITY AND ACCOUNTANTS’ ETHICALLY SENSITIVE DECISIONS Robin R. Radtke v

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CONTENTS

THE EFFECT OF MANAGER’S MORAL EQUITY ON THE RELATIONSHIP BETWEEN BUDGET PARTICIPATION AND PROPENSITY TO CREATE SLACK: A RESEARCH NOTE Adam S. Maiga

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ASYMMETRIC EFFECTS OF ACTIVITY-BASED COSTING SYSTEM COST REALLOCATION M.G. Fennema, Jay S. Rich and Kip Krumwiede

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EXAMINING THE ROLE OF CULTURE AND ACCULTURATION IN INFORMATION SHARING Stephen B. Salter and Axel K.-D. Schulz

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THE EFFECTS OF VALUE ATTAINMENT AND COGNITIVE ROLES OF BUDGETARY PARTICIPATION ON JOB PERFORMANCE Vincent K. Chong, Ian R.C. Eggleton and Michele K.C. Leong

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LIST OF CONTRIBUTORS Vincent K. Chong

UWA Business School, The University of Western Australia, Australia

Christie L. Comunale

School of Professional Accountancy, Long Island University – C.W. Post Campus, USA

Ian R. C. Eggleton

Waikato Management School, University of Waikato, New Zealand

M. G. Fennema

Department of Accounting, Florida State University, USA

Noel Harding

School of Accounting, University of New South Wales, Australia

Sally Hughes

School of Accounting, University of New South Wales, Australia

James E. Hunton

Accountancy Department, Bentley College, USA and Department of Accounting and Information Management, Universiteit Maastricht, The Netherlands

Jennifer Kahle

School of Accountancy, University of South Florida, USA

Kip Krumwiede

College of Business and Economics, Boise State University, USA

Michele K. C. Leong

UWA Business School, The University of Western Australia, Australia

Adam S. Maiga

School of Business Administration, University of Wisconsin – Milwaukee, USA

Anna No¨teberg

Department of Business Studies, Universiteit van Amsterdam, The Netherlands vii

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LIST OF CONTRIBUTORS

Robin Pennington

Department of Accounting and Information Management, University of Tennessee, USA

Robert Pinsker

College of Business and Public Administration, Old Dominion University, USA

Robin R. Radtke

Department of Accounting, The University of Texas at San Antonio, USA

Jay S. Rich

College of Business, Illinois State University, USA

Stephen B. Salter

Universidad Adolfo Ibanez Escuela de Negocios, College of Business Administration, University of Cincinnati, USA

Axel K-D. Schulz

Department of Accounting and Business Information Systems, The University of Melbourne, Australia

Thomas R. Sexton

College of Business, Stony Brook University, USA

Terry L. Sincich

Information Systems and Decision Sciences Department, University of South Florida, USA

Ken T. Trotman

School of Accounting, University of New South Wales, Australia

REVIEWER ACKNOWLEDGEMENTS The Editor and Associate Editors at AABR would like to thank the many excellent reviewers who have volunteered their time and expertise to make this an outstanding publication. Publishing quality papers in a timely manner would not be possible without their efforts.

Vincent Chong The University of Western Australia, Australia

Mohammed Abdolmohammadi Bentley College, USA Elizabeth Almer Portland State University, USA

Freddie Choo San Francisco State University, USA

Philip Beaulieu University of Calgary, Canada

Janne Chung York University, Canada

Jean Bedard Northeastern University, USA

Bryan Church Georgia Tech University, USA

James Bierstaker Villanova University, USA

Jeff Cohen Boston College, USA

Dennis M. Bline Bryant College, USA

William N. Dilla Iowa State University, USA

Wray Bradley University of Tulsa, USA

Jesse Dillard Portland State University, USA

Gary Braun University of Texas at El Paso, USA

Craig Emby Simon Fraser University, Canada

Rich Brody University of New Haven, USA

Dann Fisher Kansas State University, USA

Shimin Chen University of Louisiana at Lafayette, USA

Clark Hampton University of Connecticut, USA ix

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Joanne P. Healy Kent State University, USA

Ed O’Donnell Arizona State University, USA

Karen L. Hooks Florida Atlantic University, USA

Laurie Pant Suffolk University, USA

Stacy Kovar Kansas State University, USA

Robert J. Parker University of New Orleans, USA

Tanya Lee University of North Texas, USA

Will Quilliam University of South Florida, USA

Theresa Libby Wilfred Laurier University, Canada

Randall Rentfro Florida Atlantic University, USA

Tim Lindquist The University of Northern Iowa, USA

Andrew J. Rosman University of Connecticut, USA Scott Summers Brigham Young University, USA

Jill McKinnon Macquarie University, Australia

Steve Sutton University of Connecticut, USA

Mario Maletta Northeastern University, USA

Linda Thorne York University, Canada

Maureen Mascha Marquette University, USA

Kristin Wentzel La Salle University, USA

Elaine Mauldin University of Missouri, USA

John Wermert Drake University, USA

Rob Nieschwietz University of Colorado at Denver, USA Andreas Nikolaou Bowling Green State University, USA Hossein Nouri The College of New Jersey, USA

Patrick Wheeler University of Missouri, USA Brett Wilkinson Baylor University, USA Bernard Wong-On-Wing Washington State University, USA Alex Yen University of Connecticut, USA

EDITORIAL POLICY AND SUBMISSION GUIDELINES Advances in Accounting Behavioral Research (AABR) publishes articles encompassing all areas of accounting that incorporate theory from and contribute new knowledge and understanding to the fields of applied psychology, sociology, management science, and economics. The journal is primarily devoted to original empirical investigations; however, literature review papers, theoretical analyses, and methodological contributions are welcome. AABR is receptive to replication studies, provided they investigate important issues and are concisely written. The journal especially welcomes manuscripts that integrate accounting issues with organizational behavior, human judgment/decision making, and cognitive psychology. Manuscripts will be blind-reviewed by two reviewers and an associate editor. The recommendations of the reviewers and associate editor will be used to determine whether to accept the paper as is, accept the paper with minor revisions, reject the paper or to invite the authors to revise and resubmit the paper. Manuscript Submission Manuscripts should be forwarded to the editor, Vicky Arnold, at [email protected] via e-mail. All text, tables, and figures should be incorporated into a Word document prior to submission. The manuscript should also include a title page containing the name and address of all authors and a concise abstract. Also, include a separate Word document with any experimental materials or survey instruments. If you are unable to submit electronically, please forward the manuscript along with the experimental materials to the following address: Vicky Arnold, Editor Advances in Accounting Behavioral Research, Department of Accounting U41A School of Business University of Connecticut Storrs, CT 06269-2041

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References should follow the APA (American Psychological Association) standard. References should be indicated by giving (in parentheses) the author’s name followed by the date of the journal or book; or with the date in parentheses, as in ‘suggested by Earley (2000)’. In the text, use the form Rosman et al. (1995) where there are more than two authors, but list all authors in the references. Quotations of more than one line of text from cited works should be indented and citation should include the page number of the quotation, e.g. (Dunbar, 2001 p. 56). Citations for all articles referenced in the text of the manuscript should be shown in alphabetical order in the Reference list at the end of the manuscript. Only articles referenced in the text should be included in the Reference list. Format for references is as follows: For journals: Dunn, C.L., & Gerard, G.J. (2001). Auditor efficiency and effectiveness with diagrammatic and linguistic conceptual model representations. International Journal of Accounting Information Systems, 2(3), 1–40. For books: Ashton, R.H., & Ashton, A.H. (1995). Judgment and decision-making research in accounting and auditing. New York, NY: Cambridge University Press. For a thesis: Smedley, G.A. (2001). The effects of optimization on cognitive skill acquisition from intelligent decision aids. Unpublished doctoral dissertation, University. For a working paper: Thorne, L., Massey, D.W., & Magnan, M. (2000). Insights into selectionsocialization in the audit profession: An examination of the moral reasoning of public accountants in the United States and Canada. Working paper York University, North York, Ontario. For papers from conference proceedings, chapters from book, etc.: Messier, W.F. (1995). Research in and development of audit decision aids. In: R.H. Ashton, & A.H. Ashton (Ed.), Judgment and decision making in accounting and auditing (pp. 207–230). New York: Cambridge University Press.

BELIEF REVISION IN ACCOUNTING: A LITERATURE REVIEW OF THE BELIEF-ADJUSTMENT MODEL Jennifer Kahle, Robert Pinsker and Robin Pennington ABSTRACT The belief-adjustment model has been an integral part of accounting research in belief revision, especially in the examination of order effects. Hogarth and Einhorn ((1992) Cognitive Psychology, 24, 1–55) created the belief-adjustment model to serve as a theoretical framework for studying individuals’ decision-making processes. The model examines several aspects of decision-making, such as encoding, response mode, and task factors. The purpose of this chapter is to provide a comprehensive examination of the accounting studies that have used the theoretical framework of the belief-adjustment model in auditing, tax, and financial accounting contexts. Roberts’ ((1998) Journal of the American Taxation Association, 20, 78–121) model of tax accountants’ decision-making is used as a guideline to organize the research into categories. By using Roberts’ categorization, we can better sort out the mixed results of some prior studies and also expand the review to include a more comprehensive look at the model and its application to accounting. While many variables have been examined with respect to their effect on accounting professionals’ Advances in Accounting Behavioral Research Advances in Accounting Behavioral Research, Volume 8, 1–40 Copyright r 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1475-1488/doi:10.1016/S1475-1488(04)08001-9

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belief revisions, most studies examine them in isolation and do not consider the interaction effects that these variables may have. Our framework also identifies areas of the belief-adjustment model that need further research.

INTRODUCTION Professional judgment in accounting has been described in general terms as a continuous and incremental process (Gibbins, 1984). Most judgment tasks involve evidence that is evaluated in a sequential nature. Although sequential processing can provide economy in decision-making in terms of smaller demands on memory and information-processing load, it can also have detrimental effects, such as order effects in belief updating. Order effects, biases, and the use of heuristics resulting from the method by which individuals update their beliefs have been found in various areas of accounting, including auditing (e.g., Ashton & Ashton, 1988; Asare, 1992), tax (e.g., Pei, Reckers, & Wyndelts, 1990), and financial reporting (Pinsker, 2004). Much of the judgment and decision-making accounting literature has been influenced by Hogarth and Einhorn’s (1992) theory of belief revision. The theory accounts for order effects as they arise from the interaction of information-processing strategies and task characteristics. In particular, Hogarth and Einhorn’s belief-adjustment model assumes people handle belief revision tasks by a general, sequential anchoring and adjustment process in which current opinion, or the anchor, is adjusted by the impact of succeeding pieces of evidence. The model predicts that under conditions of evaluating a short series of simple information, a primacy effect will occur (i.e., the decision-maker will place more weight on the earliest information received) if the judgment is made after viewing all the evidence. However, if a short series of mixed (i.e., both positive and negative relative to a current hypothesis) information is evaluated piece-by-piece, differential weighting of the mixed information will produce a recency effect (i.e., the decision-maker will place more weight on the latest information received). An abundance of accounting literature examining recency effects has been motivated by the belief-adjustment model and findings of order effects in the psychology literature. Findings suggest that, in the absence of some mitigating influence, many accounting judgments are subject to recency effects. From a normative perspective, the sequence of evidence evaluation should not affect the conclusion drawn from the evidence (Christian & Reneau, 1990). The implications for accounting professionals may be

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systematic biases in judgments leading to reduced efficiency and effectiveness. The purpose of this chapter is to provide a comprehensive review of the accounting studies that have used the theoretical framework of the beliefadjustment model in auditing, tax, and financial accounting contexts. The emphasis will be on the factors that have been shown to influence or mitigate order effects. Roberts’ (1998) model of tax accountants’ decision-making will be used as a guideline to organize the factors into five main categories, including (1) individual psychological factors, (2) environmental factors, (3) input task factors, (4) processing factors, and (5) output task factors. This categorization is similar to that of Gibbins’ (1984) model of professional judgment in public accounting, whose groups include the person, stimulus, environment, decision, and judgment process. Categorization in this manner recognizes the separate influences of both external (environmental and task) and internal (individual) factors and reflects the recommendation by Hogarth and Einhorn (1992) to study further procedural and task variables that can affect belief revision. In an evaluation of recency effects in audit judgments, Trotman and Wright (2000) noted that results have been mixed across studies. By using Roberts’ (1998) categorizations, we can better understand the mixed results of the prior auditing studies, and also expand the review to include a more comprehensive look at the model and its application to various areas of accounting. The remainder of this chapter is organized into four sections. The first section describes the belief-adjustment model as proposed by Hogarth and Einhorn (1992). The following section discusses the general applications of the belief-adjustment model to accounting. Next, factors tested for association with belief revision are reviewed and categorized. The final section provides some concluding remarks as well as suggested directions for future research.

THE BELIEF-ADJUSTMENT MODEL Order Effects Bayes’ theorem was the dominant normative model of belief revision in accounting prior to 1988. The theorem gained popularity because it is a logical consequence of conditional probabilities. However, research in behavioral decision-making suggests that it is incomplete as a descriptive model of belief revision as it cannot adequately predict intuitive revision

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(Ashton & Ashton, 1988). Some researchers suggest that the discrepancy is due to the tendency for intuitive revisions to be driven by task characteristics, such as presentation order of information, which are irrelevant to the normative model (Pitz, Downing, & Reinhold, 1967). A large body of literature in psychology and accounting (e.g., Hogarth & Einhorn, 1992; Ashton & Ashton, 1988; Pei et al., 1992a, b; Kennedy, 1993) has confirmed the existence of presentation order effects on individual belief revisions. Hogarth and Einhorn (1992, p. 3) define order effects with the following example: There are two pieces of evidence, A and B. Some subjects express an opinion after seeing the information in the order A–B; others receive the information in the order B–A. An order effect occurs when opinions after A–B differ from those after B–A.

Primacy occurs when an individual places more weight on the earlier evidence in the sequence, while recency occurs when an individual places more weight on the latter (more recent) evidence in the sequence. Hogarth and Einhorn (1992) developed a ‘‘belief-adjustment model’’ to more fully explain how evidence is encoded and processed. They adapted the general concept of anchoring and adjustment (i.e., forming a belief and then adjusting it based on new information to form a new belief) to include heuristics into the model. Research since 1988 has provided descriptive validity for using the belief-adjustment model, rather than Bayes’ theorem, to explain belief revision (e.g., Ashton & Ashton, 1988; Pei et al., 1990; Bamber, Ramsay, & Tubbs, 1997). There are four distinct aspects in which the belief-adjustment model differs from Bayesian probability (Krishnamoorthy, Mock, & Washington, 1999). The belief-adjustment model (1) predicts that belief revision is influenced by the order in which evidence is evaluated; (2) entails an anchoring and adjustment strategy where the extent of belief revision is based on the size of the anchor (current opinion), a strategy that violates the fundamental tenets of Bayesian probability theory; (3) explicitly models the decision-maker’s sensitivity toward evidence; and (4) allows one to increase or decrease support for a hypothesis (e.g., that an account is fairly stated) without affecting support for its complement (e.g., that an account is not fairly stated). In an examination of four theoretical models of belief revision, including a version of Bayesian inference, Krishnamoorthy et al. found that the belief-adjustment model is the only model that captures both the direction and magnitude of auditors’ belief revisions. Hogarth and Einhorn’s (1992) theory of belief revision accounts for order effects by examining the interaction of task characteristics and information processing strategies.

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Task Variables In forming their model, Hogarth and Einhorn (1992) considered three task variables: (1) the complexity of the individual items of evidence to be processed, (2) the length of the series of items, and (3) the manner in which judgments are elicited, which will be referred to as response mode.1 Complexity is a function of the amount of information for each piece of evidence that needs to be processed as well as the lack of familiarity with the task (i.e., less familiar equals more complex and vice versa). Complexity is important to belief revision since it relates to human processing ability. As complexity increases, people may resort to simplifying strategies to ease cognitive strain (Hogarth & Einhorn, 1992). Length of series refers to the number of pieces of evidence to be evaluated. Based on their review of several studies, Hogarth and Einhorn (1992) consider a series of between 2 and 12 items to be ‘‘short’’ and a series of 17 or more items ‘‘long.’’ To distinguish length of series from complexity, remember that complexity results from the amount of information processing required and not necessarily the length of the series. As Arnold, Collier, Leech, and Sutton (2000) indicate, a ‘‘complex’’ task is one that is unfamiliar to the decision-maker (as noted in the previous paragraph) or one that requires heavy information processing (defined as either a large number or a long series of items). Therefore, a complex task could result from a short series of evidence (if unfamiliar or full of detail) or automatically from a long series. A ‘‘simple’’ task results from a short series of familiar items. Response mode concerns the manner in which judgments are elicited. Hogarth and Einhorn (1992) consider two types: Step-by-Step (SbS) and End-of-Sequence (EoS). The SbS mode is a ‘‘sequential’’ procedure whereby participants express their beliefs each time they integrate a new piece of evidence. On the other hand, the EoS mode is a ‘‘simultaneous’’ procedure in which participants express their opinions only after all the information has been presented.

Encoding Hogarth and Einhorn (1992) also acknowledge the impact of the method by which individuals encode or process information on their subsequent judgments. Accordingly, the predictions of the belief-adjustment model are affected by two additional encoding variables: (1) processing mode (SbS versus EoS), and (2) task type (evaluation versus estimation).

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While related, the processing mode cannot be solely determined by the required response mode. When the task is completed in an SbS response mode, the individual must employ an SbS process by adjusting his or her opinion incrementally for each piece of evidence processed. However, if a task is completed in an EoS response mode, an individual can employ either an SbS or an EoS processing mode. The EoS processing mode requires an individual to aggregate all items prior to integrating them with the anchor, which can be cognitively demanding. Thus, the processing mode should depend on the cognitive demands of the task. Generally, the SbS processing mode will be used when the task is more complex. This allows the individual to continually integrate information with the anchor. The EoS mode is expected to be used for a simpler task where aggregating the latter information is cognitively easier. In addition to task complexity, psychology and accounting research have examined processing mode with respect to level of experience. Specifically, Yates (1990) indicated less experienced individuals employ a sequential (SbS) decision-making process. As individual decision-makers gain experience, they develop more ‘‘sophisticated’’ decision-making processes, consistent with an EoS processing mode (Anderson, 1988). Further, lessexperienced individuals may employ an SbS processing mode to reduce cognitive load (Arnold et al., 2000) or to reduce the effort necessary to process a task (Hunton & McEwen, 1997). Hogarth and Einhorn (1992) also make a distinction between evaluation and estimation tasks. In evaluation tasks, information is encoded as positive or negative relative to the hypothesis under consideration. Here, evidence is seen as bipolar relative to the hypothesis and can be expressed by some value on the continuum between ‘‘false’’ and ‘‘true.’’ On the other hand, estimation tasks involve assessing a ‘‘moving average’’ that reflects the position of each new piece of evidence relative to current opinion (involving a unipolar scale). Research in accounting judgments has generally required and found support for the use of the evaluation form of the model (Ashton & Ashton, 1988; Tubbs, Messier, & Knechel, 1990). To our knowledge, no accounting study has used the estimation task. As Hogarth and Einhorn (1992, p. 9) indicated, estimation tasks use data that fit ‘‘averaging models;’’ whereas, evaluation tasks use data that fit ‘‘adding models.’’ In the auditing domain (where the majority of belief-adjustment model research has taken place), Messier and Tubbs (1994) contend that an auditor would evaluate an item and its relationship to an audit assertion before revising beliefs about the assertion. Thus, auditors in particular are assumed to generally employ an additive model when revising beliefs

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(consistent with an evaluation task). Although there have been accounting studies that used only positive or negative information in their tasks (e.g., Ashton & Ashton, 1988; Tubbs et al., 1990), the results obtained were measured and analyzed in an additive fashion.2

Anchoring and Adjustment Process The belief-adjustment model assumes that people revise beliefs through a sequential anchoring and adjustment process in which the current opinion (an anchor) is adjusted by the impact of subsequent pieces of information. The algebraic form of the belief-adjustment model can generally be written as3 Sk ¼ Sk

1

þ wk ½sðxk Þ

RŠ

where S k ¼ degree of belief in some hypothesis, after evaluating k pieces of evidence (0pS k p1) S k 1 ¼ anchor or prior belief. The initial strength of belief is denoted S0 sðxk Þ ¼ subjective evaluation of the kth piece of evidence (Different people may accord the same evidence, xk, different evaluations) R ¼ the reference point against which the impact of the kth piece of evidence is evaluated wk ¼ the adjustment weight for the kth piece of evidence (0pwk p1). The adjustment weight for the kth piece of evidence, wk, can be further defined as ( when sðxk ÞpR aSk 1 wk ¼ bð1 S k 1 Þ when sðxk Þ4R where a ¼ the sensitivity toward negative evidence, and b ¼ the sensitivity toward positive evidence. The formula implies that wk is related to the strength of the anchor through a ‘‘contrast’’ effect such that large anchors are ‘‘hurt’’ more than smaller ones by the same negative evidence. Thus, the magnitude of the belief revision is proportional to the prior belief, Sk 1 ; for negative evidence and proportional to the inverse of the prior belief, 1 S k 1 ; for positive evidence. The a and b variables are constants, which represent an

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individual’s sensitivity toward negative and positive evidence, respectively, and are posited to be functions of both individual and external variables. For SbS processing, the adjustment weight, wk, will depend on the sign of the new evidence and the level of the anchor, S k 1 ; as described above. However, under an EoS processing mode, the individual makes only one adjustment, and the model can be simplified to S k ¼ S0 þ wk ½sðx1 ; :::; xk Þ

RŠ,

where s(x1,y, xk) is some function, possibly a weighted average of the items that follow the anchor. The contrast assumption makes a prediction regarding the information being evaluated. Specifically, it predicts whether primacy, recency, or no order effect will occur in belief revision (Table 1). Under a short series of simple information evaluated with EoS processing, the model always predicts primacy. Conversely, the model predicts recency for SbS processing of mixed evidence and no effect for consistent evidence. Under a short series of complex information (i.e., full of details), the model always predicts recency for the evaluation of mixed evidence and no order effect for the evaluation of consistent evidence. Finally, as more information is processed (long series), decrements in a and b are expected, which eventually leads to predictions of primacy.

Table 1.

Order Effect Predictionsa.

Type of Evidence Response mode

Mixed b

EoS Short series Simple Complex Long series a

Consistent c

SbS

EoSb

SbSc

Primacy Recency Primacy No effect Recency Recency No effect No effect Toward primacy Toward primacy Primacy Primacy

Predictions assume the evaluation mode (R ¼ 0) of encoding, which is consistent with studies in accounting (see Note 1). Predictions under the estimation mode for both mixed and consistent evidence would be exactly the same as the above predictions for mixed evidence under the evaluation mode. b EoS ¼ End-of-Sequence or simultaneous processing. c SbS ¼ Step-by-Step or sequential processing.

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BELIEF REVISION IN ACCOUNTING Research in accounting examining the belief-adjustment model, with the exception of Anderson and Maletta (1999), has generally been interested in the predicted recency effects for a short series of mixed evidence where participants use sequential (SbS) processing. Fig. 1 shows the beliefadjustment model’s predicted ‘‘fishtail’’ effect for this type of information. Bayes’ theorem would dictate no order effects; however, recency has prevailed in most short-series studies. Information that is received in a short series and is used to adjust beliefs in a sequential (SbS) processing mode characterizes many tax and audit tasks; whereas, relatively longer series of information is characteristic of financial reporting tasks. For example, prior research in tax has focused on sequential, directed information searches of ambiguous tax law, whereby tax professionals make judgments or recommendations to clients. This type of task is quite common and generally requires the sequential review of both positive and negative evidence, such as court cases, legislation, and administrative rulings. In audit, research has generally focused on the sequential presentation of evidence related to such items as the existence of material Sk −

+ S0

−

0

+

1

2

k

Time (k) Sk: S0:

Degree of belief after k pieces of evidence, 0<Sk<1 Initial belief (anchor)

+/-:

Direction of evidence evaluated – (confirming or disconfirming with respect to initial belief, respectively)

Fig. 1. Hypothetical Recency Effects on Belief Revisions for Mixed Evidence. Note: Recency is implied because the final position for the (+, ) order is lower than the final position for the ( ,+) order. Adapted from Ashton and Ashton (1988).

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errors or fraud, going concern judgments, inventory write-downs, or internal control evaluations. As mentioned by Trotman and Wright (2000), due to time constraints, a short series of information is relevant since auditors typically are not expected to obtain an extended set of evidence (e.g., over 15 items) for a particular assertion. In financial reporting, the sequential release of a longer series of information items is characteristic of the new online business-reporting model favored by the American Institute of Certified Public Accountants (AICPA) and other regulatory constituents (2002). Initial studies of the belief-adjustment model in accounting were primarily concerned with testing the applicability of the model to a particular accounting area (e.g., audit or tax). The initial belief revision study in accounting by Ashton and Ashton (1988) tested the strength of the evidence presented, the response mode, and the initial anchor. Since then, a variety of other individual and external variables have been included as factors important in the prediction of order effects. Recency effects consistent with the belief-adjustment model have generally been found to be robust in the absence of some mitigating factor.

FACTORS TESTED FOR ASSOCIATION WITH BELIEF REVISION EFFECTS The factors that have been tested for association with belief revision effects in accounting have been categorized in Table 2 using Roberts’ (1998) economic psychology-processing (EPP) model that was developed for tax accountants’ judgment and decision-making (JDM). The EPP model emphasizes the cognitive decision-making process, with both individual (internal) and economic (external) factors having varying degrees of influence on cognitive processing for specific tasks. By using Roberts’ model as a framework, we begin with a solid foundation of the factors which have been examined in prior literature, and we have an organization that follows Hogarth and Einhorn’s (1992) suggestion to consider the influence of individual and external factors on belief revision. While Roberts’ model was created to better understand tax JDM, the categories are not tax specific. The factors within each group (e.g., experience and task complexity) also have been examined in audit JDM and, more specifically, the overall belief revision process. From a broad perspective, the EPP model is similar to Gibbins’ (1984) model of professional judgment in public accounting, which is derived from

Belief Revision in Accounting

Table 2.

11

Categorization of Factors Tested for Association with Belief Revision.

Individual psychological factors Cognitive Years of experience Style Task relevant knowledge Affective Initial beliefs Sensitivity toward evidence Professional attitudes Environmental factors Professional roles Inherent risk Experimental markets Client characteristics Economic benefit to firm

Input task factors Strength of evidence Type of evidence (consistent or mixed) Response mode (SbS vs. EoS) Task complexity Number of cues Processing factors Processing Mode (SbS vs. EoS) Accountability (cognitive effort) Documentation (cognitive effort) Review process Control over evidence order Group discussion Time pressure (cognitive effort) Output task factors Likelihood judgment Tax decision (e.g., recommendation to client) Audit decision (e.g., going concern)

general learning models and includes the person, stimulus, environment, decision, and judgment process. Comparable with Gibbins’ model, Roberts’ (1998) model contains five groups including individual, external, and processing categories. We chose to use Roberts’ model because it takes a more detailed approach than Gibbins. Specifically, Roberts identifies actual factors that have been examined in prior JDM literature based upon his review of 52 tax JDM papers. Roberts’ five groups include the following: 1. Individual factors: internal characteristics unique to the decision-maker. These include cognitive factors, such as knowledge, as well as affective factors, such as ethical attitudes or risk preferences. 2. Environmental factors: economic risks and rewards associated with the decision-making environment. These include external influences such as the incentives or pressures due to the professional role (e.g., tax versus audit), client preference pressures, or the inherent risk in the environment (e.g., fraud detection). 3. Task input factors: external characteristics of the decision-making task, which may impact how information is processed. These include items such as information load and task complexity.

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4. Decision-processing factors: strategies used by decision makers to simplify the judgment process. These strategies generally have been found to influence cognitive effort through such pressures as accountability, decision aids, or the review process. 5. Task output factors: type of decision required. For instance, consulting and reviewing tasks are expected to influence the decision-making process differently. Similar to the broader JDM literature, the extant belief revision literature has examined a multitude of variables and their effects on belief revision. However, until now, there has been no clear, overall organization of these variables. Using Roberts’ (1998) groupings will allow future research to more directly draw conclusions about any general effects of each group and potential interactions between the groups of factors. A solid organizational framework will allow us to identify which areas are in need of additional research. It also will enable researchers to better ensure that they have considered (or controlled for) all possible influences on the belief revision process. Finally, this categorization may allow future research to examine the belief revision process as a whole by simultaneously examining factors from each fundamental group mentioned earlier. Table 3 provides chronologically a list of belief revision studies in accounting and highlights the factors listed in Table 2. Specifically, of the 25 accounting studies listed, 19 have used the SbS processing mode. While a few studies have used the EoS mode or consistent information, these have generally been examined in addition to the SbS mode and mixed information for comparison purposes. All but one of the studies have used a short series of information (between 4 and 10 cues), with 4 cues being the most common choice. These studies, along with the related findings, will be discussed as they relate to the following sections.

Individual Psychological Factors Individual psychological factors include both cognitive and affective factors. Sixteen of the 25 studies listed examine, directly or indirectly, at least one individual psychological factor. Studies of individual factors in accounting have primarily focused on experience, a cognitive factor, and sensitivity toward evidence, an affective factor. Task-relevant knowledge and individual attitudes, including prior beliefs, have also been examined.

Study

Task

Initial Belief

Belief Revision Studies In Accounting. Measurement of Belief Revision

Order of Evidence Manipulation

Other Factors Manipulated

Factor Category

Results

Likelihood judgment Manipulated at 0.20, Final likelihood regarding internal controls 0.50, or 0.80 for minus the over payroll and salesexperiments 1 initial anchor receivable and 2 and 0.50 for experiment 3 Likelihood that internal Only final Manipulated to be controls would detect positive or likelihood was material error. Rate the negative used relevance of each piece of information

SbS and EoS modes 4 cues

Strength /type of evidence Initial anchor Response mode

3 1 3

No order effects for consistent information. Recency effects found for mixed evidence. Less extreme belief revisions with EoS than with SbS

EoS mode (after each set of 5 cues) 10 total cues

Prior beliefs (manipulated) Hypothesis-testing strategy (manipulated) Prof. Roles

1

Subjects with high (low) prior beliefs showed no (a marginally significant) recency effect. Unless specifically instructed to do so, auditors do not use a confirming strategy

Ashton and Ashton (1990)

Likelihood judgment Manipulated at 0.50 Final likelihood regarding internal controls for all four minus the over payroll, accounts experiments initial anchor receivable, health, and marbles

SbS and EoS modes 4 cues

Response mode Cue type

3 3

Pei et al. (1990)

Likelihood treatment as a Not measured dealer (investor) would be upheld in court; Recommendation to client

SbS mode 4 cues

Tax professional attitudes Client preference Judgment vs. choice Response modeSbS vs EoSwith consistent information

1

Auditors were more responsive to disconfirming than confirming evidence & revised their beliefs to a greater extent under SbS than EoS. Business executives did not exhibit same effects in non-audit tasks Both the likelihood and recommendation indicate recency effects. Client preference does not affect likelihood or recommendation. Individual attitudes affect only recommendations

Ashton and Ashton (1988)

Butt and Campbell (1989)

Christian and Reneau (1990)

Asare (1992)

Final likelihood

Likelihood of excludability of Manipulated at 20 Final likelihood and 80% fellowships from taxable minus initial (experiment 1). income; Employee vs. position Set at 50% for Independent contractor; other experiments Deductibility of travel expenses Likelihood that a firm will Measured after Final likelihood continue (fail) and going review of minus initial concern report decision background likelihood information

SbS mode 4 cues

SbS mode 4 cues

Judgment vs. choice Initial beliefs measured Prof. Roles

3 5 3

5 1 2

Belief Revision in Accounting

Table 3.

Recency for students and professionals in SbS processing of mixed evidence. Students (but not experienced participants) in EoS mode had larger revisions than those in SbS mode with consistent information Recency effects found in likelihood judgments and manifested in the subjects’ opinion choice. Hypothesis frame did not affect the existence of recency effects

13

Study

Task

Initial Belief

Pei et al. (1992a)

Likelihood that dealer Not measured (investor) treatment would prevail if audited

Kennedy (1993)

Likelihood that a firm with fail

Krull et al. (1993)

Probability of need for a write Not measured down of inventory

Johnson (1995)

Order of Evidence Manipulation

Other Factors Manipulated

Factor Category

Results

Experienced professionals’ belief revisions are affected by presentation order but not client preference. Inexperienced professionals display a reverse pattern Recency effects found in complex tasks. Accountability mitigated recency

Experience Client preference

1 2

EoS response mode/SbS processing mode 8 cues SbS mode 4 cues

Accountability Task complexity (via subjects)

4 1, 3

Presence of fraud risk factors

2 1

Experience accentuated recency effects. Fraud factors did not affect recency

1. ‘‘Absolute’’ final likelihood minus final likelihood 2. ‘‘Relative’’ change in likelihood

EoS mode 4 cues

Experience Hypothesis frame Experience Prof. Roles

1 2

Probability of collection of an Measured after background account receivable; Review evidence of subordinate information and make likelihood judgment; Assign strengths to evidence Manipulated at 50 Likelihood the accounts receivable balance is fairly stated

Final likelihood minus initial likelihood

EoS mode 4 cues

Experience Review

1 4

Auditors react differently to evidence depending on favored hypothesis frame and belief extremity. Auditors were more responsive to disconfirming evidence than confirming evidence when belief revision was measured on an absolute scale, but not when relative change in belief revision was measured on a proportional scale Experience mitigates the recency effect. Weak support that experience will interact with the review process in predicting recency for the review of a subordinate’s work containing recency

Final likelihood minus initial anchor

SbS mode 4 cues

Cognitive style

1

Probability of need for a write-down of inventory

Final probability minus initial probability

SbS mode 4 cues

Manipulated at 50% Final likelihood judgment

Measured after selection of hypothesis frame

Measured after review of background information

Final likelihood judgment

Group vs. individuals Age Experience

4 1 1

Significant interaction between cognitive style and recency effects. Field dependent auditors show greater recency effects than field independent auditors Group-assisted final beliefs represented a choice shift toward risk. Recency found; age, but not years of experience, significantly related to more conservative final beliefs

JENNIFER KAHLE ET AL.

Chan (1995)

Final likelihood

(Continued)

SbS mode 4 cues

McMillan and Select hypothesis frame for White fluctuation in financial (1993) statement ratio and indicate likelihood of that hypothesis

Messier and Tubbs (1994)

Measurement of Belief Revision

14

Table 3.

Likelihood that a firm will continue and going concern report decision

Measured after background information

Final likelihood minus initial likelihood

SbS mode 4 cues

Hite and Stock (1996)

Probability of employee vs. independent contractor; Rate the strength of each piece of evidence

Measured after background information

Final likelihood minus initial likelihood

Trotman and Wright (1996)

Likelihood that a firm will continue; Assess the strength of internal controls

Measured after background information

Final likelihood minus initial likelihood

Bamber et al. (1997)

Likelihood that Inventory Manipulated at 50% Final likelihood fraud exists and likelihood minus the of collecting account initial anchor receivable; Rate each piece of evidence Stock trades in experimental Measured as initial Final price minus market (revise security security price initial price price beliefs) Likelihood of viability of Measured after Final likelihood client (going concern) background minus initial information likelihood

Tuttle et al. (1997) Ahlawat (1999)

Documentation requirement Judgment vs. choice

4

SbS and EoS modes 4 cues

Response mode prior beliefs

3 1

SbS and EoS 4 cues

Response mode Experience

3 1

SbS mode 2 cues

2

SbS mode 6 cues

Group vs. individual accuracy, confidence, and severity of recency

4

Recency found in individual auditors, but not groups. Group memory was more accurate and groups were more confident than individuals

Inherent risk Proportional vs. Absolute Measure Staff conclusion

2 1

Auditors are susceptible to primacy effects in settings that are relatively low in inherent risk

4

Order of arguments did not affect reviewers’ likelihood assessments or final recommendation. Staff conclusion did affect likelihood assessment

SbS mode 4 cues

Mayper et al. (1999)

Final likelihood judgment with initial likelihood as a covariate

EoS mode 4 cues

15

Experimental market setting

Final likelihood minus initial likelihood

Measured after background information

1 3

SbS mode 4 cues

Anderson and Likelihood of a material error Measured after Maletta in sales/receivables and review of (1999) number of planned audit background hours information Likelihood that court will uphold deduction of worthless security; Final recommendation to client

Experience Task (within subj)

5

Recency effect in the audit judgments of no-documentation subjects, but not among subjects who performed the documentation task. No recency effect on decision for type of report to issue Recency effects for SbS mode, even when subjects have internalized prior beliefs. Taxpayers were more sensitive to negative than positive information. Assigning (rather than measuring) prior beliefs can lead to incorrect inferences Experienced managers do not display recency effects. Students exhibit recency for both tasks and both response modes. Seniors exhibit recency in all cases except EoS mode for control evaluation task Recency supported. Confirmation proneness holds over both experience levels and both contexts. Auditors are also more sensitive to evidence when the possibility of fraud exists Recency effect was found in experimental asset markets

Belief Revision in Accounting

Cushing and Ahlawat (1996)

Task

Initial Belief

Arnold et al. (2000)

Likelihood audit would result Measured after in going concern opinion; review of insolvency background information

Cuccia and McGill (2000)

Likelihood that a court would Measured after find a settlement background excludable from taxable information income; Reporting recommendation

Monroe and Ng (2000)

(Continued) Order of Evidence Manipulation

Other Factors Manipulated

Factor Category

Results

Final belief revision minus 2nd belief revision compared to 2nd belief revision minus initial belief Final likelihood with initial likelihood as covariate

Eos mode 10 cues (experiment 1) 12 cues (experiment 2)

Task complexity Going concern vs, insolvency Experience

3 2 1

Experience did not mitigate order/recency effects under conditions of heavy information load

EoS mode 4 cues

Control over rder Pre-evaluation information Task knowledge Client advocacy

4 3 1 2

Measured after review of background information

Final likelihood minus initial likelihood

Sbs mode 4 cues

Inherent risk

2

The ability to control the order in which conflicting evidence is evaluated eliminates recency, but only in a familiar task. Advocacy is not related to choice of order. Mere awareness of conflicting information does not mitigate recency No recency. Auditors’ judgments were not influenced by order effects. Judgments of inherent risk may be biased toward conservatism

Manipulated at $50

Midpoint price minus initial anchor Final price minus midpoint

Sbs and EoS modes 20 cues

Response mode

3

EoS, End-of-Sequence (simultaneous); SbS, Step-by-Step (sequential).

Recency found for both modes, but stronger for SbS. Practically, valuation was more pronounced for SbS conditions

JENNIFER KAHLE ET AL.

Likelihood that there were material errors or misstatements in the client’s financials, in the absence of any specific internal controls Pinsker (2004) Stock price valuations

Measurement of Belief Revision

16

Table 3. Study

Belief Revision in Accounting

17

Cognitive-Experience A large amount of accounting literature has found support for the idea that a decision-maker’s experience has an effect on his or her judgment, particularly for complex tasks (e.g., Davis & Solomon, 1989; Church, 1990; Libby & Luft, 1993). Specifically, society expects more experienced professionals to make higher quality judgments. Arguably, this should occur because experienced decision-makers possess richer knowledge and memory structures, as well as higher levels of confidence, which leads to lower levels of sensitivity toward evidence received (Trotman & Wright, 2000). Based on this prior research, studies in belief revision posit that greater order effects (lower performance) should occur among less experienced individuals. However, findings in the belief revision literature generally have not supported this view. Experience in belief revision has been studied by examining the number of years of experience (Pei et al., 1992a; Krull et al., 1993), age (Johnson, 1995), experience titles (McMillan & White, 1993; Messier & Tubbs, 1994; Trotman & Wright, 1996; Bamber et al., 1997), and task-related experience/knowledge (Kennedy, 1993; Arnold et al., 2000; Cuccia & McGill, 2000). Task-related experience, however, may be confounded with input task factors. For example, an individual who has less experience with a task is likely to consider the task more complex. Further, Trotman and Wright (2000) argue that besides the task, experience is likely to have an interactive effect with response mode and amount of information. Measuring any single factor in isolation may be a cause of the lack of consistent results supporting recency effects in experience studies. The studies that examined task-related experience with task complexity will be discussed later in conjunction with the discussion of input task factors. Pei et al. (1992a) examined the impact of tax professionals’ experience on belief revisions about ambiguous tax treatments. They found a significant recency effect for experienced (5–13 years) tax managers, but not for inexperienced (2–4 years) tax managers. One explanation for this result is that a second manipulated factor, client preference, may have dominated the results for the inexperienced participants. (Client preference is an environmental factor and will be discussed in greater detail in the section ‘‘Environmental Factors.’’) Krull et al. (1993) found similar results, whereby more experienced audit managers exhibited greater recency effects than less experienced managers. They, however, expected these somewhat counter-intuitive results. They provided their participants with little background information, leading them to predict that experienced participants would be more likely to recognize

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JENNIFER KAHLE ET AL.

the inadequacy of this information and place greater weight on the subsequent information received. Asare (1992) investigated order effects with different hypothesis frames (viable or failing audit client) using audit partners and managers. He used four pieces of evidence (two contrary and two mitigating) in both frames. Significant recency order effects were found for both frames for these experienced auditors. Messier and Tubbs (1994) contend that the magnitude of the contrast effect could be reduced if the participants had additional experience with the stimuli. Consistent with this reasoning, they found that experience mitigated recency effects. Only weak support was found for the proposition that experience and the review process interacted to reduce recency effects. The inconsistencies were arguably due to their use of different experience levels (seniors and managers), task differences (more versus less background information), and response mode (SbS versus EoS). While not specifically hypothesized, McMillan and White (1993) found that experience level (staff, seniors, and manager/partners) had no significant effect on belief revisions for auditors after they evaluated confirming or disconfirming evidence relative to a chosen hypothesis frame. Similarly, Bamber et al. (1997) examined experience as it related to evidence sensitivity. They concluded that inexperienced auditors (staff) were not more sensitive to confirming evidence than experienced auditors (seniors). Johnson (1995) examined two measures of experience: years on the job and age of the participant. Age, but not years of experience, was significantly associated with more conservative final beliefs. Despite the conservatism related to age effect, recency did exist in an inventory write-down task. In sum, the majority of studies find either no recency effect for experienced participants or more of a recency effect with experience. Therefore, we conclude that the evidence suggests that experience does not necessarily mitigate recency. However, task and response mode differences, the use of different experience manipulations, and interactions between individual and external factors make interpretation of the results across studies difficult. Future research should continue to consider experience as an important cognitive factor and seek to determine if experience only fails to mitigate recency bias or actually increases recency bias. Cognitive–Style One accounting study manipulated cognitive style to find out if there was an interaction with order effects. Chan (1995) had professional auditors assess

Belief Revision in Accounting

19

the likelihood that the accounts receivable balance of a fictional company was fairly stated. Using the Group Embedded Figure Test (GEFT), he separated auditors into field-dependent (those that have ‘‘spectator’’ learning approaches and are not able to separate items of information from their contexts) and field-independent (those who have ‘‘active’’ learning approaches and tend to differentiate information into welldistinguished components). Results indicated a significant interaction between cognitive style and order effects, as the field-dependent auditors showed greater recency effects than their field-independent counterparts. These results add credence to understanding the learning and processing behaviors of decision-makers and perhaps even training them to be more active in learning about task procedures. Affective–Sensitivity to Evidence Hogarth and Einhorn (1992) discuss the importance of an individual’s sensitivity to negative and positive evidence. These parameters are often described as sensitivity toward disconfirming and confirming evidence, respectively. Confirming or disconfirming evidence is determined with respect to a professional’s initial hypothesis. Similarly, an individual who is sensitive to negative or positive evidence may be paralleled to someone who is an advocate or a skeptic, respectively. That is, the individual views information as positive or negative with respect to the client-favored position. Sensitivity is a critical element of the belief-adjustment model. It is posited to be a constant for each individual decision-maker. The contrast effect, which determines whether there will be an order effect, is diluted as one’s sensitivity to evidence decreases. Sensitivity, as posited by Hogarth and Einhorn (1992), is a function of both individual factors (e.g., experience) and external factors (e.g., client preference). The model suggests that in the absence of prior information or biases, people will be highly sensitive to evidence, but as they become more committed to their beliefs, as with a long series of information, sensitivity will decline. For example, as discussed by Messier and Tubbs (1994), individuals with more experience (an individual factor) should be more confident in an initial impression of a problem. This higher confidence should lead the individual to be less sensitive toward confirming and disconfirming evidence, resulting in a smaller recency effect. Messier and Tubbs’ research supports this theory. However, Krull et al. (1993) found opposite results for their experienced versus inexperienced participants. They suggest that differences in the ambiguity of the initial information

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JENNIFER KAHLE ET AL.

provided to the participants may help explain the seemingly conflicting results. Experienced auditors may have been better able to appreciate the inadequacy of the initial information and were more sensitive, in general, to subsequent information. An individual may inherently be more prone to a particular sensitivity toward evidence, or the individual’s professional role may affect his or her sensitivity. There is evidence from psychology as well as accounting that individuals have a general tendency to search for or give more weight to evidence that confirms an initial hypothesis (e.g., Church, 1990; Klayman & Ha, 1987) and is often referred to as a confirmation bias. Conversely, some evidence from the auditing literature supports disconfirmation bias, arguably due to the auditor’s role requiring professional skepticism (e.g., Ashton & Ashton, 1990; McMillan & White, 1993). These biases affect an individual’s weighting of evidence, and consequently, can affect the predictions of Hogarth and Einhorn’s (1992) model. In fact, recency effects may become negligible if the decision-maker maintains an extremely asymmetrical sensitivity toward evidence (Pei et al., 1990). Eight accounting studies in belief revision have looked at an individual’s sensitivity toward evidence. These studies primarily looked at sensitivity as it is influenced by the accountant’s professional role. For example, several studies examined whether auditors were more sensitive to disconfirming evidence or whether tax professionals exhibited biases related to their professional roles as client advocates. The studies will be detailed in the discussion for ‘‘Professional Roles’’ in the section on ‘‘Environmental Factors.’’ Bamber et al. (1997) attempted to measure an individual’s sensitivity to evidence. By controlling for the subjective strength of evidence and the degree of the contrast, they examined whether auditors were more sensitive to a particular type of evidence. Bamber et al. discussed five possible sensitivities, including evidence neutrality, confirmation bias, disconfirmation bias, positive evidence bias, and negative evidence bias. They found that auditors were more sensitive to confirming evidence, which is consistent with the confirmation bias findings from the psychology literature (e.g., Lord, Ross, & Lepper, 1979; Nisbett & Ross, 1980). Affective–Professional Attitudes Pei et al. (1990) suggested that tax preparers’ attitudes might affect their recommendations to clients and possibly their decision processes as well. They explored tax professionals’ attitudes relative to (1) the relative frequency of evasion, (2) the morality of evasive or aggressive tax reporting,

Belief Revision in Accounting

21

(3) the individual’s self-insight as to their aggressiveness, and (4) the perception of the tax professional’s responsibilities to client and government. In the study, tax professionals were asked to express a belief about the likelihood of an ambiguous tax treatment (dealer versus investor classification) prevailing upon an audit and to provide a recommendation to the client. A significant recency effect was found for both the likelihood and recommendation. Only the morality and self-insight attitudes were associated with the client recommendation, and none were significantly related to the likelihood judgment. Therefore, this study provides some evidence that individual attitudes can impact a tax professional’s final judgment. Affective–Initial Beliefs Accounting studies have recognized the importance of correctly measuring the initial anchor (belief) and the participants’ belief revisions. The beliefadjustment model relies heavily on prior beliefs as an input into the model. Prior beliefs provide an anchor from which decision-makers adjust when presented with additional evidence (Butt & Campbell, 1989). Originally, many studies used background facts to manipulate a participant’s initial belief. Ashton and Ashton (1988) manipulated initial belief by describing the likelihood that controls would prevent or detect material error to be 20, 50, or 80%. They found support for Hogarth and Einhorn’s (1992) contention that smaller (larger) anchors are helped (hurt) more by positive (negative) evidence than larger (smaller) anchors. Many later studies (Ashton & Ashton, 1990; Kennedy, 1993; Bamber et al., 1997; Pinsker, 2004) assigned an initial belief to the participants in order to provide a consistent starting point for measuring recency. Rather than assigning an initial belief, Butt and Campbell (1989) recognized that having participants come to their own conclusions about the initial belief gave more assurance that the researcher had actually captured their priors. They measured participants’ initial beliefs after presenting them with background information designed to induce either low or high priors, and found recency effects only for participants with induced low prior beliefs. Asare (1992) recognized that there also could be possible source credibility issues in manipulating initial beliefs and similarly, measured participants’ initial beliefs on likelihood scales. Unlike Butt and Campbell, Asare found recency effects in both groups examined. Hite and Stock (1996) found significant differences between belief revisions for participants receiving assigned prior beliefs and those whose prior beliefs were measured. They theorized that participants do not

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internalize an assigned prior belief, therefore, the mean belief revision for these participants may include measurement error. Hite and Stock concluded that assigned prior beliefs are appropriate only when: (1) actual priors are close to those assigned, (2) participants have no prior beliefs, or (3) participants have weak internal priors. McMillan and White (1993) recognized the possible scale effects that could result from the strength of the initial belief in measuring a participant’s belief revision. They pointed out, for example, that if a participant’s initial likelihood score was 70% and the participant evaluates confirming evidence, a 30-point range is available for upward revision. Conversely, if the participant evaluates disconfirming evidence, a 70-point range is available for downward revision. To control for scale effects, they used a proportional measure for the participants’ belief revisions in addition to the usual absolute measure. They found that the two measures of belief revision do not provide consistent results for the effect of evidence direction on the magnitude of belief revision. However, Anderson and Maletta (1999) found that the scaled (proportional) measure of belief revision is consistent with the unscaled (absolute) measure in their study of primacy effects. With the exception of the last two studies mentioned above, the vast majority of the accounting studies have used the final judgment minus the beginning judgment as the measurement of belief revision. However, it is important for future research to recognize the potential limitation of an absolute measure due to the scale effects discussed above. Furthermore, a comparison of the proportional measure to the belief-adjustment model’s measurement of belief revision (as used by Bamber et al., 1997) reveals that both have a similar method for examining belief revision processes when the strength of evidence is constant for all evidence. In general, the research to date suggests that individual psychological factors, such as experience, fail to mitigate recency bias. However, limited research concerning cognitive learning style shows active learning approaches may mitigate some recency bias. Affective factors such as sensitivity to evidence, professional attitude, and initial belief have been shown to affect final judgments either directly or indirectly. Both confirmation and disconfirmation biases have been documented with respect to sensitivity to evidence and professional role. Initial beliefs have been shown to effect belief revision differentially depending on whether the initial belief has been assigned or measured. With respect to cognitive and affective factors, limited research demonstrates that experience does not necessarily reduce sensitivity to evidence. Studies have provided conflicting results, which indicates the need for future research in the area of individual affective psychological factors.

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23

Environmental Factors As previously discussed, Hogarth and Einhorn’s (1992) model outlines a framework explaining how external variables and processing factors interact to produce order effects in belief updating. There are two types of external variables – environmental and input task factors. Environmental factors concern risks and rewards that are present in the decision-maker’s environment. This may be due to the role of the decision-maker, the particular characteristics of the client she/he is serving, or the inherent risk present in the decision environment. Decision-makers do not operate in a vacuum, and cognitive models should reflect the potential influence of items such as the client, regulatory agencies, and professionals’ employers (Roberts, 1998). Studies which have examined these environmental factors are discussed below. Input task factors are described and discussed in the following section. Professional Roles Hogarth and Einhorn (1992) suggest that an individual’s professional role might produce differential ‘‘sensitivities’’ to evidence, and they discuss how these differential sensitivities are considered to be advocate or skeptic attitudes. Within each decision context, accounting professionals face pressures that may cause them to be differentially sensitive to positive and negative evidence. Accountants are charged with professional skepticism in audit and assurance services (AICPA, 1995, AU, 316.16). On the other hand, tax professionals have an obligation to act as a client advocate and to interpret the law in the client’s best interests (AICPA, 1995). As discussed by Cuccia and McGill (2000), the tax professional is a goal-oriented decisionmaker with an incentive to propose and defend client-favored propositions. All accounting professionals must maintain objectivity in their professional judgments (AICPA, 1995) and face litigation risks in both audit and tax for non-objectivity. (Tax professionals face penalties for overly aggressive positions under Code Section 6694.) Thus, the role of the accountant should influence the process of belief revision. Seven studies in addition to the Bamber et al. (1997) study mentioned previously, examine professional roles and their influence on sensitivity to evidence in the belief-adjustment process. The studies that have examined auditor belief revision will be discussed first followed by the studies that have examined tax professionals’ belief revision. Ashton and Ashton (1990) used both auditors and business executives for auditing and non-auditing tasks. Their results indicated auditors were more

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sensitive to disconfirming than confirming evidence, but business executives were statistically indifferent. Butt and Campbell (1989) had participants use a confirming, disconfirming, or neutral strategy to evaluate the strength of an internal control system. They found recency effects with participants who had low initial beliefs. However, they found no support for the use of a confirming strategy, and concluded that auditors were more naturally disconfirming. Asare (1992) manipulated the participants’ hypothesis frame to investigate the effects of confirmation bias and order effects. He had participants make judgments about the failure (viability) of a firm and choose the type of opinion they would issue. In support of the belief-adjustment model, he found that recency exists in both belief revisions and audit report choices. Hypothesis frame did not affect the existence of recency, and thus, experienced auditors did not exhibit a confirmatory strategy. McMillan and White (1993) found that auditors’ belief revisions were significantly larger for disconfirming evidence than for confirming evidence when the absolute measure is used (similar to Ashton & Ashton, 1988, 1990), but not significantly different when the proportional measure is used. This raises issues about whether the absolute measure, which has been used by most studies in belief revision, is the most appropriate way to measure belief revisions. Overall, studies examining audit professionals tend to find that auditors are more sensitive to disconfirming evidence. This is not surprising in light of their role and their requirement to exhibit professional skepticism. Conversely, Christian and Reneau (1990) found that professional tax participants were much more influenced by evidence supporting than by evidence opposing the client’s preferred treatment. This was contrary to the equal sensitivity displayed by student participants in the same ambiguous tax judgment case. Christian and Reneau suggested that the professionals’ asymmetric sensitivity may be due in part to the client advocacy relationship between tax professionals and their clients. Consistent with the idea that the advocacy role of tax professionals may influence belief revision, Pei et al. (1992a) found that the client preference induced an attention directing effect such that the tax professional was more sensitive to evidence favoring the client’s preferred treatment. Cuccia and McGill (2000) included a measure of client advocacy in their experiment examining tax professionals’ judgments of an ambiguous tax issue, and the potential mitigating influence of control over order of evidence evaluation. They found that 57% of participants chose to examine positive evidence first, but that client advocacy was not related to that

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choice. Thus, confirmation proneness was ruled out as an alternative explanation to the mitigating influence of control. Tax professionals appear to be driven by the client preference in their judgments, which seems reasonable given their charge to be client advocates. Importantly, these studies appear to find differences in belief revision between auditors and tax professionals, indicating the important influence of professional roles on sensitivity to evidence. The evidence regarding tax professionals reflects a tendency toward a client advocacy in most cases, rather than the tendency to be biased toward disconfirming evidence as shown by auditors. Future research should also examine whether professional role effects are dependent upon the particular decision environment (audit or tax task). Inherent Risk Four studies have examined audit fraud or inherent risk as an environmental factor that can influence the robustness of the belief-adjustment model. Krull et al. (1993) suggested that perceptions of very high (low) risk of fraud factors should foster heightened (lessened) sensitivity to evidence suggestive that fraud exists. Results indicated that the presence or absence of fraud signals did not mitigate or accentuate order effects. Thus, auditors’ sensitivity to information may not be differentially affected by the perceived risk of fraud. However, this is based on the effect of fraud factors on the belief revision. They note the limitation that no attempt was made to separately measure the effect of the fraud factors on sensitivity to evidence. Bamber et al. (1997) investigated whether auditors exhibited different sensitivity toward evidence when the initial hypothesis involved a possible irregularity. They measured sensitivity to evidence for a non-fraud case and a fraud case and found that auditors’ attitudes changed in response to the audit context. Auditors exhibited a heightened sensitivity (conservatism) when confronted with the possibility of fraud. In a somewhat atypical belief revision study in accounting, Anderson and Maletta (1999) examined auditors’ susceptibility to primacy. They predicted that primacy would result when there was a low-risk audit environment and an information sequence with late positive information. Their argument was based on prior psychology research, which states that primacy is a function of diminishing cognitive effort in the evaluation of a sequence of information, and that cognitive effort depends upon the risk associated with the decision task (Anderson, 1981; Fiske, Kinder, & Larter, 1983). In addition, Anderson and Maletta argued that auditors, by nature, have a heightened sensitivity to negative information, and thus, may not exhibit

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diminished cognitive effort with late negative information. The results were consistent with their predictions for primacy in a low-risk, late positive information situation. Finally, Monroe and Ng (2000) had auditors investigate the likelihood there were material errors or misstatements in a fictional client’s financial statements, in the absence of any specific, related internal controls. No recency effects were found, as auditor judgments were not influenced by order. Similar to Bamber et al. (1997), judgments of inherent risk may have been biased toward conservatism, and thus outweighed recency effects. It appears that auditors tended to be more sensitive toward disconfirming evidence, potentially due to their role requirement to maintain professional skepticism. In other words, they placed more weight on disconfirming evidence regardless of presentation order. Thus, it appears that increased inherent risk may exacerbate the tendency toward conservatism. Experimental Markets In a unique application of the belief-adjustment model to a more complex setting, Tuttle, Coller, & Burton (1997) examined the basic order effect predictions using an experimental assets market. Contrary to the efficient market hypothesis, which states that the market impounds new information quickly and without bias into security prices, they found the existence of recency effects in a market setting. Their study provides support for the robust nature of the belief-adjustment model and demonstrates that systematic, individual biases can survive in a market setting. Client Characteristics Client characteristics, such as the size or importance of a client, revenue potential, and risk preference/aggressiveness are expected to influence accountants’ decisions. For instance, studies have found a link between client risk preference and aggressiveness of tax recommendations (e.g., Schisler, 1994). While these are important environmental characteristics, we could find no studies that specifically examined the impact of these variables on belief revision. This is an area that future research may want to address.

Input Task Factors Task factors are the second type of external variables discussed. Beginning with Ashton and Ashton (1988), some of the basic predictions for task variables were tested in accounting. As the literature has developed, belief

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revision predictions have been examined under more complex environments, including those where the individual has control over the order of evidence, where documentation is required, where accountability is salient, where the information load is higher, and even in an experimental market setting. Nineteen of the 25 accounting belief revision studies cited have examined at least one of the following task factors. Type of Evidence and Response Mode It may be recalled that response mode, in combination with the complexity of the information, should dictate the processing mode an individual will use. According to the belief-adjustment model, SbS processing must be used with SbS response mode. However, SbS or EoS processing can be used with EoS response mode depending on cognitive demands. Predictions of the belief-adjustment model also depend on the type of the evidence (e.g., whether it is mixed or consistent). Ashton and Ashton (1988) were the first to test Hogarth and Einhorn’s (1992) model in an accounting environment. They used a series of five experiments to test the basic model predictions for consistent versus mixed information, EoS versus SbS response modes, and the strength of initial evidence with consistent information (two task variables). No order effects were found for consistent information, but recency was found for mixed evidence. Results generally supported the belief-adjustment model predictions of no order effects for consistent information, recency effects for mixed evidence, and less extreme revisions with the EoS than the SbS response mode. A later study by Ashton and Ashton (1990) using a series of two ‘‘accounting’’ studies and two ‘‘non-accounting’’ studies confirmed the results. Most studies in accounting have investigated the SbS mode with mixed evidence, and have generally found the existence of recency effects. A few studies, in addition to Ashton and Ashtons’ (1988, 1990), have specifically looked at comparing EoS versus SbS response mode and mixed versus consistent evidence. Consistent with the model predictions, Christian and Reneau (1990) found that recency effects existed for both students and experienced tax professionals when mixed evidence regarding an ambiguous tax case is presented in a SbS mode. However, they found conflicting results for consistent evidence. Students exhibited larger belief revisions in the EoS mode than in the SbS mode, but experienced participants were unaffected by response mode. Christian and Reneau pointed to the need to measure an individual’s sensitivity to evidence before conclusions regarding response mode can be appropriately drawn.

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Hite and Stock (1996) also supported model predictions with their study involving taxpayer judgments of an ambiguous tax issue (independent contractor versus employee). In the participants’ evaluation of mixed evidence, results were consistent with a recency effect in the SbS response mode and a tendency toward primacy in the EoS mode. Similarly, Trotman and Wright (1996) found that, for less experienced participants, recency existed for a going concern task in either response mode, but not in the EoS mode for a control evaluation task. However, they did not find recency effects for more experienced participants in either response mode. Pinsker (2004) examined stock price valuations from unsophisticated investors given a different frequency of consistent information releases. Continuous (SbS) conditions had significantly different valuations in the direction of the information provided than periodic (EoS) conditions at the midpoint evaluation of information releases, but not after all of the information had been released. However, recency was detected after all information items were released. Making accurate conclusions regarding the EoS response mode is difficult since the particular response mode does not automatically guarantee that the EoS processing mode will be used. Overall, it appears that the SbS processing model predictions are supported for recency in the evaluation of mixed evidence and mixed for consistent evidence. The results of these studies have important implications for practice in both audit and tax tasks. In particular, when a task requires SbS processing and responses, a recency bias may be inherent. Structuring the review process as EoS may be helpful in alleviating some of the bias introduced during the evidence collection stage. Future research should consider task processing characteristics and examine task structure as a means of potentially mitigating the recency bias. Task Complexity As previously discussed, predictions regarding task complexity are often commingled with experience predictions. Hogarth and Einhorn (1992) define complexity as a function of (1) the amount of information that needs to be processed (task factor), as well as (2) the lack of familiarity with the task (individual factor). Familiarity with a task can overcome task characteristics and render an inherently complex task to be one that is relatively ‘‘simple’’ for an experienced person (Trotman & Wright, 1996). Although the belief-adjustment model predicts recency effects for the SbS mode regardless of task complexity, several authors (e.g., Kennedy, 1993; Trotman & Wright, 1996; Arnold et al., 2000) have suggested that complexity or experience may mitigate the effect.

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Kennedy (1993) explored task complexity by using two groups of participants; those who should be unfamiliar (M.B.A. students) and those who should be familiar (auditors) with a going concern judgment task. She suggested that task complexity can affect the processing mode used by an individual. When the task is complex, individuals are likely to use a SbS processing strategy to ease the cognitive demands of the task. Conversely, easier tasks should elicit an EoS processing strategy and be less susceptible to recency effects. She found evidence consistent with EoS processing and no recency effects for auditors. Consistent with expectations, she did find recency effects for the MBA students. Similarly, Trotman and Wright (1996) examined task complexity and experience, suggesting that the interaction of these variables was likely to be instrumental in determining whether a task was ‘‘simple’’ or ‘‘complex’’ for an individual to perform. They considered the judgments of students, seniors, and managers in both a going concern case and an internal control evaluation case. Trotman and Wright expected that the familiarity of each task would depend on the participants’ experience level, and that participants performing more familiar (simpler) tasks would exhibit less recency. Their results support the predicted experience effect. A recency effect was found for students and seniors for both tasks. The use of EoS response mode mitigated this effect for seniors in the more familiar task. In contrast, managers did not exhibit recency effects for either task. One of the more recent studies to include the effect of task experience on belief revision is Cuccia and McGill (2000). They examined tax professionals’ likelihood judgments in a familiar versus an unfamiliar task. Similar to Trotman and Wright (1996), Cuccia and McGill found that if an individual has control over the order in which evidence is examined, recency is mitigated in a familiar task, but not in an unfamiliar task. Arnold et al. (2000) took a different approach to task complexity. They operationalized complexity as high information load (number of cues). They argued that even given a high level of task familiarity, a task could still be considered complex within the definition set forth by the belief-adjustment model if the information load received by the decision-maker was high (a condition consistent with many accounting tasks). Under both going concern and insolvency experiments, Arnold et al. found experience did not mitigate recency under conditions of high information load. This result raises some questions as to the methodology for measuring task complexity in future research. Specifically, it would appear that an interaction of response mode, task familiarity/experience, and information load is present and needs to be considered simultaneously in future studies.

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Environmental factors and input task factors are the external factors that influence the belief revision process of accounting professionals. As discussed above, research has found that these factors influence how individuals interpret information and ultimately make decisions. The specific environment created by items such as client factors, the decisionmaker’s role, and inherent risk in the decision-maker’s environment can differ for each decision-maker. To completely understand the belief revision process, researchers must account for the effects of these factors. By grouping the factors according to Roberts’ classifications, we separate these external factors from those that are internal (unique characteristics of the specific decision-maker). Thus, we can more appropriately identify what factors may be able to be externally controlled (e.g., choosing more conservative clients) versus those that may be need to be internally controlled (e.g., gaining more experience or knowledge with an issue).

Processing Factors Seven studies have attempted to identify factors that may mitigate or eliminate the recency bias. Aside from the mixed results for experience as a mitigating factor, other potential mitigating factors that are particularly meaningful to accounting have been examined. These factors include accountability, required documentation, the review process, use of groups, and control over the order of evidence evaluation. Kennedy (1993) examined whether accountability, defined as the requirement to justify one’s judgments to others, mitigates recency. Generally, accountability is thought to have the ability to influence the information attended to, the complexity and type of information processing, and ultimately the decision or judgment made. Kennedy suggested that accountability may induce individuals who resort to effort-saving strategies, such as SbS processing, to supply the requisite effort for EoS processing, thereby overcoming recency. She asked MBA students and audit managers to make going concern judgments using EoS response mode. MBA students who were less familiar with the task exhibited recency effects. However, when accountability was imposed, no recency effects were found, suggesting that accountability mitigates recency. Audit managers who were familiar with the task were not expected to, and did not, exhibit recency under either condition. Using similar logic (e.g., that greater effort will mitigate recency), Cushing and Ahlawat (1996) examined documentation as a potential mitigating

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factor. Documentation can be viewed as a processing factor eliciting greater cognitive involvement, similar to accountability. Cushing and Ahlawat asked auditors to draft a memorandum to the audit partner providing reasons supporting the recommended audit opinion. The documentation task was expected to induce greater effort resulting in better information recall and comprehension, greater levels of attention to the task, and improved problem-solving representation. All of these factors should contribute to a process that did not place undue weight on information received most recently. As predicted, a recency effect was present in the audit judgments of no-documentation participants, but disappeared among those who performed the documentation task. Two studies have examined how the review process affects the predictions of the belief-adjustment model. Messier and Tubbs (1994) proposed that a review responsibility produces an averaging process when integrating the beliefs of one’s own with a subordinate. They predicted that experience and the review process would interact in predicting recency effects when an auditor reviewed a subordinate’s judgment that contained a recency effect. However, their results did not support their prediction. Mayper, Anderson, and Kilpatrick (1999) examined the impact of the review process in a tax setting using a staff’s tax memorandum. The memorandum included the staff’s arguments for and against the deduction of a worthless security and their final conclusion on the judgments of a tax manager or partner reviewer. Mayper et al. found that the order of argument presentations in the memorandum did not produce a recency (or primacy) effect in the reviewers’ judgments. This provides support for the effectiveness of the review process in reducing recency effects. However, Mayper et al. were careful to point out that experience (partner and managers were used here) may be an alternative explanation for the mitigated recency effect. Two auditing studies examined whether or not use of groups in the decision-making process would mitigate recency. Johnson (1995) investigated the probability of a need for the write-down of inventory in both group and individual decision-making contexts. Although recency was discovered overall, information order did not result in recency for the group-assisted condition. In fact, for the bad news–good news sequence, the group-assisted condition actually revised their final valuation downward, indicating a primacy effect. Additionally, the manner of the revision for the group-assisted conditions was in the direction of greater risk. The groups actually revised their probabilities of inventory obsolescence downward for all information orders.

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Ahlawat (1999) had auditors state their likelihood of client viability in a going concern case using either group or individual decision-making processes. Results indicated recency effects for individual auditors, but not for groups. Additionally, group memory was more accurate and groups were more confident as compared to individuals. A final processing factor, control over the order in which evidence is evaluated, was examined by Cuccia and McGill (2000). Control over order is important in tasks such as the search of tax databases, which allow organized access to selected pieces of evidence. For example, a search of tax court cases may allow the professional to review all the positive cases first or all of the negative cases first. As discussed by Cuccia and McGill, an individual’s ability to process information depends on the congruence of the information’s organization with its intended use. Allowing control over evidence evaluation order would facilitate the use of a preferred decision strategy, thereby reducing the complexity of the task and the susceptibility to recency. Results of the study found ability to control the order of evaluation of conflicting evidence eliminated recency in a familiar task. Recency was observed only when participants had no context-relevant knowledge or were precluded from structuring the task. Additionally, mere knowledge (pre-evaluation information) that conflicting evidence would exist was not sufficient to mitigate recency. The factors discussed above examine order effects in a more complex and realistic environment. Results indicate that accountability, documentation, reviewing, using groups, and controlling evidence order can be effective in at least reducing, if not eliminating, recency effects. Given that many of these characteristics (e.g., accountability, review) are present in both audit and tax contexts, the previously observed recency biases may be easily mitigated in a real-world environment. Justification requirements and accountability pressures influence the amount of cognitive effort individuals will exert on a decision task. Thus, research on processing factors provides further evidence that researchers cannot look at the effect of one variable in isolation of other factors. Inclusion of these processing factors provides researchers a path for examining ways to overcome many effort-related decision biases. It is important for future research to continue to consider the effects of recency and other biases within the context of these processing factors to enable accurate conclusions to be drawn about the true consequences to accounting decisions. While recent research has examined more complex environments, there are still additional variables as recommended by Hogarth and Einhorn

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(1992) that have not been previously examined. These variables include: time pressure, temporal delays, the effects of interrupting updating tasks, and how roles and incentives might affect differential sensitivity to negative and positive information. These factors are relevant in an accounting environment and deserve attention in belief revision research.

Output Task Factors An output task factor refers to the actual decision of the participant in an experiment (as opposed to the judgment process used to arrive at the decision). In the accounting literature, the most common decisions requested from participants have been likelihood judgments. Audit participants have judged items such as the likelihood of material error in an account, the likelihood of collection of an account, and the likelihood that a firm will continue. Tax participants have been asked to judge the likelihood of events such as whether a court will rule in favor of an ambiguous expense deduction (income exclusion), whether an individual should be considered an independent contractor, or whether a client meets the requirements of a real estate investor or dealer. Financial participants have judged the price for one share of stock. Although all studies have an output task factor, three studies have explicitly discussed this factor as a variable in their results. Asare (1992) pointed out that there is a distinction between judgment and choice. The observed differences in likelihood assessments do not allow an inference that different actions always will result. Therefore, he examined the separate effects of order presentation on likelihood judgments and choices. In addition to asking participants to assess the likelihood that a firm will continue, Asare also asked them to make a going concern report decision. He found support for recency in both the judgment and the choice. Inconsistent with Asare’s (1992) results, Cushing and Ahlawat (1996) were not able to replicate the recency effects for the decision in a similar going concern task. However, consistent with Asare (1992), in a tax experiment where subjects provided the client recommendation as well as a likelihood assessment, Pei et al. (1990) found that both dimensions exhibited recency effects. To date, the limited research in this area suggests that perhaps recency effects carry over from the judgment task and influence output task factors, such as choice. Although most researchers have been concerned with whether presentation order affects the judgment processes of individuals, it

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is often a dichotomous decision (e.g., recommendation whether or not to deduct and expense, decision to issue a going concern opinion, etc.) that affects the client. Thus, there is value in understanding how likelihood judgments are manifested into actions or decisions. Future research should examine if recency effects automatically carry over from the judgment process to the action or if this occurs in only certain situations.

CONCLUDING REMARKS In the absence of some mitigating factor, recency predictions of the beliefadjustment model have generally been supported in auditing, tax, and financial reporting tasks. Of the 25 studies we have included in this paper, 21 found recency effects for at least some combination of the factors researchers included in their studies. This is consistent with the basic predictions of Hogarth and Einhorn’s (1992) belief-adjustment model. Many of these 21 studies, along with the other 4 studies, also found specific factors that either outweighed or mitigated recency effects or created entirely different biases (e.g., confirmation bias). Trotman and Wright (2000) suggested that the Hogarth and Einhorn (1992) model needs to be adapted and revised to include the uniqueness of auditing (accounting) tasks. While the belief-adjustment model appears to be a reasonable model for examining accountant’s belief revisions, we agree that the model can be expanded to better fit accounting tasks. While Hogarth and Einhorn (1992) suggest that individual and external variables influence the belief-adjustment process, they only distinguish their overall predictions for three task variables (complexity, length of the series of items, and response mode) and two encoding variable (processing mode and task type). The model does not make specific predictions about experience, client pressures, accountability, professional roles, time pressure, the review process, etc. These are factors that are specifically important to belief revision in accounting and should be incorporated into the model. The 25 studies provide clear evidence that (1) individual psychological factors such as experience, initial beliefs, and sensitivity toward evidence; (2) environmental factors such as individual role and inherent risk; (3) input task factors such as type of evidence and response mode; (4) processing factors such as processing mode and accountability; and (5) output task factors such as likelihood judgment and recommendation (or choice), all can impact the process by which accountants update their beliefs. The current

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chapter’s application of Roberts’ (1998) framework to the belief-adjustment model literature seems appropriate as a framework and represents a muchneeded summation of the research stream. Prior studies have not provided a general framework from which to view the belief revision process. By using Roberts’ (1998) categorization, we have provided such a framework. As can be seen from the studies reviewed in the current chapter, each of these categories of factors affects the way accountants make decisions. More importantly, as several of the studies indicate, the factors can interact with one another, thus, providing detailed information about the belief revision process. The factor categories may be thought of as an overall framework where each category influences other categories within the model. A true understanding of the process by which individuals revise beliefs should be examined with all of these categories in mind. For instance, an individual’s experience level may influence his or her initial belief (as well as subsequent belief revisions) in a sequential decision task. The subsequent belief revisions also may be impacted by that individual’s sensitivity to evidence, which may first be influenced by their professional role (e.g., as an advocate or a skeptic). Additionally, task factors, such as response mode, and processing factors that may mitigate biases (e.g., accountability) or otherwise influence how individuals revise their beliefs should be considered in understanding the complete process. As presented in Table 3, our review indicates that 17 of the studies included one or more variables from at least two of the factor categories. Sixteen studies examined individual psychological factors, with six of those studies examining multiple individual factors. Ten studies examined at least one environmental factor, while 11 studies examined at least one task factor. Seven studies specifically investigated processing factors, and three studies directly compared output task factors. Most studies examined variables from either the individual or the external factor categories. These processing factors were examined primarily to understand whether they had any mitigating influence on the biases associated with the belief-adjustment process (e.g., recency effects). There is room for additional examination of processing and mitigating factors, especially in conjunction with the other factor categories. These mitigating factors are important in forming more realistic expectations about how presentation order might affect accounting professionals in their true environment. Accountability pressures, the review process, control over order evaluation, and group discussion are all relevant factors that influence an accounting professional’s decisions. These items should be considered in

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order to more accurately generalize the findings from behavioral experiments to accounting practice. Some of the conflicting evidence found in the accounting studies, especially in the area of experience, may be attributable to differences in the researchers’ experimental designs. The differences include the actual task used, response mode variations, number and length of cues used, control of the initial anchor, and belief revision measurement. By controlling for these differences in future studies, researchers can begin to understand better under what conditions experience, and other factors, influence accountants’ belief revisions. Conflicting results may also be due to the moderating influences of some factors on other factors. For example, accountability may mitigate order effects for inexperienced accountants, but not experienced accountants. Knowledge of the particular individual, environmental, input task, processing, and output factors present in the environment under investigation will enable more accurate conclusions to be drawn about these variables’ effects on accountants’ decisions.

Future Directions Based upon the research in this area, all individuals, regardless of experience level, appear to be susceptible to biases in judgment and decision making. Thus, we suggest rather than focusing on whether a particular bias exists, the aim of future research should be not only to better understand the judgment process, but more importantly to improve judgment and decisionmaking. Using our framework, we can identify areas of the belief-adjustment model that need further research. One aspect of the belief-adjustment model that has not been fully examined is the effect of individual and environmental factors on an individual’s sensitivity to evidence. Prior research has generally failed to separate this effect from the impact that these factors have on belief revision. An individual’s sensitivity to evidence is a large factor in whether the predicted contrast effect will occur. While Bamber et al. (1997) measured sensitivity, they did not examine whether greater sensitivity translates into a larger recency effect. Additionally, examining sensitivity more closely may provide more insight into how information is actually processed. Related to this issue is the use of process-tracing measures to more closely examine cue usage by individuals. If individuals use the anchoring and adjustment heuristic, the contrast effect may result in recency effects.

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However, individuals may not necessarily spend more time on these later cues. Thus, training accountants to allocate their time spent evaluating evidence in a particular manner may be able to mitigate recency effects. The effects of temporal delay and interruptions are additional factors that are particularly interesting in the accounting environment. Information in audit, reporting, and tax tasks is often received piecemeal. Interruptions during tasks may cause delays or pauses between the review of evidence, and possibly reduce the effect of the contrast. As Kennedy (1993) indicated, recency can result from an effort-related judgment bias. If recency can be mitigated by effort-inducing strategies, it is possible that time pressure and payment incentives may induce the requisite effort needed to mitigate recency as well. Much more research is needed in examining plausible methods for mitigating recency effects. As Arnold et al. (2000), Krull et al. (1993), and Asare (1992) found, experience alone does not mitigate recency. Strategies are necessary to avoid potentially disastrous results (e.g., not identifying a going concern problem when one exists (Asare, 1992)). Previous strategies examined for mitigating recency effects have included making group decisions, explanation, (which is related to accountability) as well as counterexplanation. As Ahlawat (1999) found, group decisionmaking helped ease cognitive load, so that effort-reducing strategies (e.g., recency) were not needed. Additionally, making auditors (for example) accountable for their decisions (by writing explanations/reasons for their decisions) did result in reduced recency effects as compared to those not held accountable (Kennedy, 1993). However, Koonce (1992) argued that auditors could still arrive at lower quality decisions (including order effects), because they would not investigate the reasons why a result occurred (i.e. counterexplanation). Psychology research has shown that the use of counterexplanation will negate any observed explanation effect (Anderson & Sechler, 1986). Thus, one potentially rich area for future research in all accounting domains is to follow the psychological literature by examining whether or not counterexplanation ‘‘recreates’’ recency effects previously mitigated by use of accountability/explanation.

NOTES 1. ‘‘Response mode’’ and ‘‘presentation mode’’ have been used interchangeably in the literature. Both, essentially, are referring to the same concept. Similar to Arnold et al. (2000), we use the term ‘‘response mode’’ in this chapter to be consistent with Hogarth and Einhorn’s (1992) terminology.

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2. Psychology studies have extensively used estimation tasks (see Lopes, 1985, 1987; Anderson, 1981). Results have generally supported Hogarth and Einhorn’s (1992) model predictions. 3. The evaluation form of the model is presented (see Hogarth & Einhorn, 1992, pp. 9–12.) The reasons, similar to those cited by Bamber et al. (1997), are that (1) in evaluation tasks, evidence is often seen as bipolar relative to the hypothesis, which is similar to many accounting tasks; (2) previous research in accounting has tended to employ the evaluation form; and (3) previous research in accounting supports the predictions of the evaluation form, but has not examined the estimation form of the model (Ashton & Ashton, 1988; Tubbs et al., 1990).

ACKNOWLEDGMENTS This chapter has benefited from the comments and suggestions from Vicky Arnold, the editor. We are also grateful to Rich White for his suggestions on an earlier version of this chapter.

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Lopes, L. L. (1985). Averaging rules and adjustment processes in Bayesian influence. Bulletin of the Psychonomic Society, 23, 509–512. Lopes, L. L. (1987). Procedural debiasing. Acta Psychologica, 64, 167–185. Lord, C., Ross, L., & Lepper, M. (1979). Biased assimilation and attitude polarization: The effects of prior theories on subsequently considered evidence. Journal of Personality and Social Psychology, 37, 2098–2109. Mayper, A. G., Anderson, U., & Kilpatrick, B. G. (1999). Tax workpaper review: The impact of staff conclusions and argument presentation. Advances in Taxation, 11, 145–167. McMillan, J. J., & White, R. A. (1993). Auditors’ belief revisions and evidence search: The effect of hypothesis frame, confirmation bias, and professional skepticism. The Accounting Review, 68, 443–465. Messier, W. F., Jr., & Tubbs, R. M. (1994). Recency effects in belief revision: The impact of audit experience and the review process. Auditing: A Journal of Practice and Theory, 13, 57–72. Monroe, G. S., & Ng, J. (2000). An examination of order effects in auditors’ inherent risk assessments. Accounting and Finance, 40, 151–166. Nisbett, R., & Ross, L. (1980). Human inference: Strategies and shortcomings of social judgment. Englewoods Cliffs, NJ: Prentice-Hall. Pei, B. K. W., Reckers, P. M. J., & Wyndelts, R. W. (1990). The influence of information presentation order on professional tax judgment. Journal of Economic Psychology, 11, 119–146. Pei, B. K. W., Reckers, P. M. J., & Wyndelts, R. W. (1992a). Tax professionals’ belief revision: The effects of information presentation sequence, client preference, and domain experience. Decision Sciences, 23, 175–199. Pei, B. K. W., Reed, S., & Koch, B. (1992b). Auditor belief revision in a performance auditing setting: An application of the belief adjustment model. Accounting, Organizations, and Society(17), 169–183. Pinsker, R. (2004). Order effects in a more frequently reported information environment: Laboratory evidence opposing the belief-adjustment model’s primacy prediction. Working paper, Old Dominion University. Pitz, G. F., Downing, L., & Reinhold, H. (1967). Sequential effects in the revision of subjective probabilities. Canadian Journal of Psychology, 21, 381–393. Roberts, M. (1998). Tax accountants’ judgment-decision-making research: A review and synthesis. Journal of the American Taxation Association, 20, 78–121. Schisler, D. L. (1994). An experimental examination of factors affecting tax preparers’ aggressivness – A prospect theory approach. The Journal of the American Taxation Association, 16(2), 124–142. Trotman, K., & Wright, A. (1996). Recency effects: Task complexity, decision mode, and taskspecific experience. Behavioral Research in Accounting, 8, 175–193. Trotman, K., & Wright, A. (2000). Order effects and recency: Where do we go from here? Accounting and Finance, 40, 169–182. Tubbs, R. M., Messier, W. F., Jr., & Knechel, W. R. (1990). Recency effects in the auditor’s belief revision process. The Accounting Review, 65, 452–460. Tuttle, B., Coller, M., & Burton, F. G. (1997). An examination of market efficiency: Information order effects in a laboratory market. Accounting, Organization and Society, 22, 89–103. Yates, F. J. (1990). Judgment and decision making. Englewood Cliffs, NJ: Prentice-Hall.

AUDITOR CALIBRATION IN THE REVIEW PROCESS Noel Harding, Sally Hughes and Ken T. Trotman ABSTRACT A recent change to audit workpaper review has been the movement toward delegating more review tasks to senior auditors and including more staff auditors in the review process. This study investigates the efficiency and effectiveness implications of this change. It considers the calibration of reviewers of different levels of experience on both conceptual and mechanical errors. The results reveal that reviewers are miscalibrated (overconfident) in their workpaper error judgments. No differences are found in the calibration of staff and senior auditors across hierarchical level or type of error. The implications for audit effectiveness are discussed in the paper.

INTRODUCTION The review process plays a critical role in maintaining the quality of an audit (Bamber & Ramsay, 1997; Rich, Solomon, & Trotman, 1997a, b). It provides additional assurance that sufficient and appropriate audit work has been performed and that all audit objectives have been achieved. Traditionally, a team of auditors performed an audit via a sequential and iterative Advances in Accounting Behavioral Research Advances in Accounting Behavioral Research, Volume 8, 41–57 Copyright r 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1475-1488/doi:10.1016/S1475-1488(04)08002-0

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process with multiple, hierarchical layers of review (Solomon, 1987). Although seniors were involved in the review process, managers and partners performed the majority of the review activities. Due to competitive pressures for cost-effective audits and the expense associated with hierarchical reviews performed by experienced auditors, the review process has become a target for possible cost savings. In this regard, some audit firms have redesigned their review practices and procedures. These changes include a review process now characterized by real-time coaching, the review of audit work being conducted by interview, the delegation of more review tasks to seniors and the inclusion of staff auditors in the review process (Rich et al., 1997b; Winograd, Gerson & Berlin, 2000). Several studies have examined the effectiveness and efficiency implications of these changes (Ramsay, 1994; Bamber & Ramsay, 1997; Harding & Trotman, 1999; Bamber & Ramsay, 2000). This study extends these earlier studies by examining reviewer calibration, a measure of reviewer performance that has not previously been examined in the literature. When conducting workpaper review, the reviewer is required to examine large amounts of workpaper documentation that must be recalled when considering the probity of subsequent workpaper documentation and the conclusions contained therein. While reviewers have the opportunity to refer back to the previously reviewed workpapers, they will often rely on their memory in order to improve review efficiency (Libby & Trotman, 1993). This reliance on memory, however, can jeopardize review effectiveness and efficiency if the reviewer’s memory is incomplete and/or inaccurate. A reviewer’s confidence in their memory for workpaper documentation is, therefore, important as it will determine how the reviewer will use the judgment, and the subsequent action they will take (Sniezek & Henry, 1989). Calibration is a measure of the appropriateness of the expressed level of confidence. It assesses the proficiency of a judge (Oskamp, 1962) since it measures the relationship between judgment accuracy and judgment confidence. Ideally, confidence should increase with accuracy to give a well calibrated reviewer. An auditor who is miscalibrated can be either overconfident or underconfident in their judgment. Overconfidence occurs when, on average, a reviewer’s confidence is greater than their accuracy. Underconfidence occurs when, on average, a reviewer’s accuracy is greater than their confidence. A miscalibrated reviewer is likely to make poor judgments, which in turn, will impact upon the effectiveness and efficiency of the review process (Beck, Solomon, & Tomassini, 1985; Sprinkle & Tubbs, 1998). There can be several costs incurred if a decision-maker is miscalibrated. Overconfidence might mean that a decision-maker disregards contradictory

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evidence or, alternatively, fails to search for additional information that would lead them to question their original decision (Pincus, 1991). In contrast, underconfidence may result in excessive collection of evidence to support a judgment, leading to a reduction in efficiency. Therefore, delegation of review tasks to miscalibrated reviewers can lead to poor performance and/or increased audit hours, both of which are undesirable in an environment increasingly characterized by competition, fee, and legal pressures (e.g., Winograd et al., 2000). A consistent finding in the literature is that individuals, including auditors, are overconfident (e.g., Plous, 1993). If workpaper reviewers; are similarly overconfident in their review judgments, the reliance that audit firms place on the review process may not be justified. An overconfident reviewer might, for example, place excessive reliance on inaccurate memory for audit evidence contained in the workpapers previously reviewed. A number of recent studies have examined the accuracy of reviewers in detecting different types of workpaper errors (Ramsay, 1994; Bamber & Ramsay, 1997; Harding & Trotman, 1999; Tan & Trotman, 2003). Ramsay (1994) suggested that seniors and managers use different review templates to guide their review of the workpapers, arguing that managers focus more on conceptual errors while seniors focus more on mechanical errors.1 He found that managers performed better than seniors on conceptual errors and seniors performed better than managers on mechanical errors. Harding and Trotman (1999) extended these results to audit staff. They found that staff auditors identified more mechanical errors than seniors, while the opposite was true for conceptual errors. Bamber and Ramsay (1997) investigated the effect of specialized reviews on reviewers’ performance. That is, the effects of directing an auditor to perform a review that focuses on identifying particular types of errors, either mechanical (seniors) or conceptual (managers). The results revealed that reviewers were more accurate when directed to perform a comprehensive as opposed to a focused review. Tan and Trotman (2003) have recently demonstrated that these hierarchical differences in review performance may be influenced by preparer stylization and a reviewer’s sensitivity to that stylization. Despite its importance in understanding the effectiveness and efficiency of the review process, only one study has, to date, examined reviewer confidence in workpaper error judgments with no study examining the relationship between confidence and accuracy (i.e., calibration). Bamber and Ramsay (2000) examined the confidence that seniors and managers had in performing all encompassing versus specialized reviews. They found that

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seniors were more confident when performing specialized as compared to all encompassing reviews. There was no difference in confidence for managers.2 The present study extends earlier research examining hierarchical differences in reviewer accuracy (e.g., Ramsay, 1994; Harding & Trotman, 1999) by investigating hierarchical differences in reviewer calibration. This study allows us to assess more completely the appropriateness of moving the review process to lower levels in the audit firm hierarchy. The results of our study reveal that reviewers are miscalibrated (overconfident) in their workpaper error judgments. Contrary to expectations, there are no differences in the level of overconfidence across hierarchical level or type of error. The results suggest that while the inclusion of staff auditors in the review process might improve review efficiency, their inclusion in this study neither adds to nor detracts from review effectiveness.

HYPOTHESIS DEVELOPMENT Calibration has been extensively studied in the psychology literature (see Lichtenstein, Fischhoff & Phillips (1982) and Plous (1993) for reviews). With a few exceptions, when making judgments for at least moderately difficult tasks, miscalibration in the form of overconfidence is the norm. Underconfidence, however, is often identified for simple tasks where the percentage of subjects making the correct judgment is above 70%. This is referred to as the hard–easy effect. In auditing, initial research suggested that auditors were generally well calibrated and might even tend towards underconfidence (Tomassini, Solomon, Romney, & Krogstad, 1982). Mladenovic and Simnett (1994), consistent with the hard–easy effect identified in the psychology literature, found that overconfidence increases as the task becomes less predictable (more difficult). Given the difficult nature of audit judgments, it is perhaps not surprising that recent studies show auditors to be overconfident. For example, Simnett (1996) found auditor overconfidence on a prediction of failure task. Kennedy and Peecher (1997) found that audit staff, seniors, and managers were all overconfident in their technical knowledge. Moeckel and Plumlee (1989) investigated auditor’s confidence in the recognition of audit evidence and found that auditors can be just as confident in their inaccurate memories as their accurate memories. Workpaper review is of moderate to high difficulty. It requires a considerable amount of time and is characterized by multiple levels of review. It

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involves the examination and integration of large amounts of information that, of itself, might not be important, but could be critical when combined with other information. Given the difficulty of the review process and the consistent findings of overconfidence, we anticipate that the auditors in the present study will be miscalibrated (overconfident) in their recognition of workpaper errors. We test the following hypothesis. H1. Auditors are overconfident in their recognition of workpaper errors. While we argue that auditors will be overconfident in their workpaper judgments, the long standing hierarchical nature of the review process, and research findings that report hierarchical differences in reviewer accuracy across error types (e.g., Ramsay, 1994; Bamber & Ramsay, 1997; Harding & Trotman, 1999), suggest that there might be differences across hierarchical levels. A reviewer’s confidence in their memory for workpaper documentation is affected by their ability to retrieve information from their memory, which in turn, is a function of the type of elaboration that occurred during the encoding of that information (Craik & Tulving, 1975). Elaboration at encoding is a function of the depth of processing. Craik and Tulving identified two types of processing; deep and shallow processing. Deep processing at encoding occurs when evidence receives full attention, is entirely analyzed and ‘‘enriched by association’’ (p. 270). Shallow processing of information occurs when evidence is not attended to fully and is only analyzed at a surface level. If a reviewer has performed an in depth analysis of certain aspects of the workpapers, their ability to retrieve information from their memory is greater for those areas and likely to lead to increased confidence. Depth of processing is also argued to impact accuracy. Indeed, this has been the theoretical foundation of studies reporting hierarchical differences in reviewer accuracy (e.g., Ramsay, 1994; Bamber & Ramsay, 1997; Harding & Trotman, 1999). These studies argue that accuracy for conceptual errors increases with experience due to increased attention directed toward this aspect of the workpapers. Accuracy for mechanical errors, they argue, decreases with experience, as these errors are no longer the primary focus of the reviewer’s attention. It is, therefore, anticipated that confidence and accuracy will increase with the amount of elaboration at encoding. If, for example, mechanical errors are the focus of the reviewer’s attention, both confidence and accuracy will increase for these types of errors. In terms of the relationship between accuracy and confidence, Glenberg and Epstein (1985), Brothwell, Deffenbacher, and Brigham (1987) and

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Glenberg and Epstein (1987) all provide evidence of a positive association. It might, therefore, be expected that there are no hierarchical differences in overconfidence as changes in accuracy are associated with corresponding changes in confidence. However, highlighting that the relationship may be more complex in an auditing context, Moeckel and Plumlee (1989) report results showing both a positive and negative association. In addition, experience may have an effect in addition to influencing accuracy and the amount of elaboration. If this is the case, hierarchical differences in the level of overconfidence might be expected. There is only a limited literature examining the effect of experience on confidence. In accounting domains, the results have been mixed. Raiborn and Estes (1986) (cited in Pincus, 1991) observed a positive relationship, while Snowball (1980) observed a negative relationship. Weber (1978) and Pincus (1991) both observed no significant relationship. While Pincus (1991) found no relationship between experience and confidence across her entire sample, she did observe a positive relationship when the decision made was consistent with an unqualified opinion and a negative relationship when the decision made was consistent with a modified opinion. This, she speculated, might be the result of an availability heuristic in that auditors are more commonly faced with a situation where an unqualified opinion is issued. An alternative explanation for the results reported in Pincus (1991) relates to the implications of the decision (see Solomon, Ariyo, & Tomassini, 1985; Mladenovic & Simnett, 1994). A decision leading to a modified opinion might be argued to have greater implications than one leading to an unqualified opinion. Auditors in Pincus’ study may have been less confident in a decision leading to a modified opinion and this lack of confidence was amplified with experience as auditors become more aware of the implications of their decisions. In this regard, Sprinkle and Tubbs (1998) found that auditors were less willing to rely on their memory (less confident) for information of greater importance. Seniors being more experienced than staff auditors at workpaper review are argued to be more confident than staff auditors in their review judgments. This is particularly the case for mechanical errors, as there are also likely to be differences in confidence derived from the perceived importance and mechanical errors across hierarchical levels. While there may be some variations depending on the next level of review (Rich et al., 1997a; Tan & Trotman, 2003) the importance for seniors of mechanical errors, by comparison to conceptual errors, is low. Staff auditors, however, view mechanical errors as critical to their career progression. The prevention and/or

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identification of mechanical errors is an important factor in staff auditors’ performance evaluation. We argue that these factors will lead to a decrease in confidence for staff auditors and an increase in confidence for seniors. If reviewers are overconfident (as proposed in H1), these changes in confidence will reduce overconfidence for staff auditors and increase overconfidence for seniors. We test the following hypothesis. H2. Staff auditors will be better calibrated (less overconfident) in the recognition of mechanical errors than senior auditors. On the other hand, seniors are acutely aware of the importance of conceptual errors thereby reducing their confidence. Staff auditors, on two counts, are likely to be more confident. First, these errors are less the focus of their work compared to mechanical errors. In addition, staff auditors are unlikely to have the same appreciation of the importance of conceptual errors as their senior colleagues. Given that experience is argued to temper confidence on review matters relating to conceptual errors (and inexperience increases confidence) we argue that staff auditors will be more confident (and more overconfident) than seniors on conceptual errors. We test the following hypothesis. H3. Senior auditors will be better calibrated (less overconfident) in the recognition of conceptual errors than staff auditors.

METHOD The same participants and research instrument in Harding and Trotman’s (1999) study were used in this study. However, Harding and Trotman only analyzed the accuracy of participants’ responses to workpaper error questions. This paper analyzes the calibration of reviewers’ workpaper error judgments. A 2  (2) experiment was designed to examine the impact of experience and the type of error on reviewer calibration. The between-subject variable was experience (staff, senior) and the within-subject variable was the error type (mechanical, conceptual).

Subjects Our analysis is based on a sample of 20 senior auditors and 20 staff auditors from a then ‘‘big 6’’ accounting firm.3 The mean experience of the staff and

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senior auditors in our sample was 2.13 and 4.77 years respectively. The experience of senior and staff auditors was significantly different (t ¼ 10.653, two-tailed p ¼ 0.001). From the original sample of 42 in Harding and Trotman (1999), one auditor was dropped because of incomplete confidence levels and one was dropped because their review notes were not legible.

Research Instrument and Administration The research instrument used in Harding and Trotman (1999) and this study is the same as that developed by Ramsay (1994).4 Subjects were asked to perform a review of a set of hypothetical workpapers relating to the provision for doubtful debts account for a manufacturing firm. Subjects were given three envelopes, each individually labeled (A, B, C) and a sheet outlining the general instructions. After the subjects read the general instructions they were instructed to open envelope A, which contained the workpapers and blank pages upon which to write review notes. Subjects were given 40 min in order to complete the review. Subjects were instructed to review the workpapers as they would in a real audit, but were not told that they would be using their review notes or that they would be tested on the content of the workpapers. Following the review, the workpapers were collected and a line was ruled under the last review note on each page. Thereafter, subjects were asked to open envelope B, which contained a true/ false questionnaire containing 16 questions. These included errors that existed in the workpapers and filler items, that is, errors that did not exist in the workpapers. The recognition test included seven conceptual errors, four mechanical errors, four mechanical filler items, and one conceptual filler item. This was the same true/false test as used by Ramsay (1994). Following Ramsay (1994) and Bamber and Ramsay (2000) subjects were also asked to indicate the confidence they had in their response to each of the 16 questions on a 3-point scale: 1 – I am certain of my answer; 2 – I believe my answer is correct; and 3 – I guess my answer is correct. Materials in envelope C elicited demographic information. Subjects completed the research materials in one of three sessions conducted at the offices of the then ‘‘big 6’’ firm involved. At least one of the authors was present on each occasion in order to administer the materials. Each session began with an introduction of the author(s) by a senior member of the firm who also emphasized the firm’s support for the research being

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conducted. In each session, subjects worked diligently and appeared to take the task seriously. Method of Analysis Calibration refers to the accuracy of the decision-maker’s confidence. That is, is the confidence level assigned appropriate given the accuracy of the responses? The greater the confidence, the greater should be the accuracy. While there are a number of methods of calculating calibration (see Yates, 1990) we chose the method that distinguishes between over and under confidence given the differences in consequences for auditing. This method which allows an assessment of direction and magnitude of any miscalibrated judgments has been used in other accounting/auditing studies (e.g., Dilla, File, Solomon, & Tomassini 1991; Pincus, 1991; Simnett, 1996; Kennedy & Peecher, 1997). It provides an over/underconfidence score calculated using the following equation: Over=underconfidence ¼

T 1X ni ð P i N i¼1

Ci Þ

where N is the total number of probability assessments, ni the number of times a probability response was used, Pi the probability response category (i.e., 1.0, 0.75, 0.50), Ci the percentage of correct responses for each probability category and T the total number of probability response categories (three in the present study). A positive score indicates overconfidence while a negative score indicates underconfidence. Interpretation of this score, however, must proceed with caution. A score of zero may represent one of two possibilities. It may indicate that the decision maker is perfectly calibrated or that overconfidence/underconfidence at one probability response category is perfectly offset by underconfidence/overconfidence at another probability response category. Similarly, a positive (negative) score should be interpreted as indicating that, over the three probability response categories, the decisionmaker is, on average, overconfident (underconfident). It is therefore necessary to interpret this score in conjunction with calibration curves, which plot perfect calibration (called the ‘‘identity line’’) against the average percentage correct answers at each probability level. These calibration curves are shown in Table 2. As noted above, subjects indicated their confidence on a 3-point scale: 1 – I am certain of my answer; 2 – I believe my answer is correct; and 3 – I guess

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my answer is correct. It was therefore necessary to assign percentages to each of these responses. Responses 1, 2 and 3 were coded 100%, 75% and 50%, respectively.5 Subjects had access to their review notes (but not the workpapers) when completing the recognition test. In order to calculate calibration, it was necessary to exclude the responses to questions that were noted in the subject’s review notes. In doing so, the questions that remained represented a true test of memory and calibration. Two of the authors independently examined each of the review notes with a view to identifying those that related to a question on the recognition test. The small number of disagreements between coders were resolved. For each subject, all recognition test questions identified in the workpapers were removed from the data set. This meant that from zero to three questions were removed for each staff auditor (average 1.45) and zero to five questions removed for each senior (average 2.15).

RESULTS Consistent with Bamber and Ramsay (2000), our analysis is based on the actual errors contained in the true–false test.6 Table 1 reports the over/ underconfidence score and the number of subjects who were overconfident, perfectly calibrated, and underconfident. Table 1 (panel A) reveals that subjects were generally overconfident in their workpaper review judgments. Of the 40 subjects, 29 were overconfident.7 The mean over/underconfidence score of 0.151 was significantly different from zero (t ¼ 5.124, two-tailed po0.001). There was no difference in the over/underconfidence score between senior (0.139) and staff auditors (0.163) (t ¼ 0.396, p ¼ 0.694). While subjects were well calibrated at the 50% probability response category (percent correct 51.06), the percentage correct was significantly different from that expected for perfect calibration at the 75% probability response category (percent correct 59.33; two-tailed p ¼ 0.005) and 100% probability response category (percent correct 80.73; two-tailed po0.001). These results provide support for H1. Auditors in our study were overconfident in their recognition of workpaper errors. We discuss the implications of this finding in the following section. Table 1 (panel B) further disaggregates this overconfidence across type of error and hierarchical level. Table 2 reports the percentage of correct responses (accuracy) at each probability response category for each of the four research conditions. The table also plots accuracy against the identity line

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Table 1. Over/Underconfidence Score Number of Subjects Underconfident Overconfident Perfectly Calibrated. Panel A: Aggregated Results (n ¼ 40) Over/underconfidencea t statisticb Significanceb Number of subjects Overconfident Perfectly calibrated Underconfident Panel B: Disaggregated Results

Conceptual errors Over/underconfidencea T statisticb Significanceb

0.151 5.124 po0.001 29 3 8

Staff Auditors (n ¼ 20)

Senior Auditors (n ¼ 20)

0.18 3.047 p ¼ 0.01

0.09 2.013 p ¼ 0.06

Number of subjects Overconfident Perfectly calibrated Underconfident

15 0 5

14 2 4

Mechanical errors Over/underconfidencea T statisticb Significanceb

0.11 1.644 p ¼ 0.12

0.22 2.70 p ¼ 0.01

Number of subjects Overconfident Perfectly calibrated Underconfident

10 2 8

13 4 3

a

Over/underconfidence is calculated with reference to the formula provided in the text. A positive (negative) score indicates overconfidence (underconfidence). b This t statistic tests whether the calibration score is significantly different from zero.

representing perfect calibration. Points above and below the identity line represent overconfidence and underconfidence, respectively. Tables 1 and 2 reveal that seniors were overconfident for both conceptual errors (t ¼ 2.013, p ¼ 0.06) and mechanical errors (t ¼ 2.70, p ¼ 0.01). While staff auditors were overconfident for conceptual errors (t ¼ 3.047, p ¼ 0.01) the over/underconfidence score for mechanical errors, although in

52

Table 2.

Calibration Curves.

Staff Auditors

0.18 75 55.8

100 82.3

0.09 75 67.4

50 62.5 Percentage correct

50 48.7 Percentage correct

Conceptual Errors Over/underconfidencea Probability response cat. Percentage correct

Senior Auditors

100 90 80 70 60 50 40 30 20 10 0 50

75

100 90 80 70 60 50 40 30 20 10 0

100

50 75 100 Probability response category

Probability response category Identity line

% Correct

0.11 75 50.0

50 45.8

100 88.2

50 75 100 Probability response category Identity line

a

% Correct

% Correct

0.22 75 30.0

50 57.3 Percentage correct

100 90 80 70 60 50 40 30 20 10 0

Identity line

100 83.3

100 90 80 70 60 50 40 30 20 10 0

50 75 100 Probability response category Identity line

% Correct

Over/underconfidence is calculated with reference to the formula provided in the text. A positive (negative) score indicates overconfidence (underconfidence).  Significant at p ¼ 0.01 (significantly different from the identity line).

NOEL HARDING ET AL.

Percentage correct

Mechanical Errors Over/underconfidencea Probability response cat. Percentage correct

100 84.4

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the direction of overconfidence, was not statistically significant (t ¼ 1.644, p ¼ 0.12). In each of the four research conditions, at least 50% of the subjects were overconfident. As was the case for the aggregate analysis, our subjects were well calibrated at the 50% probability response category, and overconfident at the 75% and 100% probability response categories in each of the four research conditions. H2 and H3, taken together, predict a significant interaction between reviewers’ calibration for the nature of the item in the recognition test (mechanical or conceptual) and the experience of the reviewer (staff or senior). Contrary to expectations, the results from a 2  (2) repeated measures ANOVA revealed no significant interaction between error type and experience (F ¼ 2.125, p ¼ 0.153). H2 predicted that staff auditors would be better calibrated than senior auditors in the recognition of mechanical errors while H3 predicted that the opposite would be true for conceptual errors. Although in the expected direction, there was no significant difference in the calibration scores between staff and senior auditors for mechanical errors (t ¼ 1.056, one-tailed p ¼ 0.149) or conceptual errors (t ¼ 1.102, one-tailed p ¼ 0.139). Therefore, our data do not support H2 or H3.8

DISCUSSION AND CONCLUSIONS The literature on the ability of reviewers to identify various workpaper errors in the review process (Ramsay, 1994; Bamber & Ramsay, 1997, 2000; Harding & Trotman, 1999) has provided useful insights into differences in reviewer accuracy and efficiency across different experience levels of reviewers. These studies, however, have not examined the calibration of reviewer judgments. Calibration measures the relationship between judgment accuracy and judgment confidence. A well-calibrated judge is more likely to act appropriately on their judgment, irrespective of whether that judgment is correct or not. An overconfident reviewer, on the other hand, believes they are correct when, in fact, they are incorrect. Overconfidence could mean, for example, that the reviewer chooses not to reinspect the workpapers or ignores contradictory evidence. Given the importance of the review process in ensuring that errors are identified and corrected before the final opinion is issued, calibration is an important element of a reviewer’s performance. This study provides insights into the effectiveness of the review process and the appropriateness of delegating specific aspects of the review process to less experienced auditors.

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The results revealed that senior and staff auditors were miscalibrated (overconfident) in their workpaper review judgments. When reviewers confidently rely on their inaccurate memory, they are less likely to re-inspect the workpapers. An incomplete or inaccurate recall of workpaper contents might mean that deficient or incomplete audit procedures are not identified. Given that the identification and correction of deficient or incomplete work is the primary purpose of the review process, reviewer overconfidence is an issue that should be addressed by audit firms. Audit firms should exercise caution when delegating review tasks to seniors and staff auditors. The results show that there was no difference in the levels of overconfidence between seniors and staff auditors on either conceptual or mechanical errors. That is, staff and senior auditors are equally aware (or more precisely based on the results of this study, equally unaware) of the accuracy and completeness of their memory for workpaper contents. Combining these results with Bamber and Ramsay (1997, 2000) and Harding and Trotman (1999), there are some important practical implications for audit firms with respect to changes in the review process. The effectiveness of staff auditors in identifying mechanical errors, the fact that they have similar calibration levels to seniors on these errors, and their lower cost means that it is appropriate for staff auditors to play a role in the review process. As noted above, the results do not suggest that a greater number of mechanical errors will be found with the inclusion of staff auditors. Rather, they suggest that a similar outcome will be achieved, at lower cost, if staff auditors instead of seniors perform the review work; however, the same conclusion could not be drawn for conceptual errors. While staff and senior auditors might be equally aware (or unaware) of when they are incorrect, only senior auditors are likely to be able to correct their inaccurate understanding by reinspecting the workpapers. Staff auditors will not have this ability, even if they do re-inspect the workpapers. From these results, it is tempting to suggest a specialized review with staff considering mechanical errors and seniors considering conceptual errors. However, Bamber and Ramsay (1997, 2000) indicate that these specialized reviews are less effective and efficient than comprehensive reviews. Therefore, our overall conclusion based on the combined research is that there are benefits of having both staff auditors and seniors carrying out comprehensive reviews for a client. The addition of the staff auditors will add to the quality of documentation and provide training in review. These benefits need to be compared to the additional cost of the time they spend on reviews.

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NOTES 1. Examples of mechanical errors include missing tick marks and numbers on one workpaper not agreeing with the original calculation of the numbers located on another workpaper. Examples of conceptual errors include use of an improper materiality threshold for the type of client and the inadequate explanation or justification of audit conclusions (Bamber & Ramsay, 2000). 2. As part of additional analysis, Bamber and Ramsay (2000) calculated a surrogate for calibration. This measure involved examining accuracy when subjects indicated they were certain of their answer. No analysis was performed when subjects indicated they were less than certain. 3. Subjects were selected by the firm involved on the basis of experience and ability. 4. Minor ‘‘semantic changes’’ were made to the workpapers and the True/False questionnaire in order to make them appropriate to Australian auditors. 5. The descriptions attached to responses 1 and 3 meant that coding did not represent a major problem. Coding for response 2, however, was problematic. Given the absence of any compelling reason to code otherwise, a response of 2 was coded 75%. It is recognized, however, that subjects may have inferred that I believe my answer is correct to mean something other than being 75% confident. With this in mind, we re-calculated all scores using 80% and 70%. Our statistical inferences were unchanged with the use of these alternative figures. 6. We re-analyzed the data including both actual errors and filler items. All inferences were unchanged. 7. Of the eight subjects who were underconfident, four were seniors and four were staff auditors. Two staff auditors and one senior were perfectly calibrated. 8. Similar results were obtained when the analysis was repeated for all questions (actual errors and filler items).

ACKNOWLEDGMENTS We thank Michael Bamber, Amna Khalifa, and Roger Simnett for their useful comments. We also acknowledge the financial support of an Australian Research Council Discovery Grant to Ken Trotman.

REFERENCES Bamber, E. M., & Ramsay, R. J. (1997). An investigation of the effects of specialization in audit workpaper review. Contemporary Accounting Research, 14(3), 501–513. Bamber, E. M., & Ramsay, R. J. (2000). The effects of specialization in audit workpaper review on review efficiency and reviewer’ confidence. Auditing: A Journal of Practice and Theory, 19(2), 147–157.

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Beck, P. J., Solomon, I., & Tomassini, L. A. (1985). Subjective prior probability distributions and audit risk. Journal of Accounting Research, 23(1), 37–56. Brothwell, R., Deffenbacher, K., & Brigham, J. (1987). Correlation of eyewitness accuracy and confidence: Optimality hypothesis revisited. Journal of Applied Psychology, 72(November), 691–695. Craik, F., & Tulving, E. (1975). Depth of processing and the retention of words in episodic memory. Journal of Experimental Psychology: General, 104(3), 268–294. Dilla, W., File, R. D., Solomon, I., & Tomassini, L. A. (1991). Predictive bankruptcy judgments by auditors. In: L. Poneman & D. Gabhat (Eds), Probabilistic approach auditing: advances in behavioral research. Berlin: Springer. Glenberg, A., & Epstein, W. (1985). Calibration of comprehension. Journal of Experimental Psychology: Learning Memory and Cognition, 11(October), 702–718. Glenberg, A., & Epstein, W. (1987). Inexpert calibration of comprehension. Memory and Cognition, 15(January), 84–93. Harding, N., & Trotman, K. (1999). Hierarchical differences in audit workpaper review performance. Contemporary Accounting Research, 16(4), 671–684. Kennedy, J., & Peecher, M. (1997). Judging auditors’ technical knowledge. Journal of Accounting Research, 35(2), 279–293. Libby, R., & Trotman, K. T. (1993). The review process as a control for differential recall of evidence in auditor judgments. Accounting, Organizations and Society, 18(6), 559–574. Lichtenstein, S., Fischhoff, B., & Phillips, L. D. (1982). Calibration of probabilities. The state of the art to 1980. In: D. Kahneman, P. Slovic & A. Tversky (Eds), Judgment under uncertainty: Heuristics and biases. Cambridge: Cambridge University Press. Mladenovic, R., & Simnett, R. (1994). Examination of contextual effects and changes in task predictability on auditor calibration. Behavioral Research in Accounting, 6, 178–203. Moeckel, C. L., & Plumlee, R. D. (1989). Auditors’ confidence in accurate and inaccurate recognition of audit evidence. The Accounting Review, 64(4), 653–666. Oskamp, S. (1962). The relationship of clinical experience and training methods to several criteria of clinical prediction. Psychological Monographs, 76(28), 547. Pincus, K. V. (1991). Audit judgment confidence. Behavioral Research in Accounting, 3, 39–64. Plous, S. (1993). The psychology of judgment and decision making. New York: McGraw Hill. Raiborn, D. D., & Estes, R. (1986). The impact of personal characteristics on materiality decisions. In: M. Massoud (Ed.), Proceedings-1986 western accounting association annual meeting: the economics of accounting. Ramsay, R. (1994). Senior/manager differences in audit workpaper review performance. Journal of Accounting Research, 32(1), 127–135. Rich, J. S., Solomon, I., & Trotman, K. T. (1997a). The audit review process: A characterization from the persuasion perspective. Accounting, Organizations and Society, 22(5), 481–505. Rich, J. S., Solomon, I., & Trotman, K. T. (1997b). Multi-auditor judgment/decision making research: A decade later. Journal of Accounting Literature, 16, 86–126. Simnett, R. (1996). The effect of information selection, information processing and task complexity on predictive accuracy of auditors. Accounting, Organizations and Society, 21(7/ 8), 699–719. Sniezek, J. A., & Henry, R. A. (1989). Accuracy and confidence in group judgments. Organizational Behavior and Human Decision Processes, 43(1), 1–28.

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Snowball, D. (1980). Some effects of accounting expertise and information load: An empirical study. Accounting Organizations and Society, 5, 323–338. Solomon, I. (1987). Multi-auditor judgment/decision making research. Journal of Accounting Literature, 6, 1–25. Solomon, I., Ariyo, A., & Tomassini, L. A. (1985). Contextual effects on the calibration of probabilistic judgments. Journal of Applied Psychology, 70(3), 528–532. Sprinkle, G. B., & Tubbs, R. M. (1998). The effect of audit risk and information importance on auditor memory during working paper review. The Accounting Review, 73(4), 475–502. Tan, H. T., & Trotman, K. T. (2003). Reviewers’ responses to anticipated stylization attempts by preparers of audit workpapers. The Accounting Review, 78(2), 581–604. Tomassini, L. A., Solomon, I., Romney, M. B., & Krogstad, J. L. (1982). Calibration of auditor’s probabilistic judgments: Some empirical evidence. Organizational Behavior and Human Performance, 30(3), 391–406. Yates, J. F. (1990). Judgment and decision making. Englewood Cliffs, NJ: Prentice-Hall. Weber, R. (1978). Auditor decision making on overall system reliability: Accuracy, consensus and the usefulness of a simulation decision aid. Journal of Accounting Research, 16(2), 368–388. Winograd, B. N., Gerson, J. S., & Berlin, B. L. (2000). Audit Practices of PricewaterhouseCoopers. Auditing: A Journal of Practice and Theory, 19(2), 175–182.

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LINGUISTIC DELIVERY STYLE, CLIENT CREDIBILITY, AND AUDITOR JUDGMENT Christie L. Comunale, Thomas R. Sexton and Terry L. Sincich ABSTRACT This chapter introduces linguistic delivery style to auditing research, demonstrates how linguistic delivery style relates to client credibility, and shows how linguistic delivery style and client credibility influences auditors’ judgment. Two hundred auditors participated in an analytical procedures task. The results indicate that high client credibility and powerful linguistic delivery style increase the auditor’s assessed likelihood that the explanation accounts for the fluctuation and decrease their intent to perform additional testing. Moreover, powerless linguistic delivery style from an otherwise high credibility client leads to auditor judgments and intentions that are indistinguishable from those that arise from a low credibility client. Finally, evidence indicates that linguistic delivery style is a fourth component of credibility.

Advances in Accounting Behavioral Research Advances in Accounting Behavioral Research, Volume 8, 59–86 Copyright r 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1475-1488/doi:10.1016/S1475-1488(04)08003-2

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INTRODUCTION SAS No. 56 (America Institute of certified Public Accountants, AICPA, 1988) requires that auditors use analytical procedures in the planning and review stages of the audit. In addition, SAS No. 99 (AICPA, 2002) requires that auditors gather information necessary to identify risks of material misstatement due to fraud by considering the results of the analytical procedures performed in planning the audit. Frequently, while employing analytical procedures, the auditor must engage in management inquiry to investigate unexpected findings. In addition, SAS No. 99 directs auditors to inquire of management and others within the entity about the risks of fraud. Such inquiry often takes the form of oral exchanges of questions and responses. The effectiveness and efficiency of the management inquiry process depend heavily on the perceived credibility of the management source (Mautz 1958). Nonaudit research indicates that perceived credibility is composed of three factors: competence, trustworthiness, and objectivity (Berlo, Lemert, & Mertz, 1969; McCroskey, 1966). Audit research demonstrates that auditors are sensitive to all three factors and illustrates a positive relationship between each factor and perceptions of messenger credibility and acceptability of audit evidence (Joyce & Biddle, 1981; Bamber, 1983; Rebele, Heinz, & Briden, 1988; Anderson, Koonce, & Marchant, 1994; Hirst, 1994; Peecher, 1996; Ayers & Kaplan, 1998; Goodwin, 1999; Beaulieu, 2001). Research in communications suggests that linguistic delivery style – the presence of hedges, hesitations, and faulty grammar – also influences perceived credibility. Interestingly, audit research literature has not explored linguistic delivery style. This study introduces linguistic delivery style to the audit literature by showing the effects of powerful or powerless language on the auditor’s evaluation of the client’s credibility and hence on the auditor’s likelihood assessments and planning decisions in the management inquiry process. Three possible outcomes may result from the influence of linguistic delivery style. First, linguistic delivery style may not have a discernible effect. Second, the auditor may be inefficient if he or she appropriately questions a valid explanation when the client uses a particularly powerless form of speech. Finally, the auditor may be ineffective if he or she inappropriately accepts an insufficient explanation because the client uses a particularly powerful form of speech. While the two latter outcomes are important, this study examines only the first kind, that is, the effects of client credibility and linguistic delivery style on auditors’ likelihood assessments and planning decisions in the case where

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the client’s explanation is valid. This outcome was chosen as the focus of this study primarily because both auditors and clients seek to streamline the audit process, thereby reducing audit effort and costs. Consistent with the existing audit literature, this study provides evidence that client credibility influences auditors’ likelihood assessments and planning decisions. In addition, the evidence suggests that the client’s linguistic delivery style influences auditors’ likelihood assessments and planning decisions. Finally, while researchers have recognized competence, trustworthiness, and objectivity as components of credibility, linguistic delivery style appears to be a fourth component. Moreover, the supplemental results suggest that powerless linguistic delivery style from an otherwise high-credibility client leads to auditor judgments and intentions that are indistinguishable from those that arise from a low-credibility client, thereby requiring the auditor to perform additional work.

MODELING FRAMEWORK AND HYPOTHESIS DEVELOPMENT This study relies on research in psychology, communications, and auditing to generate the hypotheses, identify the components of credibility and linguistic delivery style, and build the research model (shown in Fig. 1). The model posits that client credibility and linguistic delivery style have direct effects on auditors’ likelihood assessments and planning decisions during analytical procedures. In addition, the model suggests that linguistic delivery style influences the auditor’s perceptions of client credibility.

Message Content During analytical procedures, the auditor may request an explanation from the client for a significant account balance fluctuation. The client’s oral explanation constitutes the message in this study. Message content is held constant and thus is not shown in the research model. Message content is theoretically independent of the linguistic delivery style of the client and refers to the literal meaning of the words used in the explanation, separate and distinct from any linguistic cues regarding the strength of the client’s belief and confidence in the explanation. Specifically, the power of the client’s linguistic delivery style does not alter the message content although it

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Competence Trustworthiness Objectivity

Client Credibility

Likelihood Assessments and Planning Decisions Likelihood Assessment Confidence in Assessment Likelihood of Substantive Tests Further Client Inquiry

Linguistic Delivery Style

Hedges Hesitations Grammar

Fig. 1.

Research Model.

may be an imperfect indicator of the strength of the client’s belief and confidence in the explanation. Client Credibility Following O’Keefe (1990), client credibility refers to the auditor’s perception of the client’s believability. Early factor-analytic investigations of credibility reveal three dimensions: competence, trustworthiness, and objectivity (Berlo et al., 1969; McCroskey, 1966). In the following three subsections, these dimensions are discussed as they relate to auditor judgment and behavior.

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Competence Audit research suggests that auditors are sensitive to the expertise or competence of the source (Bamber, 1983). Rebele et al. (1988) find that auditors place more reliance on evidence obtained from a high-expertise source than on evidence obtained from a low-expertise source in an accounts receivable collections task. In the same light, Anderson et al. (1994) find that auditors judge evidence gained from a more competent source as more reliable than evidence gained from source of lesser competence. Trustworthiness Audit research finds mixed results concerning auditor sensitivity to source trustworthiness. Bernardi (1994) finds no difference in fraud detection rates among auditors considering information from a low- versus a high-integrity client. Likewise, Kaplan and Reckers (1984) find that auditors are insensitive to client integrity in the initial audit planing process. On the other hand, Goodwin (1999) finds that auditors evaluating management-provided evidence involving obsolete inventory are sensitive to management’s integrity. Beaulieu (2001) finds that judgments of client integrity relate negatively to risk judgments, audit evidence extent recommendations, and fee recommendations. Ayers and Kaplan (1998) discover that audit-firm partners utilize their assessments of client integrity in client acceptance decisions. Finally, Peecher (1996) reports that management’s integrity influences the auditor’s acceptance of a client-provided explanation in analytical procedures. Objectivity Audit research demonstrates that auditors are more sensitive to the objectivity of the source when the source is an individual within, as opposed to outside, the firm under investigation. Hirst (1994) finds that auditors assess explanations received from fellow auditors as more diagnostic than those provided by the client’s chief financial officer. Joyce and Biddle (1981) discover that auditors assess information from an independent credit agency as more diagnostic than information received from the client’s credit manager. Finally, Brown (1983) finds that auditors evaluating internal auditor reliability consider objectivity more important than competence and performance. Linguistic Delivery Style O’Keefe (1990) notes that the factors of competence, trustworthiness, and objectivity represent only the most general dimensions of perceived

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credibility. He argues that the delivery characteristics of the message also may influence perceived credibility and thus warrant further investigation. Erickson, Lind, Johnson, and O’Barr (1978), Lind and O’Barr (1979), and O’Barr (1982) examine courtroom transcripts and develop linguistic clusters that, they argue, exhibit high and low communicative power. They find that, on an average, uneducated witnesses use ‘‘low-social power’’ forms of speech – hedges, hesitations, intensifiers, tag questions, deictic words or phrases,1 and polite form – whereas lawyers, judges, and expert witnesses typically use ‘‘high-social power’’ forms of speech. Their findings suggest that speakers using high-social power speech are perceived as possessing a greater depth of knowledge about the topic and therefore more credible than speakers using low-social power speech. Haleta (1996) finds that students rate teachers using language devoid of hedges, intensifiers, deictic phrases, and hesitation forms as significantly more credible than teachers who use these linguistic characteristics. A number of studies find that hedges and hesitations are often perceived as lowest in power and more strongly associated with lower perceptions of messenger credibility and message believability. Wright and Hosman (1983) discover that high levels of hedging significantly reduce credibility ratings. Vinson and Johnson (1989) reveal that both hedges and hesitations have negative effects on perceptions of messenger credibility. Hosman and Wright (1987) find that lower levels of hedges and hesitations produce more positive speaker and message evaluations. More recently, Adkins and Brashers (1995) show that the user of a powerful language style in a computer-mediated group is generally perceived as more credible, attractive, and persuasive than the user of a powerless language style. In addition to hedges and hesitations, linguistic research indicates that other delivery characteristics such as mispronunciation, poor organization, slow speech rate, low levels of diversity, and faulty grammar are associated with decreased ratings of speaker effectiveness and credibility (Harms, 1961; Miller & Hewgill, 1964; McGuire, 1973). Koonce and Phillips (1996) find in an analytical procedures task that when information pertaining to the client’s suggested non-error cause is easy to comprehend, auditors judge that cause more plausible than when the same information is difficult to comprehend. Thus, research in both communication and audit judgment suggest that linguistic delivery style may influence the auditor’s likelihood assessments and planning decisions, a phenomenon that has not been studied in the audit literature.

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Research Hypotheses Based on the research cited above, this study investigates whether the manipulation of linguistic delivery style influences the auditor’s perception of the client’s credibility. In other words, while the manipulation of the client’s credibility is expected to influence the auditor’s perception of the client’s credibility, the manipulation of the linguistic delivery style of the client is also expected to affect the auditor’s perception of the client’s credibility. H1. The auditor’s perception of client credibility will be higher when manipulated linguistic delivery style is more powerful. Also based on the research cited above, this study examines the effects of client credibility and linguistic delivery style on auditor likelihood assessments and planning decisions. The research hypotheses are as follows: H2. High client credibility will:  Increase the auditor’s likelihood that the client-provided explanation accounts for the fluctuation.  Increase the auditor’s confidence in his/her likelihood assessment.  Decrease the auditor’s intention to perform additional substantive testing.  Decrease the auditor’s intention to gather additional client-provided explanations. H3. Powerful linguistic delivery style will:  Increase the auditor’s likelihood that the client-provided explanation accounts for the fluctuation.  Increase the auditor’s confidence in his/her likelihood assessment.  Decrease the auditor’s intention to perform additional substantive testing.  Decrease the auditor’s intention to gather additional client-provided explanations.

RESEARCH METHODOLOGY Experimental Procedure The context of this study is preliminary analytical procedures. Two hundred auditors employed by two of the Big 4 accounting firms participated in the

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experiment at the firms’ annual professional training workshops. One hundred eighty-one study participants were audit seniors; the remaining 19 participants were audit managers. Each auditor was asked to assume that he or she was an audit team supervisor of a new manufacturing client and was performing preliminary analytical procedures. Each auditor was provided with the firm’s financial information including comparative financial statements of the current year’s unaudited account balances and the prior year’s audited (by a predecessor firm) account balances. The account balances included a sizable inventory fluctuation (19.3%), although no reason was given for the cause of the fluctuation or whether it reflected a change or error in the account balance. The auditors were asked to assume that this fluctuation warranted investigation through a client-provided explanation.2 The auditors were then provided with background information about the assistant controller, the member of the firm who would provide the explanation for the fluctuation. This information referenced the assistant controller’s competence, trustworthiness, and objectivity – the three factors comprising credibility. These factors were manipulated as either consistently high or consistently low. The high client credibility condition portrayed the assistant controller as qualified, candid, and impartial, while the low client credibility condition portrayed the assistant controller as unqualified, not candid, and potentially biased. The wordings of the credibility manipulations are shown in the appendix. Next, the auditor groups were provided with the assistant controller’s explanation for the fluctuation, delivered once using a compact disc player. There was no opportunity for clarification of the explanation and no assumption was made regarding its validity. The linguistic delivery style of the explanation was manipulated as either powerful or powerless. Powerful linguistic delivery style is free of hedges, hesitations, and faulty grammar. In contrast, powerless linguistic delivery style includes hedges, hesitations, and faulty grammar. The wordings of the linguistic delivery style manipulations are shown in the appendix. Auditors then indicated their assessment of the likelihood that the client’s explanation accounted for the fluctuation and their confidence in that assessment. They also indicated the likelihoods that they would perform substantive tests and that they would conduct further client inquiry. The first three responses were on 0–100 scale and the client inquiry response was on a 7-point Likert-type scale, with endpoints of ‘‘unlikely’’ and ‘‘likely.’’ See Table 1. Next, they replied to the items designed to check the manipulations of credibility and linguistic delivery style (shown in Table 2). Finally, the

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Table 1.

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Questions Designed to Elicit the Dependent Variables.

Likelihood Assessment

How likely do you think it is that Joe Smith’s explanation accounts for the fluctuation in inventory? Please indicate with a percentage of 0–100%, where 0 is least likely and 100 is most likely: _________%

Confidence in Likelihood Assessment

How confident are you in your likelihood percentage? Please indicate with a percentage of 0–100%, where 0 is least confident and 100 is most confident: _________%.

Change in Substantive Tests

Given Joe’s explanation, how likely are you to change your substantive test of details relating to the inventory account? Please indicate with a percentage of 0–100%, where 0 is least likely and 100 is most likely: _________%

Further Client Inquiry

How likely are you to inquire of another member of WEBER’s management regarding the significant fluctuation in the inventory account?

auditors provided demographic information. The entire experiment took 1 h to complete. Data were collected on three dates at three US locations, with two datacollection sessions conducted at the same time on the same day (in different rooms) in the Northeast, a third data-collection session in the Southeast, and a fourth in the West. In the Northeast, the manipulated linguistic delivery style (powerful or powerless) was randomly assigned to the rooms in which the auditors were already present.3 At the data-collection sessions in the Southeast and in the West, all the auditors were in the same room and thus were constrained to hear the same manipulated linguistic delivery style. However, participants were randomly assigned to either the high or low manipulated credibility group.4 Table 3 presents sample demographics across locations and across treatments. For quantitative demographic measures (e.g., age and experience), the means for the four experimental conditions were compared using a nonparametric analysis of variance (Kruskal–Wallis test). Table 3, panel B, shows statistically significant differences (po0.001) for age and months of overall audit experience, but not months of manufacturing experience. For qualitative demographic measures (e.g., gender and current position), the proportions of auditors in each of the four experimental conditions were compared using a w2 : Table 3, panel B, shows statistically significant differences (po0.001) for current position (supervising senior or manager), but not for gender or audit experience in manufacturing.

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Table 2.

Items Relating to Client Credibility and Linguistic Delivery Style.

Panel A: Items Relating to Client Credibility Competence  I think that Joe Smith possesses an adequate amount of experience to provide an accurate explanation for the financial statement fluctuation.  It is my perception that Joe Smith is qualified to provide an accurate explanation for the financial statement fluctuation.  I think that Joe Smith possesses the requisite knowledge to provide an accurate explanation for the fluctuation. Trustworthiness  I feel that Joe Smith was candid in response to my inquiry.  Joe Smith may not have been totally honest when responding to my inquiry.  I feel that Joe Smith was truthful in response to my inquiry. Objectivity  I feel that Joe Smith was impartial when providing his explanation.  Joe Smith may have been biased when responding to my inquiry.  Joe Smith was probably objective when providing his explanation. Panel B: Items Relating to Linguistic Delivery Style Hedges  Joe Smith’s explanation seemed to be unclear.  I feel Joe Smith was confident in the explanation he provided.  Joe Smith seemed to be direct when providing his explanation. Hesitation  Joe Smith’s explanation flowed smoothly.  Joe seemed to hesitate when providing his explanation.  I think that Joe Smith was fluent when providing his explanation. Grammar  I think that Joe Smith’s explanation was grammatically correct.  I feel that Joe Smith’s explanation was articulate.  Joe Smith’s explanation seemed to be well spoken. Note: All competence items were adapted from Leather’s Personal Credibility Scale (1992). All other credibility items were adapted from McCroskey’s Scales for the Measurement of Ethos or developed by the authors. All linguistic delivery style items were adapted from scales used in Bradac, Konsky, and Davies (1976) or developed by the authors.

There are statistically significant differences in some of the demographic variables across different levels of both linguistic delivery style and client credibility. Specifically, the findings indicate that participants who experienced powerful linguistic delivery were 1.3 years younger (p ¼ 0:001) and had 14.4 months less experience (po0.001) on an average and were less

Participant Demographics by Location and Treatment.

Panel A: Participant Demographics by Location Firm 1 Demographic Item

1st Collection

Firm 2

2nd Collection

1st Collection

2nd Collection

All

Frequencies Number of Participants Male Female Current Position: Supervising Seniors1 Current Position: Managers Number with Audit Experience in Manufacturing

69 31 38 69

70 29 41 69

29 17 12 11

32 14 18 32

200 91 109 181

0 49

1 48

18 23

0 22

19

Means (Standard deviations) Months of Manufacturing Experiencea,2 Age (years)3 Months Overall Audit Experience3

27.0 (19.9)

24.1 (18.7)

37.3 (29.1)

12.0 (8.1)

25.4 (21.1)

27.0 (2.63) 41.7 (10.6)

27.4 (3.44) 43.1 (10.4)

27.8 (1.98) 67.2 (21.6)

25.1 (1.54) 26.7 (5.43)

26.9 (2.86) 43.6 (16.6)

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Table 3.

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Table 3. (Continued ) Panel B: Participant Demographics by Treatment Client Credibility Linguistic Delivery Style

High Powerful

Low Powerful

High Powerless

Low Powerless

All All

Frequencies Number of Participants Male Female Current Position Supervising Seniors4 Managers Number with Audit Experience in Manufacturing

52 19 33

53 22 31

49 28 21

46 22 24

200 91 109

52 0 38

52 1 35

35 14 36

42 4 33

181 19 142

Means (Standard deviations)

a

23.3 (20.2) 26.3 (1.91) 37.6 (9.72)

Restricted to those auditors with audit experience in manufacturing. po0.001 in a 2  4 w2 test for current position by location. 2 p ¼ 0.009 in a Kruskal–Wallis nonparametric ANOVA. 3 po0.001 in a Kruskal–Wallis nonparametric ANOVA. 4 po0.001 in a 2  4 w2 test for current position by treatment. 1

19.9 (15.6) 26.3 (3.09) 35.8 (12.1)

28.8 (24.4) 28.2 (3.45) 53.9 (21.6)

29.8 (22.6) 27.0 (2.36) 48.0 (13.7)

25.4 (21.1) 26.9 (2.86) 43.6 (16.6)

CHRISTIE L. COMUNALE ET AL.

Months of Manufacturing Experience Age (years)3 Months of Overall Audit Experience3

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likely to be managers (po0.001) relative to those participants who experienced powerless linguistic delivery style. In addition, the findings show that participants who experienced high credibility were more likely to be managers (p ¼ 0:034).

Independent Variables As described above, the two key independent variables in the study are client credibility (high or low) and linguistic delivery style (powerful or powerless). To determine whether the client credibility manipulation was successful, the auditors’ perceptions of client credibility in the high and low groups were compared. The auditors’ perceptions of linguistic delivery style in the powerful and powerless groups were compared to determine whether the linguistic delivery style manipulation was successful. Three items were used to represent each of the three dimensions of client credibility, resulting in nine items, as shown in Table 2. Descriptive statistics for these items are presented in Table 4, panel A. Similarly, three items were used to represent each of the three dimensions of linguistic delivery style, again resulting in nine items, as shown in Table 2. Descriptive statistics for these items are shown in Table 4, panel B. The nine items related to client credibility were summed to obtain an overall construct score for perceived client credibility. (This summation is justified by an inter-item reliability of a ¼ 0:84:) Similarly, the nine items related to linguistic delivery style were summed to obtain an overall construct score for perceived linguistic delivery style. (This summation is justified by an inter-item reliability of a ¼ 0:96:)

Statistical Analysis To perform a manipulation test on linguistic delivery style, a one-way ANOVA using perceived linguistic delivery style as the dependent variable and using manipulated linguistic delivery style (powerful and powerless) as the factor was conducted. Moreover, separate one-way ANOVAs for each of the three components of linguistic delivery style were conducted. To perform a manipulation test on client credibility and to test H1, a twoway ANOVA with perceived client credibility as the dependent measure and with manipulated client credibility (high and low) and manipulated linguistic delivery style (powerful and powerless) as the independent factors was

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Table 4.

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Descriptive Statistics for the Components of Client Credibility and Linguistic Delivery Style.

Manipulated Client Credibility Manipulated Linguistic Delivery Style

High

Low

High

Low

Powerful

Powerful

Powerless

Powerless

Panel A: Client Credibility Sample Size Perceived Credibility Perceived Competence Perceived Trustworthiness Perceived Objectivity

52 40.9 7.4 17.6 2.5 12.9 3.8 10.4 3.2

53 31.8 7.9 10.7 3.6 11.0 3.6 10.1 3.0

49 36.5 8.8 14.6 4.6 11.3 3.9 10.6 4.1

46 25.8 6.6 7.2 3.3 9.3 3.3 9.3 2.8

52 48.5 7.4 15.7 3.2 16.7 2.7 16.1 2.8

53 44.8 7.8 14.6 3.3 15.4 3.1 14.8 2.9

49 20.1 7.4 6.9 3.0 6.4 2.8 6.9 2.9

46 16.7 5.7 5.7 2.3 5.1 2.0 6.0 2.0

Panel B: Linguistic Delivery Style Sample Size Perceived Linguistic Delivery Style Perceived Hedges Perceived Hesitations Perceived Grammar

Note: Top entry in each cell is the mean; bottom entry is the standard deviation.

conducted. Moreover, separate two-way ANOVAs for each of the three components of client credibility were performed. To test H2 and H3 concerning the impacts of client credibility and linguistic delivery style on auditors’ likelihood assessments and planning decisions, a two-way ANOVA was employed. The two independent factors, each at two levels, were manipulated – client credibility (high and low) and manipulated linguistic delivery style (powerful and powerless). A test for the presence of interaction between the independent factors was also conducted. Four dependent measures were investigated – likelihood assessment, confidence in likelihood assessment, change in substantive tests, and further client inquiry.

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To remove unwanted sources of variation attributable to the participant demographic variables, gender, current position, existence of audit experience in manufacturing, months of manufacturing experience, age, and months of overall audit experience were included in the ANOVA models as covariates. Results were substantively equivalent in models run with and without covariates. Therefore, all results are shown without covariates.

RESULTS Manipulation Testing of Linguistic Delivery Style The F-value for manipulated linguistic delivery style is 733.0 (po0.001). In addition, the findings indicate manipulated linguistic delivery style is statistically significant in all three models (po0.001) for the separate components of linguistic delivery style. This confirms that the manipulation of linguistic delivery style was successful.

Manipulation Testing of Client Credibility and Test of H1 The F-value for manipulated client credibility is 81.71 (po0.001). In addition, manipulated client credibility is statistically significant in the models for competence and trustworthiness (po0.001), but not in the model for objectivity (p ¼ 0:154). With statistically significant differences in the overall means and in the competence and trustworthiness components, the manipulation of credibility was partially successful. While the manipulation of objectivity was not successful, the manipulation of the other two components of credibility did have the intended effect. H1 specifies that auditors’ perceptions of client credibility are higher when manipulated linguistic delivery style is powerful. The F-value for manipulated linguistic delivery style is 22.56 (po0.001). The means for perceived client credibility for the four experimental conditions and the overall means for each main effect are shown in Table 4, panel A. As hypothesized by H1, auditors assigned to the powerful linguistic delivery style condition perceive a higher mean client credibility rating (36.3) than auditors in the powerless linguistic delivery style condition (31.3). The results support hypothesis H1 that when linguistic delivery style is powerful, the auditors perceive client credibility at a higher level, on average.

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Tests of H2 and H3 H2 and H3 postulate that high client credibility and powerful linguistic delivery style, respectively, influence the four dependent measures. The means of each dependent variable for each experimental condition are shown in Table 5. The ANOVA results5 are shown in Table 6. Table 5 shows that the differences in mean values across the four dependent measures are all in the direction hypothesized by H2 and H3. Table 6 indicates that the main effects for client credibility and linguistic delivery style are statistically significant for three of the four dependent measures, but the interaction term is not statistically significant in any of the models. Specifically, client credibility is statistically significant in the models for likelihood of client explanation (F ¼ 8:26; p ¼ 0:005), likelihood of substantive tests (F ¼ 8:18; p ¼ 0:005), and further client inquiry (F ¼ 6:48; p ¼ 0:012) but not in the model for confidence in assessment (F ¼ 0:44; p ¼ 0:508). In addition, linguistic delivery style is statistically significant in the models for likelihood of client explanation (F ¼ 42:00; po0.001), likelihood of substantive tests (F ¼ 12:46; po0.001), and further client inquiry (F ¼ 11:92; po0.001) but not in the model for confidence in assessment (F ¼ 1:11; p ¼ 0:294). Therefore, there is support for the components of H2 and H3 related to likelihood of client explanation, likelihood of substantive tests, and further client inquiry.

Table 5.

Comparison of Means across Experimental Conditions.

Manipulated Client Credibility Manipulated Linguistic Delivery Style Sample Size Likelihood of Client Explanation Confidence in Assessment Likelihood of Substantive Tests Further Client Inquiry

High

Low

High

Low

Powerful

Powerful

Powerless

Powerless

52 54.5 29.9 75.7 19.3 51.3 32.6 6.15 1.55

53 42.6 26.6 74.2 18.9 65.0 33.8 6.74 0.90

49 30.1 19.1 73.0 22.4 68.0 29.5 6.86 0.41

46 21.9 20.1 70.3 28.1 79.6 28.3 6.96 0.21

Note: Top entry in each cell is the mean; bottom entry is the standard deviation.

Linguistic Delivery Style, Client Credibility, and Auditor Judgment

ANOVA Results.

Table 6. Source

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DF

Type III SS

Mean Square

F Value

p4F

1 1 1

4978.3 25309.2 166.3

4978.3 25309.2 166.3

8.26 42.00 0.28

0.005 o0.001 0.600

1 1 1

217.6 548.0 17.0

217.6 548.0 17.0

0.44 1.11 0.03

0.508 0.294 0.853

1 1 1

7978.7 12147.6 50.1

7978.7 12147.6 50.1

8.18 12.46 0.05

0.005 o0.001 0.821

1 1 1

5.8 10.6 2.9

5.8 10.6 2.9

6.48 11.92 3.25

0.012 o0.001 0.073

Likelihood Assessment Manipulated credibility Manipulated Linguistic delivery style Interaction Confidence in Assessment Manipulated credibility Manipulated Linguistic delivery style Interaction Likelihood of Substantive Tests Manipulated credibility Manipulated Linguistic delivery style Interaction Further Client Inquiry Manipulated credibility Manipulated Linguistic delivery style Interaction

Supplemental Analysis: Investigation of Perceptions and the Dependent Variables In addition to testing the effects of the experimental manipulations on the dependent variables, the effects of perceived client credibility and linguistic delivery style on the dependent variables are also examined. Multiple regression models are built for each dependent variable using the following form: EðY Þ ¼ b0 þ b1 C P þ b2LP þ b3 ðCPnLP Þ

(1)

where Y ¼ dependent measure; CP ¼ measure of perceived client credibility; LP ¼ measure of perceived linguistic delivery style. Table 7 presents the regression results. Note that, for each dependent measure, the interaction term in the model is statistically significant. The

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Table 7.

Multiple Regression Results for Each Dependent Variable. Least Squares Prediction Equation Y^ ¼ 15:87 þ 0:037C p

Confidence in assessment

Y^ ¼ 98:94

0:0915C p

Likelihood of substantive tests

Y^ ¼ 76:88

0:007C p þ 0:617Lp

Further client inquiry

Y^ ¼ 6:06 þ 0:031C p þ 0:056Lp

0:6781Lp þ 0:036ðC p  Lp Þ 0:744Lp þ 0:25ðC p  Lp Þ

0:027ðC p  Lp Þ

0:002ðC p  Lp Þ

Note: The p value for the global F-test of H0: b1 ¼ b2 ¼ b3 ¼ 0 are given in parentheses next to the F value.

F ¼ 55:35 (po0:001) Adjusted R2 ¼ 0:450 po0:005 for interaction F ¼ 2:73 (po0:001) Adjusted R2 ¼ 0:025 p ¼ 0:010 for interaction F ¼ 10:10 (po0:001) Adjusted R2 ¼ 0:121 p ¼ 0:054 for interaction F ¼ 24:42 (po0:001) Adjusted R2 ¼ 0:261 po0:005 for interaction

CHRISTIE L. COMUNALE ET AL.

Likelihood of client explanation

Model Statistics

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estimates of b3 given in the least squares prediction equation have the expected signs (positive for likelihood of client explanation and confidence in assessment; negative for likelihood of substantive tests and further client inquiry). If b3 ¼ 0; then the rate of change in the dependent variable with respect to perceived linguistic delivery style equals b2 in Eq. (1) – a rate of change that is independent of the level of perceived client credibility. On the other hand, when b3 differs from zero, the rate of change in the dependent variable with respect to perceived linguistic delivery style is represented by b2 þ b3 C P in Eq. (1) – a rate of change that depends on the level of perceived client credibility. To understand better the nature of the interactions detected, the regression models are examined at both a low and high level of perceived client credibility (Neter, Kutner, Wasserman, & Nachtsheim, 1996). The 10th percentile of the distribution is selected for the low level – the value 22. The 90th percentile of the distribution is selected for the high level – the value 46. These percentile values are referred to as ‘‘low’’ and ‘‘high’’ perceived client credibility. At each of these levels, the estimated model parameters are used to estimate the slope of the regression line relating the dependent measure to perceived linguistic delivery style. From Eq. (1), this slope is b2 þ b3 C P ; which represents the change in the dependent variable for a unit increase in perceived linguistic delivery style when perceived client credibility is fixed at the value CP. Thus, the slope is b2 þ 22b3 at the low level of credibility and b2 þ 46b3 at the high level of perceived credibility. Table 8 shows the estimated slopes and corresponding significance tests for each regression model; and Fig. 2 presents graphs of the estimated lines for low and high perceived client credibility. For three of the four dependent measures, the estimated slopes of the regression lines are not statistically significantly different from zero when the auditors perceived client credibility is low. In the case of the dependent measure further client inquiry, the estimated slope is positive and statistically significant. However, this result is misleading due to the bounded nature of the dependent variable. In fact, the sample data reveal that all auditors who perceived client credibility at 27 or below answered the ‘‘inquiry’’ question the same (at a level of ‘‘7’’).6 Therefore, linguistic delivery style has little influence on any of the four dependent variables when perceived client credibility is low. When the auditors perceive client credibility as high, the estimated slopes of the regression lines for all four dependent measures are statistically significantly different from zero. Note also that the slopes are positive for likelihood of client explanation and confidence in assessment, and negative

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Table 8.

Estimated Regression Slopes for Linguistic Delivery Style. Likelihood of Client Explanation

Perceived low credibility (10th percentile) Perceived high credibility (90th percentile)

Confidence in Assessment

Likelihood of Substantive Tests

Further Client Inquiry

Slope ¼ 0:123 P ¼ 0:414

Slope ¼ 0:183 p ¼ 0:262

Slope ¼ 0:032 p ¼ 0:889

Slope ¼ 0:013 p ¼ 0:037

Slope ¼ 0:990 P ¼ 0:001

Slope ¼ 0:421 p ¼ 0:007

Slope ¼ 20:061 p ¼ 0:006

Slope ¼ 0:033 p ¼ 0:001

Note: Values are estimated slopes of the dependent variables with respect to linguistic delivery style, holding client credibility fixed at a low (10th percentile) and high (90th percentile) level. The observed significance levels (p values) for a test of zero slope are given in parentheses.

for likelihood of substantive tests and further client inquiry. Thus, linguistic delivery style is associated in the predicted direction with all four dependent variables when perceived client credibility is high. The findings indicate that the influence of perceived linguistic delivery style on the auditor’s likelihood assessments and planning decisions is greater when perceived client credibility is high. An interaction of this nature is plausible because, when perceived client credibility is low, there is a conservative tendency on the part of the auditor to obtain additional evidence beyond a client-provided explanation regarding an unusual account fluctuation. Hence, when perceived client credibility is low, a powerful linguistic delivery style may be insufficient to overcome the auditor’s skepticism and the auditor will continue to gather additional evidence. On the other hand, when perceived client credibility is high, powerful linguistic delivery style may reinforce the faith in the client-provided explanation and limit further investigation by the auditor.

DISCUSSION This study introduces linguistic delivery style to auditing research, demonstrates how linguistic delivery style relates to client credibility, and shows how the two act independently (and together) to influence auditors’ likelihood assessments and planning decisions. The evidence in this study indicates that client credibility and linguistic delivery style influence

Likelihood

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Panel A: Likelihood of Client Explanation

100 90 80 70 60 50 40 30 20 10 0 9

13

17

21

25

29

33

37

41

45

49

53

57

61

57

61

Linguistic Delivery Style

High Credibility

Low Credibility

Confidence

Panel B: Confidence in Assessment 100 90 80 70 60 50 40 30 20 10 0 9

13

17

21

High Credibility

Fig. 2.

25

29 33 37 41 45 Linguistic Delivery Style

49

53

Low Credibility

Graphs Depicting the Interaction Term in Each Dependent Variable.

auditors’ likelihood assessments and planning decisions in the expected directions. Moreover, the supplemental results suggest that powerless linguistic delivery style from an otherwise high credibility client leads to auditor judgments and intentions that are indistinguishable from those that arise from a low-credibility client. The interaction, which is statistically significant using the perceived variables but not statistically significant using the manipulated

CHRISTIE L. COMUNALE ET AL.

Likelihood

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Panel C: Likelihood of Substantive Tests

100 90 80 70 60 50 40 30 20 10 0 9

13

17

21

25

29

33

37

41

45

49

53

57

61

Linguistic Delivery Style High Credibility

Panel D: Request Client Inquiry

8 Intention To RequestFurther Client Inquiry

Low Credibility

7 6 5 4 3 2

9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63

1

Linguistic Delivery Style High Credibility

Low Credibility

Fig. 2.

(Continued).

variables, may be because the perceived variables are measured on a continuous, rather than a binary, scale, thereby providing more precise measures of client credibility and linguistic delivery style. This greater precision allows us to detect an effect that would otherwise remain unseen. Finally, while researchers have recognized competence, trustworthiness, and

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objectivity as components of credibility, the evidence from this study indicates that linguistic delivery style may be a fourth component. The good news is that the results indicate that a low-credibility client cannot alter the auditor’s likelihood assessments and planning decisions using powerful linguistic delivery style. The bad news, for the client, is that a high-credibility client with powerless linguistic delivery style may induce auditor responses equivalent to those of a low-credibility client. This may occur due to obscurities introduced by the elements of powerless linguistic delivery style. This may result in greater audit work and greater audit cost to the client. These results imply that clients should communicate in powerful linguistic delivery style. Failure to do so may result in increased audit effort and costs, the allocation of which will depend on the extent to which the auditor can charge the client for additional audit work arising from this inefficiency. Whatever the ultimate allocation, powerless linguistic delivery style may lead to higher overall audit costs when the client, ironically, has otherwise high credibility. While formal communication skills have long been emphasized as important for auditors (Brune, 2003; Canadian Institute of Chartered Accountants, 2000), this study also demonstrates the need for good interviewing skills, from the interviewee’s perspective.

LIMITATIONS AND FUTURE RESEARCH This study has two primary limitations that provide opportunities for future research. First, the scenario limits the examination of linguistic delivery style to the issue of efficiency (Type I error), which is studied only when sufficient explanations exist. In other words, it is not possible to address whether linguistic delivery style influences effectiveness (Type II error) and whether linguistic delivery style matters when insufficient explanations exist. Second, the auditor’s likelihood assessment of the client’s explanation and the confidence in that explanation occur simultaneously with the auditor’s planning decisions, which are intermediate judgments regarding the need for additional information. Therefore, we cannot be sure that all of the dependent measures are not simply capturing the same underlying uncertainty at this intermediate point. Thus, the iterative nature of the process leading to the final judgment (can the auditor believe the client’s representation after additional audit work) cannot be studied given the current design.

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The influence of the individual components of linguistic delivery style and client credibility on the dependent variables cannot be assessed due to the experimental design utilized in this study. However, the primary concern was that the manipulation of linguistic delivery style and client credibility was sufficiently strong to produce the hypothesized effects if indeed they existed. In fact, the powerless linguistic delivery style and low client credibility manipulations used in this study were quite strong; hence, the research findings may be amplified. Every effort was made to ensure that the message content was independent of the linguistic delivery style. For example, the sentences ‘‘Sales were lower in the third quarter relative to the second quarter’’ and ‘‘Sales were um y lower in the third quarter relative to the second quarter’’ carry the same content despite the introduction of the hesitation ‘‘um.’’ All the participants in the Southeast heard the same linguistic delivery style manipulation. Similarly, all the participants in the West heard the other linguistic delivery style manipulation. Since participants may vary in their sensitivity to linguistic delivery style across geographic locations, this unintended crossing of linguistic delivery style and region could explain in part the absence of an interactive effect using the manipulated variables while an interactive effect was observed using the perceived variables. In addition, a 7-point scale was used to measure the dependent variable ‘‘Further Client Inquiry,’’ which creates endpoint problems that violate statistical assumptions used in the analysis. Consequently, this may present difficulties in analyzing and interpreting results. Only one scenario was used in this study so the results may not generalize to other scenarios. Moreover, participants were exposed to audio explanations only, while a video explanation might have been more realistic. In addition, the auditors in this experiment were not subject to review as they are in a typical audit engagement. Finally, the focus is on one kind of undesirable outcome (performing additional audit work when no errors exist) and a second kind of undesirable outcome (accepting a client’s faulty explanation when errors do exist) may be of even greater importance to the auditor. Despite these limitations, the results of this study represent an important contribution to the understanding of the client–auditor relationship by demonstrating the role of linguistic delivery style in the client inquiry process. In an era of increasing scrutiny of the audit function, client–auditor communication has become even more crucial to audit success, and these results can assist auditors in improving this vital audit process.

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NOTES 1. These are words or phrases that show or point out directly, such as the words this, that, and those. For example, ‘‘I never work that hard’’ is weaker than ‘‘I never work hard.’’ 2. Three separate pilot studies were conducted using student participants to test the efficacy of the linguistic delivery style and client credibility manipulations, as well as to assess overall understandability of the instrument. Based on pilot study feedback, the wording of several variable items was refined to improve the clarity of experimental scripts. 3. Auditors were assigned to rooms according to training session, which were unrelated to the purposes of this study. There is no reason to suspect that the auditors in one room differed from those in the other room with respect to assessment of credibility or sensitivity to linguistic delivery style. 4. Initial credibility assessments were not captured at this stage to avoid potential biases that might occur if participants anchored on these assessments when their final assessments were made later in the study. 5. Because the dependent variables violate the normal distribution and constant variance assumption, an ANOVA based on ranks is also conducted. The results are essentially identical to the parametric ANOVAs. Therefore, the ANOVA results based on the actual data are reported. 6. There are very few observations at the ‘‘low’’ level of credibility. Consequently, slope estimates at the ‘‘low’’ level yield potentially unreliable results.

ACKNOWLEDGMENTS The authors gratefully acknowledge the guidance and advice of Drs. Gary L. Holstrum, James Hunton, Rosann W Collins, and Jacqueline Reck of the University of South Florida, who were members of Dr. Comunale’s dissertation committee. We are also indebted to the referees whose comments and suggestions greatly improved the quality and the presentation of this chapter.

REFERENCES Adkins, M., & Brashers, D. E. (1995). The power of language in computer-mediated groups. Management Communication Quarterly, 8(3), 289–322. American Institute of Certified Public Accountants (AICPA). (1988). Statement on Auditing Statements No. 56: Analytical Procedures. New York: AICPA. American Institute of Certified Public Accountants (AICPA). (2002). Statement on Auditing Statements No. 99: Consideration of Fraud in a Financial Statement Audit. New York: AICPA.

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Anderson, U. L., Koonce, L., & Marchant, G. (1994). The effects of source-competence information and its timing on auditor’s performance of analytical procedures. Auditing: A Journal of Practice and Theory, 13(Spring), 137–148. Ayers, S., & Kaplan, S. (1998). Potential differences between engagement and risk review partners and their effect on client acceptance judgments. Accounting Horizons (June), 12(2), 139–153. Bamber, E. M. (1983). Expert judgment in the audit team: A source reliability approach. Journal of Accounting Research, 21(Autumn), 396–412. Beaulieu, P. R. (2001). The effects of judgments of new client’s integrity upon risks judgments, audit evidence, and fees. Auditing: A Journal of Practice and Theory, 20(September), 85–99. Berlo, D. K., Lemert, J. B., & Mertz, R. J. (1969). Dimensions for evaluating the acceptability of message sources. Public Opinion Quarterly, 33(4), 563–576. Bernardi, R. A. (1994). Fraud detection: The effect of client integrity and competence and auditor cognitive style. Auditing: A Journal of Practice and Theory, 13(Supplement), 68–84. Bradac, J. J., Konsky, C. W., & Davies, R. A. (1976). Two studies of the effects of linguistic diversity upon judgments of communicator attributes and message effectiveness. Communication Monographs, 4, 70–79. Brown, P. R. (1983). Independent auditor judgment in the evaluation of internal audit functions. Journal of Accounting Research, 21(Autumn), 444–455. Brune, C. (2003). The artful interviewer. The Internal Auditor, 60(2), 25–27. Canadian Institute of Chartered Accountants (CICA). (2000). Audit enquiry: Seeking more reliable evidence from audit enquiry. Toronto, Canada. (CICA). Erickson, B., Lind, A. E., Johnson, B. C., & O’Barr, W. M. (1978). Speech style and impression formation in a court setting: The effects of ‘‘powerful’’ and ‘‘powerless’’ speech. Journal of Experimental Social Psychology, 14(May), 266–279. Goodwin, J. (1999). The effects of source integrity and consistency of evidence on auditors’ judgments. Auditing: A Journal of Practice and Theory, 18(Fall), 1–16. Haleta, L. L. (1996). Student perceptions of teachers’ use of language: The effects of powerful and powerless language on impression formation and uncertainty. Communication in Education, 45(January), 16–28. Harms, L. S. (1961). Listener judgments of status cues in speech. Quarterly Journal of Speech, 47, 164–168. Hirst, D. E. (1994). Auditor’s sensitivity to source reliability. Journal of Accounting Research, 32(Spring), 113–126. Hosman, L. A., Wright, J. W. (1987). The effects of hedges and hesitations on impression formation in a simulated courtroom context. Western Journal of Speech Communication, 51(Spring), 173–188. Joyce, E. J., Biddle, G. C. (1981). Are auditor’s judgments sufficiently regressive? Journal of Accounting Research, 19(Autumn), 323–349. Kaplan, S. E., & Reckers, P. M. J. (1984). An empirical examination of auditors’ initial planning process. Auditing: A Journal of Practice and Theory (Fall), 4(1), 1–19. Koonce, L., & Phillips, F. (1996). Auditor’s comprehension and evaluation of client-suggested causes in analytical procedures. Behavioral Research in Accounting, 8, 32.

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Lind, E. A., & O’Barr, W. M. (1979). The social significance of speech in the courtroom. In: H. Giles & R. St Clair (Eds), Language and social psychology (pp. 66–87). England: Basil Blackwell (U.S. distribution, Baltimore MD: University Park Press). Mautz, R. K. (1958). The nature and reliability of audit evidence. The Journal of Accountancy (May), 105(5), 40–47. McCroskey, J. C. (1966). Scales for the measurement of ethos. Speech Monographs, 33, 65–72. McGuire, W. J. (1973). Persuasion, resistance, and attitude change. In: I. Pool (Ed.), Handbook of Communication (pp. 16–252). Chicago, IL: McNally College Publishing Company. Miller, G. R., & Hewgill, M. A. (1964). The effect of variations in nonfluency on audience ratings of source credibility. Quarterly Journal of Speech, 50, 36–44. Neter, J., Kutner, M. H., Wasserman, W., & Nachtsheim, C. J. (1996). Applied linear statistical models (4th ed.). New York, NY: McGraw-Hill/Irwin. O’Barr, W. M. (1982). Linguistic evidence: Language, power, and strategy in the courtroom. New York: Academic Press. O’Keefe, D. J. (1990). Persuasion: Theory and research. Newbury Park: Sage Publications. Peecher, M. E. (1996). The influence of auditors’ justification processes on their decisions: A cognitive model and experimental evidence. Journal of Accounting Research (Spring), 34(1), 125–140. Rebele, J. E., Heintz, J. A., & Briden, G. E. (1988). Independent auditor sensitivity to evidence reliability. Auditing: A Journal of Practice and Theory, 8 (Fall), 43–52. Vinson, L., & Johnson, C. (1989). The use of written transcripts in powerful and powerless language research. Communication Reports, 2, 16–21. Wright, J. W., & Hosman, L. A. (1983). Language style and sex bias in the courtroom: The effects of male and female use of hedges and intensifiers on impression formation. Southern Speech Communication Journal, 48, 137–152.

APPENDIX Client Credibility Manipulations High Client Credibility During your controls testing, you had one other interaction with Joe Smith. At that time, it was revealed to you that Joe possessed both a CPA and an MBA and has over 20 years of accounting experience (competence). Moreover, during this previous interaction, he seemed, in your opinion, to provide candid responses to your questions (trustworthiness). As far as you know, Joe had no vested interest that would have caused him to provide biased responses (objectivity). Low Client Credibility During your controls testing, you had one other interaction with Joe Smith. At that time, it was revealed to you that this is Joe’s first year as assistant controller. Moreover, it is your understanding that Joe does not possess any

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professional certifications and has not completed any post-graduate work (competence). During this previous interaction, you were not quite sure whether Joe was entirely candid when responding to your questions (trustworthiness). As far as you know, Joe could have a vested interest that would have caused him to provide biased responses (objectivity). Linguistic Delivery Style Manipulations Powerful Linguistic Delivery Style The year over year increase in inventory is part of our overall strategic plan. One of our goals at Weber is to be the leading provider of appliances in the Southeast. To accomplish this goal we must deliver quality products to our customers, on time, every time. Unfortunately, we received numerous complaints last year regarding product shortages. Therefore, we increased the production of our more popular models. The elimination of product shortages at our retail outlets will greatly improve customer satisfaction. Powerless Linguistic Delivery Style Hmmm (hesitation)yI’m thinkin’ (grammar and hedge) that, that has somethin’ (grammar) to do with our um (hesitation)y.overall plan. A goal here, you know, (hedge) is to be ah (hesitation)yleading provider of appliances. So, I believe (hedge) we’ve been tryin’ (grammar) to deliver products, quality products, on time. I’m pretty sure (hedge) that we got (grammar) some complaints last year about runnin’ outta (grammar) our stuff. Maybe (hedge) we started increasin’ (grammar) the production, probably (hedge) of our better sellers. By steppin’ (grammar) up production, we ah (hesitation) ycan maybe (hedge) make our customers happy.

CLIENT INQUIRY VIA ELECTRONIC COMMUNICATION MEDIA: DOES THE MEDIUM MATTER? Anna No¨teberg and James E. Hunton ABSTRACT Face-to-face meetings between auditors and clients are becoming increasingly more difficult and expensive to arrange, due in large part to the ceaseless expansion of commerce across the globe. Relying on electronic communication media such as e-mail messaging or video-conferencing for auditor–client inquiry purposes is one way to enhance the timeliness of such communications; however, questions arise with respect to potentially biasing influences of certain technical aspects of electronic media on auditors’ judgment and decision-making processes. Drawing on information processing theories, the current study posits that media and message attributes can interact, thereby differentially affecting auditors’ belief revisions – holding information content constant. The media attributes examined in the current study are cue multiplicity (i.e., the range of central and peripheral cues a medium is capable of transmitting) and message reprocessability (i.e., the extent of archival and retrieval features a medium is capable of handling); and the message attribute studied is evidence strength (e.g., the credibility of client-provided evidence). Advances in Accounting Behavioral Research Advances in Accounting Behavioral Research, Volume 8, 87–112 Copyright r 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1475-1488/doi:10.1016/S1475-1488(04)08004-4

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Research findings from a laboratory experiment with 189 graduate accounting students indicate the following: (1) when client-provided evidence is strong, neither message reprocessability nor cue multiplicity significantly affect the auditors’ belief revisions; (2) when evidence is weak and reprocessability is present, higher cue multiplicity leads to significantly greater belief revision in favor of the client; (3) when evidence is weak and reprocessability is absent, lower cue multiplicity results in significantly greater belief revision in favor of the client. Study results suggest theoretical and practical implications for globally distributed auditor–client communications.

INTRODUCTION The use of electronic communication media within and among organizations is increasing at an exponential rate (Strauss & McGrath, 1994; Baltes, Dickson, Sherman, Bauer, & LaGanke, 2002). The integration of digital communication technologies into business entities provides opportunities for people in geographically dispersed locations to rapidly contact one another for a variety of purposes, such as sharing information, discussing ideas, forming relationships, and making decisions. The primary driver of this technological trend is the growing dispersion of commercial enterprises across the globe. Audit firms are expanding their organizational boundaries across the world as well, primarily because their clients are globally extending their commercial reach. Thus, auditors are making increasing use of electronic communication media for intra-firm and auditor–client communications, as the relatively slow speed and high cost of face-to-face exchanges are quite constraining. While one objective of incorporating electronic media into auditor–client communications is to enhance the timeliness of such exchanges, questions arise as to whether certain media and message attributes can bias the interpretation of the message content. The purpose of the current study is to examine the potentially biasing effects of two media attributes and one message attribute. The first media attribute, cue multiplicity, reflects the range of central and peripheral message cues that can be transmitted by a particular medium. The second media attribute, message reprocessability, refers to the extent to which a medium is capable of archiving and retrieving messages. The message attribute under examination is evidence strength, or the credibility of evidential matter

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underlying a client’s argument. Information processing theories suggest that the media and message attributes will interact such that auditors’ belief revisions will be differentially affected, depending on the attribute states. In this study, we develop hypotheses based on the belief adjustment model (Einhorn & Hogarth, 1985; Ashton & Ashton, 1988; Hogarth & Einhorn, 1992) and dual processing models (Chaiken, 1980; Petty & Cacioppo, 1981) of information processing. We conduct a 2  2  2 between-subjects experiment to test the hypotheses. The independent variables are cue multiplicity (low, high), message reprocessability (absent, present), and evidence strength (weak, strong). The scenario involves an auditor who identifies a potentially serious problem – a large part of the client’s inventory is seemingly obsolete and the auditor believes that the inventory may be overvalued by a material amount. Using electronic media for communication, the chief financial officer (CFO) expresses disagreement with the auditor’s conclusions and explains why an inventory write-down is not necessary. The dependent variable reflects the extent of belief revision, in favor of the client’s position, exhibited by the participants. Study findings indicate that belief revision is unaffected by cue multiplicity, message reprocessability or the interaction of both when the clientprovided evidence is strong. However, when evidence is weak, the effect of cue multiplicity inverts depending on the state of message reprocessability. Specifically, in the presence of reprocessability, higher cue multiplicity leads to greater belief revision toward the client’s preferred position, as predicted; unexpectedly however, when reprocessability is absent, lower cue multiplicity results in greater belief revision in favor of the client. This is the first study to examine how auditors’ belief adjustments can be differentially affected by interactions among certain electronic communication media and message attributes. The value of this research lies in its contribution to extant information processing theory and audit practice. The next section discusses background literature and develops study hypotheses. The following sections explain the research method, present the study findings, and discuss the implications for information processing theory and audit practice.

BACKGROUND AND HYPOTHESES Auditor–Client Inquiry The planning phase of an audit includes performing analytical review procedures aimed at directing attention to potential problem areas. Analytical

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review procedures involve four phases: (1) developing an understanding of the client’s business activities and searching for unusual fluctuations in a company’s financial statements (mental representation); (2) generating potential causes for any observed inconsistencies (hypothesis generation); (3) gathering diagnostic information (information search); and (4) analyzing findings to identify the most likely hypothesis and arrive at a diagnosis (hypothesis evaluation) (Koonce, Walker, & Wright, 1993). The current study focuses on the information search phase of analytical review procedures. More specifically, this study examines a particular information source – client inquiry (Hirst & Koonce, 1996). During analytical review, auditors might notice one or more unusual relationships in the clients’ account representations. When this happens, auditors often ask managers to explain how and why such situations arose. During inquires, auditors are aware that managers’ explanations can be biased toward supporting the underlying transactions giving rise to the unusual relationships. Accordingly, auditors are professionally skeptical in such situations. Possible reasons why managers can bias their arguments toward supporting the current state of accounts are found in agency theory (e.g., Jensen & Meckling, 1976). One assumption in agency theory is that managers (agents) will attempt to take self-serving advantage of private information they possess of which owners (principals) are unaware. Intervening in the principal–agent relationship are independent auditors who represent the principals’ interests. Agency theory suggests that managers with a higher (lower) motivation to maintain their information-asymmetric advantage over the principals will likely be less (more) honest with and forthcoming toward auditors (Hirst & Koonce, 1996). Under such circumstances, when auditors attempt to gather information via client inquiry about unusual account fluctuations, managers with higher levels of self-serving interests might try harder to persuade the auditors to accept current account representations. From both academic and practical standpoints, it is important to understand how persuasive intentions of this nature can influence auditors’ belief revision processes and judgments, as auditors might unknowingly make suboptimal choices and decisions under certain conditions (e.g., Jensen & Meckling, 1976). The condition of interest in the current study involves the electronic communication media through which auditor–client communications are exchanged. Although auditors and clients may be scattered across the world, they nevertheless need to communicate with each other about important audit issues in a timely manner. One way for auditors and clients to establish

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prompt communication exchanges is through the use of electronic communication media, such as e-mailing or video-conferencing. While this solution might be efficient from time and cost perspectives, questions arise regarding the effectiveness of such media. The media-task fit model developed by No¨teberg, Benford, and Hunton (2003) proposes that the effectiveness of auditor–client inquires are affected by the fit between task and technology attributes. In the current study, we test a proportion of the media-task fit model, and predict that certain media attributes can unintentionally bias auditors’ belief revisions and judgments, as next discussed.

Belief Adjustment According to the anchoring-and-adjustment heuristic, individuals anchor on initial beliefs regarding a subject matter, obtain and evaluate additional evidence, and then adjust their initial anchor accordingly. The belief adjustment model (BAM) (Einhorn & Hogarth, 1985; Ashton & Ashton, 1988; Hogarth & Einhorn, 1992) predicts that individuals revise their beliefs on the basis of evidence direction and strength. The current study holds evidence direction constant, meaning that the client presents evidence that attempts to disconfirm the auditor’s initial belief state. This factor is held steady because it reflects the audit condition of interest: a client is attempting to persuade an auditor to accept a current account representation. However, the client may provide either strong or weak evidential cues to the auditor in this circumstance. While the positive main effect of evidence strength on judgments is not a new issue to investigate (e.g., Hogarth & Einhorn, 1992), the current study is unique, as it examines the potential interaction of evidence strength with two electronic communication media attributes – reprocessability and cue multiplicity.

Reprocessability Reprocessability refers to an electronic communication media feature that allows communicators to re-examine messages (Dennis & Valacich, 1999). Reprocessability is particularly prominent in electronic media that allow for vast information storage, such as e-mail or voice mail. It is a media attribute that acts as an ‘‘externally recorded memory’’ (Sproull, 1991) and thereby aids in understanding the situation, particularly as the volume, complexity

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and equivocality of messages increase (No¨teberg et al., 2003). The current study examines the effect of reprocessability on auditors’ belief revisions. Two processing strategies implicit in the BAM (sequential and simultaneous) are reviewed to explain potential effects of reprocessability on belief revision. According to the BAM, individuals use one of two informationprocessing strategies when faced with multiple pieces of evidential matter. Sequential processing takes place when individuals adjust their beliefs incrementally after evaluating each piece of evidence. Simultaneous processing means that individuals adjust their initial anchor only after evaluating the full aggregate set of evidential matter. According to the ‘‘dilution effect’’ (Ashton & Ashton, 1988), disconfirmation-prone individuals make stronger belief revisions when information is elicited in a sequential format, as compared to a simultaneous format. Ashton and Ashton (1988) note that auditors are generally disconfirmationprone; therefore, sequential evaluation of consistently negative evidence will result in more extreme belief change than simultaneous processing of the same evidence. Consequently, belief revision should be stronger when individuals process evidence sequentially as opposed to simultaneously. The primary reason for the dilution effect is that sequential evidence processing offers more opportunities to anchor and adjust than does simultaneous processing, thereby resulting in more extreme revisions in the direction of the evidence (Francis & Schipper, 1999). Returning to the media attribute of interest, let us first contemplate the use of a medium that lacks reprocessability, i.e., messages are not stored for later re-examination. For instance, if an auditor holds a telephone conversation with a client during which multiple issues are discussed, the auditor is likely to process the evidential matter simultaneously upon reflection of the whole conversation. One could argue that the auditor sequentially processes information as received during the conversation, but research evidence suggests that cognitive conversation processes (e.g., listening, speaking, adjusting, and coordinating) effectively block sequential anchoring and adjusting (Schober & Brennan, 2003). Even if some degree of sequential processing takes place during conversation, the lack of a message archive effectively prevents post-conversation sequential adjusting (Schober & Brennan, 2003). Hence, simultaneous post-conversation processing of all evidential matter is likely to occur.1 Next, assume that the medium used for message conveyance allows the auditor to re-examine evidential cues after an initial exposure to evidence collection. To the extent that auditors utilize a medium’s reprocessing capability, the subsequent assessment of each piece of information can evoke a

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sequential processing strategy.2 Hence, we expect that the extent of belief revision will increase with the presence, as compared to the absence, of reprocessability, as stated in the first Hypothesis H1. There is a positive relationship between reprocessability and belief revision. While we assume that the second media attribute examined in this study (cue multiplicity) will also positively affect belief revision, we posit that its impact will vary depending on the interaction of evidence strength and the medium’s reprocessing capability, as next discussed.

Cue Multiplicity While the BAM only considers content-related evidence cues in its prediction of individuals’ belief revision processes, we extend the model by incorporating peripheral (i.e., non-content related) cues. If given the opportunity, it is likely that message senders with persuasive intentions (e.g., clients) will employ peripheral cues (e.g., body language and voice intonation) to persuade the recipients (e.g., auditors) of their messages. Given that some electronic media allow for the conveyance of such peripheral cues while others do not, it is important to consider both central and peripheral cues, in order to understand decision-makers’ potential reactions to persuasive evidence.3 According to dual processing models, such as the elaboration likelihood model (Petty & Cacioppo, 1981) and the heuristic-systematic model (Chaiken, 1980), message recipients can sequentially or simultaneously follow two routes when processing persuasive information – a central route (systematic) and a peripheral (heuristic) route.4 When following the central route, message recipients systematically scrutinize the validity of arguments (i.e., central cues) contained in a persuasive message, and revise their initial beliefs based on the content-related cues included in the message. The current study incorporates two levels of evidence strength (weak and strong) into the experimental design. The purpose of manipulating the validity of arguments or central cues in this manner is to discriminate message effects, which are processed via the central route, from media effects, which trigger peripheral route processing. When following a peripheral information-processing route, decisionmakers factor secondary, personal, and social cues into their belief revisions, which may or may not be related to the central message (Chen & Chaiken,

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1999). For instance, peripheral cues such as source attractiveness and voice inflections can hold persuasive powers without demanding considerable mental effort. In a laboratory experiment, Comunale, Sexton, and Sincich (2005) demonstrated that auditors were affected by the linguistic manner (e.g., hedges and hesitations in speech) in which a client delivered explanations. They found that auditors incorporate linguistic delivery style into their evaluation of client credibility, thereby supporting the assumption that peripheral cues play an important role in auditors’ belief revisions. In the current study, cue multiplicity represents the extent to which the medium makes available various peripheral cues. Communication media may hold a high or low level of cue multiplicity. Media that restrict the availability of peripheral factors offer a low level of cue multiplicity (e.g., e-mail messages), whereas those that provide access to such cues provide a high level of cue multiplicity (e.g., audio–video presentations). Previous research has examined the impact of cue multiplicity on various communication outcomes, such as effectiveness and efficiency of managers’ decisions (e.g., Daft & Lengel, 1986; Daft, Lengel, & Klebe Trevino, 1987; Kraut, Galegher, Fish, & Chalfonte, 1992; Rice, 1992; Hollingshead, McGrath, & O’Connor, 1993; Dennis & Kinney, 1998; Suh, 1999), attitude change (Matheson & Zanna, 1989), and communicator likeability (Weisband & Atwater, 1999). The current study examines the effect of cue multiplicity on auditors’ belief revisions, assuming that the peripheral cues support the central message. As suggested by Chaiken and Eagly (1983), we posit that high cue multiplicity enables peripheral processing by drawing the message recipient’s attention to the sender’s peripheral cues. Thus, assuming that peripheral signals are directionally aligned with the central cues, a medium with high cue multiplicity is expected to enhance the persuasive power of the message, as compared to a medium with low cue multiplicity. However, we also hypothesize that the belief revision effect of cue multiplicity is moderated by the strength of the evidence and the presence or absence of reprocessability, as reviewed next.

Interactive Effects When evidence is weak and reprocessability is absent, greater cue multiplicity is likely to have a positive effect on belief revision because the peripheral cues are supporting the central message. When evidence is weak and reprocessability is present, the positive effect of cue multiplicity will be

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amplified significantly because individuals are repeatedly exposed to the (weak) central message and (supportive) peripheral cues. Thus, we offer Hypothesis 2 (see Fig. 1 for a graphic depiction): H2a. When evidence is weak and reprocessability is absent, belief revision is greater when cue multiplicity is high as compared to low. H2b. When evidence is weak and reprocessability is present, belief revision is greater when cue multiplicity is high as compared to low. H2c. When evidence is weak, the difference in belief revision between high and low cue multiplicity is greater in the presence, as compared to absence, of reprocessability. Given strong evidence and the absence of reprocessability, increased cue multiplicity is still expected to result in significantly greater belief adjustment. However, we predict no effect of cue multiplicity when strong evidence can be reprocessed. This prediction considers the occurrence of a ceiling effect when evidence is strong and reprocessability is present. We argue that the combined effect of strong evidence and reprocessability will

Predicted post-test belief

Weak Evidence

H2a

Low Cue Multiplicity High Cue Multiplicity

H2b H2c = H2a < H2b Reprocessability absent

Reprocessability present

Fig. 1. Hypothesis 2: Predicted Downward Belief Revision in Favor of the Client’s Desired Position. (Scenario: The Auditor Initially Sets a Relatively High Belief Anchor with Regard to a Proposed Adjusting Journal Entry. Afterward, the Client Explains Why the Adjusting Journal Entry is Unnecessary. The Auditor then Revises His/Her Initial Belief. A Downward Revision Suggests that the Auditor is Lowering His/Her Initial Belief in Favor of the Client’s Position.)

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result in decision-makers reaching a maximum belief revision. Adding peripheral cues to the message will not make a significant difference because the decision-makers have reached a belief revision ceiling. Accordingly, Hypothesis 3 is offered (see Fig. 2 for an illustration). H3a. When evidence is strong and reprocessability is absent, belief revision is greater when cue multiplicity is high as compared to low. H3b. When evidence is strong and reprocessability is present, belief revision is not significantly different when cue multiplicity is high as compared to low. H3c. When evidence is strong, the difference in belief revision between high and low cue multiplicity is greater in the absence, as compared to presence, of reprocessability.

Predicted post-test belief

Strong Evidence

H3a

Low Cue Multiplicity High Cue Multiplicity

H3b Reprocessability absent

H3c = H3a > H3b

Reprocessability present

Fig. 2. Hypothesis 3: Predicted Downward Belief Revision in Favor of the Client’s Desired Position. (Scenario: The Auditor Initially Sets a Relatively High Belief Anchor with Regard to a Proposed Adjusting Journal Entry. Afterward, the Client Explains Why the Adjusting Journal Entry is Unnecessary. The Auditor then Revises His/Her Initial Belief. A Downward Revision Suggests that the Auditor is Lowering His/Her Initial Belief in Favor of the Client’s Position.)

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METHOD We designed and administered a computerized laboratory experiment to test the research hypotheses. The experiment reflects a 2 (cue multiplicity: low, high) 2 (message reprocessability: absent, present) 2 (evidence strength: weak, strong) between-subjects design. Participants were randomly assigned to treatment conditions. Experimental Procedure Participants were instructed to assume the role of an audit partner of a large accounting firm. The client firm was a computer manufacturer called ‘‘MicroClone.’’5 The senior manager on the audit had uncovered a potential problem with the client’s finished goods inventory, which was currently valued at h2 million. Specifically, due to the recent introduction of 5th generation computers, part of the inventory (4th generation computers) might be over-valued by about h400,000 (see the appendix for complete wording of the background information presented to all participants). Next, the company’s CFO reacted to the auditor’s concern with five arguments defending why the 4th generation computers should not be written down.6 All arguments, whether strong or weak, attempted to persuade the auditor not to book the recommended inventory write-down. Experimental Manipulations Evidence strength is a function of source objectivity (e.g., Abdel-Khalik, Snowball, & Wragge, 1983), source independence (e.g., Brown, 1983), and evidence verifiability (e.g., Spires, 1991; Goodwin, 1999). All three aspects were used to manipulate the relative strength of CFO-provided arguments. Following each of the five arguments, the CFO referred to a secondary source who confirmed the argument. In the weak evidence condition, the CFO referred to an internal source (i.e., a mid-level employee). This treatment was designed to weaken the perception of source objectivity and independence. Strong arguments contained the same message content as the weak arguments, but the CFO referred to ‘well-known experts’ in the field who confirmed the arguments in published articles. The use of an external source was intended to strengthen the perception of source objectivity and independence. To further reinforce the strong argument manipulation, the senior manager on the audit verified the genuineness of the published article;

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on the other hand, participants in the weak message condition were not explicitly told that the evidence provided by the secondary source was verified.7 Following the first round of arguments, participants in the reprocessability absent (present) condition were not (were) allowed to re-examine the CFO-provided arguments. To ensure that participants in the ‘reprocessability present’ condition actually reprocessed the messages, the computer software presented for a second time each of the five arguments (in random order) before allowing the participants to record their post-argument belief state.8 We manipulated cue multiplicity by presenting the CFO’s arguments in either e-mail or audio–video format. E-mail is low in cue multiplicity, as it does not allow for any peripheral cues, such as body language and voice intonation. In the audio–video clips, the CFO conveyed the arguments using the same wording as the e-mails. The actor was male, around 50 years of age, dressed in a dark suit and wore glasses, i.e., a stereotypical business professional. The video clips showed his head and upper torso in an office environment, i.e., seated at a desk in daylight next to a computer. The actor asserted the same serious tone in his voice across the five clips. His presentation style was free of hedges, hesitations, and faulty grammar in an attempt to induce a perception of high source credibility (Comunale et al., 2005). Thus, the peripheral cues present in the audio–video clips, but not available in the e-mail treatment, were (1) professional looking businessman, (2) sincere look on his face, (3) credible grammar, (4) serious voice tone, and (5) orderly business office environment.

Dependent Measure Recall that the CFO in the case provided evidence aimed at persuading the auditor to revise his/her initial belief from a relatively high (strong) position to a lower (weaker) state. Hence, the dependent variable used to test the hypotheses was the extent of downward belief revision regarding the proposed inventory valuation write-down, after being exposed to the client’s arguments. The participants responded to the following statement: ‘‘Given the available information, I strongly believe that MicroClone should write down its 4th generation inventory by h400,000’’ (1 ¼ strongly disagree, 7 ¼ strongly agree). The participants’ initial belief anchor was first measured after reading the background information. Then, once the participants were exposed to the five counter-arguments offered by the chief executive officer (CEO) (i.e., the treatments), they responded to the same statement once

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again. Thus, the dependent variable metric is calculated as the post-test belief, adjusted for the effect of the covariate (pre-test belief). This metric is referenced as ‘‘belief revision.’’

RESULTS Sample Demographics The sample is comprised of 189 part-time graduate students in accounting with prior work experience. Individual cell sizes for all eight conditions range from 21 to 26, with a median cell size of 23 and a mode of 26. The mean (s.d.) age of participants is 26.67 (5.21) and the age range is between 20 and 52 years. There are 123 male participants (65.1%) and 66 (34.9%) female participants. The number of participants in their first, second, and third year of graduate education, respectively, are 131 (69.3%), 38 (20.1%), and 20 (10.6%). Most participants have some work experience in the field of accounting and/or auditing; specifically, 13 (6.9%) indicate no work experience, 21 (11.1%) record less than a year of experience, 44 (23.3%) specify between 1 and 2 years of experience, 96 (50.8%) note between 3 and 5 years of experience, 13 (6.9%) hold between 5 and 10 years of experience, and 1 (0.5%) possesses more than 10 years of work experience. At the time of the experiment, each participant was enrolled at one of two Dutch universities.9 Manipulation Checks Participants were asked the extent to which they agreed with a statement indicating that the messages were strong (1 ¼ strongly disagree, 7 ¼ strongly agree). The response means (s.d.) are as follow: weak evidence treatment 4.44 (1.81) and strong evidence treatment 5.16 (1.46). An ANOVA indicates statistical significance between the two treatments (F ¼ 8:88; po0:01). As a further check on evidence strength, participants were asked to state the extent to which they agreed (1 ¼ strongly disagree, 7 ¼ strongly agree) with the following statements: (1) ‘‘The arguments in favor of a lower write-down provided by Tom van Breukelen were convincing,’’ and (2) ‘‘The arguments in favor of a lower write-down provided by Tom van Breukelen were credible.’’ The mean (s.d.) for the two questions are: (1) convincing – 3.90 (4.58) for weak evidence, 4.76 (4.58) for strong evidence; (2) credible – 4.57 (1.63) for weak evidence, 5.28 (1.35) for strong evidence. Based on ANOVA tests, the means for both questions are significantly different (po0:001)

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between the weak and strong evidence conditions, and they are directionally consistent with expectations. Hence, the manipulation of evidence strength was successful. Participants also stated the extent to which they agreed with a statement indicating that they were allowed to review the five arguments after receiving the first exposure but before recording their second belief (1 ¼ strongly disagree, 7 ¼ strongly agree). The response mean (s.d.) is 3.72 (2.46) for the ‘reprocessability absent’ group and 6.31 (1.46) for the ‘reprocessability present’ group. An ANOVA indicates a significant difference between the two means (F ¼ 78:16; po0:01), suggesting a successful manipulation of reprocessability. To test for cue multiplicity, participants were asked whether they were exposed to the CFO’s arguments via e-mail or audio–video media. All participants in the e-mail condition correctly responded to this question. However, out of 98 subjects in the high cue multiplicity condition, 16 stated that they had been exposed to e-mail messages. One possible reason for some participants responding incorrectly could be that they misinterpreted the question; that is, all participants were exposed to written attachments following each e-mail or audio–video message (i.e., either an internal or external source verified each argument in writing); hence, participants in the high cue multiplicity condition might have thought that the verifying attachments were e-mail messages when responding to this manipulation check question. A Pearson w2 test (w2 ¼ 136:99; po0:01) indicates a significant difference between treatment groups, and additional sensitivity testing of participants in the high cue multiplicity condition who did and did not respond correctly to this question reveals no significant difference in their pre–post belief revisions (p40:90). Thus, the manipulation of cue multiplicity was successful.

Parametric Assumptions To assess whether the post-test belief scores meet the normality assumption, Kolmogorov–Smirnov and Shapiro–Wilk tests were conducted. Both tests are significant at po0:001; indicating that the data are not normally distributed. However, between-subjects ANCOVA models are robust to nonnormality if the skewness of each treatment condition is in the same direction and the largest variance is o4 times the smallest variance (Torrance, 2002). The study meets both conditions, as response distributions in all eight treatment conditions are positively skewed and the largest variance (s ¼ 4:06) is 3.87 times the smallest variance (s ¼ 1:05). An additional

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analysis for homogeneity of variance (Levene’s test) is non-significant (F ¼ 1:139; p ¼ 0:341), indicating that cell variances are statistically equivalent across experimental conditions. Based on the results of normality and variance testing, we conclude that the use of ANCOVA for hypothesis testing is appropriate.10 The design employed in this study is a pre–post measurement design, for which ANCOVA is a commonly employed method. The initial belief anchor measure is incorporated as a covariate in the final model to adjust for pretest differences. ANCOVA was used to test for main and interactive effects of message reprocessability (present or absent), evidence strength (high or low), and cue multiplicity (high or low) on belief revision. ANCOVA Model Treatment means, standard deviations, and sample sizes are shown in Table 1, and the ANCOVA results are presented in Table 2. The covariate, pretest belief, is significant (po0:001), the main effect of evidence strength is marginally significant (p ¼ 0:092), a two-way interaction between cue multiplicity and reprocessability is significant (p ¼ 0:009), and the three-way interaction among message reprocessability, evidence strength and cue multiplicity is significant (p ¼ 0:025). The results of hypothesis testing are shown next. Hypothesis Testing H1 predicts a positive effect of reprocessability on belief revision. This hypothesis is not supported (see Table 2), as the main effect for reprocessability is non-significant (p ¼ 0:834). However, as indicated by the significant three-way interaction, the effect of reprocessability is contingent on the state of the other two variables – evidence strength and cue multiplicity. To test H2 and H3, planned comparison tests were conducted and are shown in Table 3. H2a predicts that, in light of weak evidence and no reprocessability, higher cue multiplicity will result in greater belief revision. As illustrated in Fig. 3 and reported in Table 3, the means are significantly different (difference score ¼ 0:90; p ¼ 0:022), but in the opposite direction as expected; hence, H2a is not supported. Testing for H2b indicates significantly different means (difference score ¼ 1:02; p ¼ 0:006) in the anticipated direction; thus, H2b is supported. To test for H2c, the high cue multiplicity mean was subtracted

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Table 1.

Means, Standard Deviations (s.d.) and Sample Sizes by Treatment Condition. Message Reprocessability

Cue Multiplicity

Belief PreTest Mean (s.d.)

Belief PostTest Mean (s.d.)

Sample Size

Weak

Absent

High

Absent

Low

Weak

Present

High

Weak

Present

Low

Strong

Absent

High

Strong

Absent

Low

Strong

Present

High

Strong

Present

Low

3.41 (2.02) 2.46 (1.58) 2.08 (1.20) 3.04 (1.78) 2.39 (1.53) 2.00 (1.02) 2.52 (1.66) 2.43 (1.50)

22

Weak

4.64 (1.53) 4.54 (1.94) 4.27 (1.89) 4.12 (2.10) 4.30 (1.43) 4.14 (1.55) 4.14 (1.56) 4.30 (1.82)

Evidence Strength

26 26 26 23 22 21 23

from the low cue multiplicity mean in each reprocessability treatment, and the difference-in-difference between the reprocessability present and absent conditions was compared. As predicted, the ‘reprocessability present’ mean difference (1.02) is greater than the ‘reprocessability absent’ mean difference ð 0:90Þ; as the difference-in-difference of 1.92 ½1:02 ð 0:90ފ is significant (t ¼ 10:69; po0:001). Thus, H2c is conditionally supported; that is, when interpreting H2c, one must consider that the relationship in H2a is opposite from expectations. H3 holds evidence strength constant at strong (see Fig. 4 for an illustration of the results). We first examine the difference in belief revision means between high and low cue multiplicity when reprocessability is absent (Table 3). The difference in means ð 0:31Þ is in the opposite direction from expectations and non-significant (p ¼ 0:434); hence, H3a is not supported. Difference in means between high and low cue multiplicity when reprocessability is present is tested next. Once again, the mean difference ð 0:16Þ is in the opposite direction as anticipated and non-significant (p ¼ 0:691); therefore, H3b is not supported. While the difference-in-difference between reprocessability conditions of 0:15 ½ð 0:31Þ2ð 0:16ފ is significant

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Table 2. Source

Corrected model Intercept Pretest belief Evidence strength Reprocessability Cue multiplicity Evidence strength  Reprocessability Evidence strength  Cue multiplicity Reprocessability  Cue multiplicity Evidence strength  Reprocessability  Cue multiplicity Error Total Corrected total

Type III Sum of Squares

103

ANCOVA Results. d.f.

Mean Square

F

p-value

150.075 9.336 114.079 5.201 0.080 0.359 2.712

8 1 1 1 1 1 1

18.759 9.336 114.079 5.201 0.08029 0.359 2.712

10.330 5.141 62.819 2.864 0.044 0.198 1.493

0.000 0.025 0.000 0.092 0.834 0.657 0.223

1.073

1

1.073

0.591

0.443

12.784

1

12.784

7.040

0.009

9.296

1

9.296

5.119

0.025

326.877

180

1.816

1,696.000

189

476.952

188

Note: R2 ¼ 0:315 (adjusted R2 ¼ 0:284).

(t ¼ 3:68; po0:001), the finding does not support H3c, as the differential effects between reprocessability conditions is in the opposite direction as predicted.11

DISCUSSION The current study provides theoretical contributions to both information systems (IS) and auditing literatures, as it combines IS research examining media attributes with auditing theory involving belief adjustment. Whereas previous media studies in IS have primarily examined the impact of communication media attributes on users’ preferences, this study is unique in that it investigates belief revision effects on auditors’ judgments. Some research findings support extant theory, while other findings disconfirm theoretical propositions. When strong evidence was presented to the participants, belief revision was unaffected by cue multiplicity. While statistical testing revealed

104

Table 3. Hypothesis

H2b H3a H3b a

Evidence Strength

Message Reprocessability

Cue Multiplicity

Adjusted Post-Test Meana,b

Weak Weak Weak Weak Strong Strong Strong Strong

Absent Absent Present Present Absent Absent Present Present

High Low High Low High Low High Low

3.26 2.36 2.10 3.12 2.39 2.08 2.60 2.44

Cue Multiplicity Differencec

p-value

0.90

0.022

1.02

0.006

0.31

0.434

0.16

0.691

Adjusted for the covariate (pre-test belief). Note that lower mean scores indicate stronger belief revision in favor of the client, as the experimental task concerned the auditors’ downward belief revision from their initial (relatively high) anchor toward the client’s desired position. c Low cue multiplicity minus high cue multiplicity. b

ANNA NO¨TEBERG AND JAMES E. HUNTON

H2a

Planned Comparisons.

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Weak Evidence

Adjusted post-test belief

3.5 3.0 2.5 2.0

Low Cue Multiplicity

1.5

High Cue Multiplicity

1.0 0.5 0.0 Reprocessability absent

Fig. 3.

Reprocessability present

Hypothesis 2: Observed Downward Belief Revision. Strong Evidence

Adjusted post-test belief

3.5 3.0 2.5 2.0

Low Cue Multiplicity

1.5

High Cue Multiplicity

1.0 0.5 0.0 Reprocessability absent

Fig. 4.

Reprocessability present

Hypothesis 3: Observed Downward Belief Revision.

significant cue multiplicity differences between reprocessability conditions (H3c), we question the practicality of this finding, as it is inconsistent with expectations and there are no significant cue multiplicity effects within each reprocessability condition. Thus, from a pragmatic point of view, we suggest that belief revision was unaffected by cue multiplicity, message reprocessability or their interaction in the presence of strong evidence. The most likely explanation for this finding is a ceiling effect; that is, the central message of the argument was so convincing that neither the presence of

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congruent persuasive peripheral cues in the high multiplicity condition nor the ability to reprocess the messages multiple times afforded significant incremental weight in the participants’ cognitive processes. Results obtained in the strong evidence condition may be an experimental artifact or it might reflect a theoretically sound inference that can be generalized to different populations, times and settings. Future research in this area is needed to tease apart these two possible explanations. When evidence was weak, however, some of the media attributes examined herein played a significant role in the auditors’ belief revision processes; specifically, research findings reveal that the effect of cue multiplicity inverted, depending on whether participants reprocessed evidence cues. When the reprocessability feature was available, the extent of belief revision toward the client’s position was greater in the audio–video (high cue multiplicity) condition, as compared to the e-mail (low cue multiplicity) condition. This finding is consistent with information processing theory. However, when reprocessing was not available, the opposite results were unexpectedly obtained. We offer the following possible explanation for the latter finding. When individuals are exposed to weak evidence yet fairly persuasive peripheral cues (e.g., a professional looking and sounding businessperson situated in an orderly office environment), the seeming inconsistency between the strength of the central message (weak) and the strength of the peripheral cues (strong) might trigger professional skepticism in the auditors’ minds with respect to the believability of the central message. Such skepticism might become manifest in an unexpected way; that is, the central message is perceived as weaker than it would have been had the inconsistently strong peripheral cues not been available. This might explain why belief revision was greater in the email as opposed to the audio–video condition when evidence was weak and reprocessing was not available. However, one must reconcile this observation with the opposite effect in the presence of reprocessability. The cognitive processing weight given to a one-time exposure to inconsistently strong peripheral cues might dissipate or disappear altogether with repeated exposure. We term this phenomenon ‘peripheral cue attenuation’ – an effect that has not been recognized or tested in prior research. To the extent that the peripheral cue weights are diminished, the cognitive processing weight assigned to the central cue increase relatively. Consistent with anchoring and adjustment theory, multiple exposures to central messages result in more opportunities to anchor and adjust (i.e., sequential updating), hence theoretically, belief revision would be greater in the high cue multiplicity condition when reprocessing was available. Perhaps this could

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partially account for the higher belief revision in the audio–video condition, as compared to the e-mail condition, when reprocessability was present and evidence strength is weak. Future research should focus more attention on the peripheral cue attenuation effect. We admit, however, that the unexpected negative effect of cue multiplicity when reprocessability was absent could be due to an experimental artifact; that is, in the absence of reprocessability, the participants’ cognitive capacity was possibly overtaxed when they processed high cue multiplicity (audio– video) messages. Participants had no control over the speed of audio–video message presentation, other than pushing the play or stop button. Thus, audio–video clips were played at a pre-determined speed. These factors could have increased complexity and uncertainty among participants due to: (1) the presence of peripheral cues that taxed the cognitive processing load and (2) the participants’ inability to control the speed of information reception. As a result, participants may have preferred to be conservative in their beliefs, thereby revising their beliefs only to a very limited extent. On the other hand, e-mail recipients in the absence of reprocessability were able to read messages at their own pace and they revised their beliefs more strongly than the video recipients. This may have occurred because (1) they had no peripheral cues to process thereby focusing more cognitive processing attention on the central, albeit weak, message and (2) the technology did not force them to process the information at a certain speed. As a result, they were less uncertain about the conveyed information and less conservative in their belief revisions. Preliminary evidence for the suggested explanation of our unexpected finding can be found in post-hoc testing. Namely, given weak evidence and the absence of reprocessability, higher response variance was found when cue multiplicity was high as compared to low. This finding suggests that participants who were exposed to weak messages in audio– video format were significantly less consistent in their judgments than e-mail recipients, suggesting some level of confusion and possibly information overload caused by the combination of high cue multiplicity and no reprocessability. From a practical standpoint, results from the current study suggest that the auditing profession should consider various advantages and disadvantages offered by computer-mediated communication with clients. While the use of electronic communication media to collect evidential matter from clients is timely and cost efficient, auditors should be aware that certain media attributes might unintentionally bias their judgments, particularly when the clients hold persuasive intentions. Naturally, definitive conclusions in this regard cannot be made from a single study, as more research is needed in this area.

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For instance, future studies might focus more on peripheral cues. Only peripheral cues that generally support the direction of the central message cues were considered in this study. It would be interesting to investigate the interactive effect of cue format (central vs. peripheral), cue strength (strong or weak), and cue direction (confirming or disconfirming an initial belief) on belief revision. We also recommend that future research study the impact and use of electronic media for two-way communication, i.e., where communication parties interact with each other. In such scenarios, attributes other than those examined in this study could be investigated. Most of the other attributes would revolve around the concept of synchronicity, i.e., whether interaction occurs in real-time (synchronous) or with a time delay (asynchronous) (e.g., Burgoon et al., 2000). Related to the concept of synchronicity, one could also study the impact of another media attribute, rehearsability (e.g., Dennis & Valacich, 1999) or the extent to which the medium allows for message rehearsal before transmission, on audit judgment and decision making. Finally, future research should explore how audit teams can effectively use computer-mediated communication technology during all phases of the audit. Indeed, this is an exciting line of research – one that is relevant to the burgeoning demand for and growing use of electronic communication media in global commerce.

NOTES 1. One could argue that some auditors would write down what they discuss with clients via a telephone conversation and such notes would serve as a reprocessable archive. To the extent that auditors re-examine and reflect on such written records, post-conversation sequential reprocessing of evidence might occur. However, even if this were the case, personally recorded evidence of this nature is often incomplete, inaccurate and biased to some degree due to simultaneous demands of mental filtering, linguistic writing, and cognitive conversation processing (Schober & Brennan, 2003). One could further argue that the auditor could mentally recall and re-examine each piece of evidence gathered during a conversation. However, memory decay and recall filtering would likely dilute and bias this form of sequential processing. 2. This would be the case even if auditors engaged in some degree of sequential processing during conversation, and/or simultaneous anchoring and adjusting postconversation before subsequent reprocessing of the messages. 3. Note that this study is limited in that it considers only peripheral cues that support the underlying, disconfirming message provided by central cues. Thus, our predictions apply only to scenarios where the message source is perceived as persuasive both in terms of central as well as peripheral message cues. 4. Although the Elaboration Likelihood Model and the Heuristic–Systematic Model differ in some important aspects (see Eagly & Chaiken (1993) for a detailed

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review), these differences are not central to our study. For this reason, the terms central and peripheral are used interchangeably with the terms systematic and heuristic here. 5. The scenario is based on the classroom case ‘‘MicroClone, Inc.’’ (Kistler & Strickland, 1997). 6. The five arguments provided by MicroClone’s CFO were presented in random order. 7. Pilot tests revealed that explicitly mentioning verification in the ‘strong evidence’ treatment and not mentioning verification in the ‘weak evidence’ treatment was an effective manipulation. However, we recognize that participants in the ‘weak evidence’ condition may have believed that the internal source was either easily verifiable or presumed verified. Either belief would drive the results toward the null, not the alternative, hypothesis. 8. Participants could re-examine the arguments as many times as they desired; however, they were all purposefully exposed to the messages for at least a second time because the cognitive aspect of the reprocessability hypothesis assumes that the electronic media is capable of reprocessability and that the message recipient takes advantage of such functionality. 9. There are no significant differences ðp40:10Þ across treatment conditions for the following demographic variables: university, experimental session, age, gender, year of graduate study, educational background, and work experience. 10. Additional testing using non-parametric tests agree with the results of upcoming parametric tests. 11. The difference-in-difference analysis (H3c) results in a considerably smaller standard deviation than the single difference scores for H3a ð 0:31Þ and H3b ð 0:16Þ: Hence, we find statistical significance regarding the former but not the latter mean comparisons.

ACKNOWLEDGMENTS We thank the reviewers and participants of the European Conference of Information Systems, where an earlier version of this chapter was presented in 2004 and many useful comments were provided.

REFERENCES Abdel-Khalik, A. R., Snowball, D. A., & Wragge, J. H. (1983). The effects of certain internal audit variables on the planning of external audit programs. Accounting Review, 58(2), 215–227. Ashton, A. H., & Ashton, R. H. (1988). Sequential belief revision in auditing. Accounting Review, 63(4), 623–641. Baltes, B. B., Dickson, M. W., Sherman, M. P., Bauer, C. C., & LaGanke, J. S. (2002). Computer-mediated communication and group decision making: A meta-analysis. Organizational Behavior and Human Decision Processes, 87(1), 156–179.

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Brown, P. R. (1983). Independent auditor judgment in the evaluation of internal audit functions. Journal of Accounting Research, 21(2), 444–455. Burgoon, J. K., Bonito, J. A., Bengtsson, B., Cederberg, C., Lundeberg, M., & Allspach, L. (2000). Interactivity in human–computer interaction: A study of credibility, understanding and influence. Computers in Human Behavior, 16, 553–574. Chaiken, S. (1980). Heuristic versus systematic information processing and the use of source versus message cues in persuasion. Journal of Personality and Social Psychology, 39, 752–766. Chaiken, S., & Eagly, A. H. (1983). Communication modality as a determinant of persuasion: The role of communicator salience. Journal of Personality and Social Psychology, 45(2), 241–256. Chen, S., & Chaiken, S. (1999). The heuristic-systematic model in its broader context. In: S. Chaiken & Y. Trope (Eds), Dual-process theories of social psychology. New York: Guildford Press. Comunale, C. L., Sexton, T. R., & Sincich, T. (2005). Linguistic delivery style, client credibility, and auditor judgment. Advances in Accounting Behavioral Research, 8, 61–90. Daft, R. L., & Lengel, R. H. (1986). Organizational information requirements, media richness and structural design. Management Science, 32, 554–571. Daft, R. L., Lengel, R. H., & Klebe Trevino, L. (1987). Message equivocality media selection and manager performance: Implications for information systems. MIS Quarterly, September 1987, 11(3), 335–366. Dennis, A. R., & Kinney, S. T. (1998). Testing media richness theory in the new media: The effects of cues, feedback, and task equivocality. Information Systems Research, 9(3), 256–274. Dennis, A. R., & Valacich, J. S. (1999). Rethinking media richness: Toward a theory of media synchronicity. Paper presented at the 32nd Hawaii international conference on system sciences, Maui, Hawaii. Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Fort Worth, TX: Harcourt Brace Jovanovich. Einhorn, H. J., & Hogarth, R. M. (1985). A contrast/surprise model for updating beliefs. Working Paper, University of Chicago. Francis, J., & Schipper, K. (1999). Have financial statements lost their relevance? Journal of Accounting Research Autumn, 37(2), 319–352. Goodwin, J. (1999). The effects of source integrity and consistency of evidence on auditors’ judgments. Auditing: A Journal of Practice & Theory, 18(2), 1–16. Hirst, E., & Koonce, L. (1996). Audit analytical procedures: A field investigation. Contemporary Accounting Research, 13(2), 457–486. Hogarth, R. M., & Einhorn, H. J. (1992). Order effects in belief updating: The belief-adjustment model. Cognitive Psychology, 24, 1–55. Hollingshead, A. B., McGrath, J. E., & O’Connor, K. M. (1993). Group task performance and communication technology: A longitudinal study of computer-mediated versus face-toface work groups. Small Group Research, 24(3), 307–333. Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Behavior, agency costs and ownership structure. Journal of Financial Economics, 3, 305–360. Kistler, L. H., & Strickland, S. (1997). Instructional case: Microclone Inc. Issues in Accounting Education, 12(2), 427–434. Koonce, L., Walker, N. R., & Wright, W. F. (1993). A cognitive characterization of audit analytical review. Auditing: A Journal of Practice & Theory, 12(Supplement), 57–76.

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Kraut, R. E., Galegher, J., Fish, R., & Chalfonte, B. (1992). Task requirements and media choice in collaborative writing. Human–Computer Interaction, 7(4), 375–407. Matheson, K., & Zanna, M. P. (1989). Persuasion as a function of self-awareness in computermediated communication. Social Behavior, 4, 99–111. No¨teberg, A., Benford, T. L., & Hunton, J. E. (2003). Matching electronic communication media and audit tasks. International Journal of Accounting Information Systems, 4, 27–55. Petty, R. E., & Cacioppo, J. T. (1981). Attitudes and persuasion: Classic and contemporary approaches. Dubuque, Iowa: W.C. Brown. Rice, R. E. (1992). Task analyzability, use of new media, and effectiveness: A multi-site exploration of media richness. Organization Science, 3, 457–500. Schober, M. F., & Brennan, S. E. (2003). Processes of spoken discourse: The role of the partner. In: A. C. Graesser, M. A. Gernsbacher & S. R. Goldman (Eds), Handbook of discourse processes (pp. 123–164). Hillsdale, NJ: Lawrence Erlbaum. Spires, E. (1991). Auditors’ evaluation of test-of-control strength. The Accounting Review, April, 66(2), 259–276. Sproull, R. F. (1991). A lesson in electronic mail. In: L. Sproull & S. Kiesler (Eds), Connections: New ways of working in the networked organization (pp. 177–184). Cambridge, MA: MIT Press. Strauss, S. G., & McGrath, J. E. (1994). Does the medium matter? The interaction of task type and technology on group performance and member reactions. Journal of Applied Psychology, 79, 87–97. Suh, K. S. (1999). Impact of communication medium on task performance and satisfaction: An examination of media-richness theory. Information & Management, 35, 295–312. Torrance, M. (2002). Introduction to analysis of variance, from http://www.staffs.ac.uk/personal/sciences/smt15/mt15/rm3/lecture_web/index.htm. Accessed on December 1, 2004. Weisband, S., & Atwater, L. (1999). Evaluating self and others in electronic and face-to-face groups. Journal of Applied Psychology, 84, 632–639.

APPENDIX Experimental Case Please assume the role of a partner at a large audit firm. You are the partner in charge of the audit of MicroClone Inc., a manufacturer of IBM-compatible personal computers. The senior manager on the audit, Steven de Wit, who reports directly to you, has extensively analyzed the company’s finished goods inventory. 25% (2,000 units) of the finished goods inventory is comprised of 4th generation computers and the remaining 75% (6,000 units) is represented by 5th generation computers (the latest microprocessor chip available on the market). MicroClone’s inventory of 2,000 units of 4th generation computers is presently valued at cost (h1,000 per unit), for a total of h2,000,000.

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Steven de Wit has identified what might be a problem concerning the 4th generation inventory. He noted that, during the 4th quarter, MicroClone began shipping personal computers using the 5th generation chips; thus, he is concerned that the value of the 4th generation computers remaining in the inventory might be overstated. After listening to Steven, you are also concerned about a potential overvaluation problem because 4th generation computers will eventually become obsolete by industry standards. Your and Steve’s analyses suggest that each 4th generation computer in the inventory should be valued at h800, not h1,000. Thus, you believe that MicroClone’s estimate of 4th generation computers could be overstated by as much as h200 per unit, yielding a potential over-valuation of h400,000 – at the most. You have decided to take the inventory issue directly to Tom van Breukelen, the chief financial officer (CFO) of MicroClone. Your aim is to negotiate with van Breukelen the appropriate over-valuation estimate, which can range from h0 to h400,000. You ask van Breukelen to explain his point of view regarding the 4th generation issue by sending him an e-mail message with the following content: To: Tom van Breukelen From: You Dear Mr van Breukelen, I have some questions about the finished goods inventory. Specifically, I want to ask you about the h2 million valuation of the 4th generation microcomputers. By industry standards, the 4th generation computers will likely become obsolete as the new, faster 5th generation models become more widely adopted. According to your records, about 25% (2,000 units) of your company’s finished goods inventory is in 4th generation models, which are valued at h1,000 each, for a total inventory valuation of h2,000,000. Based on my preliminary analyses, your 4th generation computers might be over-valued by around h200 per unit. Therefore, the finished goods inventory appears to be overvalued by h400,000 at the most, which means that your pre-tax profits also could be overstated by that amount. I am concerned about this situation because h400,000 is material to your financial statements taken as a whole. Kind regards,

ROLE MORALITY AND ACCOUNTANTS’ ETHICALLY SENSITIVE DECISIONS Robin R. Radtke ABSTRACT If individuals exhibit less ethical behavior in the workplace than in their personal decisions, this may constitute evidence of role morality behavior. Role morality can be defined as ‘‘claim(ing) a moral permission to harm others in ways that, if not for the role, would be wrong’’ (Applbaum, 1999. Ethics for adversaries: The morality of roles in public and professional life (p. 3). Princeton, NJ: Princeton University Press.) To investigate this issue, 55 practicing accountants completed and returned the experimental survey. Results show that in many situations, business decisions were less ethical than personal decisions, consistent with the theory of role morality. The implications and limitations of this study as they relate to practicing accountants are discussed.

INTRODUCTION The importance of ethical decision-making in the accounting profession has recently received renewed attention. Concerns about unethical activities Advances in Accounting Behavioral Research Advances in Accounting Behavioral Research, Volume 8, 113–138 Copyright r 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1475-1488/doi:10.1016/S1475-1488(04)08005-6

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related to the failure of Enron is one of the latest examples. Accountability to the client in terms of pressure to present a favorable financial picture of a corporation’s health may often lead to ethical breaches (Chartier, 2002). Because of the Enron example concerning dubious accounting practices and other documented cases of fraud over the last few years, public confidence in the accounting profession may be diminishing. While calls for effective codes of conduct and increased monitoring and accountability of the accounting profession are not new (National Commission on Fraudulent Financial Reporting, 1987), the credibility crisis remains. New calls for regulation both within and outside the accounting profession are specifically concerned with violations of ethical standards (Pitt, 2002). Within this climate, investigation of accountants’ ethical decision-making processes is particularly important. Since recent examples suggest that at least some accountants are making unethical business decisions, research is needed to investigate whether these accountants make unethical decisions in both personal and business situations or whether they exhibit more ethical decisions in personal situations, consistent with the theory of role morality. Implications for ethical training and/or screening programs may vary significantly based on these findings. The remainder of the chapter is organized as follows. In the first section of the chapter, relevant literature is reviewed and research questions are developed. Next, the experimental design is described. The data analysis and results are presented in the third section. The chapter concludes with a discussion of the limitations and implications for future research.

RELEVANT THEORY AND RESEARCH QUESTIONS Role Morality and Ethically Sensitive Decisions Mostly everyone deals with situations that contain ethical issues on a regular basis in both personal and business decisions. Undoubtedly, many of these situations are quite complex and difficult to reconcile. When pondering an ethically sensitive situation, many factors may come into play. One commonly accepted ethical principle is universalism, which suggests that once a certain action, such as theft, is determined to be wrong, it should always be considered wrong. Thus, if one ascribes to the principle of universalism and agrees that theft is wrong, one would not steal regardless of whether the situation is of a personal or business nature.

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If individuals exhibit less ethical behavior in the workplace than they do in their personal decisions, this is consistent with the theory of role morality. Role morality can be defined as ‘‘claim(ing) a moral permission to harm others in ways that, if not for the role, would be wrong’’ (Applbaum, 1999, p. 3). Role morality at its extremes can be seen in such examples as Sanson, the executioner of Paris (Applbaum, 1999, p. 15) and Adolf Eichmann, a middle-level Schutzstaffel (SS) officer who was responsible for the deportation of millions of European Jews to Nazi death camps (Applbaum, 1995, p. 470). Less extreme examples are present everyday in many professional realms and include a doctor denying a patient involved in a clinical trial, a treatment that the doctor has reason to believe is beneficial (Applbaum, 1999, p. 48), a CEO engaging in a strategy of secrecy wherein certain information is concealed from the public to advance the purposes of the CEO (Applbaum, 1999, p. 183), and a campaign strategist who willfully distorts an opponents’ record to smear his/her reputation in the eyes of the public (Applbaum, 1999, p. 3). An alternative, less extreme definition of role morality is ‘‘the extent to which one succeeds in meeting the demands and obligations of one’s role’’ (Werhane & Freeman, 1999, p. 3). This definition is consistent with the view that role morality and common-sense morality often prescribe the same action. Specifying in more detail how role morality relates to common-sense morality is the starting point in developing an understanding of how individuals can undertake actions consistent with the former and not the latter. Fig. 1 shows a graphical depiction of the overlaps among role morality, common-sense morality, and self-interest, as suggested by Kerssens-van Drongelen and Fisscher (2003). Much of role morality overlaps with common-sense morality, such that only infrequently will individuals be faced with situations where they must choose between the two. In these situations, however, in order for an individual to choose role morality over commonsense morality, the individual must ‘‘excuse oneself from a common moral obligation by appealing to a role or the institution that creates the role’’ (Luban, 1988, p. 116). One way to achieve this end is through moral disengagement. Bandura, Barbaranelli, Caprara, and Pastorelli (1996) suggest that moral disengagement allows individuals to displace their moral control in order to engage in detrimental conduct. Several methods may be used to achieve this disengagement including displacement of responsibility, moral justification, disregarding or distorting the consequences of actions, and dehumanization. Displacement of responsibility allows individuals to pass on responsibility for actions to a legitimate authority who has dictated the actions to be

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Organizational factors:

Personal factors: Accountability

1) Company type 2) Length of employment

1) Gender 2) Age

Role morality

Self-interest

Common-sense morality

Fig. 1. Theoretical Model. (Based on Kerssens-van Drongelen and Fisscher’s (2003) Graphical Representation of the Relationships among Role Morality, Common-Sense Morality, and Self-Interest. This Figure Incorporates the Factors of the Current Study and Depicts their Posited Impacts on both Role Morality and Common-Sense Morality.)

undertaken. In this situation, the individual eliminates self-censuring reactions normally attributable to reprehensible conduct. When detrimental conduct is portrayed as being in the service of a valued social purpose, moral justification makes the culpable action righteous. By focusing only on the positive benefits of personal gain while ignoring the harm caused to others, one disregards or distorts consequences. Dehumanization treats people as being devoid of human qualities. McPhail (2001, p. 280) suggests that because accounting is technical and mathematical in nature, it ‘‘dehumanises individuals and consequently makes it easier for some people to treat other people cruelly.’’ Taken together, these and several other disengagement practices may sufficiently disinhibit an individual such that detrimental action is undertaken. Multiple studies from the 1970s provide evidence of the detrimental effects of moral disengagement. Tilker (1970) found that an individual who is forced to feel responsible for the condition of another who is providing feedback on their well-being is most likely to act socially responsible. Diener, Dineen, Endresen, Beaman, and Fraser (1975) found that the presence of several disinhibiting forces may lead to increases in antisocial

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behavior, while Bandura, Underwood, and Fromson (1975) found that lower personal responsibility and dehumanization led to increased aggressiveness. More recent efforts in the area have found that moral disengagement reduced anticipatory self-censure and prosocialness and promoted affective and cognitive reactions conducive to detrimental conduct and aggression (Bandura et al., 1996). Additionally, Bandura, Caprara, Barbaranelli, Pastorelli, and Regalia (2001) found that both perceived academic and self-regulatory efficacy reduced transgressiveness both directly and by increasing adherence to moral self-sanctions for harmful conduct and prosocialness. Bandura (2002, p. 114) also comments that industrywide collective moral disengagement practices such as those associated with the tobacco industry ‘‘require a large network of otherwise considerate people performing jobs drawing on their expertise and social influence in the service of a detrimental enterprise.’’ Thus, moral disengagement provides a basis for understanding how role morality behavior could depart from common-sense morality. Within the accounting profession examples of role morality may include such behavior as earnings management or allowing a questionable audit adjustment or tax treatment.

Other Factors Potentially Impacting Ethically Sensitive Decisions Accountability Accountability is one factor that may impact individuals’ ethically sensitive decision-making and role morality behavior. Defined as having to justify a decision to an external party, accountability may alter one’s thought process (Weigold & Schlenker, 1991) and compel an individual to conform to the expectations of the evaluator, when those expectations are made clear to the individual (Tetlock, 1985; Tetlock, Skitka, & Boettger, 1989). This induces the individual to make that decision, which the evaluator is expecting when the individual knows he or she must explain the decision to the evaluator. Absence of accountability leaves the individual without certain knowledge of the expectations of the evaluator and without the perceived benefits of conformity. Ashton, Kleinmuntz, Sullivan, and Tomassini (1988, p. 130) attest to the importance of accountability within an auditing setting by stating that ‘‘because of the environment, the professional auditor must be prepared to justify, document, and take responsibility for his/her judgments and decisions.’’ Related research has shown that auditors have a good understanding

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of the inherent accountability in their profession (Emby & Gibbins, 1988). Accountability has also been shown to affect various auditing decisions in experimental settings. Johnson and Kaplan (1991) found that accountable auditors showed higher consensus and self-insight (the extent to which an auditor is aware of his/her own judgment process) than those who were not accountable. Accountable audit managers were found to be more likely to issue qualified opinions in a study by Lord (1992), while Buchman, Tetlock, and Reed (1996) found that the audit report chosen by experienced auditors matched the expectation of the evaluator (the client or the partner in charge). In a study involving both executive MBA students and auditors, Kennedy (1993) concluded that effort-related biases such as recency can be mitigated by accountability. While several earlier studies found that accountability exacerbates the dilution effect (Hackenbrack, 1992; Messier & Quilliam, 1992), more recent results have not supported this finding (Glover, 1997; Hoffman & Patton, 1997). More recently, Turner (2001) found support for the results of Peecher (1996) in that accountability demands may represent bias-instigators instead of bias-mitigators, while Asare, Trompeter, and Wright (2000) found accountable auditors focused more on testing potential error hypotheses and increased the breadth of hypotheses tested. In summary, accountability can have varying impacts in different decision contexts. Several accounting studies have shown, however, that accountable subordinates often make judgments consistent with the views of their supervisors or clients in situations such as inventory valuation (Ponemon, 1995), financial reporting (Hackenbrack & Nelson, 1996), interpretation of tax standards (Cuccia, Hackenbrack, & Nelson, 1995), and litigation disclosure (Buchman et al., 1996). These results suggest that accountability may compel accountants to make decisions they otherwise might not make. If these decisions are unethical, then accountability may be a factor that impacts role morality behavior as is posited in Fig. 1.

Additional Organizational and Personal Variables While accountability may be the primary variable affecting role morality behavior, various organizational and personal variables may also be related to accountants’ reactions to ethically sensitive situations of a personal and business nature. Specifically, two organizational and two personal factors are included in the regression analyses of this study – company type (public accounting versus private industry), and length of employment with the firm, and gender, and age.1 A discussion of each of these variables follows and a graphical depiction is included in Fig. 1.

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Undoubtedly, many differences exist between accountants working in public accounting and private industry. For example, the hierarchical nature and low retention rates of the public accounting profession suggest that conformity pressures may be particularly acute. Although accountants working in private industry may also face various pressures within the company, these pressures may not be as severe as in public accounting. Thus, accountants may self-select into public or private accounting as a result of the firm socialization process. Ponemon (1992a) found that the ethical development of staff and seniors was higher than that of managers and partners and posited that accountants with higher levels of ethical development (than the firm norm) may leave public accounting for private industry before entering the management ranks. Jeffrey and Weatherholt (1996) suggest that this migration may cause accountants in private industry to exhibit higher levels of ethical development, despite similar educational and experience bases. This may cause fewer differences in responses to ethically sensitive situations of a personal and business nature in accountants working in private industry. Length of employment with the firm may also impact whether accountants are susceptible to role morality behavior. Results of firm socialization studies suggest that promotion constitutes an employee screening process, signaling to upper management, which employees should be retained (Nystrom & McArthur, 1989). More specifically, individuals who are promoted are perceived by upper management to have personal characteristics similar to those of the corporate culture (Weick, 1979; Lockheed, 1980; Smircich, 1983). Individuals’ cognitive characteristics, including moral reasoning, have been found to become more homogeneous as they advance in management levels within an organization (Fisher, Merron, & Torbert, 1987; Avolio & Gibbons, 1988). The result of this firm socialization process may promote different responses to ethically sensitive situations of a personal and business nature in those accountants with a longer tenure with the firm. The personal variables of gender and age may also affect an accountant’s susceptibility to role morality behavior. In most cases, age may be correlated with length of employment with the firm (r ¼ 0:8930; p ¼ 0:0001 in the current study), causing the effects of these variables to be similar. Gender is a variable that has traditionally been shown to affect many decision contexts. Specifically, males and females in the general population have been found to be virtually identical with respect to moral development (Rest, 1986), while several studies of accounting professionals and students, alternatively, show that female accountants demonstrate a higher level of moral reasoning (Shaub, 1994; Ameen, Guffey, & McMillan, 1996). Further,

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recent evidence of a growing number of female whistle-blowers (Moak, 2004; www.cnn.com, 2002) lends support to the contention that females may be less susceptible to firm socialization and may exhibit fewer differences in responses to ethically sensitive situations of a personal and business nature.

Development of Research Questions In order to measure whether accountants exhibit behavior consistent with the theory of role morality, two sets of responses to ethically sensitive situations are necessary for each accountant. Specifically, comparison of accountants’ responses to ethically sensitive situations framed in both personal and business settings would allow identification of those accountants whose decisions differ. Thus, RQ1. Do accountants react to ethically sensitive situations of a personal and business nature in a similar manner (which is inconsistent with the theory of role morality)? RQ2. Is potential role morality behavior affected by accountability, company type, length of employment with the firm, gender, or age?

METHODOLOGY Participants Accountants from both public accounting and private industry were solicited to act as participants. Companies of both types with local offices in a metropolitan city in the southwest were randomly selected. Appropriate contact personnel were requested to garner participation from their staffs. These personnel were asked to provide the number of staff who would participate in a study of ‘‘reactions to ethically sensitive situations.’’ The surveys were then sent to contact personnel for distribution. Of the 116 surveys sent for distribution, 55 (47%) were returned completed.

Instrument The survey instrument was comprised of two main parts. In one section, participants responded to two matched sets of ethically sensitive situations:

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of a personal and business nature.2 The second section elicited demographic and work environment data, including measures of perceived accountability pressure.3 The ethically sensitive situations were used in a previous study by Radtke (2000) and were generally consistent with the definition of an ethical issue as proposed by both Velasquez and Rostankowski (1985) and Jones (1991): an ethical issue occurs when a person’s actions, freely performed, may harm or benefit others. The 16 ethically sensitive situations corresponded to two matched sets of eight situations each, when personal and business situations dealing with the same issue were paired together. Of the eight issues, four were patterned after those of the Defining Issues Test (Rest, 1979) – situations dealing with theft, withholding information from authorities, freedom of speech, and racial discrimination. The other four issues were similar, but more situation specific, dealing with copying software, cheating on taxes, deception/honesty, and trading on inside information. In order to obtain two matched sets of eight situations each, every situation was chosen specifically for its ability to be framed in both a personal and business setting (although ensuring that participants perceive the matched situations to be comparable is impossible). Also, each business situation was general enough such that a practicing accountant working in any business area should be able to formulate an answer. For each situation, participants were asked to indicate their probable action on the issue on the five-point scale of yes, probably yes, unsure, probably no, and no. The resulting 16 ethically sensitive situations are presented in the appendix.4 Consistent with Dreike and Moeckel (1995), the survey instrument included a combination of situations representing some actions that are obviously illegal, some that would fall under issues addressed by the AICPA Code of Professional Conduct (AICPA, 2003), and some that may be considered as inappropriate or gray areas. Including situations of varying degrees of ethical content makes the survey instrument richer and less repetitive. The issue of whether the participants perceived the situations to have significant ethical content is difficult to ascertain and control. Dreike and Moeckel (1995) point out that even though they used a survey containing ethical situations consistent with the Velasquez and Rostankowski (1985) definition, the 66 auditors in their study who rated eight ethical situations each only viewed 56% of the situations as actually containing an ethical issue. Pretesting the ethically sensitive situations consisted of several steps. First, several colleagues familiar with ethics research reviewed the situations for general readability and design. Second, the entire instrument was completed by 44 cost accounting students (32 undergraduate and 12 graduate students

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who would be reasonably familiar with the types of situations presented). Third, a number of colleagues and doctoral students reviewed the comparability of the situations that were matched in terms of personal and business settings. In nearly all cases, the vast majority of the matched situations were easily judged as comparable.

RESULTS Preliminary Analyses For the 55 participants, the average age was 34, while 20 of the participants who reported their gender were female and 31 were male. Nearly 40% of the participants were from public accounting, while most of the private industry participants worked at oil and gas firms. The sample was almost equally split between CPA and non-CPA respondents. Table 1 shows the remaining sample characteristics. A summary of the responses to the 16 ethically sensitive situations is shown in Table 2. Interestingly, responses to some of the situations were quite homogeneous (situations 1, 4, and 8 had one response chosen by over half the participants), while responses to the majority of the situations were quite diverse. At the extremes, 40 (72.7%) respondents answered ‘‘No’’ to situation 8, while responses to other situations were extremely diverse (e.g., situations 7, 10, and 14 had at least 10% of the respondents choosing each of the possible responses, while nearly half of the respondents chose yes or probably yes and over half of the respondents chose no or probably no for situation 2). For further statistical analyses, the responses ranging from yes to no were coded from 1 to 5, with 1 representing the least ethical response and 5 representing the most ethical response. Due to the phrasing of some questions, the order of the responses was reversed (such that a response of yes would be representative of the most ethical response and would be coded as 5), so that participants would not always expect the least ethical response to appear first. Specifically, questions 5, 6, 7, 8, 11, 13, 14, 15, and 16 were reverse ordered and coded accordingly. Based on this coding, a mean response was calculated for each question and for each participant. The mean responses (shown in Table 2) for the questions range from 1.42 on situation 8 to 4.29 on situation 1. The mean response of 1.42 on situation 8 represents a somewhat unethical response about reporting/withholding information in a business setting. The mean response of 4.29 on situation 1 represents a somewhat ethical response

Role Morality and Accountants’ Ethically Sensitive Decisions

Table 1.

Sample Demographics.

Company type Public accounting Oil and gas Insurance Manufacturing Other

21 24 5 2 2

Ethnic background Caucasian Hispanic African American Asian

35 5 1 2

Undergraduate degree Accounting Non-accounting

27 20

Graduate degree Yes No

12 40

CPA Yes No

28 26

Years with the company Years in current position Years of accounting experience

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Male

Female

10 16 3 1 1

10 7 1 1 1

Mean

Median

Standard Deviation

8.36 3.32 9.93

5.0 1.0 6.5

8.55 4.59 8.59

Note: All respondents did not answer all questions.

about cheating on taxes in a personal setting. For the 55 sample participants, the overall mean response for all 16 situations was 2.93, which represents a middle response between the two endpoints of ethical and unethical behaviors. The range of mean responses across participants was from 1.94 to 3.75, ranging from somewhat unethical to ethical behavior. Tests of Research Questions Univariate tests were first used to address the first research question of the study, that accountants will react to ethically sensitive situations of a personal and business nature in a similar manner. For these tests, responses

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Table 2.

Sample Responses to Ethically Sensitive Situations.

Situation/Response

Mean

Yes

Probably Yes

Unsure

Probably No

No

1. Personal case of cheating on taxes

4.29

2 (3.6%)

6 (10.9%)

1 (1.8%)

11 (20.0%)

35 (63.7%)

2. Business case of cheating on taxes

3.22

7 (12.7%)

17 (30.9%)

3 (5.5%)

13 (23.6%)

15 (27.3%)

3. Personal case of copying software

2.36

13 (23.6%)

27 (49.1%)

3 (5.5%)

6 (10.9%)

6 (10.9%)

4. Business case of copying software

2.00

17 (30.9%)

31 (56.4%)

1 (1.8%)

2 (3.6%)

4 (7.3%)

5. Personal case of theft

2.42

8 (14.6%)

11 (20.0%)

0 (0%)

13 (23.6%)

23 (41.8%)

6. Business case of theft

1.76

0 (0%)

3 (5.5%)

5 (9.1%)

23 (41.8%)

24 (43.6%)

7. Personal case of reporting/withholding information

3.18

7 (12.7%)

22 (40.0%)

9 (16.4%)

8 (14.5%)

9 (16.4%)

8. Business case of reporting/withholding information

1.42

0 (0%)

3 (5.5%)

2 (3.6%)

10 (18.2%)

40 (72.7%)

9. Personal case of trading on inside information

4.02

0 (0%)

8 (14.5%)

8 (14.5%)

14 (25.5%)

25 (45.5%)

2.93

8 (14.5%)

19 (34.6%)

8 (14.5%)

9 (16.4%)

11 (20.0%)

11. Personal case of deception/honesty

3.84

17 (30.9%)

26 (47.3%)

3 (5.4%)

4 (7.3%)

5 (9.1%)

12. Business case of deception/honesty

3.89

4 (7.3%)

9 (16.4%)

3 (5.4%)

12 (21.8%)

27 (49.1%)

13. Personal case of racial discrimination

3.53

13 (23.6%)

19 (34.5%)

10 (18.2%)

10 (18.2%)

3 (5.5%)

14. Business case of racial discrimination

2.82

6 (10.9%)

15 (27.3%)

7 (12.7%)

17 (30.9%)

10 (18.2%)

15. Personal case of freedom of speech

2.16

1 (1.8%)

8 (14.6%)

7 (12.7%)

22 (40.0%)

17 (30.9%)

16. Business case of freedom of speech

3.05

9 (16.3%)

16 (29.1%)

4 (7.3%)

21 (38.2%)

5 (9.1%)

ROBIN R. RADTKE

10. Business case of trading on inside information

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to personal and business situations for each participant were matched by situation type. This resulted in a total of eight matched situations. The difference between the personal and business response (which could range between 4 and 4) was then calculated for each type of situation. The average difference was then calculated for each participant. For the 55 participants, the mean average difference was 0.59, indicating that business responses were on average less ethical than personal responses. This difference was statistically significantly different from 0 at p ¼ 0:0001 using a univariate test. Further analysis of the differences between personal and business responses by type of situation shows interesting results. Table 3 shows average differences by situation type, as well as the p values of the univariate tests. Only differences 2 and 6, dealing with copying software and deception/ honesty, respectively, are insignificant at a ¼ 0:05: This suggests that participants’ responses on these two issues were similar for personal and business situations. All other differences were positive and statistically significantly different except difference 8, which was negative. The positive differences imply that business responses were less ethical than personal responses in situations dealing with cheating on taxes, theft, reporting/ withholding information, trading on inside information, and racial Table 3.

Differences in Responses to Personal and Business Ethically Sensitive Situations by Situation Type.

Differences by Situation Type

Average Difference

D1 ¼ S1 S2 Cheating on taxes D2 ¼ S3 S4 Copying software D3 ¼ S5 S6 Theft D4 ¼ S7 S8 Reporting/withholding information D5 ¼ S9 S10 Trading on inside information D6 ¼ S11 S12 Deception/honesty D7 ¼ S13 S14 Racial discrimination D8 ¼ S15 S16 Freedom of speech

t-Test on Sample Wilcoxon Test on Sample Means (p value) Medians (p value)

1.05

0.0001

0.0001

0.36

0.0534

0.0516

0.64

0.0029

0.0022

1.73

0.0001

0.0001

1.07

0.0001

0.0001

0.05

0.8286

0.9778

0.70

0.0012

0.0007

0.88

0.0001

0.0001

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discrimination. The one negative difference implies that business responses were more ethical than personal responses in situations dealing with freedom of speech. These results suggest that accountants react differently to ethically sensitive situations of a personal and business nature. Specifically, in the majority of the situations examined, business responses were less ethical than personal responses, consistent with the theory of role morality. A more complete analysis of these differences requires the use of regression analysis with control variables.5 The dependent variables for regression purposes are the matched responses by situation type. This allows for comparison of responses for each participant for each situation type based on varying the situation setting between the two options of personal and business. The control variables previously identified are accountability, company type (public accounting versus private industry), length of employment with the firm, gender, and age. A total of nine regressions were run, one for each type of ethically sensitive situation previously identified, and one across all participants’ responses to all situations. The regression equation is as follows: Matched Responses ¼ a þ b1 P=B þ b2 ACCT þ b3 COT þ b4 LENGTH þ b5 GENDER þ b6 AGE þ x Variables included in the regression have been previously discussed in the development of research questions section of the chapter. Variable coding is as follows: P/B (0 ¼ business setting and 1 ¼ personal setting), COT (0 ¼ private industry and 1 ¼ public accounting), and GENDER (0 ¼ male and 1 ¼ female). The accountability variable, ACCT, represents the accountability pressure felt by each participant in either the business or personal setting. The business accountability equals the sum of each participants’ responses to the four accountability scales for the business setting (accountability to superiors/company, accounting profession, clients/other companies, and general public), while the personal accountability represents the accountability felt to oneself.6 Each of these scaled measures can potentially range from 1 (low) to 7 (high); the business measure can thus range from 4 to 28. For the sample participants the average for the personal measure is 6.64, with a range of 4–7, while the average for the business measure is 22.95, with a range of 14–28, indicating that most participants felt a relatively sizable amount of accountability pressure both to themselves and to the business-related entities included in the study. Since the personal and business measures are based on different scales (i.e., a 4 on the personal

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127

measure represents the midpoint of the scale, while a 4 on the business measure represents the minimum value), the scales were standardized by dividing the personal measure by 7 and the business measure by 28. Results of the regression are shown in Table 4 and support the proposition that business responses were less ethical than personal responses, consistent with the theory of role morality. The personal or business setting variable is significant in six of the nine regressions. In five of these cases, including across all situations, the parameter estimate was positive, indicating that the business response was less ethical than the personal response. The significance of the regression across all situations including all participants’ responses addresses the potential concern over the matching of situations by situation type. The only control variables that were significant were accountability in the situation dealing with copying software, and age in the situation of trading on inside information. Taken as a whole, the results of this study suggest that business responses to ethically sensitive situations are less ethical than personal responses, consistent with the theory of role morality. To further investigate the effect of accountability in the business situations, a regression was run including only the business responses, excluding the setting (P/B) variable and including the other five variables mentioned above. As such, the accountability measure in this regression represents the accountability felt by each participant in the business setting. Results show that the accountability measure is positive and significant (b ¼ 0:050; t ¼ 2:12), indicating that those participants who felt greater accountability pressure at work were more likely to choose more ethical responses, contrary to the expectations of this study. None of the other control variables show significant results.

DISCUSSION The results of this study support the premise that accountants react differently to ethically sensitive situations of a personal and business nature, consistent with the theory of role morality and inconsistent with the concept of universalism. Specifically, in the majority of the situations examined, business decisions were less ethical than personal decisions, consistent with the theory of role morality. Whether accountants work in public accounting or private industry, length of employment with the firm, age, and gender did not significantly affect accountants’ reactions to ethically sensitive situations. Additional results support the contention that greater perceived

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Table 4.

Coefficient Estimates of Regressions by Situation Type (b Coefficient and t-Statistic Reported).

Situation Type/Variable

Accountability

Company Type

Length of Employment

0.925 (2.84)

0.127 (0.77)

0.051 (0.19)

0.007 (0.22)

0.449 ( 1.45)

0.028 ( 0.79)

0.1263

Copying software

0.041 ( 0.14)

0.297 (2.02)

0.336 ( 1.40)

0.006 (0.22)

0.039 ( 0.14)

0.009 (0.30)

0.0700

Theft

0.738 (2.42)

0.048 ( 0.31)

0.118 (0.46)

0.046 ( 1.47)

0.338 (1.16)

0.027 (0.81)

0.0845

Reporting/withholding information

2.031 (7.36)

0.190 ( 1.35)

0.093 ( 0.41)

0.022 (0.78)

0.474 (1.80)

0.011 ( 0.38)

0.4084

Trading on inside information

0.812 (2.89)

0.187 (1.31)

0.434 ( 1.85)

0.025 ( 0.87)

0.416 (1.55)

0.085 (2.75)

0.3315

Deception/honesty

0.264 ( 0.85)

0.251 (1.59)

0.442 ( 1.71)

0.032 (1.02)

0.169 ( 0.57)

0.015 ( 0.43)

0.0531

Racial discrimination

0.595 (1.87)

0.012 ( 0.07)

0.388 ( 1.46)

0.005 (0.16)

0.455 (1.50)

0.007 (0.21)

0.0345

Freedom of speech

0.637 ( 2.15)

0.200 ( 1.32)

0.045 (0.18)

0.015 ( 0.50)

0.102 (0.36)

0.014 (0.42)

0.0972

Across all situations ðn ¼ 800Þ

0.520 (4.04)

0.052 (0.79)

0.185 ( 1.72)

0.002 ( 0.12)

0.141 (1.15)

0.011 (0.78)

0.0421

Cheating on taxes

Gender

Age

Adjusted R2

Personal or Business

Matched Responses ¼ a þ b1 P=B þ b2 ACCT þ b3 COT þ b4 LENGTH þ b5 GENDER þ b6 AGE þ x  t-value is significant at a ¼ 0:05:

ROBIN R. RADTKE

Note: The sample size for the regression analysis was 50 participants, since some participants did not supply all needed data. Thus, for the first eight regressions, n ¼ 100; which includes both the personal and business response for each participant for each situation type.

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accountability pressure is associated with more ethical responses in the business setting. This evidence implies that accountants may tend to view ethically sensitive situations of a business nature differently from those of a personal nature. Specifically, they may not perceive acting less ethically in business situations negatively; they may believe they are expected to act in a certain manner that is consistent with their role. Firm socialization may be one factor related to this behavior. As previously discussed, firm socialization theory suggests that individuals who are promoted are perceived by upper management to have personal characteristics similar to those of the corporate culture (upper management) (Weick, 1979; Lockheed, 1980; Smircich, 1983). The results of the current study suggest that accountants may not actually change their moral beliefs to be consistent with those of upper management, but may merely change their business ethics so as to appear to have consistent beliefs and increase their promotion potential. These results are somewhat conflicting with those of Ponemon (1990, 1992a) and Shaub (1994) who found that moral reasoning level and position level within the firm are inversely related. These studies suggest that those who are retained by public accounting firms have lower moral reasoning levels. These results are at least partially tempered by the accountability findings that greater perceived accountability pressure is associated with more ethical responses in the business setting. This suggests that one way to try to avoid role morality behavior is to make individuals accountable for their actions. This is consistent with an increase in perceptions of personal responsibility for actions as a means to avoid moral disengagement. In the current climate of diminishing confidence in the accounting profession, the policy implications of this result are promising, given calls for increased monitoring of auditors by the Sarbanes-Oxley Act, for example. Focusing on additional factors shown to be consistent with moral disengagement could also aid in avoiding role morality behavior. Specifically, avoiding distortion of consequences and dehumanization by being aware of the harm caused to others and focusing on the end users of the accounting process would minimize chances for moral disengagement, whereby an individual would be more prone toward role morality behavior. Firm training programs that recognize the importance of these sorts of factors may prove beneficial. The results of the current study represent a new avenue of investigation into understanding accountants’ decision-making in ethically sensitive situations. Previous studies have focused on associating certain behaviors such as making questionable independence judgments (Ponemon & Gabhart, 1990), underreporting audit time (Ponemon, 1992b), and avoidance of

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whistle-blowing (Brabeck, 1984; Arnold & Ponemon, 1991) with lower levels of moral reasoning. The present study shows that the nature of the decision (whether the decision is personal or business) may be another critical factor in influencing accountants’ ethically sensitive decisions. Another interesting finding of the study is the lack of differences between public accountants and non-public accountants, as well as among individuals based on the length of tenure with the firm. Since these factors have been shown to be significant in other studies, additional analysis is warranted. Results of this study should be interpreted with caution due to several limitations. First, the survey elicited responses to eight matched sets of ethically sensitive scenarios. These responses may not be representative of accountants’ responses to other types of ethically sensitive situations. Second, the majority of the sample participants from private industry were from oil and gas firms. Inclusion of more participants from other types of private industry firms would broaden the generalizability of the results. Third, perceptions of the ethical nature of the ethically sensitive situations may have varied across participants (i.e., not all participants may have considered all situations to contain significant ethical issues). A related concern is that the magnitude of the ethically sensitive situations may have been interpreted to be different across the personal and business settings. Also, participants may not have perceived situations that were paired together as a personal and business example of a similar situation to really be representative of similar situations (although obviously, a personal and business setting cannot be identical). Additionally, limitations typically associated with exploratory research and survey instruments apply to this study as well. Notwithstanding these general limitations, the results of this study support the premise that accountants react differently to ethically sensitive situations of a personal and business nature. Specifically, in the majority of the situations examined, business decisions were less ethical than personal decisions, consistent with the theory of role morality. This evidence implies that accountants may be susceptible to role morality behavior in the workplace by means of moral disengagement, as well as the effects of firm socialization. Whether this affects the quality of accountants’ ethically sensitive decisions has yet to be determined.

NOTES 1. Education level was also considered as a control variable, since previous research has found that an individual’s level of moral reasoning increases with education (Thoma, 1986; McNeel, 1994). In the current sample, 40 participants reported having an undergraduate degree, while only 12 reported having a graduate

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degree. Despite this small variation in education level across participants, the regressions were run including a variable representing education level. Results showed no significant variation from those not including an education level variable. 2. Ethically sensitive scenarios (or vignettes) were used in which the participants are asked to place themselves in the situations and indicate their probable reaction. Vignettes were deemed to be the preferable research technique by Cavanaugh and Fritzsche (1985, p. 291) in their study of techniques available to investigate individuals’ ethical principles and behavior and have been used in many previous studies on ethical behavior (Reidenbach & Robin, 1988, 1990; Flory, Phillips, Reidenbach, & Robin, 1992; Hunt & Vasquez-Parraga, 1993; Dreike & Moeckel, 1995). 3. Additional measures pertaining to daily activities on the job and whether the questionnaire was completed at work or at home were also included in this section. Statistical analysis of these measures showed that they were not significantly related to the variables investigated in the study. 4. Note that the ethically sensitive situations as they appear in the appendix are ordered such that the personal and business scenarios dealing with each type of situation are grouped together. This ordering is solely for presentation purposes; the 16 situations were randomly ordered in the actual questionnaire. 5. Given the continuous nature of two of the regression variables, natural log transformations were performed on age and length of employment. Regression results using the transformations were virtually identical to those without the transformations. Consequently, regression results with the transformations are not reported in the chapter. As mentioned earlier in the chapter, age and length of employment were highly positively correlated at 0.8930. To ensure that multicollinearity was not a problem in the regressions, variance inflation factors (VIFs) were computed. Since none of the VIFs were greater than the threshold value of 10 (Neter, Wasserman, & Kutner, 1990, p. 409), both variables were included in the regressions. 6. Generally, accountability is thought of in terms of external parties. Given the wide range of settings in the personal ethically sensitive situations included in the study, however, it was considered to be quite difficult to determine to whom an individual may feel responsible across the various settings. Thus, the measure of personal responsibility or accountability included in the study represents a constant measure across all personal ethically sensitive situations.

ACKNOWLEDGMENTS The author expresses appreciation for the helpful comments of Scott Jackson, Timothy Louwers, Austin Reitenga, Richard Scamell, editor Vicky Arnold, associate editor Robin Roberts, and two anonymous reviewers.

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Nystrom, P., & McArthur, A. (1989). Organizations’ roles in career theory. In: M. Arthur, D. Hall & B. Lawrence (Eds), Handbook of career theory (pp. 490–505). Cambridge, England: Cambridge University Press. Peecher, M. (1996). The influence of auditors’ justification processes on their decisions: A cognitive model and experimental evidence. Journal of Accounting Research, 34(1), 125–140. Pitt, H. (2002). Public statement by SEC chairman: Regulation of the accounting profession (January 17). Washington, DC: U.S. Securities & Exchange Commission. Ponemon, L. (1990). Ethical judgments in accounting: A cognitive-developmental perspective. Critical Perspectives in Accounting, 1(2), 191–215. Ponemon, L. (1992a). Ethical reasoning and selection-socialization in accounting. Accounting, Organizations and Society, 17(3–4), 239–258. Ponemon, L. (1992b). Auditor underreporting of time and moral reasoning: An experimental lab study. Contemporary Accounting Research, 9(1), 171–189. Ponemon, L. (1995). The objectivity of accountants’ litigation support judgments. The Accounting Review, 70(3), 467–488. Ponemon, L., & Gabhart, D. (1990). Auditor independence judgments: A cognitive-developmental model and experimental evidence. Contemporary Accounting Research, 7(1), 227–251. Radtke, R. (2000). The effects of gender and setting on accountants’ ethically sensitive decisions. Journal of Business Ethics, 24(4), 299–312. Reidenbach, R., & Robin, D. (1988). Some initial steps toward improving the measurement of ethical evaluations of marketing activities. Journal of Business Ethics, 7(11), 871–879. Reidenbach, R., & Robin, D. (1990). Toward the development of a multidimensional scale for improving evaluations of business ethics. Journal of Business Ethics, 9(8), 639–653. Rest, J. (1979). Development in judging moral issues. Minneapolis, MN: University of Minnesota Press. Rest, J. (1986). Moral development: Advances in research and theory. New York, NY: Praeger. Shaub, M. (1994). An analysis of factors affecting the cognitive moral development of auditors and auditing students. Journal of Accounting Education, 12(1), 1–24. Smircich, L. (1983). Concepts of culture and organizational analysis. Administrative Science Quarterly, 28(3), 339–358. Tetlock, P. (1985). Accountability: The neglected social context of judgement and choice. In: B. Staw & L. Cummings (Eds), Research in organizational behavior, 9 (pp. 279–332). Greenwich, CT: JAI Press. Tetlock, P., Skitka, L., & Boettger, R. (1989). Social and cognitive strategies for coping with accountability: Conformity, complexity, and bolstering. Journal of Personality and Social Psychology, 57(4), 632–640. Thoma, S. (1986). Estimating gender differences in the comprehension and preference of moral issues. Developmental Review, 6(2), 165–180. Tilker, H. (1970). Socially responsible behavior as a function of observer responsibility and victim feedback. Journal of Personality and Social Psychology, 14(2), 95–100. Turner, C. (2001). Accountability demands and the auditors’ evidence search strategy: The influence of reviewer preferences and the nature of the response (belief vs. action). Journal of Accounting Research, 39(3), 683–706. Velasquez, M., & Rostankowski, C. (1985). Ethics: Theory and practice. Englewood Cliffs, NJ: Prentice-Hall. Weick, K. (1979). Cognitive processes in organizations. In: B. Staw (Ed.), Research in organization behavior 1 (pp. 41–74). Greenwich, CT: JAI Press.

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Weigold, M., & Schlenker, B. (1991). Accountability and risk taking. Personality and Social Psychology Bulletin, 17(1), 25–29. Werhane, P., & Freeman, R. (1999). Business ethics: The state of the art. International Journal of Management Reviews, 1, 1–16. www.cnn.com. (2002). Time names whistleblowers as persons of the year (December 23). http:// www.cnn.com/2002/US/12/23/time.persons.of.year

APPENDIX Ethically Sensitive Situations (1)

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You are preparing your personal tax return. During the year you made several substantial charitable contributions which totaled $1,700. Additionally, you made several smaller contributions for which you did not receive receipts. You estimate the value of these items at no more than $150. When preparing this year’s tax return, you notice that by claiming just over $2,000 in charitable expenses (instead of $1,850) you would drop to a lower tax bracket which would save you about $100 in taxes. Do you claim over $2,000 in charitable contributions? Assume you are a tax accountant within your company. During the prior year, the company purchased an asset for $55,000. Normal shipping, handling, insurance, and setup costs totaled $7,000. This makes the cost basis of the asset $62,000. Owing to a problem installing the asset, however, an additional cost of $5,000 was incurred. You are aware that unusual expenditures on asset installment should be capitalized (making the cost basis of the asset $67,000), but expensing these costs for tax purposes lowers the company’s taxes for the current year. The controller of your company has made it clear that she favors aggressive tax positions and that the probability of these expenditures being audited is minimal. Do you expense the additional cost of $5,000 for tax purposes? You and your brother regularly enjoy competing at computer games. He recently purchased a relatively costly new game and installed it on his PC. You have since been playing the game at his house, but he has gotten the upper hand, since he can practice anytime he wants. He offers to install the game on your PC from the same disk. You’re not sure if you should do this since the software is licensed to be installed on only one computer, but he says everyone does it all the time. Since you hate losing to him, you consider it. Do you copy the game onto your PC? At work you use many different software packages. Several weeks ago your supervisor ordered a new package for you that several of your

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colleagues are currently using. The software is now late in arriving. The package would aid you tremendously in completing your current project, but is not absolutely necessary. Earlier today your supervisor brought her copy of the software over to you and suggested that you copy it onto your computer for use until your copy arrives. You know that the software is licensed to be installed onto only one computer. Do you copy the software? Yesterday you drove to the store with your neighbor and her young son. When you got back to the car, your neighbor noticed that her son picked up a small item from the store worth about $5 that wasn’t paid for. Your neighbor reprimanded the child and then turned to you and said she was ready to go. You asked her if she was going to go back into the store to pay for the item. She said it’s not worth the hassle. Do you refuse to drive her home unless she goes back to the store and pays for the item? While at a colleague’s home you notice several piles of supply items from the office including notepads, diskettes, and boxes of pens. When you ask your colleague about this, she explains that she often brings work home from the office and even if she uses some of the work supplies for personal projects, she sometimes uses personal items for work-related efforts, so it all washes out in the end anyway. Although the supplies don’t total a substantive amount, you are concerned that they have been removed from the office and wonder what else your colleague may ‘‘borrow’’ for personal use. Do you tell your superior about your colleague’s actions? You and your friend are watching the evening news when a picture of a man wanted for questioning is shown. You recognize the man as an old acquaintance of your friend. You ask your friend when was the last time he saw the man. He tells you he actually ran into him last week, but had no idea of any illegal activities he may have been engaged in. You ask your friend whether he is going to call the authorities to report his encounter with the man and he says that he doesn’t want to get involved. Do you call the authorities to report your friend’s association with the man? You and a colleague of yours started work at the same time after graduating college. Both of you are quick learners, but he seems to always get things done faster and is continually receiving praise from your superior who supervises you both. Since your company operates on flexible hours, you and your colleague rarely leave the office at the same time. Yesterday you left at the same time, however, and you

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noticed that he was taking files home with him to work on. Since your company considers you salaried employees, you don’t receive overtime. On the one hand you think that if your colleague wants to spend his personal time on work that’s his problem, but on the other hand he’s making you look bad. Do you tell your superior about your colleague’s actions? Last week you discussed your personal investments over lunch with a friend of yours from college. Your friend told you that since he has been working at a brokerage firm, he’s privy to a wealth of inside information about a variety of stocks. Over the course of the meal, several pieces of information about stocks that you own are disclosed. Your friend also tells you how to use this information discreetly and suggests that you may want to use it to your advantage. He divulges that he has done this several times without being caught. Do you trade your stock based on this inside information? While at lunch with several of your colleagues last week you overheard a discussion about a client company’s financial situation. An accountant working closely with the company noticed significant decreases in sales and receivables. He wasn’t sure exactly how bad it was until he heard a rumor at the company about the possibility of filing for bankruptcy. You’re now worried because you own a significant block of shares in the company. Do you sell the shares based on this inside information? Two of your good friends are engaged in negotiations for the sale/ purchase of a used car. Several years ago the seller of the car was involved in an accident which you witnessed, but the buyer is unaware of. The car sustained substantial damage, but was repaired. Now the seller is representing the car as never having been in an accident. The difference in the appraisal value of the car is difficult to measure. Do you tell the buyer of the car about the accident? While on a trip out of town on business you had dinner with your sister. Your company has a policy of reimbursing dinner expenses up to $50 per meal. The total cost for this meal for both you and your sister was $35.70. The cost of your meal alone was $16.30. You know that others in your company routinely submit claims for dinner expenses for nonbusiness parties. Do you claim the entire amount for reimbursement? Yesterday your second grader came home and started watching cartoons. During one of the programs he said ‘‘see that little boy, he’s not as smart as I am.’’ The little boy was of a different race and when you asked your son why he thought that, he said his teacher had told

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him so during school. Do you take action to reprimand the teacher on the basis of racial discrimination? A couple of weeks ago your division posted a job opening. You told a good friend of yours who was looking to switch jobs about the position and she submitted a resume. She met the job qualifications and was one of three candidates interviewed. She told you that she thought the interview went very well and was excited about the possibility of being hired. Your direct superior also told you that he enjoyed interviewing your friend and that in his opinion, she should get the job. Yesterday when you came into the office you found out that one of the other candidates got the job. When inquiring as to the reasons that your friend didn’t get an offer, you were told that although her credentials were the best of the three, her personality just didn’t fit with the rest of the employees in the division. You are somewhat suspicious of this explanation, since your friend is a minority and you have heard some of the senior personnel in the division make racial slurs. Do you investigate the matter further on the grounds of racial discrimination? While talking with your neighbor he tells you that at the next neighborhood association meeting he intends to use the open forum time to plug his company’s new product line. The neighborhood association has rules about this, but the president is often somewhat negligent in enforcing them. When you tell your friend that he probably shouldn’t use the meeting time in this way, he responds by saying that it’s a free country and he can speak about anything he wants to. Do you attempt to contact the president and others in the association to try to block your friend from speaking? Based on your recent work-team experiences you were asked by your division head to draft a memo highlighting the good and bad points. You recalled many of each and drafted the memo accordingly. After your supervisor reviewed the memo she told you to tone it down a bit. You ask her what she means by that and she basically tells you that in her opinion it’s not good to give either too much praise or too many admonishments. You say that is counterproductive to the purpose of the memo to not disclose the actual occurrences. She tells you to print the memo as is at your own risk. You feel that you’ve done the job that was asked of you and you have a right to tell it like it is. Do you print the memo as is?

THE EFFECT OF MANAGER’S MORAL EQUITY ON THE RELATIONSHIP BETWEEN BUDGET PARTICIPATION AND PROPENSITY TO CREATE SLACK: A RESEARCH NOTE Adam S. Maiga ABSTRACT This chapter uses agency theory and ethics literature to assess the moderating effect of manager’s moral equity on the relation between budget participation and propensity to create slack. Moral equity is the major evaluative criterion for ethical judgment, is based on the overall concept of fairness, justice and right and is often very influential in contemporary moral thought (Robin & Reidenbach (1996) Journal of Business, 5(1) 17–28). The results indicate that a manager’s moral equity moderates the effect of budget participation. For managers with high moral equity, the relationship between participation and manager’s propensity to create slack is significantly negative while, for managers with low moral equity, the relationship is significantly positive. Further analyses indicate that

Advances in Accounting Behavioral Research Advances in Accounting Behavioral Research, Volume 8, 139–165 Copyright r 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1475-1488/doi:10.1016/S1475-1488(04)08006-8

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high budget participation and high moral equity result in less propensity to create slack than high budget participation and low moral equity.

INTRODUCTION A major concern expressed in the literature is that the use of participative budgetary control processes may result in the generation of slack budgets (Antle & Eppen 1985). Budgetary participation is the means by which subordinate managers influence plans and their methods of implementation, thereby sharing in the decision-making process with their superiors on matters that affect their areas of responsibility (Milani, 1975; Brownell, 1982). Budget slack, defined as overstated expenses, understated revenues, or underestimated performance capabilities, allows managers to obtain excess resources and to shirk more effectively (Lukka, 1988).1 Several accounting studies invoke the principal-agent framework to argue that subordinates have more accurate information than their superiors regarding local conditions, i.e., private information about local conditions (Merchant, 1981; Christensen, 1982; Chow, Cooper, & Waller, 1988; Waller, 1988). Subordinates’ participation in the budget-setting process may give superiors the opportunity to gain access to local information (Baiman, 1982; Baiman & Evans, 1983; Magee, 1980). Participation allows subordinates to communicate or reveal some of their private information that may be incorporated into the standards or budgets against which their performance would be assessed (Baiman & Evans, 1983). But subordinates may misrepresent or withhold some of their private information, resulting in understated revenues and overstated expenses, which could lead to budgetary Slack (Christensen, 1982; Merchant, 1985; Pope, 1984; Young, 1985). The results of prior studies examining the relationship between subordinates’ participation and budgetary slack have not been consistent. For example, Dunk (1993), Merchant (1985), Cammann (1976), and Onsi (1973) report evidence suggesting that participation reduces the amount of budget slack. On the other hand, Lukka (1988) and Young (1985) report that budget participation and the creation of slack may be positively related; and Collins (1978) reports that the relationship between budgetary participation and budgetary slack is not significant. The above studies view budgetary slack as an organizational or behavioral issue (Douglas & Wier, 2000) based on moral hazard2 and are not designed to distinguish wealth maximization from alternative preferences such as ethics,

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fairness, trust, accountability or integrity, and conscience (Luft, 1997). In agency theory, moral hazard is the basis for a manager’s economic decision behavior, while moral development theory suggests that individuals’ decision behavior is influenced by their level of ethical principles (Rutledge & Karim, 1999; Douglas & Wier, 2000; Luft, 1997; Noreen, 1988). Forsyth (1992) proposes that individuals judge or approach business practices, such as the creation of budgetary slack, as ethical or unethical, and then decide whether or not to engage in those practices. Noreen (1988) suggests that while there may be some people who are unreservedly opportunistic, others do constrain their own behavior out of an ethical sensibility or conscience. As depicted in Fig. 1, this paper suggests that the relation between manager’s budget participation and his/her propensity to create budgetary slack may be moderated by his/her moral equity. The moral equity factor used in past empirical research accounts for the major explanatory impact in the ethical evaluation process. Moral equity represents a universal ethics construct, and it is the major evaluative criterion for ethical judgment (Robin & Reidenbach, 1996; Flory, Phillips, Reidenbach, & Robin, 1992). The philosophy of moral equity is based on the overall concept of fairness, justice and right, and has been very influential in contemporary moral thought. Moral equity can be used as the basis for arguing against unethical business practices. Organizational policies are likely to be most effective if they can be justified according to the ethical concepts used by the people involved (Arrow, 1985; Baiman, 1990; Douglas & Wier, 2000). Jones (1991) suggests that concerns for ethics are jointly determined by characteristics of the situation and the individual. Hence, moral equity could be a useful tool for looking at how managers make certain ethical judgments in a participative budget setting. Essentially, as both the subject and consequence of unethical business behavior grow in importance, so does the need to know

Moral Equity

Budget Participation

Fig. 1.

Budget Slack

Model Showing Moral Equity as Moderating Variable.

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the grounds on which managers tend to make ethical judgments and their impact of those judgments. Such insights will help to expand upon previous studies. Also, the empirical investigation of this study will help to understand how the manager’s level of moral equity impacts the relationship between budget participation and propensity to create slack. Many scholars have highlighted, from both the theoretical and empirical perspectives, the impact of a manager’s budget participation on budget slack. Most contributions, however, did not analyze the link between a manager’s budget participation and his/her propensity to create slack within the context of the manager’s moral equity. The purpose of this study is to assess the moderating effect of manufacturing business unit3 managers’ moral equity on the relationship between the extent of their budget participation and propensity to create slack. This study proposes that moral equity influences managers’ behavior in participative budgeting. Specifically, managers with high moral equity may use their influence primarily to reduce slack while managers with low moral equity may use their influence primarily to create slack. The remainder of this chapter is organized as follows. The next section draws upon previous literature to develop a theoretical framework linking the variables of interest. The methodology and statistical results are discussed in the third and fourth sections, respectively. The paper concludes with a discussion of the findings and suggestions for future research.

LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT Agency theory posits that information asymmetry may systematically influence the extent to which participation leads to slack budgets. Information asymmetry arises when subordinates (agents) are in possession of information that affects the decision process between subordinates and superiors (principals) (e.g., Baiman & Evans, 1983; Penno, 1984; Coughlan & Schmidt, 1985). When the agent has private information that is not available to the firm (i.e., information asymmetry), the principal can no longer verify if the agent’s decisions are in accordance with the firm’s interests. This provides the agent with the opportunity to shirk by making decisions that conflict with the interests of the firm. When the agent is under conditions of an incentive to shirk and an opportunity to shirk (e.g., private information), the problem of moral hazard can occur. Magee (1980) proposes that budgets could be improved if principals were aware of local information held by subordinates prior to the budgets being set,

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i.e., information asymmetry is eliminated. Baiman (1982), Chow (1988), Blanchard and Chow (1983), and Waller (1988) argue that subordinates in many organizational settings have more accurate information than their superiors on the factors influencing performance. Baiman and Evans (1983) propose that firms in which subordinates have such information, participation-based management control systems allow subordinates to reveal or communicate some of their private information that may then be incorporated into the standards or budgets against which their performance is evaluated. Unfortunately, agents may misrepresent or withhold from their principals some or all of their locally- based information, which could lead to budgets containing slack (Christensen, 1982; Baiman & Sivaramakrishnan, 1991). The argument for agents either falsifying or withholding their private information is that managers plan to have slack in their budgets to enable budgeted objectives to be met and improve the likelihood that they will be compensated for their efforts. For example, Waller (1988) argues that if subordinates believe their communicated private information will be employed in standard setting for the purpose of performance evaluation, they may have an incentive to bias their communications to facilitate the setting of relatively easy standards. This problem, Waller (1988) stresses, is particularly salient when subordinates’ pay schemes are budget-based and the budget-setting process is participative. As managerial compensation is often based on budget achievement and information provided by agents is likely to be used in their performance evaluation (Christensen, 1982; Baiman & Evans, 1983), the prospect of dysfunctional consequences arising from the presence of information asymmetry may be non-trivial. Young (1985) warns that the existence of private information in conjunction with participation may result in subordinates intentionally building excess resource requirements into budgets or consciously understating production capabilities. These agency-based studies capture behavior shifts in response to incentives in particular settings, but do not provide decisive tests of the simple self-interest model vs. plausible alternative utility functions. Luft (1997) questions the commonly cited belief that self-interest is a good approximation for behavior and argues that prior tests have lacked the power to distinguish between self-interested and ethical models of behavior. Thus, while the agency-based studies offer evidence for the existence of a preference for wealth, they do not offer evidence against the existence of other significant preferences (Luft, 1997). Hence, this study proposes that whether managers are predisposed to pursuing self-interests or organizational interests depend upon their level of moral equity. Ethical concerns typically arise in situations where self-interest

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conflicts with a moral duty to others (Bowie & Duska, 1990). DeGeorge (1992) asserts that ethically motivated agents exercise effective self-control that no amount of external control can match, and that researchers should utilize, promote, and incorporate such motivation. A person who fails to recognize a moral issue will fail to employ moral decision-making schemata (Jones, 1991). The consideration of budget slack from the perspective of ethical decision-making assumes that a person must be able to recognize budget slack creation as a moral issue. Slack creation may be inconsistent with role-related norms and desired virtues of professional managers, and the resource misallocation that results is detrimental to other organizational units and to investors (Merchant, 1995). Thus, creation of budget slack is an ethical dilemma – a predicament with a moral component (Douglas & Wier, 2000). Opportunistic behavior on the part of the agent may be controlled in part by the agents’ concerns for reputation or ethics (see Arrow, 1985; Baiman, 1990), and the idea that individuals can be strongly motivated to pursue organizational interests (i.e., without the prospect of self-interest) has been shown in the literature. For example, in an experimental study, Stevens (2002) investigated the effects of two potential controls for opportunistic self-interest – reputation and ethics. The results provide strong evidence that reputation and ethics reduce budgetary slack. While slack levels under a slack-inducing pay scheme were higher than in prior experimental studies, subjects still restricted the amount of slack in their budgets below the maximum, and thereby failed to maximize their pay. This result is consistent with findings of Evans, Hanna, Krishnan, Moser (2001) that subjects often sacrifice wealth to make honest reports of productive capability. In Evans et al. (2001), budgetary slack is negatively associated with a measure of ethical responsibility from the pre-experiment personality questionnaire as well as reputation and ethical concerns expressed in the exit questionnaire. As information asymmetry regarding productive capability increased, subordinates expressed lower reputation concerns, thereby reducing the superior’s ability to monitor the slack in their budget. Ethical concerns, however, were not diminished with increases in information asymmetry. These results suggest that reputation is a socially mediated control whereas ethics is an internally mediated control for opportunistic self-interest. In an experimental study, Douglas, Davidson, & Schwartz (2001) investigated auditors’ ethical judgments in situations typical of those they face in practice. Results indicate that ethical orientation is related to ethical judgments in high (but not low) moral intensity situations. These results support Jones’ (1991) issue-contingent argument that suggests that differences in

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characteristics of a moral issue itself, its moral intensity, affect individuals’ responses to the issue. The chapter paper proposes that in participatory budgeting, the individual’s level of moral equity influences his or her attempts to create budgetary slack. For subordinates with high moral equity, budget participation and budgetary slack may be inversely related. In a high budget participation setting, managers are likely to make use of all possible sources of information in order to increase the accuracy of the budget decision (Gul, 1991). Since information availability is improved through participation, participative budgeting will lead to more analytical and accurate decision-making. Therefore, in a high participatory budget, subordinates with high moral equity will use their private information to produce an accurate budget, i.e., to reduce slack and benefit the organization. In contrast, when budget participation is low, subordinates with high moral equity will have little opportunity to share their private information, and superiors will have only limited success in attempting to reduce slack. Therefore, there is less opportunity to reduce budgetary slack. For individuals with low moral equity, budget participation provides opportunities for the subordinate to create slack. For these individuals, the relation between budget participation and budgetary slack is positive, i.e., as budget participation increases, budgetary slack increases. Since the subordinate has low moral equity, he/she may use participation to introduce slack and gain favorable future evaluations (i.e., the subordinate seeks to maximize self-interest). The proposed effects of moral equity on the relationship between budget participation and budgetary slack results in the following hypothesis, stated in alternative form: H1. There is an interaction between moral equity and participative budgeting that affects budgetary slack. For managers with high moral equity, increasing budget participation will decrease budgetary slack. For managers with low moral equity, increasing budgetary participation will increase budgetary slack.

RESEARCH METHOD Sample A questionnaire was administered to a sample of managers (plant managers, manufacturing managers, operations managers, marketing managers,

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research managers, distribution managers) from manufacturing companies in the USA Manufacturing organizations were selected for study, as the use of budgets in such organizations is common. The primary source of sample selection was the Industry Week series. For this study, an initial mailing list of 1,103 business units was obtained and a random sample of 650 names was selected.4 A cover letter explained the purpose of the study with an exhortation for participation and cooperation. Based on the survey responses, two criteria were used to select the participants: (1) each participant had budget responsibility in the subunit and (2) each unit was a profit-center (see appendix, part II). An abbreviated copy of the questionnaire used in the study appears in the appendix. In the first 3 weeks, 167 questionnaires were returned; that was followed by a second mailing which resulted in 56 new responses. Of the 223 returned questionnaires, only 193 were usable.5 In an attempt to increase the number of respondents, 150 non-respondents were chosen randomly and contacted by telephone; that resulted in a return of 78 questionnaires of which 58 were usable.6 Overall, this data collection led to 251 usable responses7 with a 38.61% response rate.

Measurement and Validation of Variables The variables used to answer the research question are budget slack, budget participation, and moral equity. The measurement of the variables is obtained from average responses from the questionnaire results. The factor loadings, explained variances, and reliability measures are reported in Table 1. The appendix contains an abbreviated copy of the research questionnaire used to measure the self-reported variables in this study. Propensity to Create Slack Propensity to create slack is operationalized using the three-item scale used in Kren (1993) and adapted from Merchant (1985). Merchant’s original fouritem scale was examined by Hughes and Kwon (1990) who suggested deleting one item to improve the scale’s reliability. Thus this study uses the three items suggested by Hughes and Kwon (1990). The response scale is a 7-point Likert-type scale ranging from one (strongly disagree) to seven (strongly agree). Principal component analysis with varimax rotation was used to examine the extent to which these measures are interrelated and produced one factor with total variance of 88.208% and an eigenvalue greater than one. Cronbach alpha was 0.928, indicating that the measures are reliable.

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Table 1.

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Factor Loadings, Explained Variance and Reliability Measures. Factor Loading

Budget slack To protect himself, a manager submits a budget that can safely be attained In good business times, your superior is willing to accept a reasonable level of slack in the budget Slack in the budget is good to do things that cannot be officially approved Budget participation I am involved in setting all of my budget My superior clearly explains budget revisions I have frequent budget-related discussions with my superior I have a great deal of influence on my final budget My contribution to the budget is very important My superior initiates frequent budget discussions when when the budget is being prepared Moral equity Scenario A Fair/unfair Just/unjust Morally right/ not morally right Acceptable/unacceptable to family Scenario B Fair/unfair Just/unjust Morally right/not morally right Acceptable/unacceptable to family Scenario C

Explained Variance

Reliability Coefficient (Cronbach Alpha)

88.208

0.928

86.154

0.905

72.519

0.873

69.472

0.852

63.598

0.808

0.764

0.673

0.782

0.864 0.793 0.914 0.696 0.731 0.805

0.859 0.925 0.903 0.680

0.873 0.920 0.885 0.620

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Table 1. (Continued ) Factor Loading

Budget slack Fair/unfair Just/unjust Morally right/not morally right /unacceptable to family Scenario D Fair/unfair Just/unjust Morally right/ not morally right Acceptable/ unacceptable to family

Explained Variance

Reliability Coefficient (Cronbach Alpha)

88.208

0.928

65.561

0.825

0.799 0.886 0.774 0.706 0.884 0.851 0.799 0.686

Budget Participation Budget participation is measured using the Milani (1975) six-item measure. The response scale is a 7-point Likert-type scale ranging from one (strongly disagree) to seven (strongly agree). A principal component analysis with varimax rotation produced one factor with total variance of 86.154% and an eigenvalue greater than one. A reliability check for the measures produced a Cronbach alpha of 0.905, indicating that the measures are reliable. Moral Equity Four scenarios that the IMA Resources Center developed and that Flory et al. (1992) used in a subsequent study are used in developing a measure of moral equity because they portray substantially more involved, realistic situations. Each scenario included an action statement to assure that all respondents were reacting to the same stimulus. The action statement was particularly necessary with the situations described in the present study. Consequently, the four scenarios are used in this study. Each scenario portrays a different sort of ethical dilemma. Scenarios A and D describe actions that might not be perceived as explicitly ethical or unethical, while scenarios B and C feature what most would label as definitely unethical behavior. Scenario A describes a superior who is making questionable expenditures that he claims meet upper management’s approval. The manager, who may find himself in a marketing environment different from his background, is faced with establishing the proper lines of

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authority in connection with an issue that may not be a violation of company policy. Scenario B involves a controller who is asked to falsify external financial statements for the purpose of procuring additional working capital. Although this may actually happen in some companies, managers typically agree that falsification of external statements is wrong. This is also true of the specific violations of company policy shown in scenario C. A difference in scenario C, besides the fact that it is an internal situation, is that the manager had previously violated company policy, and now, in an attempt to rectify a resulting failure, decides to violate the policy again. Scenarios A, B, and C all implicitly involve a manager’s job security; but in each situation, the individuals are seemingly concerned with their company’s welfare. In contrast, scenario D emphasizes the manager’s personal problems. In this scenario, company policy is not clearly delineated, and there could be some uncertainty whether the manager’s action is unethical. The additional background information provided in scenario D allows the respondent to empathize with the manager’s personal difficulties, although it is unclear whether his personal situation has any bearing on his decision. Respondents were asked to react to each scenario using a set of measures developed by Reidenbach and Ronald (1991). The set focuses on the dynamics of decision making regarding a manager’s moral equity. It consists of four bipolar seven-point Likert scales (Fair/Unfair, Just/Unjust, Morally right/Not morally right, Acceptable to my family/Unacceptable to my family). Factor analysis with principal component analysis with varimax rotation is used to examine the extent to which the moral equity measures under each scenario are interrelated. One factor with eigenvalue 41 emerged from the analysis for each scenario, with corresponding varimax rotation factor solution retaining at least 67.31% of the total variance in the data. The Cronbach alphas were 0.873, 0.852, 0.808, and 0.825, respectively, suggesting that the measures are reliable. To assess the content validity of the scales, each moral equity measure is regressed on its corresponding ethical intention measure to test whether the constructs in fact measure manager’s moral equity (Flory et al. 1992). Manager’s ethical intention to each scenario is measured on a 7-point bipolar scale range from (1) ethical to (7) unethical. This is a common validation procedure in the social sciences. A high covariation (R2) between ethical intention and moral equity suggests that the moral equity captures much of what the respondents mean by ‘‘ethical’’ (Flory et al. 1992). The individual b values also help define the concept of ‘‘ethics’’ for the respondents. The results which appear in Table 2 indicate that under the four

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Table 2. Scenario

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A Comparison of the Moral Equity Measure and the Ethical Intention Measure. Regression Results

Moral Equity b1

2

R A B C D

0.608 0.493 0.567 0.654

0.523 0.691 0.709 0.653

scenarios the moral equity measures explain 49.3–65.4% of the variance in what the managers defined as ethical. Corresponding b values, ranging from 0.523 to 0.709, suggest that the ethical intention measures capture much of what the respondents mean by ‘‘ethical.’’ Research Model and Testing Procedures The average for the six responses for budget participation, the overall average for the four scenarios measuring moral equity, and the average for the three responses for propensity to create slack were computed in order to test the hypothesis. The hypothesis posits a moderating effect of manager’s moral equity on the relationship between budget participation and propensity to create slack. Based on this approach, the following regression model is employed: PCS ¼ a0 þ b1 BP þ b2 ME þ b3 ðBP  MEÞ þ 

(1)

Where PCS is the propensity to create slack; BP the budget participation; ME the moral equity; a0 the intercept; b1 ; b2 ; and b3 are the regression coefficients; and  the error term.

RESULTS Descriptive Statistics In Table 3, the mean and standard deviation values of the variables used to answer the research question denote that many respondents indicated some probability of engaging in the activity specified in the scenarios and their level of budget participation. Additional information on respondents’ characteristics is provided in Table 3. The respondents to the question regarding

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Descriptive Statistics.

Table 3. Variables

Number of Items

Propensity to create slack Budget participation Moral equity Size (number of employees) Years at division Years in management position Net sales (million) a

Standard

3

2.685

Mean

Theoretical Range

Actual Range

1.392

1–7

1.00–6.66

1.529 0.722 148.897

1–7 1–7 N/A

2.33–6.66 1.66–4.66 32–764

6 4 N/A

4.890 3.817a 240.841

N/A N/A

9.143 13.116

8.999 9.035

N/A N/A

4–16 5–26

N/A

$5.524

$1.339

N/A

1.371–12.623

Overall mean.

Table 4.

Correlations among Variables. BP

BP ME BP  ME PCS

ME

1 0.121 0.055 0.195 0.002 0.104 0.100

— 1 — 0.012 0.844 0.046 0.471

BP  ME — — — 1 — 0.498** 0.000

 po0.001.

number of years with the division have a mean of 9.14 in their current position. To the number of years in management question, respondents indicated a mean of 13.12 years. The results also show that the average number of employees equals 241. For the 194 divisions that provided sales figures, the mean was $5.4 million. Table 4 presents the correlation matrix for the variables in the study. Both budget participation and propensity to create slack negatively correlate with the interaction term (r ¼ 0:195; p ¼ 0:002; r ¼ 0:498; p ¼ 0:000 respectively). Hypothesis Test To test the hypothesis, standard scores8 are used for the independent variables in order to provide a clearer basis to interpret signs of the interaction

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coefficient (Brownell & Hirst, 1986) and to minimize mulitcollinearity between main and cross-product effects (Cronbach, 1987). The interaction term is constructed by multiplying the standardized scores of budget participation and moral equity for each respondent. Tolerance greater than 0.10 is achieved. Variance inflation factor values from the regression analyses conducted for all the variables are less than 2. Hence, mulitcollinearity does not appear to be a problem. To permit an acceptance of the hypothesis, the coefficient b3 in Eq. (1) is required to be (a) significant, indicating an interaction between participation and moral equity affecting manager’s propensity to create slack and (b) negative to support the direction of the hypothesis. The results of the regression analysis using Eq. (1) appear in Table 5 and indicate that the b3 coefficient is statistically significant and negative (t ¼ 8:840; p ¼ 0:000). As predicted by the hypothesis, budget participation and moral equity interact to affect manager’s propensity to create slack. To provide additional support for the hypothesis, the data are divided into two groups on the basis of the overall mean score for moral equity – high moral equity and low moral equity categories. Moral equity scores below the mean are classified as low, while moral equity scores above the mean are classified as high. Next, budget participation is regressed on propensity to create slack for each group. Results in Table 6 show that budget participation is significant and positive with low moral equity group (b ¼ 0:488; t ¼ 6:131; po0:001 with R2 ¼ 0:239; and adjusted R2 ¼ 0:232). For the high morality group, results show that budget participation is significant and negative (b ¼ 0:355; t ¼ 4:279; po0:001 with R2 ¼ 0:126; and adjusted R2 ¼ 0:119). Therefore, the results provide additional support for the hypothesis.

Regression Analysis.

Table 5. Standardized Coefficients b

Intercept BP ME BP  ME

— 0.002 0.039 0.497

T

Significance

Colinearity Statistics Tolerance

Variance Inflation Factor

55.595 0.041 0.707 8.840

0.000 0.968 0.480 0.000

— 0.948 0.985 0.962

— 1.055 1.015 1.040

R2 ¼ 0:249; n ¼ 251; F ð3; 247Þ ¼ 27:361; po0.0001.

Regression Results for Low Moral Equity b Intercept BP R2 Adjusted-R2 F N

2.740 0.419

S.E. 0.074 0.068 0.239 0.232 37.586 (po0.000) 122

t

Significance

37.144 6.131

0.000 0.000

Regression Results for High Moral Equity b 2.729 0.335

S.E. 0.072 0.078

t

Significance

38.093 4.279

0.000 0.000

Effect of Manager’s Moral Equity

Regression Results For Low And High Moral Equity Groups.

Table 6.

0.126 0.119 18.307 (po0.000) 129

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A Further Analysis of the Interaction Effects In order to facilitate understanding of the interaction effect, the interaction term was investigated mathematically, as shown in Eqs. (2) and (3) below, and then graphically presented in Fig. 2. The steps taken for this analysis are: (a) taking the partial derivative of Eq. (1) over ME, (b) determining the value of BP (the inflection point) at which this variable would have no moderating effect on the relationship between ME and (ME  BP), and (c) plotting the joint effect of the main and interaction terms (see Schoonhoven, 1981; Southwood, 1978). The use of steps (a) and (b) above produced Eqs. (2) and (3) as @PCS=@BP ¼ b2 þ b3 ME

(2)

and ME ¼

b2 =b3

(3)

Substituting the values for b2 and b3 from Table 4 into Eq. (3) produced ME ¼ 0:004 (the inflection point of the standardized equation). Again, substituting the values for b2 and b3 from Table 5 and values for ME into Eq. (2), the joint effect of the main and interaction terms was determined. The results presented in Fig. 2 show the relationship between participation and budget slack due to the moderating effect of moral equity. For high moral equity scores, i.e., moral equity values greater than 3.821 (3.817, the mean+0.004, the standardized inflection point), budget participation is associated with low budget slack. For moral equity values o3.821, budget participation is associated with high budget slack. For moral equity value 7

∂B-Slack / ∂BP

6 4.968 1

2

3

3.821 4

4

3

Fig. 2.

The Effect of Moral Equity on the Relationship between Budget Participation and Propensity to Create Slack.

Effect of Manager’s Moral Equity

Table 7.

155

Mean Scores for Propensity to Create Slack. Low Budget Participation

High Budget Participation

Low Moral Equity

a

2.329 0.876b n ¼ 62

3.049 0.827 n ¼ 60

High Moral Equity

2.977 0.839 n ¼ 60

2.434 0.798 n ¼ 69

a

Mean. Standard deviation.

b

C3.821, the association between budget participation and budget slack is found to be neutral. Note that the value which is 3.821 for moral equity is (a) within the range of values observed in this study and (b) close to the mean value for moral equity (see Table 3 above). This value therefore represents the average moral equity in this study. To investigate further the nature of the interaction, measures of moral equity and budget participation are dichotomized (high, low) based upon sample means. Table 7 indicates mean slack scores for the groups formed by the process. A post ad hoc Scheffe test was carried out to compare the cell means. The information reveals that when there is high budget participation, slack differs between levels of moral equity. Mean slack for the high moral equity group, 2.434, is significantly less than mean slack for the low group, 3.049 (p ¼ 0:001). Also, with low budget participation, slack differs between the levels of moral equity. Mean slack for low moral equity, 2.329, is significantly lower than mean slack for high group, 2.977 (p ¼ 0:000). However, there is no significant difference between low budget participation/high moral equity group and high budget participation/low moral equity group means (2.977 vs. 3.049; p ¼ 0:973), and low budget participation/ low moral equity group mean slack is not significantly different from that of high budget participation/high moral equity (2.329 vs. 2.434, p ¼ 0:915).

CONCLUSION This study extends our understanding of budgetary slack by investigating the moderating effect of a manager’s moral equity on the relationship between budget participation and manager’s propensity to create slack. The results suggest that the relationship between budgetary participation and

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budgetary slack is moderated by moral equity. For managers with high moral equity, the relationship between participation and slack is significantly negative while, for managers with low moral equity, the relationship is significantly positive. However, as reported in Table 7, when budget participation is low, managers with low moral equity create significantly less slack than those with high moral equity. This analysis seems counterintuitive. Accordingly, different possible explanations are provided below: (1) Given that slack creation was so low in the sample (overall mean for slack of 2.685, which is well below the scale mid-point of 4) and the means of the four cells in Table 7 were also below this mid-point, one cannot rule out that the results are an artifact of this sample. (2) As a construct, moral equity and slack may be seen from both a clinical and an emotive sense. Also, slack creation may be situational and therefore will affect the ethical decision-making process by altering intention. That is, an individual may judge an action to be ethical, and yet act in an unethical fashion due to a greater profit gained through the unethical action (Hunt & Vitell 1986). Hence, managers’ questionnaire responses may have been founded on an emotive platform and the results could be prejudiced. (3) It may also be the case that cross-sectional survey methods are not sufficient for the investigation of moral equity and propensity to create slack, given the nature of the constructs, as well as the validity implications of examining relationships within a single industry, especially when some non-response bias may still be present. (4) Construction of the measures of budget participation, moral equity, and propensity to create slack from the literature reviews and factor analyses may have driven the results. In addition, it could be argued that the findings obtained in this research are partly attributable to a poor measure of moral equity and the dependent variable (propensity to create slack). (5) Also, as argued by Brownell (1982), the impact of budget participation on budget slack might be contingent on other groups of variables such as cultural, organizational, and interpersonal. Future research should focus on those contingent variables in order to develop a comprehensive and integrated model specifying the conditions under which budgetary participation will produce favorable outcomes. Future research could go well beyond the specific suggestions made here. The treatment of ethical issue was limited to moral equity. The ethics literature suggests that a number of other ethical factors, such as relativism,

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idealism, and contractualism, may have significant effects on behavior. Also, future research may need to investigate whether individuals have voice, choice, or both in participative budgeting (Libby, 1999) and assess the impact on budget slack within the ethical context. The implications of these effects for budgeting remain to be investigated. Finally, field evidence of these issues and well-designed experimental and case studies, and archival tests are needed to distinguish among different explanations for observed behavior to support the predictions being tested. Despite these limitations, the current research is important. The current study demonstrates that moral equity is an important variable in the relationship between budget participation and budget slack. The findings, based on the research question, suggest that organizational goals may take precedence over self-interest for those managers with high moral equity. Since this study is the first systematic investigation on the moderating effect of moral equity on the relationship between budget participation and manager’s propensity to create slack, it may enhance the richness of agency research and provide additional insights to resolve some of the complex relationships and issues in this traditional, but still important and interesting, research area.

NOTES 1. Slack can also serve a positive purpose in the organization. For example, Cyert and March (1992) suggest that slack can be used to absorb fluctuations in an uncertain operating environment. Merchant (1989) suggests that superiors may allow slack in subordinates’ budgets to encourage coordination, motivation, and innovation. This contrasts with the agency theory perspective that budgetary slack is an inefficiency reflecting the effect of moving from an environment with perfect information to one with information asymmetry (e.g., Magee, 1980; Christensen, 1982; Baiman & Evans, 1983; Penno, 1984). 2. Moral hazard is defined as an incentive to act in one’s self-interest in conflict with the organization’s overall goals while being able to hide those actions through privately held information (Baiman, 1982). 3. The term business unit is used to refer to a self-contained sub-unit (e.g., division) of a larger corporation. 4. Research budget constraints did not allow larger sample size selection. 5. The unusable returned questionnaires were either incomplete or did not meet the two selection criteria. 6. Because of contravening company policy, some preferred not to participate. 7. Discriminant analysis was used to compare the early vs. late respondents (Fowler, 1993). Results revealed that the two groups did not differ significantly in either the level of the variables or in the relationship between the variables at the 0.05 level. This suggests that non-response bias is not a problem.

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8. The independent variables were transformed into new measurement variables with a mean of 0 and standard deviation of 1.

ACKNOWLEDGMENTS The author wishes to thank the editor, associate editor, and two anonymous reviewers for the helpful comments. Additionally, the author acknowledges the comments and suggestions provided by Fred A. Jacobs of Georgia State University.

REFERENCES Antle, R., & Eppen, G. D. (1985). Capital rationing and organizational slack in capital budgeting. Management Science, 31, 163–174. Arrow, K. J. (1985). The economics of agency. In: J. W. Pratt & R. J. Zeckhauser (Eds), Principals and agents: The structure of business (pp. 37–51). Boston, MA: Harvard Business School Press. Baiman, S. (1982). Agency research in managerial accounting: A survey. Journal of Accounting Literature, (Spring), 154–213. Baiman, S. (1990). Agency research in managerial accounting: A second look. Accounting, Organizations and Society, 15(4), 341–371. Baiman, S., & Evans, J. H., III (1983). Pre-decision information and participative management control systems. Journal of Accounting Research, (Autumn), 21(2), 371–395. Baiman, S., & Sivaramakrishnan, K. (1991). The value of private pre-decision information in a principal-agent context. The Accounting Review, 66(4), 747–766. Blanchard, G. A., & Chow, C. W. (1983). Allocating indirect costs for improved management performance. Strategic Finance, 64(9), 38–41. Bowie, N., & Duska, R. (1990). Business Ethics (2nd ed.). Englewood Cliffs, NJ: Prentice Hall. Brownell, P. (1982). The role of accounting data in performance evaluation, budgetary participation, and organizational effectiveness. Journal of Accounting Research, 20, 12–27. Brownell, P., & Hirst, M. (1986). Reliance on accounting information, budgetary participation, and task uncertainty. Journal of Accounting Research, 24(2), 241–249. Cammann, C. (1976). Effects of the use of control systems. Accounting, Organizations and Society, 1, 301–313. Chow, C. W., Cooper, J. C., & Waller, W. S. (1988). Participative budgeting: Effects of a truthinducing pay scheme and information asymmetry on slack and performance. The Accounting Review, 63, 111–122. Christensen, J. (1982). The determination of performance standards and participation. Journal of Accounting Research, 20, 589–603. Collins, F. (1978). The interaction of budget characteristics and personality variables with budgetary response attitudes. The Accounting Review, (April), 53(2), 324–335. Coughlan, A. T., & Schmidt, R. M. (1985). Executive compensation, management turnover, and firm performance: An empirical investigation. Journal of Accounting & Economics, 7, 43–66.

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Cronbach, L. J. (1987). Statistical tests for moderator variables: Flaws in analysis recently proposed. Psychological Bulletin, 102(3), 414–417. Cyert, F. L., & March, J. (1992). The Behavioral Theory of the Firm (2nd ed.). Cambridge, MA: Blackwell Publishers. DeGeorge, R. (1992). Agency theory and the ethics of agency. In: N. E. Bowie & R. E. Freeman (Eds), Ethics and agency theory: An introduction (pp. 59–72). New York, NY: Oxford University Press. Douglas, P. C., Davidson, R. A., & Schwartz, B. N. (2001). The effect of organizational culture and ethical orientation on accountants’ ethical judgments. Journal of Business Ethics, 34(2), 101–121. Douglas, P. C., & Wier, B. (2000). Integrating ethical dimensions into a model of budgetary slack creation. Journal of Business Ethics, 28, 267–277. Dunk, A. (1993). The effect of budget emphasis and information asymmetry on the relation between budgetary participation and slack. The Accounting Review, (April), 68(2), 400–410. Evans, J. H., III, Hannan, R., Krishnan, R., & Moser, D. (2001). Honesty in managerial reporting. The Accounting Review, 76, 537–559. Flory, S., Phillips, T., Reidenbach, E., & Robin, D. (1992). A multidimensional analysis of selected ethical issues in accounting. The Accounting Review, (April), 67(2), 284–302. Forsyth, D. R. (1992). Judging the morality of business practices: The influence of personal moral philosophies. Journal of Business Ethics, 11(5), 461–475. Fowler, F. J., Jr. (1993). Survey Research Methods. Newbury Park, CA: Sage. Gul, F. A. (1991). The effects of management accounting systems and environmental uncertainty on small business manager’s performance. Accounting and Business Research, 22, 57–61. Hughes, M. A., & Kwon, S. Y. (1990). An integrative framework for theory construction and testing. Accounting, Organizations and Society, 15, 179–191. Hunt, S. D., & Vitel, S. J. (1986). A general theory of marketing ethics. Journal of Macromarketing, 6(1), 5–16. Jones, T. M. (1991). Ethical decision making by individuals in organizations: An issue-contingent model. Academy of Management Review, 15, 366–392. Kren, L. (1993). Control system effects on budget slack. Advances in Management Accounting, 2, 109–118. Libby, T. (1999). The influence of voice and explanation on performance in a participative budgeting setting. Accounting, Organizations and Society, 24(2), 125–137. Luft, J. L. (1997). Fairness, ethics and the effect of management accounting on transaction costs. Journal of Management Accounting Research, 9, 199–216. Lukka, K. (1988). Budgetary biasing in organizations: Theoretical framework and empirical evidence. Accounting, Organization and Society, 13, 281–301. Magee, R. P. (1980). Equilibria in budget participation. Journal of Accounting Research, (Autumn), 18(2), 551–573. Merchant, K. A. (1981). The design of the corporate budgeting system: Influences on managerial behavior and performance. The Accounting Review, 56, 813–829. Merchant, K. A. (1985). Budgeting and the propensity to create budgetary slack. Accounting, Organizations and Society, 10, 201–210. Merchant, K. A. (1989). Rewarding Results: Motivating Profit Center Managers. Boston, MA: Harvard Business School Press.

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Merchant, K. A. (1995). Ethical Issues Related to ‘‘Results-Oriented’’ Management Control Systems. In: Ethical issues in accounting: Proceedings of the professionalism and Ethics seminar. Professionalism and Ethics Committee of the American Accounting Association, St. Charles, IL, (June), 3(1), 2–4. Milani, K. (1975). The relationship of participation in budget-setting to industrial supervisor performance and attitudes: A field study. The Accounting Review, 50(2), 274–284. Noreen, E. (1988). The economics of ethics: A new perspective on agency theory. Accounting, Organizations and Society, 13(4), 359–369. Onsi, M. (1973). Factor analysis of behavioral variables affecting budgetary slack. The Accounting Review, (July), 48(3), 535–548. Penno, M. (1984). Asymmetry of pre-decision information and managerial accounting. Journal of Accounting Research, 22, 177–191. Pope, P. F. (1984). Information asymmetries in participative budgeting: A bargaining approach. Journal of Business Finance & Accounting, 11, 41–59. Reidenbach, R. E., & Ronald, D. P. (1991). An Application and extension of a multidimensional ethics scale to selected marketing practices and marketing groups. Academy of Marketing Science, 19(2), 83–92. Robin, D. P., & Reidenbach, R. E. (1996). The perceived importance of an ethical issue as an influence on the ethical decision-making of ad managers. Journal of Business Research, 5(1), 17–28. Rutledge, R. W., & Karim, K. E. (1999). The influence of self-interest and ethical considerations on manager’s evaluation judgments. Accounting, Organizations and Society, 24, 173–184. Schoonhoven, C. B. (1981). Problems with contingency theory: Testing assumptions hidden within the language of contingency theory. Administrative Science Quarterly, 26, 349–377. Southwood, K. E. (1978). Substantive theory and statistical interaction: Five models. American Journal of Sociology, 83, 1154–1203. Stevens, D. E. (2002). The effects of reputation and ethics on budgetary slack. Journal of Management Accounting Research, 14, 153–171. Waller, W. S. (1988). Slack in participating budgeting: The joint effect of a truth-inducing pay scheme and risk preferences. Accounting, Organizations and Society, 13, 87–98. Young, S. M. (1985). Participative budgeting: The effects of risk aversion and asymmetric information on budgetary slack. Journal of Accounting Research, (Autumn), 23(2), 829–842.

APPENDIX PART I SCENARIOS (Adopted from IMA Research Center) The following four scenarios were used in this study. Each scenario appeared on a separate page followed by brief instructions, a randomized presentation of the scales, the univariate ethics measure, and the behavioral intention measure as shown below.

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Scenario A. Tom Waterman is a young management accountant at a large, diversified company. After some experience in accounting at headquarters, he has been transferred to one of the company’s recently acquired divisions run by its previous owner and president, Howard Heller. Howard has been retained as vice president of this new division, and Tom is his accountant. With a marketing background and a practice of calling his own shots, Howard seems to play by a different set of rules than those to which Tom is accustomed. So far, it is working as earnings are up and sales projections are high. The main area of concern to Tom is Howard’s expense reports. Howard’s boss, the division president, approves the expense reports without review, and expects Tom to check the details and work out any discrepancies with Howard. After a series of large and questionable expense reports, Tom challenges Howard directly about charges to the company for typing that Howard’s wife did at home. Although company policy prohibits such charges. Howard’s boss again signed off on the expense. Tom feels uncomfortable with this and tells Howard that he is considering taking the matter to the Board Audit Committee for review. Howard reacts sharply, reminding Tom that ‘‘the Board will back me anyway’’ and that Tom’s position in the company would be in jeopardy. ACTION: Tom decides not to report the expense charge to the Audit Committee. Please evaluate this action of Tom Waterman. Fair___ ___ ___ ___ ___ ___ ___Unfair Just___ ___ ___ ___ ___ ___ ___Unjust Morally right___ ___ ___ ___ ___ ___ ___Not morally right Acceptable to my family___ ___ ___ ___ ___ ___ ___Unacceptable to my family If you were responsible for making the decision described in the scenario, how would you judge the decision? Ethical ___ ___ ___ ___ ___ ___ ___ Unethical Scenario B. Anne Devereaux, company controller, is told by the chief financial officer that in an executive committee meeting the CEO told them that the company ‘‘has to meet its earnings forecast, is in need of working capital and that’s final.’’ Unfortunately, Anne does not see how additional working capital can be raised even through increased borrowing, since

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income is well below the forecast sent to the bank. Seth suggests that Anne review bad debt expense for possible reduction and holding sales open longer at the end of the month. He also brushes off the management letter request from the outside auditors to write down the spare parts inventory to reflect its ‘‘true value.’’ At home on the weekend, Anne discusses the situation with her husband, Larry, a senior manager of another company in town. ‘‘They’re asking me to manipulate the books,’’ she says. ‘‘On the one hand,’’ she complains, ‘‘I’m supposed to be the conscience of the company and on the other, I’m supposed to be absolutely loyal.’’ Larry tells her that companies do this all the time, and when business picks up again she’ll be covered. He reminds her how important her salary is to help maintain their comfortable lifestyle, and that she should not do anything drastic that might cause her to lose her job. ACTION: Anne decides to go along with the suggestions proposed by her boss. Please evaluate this action of Anne Devereaux. Fair___ ___ ___ ___ ___ ___ ___Unfair Just___ ___ ___ ___ ___ ___ ___Unjust Morally right___ ___ ___ ___ ___ ___ ___Not morally right Acceptable to my family___ ___ ___ ___ ___ ___ ___Unacceptable to my family If you were responsible for making the decision described in the scenario, how would you judge the decision? Ethical ___ ___ ___ ___ ___ ___ ___ Unethical

Scenario C. Drew Isler, the plant’s chief accountant, is having a friendly conversation with Leo Sullivan, operations manager and old college buddy, and Fred LaPlante, the sales manager. Leo tells Drew that the plant needs a new computer system to increase operating efficiency. Fred interjects that with the increased efficiency and decreased late deliveries their plant will be the top plant next year. However, Leo wants to bypass the company policy which requires that items greater than $5,000 receive prior Board approval and be capitalized. Leo would prefer to generate purchase orders for each component part of the system, each being under the $5,000 limit, and thereby avoid the approval ‘‘hassle.’’ Drew knows this is clearly wrong from a company and an

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accounting standpoint, and he says so. Nevertheless, he eventually says that he will go along. Six months later, the new computer system has not lived up to its expectations. Drew indicates to Fred that he is really worried about the problems with the computer, and the auditors will disclose how the purchase was handled in the upcoming visit. Fred acknowledges the situation by saying that production and sales are down and his sales representatives are also upset. Leo wants to correct the problems by upgrading the system (and increasing the expenses), and urges Drew to ‘‘hang in there.’’ ACTION: Feeling certain that the system will fail without the upgrade, Drew agrees to approve the additional expense. Please evaluate this action of Drew Isler. Fair___ ___ ___ ___ ___ ___ ___Unfair Just___ ___ ___ ___ ___ ___ ___Unjust Morally right___ ___ ___ ___ ___ ___ ___Not morally right Acceptable to my family___ ___ ___ ___ ___ ___ ___Unacceptable to my family If you were responsible for making the decision described in the scenario, how would you judge the decision? Ethical ___ ___ ___ ___ ___ ___ ___ Unethical

Scenario D. Paul Tate is the assistant controller at Stern Electronics, a medium-sized manufacturer of electrical equipment. Paul is in his late 50’s and plans to retire soon. His daughter has been accepted into medical school, and financial concerns are weighing heavily on his mind. Paul’s boss is out of the office recuperating from health problems, and in his absence Paul is making all decisions for the department. Paul receives a phone call from an old friend requesting a sizable amount of equipment on credit for his new business. Paul is sympathetic but cognizant of the risk of extending credit to a new company, especially under Stern’s strict credit policy for such transactions. When Paul mentions this conversation to Warren, the general manager, he is immediately interested. Warren notes that the company needs an additional $250,000 in sales to meet the quarterly budget and, thus, ensure bonuses for management, including Paul.

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ACTION: Paul decides to make the sale to his friend’s new business. Please evaluate this action of Paul Tate. Fair___ ___ ___ ___ ___ ___ ___Unfair Just___ ___ ___ ___ ___ ___ ___Unjust Morally right___ ___ ___ ___ ___ ___ ___Not morally right Acceptable to my family___ ___ ___ ___ ___ ___ ___Unacceptable to my family If you were responsible for making the decision described in the scenario, how would you judge the decision? Ethical ___ ___ ___ ___ ___ ___ ___ Unethical

PART II 1. Is your division a profit center? ____Yes ______ No 2. Do you have a budget responsibility in your division? ______ Yes _____ No

PART III If your answer to both 1 and 2 in Part IV is yes, please answer the remaining parts of the questionnaire, otherwise stop at Part IV and return the questionnaire.

Participation (response anchors: 1 ¼ stronglydisagree; 2 ¼ moderatelydisagree; 3 ¼ mildly disagree, 4 ¼ neutral; 5 ¼ mildly agree, 6 ¼ moderately agree, 7 ¼ strongly agree) 1. I am involved in setting all of my budget. 2. My superior clearly explains budget revisions. 3. I have frequent budget-related discussions with my superior.

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4. I have a great deal of influence on my final budget. 5. My contribution to the budget is very important. 6. My superior initiates frequent budget discussions when the budget is being prepared. Budget Slack (response anchors: 1 ¼ strongly disagree, 2 ¼ moderately disagree, 3 ¼ mildly disagree, 4 ¼ neutral; 5 ¼ mildly agree, 6 ¼ moderately agree, 7 ¼ strongly agree) 1. To protect himself, a manager submits a budget that can safely be attained. 2. In good business times, your superior is willing to accept a reasonable level of slack in the budget. 3. Slack in the budget is good to do things that cannot be officially approved.

PART IV Please answer the following: 1. 2. 3. 4.

What is the number of employees is your company? ___________ What is your approximate dollar volume of sales? _____________ Number of years is this position? ___________ Number of years in management? __________

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ASYMMETRIC EFFECTS OF ACTIVITY-BASED COSTING SYSTEM COST REALLOCATION M. G. Fennema, Jay S. Rich and Kip Krumwiede ABSTRACT Despite the many proposed benefits of activity-based costing (ABC), many managers oppose implementing it. One important reason for this resistance that has generally not been addressed in the literature is the effect of cost reallocations on managers’ evaluations and compensation. This study examines how the impact of installing an ABC system on managers’ bonuses affects their support for ABC implementation. Because ABC systems usually allocate costs in different proportions than traditional systems, some products may appear to be more profitable while others may appear less so. This, in turn, causes the business units responsible for the products to appear to be more or less profitable. Based on prospect theory (Kahneman & Tversky (1979). Econometrica, 47, 263–291; Tversky & Kahneman (1992). Journal of Risk and Uncertainty, 5, 297–323), we predict that the negative effect on managers’ support for ABC in business units reporting less profit is greater than the positive effect on managers’ support in the more profitable units. The results of an experiment support this prediction. Since management support is critical to successful system implementation, this asymmetric effect has implications for cost management system changes. Advances in Accounting Behavioral Research Advances in Accounting Behavioral Research, Volume 8, 167–187 Copyright r 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1475-1488/doi:10.1016/S1475-1488(04)08007-X

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INTRODUCTION Increased global competition has motivated organizations to improve their products and customer service. Such improvements typically include the development of superior information systems that allow managers to track customers, increase production efficiencies, and manage costs. One such information system involves the calculation of product costs. Having accurate product costs is critical in deciding whether the product can compete against other companies’ products and also in the pricing of the item. Determining product costs, however, is generally difficult to do. While some costs are easily traced to specific products, many costs are expended to produce a wide range of products. Assigning these ‘‘indirect’’ costs to individual products has always been difficult but has become increasingly so as more automation is introduced into the production process. This automation has led to a higher percentage of indirect costs and therefore less accurate product costs. A highly publicized innovation developed over the last two decades to produce better product cost information is activity-based costing (ABC). When properly implemented and maintained, ABC systems assign indirect costs based on resource consumption and generally provide more accurate product costs than traditional cost allocation systems. However, replacing old product cost information systems with newer ABC systems is not a simple matter. ABC systems are generally complex and pose significant implementation challenges (Kennedy & Affleck-Graves, 2001). Thus, in spite of many proposed benefits of ABC, the majority of firms have not implemented it (Kennedy & Affleck-Graves, 2001). Gosselin (1997) referred to this as the ‘‘ABC paradox.’’ Because the information generated by the system affects many managers in an organization, significant resistance to successful implementation can arise. For example, Argyris and Kaplan (1994) describe situations in which individuals engage defensive routines to block implementation when the information from an ABC system will be highly embarrassing or potentially threatening to them. An inability to overcome opposition can result in costly failure of the implementation effort. One important reason for this resistance that has generally not been addressed in the literature is how the impact of cost reallocations on managers’ evaluations and compensation affects their support for the new cost system. Because an ABC system reallocates indirect costs, some products may appear more profitable and others may appear less so. The managers responsible for those products will likewise be affected, since their divisions will appear to be more or less

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profitable. Thus, their support for the adoption of the new system may be related to the profit effect on their division. The major premise of this study is that although losses in profitability for some products will be more or less mirrored by increases in profitability in others, the reactions to prospective gains and losses by the managers responsible for the affected products will not be identical. Specifically, we predict that the negative effect on managers’ support for ABC in business units that will become less profitable is greater than the positive effect on managers’ support in units that become more profitable. This prediction is derived from the asymmetric value function in prospect theory (Kahneman & Tversky, 1979; Tversky & Kahneman, 1992). The following sections describe the process of product costing, the proposed effect of ABC system implementation on manager resistance, and the results of an experiment that support this prediction.

BACKGROUND Product Costing The determination of a product’s cost requires an analysis of the resources expended to create the product. Such costs are categorized as either direct or indirect costs. Direct costs are those that are traceable to the final product while indirect costs are those that are not. For example, the production of a wooden desk involves several direct material costs including a certain amount of wood and a set number of pieces of hardware. Determining the cost of these inputs is relatively easy. In addition, human labor to cut the wood or assemble the desk is also considered a direct cost since a standard amount of time could be established for these operations. The calculation of indirect costs is much more difficult than that of direct costs. In the desk example, indirect costs might include items such as the depreciation (or rent) expense for cutting equipment. Also included might be expenditures for property taxes on the factory building and the salaries of factory supervisors. Although all of these costs are incurred to produce the desks, they are not traceable to specific desks. Therefore, these costs are more difficult to assign to each desk. Traditional cost accounting systems generally pool all indirect costs together and then spread them to products based on some volume-based measure such as direct labor hours. For the desk example, the estimated total annual amount of factory indirect costs might be divided by the

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estimated total annual direct labor hours to get a predetermined overhead rate. This rate would then be applied to each desk based on the number of direct labor hours required to produce it. This type of cost allocation system yields reasonably accurate product costs as long as all the products made in the factory consume similar percentages of indirect costs. Of course, if the desk factory only made one type and size of desk, the traditional system would be highly accurate. However, if a factory makes diverse products, the traditional system is not likely to provide accurate product costs. If the plant that makes desks also makes tables, it is unlikely that a traditional system will allocate indirect costs in the proportion that resources are consumed. For example, if tables require less cutting of wood than do desks, the traditional system would assign less indirect costs to each table based simply on the number of direct labor hours consumed by each product. Even if the tables require more setup and material handling costs than desks, they would still be allocated lower overhead costs. The result would be distorted product costs for both products. Such distortions caused by the limitations of traditional volumebased allocation methods have been widely discussed in the literature (see Kaplan & Cooper, 1998, Chapters 3 and 6, or Cokins, 2001, Chapter 1, for overviews). The goal of a cost system is to assign indirect costs to products in the proportion that they are consumed by the product. Since traditional systems often fail to achieve this goal, many companies have attempted to implement ABC. However, implementing ABC can be quite difficult. The next section discusses ABC system implementation and the problems that can arise.

ABC System Implementation and Manager Resistance Organizational change has been the subject of much study (e.g., Goodman & Dean, 1982; Beer, Eisenstat, & Spector, 1990; Huff, Huff, & Barr, 2001). One example of organizational change is the implementation of a new management accounting information system. Probably the most widely discussed innovation of the past two decades in management accounting information systems is ABC. The promise of ABC is quite inviting. By focusing on the activities that a product requires, ABC can provide a more precise allocation of indirect costs, resulting in more accurate product costs. This increased accuracy enables firms to make better critical decisions such as pricing and market

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entry and exit decisions. Cooper and Kaplan (1991) also identify benefits associated with lean production, such as reducing inventories, increasing common components, increasing quality by minimizing total quality costs, and assessing customer profitability. Ness and Cucuzza (1995) suggest that once ABC is part of a firm’s critical management systems it becomes a powerful tool, supporting continuous rethinking and improving not only products and services but also process and market strategies. Ittner, Lanen, and Larcker (2002) find some evidence that using ABC enhances manufacturing performance and some weak evidence that profits are enhanced when ABC matches a plant’s operational characteristics. Kennedy and AffleckGraves (2001) find that firms adopting ABC outperform non-ABC firms by up to 27% in both higher stock returns and accounting measures such as return on equity and profit margin. Although the potential benefits are significant, the introduction of any new technology can be costly. ABC systems require significant investment in model design and data-gathering costs. But since these systems generate information that affects many individuals within a firm, behavioral and organizational factors are of greater concern. In their models of cost management system implementation, Shields and Young (1989, 1995) identify the linkage between the cost management system and performance evaluation and compensation as one of seven behavioral and organizational variables that are required for successful system implementation. Indeed, in an empirical study of 143 firms, Shields (1995) finds this linkage to be empirically correlated with success. Cokins (2001) notes that individuals involved in successful ABC implementations spend 90% of their efforts on organizational behavior issues and only 10% on technical issues. Unfortunately, most organizations do just the opposite. The behavioral costs take the form of barriers to implementation erected by individuals who feel that they may be negatively affected by the new system. Previous studies have addressed many reasons why managers might oppose the implementation of ABC. Cooper, Kaplan, Maisel, Morrissey, and Oehm (1992) describe the process eight companies went through in trying to implement ABC. Two of the company examples address behavioral issues relating to the ABC implementation process. In Farrall, Inc. (Chapter 5), representatives from the sales department committed only 5% of their time to ABC implementation, compared with 10–100% committed from representatives in accounting and information system departments. Sales and marketing employees are often hesitant to help with ABC implementation because they either do not value ABC or worry about what the results will show. Williams Brothers Metals (Chapter 6) discusses the negative reaction

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of product-line managers whose product margins are hurt by the ABC analysis. One manager, whose two largest products now appeared unprofitable, believed the ABC information was ‘‘fundamentally flawed.’’ He questioned the assumptions that the ABC model was built on and even claimed they are ‘‘completely wrong.’’ Cooper et al. (1992) also found that most firms involved with implementing ABC systems did not create the critical link from the system to performance evaluation and compensation. Argyris and Kaplan (1994) explore such barriers, identifying potential embarrassing or threatening situations that can arise from an ABC system. Examples include the identification of non-value-added activities (e.g., rework) or finding other production inefficiencies that had been hidden by the old system. Faced with this situation, not only will affected managers not support the ABC system, they will also respond defensively to block its implementation. With barriers in place by affected stakeholders, successful implementation is unlikely. Indeed, successful implementation seems to be elusive in practice. Ittner et al. (2002) find only 26% of U.S. manufacturing plants use ABC extensively. Kennedy and Affleck-Graves (2001) find 20.1% of the top 1,000 firms in the U.K. (ranked by sales) to be using ABC. Ness and Cucuzza (1995) observe from their consulting experiences that employee resistance is the single biggest obstacle to successful ABC implementation. They find managers are commonly nervous about revealing detailed information that could be used to make them look bad or dramatically change the definitions of success and failure. Nevertheless, the authors recommend that measurement and incentive systems be tied to the ABC numbers. Academic research relating to ABC implementation has likened it to an innovation in a firm’s cost information system (Gosselin, 1997; Shields, 1995; Anderson, 1995). Studies have explained implementation of information systems using a ‘‘process stage’’ model (Cooper & Zmud, 1990; Kwon & Zmud, 1987). Anderson (1995) suggests integrating the system with management information and reward systems is critical to reaching the acceptance and usage stages. She states, ‘‘Implementation of ABC systems is unlikely to succeed until these stakeholders’ concerns are addressed’’ (p. 9). Krumwiede (1998) finds top management and nonaccounting support to be critical to reaching the highest stages of implementation success (i.e., routine and integrated). Prior behavioral research on the resistance to ABC implementation has focused on various organizational and individual factors but not on employee resistance to ABC implementation. This study uses an experimental approach to explore the effect of changes in performance evaluation and

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compensation resulting from ABC adoption on manager resistance. Clearly, changes in compensation caused by a new ABC system can result in a threatening situation for affected managers. However, while some studies (e.g., see Mak & Roush, 1994) have examined the issue of performance evaluation in an ABC environment, no study has investigated the potential effect on managers’ support when compensation is based upon division performance. In this situation, indirect costs will be reallocated, causing some divisions to appear more profitable while others appear to be less profitable. Consider the following example. A company has two divisions, one that produces product A and another that makes an equal amount of product B. Managers’ bonuses are based on a percentage of division profitability. The current product costing system calculates the cost of A to be $5 per unit and that of B to be $11 per unit. An ABC system calculates the unit costs to be $7 for A and $9 for B. The same total costs exist, but they are applied to the products in different ways. Since Division A would appear less profitable and Division B would appear more so, a critical question arises: What effect would such a shift of costs have on the two division managers’ support for the new ABC system?

THEORY DEVELOPMENT AND HYPOTHESIS Since the allocation of indirect costs to products is typically a zero-sum game (i.e., any amount taken from one product is allocated to another), it might be assumed that the effect on the support of managers responsible for those products would be opposite but equal since the effect on their bonuses would be opposite but equal. Such would be the predictions from standard economic theory. But that theory has not been found to be particularly descriptive of actual behavior in many situations (see Kahneman & Tversky, 1979). Prospect theory (Kahneman & Tversky, 1979; Tversky & Kahneman, 1992) has been proposed and widely tested as an alternative to standard economic theory. This theory suggests asymmetrical reactions to expected gains and losses. It has been applied in recent accounting studies relating to budgetary slack (Lau & Eggleton, 2003), risk-taking tendencies in capital budgeting decisions (Moreno, Kida, & Smith, 2002), framing effects in a classic Asian disease-type business scenario (Chang, Yen, & Duh, 2002), and loss aversion by traders in financial markets (Willman & FentonO’Creevy, 2002). There are two characteristics of prospect theory that are important to the current situation (refer to Fig. 1 for an example of a prospect theory value

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Gains

Losses $1,000

Fig. 1.

$1,000

Prospect Theory Value Function.

function). First, gains and losses are evaluated from some reference point. In the above example, that reference point is most likely the current product cost in each division (and thus the current bonus based on those product costs earned by the managers). With the new ABC system, the manager of the division producing product A would suffer a $2 per unit loss from the $5 reference point, and the manager of the division producing product B would enjoy a $2 gain from the $11 reference point. A second characteristic of prospect theory is a value function that is steeper for losses than for gains. If the product cost changes caused the manager of Division A to be penalized by the loss of $1,000 bonus and the manager of Division B to be rewarded with an additional $1,000 bonus, the value function in Fig. 1 posits that the increase in value experienced by the Division B manager would be significantly less than the decrease in value experienced by the manager of Division A. Thus, the negative support from the first manager would exceed the positive support of the second one. This leads to the following hypothesis: H1. The loss in support from managers whose products increase in cost due to the installation of an ABC system will exceed the increase in support from managers whose products decrease in cost.

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The following experiment was designed to test this hypothesis using two groups of participants. The first group included upper level college students who have been trained to understand how ABC systems can improve managerial decision making. The second involved practicing Certified Public Accountants (CPAs). Since prior experience in a broad domain often results in more defined knowledge structures about the decision process (VeraMunoz, Kinney, & Bonner, 2001), the experienced CPA participants were chosen to validate the results of the student group.

METHODOLOGY Participants/Task Ninety-one university business students (junior and senior level accounting majors) and 77 CPAs participated in a two-part exercise. The students were enrolled in an accounting class where traditional and ABC product costing systems had been taught. The two parts of their exercise were conducted two days apart. The CPAs had an average of 14.8 years of professional experience and their data were collected during a one-day continuing professional education class, with the first part of the exercise completed in the morning session and second part in the afternoon session. In the first part of the exercise, participants were asked to examine a company’s unit costs for a product under a current traditional costing system and under a proposed ABC system. They were then asked to assume the role of the chief financial officer (CFO) of the company and answer three questions designed to measure the amount of their support for the proposed system. The primary measure was the participant’s support for the new system, on a 7-point Likert-type scale. The scale had verbal descriptions of ‘‘Would Strongly Support’’ and ‘‘Would Strongly Object to’’ at the endpoints and ‘‘Indifferent’’ at the midpoint. Participants were also asked to assess the maximum the company should pay to install the new ABC system, and to estimate the percentage of the cost of the new system that should be allocated to each of two divisions. A copy of the instrument is included in Appendix A. In the second part (see Appendix B), participants were asked to play the role of the manager of one of two divisions, either the Standard Division or the Deluxe Division. In this part, the participants were shown the effect of the proposed ABC system on the profitability of the divisions, and therefore how it would affect their bonus. This effect was symmetric, with

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the managers of the Standard Division receiving an extra $7,200 bonus and the managers of the Deluxe Division losing the same amount. Participants were then asked to answer the same three questions again.

Design After assuming the CFO role in Part 1 of the experiment, participants were randomly assigned to one of the between-participant conditions of either the manager of the Standard Division or the Deluxe Division for Part 2. The dependent variables are the absolute value of the change (from the CFO role to the division manager role) in the three variables that were used to measure support for the new system.

RESULTS AND DISCUSSION Tables 1 and 2 contain the descriptive results for the student participants and CPA participants, respectively. All changes in support were calculated and analyzed at the individual level. Panel A in each table contains the results of the first part of the experiment for each group of participants. Recall that in the first part, the participants were all assigned to the role of the CFO of the firm. As such, it would be expected that there would be no differences between the responses of those who would be in the two manager roles. This is the case for both the students and the CPAs. It is interesting to note that although these business students were taught about the benefits of ABC systems, there was not overwhelming overall support for the proposed system (i.e., 3.4 is nearly exactly in the middle of the 7-point scale). Also note that for the CPAs the amounts to be allocated to the two divisions do not add up to 100% because three participants allocated zero to both of the divisions and one participant’s allocations did not add to 100%. In Part 2 of the experiment, participants were assigned to one of the two manager roles, and Panel B in Tables 1 and 2 contains the responses from that part of the experiment. Panel C shows the increase or decrease in support from that of the CFO role to that of the two division manager roles. As expected, overall support for the system (as measured by the 7-point scale), increased for the Standard Division managers and decreased for those in the Deluxe Division. Similarly, the amount to be paid for the system increased for those in the Standard Division and decreased for managers of the Deluxe Division. Finally, the amount of the system’s cost to be allocated

Asymmetric Effects of Activity-Based Costing System Cost Reallocation

Table 1.

177

Descriptive Results: Student Participants. Standard Division Role (n ¼ 44)

Deluxe Division Role (n ¼ 47)

3.4 322,501 48 52

3.4 314,161 52 48

2.3 329,977 42

5.6 227,226 34

Panel A: Mean Responses – CFO Role (Part 1)a Support for ABC systemb Amount to be paid for ABC system ($) Percent allocated to standard division Percent allocated to deluxe division Panel B: Mean Responses by Role (Part 2) Support for ABC systemb Amount to be paid for ABC system ($) Percent allocated your division

Panel C: Increase (Decrease) in Mean Responses from CFO Role to Manager Role Support for ABC systemb Amount to be paid for ABC system ($) Percent allocated to your division

1.1 7,476 (5)

(2.0) (86,936) (14)

a

In Part 1 of the experiment the participants were unaware of the role they would be asked to assume in Part 2. Part 1 responses are presented consistent with their Part 2 roles to better demonstrate how their judgments differed between the two parts. b 1 ¼ Strongly support, 7 ¼ Strongly object to.

to the manager’s division decreased for both divisions. Even though the managers of the Standard Division would benefit significantly from the new system, they were unwilling to accept more of the system’s cost than when they were in the CFO role. Tables 3 and 4 show the results of an ANOVA examining the differences in responses over the two parts of the experiment, for the student group and the CPA group respectively. Results for the primary measure of support (support for the ABC system) are shown in panel A. For both students and CPAs, there is a significant effect for the differential support of the new ABC system. On the 7-point scale (1 ¼ Strongly Support, 7 ¼ Strongly Object To), student participants in the Standard Division increased their support by 1.1 points while those in the Deluxe Division reduced their support by 2.0 points, or almost double that of the Standard Division. For CPAs, Standard Division support increased by 1.9 points while Deluxe Division support decreased by 2.9 points. The difference in the absolute value

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Table 2.

Descriptive Results: CPA Participants. Standard Division Role (n ¼ 35)

Deluxe Division Role (n ¼ 42)

4.1 241,023 59 34

3.5 290,357 55 43

2.2 398,286 51

6.3 137,857 21

Panel A: Mean Responses – CFO Role (Part 1)a Support for ABC systemb Amount to be paid for ABC system ($) Percent allocated to standard division Percent allocated to deluxe division Panel B: Mean Responses by Role (Part 2) Support for ABC systemb Amount to be paid for ABC system ($) Percent allocated your division

Panel C: Increase (Decrease) in Mean Responses from CFO Role to Manager Role Support for ABC systemb Amount to be paid for ABC system ($) Percent allocated to your division

1.9 157,263 (8)

(2.9) (152,500) (21)

a

In Part 1 of the experiment the participants were unaware of the role they would be asked to assume in Part 2. Part 1 responses are presented consistent with their Part 2 roles to better demonstrate how their judgments differed between the two parts. b 1 ¼ Strongly support, 7 ¼ Strongly object to.

of these changes is significant for both the students (p ¼ 0:0088) and CPAs (p ¼ 0:0337). This supports the hypothesis that the decrease in support from those negatively affected by the ABC system will exceed the increase in support from those positively affected by it. In a secondary measure of support, the amount to be paid for the new ABC system, results were mixed. Student participants in the Standard Division (whose costs went down and bonus went up) were willing to pay an average of $7,476 more than when they were in the CFO role. However, Deluxe Division managers were willing to pay $86,936 less than when they were in the CFO role. The difference between the absolute value of these two amounts is marginally significant at p ¼ 0:0578 (see panel B of Table 3). For CPAs, the change in the amount to be paid for the new system did not differ. Those in the Standard Division were willing to pay $157,263 more than when they were in the CFO role while those in the Deluxe Division were willing to pay $152,500 less. Panel B of Table 4 shows that the absolute difference between these two responses is not significant.

Asymmetric Effects of Activity-Based Costing System Cost Reallocation

Table 3. Source

179

ANOVA Results of Hypothesis Test: Student Participants. DF

Sum of Squares

Mean Square

F Value

p4F

7.17

0.0088

143,482,968,757 38,835,636,163

3.69

0.0578

8,522 642

13.26

0.0005

Panel A: Support for ABC System Manager role Error Corrected total

1 89 90

22.49 278.99 301.48

22.49 3.13

Panel B: Amount to be Paid for ABC System Manager role Error Corrected total

1 89 90

143,482,968,757 3,456,371,618,501 3,599,854,587,257

Panel C: Percent Allocated to Your Division Manager role Error Corrected total

Table 4. Source

1 89 90

8,522 57,183 65,705

ANOVA Results of Hypothesis Test: CPA Participants. DF

Sum of Squares

Panel A: Support for ABC System Manager role 1 Error 75 Corrected total 76

18.69 200.45 318.14

Mean Square

F Value

p4F

4.68

0.0337

433,073,610 28,578,126,956

0.02

0.9024

16,102 866

18.58

0.0001

18.69 3.99

Panel B: Amount to be Paid for ABC System Manager role Error Corrected total

1 75 76

433,073,610 2,143,359,521,714 2,143,792,595,325

Panel C: Percent Allocated to Your Division Manager role Error Corrected total

1 75 76

16,102 65,000 81,102

Panel C of Tables 3 and 4 contains the results of the other secondary measure of support, the percent of cost managers would allocate to their divisions. For both groups of subjects, both division managers wanted to bear a smaller percent of the cost of the system (than they had allocated when they

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were in the CFO role), but Deluxe Division managers were even more unwilling to bear the costs (p ¼ 0:0005 for students, p ¼ 0:0001 for CPAs). These results also support the prospect theory-based hypothesis in this study.

CONCLUSIONS AND LIMITATIONS This study documents an asymmetric effect of reallocating product costs on managers who have an interest in ABC system information. Specifically, these results provide evidence that the loss in support from managers whose products increase in cost exceeds the increase in support from those whose products decrease in cost. This was true over three possible measures of support for the student participants and two for the CPA participants. This simple but powerful finding has consequences for both theoretical models of ABC implementation and practical applications. Many prior studies have identified various inhibitors to ABC implementation success. In their models of cost management system implementation, Shields and Young (1989, 1995) identify the link between the cost management system and performance evaluation and compensation as one of seven behavioral and organizational variables that are required for successful system implementation. As noted earlier, Shields (1995) found this compensation linkage to be correlated with success. Unfortunately, previous studies (e.g., Cooper et al., 1992) have found that most firms involved with implementing ABC systems do not create the critical link from the system to performance evaluation and compensation. The current study underscores the importance of this connection, and it also adds the finding that managers will be asymmetrically affected by equal changes in that compensation. Identifying this effect helps develop a more complete theory of individuals’ resistance to ABC implementation. On a practical level, those attempting to install a new innovation such as ABC need to anticipate individual resistance. Leonard-Barton (1987) likens system implementation to an internal marketing campaign. By building on system positives and countering negative ones, implementation managers can speed the system change effort. Since cost reallocations can cause significant loss of support from negatively affected managers, steps must be taken to reduce that effect. Such measures might include temporary compensation adjustments or permanent changes in the performance-based compensation system, such as increased use of nonfinancial measures in bonus contracts (Ittner, Larcker, & Rajan, 1997). In any case, the effect of such actions

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should be well thought out to take into account the varying effects on the managers. As with all experimental research, the findings of this study need to be considered in light of its limitations. The student participants in this study lack experience in making decisions about the value of cost management systems. However, the CPA group had much more experience and helps to validate the results of the less experienced group. The design of this study has limitations as well. The various pressures and incentives that would be present for real-world decision makers are not present in our experiment. As discussed in Lipe and Salterio (2000), experimental studies attempting to model real-world phenomenon require simplifying assumptions that impair realism. For example, the participants in this study took on both CFO and Division Manager roles. In actual practice, these decision makers would have very different backgrounds and priorities. Also, there may be other company-wide incentives (e.g., stock plan) that affect a division manager’s decision making. Another limitation of this study is that the division managers may have various products, some of which show higher costs and some showing lower costs using ABC. Finally, this study focuses on the economic and behavioral impact of ABC on the division manager. It ignores whether or not ABC is actually beneficial for the company as a whole. This study adds to the limited body of research on manager resistance to activity-based costing. Future studies might add to our knowledge by addressing ways to mitigate the asymmetric effect on managers’ support. Different compensation schemes might be examined in order to determine what measures can be taken to increase support at the lowest cost. The goal of these studies should be to increase our understanding of how managers perceive changes in their environment. In this way, more accurate costing can be achieved with increased support from affected managers.

REFERENCES Anderson, S. W. (1995). A framework for assessing cost management system changes: The case of activity based costing implementation at General Motors, 1986–1993. Journal of Management Accounting Research, 7, 1–51. Argyris, C., & Kaplan, R. (1994). Implementing new knowledge: The case of activity based costing. Accounting Horizons, 8, 83–105. Beer, M., Eisenstat, R., & Spector, B. (1990). Why change programs don’t produce change. Harvard Business Review, 68, 158–166. Chang, C. J., Yen, S., & Duh, R. (2002). An empirical examination of competing theories to explain the framing effect in accounting-related decisions. Behavioral Research in Accounting, 14, 35–64.

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Cokins, G. (2001). Activity-based cost management. New York, NY: Wiley. Cooper, R., & Kaplan, R. (1991). The design of cost management systems. Englewood Cliffs, NJ: Prentice-Hall. Cooper, R., Kaplan, R., Maisel, L., Morrissey, E., & Oehm, R. (1992). Implementing activitybased cost management. Montvale, NJ: Institute of Management Accountants. Cooper, R. B., & Zmud, R. W. (1990). Information technology implementation research: A technological diffusion approach. Management Science, 36(2), 123–139. Goodman, P., & Dean, J. (1982). Creating long-term organizational change. In: P. S. Goodman and Associates (Ed.), Changes in organizations: New perspectives on theory, research, and practice. San Francisco, CA: Jossey-Bass. Gosselin, M. (1997). The effect of strategy and organizational structure on the adoption and implementation of activity-based costing. Accounting, Organizations and Society, 22(2), 105–122. Huff, A. S., Huff, J. O., & Barr, P. S. (2001). When firms change direction. New York, NY: Oxford University Press. Ittner, C. D., Lanen, W. N., & Larcker, D. F. (2002). The association between activity-based costing and manufacturing performance. Journal of Accounting Research, 40(3), 711–726. Ittner, C. D., Larcker, D. F., & Rajan, M. V. (1997). The choice of performance measures in annual bonus contracts. The Accounting Review, 72(2), 231–255. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263–291. Kaplan, R. S., & Cooper, R. (1998). Cost & effect. Boston, MA: Harvard Business School Press. Kennedy, T., & Affleck-Graves, J. (2001). The impact of activity-based costing techniques on firm performance. Journal of Management Accounting Research, 13, 19–45. Krumwiede, K. R. (1998). The implementation stages of activity-based costing and the impact of contextual and organizational factors. Journal of Management Accounting Research, 10, 239–277. Kwon, T. H., & Zmud, R. W. (1987). Unifying the fragmented models of information systems implementation. In: R. J. Boland & R. Hirscheim (Eds), Critical issues in information systems research. New York, NY: Wiley. Lau, C. M., & Eggleton, I. R. C. (2003). The influence of information asymmetry and budget emphasis on the relationship between participation and slack. Accounting & Business Research, 33(2), 91–104. Leonard-Barton, D. (1987). Implementing structured software methodologies: A case of innovation in process technology. Interfaces, 17, 6–17. Lipe, M. G., & Salterio, S. E. (2000). The balanced scorecard: Judgmental effects of common and unique performance measures. The Accounting Review, 75(3), 283–298. Mak, Y., & Roush, M. (1994). Flexible budgeting and variance analysis in an activity-based costing environment. Accounting Horizons, 8, 93–103. Moreno, K., Kida, T., & Smith, J. F. (2002). The impact of affective reactions on risky decision making in accounting contexts. Journal of Accounting Research, 40(5), 1331–1349. Ness, J., & Cucuzza, T. (1995). Tapping the full potential of ABC. Harvard Business Review, 73, 130–138. Shields, M. (1995). An empirical analysis of firms’ implementation experiences with activitybased costing. Journal of Management Accounting Research, 7, 148–166. Shields, M., & Young, S. (1989). A behavioral model for implementing cost management systems. Journal of Cost Management, 3, 17–27.

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Shields, M., & Young, S. (1995). Behavioral and organizational issues. In: B. Brinker (Ed.), Handbook of cost management. New York: Warren Gorham Lamont. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297–323. Vera-Munoz, S. C., Kinney, W. R., & Bonner, S. E. (2001). The effects of domain experience and task presentation format on accountants’ information relevance assurance. The Accounting Review, 76(3), 405–429. Willman, P., & Fenton-O’Creevy, M. (2002). Traders, managers and loss aversion in investment banking: A field study. Accounting, Organizations & Society, 27(1, 2), 85–98.

APPENDIX A Product Costing Case: Part 1 Micro Computer Industries makes memory boards for microcomputers. They began operations in 1986 and had sales of about $10 million last year. The production process involves the assembly of various memory board circuits. Two types of memory boards are currently made, a Standard model and a Deluxe model. Prices in this industry are very competitive, so prices are based on competitors’ prices. The company is organized into two divisions, one for each type of memory board. The senior officers (i.e., the chief executive officer, chief operating officer, and chief financial officer) are salaried and are given stock options. The managers of the two divisions are paid a salary and a substantial bonus, based on the profitability of their division determined by the selling price minus product costs and a portion of allocated administrative costs. Product costs are determined by a traditional cost accounting system, where fixed overhead is allocated based on direct labor hours. Exhibit A.1 shows the unit costs for the two types of memory boards under the current traditional system. Recently, the CFO of the company has been attempting to improve the accuracy of the product costing system. One attempt involves the possible use of an ABC system. It is estimated that the cost of installing such a system would be between $200,000 and $500,000, with the most likely cost of $350,000. Four activities were identified and their rate per activity was calculated. Exhibit A.2 shows the unit costs under the proposed ABC system for the month of January, in which 1,137 memory boards were made in the Standard Division and 288 memory boards were made in the Deluxe Division.

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Exhibit A.1.

Unit Product Costs under Traditional System. Memory Boards Standard

Deluxe

Direct materials Direct labor Manufacturing overhead

100 30 150

200 60 300

Total ($)

280

560

Exhibit A.2.

Unit Product Costs under ABC System. Memory Boards Standard

Deluxe

Direct materials Direct labor Manufacturing overhead

100 30 120

200 60 360

Total ($)

250

620

Assume that you were the CHIEF FINANCIAL OFFICER. Give your OPINION (there are no right or wrong answers) to the following questions. What is the maximum that the company should pay to install the ABC system? _________ What percentage of the cost of the new ABC system would you accept to be allocated to each division? Standard __________ Deluxe __________ How much would you support or object to the purchase and installation of the new ABC system? (make a slash mark in the appropriate place along the line) Would Would Strongly Strongly Support Indifferent Object to |---------------|---------------|---------------|---------------|---------------|---------------|

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APPENDIX B Product Costing Case: Part 2 (STANDARD condition) Micro Computer Industries makes memory boards for microcomputers. They began operations in 1986 and had sales of about $10 million last year. The production process involves the assembly of various memory board circuits. Two types of memory boards are currently made, a Standard model and a Deluxe model. Prices in this industry are very competitive, so prices are based on competitors’ prices. The company is organized into two divisions, one for each type of memory board. The senior officers (i.e., the chief executive officer, chief operating officer, and chief financial officer) are salaried and are given stock options. The managers of the two divisions are paid a salary and a substantial bonus, based on the profitability of their division which is determined by the selling price minus product costs and a portion of allocated administrative costs. Product costs are determined by a traditional cost accounting system, where fixed overhead is allocated based on direct labor hours. Exhibit B.1 shows the unit costs for the two types of memory boards under the current traditional system. Recently, the CFO of the company has been attempting to improve the accuracy of the product costing system. One attempt involves the possible use of an ABC system. It is estimated that the cost of installing such a system would be between $200,000 and $500,000, with the most likely cost of $350,000. Four activities were identified and their rate per activity was calculated. Exhibit B.2 shows the unit costs under the proposed ABC system for the month of January, in which 1,137 memory boards were made in the

Exhibit B.1.

Unit Product Costs under Traditional System. Memory Boards Standard

Deluxe

Direct materials Direct labor Manufacturing overhead

100 30 150

200 60 300

Total ($)

280

560

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Exhibit B.2.

Unit Product Costs under ABC System. Memory Boards Standard

Deluxe

Direct materials Direct labor Manufacturing overhead

100 30 120

200 60 360

Total ($)

250

620

Exhibit B.3.

Current Year Actual Bonus Calculation. Division

Unit sales Selling price per unit ($) Cost per unit ($) Gross margin ($) Allocated administration costs ($) Profit ($) Bonus (2% of profit) ($)

Standard

Deluxe

12,000 450 280 2,040,000 900,000 1,140,000 22,800

6,000 950 560 2,340,000 1,100,000 1,240,000 24,800

Standard Division and 288 memory boards were made in the Deluxe Division. Remember that, as manager of the STANDARD division, your bonuses are based on the profitability of that division. Exhibit B.3 shows the bonus calculations for the current year. If the ABC system had been in place in the current year, the bonus calculation (ignoring the allocation of the system’s cost) would have been as in Exhibit B.4. Assume that you are the MANAGER of the STANDARD Division. Give your OPINION (there are no right or wrong answers) to the following questions. What is the maximum that the company should pay to install the ABC system? _________ What percentage of the cost of the new ABC system would you accept to be allocated to your division? ________________

Asymmetric Effects of Activity-Based Costing System Cost Reallocation

Exhibit B.4.

187

Current Year Bonus Calculation if ABC System had been in Place. Division

Unit sales Selling price per unit ($) Cost per unit ($) Gross margin ($) Allocated administration costs ($) Profit ($) Bonus (2% of profit) ($)

Standard

Deluxe

12,000 450 250 2,400,000 900,000 1,500,000 30,000

6,000 950 620 1,980,000 1,100,000 880,000 17,600

How much would you support or object to the purchase and installation of the new ABC system? (make a slash mark in the appropriate place along the line) Would Would Strongly Strongly Support Indifferent Object to |---------------|---------------|---------------|---------------|---------------|---------------|

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EXAMINING THE ROLE OF CULTURE AND ACCULTURATION IN INFORMATION SHARING Stephen B. Salter and Axel K.-D. Schulz ABSTRACT In the current environment, an important firm asset is the employee knowledge base, which in a large part depends on employee willingness to share information. Yet prior research has noted that while employees are delighted to reveal success they are often reluctant to reveal errors. While there are many factors affecting managers’ reluctance to reveal errors, this study focuses on cultural differences between Chinese migrants and Anglo residents as well as the role of acculturation. This is particularly relevant given the very significant foreign direct investment into China, and migration of managers and high-end technical staff from portions of Greater China to the management and higher technical classes of the Anglo world. Prior studies including Chow, Harrison, McKinnon, and Wu (1999a). Accounting, Organizations and Society, 24, 561–582, Chow, Deng, and Ho (2000). Journal of Management Accounting Research, 12, 65–95, and Tinsley and Pillutla (1998). Journal of International Business Studies, 29(4), 711–728, provide conflicting views and evidence for differences in information sharing between Chinese and Anglo managers, and there is no accounting or management literature that deals with changes in information sharing behavior in the migration process. Advances in Accounting Behavioral Research Advances in Accounting Behavioral Research, Volume 8, 189–212 Copyright r 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1475-1488/doi:10.1016/S1475-1488(04)08008-1

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This study employs an experiment to test for differences in individuals’ willingness to share information about a prior costing error. Using a sample of students from two different nationalities drawn from a major Australian university (Australian and Hong Kong SAR, China), this study finds that migrant Chinese share less information than AngloAustralians. This study further provides empirical evidence that the relative change in willingness to share this information when the supervisor is removed from the decision context is lower for the migrant Chinese than for the Anglo-Australians. Finally, this study finds evidence for acculturation as the willingness of migrant Chinese managers changes with the length of their stay in the new society. Acculturation occurs relatively quickly and highly acculturated Chinese information-sharing behavior is not significantly different from the Australian-born subjects.

INTRODUCTION In the current knowledge-based environment, a large portion of a firm’s assets are in its information and knowledge. Often, the source of this knowledge is the database of best practices and past failures that accumulates among employees.1 While there are many aspects of information sharing in organizations, this study focuses on the impact of cultural differences as well as the effect of changes in cultural differences over time (acculturation) in relation to individuals revealing past errors. In particular, this study centers on differences in culture between Anglo-Australian residents and Chinese migrants.2 How to encourage the release of information through control systems has been widely debated in the Anglo world (see, e.g., Chenhall, 1992; Nanni, Dixon, & Vollman, 1992; Peters, 1994; Smith, 1994; Levinthal & March, 1993). Periodically removing the supervisor, who has the ability to punish subordinates based on the information revealed, has been suggested as one possible solution to reduce the risk of information sharing (Peters, 1994; Chow et al., 1999a). In addition to Anglo studies, several authors have tried to extend the research on information sharing to other cultures, primarily Greater China,3 only to find that the results are inconsistent. Chow et al. (1999a) and Chow, Schulz, and Wu (1999b) find no statistical differences in base propensity to share information between Australian and Chinese (Taiwanese) managers. In contrast, Chow et al. (2000) report significantly more sharing by Peoples Republic of China (PRC) managers than American

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managers. Tinsley and Pillutla (1998), in a related negotiation study, found Hong Kong Chinese to be less willing to share information than American subjects. Further, Chow et al. (1999a, b) examined inter-cultural differences in information sharing before and after the removal of the supervisor from the decision context. Both find that Australian and Chinese managers are more likely to share information when a supervisor is absent although neither compares the relative magnitude of the change between cultures. Content analysis presented by both Chow et al. (1999a) and Chow et al. (2000) point to differences in factors underlying subjects’ information sharing decisions between the Anglo-(Australian and American) and Chinese (Taiwanese and PRC) cultures. This literature can be synthesized by the view that Anglo subjects operate primarily in an individualistic/self interest paradigm compatible with agency theory. Chinese subjects, on the other hand, are subject to opposing cultural forces; collectivism and face. Collectivism is the desire to serve the needs of the many, if necessary, at the expense of the one. Collectivism encourages information sharing. Face, the need to protect the individual’s reputation among those who judge him/her, creates a situation similar to agency where the protection of one’s reputation may require hiding or not revealing damaging information. This face effect can be further broken down by the constituencies who judge an individual: (1) face before the group (of peers) and (2) face before one’s superior/supervisor. The latter effect is magnified in high-power distance cultures. The results from Chow et al. (1999a, b) indicate that Taiwanese and possibly other members of the overseas Chinese diaspora may be harder to predict and may even be unstable in their responses to information sharing stimuli. The three previous studies are based on comparing Anglo managers, who live and work in an Anglo society (U.S.A. or Australia), with Chinese managers, who live and work in a Chinese society (PRC, Taiwan or Hong Kong). The last quarter of the 20th century and the first years of the 21st century have seen not only significant global flow of capital, but also a greatly increased global flow of labor. These are largely not the ‘‘huddled masses yearning to be free’’ of the 19th century, but rather knowledge workers who enter positions of responsibility in the new host countries.4 This migration first leads to the immigrants’ culture potentially clashing with that of his or her new home. Cross-cultural studies in general, and the first part of our study, provide some answers to this. The second and perhaps more interesting issue of migration is that the values and behavior of migrants are not static. Over time, immigrants

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change their tastes, advertising and media usage, voting patterns, consumption patterns, and behavior and values. This process is commonly referred to as acculturation (see Kim, La Roche, & Tomiuk, 2004; Yammarino & Jung, 1998; Ueltschy & Krampf, 1997; Berry, 1997 for summaries). Cross-cultural studies are insufficient to address this change. They tell only half the story (i.e., how did the migrant’s values differ from his new host country prior to migrating). The second part of our study investigates the effect of acculturation crucial to managers (i.e., changing attitudes to information sharing). Our question is how behavior changes over time, if at all. In order to be as relevant as possible, this study examines a group of the overseas Chinese diaspora that is far more likely to immigrate to Anglo-culture nations (i.e., citizens of Hong Kong SAR). The results indicate that when there are disincentives to share information, Chinese participants, with little or no acculturation, were significantly less likely to disclose information than Anglo participants. The behavior is similar to that described by Tinsley and Pillutla (1998). With the removal of the supervisor, both groups disclosed more, but the Anglos exhibited significantly larger growth in willingness to share information. Finally, the behavior of the immigrants change over time; and acculturation brings decision patterns of these immigrants closer to, but not completely in line with, that of the host society. The pace of acculturation appears to be rapid with a clear break at 5 years. These results indicate that managers in immigrant receiving countries need to manage not only the cross-cultural differences that immigrants bring with them, but also the process of change as immigrant workers adjust or fail to adjust their behavior to the norms of their adopted country. This paper now continues with a brief review of the literature followed by hypotheses, methodology, results, and conclusion.

LITERATURE REVIEW Explanations of information sharing have taken a number of different directions. A substantial body of literature has examined agency-based explanations and proposed solutions. An alternative stream of research has examined the impact of culture on behavior patterns. Two of these studies (Chow et al., 1999a; Tinsley & Pillutla, 1998) have attempted to link cultural tenets and agency explanations. Given trends in cross-national movements of persons, there also appears to be a need to examine the impact of acculturation on information sharing behavior. This section of the paper reviews relevant literature for each of these streams and proposes three hypotheses.

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Why Are Managers Reluctant to Release Information? Examining the Basic Literature Persuading employees to be open is not always easy (see, e.g., Chow et al., 1999b). Why are managers reluctant to release information? The extant literature appears to argue that economic consequences and perception of personal gain or loss drives information sharing behavior. As Chow et al. (1999b, p. 440) explain: Despite the large number of students who had indicated a concern with the company’s welfare and ethics, concern for their own job security and prospects had deterred many of them from fully sharing information about their mistakes. This deterrent effect, in turn, had depended on the expected reaction of the company and superior to the information disclosure.

This response appears to be tied closely to prior U.S. work in escalation of commitment using an agency framework.5 In the agency studies, fear of loss cause subordinates not to report mistakes if supervisors appear to have no way of finding out. Kanodia, Bushman, and Dickhaut (1989), for example, developed an equilibrium model in which deliberately failing to correct errors and, in fact, escalating them is rational in an agency framework. While this behavior may be irrational from the firm’s perspective, it may be rational to the decision maker when the decision maker’s reputation is at stake and information asymmetries are present. Harrell and Harrison (1994) present evidence that respondents not only cover up errors, but also escalate commitment to decisions when incentive to shirk and asymmetric information are both present, as predicted by agency theory. In the information-sharing literature, one proposed method of enhancing the likelihood of information sharing is by withdrawing supervisors. Chow et al. (1999a) proposed that doing so will cause a related reduction in threat to the participants and hence an increase in the willingness to share information. Such a reduced threat can have the equivalent valence of many other types of truth-inducing incentives. Why Are Managers Reluctant to Release Information? Examining the Cross-Cultural Literature The extant literature suggests that there are certain universal informationsharing behaviors by managers. However, a growing body of research, including Chow, Kato, and Merchant (1996), Chow, Kato, and Shields (1994), Birnberg and Snodgrass (1988), Harrison, McKinnon,

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Panchapakesan, and Leung (1994), Merchant, Chow, and Wu (1995), and O’Connor (1995) suggests that people in different nations often vary in how they react to given job-related conditions or decisions. In the case of the impact of culture on information sharing, there are a number of competing studies and theories. Tinsley and Pillutla (1998) and Chow et al. (1999a) provide conflicting cultural models of information sharing but the same results. Both papers find residents of greater China relatively unwilling to share information. In the case of Chow et al. (1999a), the original expectation based on Hofstede (1980) was that the Chinese were more collectivist than Anglo-Australians and would share more information for the benefit of all. The results showed this not to be the case. On the basis of open questions, the results were ascribed to the Chinese sense of face – a desire not to be shamed in front of their peers. Tinsley and Pillutla (1998), using the Schwartz (1992) value study, hypothesize that Hong Kong residents balance competing dimensions in decision making. While they value self-transcendence (collectivism), they are also conservative and hence resistant to change. An integral part of this conservatism is a desire to avoid the disruption caused by information sharing which is associated with the need of the individual to maintain face. Tinsley and Pillutla (1998) find that conservatism (face) dominates the decision process, and Hong Kong subjects share less information than Americans. Chow et al. (2000), on the other hand, studying subjects in the PRC argue and find the opposite to Tinsley and Pillutla (1998). They posit and find that a person in a collectivist society will be more inclined to share information that is beneficial to the collective. Chow et al. (2000), however, does not involve the disclosure of information before a supervisor but simply within groups of equals. Contrasting Chow et al. (2000) with the Tinsley and Pillutla (1998) model, both acknowledge the intuitive link between collectivism and information sharing in the Chinese society; however, the difference arises in the importance of the counterforce of face. Chow et al. (2000) see face as a byproduct of and dominated by collectivism, with the citizens of Greater China being collectivist and information sharing. Tinsley and Pillutla (1998) start with the Schwartz (1987) view of collectivism being overshadowed by face as part of Chinese conservatism (a desire to avoid change or suggest that something is not actually what it appears to be). Tinsley and Pillutla’s (1998) position is very much in line with the views of Ho (1976) and Redding and Wong (1986). Redding and Wong (1986) argue that one of the features that distinguish the importance of face in Chinese cultures is the sheer degree of concern with it. Ho (1976, p. 867), for example, explains the dynamic of face as follows: ‘‘Face is lost when the

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individual fails to meet essential requirements placed upon him by virtue of the social position he occupies.’’ Thus an individual who fails to achieve a promised commitment, whatever the reason, suffers a loss of face and hence a loss of psychic income in addition to any personal monetary consequences. Persons disappointing either a superior or a group may lose face. In both cases, perceived failure leads to a loss of face from a superior or from a group as a whole. Whether one loses face before a superior or subordinate, the perception of that loss is measured with regard to external constituencies. Thus, ‘‘yface is assessed in terms of what others think of him’’ (Ho, 1976, p. 871). It is possible that Chow, Deng and Ho (2000) removes part of face by removing the supervisor but the net effect of the papers cited in the preceding paragraphs is that combined effect of collectivism and face on information sharing remains an open question and one worth reexamining. Given the opposite views in the prior literature, our initial starting point or anchor is that Chinese managers who have migrated to an Anglo country will have a different propensity to reveal private information than Anglo managers residing in the same country. As such, we hypothesize H1. Chinese immigrant managers will have a different propensity to reveal private information than born and raised Anglo-resident managers. Drawing on the cultural model of Hofstede (1980, 1991) supplemented by the work of Ho (1976) on face, Chow et al. (1999a) also proposed an interaction between the cultural origin and the effect of the removal of the supervisor on information-sharing behavior. While Chow et al. (1999a) did not find statistical support for this hypothesis, as both types of managers responded similarly to the removal of the supervisor, further content analysis suggested that Anglo-Australian managers used different decision models and criteria than Taiwanese-Chinese (hereinafter referred to as Chinese) managers in their decision to reveal information. Australian subjects were presented in the Chow et al. (1999a) study as fairly simple and calculative. The role of a supervisor is to reward success and punish failure. Thus, the presence of a supervisor provides the major barrier to information disclosure and, as such, provides the incentive for subordinates not to disclose information about prior mistakes that could harm the individual’s prospects. The removal of a supervisor results in the removal of this barrier to information disclosure. The Chinese were presented as facing a more complex series of factors in making the decision to reveal information. Chinese managers face three barriers to information sharing: (1) a desire to avoid a loss of face before the group; (2) a desire to avoid a loss of face before the supervisor; and (3) the perception of the

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superior as the greater authority and the legitimate and obliged decision maker (power distance). While two of these barriers are removed when the supervisor is withdrawn, loss of face before the group remains. Thus, one might anticipate a smaller increase in the propensity to share information for members of the Chinese society vis-a`-vis the Anglo-Australians whose major significant barrier to sharing, fear of punishment, has been removed. Based on these cultural perspectives, we therefore propose that Chinese managers who have migrated to an Anglo country will exhibit a relatively lower level of willingness to release private information about an error in response to the removal of a supervisor than Anglo managers who are residing in the same country. Our second hypothesis is H2. The change in willingness to release private information about a prior mistake, as a result of the removal of the supervisor, would be lower for Chinese immigrant managers than born and raised Anglo-resident managers. Acculturation and Information Sharing If one believes the extant literature, cross-national differences in values can and do affect behavior. Even if a manager never plans to immigrate, she/he is still likely to encounter cross-cultural control issues. Large-scale immigration6 makes it increasingly unclear from whence an American, Canadian, or Australian manager has drawn their values and how they may react to particular decision-making situations. While non-native-born individuals may either immediately or never absorb the new culture, considerable evidence from marketing and other social science literature suggests that the values and behaviors of immigrants change over time as they interact with the existing workforce (see Ueltschy & Krampf (1997) and Yammarino & Jung (1998) for summaries), a process referred to as acculturation. In each of these studies, immigrant group members are found to hold norms and values somewhere between those of the culture of origin and the host society. The more acculturated the individual, the greater the progression toward the attitudes and values of the host society (Faber, O’Guinn, & Meyer, 1987). Studies of Latino immigrants have, for example, noted differences in media preferences and advertising effectiveness (Hayes-Bautista, Schinck, Chapa, & Soto, 1984; Adelson, 1989; Ueltschy & Krampf, 1997). The marketing literature

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contains a similar stream of literature on Chinese immigrants which is summarized in Lee and Tse (1994). The process of acculturation is not linear and takes place in stages (Ueltschy & Krampf, 1997). Studying Mexican immigrants to the U.S., Ueltschy and Krampf (1997) identified three statistically viable clusters of behaviors with members of each cluster varying in terms of level of acculturation to media. 1. A low acculturation cluster of persons who have lived in the U.S. between 1 and 5 years 2. A bicultural cluster of persons born in Mexico who have lived in the U.S. from 10 to 15 years 3. A high acculturation cluster of persons born in the U.S. of non-U.S. parents and grand parents; this group is not particularly dissimilar from the Anglo residents around them These differences between the groups in the sample translate into significant differences in advertising preferences. Lee and Tse (1994) use a four-part a priori classification of Hong Kong and Canadian subjects based on immigration laws and status, including English-speaking Caucasian Canadians and three levels of Chinese immigrants. Like Ueltschy and Krampf (1997), they find significantly different media usage patterns in each group but use a breaking point for immigrants of 7 years based on the elapsed time since a major change in immigration law. In summary, the extant literature seems to indicate that immigrants grow closer to the host country in tastes and behavior over time. The previous literature (Chow et al., 1999a, 2000) finds that Australian subjects are expected to begin at a lower level of information sharing than Chinese and increase their likelihood of sharing information (about a prior mistake) to a greater extent than Chinese subjects in response to the removal of the supervisor. Therefore, as the migrant Chinese group becomes more acculturated, they will also show a much greater increase in likelihood of sharing information in response to the removal of the supervisor than their un-acculturated peers. Thus our third hypothesis is

H3. There is a positive relationship between the change in the willingness to release private information about a prior mistake when the supervisor is removed and the degree of acculturation.

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RESEARCH METHODS Overview of Design An experiment was designed to focus on private information disclosure of Chinese immigrants and Australian residents and the effect of acculturation on Chinese immigrants’ disclosure behavior. A 2  (2) repeated factorial design was utilized to test hypotheses 1–3. The first factor represented the type of culture and was assessed in terms of ethnic origin and country of birth, with subjects being classified into two categories of Australian and Chinese (more detail about the categories is provided in subsequent discussion). The second factor represented the removal of the supervisor, which was manipulated within subjects. Participants were required to make two decisions with the removal of the supervisor occurring between the first and second decision. To test H3, we split the Chinese group into a high- and low-acculturated subgroup on the basis of tenure in Australia, which resulted in a 3  (2) repeated factorial design. After the split, the first factor represented the level of acculturation ranging from low-acculturated Chinese to high-acculturated Chinese to Australian groups. The second factor remained the removal of the supervisor. The dependent variable was the subjects’ degree of willingness to disclose private information revealing prior personal mistake.

Decision Task The decision task was adapted from the Chow et al. (1999a) instruments. Each subject received an experimental booklet containing instructions and the experimental material. Participants were asked to read the experimental material carefully and assume the role of a department manager of a hypothetical plant. The instructions also contained very explicit statements to the effect that there was no ‘‘correct’’ or ‘‘incorrect’’ response to the experiment, but rather that the best response was one that most closely reflected their true feelings and belief. The experimental material consisted of a background note and two questions. For the background note, subjects were provided with information about their recent promotion to department manager and a decision they had taken to endorse a new technology shortly after their promotion. Subjects were told that after endorsing the new technology, they found out that they had underestimated the variable costs of the new technology. In hindsight, if they had correctly estimated the variable cost, the best decision

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would have been to stay with the old technology. Subjects were further informed that the organization monitors performance very closely but does not track actual costs and revenues from individual projects. This allowed them to work very hard to squeeze cost savings and revenues from other projects to make up for the higher than planned costs of the new technology. Subjects were further told that they had just been invited to attend a meeting with another department manager in another plant, who was also considering adopting a new technology. Subjects were informed that the technology was very similar to the one they had adopted and that the other manager had also underestimated the variable costs associated with the technology. Following the background note, subjects had to make two decisions. For both decisions, the participants had to indicate the degree to which they would reveal their mistake in underestimating the variable cost of the new technology in order that people in the other plant would make a better decision. For the first decision, subjects were told that their supervisor would be attending the same meeting with the other department manager. For the second decision, subjects were informed that their supervisor was now not attending the meeting with the other department manager and that there was little chance of anything that was said would find its way back to the supervisor. Further, subjects were also asked to indicate the extent to which their two decisions truly reflected how they would think and act if they were in the manager’s place. Finally, subjects were asked to indicate their ethnic origin along with their country of birth and information about the tenure in Australia or any other country in which they have lived. Demographic information about their gender was also collected.

Dependent Variable The dependent variable measured the degree to which the individual would reveal their private information about prior personal failure. Subjects were asked to indicate to what extent they would definitely reveal their own mistake of underestimating. They recorded their answer on a 9-point scale ranging from 1 (definitely not reveal) to 9 (definitely reveal).

Independent Variables The independent variables were culture/degree of acculturation and the presence or absence of the supervisor. In measuring culture/acculturation,

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the prior research was examined for precedent. Researchers have used a variety of measures to operationalize culture/acculturation. These include the following: 1. Self-identification. This approach has been deemed most appropriate by cross-cultural behavioral researchers, especially those in cultural anthropology and social psychology (Cohen, 1978, Minor, 1992; Kara & Kara, 1996). 2. Place of birth. Padilla (1980) and Valencia (1985) use place of birth (U.S. vs. foreign born) to proxy for acculturation. 3. Place of birth/time in country groupings. Both Lee and Tse (1994) and Ueltschy and Krampf (1997) use time-bounded clusters, which then form the basis of testing the main hypotheses of their study.7 We divide our sample initially into Australian born and non-Australian born. All of the Australian-born participants identify themselves as Caucasian and the Chinese sample was of Chinese ethnic origin. This removes the possibility of Australian-born Chinese ethnic origin subjects and means that place of birth and self-identified origin collapse into one. The two broad categories can be described as Australian born and Chinese born. Within the Chinese-born sample, an initial regression indicates that time in country affects the difference in likelihood that the individual is going to reveal their prior error between when the supervisor is present and when the supervisor is absent. We are then faced with choosing a viable break point to group our Chinese-born sample. Fortunately, the immigration-based approaches taken by Lee and Tse (1994) and the rules of thumb of Ueltschy and Krampf (1997) can be synthesized. This study uses a categorization consistent with Ueltschy and Krampf (1997) of less than 5 years and 5 or more years living in the new country. This categorization is also consistent with Australian immigration rules specifically requiring immigration applications to be resubmitted at the end of each phase of education. Our second year students, who are resident for more than 5 years, would have to have completed at least two immigration reviews before the Australian Government (high school and college entry) or have achieved permanent residence by migrating themselves or been part of a family migration. The two groups, Australian born and Chinese born, were used to test for initial cultural differences in H1 and H2 and three groups Australian Born Culture (ABC), Chinese Born High Acculturation (CHA) and Chinese Born Low Acculturation (CLA) were used for the purpose of testing the impact of acculturation H3. The ABC group consists of participants born in Australia, essentially of Anglo ethnic origin, born in a country with a dominant Anglo

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culture. The CHA group consists of Chinese participants, born in Hong Kong, with 5 or more years of tenure in Australia. While members of this group were not born in a country with a dominant Anglo culture, their tenure for 5 or more years in Australia provided them with a high level of opportunity to become acculturated with the Anglo culture. The CLA group consists of Chinese participants, born in Hong Kong, with less than 5 years of tenure in Australia. Members of this group were born in a country that does not have a dominant Anglo culture and their tenure in Australia has been minimal, providing them with only a low level of opportunity to become acculturated with the Anglo culture. The role of the supervisor in preventing or enhancing information disclosure was manipulated within subjects by the removal of the supervisor in the second decision. For the first decision, subjects were told that the supervisor was attending the meeting between themselves and the other plant managers (‘‘supervisor present’’ condition), while for the second decision the supervisor was not attending the meeting (‘‘supervisor absent’’ condition).

Manipulation Checks A question testing for the respondents’ willingness to answer the questions truthfully was included in the experimental instrument.8 Participants were asked to respond on a 9-point scale ranging from 1 (‘‘not at all’’) to 9 (‘‘totally’’). Five participants were omitted for failing to answer the question. This resulted in 115 usable responses. The mean response of the 115 participants was 7.157 (significantly greater than the midpoint of the scale, t ¼ 18:403; p ¼ 0:000). Of the 115 participants, 7 responded below the midpoint of the scale. The 115 usable responses did not significantly differ between treatment groups on this question. These results provide comfort that participants not only responded truthfully, but also did not systematically interact with the treatment groups. As such, all 115 responses were retained for the main analysis.9

Participants All participants were enrolled in the same second-year management accounting subject at the time of the experiment.10 Demographic data was obtained for gender. While the distribution of the 57 male and 58 female

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subjects was not consistent across all treatments, a post hoc comparison between gender and the dependent variable was not significant.

RESULTS Analysis To test the hypotheses, a combination of two repeated ANOVA tests (for H1 and H2) as well as a linear regression and Bonferroni contrast tests (for H3) were employed. Bonferroni contrast tests were used to test the very specific a priori relationships of interest in H3. All directional hypotheses were tested using 1-tailed tests. All of the analysis was conducted using SYSTAT 10.

Descriptive Statistics To test the first two hypotheses, the sample was split on the basis of country of birth. A few general trends emerged from the descriptive statistics reported in Table 1. In all situations and for all sample subgroups, the willingness to reveal information was greater than the midpoint of the scale indicating a propensity to reveal private information about a prior mistake. On average, the Chinese migrant sample had a lower propensity to reveal information than the Anglo-Australian sample. This is concomitant with Tinsley and Pillutla (1998) findings that American managers show a greater willingness to share information than Chinese managers in negotiation strategies. In the supervisor absent condition, there was an increase for both the Chinese sample and Australian sample in the willingness to reveal private information about a prior mistake. To address the issue of acculturation, the complete Chinese sample was split into two subgroups resulting in three groups: ABC, CHA, and CLA, as discussed previously. This sample breakdown reveals some general trends between the CHA and CLA groups reported in the descriptive statistics (refer Table 2). On average, the CLA group was less willing to reveal information than the CHA group. Although the CHA group increased their willingness to disclose information once the supervisor was removed, the CLA group did not. Finally, examining the responses in Table 2, the results show a positive trend between the difference in willingness to reveal the information (supervisor absent vs. supervisor present) and the degree of acculturation from CLA ( 0.066)11 to CHA (+1.100) to ABC (+1.300).

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Table 1.

203

Means (Standard Deviations) for Australian Born vs. Chinese Born Participants.

Supervisor

Culture

Partial Means

Australian

Chinese

Supervisor present Mean S.D.

6.200 (2.070)

5.723 (1.972)

5.930 (2.021)

Supervisor absent Mean S.D.

7.500 (1.632)

6.554 (1.924)

6.965 (1.821)

Partial means

6.850 (1.632)

6.138 (1.956)

N

60

65

115

Note: Higher values indicate a greater willingness to definitely reveal mistakes.

Hypothesis Testing H1 postulates that migrant Chinese managers working in an Australian culture will have a different propensity to reveal information about a prior mistake than resident Australian managers. From the data shown in Table 1 and the results in Table 3, H1 was supported. The results show that Chinese participants were significantly (F ¼ 4:916; p ¼ 0:029; two-tailed) less likely to disclose information (mean ¼ 6:138) than Australian participants (mean ¼ 6:850). These results are consistent with those previously found by Tinsley and Pillutla (1998). H2 proposed that the removal of the supervisor would result in a smaller increase in information sharing for migrant Chinese than Australian resident subjects. The descriptive statistics reported in Table 1 and the results reported in Table 3 show that the tendency to release information when the supervisor is absent is marginally significantly (F ¼ 2:286; p ¼ 0:068;12 onetailed) greater for Australian subjects (difference in means ¼ 1.300) than Chinese subjects (difference in means ¼ 0.831). As the difference is only marginally significant, H2 was not supported. One of the potential explanations for the nonsignificant difference could be that subjects highly acculturated to the Australian context show behavior consistent with the Australian subjects. This explanation is tested more specifically when examining H3.

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Table 2.

Mean (Standard Deviation) for Australian vs. Chinese High/ Low Acculturation. Culture

Partial Means

ABC

CHA

CLA

Supervisor present Mean S.D.

6.200 (2.070)

5.660 (1.923)

5.933 (2.187)

5.930 (2.021)

Supervisor absent Mean S.D.

7.500 (1.632)

6.760 (1.779)

5.867 (2.031)

6.965 (1.821)

Partial means

6.850 (1.966)

6.210 (1.924)

5.900 (2.074)

N

50

50

15

115

Note: Higher values indicate a greater willingness to definitely reveal mistakes. ABC, Australian-Born Culture Group; CHA, Chinese High Acculturation Group; CLA, Chinese Low Acculturation Group.

Table 3.

Repeated ANOVA for Australian vs. Chinese Born Participants. Analysis of Variance

Source

SS

DF

MS

F

p (2-tailed)

Between subjects Culture Error

28.616 657.758

1 113

28.616 5.821

4.916

0.029

Within subjects Supervisor Supervisor  Culture Error

64.155 3.111 153.819

1 1 113

64.155 3.111 1.361

47.130 2.286

0.000 0.133

H3 proposed a positive relationship between the change in willingness to release private information about a prior mistake when the supervisor is absent and the degree of acculturation. The descriptive statistics reported in Table 2 and the results reported in Table 4 do indeed show a significant positive relationship (F ¼ 4:206; p ¼ 0:009; one-tailed) between the propensity to disclose the information (supervisor absent vs. supervisor present)

Examining the Role of Culture and Acculturation in Information Sharing

Table 4.

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Repeated ANOVA for Australian vs. Chinese High/Low Acculturation. Analysis of Variance

Source

SS

DF

MS

F

p (2-tailed)

Between subjects Acculturation Error

30.834 655.540

2 112

15.417 5.853

2.634

0.076

Within subjects Supervisor Supervisor  Acculturation Error

25.521 10.964 145.967

1 2 112

25.521 5.482 1.303

19.582 4.206

0.000 0.017

and the degree of acculturation CLA ( 0.066) to CHA (+1.100) to ABC (+1.300). A follow up reported in Table 5 shows that the difference in propensity to release information between the two Chinese groups (CHA vs. CLA) was significant (c1; F ¼ 7:083; p ¼ 0:004; one-tailed) while the difference in propensity between the high-acculturated Chinese subjects and the Australian group was not significant (c2; F ¼ 0:045; p ¼ 0:416; one-tailed). Therefore, H3 was partially supported. The sub-sample of subjects born in China was further examined in order to explore the different information disclosure behavior resulting from acculturation. A regression comparing the difference in information disclosure (supervisor present vs. supervisor absent) to the number of years these subjects have lived in Australia indicates that the coefficient is both positive (0.134) and significant (t ¼ 2:738; p ¼ 0:008; two-tailed). This provides further evidence of acculturation; with the increased number of years the Chinese migrants spent in Australia, the more their behavior resembled the high-acculturated Chinese group, which was previously shown to be insignificant from the Australian group (Table 6). In summary, Chinese migrant subjects on average were significantly less likely to reveal information. In addition, the relative difference in willingness to reveal information as the situation shifts, from supervisor present to one where the supervisor is absent, is lower for Chinese subjects than Australian subjects although the difference is only marginally significant. However, the small difference can be explained in terms of acculturation. Our results show that while the high-acculturated Chinese group did not differ significantly from the Australian subjects, the low-acculturated Chinese group did.

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Table 5. Contrast c1 c2

Bonferroni Contrasts for Acculturation.

SS

DF

MS

F

p (2-tailed)

15.705 0.099

1 1

15.705 0.099

7.083 0.045

0.009 0.833

Note: c1 ¼ (CHA vs. CLA) vs. (supervisor present vs. supervisor absent). c2 ¼ (ABC vs. CHA) vs. (supervisor present vs. supervisor absent).

Regression of Change in Willingness to Report and Acculturation.

Table 6. Effect Constant LIA Source Regression Residual

Coefficient Std. Error Std. Coefficient Tolerance 0.176 0.134

0.399 0.049

0.000 0.326

1.000

t-stat

Probability (2-tailed)

0.441 2.738

0.661 0.008

Sum of Squares

DF

Mean Square

F-ratio

11.609 97.529

1 63

11.609 1.548

7.499

p 0.008

Note: N ¼ 65; R2 ¼ 0:106; LIA ¼ years lived in Australia, Durbin Watson D Statistic ¼ 2.443, First-order Autocorrelation ¼ 0.222.

CONCLUSION AND LIMITATIONS This study examined the impact of the multiple forces that may affect the decision to release information about a prior mistake in a cross-cultural setting. It finds that while the absence of a supervisor can enhance information sharing, this effect is not constant across cultures. Further and more important, as managers migrate, a process of acculturation takes place that shifts response patterns for the migrant to a point closer to but not completely the same as persons born in his/her host culture. The results support Tinsley and Pillutla’s (1998) view that for Chinese subjects, conservatism (face) dominates their base collectivism. Thus, the willingness to share information that logically should exist in persons from a collectivist culture is not found. This raises questions as to why Chow et al. (2000) find the opposite. It is possible that the in-group portion of Chow et al.’s (2000) sample reinforces cultural collectivism that the overseas Chinese (Taiwanese and Hong Kong residents of Chinese origin) used in this sample. Tinsley and Pillutla (1998) and Chow et al. (1999a) represent a variant on

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those in the PRC or that the managers in Chow et al. (2000) represent one view of Chinese society. Further, the behavior of the immigrant appears to change over time and acculturation brings decision patterns of these immigrants closer, but not completely in line with that of the host society. This supports the extant marketing literature that indicates that changes in consumer behavior can be tied to acculturation. The pace of acculturation appears to be rapid with a clear break at 5 years.13 One of the limitations of this study lies in its use of students as surrogates for managers. While we believe that student subjects are acceptable surrogates for managers, as discussed previously in this study, it does raise an issue of the potential moderating role of general corporate culture (particularly for foreign-owned companies), which is outside the scope of our study. A second limitation lies in this study’s reliance on established cultural differences (such as differences in face) based on different nationalities origins backgrounds. Future research may strengthen this by measuring this difference more directly. Along with this limitation, this study raises questions that might well be answered by further research. Further work needs to be done to test the pace of acculturation in non-Anglo societies that accept immigrants such as France or Italy. Acculturation should also be studied using participants who are not attending university in the host country.14 Also, given the variance of responses to information sharing between Hong Kong, PRC, and Chinese members of the Chinese diaspora observed in the literature, there appears to be a need to test for differences within the Chinese culture including differences between regions of mainland China. A different direction of interest could lead to the examination of whether the cultural background of the supervisor has an affect on the willingness to disclose information about prior mistakes when they are not present. Another interesting direction is to explore the potential moderating role of general corporate culture on the relationships found in our study. Particularly where general corporate culture is at odds with the national culture in which the organization operates, managers may face competing cultural pressures in their information-sharing decisions. Other possible variables of interest may include size of business and ethnicity of fellow employees. While this study exclusively examined the sharing behavior concerning negative information, part of the information and knowledge assets also include positive information. While substantial bodies of literature examine the sharing of positive information, the authors are not aware of any acculturation work in this area.

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This study triangulates three views of culture in Schwartz (1992), Hofstede (1980) and Ho (1976) in preparing its hypotheses. However, as Harrison and McKinnon (1999) would argue, since each of these descriptions of culture focuses on the values of each society, our study may be ‘‘limited in its ability to examine and understand the dynamic process of management control systems and their cultural interplays.’’ Other lenses, particularly those emerging from anthropology, sociology, and history literatures, may provide valuable additional insights. In addition, this study is limited by the potential of a laboratory experiment involving a simplified information-sharing task. Finally, while this study is intended to build the academic knowledge base, there are certain implications for managers and management control system designers. If companies want to utilize the talents of the world’s brightest and best, they must realize that this comes at a price. That price is constant vigilance and understanding of how people from different cultures respond to and change their response to their work context over time.

ACKNOWLEDGMENTS The authors would like to acknowledge the helpful comments made by Margaret Abernethy, Andrea Drake, Chee Chow, Graeme Harrison, Jane Hronsky, and the participants of the 2001 AAANZ Conference and faculty at workshops at the University of Melbourne, University of Wisconsin and Bowling Green State University. U.S. Department of Education’s BIE program and the University of Melbourne Faculty of Economics and Commerce and Visiting Scholar Award provided financial support for this project.

NOTES 1. As Macintosh (1994) noted, the manner in which these employees gather, store and move information should be of vital interest to accounting and information system managers. 2. We are using a very general definition of migrants as ‘‘individuals who moved from their country of origin to a new country.’’ 3. Greater China includes the Peoples Republic of China and other areas where persons of Chinese origin dominate the business culture. This group of countries typically includes Singapore, Hong Kong and Taiwan. 4. See for example ‘‘After the Flood,’’ The Economist Newspaper, September 7, 2000 and Bogumil and Lawrence (2000).

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5. Agency theory has arguably had more influence on (Western) financial economics and accounting than any other theory in the last 20 years (Baiman, 1982, 1990; Watts & Zimmerman, 1990). The key feature that distinguishes it from classical economics is the assumption that there may be a divergence of goals between the manager (agent) and the firm’s owners (principal). Managers act in their self-interest rather than the firm’s interest (i.e., shirk) when two conditions are simultaneously satisfied: first, there is an incentive for them to do so (they stand to gain personally by taking a particular action), and second, information asymmetry exists between them and principals, who have less information than the agents. 6. If there is a defining demographic trend in these ‘‘immigrant Anglo countries’’ of the world, it has been the return of significant waves of immigrants from cultures that have little or no relation to existing cultures. In Australia, for example, over 20% of the population is composed of immigrants and 40% of these immigrants are Chinese (SBS Television, 2000). In the 1996 Canadian census, approximately 25% of the population is first generation immigrants and of those immigrants approximately 20% are from South and East Asia. Although the U.S. sees itself as a nation of immigrants, it has a smaller immigrant population with immigrants contributing approximately 8% of the population in 1990. However, the role of Chinese immigrants in the U.S. has been a topic of increasing importance as the percentage of Chinese immigrants among total U.S. immigrants has increased from 9.1% in 1960 to 37% in 1992 (Min, 1995). 7. Lee and Tse (1994) (Hong Kong and Canadian subjects) use a four-part a priori classification viz:(1) English-speaking Caucasian Canadians (those who had lived in Canada for more than 10 years); (2) Long-time Hong Kong immigrants (those who emigrated to Canada more than 7 years ago); (3) New Hong Kong immigrants (those who emigrated to Canada less than 7 years ago); and (4) Hong Kong residents (HK, ethnic Chinese who had lived in Hong Kong for more than 10 years). Hong Kong residents and Caucasian Canadians were included as the anchoring points for comparison purposes. Ueltschy and Krampf (1997), based on clustering of responses to a multivariate scale, define a three-part classification specifically: (1) Cluster 1 a low acculturation group that have lived in the U.S. between 1 and 5 years; (2) Cluster 2 a bicultural group born in Mexico but having lived in the U.S. from 10 to 15 years; and (3) Cluster 3 a high acculturation group born in the U.S. as were their parents and grand parents but not particularly dissimilar from the Anglo residents around them. 8. This question was included to test for any systematic differences across subject groups in terms of how truthful subjects were in responding to the questions in the instrument. 9. To gain further comfort all of the analyses reported in this study were also conducted with a covariate controlling for the response on the manipulation check question. The covariate was coded 1 for a response below the midpoint of the scale and 2 for a response above the midpoint. Results obtained were consistent with those reported in this study. 10. We believe that student subjects are acceptable surrogates for managers in our study, as these subjects were studying (i.e., working) in an environment culturally representative of their future working environment insofar as national culture. 11. CLA/Supervisor absent 5.867 minus CLA supervisor present 5:933 ¼ 0:066 in Table 2.

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12. Table 3 provides the results for a two-tailed test. The one-tail p-value is obtained by dividing the two-tail p-value by 2. 13. We tested a further break at 10 years and found no evidence of significant further acculturation. 14. It is interesting to note that the Australian government seems to recognize the value of university training as a source of acculturation by their policy that foreigners educated at Australian universities need to obtain only 90% of the points required of other potential candidates for permanent residence.

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THE EFFECTS OF VALUE ATTAINMENT AND COGNITIVE ROLES OF BUDGETARY PARTICIPATION ON JOB PERFORMANCE Vincent K. Chong, Ian R. C. Eggleton and Michele K. C. Leong ABSTRACT This chapter examines the effects of the value attainment and cognitive roles of budgetary participation on job performance. A structural model consisting of variables such as budgetary participation, job-relevant information, job satisfaction, and job performance is proposed and tested using a survey questionnaire on 70 senior managers, drawn from a crosssection of the financial services sector. Their responses are analyzed using a structural equation modeling (SEM) technique. The results reveal that budgetary participation is positively associated with job-relevant information. These results lend support to the cognitive effect of budgetary participation, which suggests that subordinates participate in the budget setting process to share information. In addition, the results suggest that budgetary participation is positively associated with job satisfaction. Advances in Accounting Behavioral Research Advances in Accounting Behavioral Research, Volume 8, 213–233 Copyright r 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 1475-1488/doi:10.1016/S1475-1488(04)08009-3

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These results support the value attainment role of budgetary participation, which increases subordinates’ levels of job satisfaction. Furthermore, the results reveal that there are positive relationships between job-relevant information and job satisfaction, job-relevant information and job performance, and job satisfaction and job performance.

INTRODUCTION Since the pioneering work of Argyris (1952) concerning the behavioral aspects of budgeting, a sizable body of research has developed in management accounting examining the effect of budgetary participation on job performance (see Covaleski, Evans, Luft, & Shields, 2003, for a comprehensive review). To date, the results of empirical evidence on the cognitive role of budgetary participation on job performance have produced consistent outcomes (Chenhall & Brownell, 1988; Kren, 1992; Chong & Chong, 2002).1 Kren (1992) developed a research model, which explicitly examined the cognitive function of budgetary participation on job performance. He argues that budgetary participation can facilitate the acquisition and use of job-relevant information. Job-relevant information, in turn, can improve performance. Specifically, Kren finds that budgetary participation affects job performance indirectly through job-relevant information. A review of the literature on participative budgeting indicates that value attainment is another role of budgetary participation (Locke & Schweiger, 1979; Locke & Latham, 1990; Shields & Shields, 1998). Indeed, numerous studies have recognized the value attainment role of budgetary participation (see e.g. Chenhall, 1986; Chenhall & Brownell, 1988; Chong & Bateman, 2000). However, no studies have explicitly tested its impact on subordinates’ job performance, and its potential influence within a cognitive model of budgetary participation. The value attainment role of budgetary participation theoretically affects subordinates’ levels of job satisfaction (Shields & Shields, 1998). Specifically, the value attainment effect of budgetary participation suggests that allowing subordinates to participate in the budgetsetting process will increase the likelihood that they will feel satisfied with their values (French, Israel, & As, 1960; Strauss, 1963; Lowin, 1968; Locke & Schweiger, 1979). Subordinates’ values may include: (1) the opportunity to express their views, (2) the feeling of being treated equally, and (3) the desire for respect or dignity (Argyris, 1955; Davis, 1957). To date, the

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existing literature has not explicitly tested the value attainment role of budgetary participation within the cognitive model of budgetary participation developed by Kren (1992). This gap in the accounting literature, which remains unexplored, constitutes the primary motivation for our study. This study extends Kren’s cognitive model by incorporating the variable of job satisfaction in our theoretical model (see Fig. 1). We argue that participation in the budget-setting process will help managers attain values, and that, subsequently, such value attainment of budgetary participation will manifest itself as higher job satisfaction, which in turn, enhances job performance. We posit that the cognitive role of budgetary participation enhances the gathering of job-relevant information (Link 1, Fig. 1), and that the value attainment effect of budgetary participation increases subordinates’ levels of job satisfaction (Link 2, Fig. 1). In addition, we argue that the availability and use of job-relevant information enhances job satisfaction and job performance (Links 3 and 4, respectively, Fig. 1). Finally, we propose that subordinates with higher levels of job satisfaction will be more likely to perform better in their job (Link 5, Fig. 1). In summary, the cognitive and value attainment roles of budgetary participation should initially

Cognitive Effect

Budgetary Participation

Performance Effect

Job-Relevant Information

Link 1

Job Performance

Link 4

Link 3 Link 2 Link 5

Job Satisfaction

Value Attainment Effect

Fig. 1.

Performance Effect

Theoretical Model.

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enhance job-relevant information and job satisfaction, which in turn, lead to higher job performance. The remainder of the chapter is organized as follows: In the next section, the relevant literature is reviewed and hypotheses underlying the study are developed. Subsequent sections present the research method, results, and conclusion and limitations of the study.

RESEARCH MODEL AND THEORETICAL DEVELOPMENT The Cognitive Effect Hypothesis: The Relationship between Budgetary Participation and Job-Relevant Information The first hypothesis is concerned with the relationship between budgetary participation and job-relevant information (Link 1 of Fig. 1).2 Prior accounting literature suggests that budgetary participation provides an opportunity for subordinates to gather job-relevant information to facilitate their decision-making process (Kren & Liao, 1988; Chenhall & Brownell, 1988; Kren, 1992; Magner, Welker, & Campbell, 1996). For example, Kren and Liao (1988) suggest that participation provides cognitive benefits, which enable the subordinates to clarify and comprehend the means by which objectives can be fulfilled. Chenhall and Brownell (1988), on the other hand, claim that budgetary participation allows managers to acquire job-relevant information that assists and clarifies their role expectations, the methods used in fulfilling their role expectations, or the consequences of role performance. Kren (1992) and Magner et al. (1996) find that budgetary participation is positively associated with job-relevant information. The psychological literature (see Latham & Saari, 1979; Campbell & Gingrich, 1986) also indicates that budgetary participation has a positive and direct effect on job-relevant information. In summary, the above literature review and empirical evidence suggest that budgetary participation serves as a cognitive function by enabling managers to obtain, exchange, and disseminate job-relevant information. Hence, this study postulates that the cognitive role of budgetary participation is positively associated with job-relevant information. The following hypothesis is tested: H1. Budgetary participation is positively associated with job-relevant information.

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The Value Attainment Effect Hypothesis: The Relationship between Budgetary Participation and Job Satisfaction The second hypothesis is concerned with the relationship between budgetary participation and job satisfaction (Link 2 of Fig. 1). The notion of the value attainment role of budgetary participation suggests that allowing subordinates to participate in the budget-setting process will increase the likelihood that they will feel satisfied with their values (French et al., 1960; Strauss, 1963; Lowin, 1968; Locke & Schweiger, 1979).3 Prior studies (e.g. Chenhall & Brownell, 1988; Lau & Tan, 2003) support the value attainment role of budgetary participation. For example, Chenhall and Brownell (1988, p. 231) conclude that ‘‘yparticipation is most helpful in decreasing managers’ role ambiguity, and that decreased role ambiguity improves job satisfaction.’’ Chenhall and Brownell attribute their findings to the managers’ opportunity to participate in a budget-setting process, which allows them to attain their values (i.e. to reduce the level of role ambiguity). Consequently, these managers felt highly satisfied with their job because of such value attainment. Similarly, Lau and Tan (2003) find that budgetary participation is likely to improve subordinates’ levels of job satisfaction because allowing them to get involved in the budget-setting process enhances their ability to meet their budget targets (i.e. to meet their values). In addition, subordinates who participate in the budget-setting process may experience feelings of dignity and self-respect (Cherrington, 1980; Shields & Shields, 1998). Shields and Shields (1998) suggest that budgetary participation helps to increase the subordinates’ self-esteem, morale, and to enhance their job satisfaction. They claim that the positive relationship between budgetary participation and job satisfaction is due to the fact that ‘‘ythe act of participation allows a subordinate to experience self-respect and feelings of equality arising from the opportunity to express his or her values’’ (Shields & Shields, 1998, p. 59). Furthermore, the opportunity to participate in the budget-setting process may cause subordinates to feel that their jobs are more fulfilling and induce them to exert greater work-related effort (see Deci & Ryan, 1985). Prior research suggests that the exertion of effort in the job itself provides fulfillment of peoples’ intrinsic needs to be competent and effective, hence contributing to job satisfaction (Aronson & Mills, 1959; Cardozo, 1965; Emmons, 1986). In summary, the above discussion suggests that the value attainment role of budgetary participation is expected to increase subordinates’ levels of job satisfaction. Thus, the formal hypothesis is as follows:

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H2. Budgetary participation is positively associated with job satisfaction. The Relationship between Job-Relevant Information and Job Satisfaction The third hypothesis is concerned with the relationship between job-relevant information and job satisfaction (Link 3 of Fig. 1). A review of the literature suggests that job-relevant information is related to subordinates’ job satisfaction (see O’Reilly & Caldwell, 1979; White & Mitchell, 1979; Griffin, 1983; Lau & Tan, 2003). Lau and Tan (2003, p. 23), for example, find that ‘‘yjob-relevant information, which promotes feeling of success through successful task completion, was associated with improved subordinates’ job satisfaction.’’ Lau and Tan’s argument was based on the needs-satisfaction model proposed by Salancik and Pfeffer (1977, 1978), which suggests that the expectation of feeling success (failure) is associated with feelings of satisfaction (dissatisfaction). In addition, the needs-satisfaction model suggests that needs fulfillment leads to increased job satisfaction. Specifically, it is argued that ‘‘yjobs which fulfill a person’s needs are satisfying; those that do not are not satisfying’’ (Salancik & Pfeffer, 1977, p. 428). In summary, the more job-relevant information that subordinates have about how to perform their job, the more job satisfaction they will have. Thus, the following hypothesis is tested: H3. Job-relevant information is positively associated with job satisfaction. The Relationship between Job-Relevant Information and Job Performance The fourth hypothesis is concerned with the relationship between jobrelevant information and job performance (Link 4 of Fig. 1). Job-relevant information gathered through a participative process could enhance an individual’s ability to perform (Beehr & Love, 1983). The existing literature suggests that the use of job-relevant information enhances job performance (see e.g. Campbell & Gingrich, 1986; Kren, 1992; Chong & Chong, 2002). Kren (1992), for example, finds that job-relevant information is positively associated with job performance. He attributes his results to the fact that job-relevant information helped subordinates to improve their action choices through better-informed effort, and consequently, improved performance. Kren (1992, p. 512) summarizes the usefulness of job-relevant information for decision making as follows:

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yJob-relevant information can improve performance because it allows more accurate predictions of environmental states and thus allows more effective selection of appropriate courses of action.

Chong and Chong (2002) conclude that the use of job-relevant information improves subordinates’ job performance because job-relevant information allows them to improve their decision choices. In summary, the above discussion suggests that the higher the level of job-relevant information, the higher the level of subordinates’ job performance. Hence, the following hypothesis is tested: H4. Job-relevant information is positively associated with job performance. The Relationship between Job Satisfaction and Job Performance The fifth hypothesis is concerned with the relationship between job satisfaction and job performance (Link 5 of Fig. 1). Recall that a primary objective of this study is to demonstrate the value attainment role of budgetary participation on subordinates’ job performance. Numerous prior accounting studies (see e.g. Choo & Tan, 1997; Poznanski & Bline, 1997) have demonstrated that job satisfaction is an antecedent to job performance. For example, Choo and Tan (1997) find that job satisfaction mediates the relationship between disagreement in budgetary performance evaluation style and job performance. Specifically, they find that when subordinates’ preferred budgetary performance evaluation styles differed from their superiors’ preferred budgetary performance evaluation styles, this disagreement leads to lower levels of job satisfaction and poorer job performance amongst subordinates. A dissatisfied subordinate may be more likely than a highly satisfied subordinate to decide simply not to perform in his or her job (see Franken, 1982). For example, Franken (1982, p. 451) claims that: Job dissatisfaction is an important issue because it has been linked toythe decision simply to not perform. ydissatisfaction is likely to lead to ysimply poor performance.

In addition, a subordinate who is satisfied with his or her job is assumed to perform better (Locke, 1986; Katzell, Thompson, & Guzzo, 1992). Katzell et al. (1992, p. 198) suggest that job satisfaction should be regarded as an attitudinal state of arousal that disposes one to exert effort. These studies attribute such findings (i.e. that there is a positive relationship between job satisfaction and job performance), to the fact that highly satisfied subordinates are more likely to exert additional effort to perform. Effort refers to ‘‘the amount of energy spent on [an] act per unit of time’’ (Naylor,

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Pritchard, & Ilgen, 1980, p. 6). It represents the force, energy, or activity by which work is accomplished (see Naylor et al., 1980; Ilgen & Klein, 1988). Brown and Peterson (1994) found that increased effort leads to improved job performance.4 In summary, the above discussion suggests that the higher the levels of job satisfaction, the higher the levels of subordinates’ job performance. Thus, the following hypothesis is tested: H5. Job satisfaction is positively associated with job performance.

RESEARCH METHOD The data collection method used in this study was a mailed survey questionnaire. This method was employed as it enables a large random sample to be tested, which enhances external validity. A total of 141 senior-level managers from firms in the financial services sector were randomly drawn from the Kompass Australia (1999) business directory. Telephone calls were made to ensure that each of the senior-level managers selected would receive the questionnaire, and would be the person involved in answering the questionnaire. Most importantly, these managers were called to ensure that they held budget responsibilities in their respective firms. Respondents were involved in the preparation of budgets for planning and control purposes. Since all of the respondents occupied positions of high responsibility and accountability, their role of budgetary participation in our sample is not one of pseudo-participation.5 Each participant was sent a survey questionnaire with a covering letter explaining the objective of the study and a reply paid self-addressed envelope. To enhance our response rate, each respondent was promised a gift voucher of 15 Australian dollars for returning the completed questionnaire. Each questionnaire was pre-coded to enable non-respondents to be traced and follow-up to be executed. A follow-up letter and another copy of the questionnaire were sent to those people who had not responded after four weeks. The response rate to the mail-out was 54%.6 Of the 77 questionnaires, seven were excluded from the study because they were considered outliers.7 This resulted in 70 usable responses for the final data analysis. The organizations surveyed have an average of 908 employees. Each manager, on average, was responsible for 98 employees. The average age of each participant was 42 years. The average length of time spent in the position

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was nearly 4 years and each manager had, on average, 10 years experience in his or her current area of responsibility.

Measurement of Variables Budgetary participation was measured by a three-item scale, similar to those used by Kren (1992) and Magner et al. (1996), which was originally developed by Milani (1975). These three similar items were chosen to insure that the budgetary participation scale would be comparable with those of Kren (1992) and Magner et al. (1996). The three items were: (1) ‘‘To what extent are you involved in setting your budget?’’, (2) ‘‘How much influence do you feel you have on the final budget?’’, and (3) ‘‘How do you view your contribution to the budget?’’. Each item was measured on a 7-point Likert-type scale (scored 1–7), with higher values on the item scale indicating higher budgetary participation. Cronbach’s alpha coefficient (Cronbach, 1951) obtained for this scale was 0.89, which indicates high internal reliability for the scale (Nunnally, 1967). Job-relevant information was measured using Kren’s (1992) three-item, 7point Likert-type scale. The objective of this measure is to assess the extent to which managers’ perceived information availability is necessary for the purposes of evaluating important decision alternatives and making effective job-related decisions. The scale ranges from 1 (strongly disagree) to 7 (strongly agree). The three items were: (1) ‘‘I am always clear about what is necessary to perform well on my job’’, (2) ‘‘I have adequate information to make optimal decisions to accomplish my performance objectives’’, and (3) ‘‘I am able to obtain the strategic information necessary to evaluate important decision alternatives’’. Cronbach’s alpha coefficient obtained for this scale was 0.77, which indicates satisfactory internal reliability for the scale. Job satisfaction was measured by a two-item, 7-point Likert-type scale developed by Dewar and Werbel (1979). The two items were: (1) ‘‘All in all, I am satisfied with my job’’, and (2) ‘‘In general, I like working in this company’’. Cronbach’s alpha coefficient obtained for this scale was 0.91, which indicates high internal reliability for the scale. A single-item, 7-point Likert-type scale was used to measure job performance as it was consistent with numerous prior accounting studies (e.g. Merchant, 1981, 1984; Mia & Chenhall, 1994; Dunk, 1995). Respondents were asked to rate their overall performance from ‘‘well below average’’ to ‘‘well above average’’ on a fully anchored 7-point Likert-type scale.

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RESULTS Table 1 shows the descriptive statistics and Pearson correlation matrix for the variables used in the study. To test the theoretical model (Fig. 1) and associated hypotheses, a structural equation modeling (SEM) technique was used. The SEM technique allows us to simultaneously test the entire budgetary participation–performance structural model. It also allows the specification of more complex models and makes allowances for errors in measurement. The SEM technique used in this study was based on the EQS structural equation computer program (Bentler, 1995). An approach recommended by Anderson and Gerbing (1988) was utilized, which involves two steps. First, the measurement model was evaluated by confirmatory factor analysis. Second, on the basis of the results of the measurement model analysis, only those items reflecting a common construct were aggregated to derive unidimensional composite scales for the structural model tests (Anderson & Gerbing, 1988).

Analysis of the Measurement Model The first step of the analysis is to develop a measurement model. The measurement model consists of three factors (budgetary participation, jobrelevant information, and job satisfaction). The w2 statistic and fit indices are summarized in Table 2, panel A. Although, the w2 statistic has been the most

Table 1.

Descriptive Statistics and Pearson Correlation Matrix ðn ¼ 70Þ.

Variable 1. Budgetary participation 2. Job satisfaction 3. Job-relevant information 4. Job performance

Actual (Theoretical) Range Mean S.D. 2.00–7.00 (1.00–7.00) 1.00–7.00 (1.00–7.00) 2.33–7.00 (1.00–7.00) 4.00–7.00 (1.00–7.00)

 Significant at the 0.01 level (2-tailed).  Significant at the 0.05 level (2-tailed).

1

2

3

4

5.16 1.58 1.00 5.53 1.22 0.39 1.00 5.39 1.06 0.24 0.32 1.00 6.08 0.81 0.25 0.36 0.44 1.00

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Table 2.

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Results of Confirmatory Factor Analysis.

Panel A: w2 Statistics and Fit Indices Fit Measures

Recommended Values

Result of This study

w2 Df w2 (p-value) w2 /df

NA NA 40.05 p3.00

36.58 17 0.003 2.15

Fit indices CFI NFI NNFI GFI

X0.90 X0.90 X0.90 X0.90

0.94 0.89 0.90 0.89

Residual analysis RMSEA p0.10 0.13 AOSR p0.05 0.06 Panel B: Standardized Loading, Composite Reliability, Variance Extracted Estimate and Cronbach’s Alpha Variable

Budgetary participation BP1 BP2 BP3

Standardized Loading

Composite Reliability

Variance Extracted Estimate

Cronbach’s Alpha

0.89

0.73

0.89

0.87

0.66

0.77

0.91

0.84

0.91

0.78 0.93 0.85

Job-relevant information JRI1 JRI2 JRI3

0.86 0.95 0.59

Job satisfaction JS1 JS2

0.85 0.98

CFI ¼ Comparative Fit Index, higher values indicate better fit. NFI ¼ Normed Fit Index, higher values indicate better fit. NNFI ¼ Non-Normed Fit Index, higher values indicate better fit. GFI ¼ Goodness-of-Fit Index, higher values indicate better fit. RMSEA ¼ Root Mean Square of Approximate, lower values indicate better fit. AOSR ¼ Average Off-Diagonal Standardized Residual, lower values indicate better fit.  All standardized loadings are significant at po0.05.

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often used indicator for model fit, it is not as useful as other fit indicators (Browne & Mels, 1994). Bollen and Long (1993) suggest the use of multiple model fit indicators. The results shown in Table 2, panel A reveal that the w2 statistic for our measurement model does not fit the data (w2 ¼ 36:58; po0.003). However, the value of the w2 divided by the degree of freedom is 2.15, which is below the cutoff value of 3 and is indicative of model fit (Hartwick & Barki, 1994; Segars & Grover, 1993). Further, the fit measures such as comparative fit index (CFI) and non-normed fit index (NNFI) are 0.94 and 0.90, respectively, which meets or exceeds the 0.90 minimum threshold (Bentler & Bonett, 1980; Anderson & Gerbing, 1988; Hair et al., 1998). The normed fit index ðNFI ¼ 0:89Þ and goodness-of-fit index (GFI ¼ 0.89) fall marginally short of the desired 0.90 criteria. The root mean square error of approximate (RMSEA) is 0.13, indicating a reasonable fit, although this value is somewhat higher than desired (Segars & Grover, 1993; Hartwick & Barki, 1994; Fogarty, Singh, Rhoads, & Moore, 2000). Finally, the average off-diagonal standardized residual is 0.06, suggesting a marginal fit. Taken together, the fit indices seem to be adequate and respecification is not necessary. Furthermore, the tests for convergent validity and discriminant validity provide further support for this decision. Convergent Validity Test The convergent validity of the scale is assessed by three measures: standardized loading, composite reliability, and variance extracted estimate (Fornell & Larcker, 1981). The results shown in Table 2, panel B, reveal that the standardized loadings for all the items of the three scales are highly significant (po0.05). The composite reliability reflects the internal consistency of the indicators measuring a given factor (Fornell & Larcker, 1981). It is analogous to Cronbach’s (1951) alpha coefficient for measuring the reliability of a multiple-item scale. The composite reliability is 0.87 or greater for each scale, indicating that the items comprising each scale are highly correlated. Variance extracted estimates assess the amount of variance that is captured by an underlying factor in relation to the amount of variance due to measurement error (see Fornell & Larcker, 1981). The variance extracted estimates for three of the factor scales exceeds the minimum level of 0.50 recommended by Fornell and Larcker (1981). Taken together, these results provide support for the convergent validity of the three scales. Discriminant Validity Test Discriminant validity is inferred when measures of each construct converge on their respective true scores, which are different from the scores of other

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constructs (see Churchill, 1979). The discriminant validity of the three scales is assessed using the procedure outlined by Bagozzi, Yi, and Phillips (1991). Specifically, a model is estimated in which the correlation between (1) budgetary participation and job-relevant information; (2) budgetary participation and job satisfaction; (3) job-relevant information and job satisfaction, respectively, are restricted to unity (i.e. the correlation is fixed at 1.0). The fits of the constrained models are then compared with those of the original unconstrained models. The results of the discriminant validity analysis for the three scales are shown in Table 3. As shown in Table 3, panel A, the w2 difference tests are significant for the models, which estimate the correlations between the budgetary participation and job-relevant information scales (w2 ð1Þ ¼ 63:45; po0.01), the budgetary participation and job satisfaction scales (w2 ð1Þ ¼ 12:26; po0.05), and jobrelevant information and job satisfaction scales (w2 ð1Þ ¼ 8:16; po0.05). These results suggest that the above-mentioned scales exhibit very strong properties of discriminant validity. A further test for the discriminant validity of the three scales is conducted by comparing the variance extracted estimates with the squared of the correlations between the latent constructs (Fornell & Larcker, 1981). As shown in Table 3, panel B, the variance extracted estimates for all constructs exceed the squared of the correlations. These results provide strong support for the discriminant validity of the three scales.

Analysis of the Structural Model The hypotheses are tested by relying on the standardized parameter estimates for the theoretical model as shown in Fig. 2. As expected, the results reveal that H1, which states that budgetary participation is positively associated with job-relevant information, is statistically significant (standardized path coefficient ¼ 0:24; po0.05). Thus, these results support H1 and lend support to the cognitive effect of budgetary participation, which suggests that subordinates participate in the budget-setting process to share information. In addition, the results shown in Fig. 2 reveal that budgetary participation is positive and statistically significantly associated with job satisfaction (standardized path coefficient ¼ 0:34; po0.05). These results support H2. Support is also found for H3, H4, and H5, since significantly positive relationships are demonstrated between job-relevant information and job satisfaction (H3), job-relevant information and job performance (H4), and job satisfaction and job performance (H5) (standardized path

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coefficients ¼ 0.24, 0.36, and 0.24, respectively po0.05). Overall, the model accounts for 24% of the variance in job performance (R2 ¼ 0:24; see Fig. 2). In order to understand better the impact of the linkage between budgetary participation and job performance, a model that excluded the value attainment effect of budgetary participation (i.e. job satisfaction) is tested for comparison purposes. As expected, budgetary participation is positive and statistically significantly associated with job-relevant information (standardized path coefficient ¼ 0:36; po0.05), and job-relevant information is also positive and significantly associated with job performance (standardized path coefficient ¼ 0:45; po0.05). The cognitive effect model accounts for 21% of the variance in job performance ðR2 ¼ 0:21Þ: Recall that the value attainment-cognitive effect model accounts for 24% of the variance in job performance, while the cognitive effect model alone accounts for only 21% of the variance in the job performance. Taken together, these results reveal that the introduction of the value attainment effect into the cognitive effect model result in a statistically significant (F change ¼ 3:06; po0.043, 1-tailed) increase in R2, suggesting that the combined value attainment and cognitive effect improve the predictive ability of our model.

CONCLUSION AND LIMITATIONS The main objective of this study is to test the impact of the value attainment role of budgetary participation on job performance, and its influence within the cognitive model. This study contributes to the participative budgeting literature in a number of ways. First, it introduces the value attainment role of budgetary participation to the accounting literature, and explicitly examines this role of budgetary participation on job performance. Second, it provides empirical support to the value attainment role of budgetary participation, which was theorized to increase subordinates’ job satisfaction. This result is consistent with our value attainment effect hypothesis and prior studies (e.g. Chenhall & Brownell, 1988; Chong & Bateman, 2000). In addition, this study extends prior studies by incorporating the value attainment role of budgetary participation into the cognitive model. The results of this study reveal that the joint effects of the value attainment and cognitive roles of budgetary participation significantly improved subordinates’ job performance. Furthermore, the results of this study provide additional empirical evidence to support the robustness of the findings of prior studies that examined the cognitive role of budgetary participation (e.g. Kren, 1992; Magner et al., 1996).

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Results of Discriminant Validity Tests.

Table 3.

Panel A: The w2 Difference Test w2

df

13.02 76.47 6.86 19.12 11.31

8 9 5 4 5

3.15

4

Model Constrained model: BP-JRI Unconstrained model: BP-JRI Constrained model: BP-JS Unconstrained model: BP-JS Constrained model: JRI-JS Unconstrained model: JRI-JS Panel B: Variance Extracted Estimate Test

Dw2 63.45 12.26 8.16

Intercorrelation

Squared Intercorrelation

Variance Extracted Estimate

0.41 0.43 0.35

0.17 0.18 0.12

0.73–0.66 0.73–0.84 0.66–0.84

BP-JRI BP-JS JRI-JS

 po0.05.  po0.01.  The squared of the correlation is less than both variance extracted estimates.

R2 = 0.06

Budgetary Participation

0.24*

0.34*

Job-Relevant Information

0.24*

Job Satisfaction

R 2 = 0.24

0.36*

Job Performance

0.24*

R 2 = 0.21

Fig. 2. Standardized Path Coefficients. (*Significant at 0.05 level. Model w2 ¼ 0:51; d.f. ¼ 1 (po0.48); Bentler-Bonnet Normed Fit Index (NFI) ¼ 0.99; Bentler-Bonnet Nonnormed Fit Index (NNFI) ¼ 1.09; Comparative Fit Index (CFI) ¼ 1.00; Goodness-of-Fit Index (GFI) ¼ 0.99; Root Mean Square Error of Approximate (RMSEA) ¼ 0.00; Average Off-Diagonal Standardized Residual (AOSR) ¼ 0.01.)

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Several limitations of this study need to be noted. First, sample was selected from the financial services sector. Hence, in generalizing the results to other industries, caution should be exercised. Further studies that compare two industries such as the manufacturing and financial services sectors would be worthwhile. A related issue to the sample is the use of relatively small sample size ðn ¼ 70Þ in this study. Prior studies (e.g. Bentler & Bonnet, 1980; Zimmerman, Eason, & Gowan, 1999) have criticized the problems associated with the use of small sample size for structural equation modeling. An alternative approach, path analysis, could be used as it would likely yield similar results to structural equation modeling regarding the significance of the relations between variables. Second, this study uses a selfrating scale to measure job performance which is likely to have resulted in higher leniency and lower variability errors in this measure (Prien & Liske, 1962; Thornton, 1968). Thus, care should be taken in interpretation of the results. Future studies could employ different research methods (e.g. longitudinal field studies) to investigate systematically the theoretical relationships proposed in this study. In addition, future study may also consider employing objective measures of performance (e.g. return-on-investment or return-on-assets to measure performance). Third, this study focuses on an examination of the value attainment and cognitive roles of budgetary participation without considering the potential motivational function of budgetary participation on job performance (Nouri & Parker, 1998; Chong & Chong, 2002; Wentzel, 2002). An attempt to test the three roles (i.e. motivational, cognitive, and value attainment) of budgetary participation in a single study would provide more insight into the process as to how budgetary participation really affects job performance. Finally, while this study tested a recursive model, a non-recursive model might be more applicable to the situation. In other words, there could be simultaneous links between (1) budgetary participation and job-relevant information; (2) job-relevant information and both job satisfaction and performance, and (3) job satisfaction and job performance. Applying the test of a non-recursive model was not possible in this study due to identification problems. Future research may attempt to test for a non-recursive model of participative budgeting.

NOTES 1. The cognitive mechanism suggests that the process of participation improves subordinate’s performance by increasing the quality of decisions as a result of the

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subordinate sharing job-relevant information with the superior (Kren, 1992; Shields & Shields, 1998, p. 59). 2. Job-relevant information refers to information that assists job-related decisionmaking (Kren, 1992). It is also known as ex-ante information (see Baiman, 1982), decision-facilitating information (see Demski & Feltham, 1976; Magee, 1986; Hilton, 1994), and task-relevant knowledge (Murray, 1990; Wier, 1993). 3. Pinder (1984, p. 95) suggests that ‘‘yvalues are those things that a person believes are conducive to his/her welfare’’, while Locke (1983, p. 1034) claims that ‘‘ya value is what a person consciously or subconsciously desires, wants, or seeks to attain’’. As noted earlier, subordinates’ values may include: (1) the opportunity to express their views, (2) the feeling of being treated equally, or (3) the desire for respect or dignity (Argyris, 1955; Davis, 1957). It is suggested that values play an important role in determining job satisfaction (Katzell, 1964). 4. In general, it is suggested that increased effort can either lead to immediate performance increases if it is directed toward current performance, or lead to delayed performance increases if it is directed toward learning (see Bonner & Sprinkle, 2002). The focus of this study is to investigate the increased effort, which is directed toward current performance (i.e. immediate performance), rather than directed toward learning (i.e. delayed performance). 5. Pseudo-participation refers to a budget-setting process in which subordinates are involved, but the superior makes the final decision. It is a consultative-type budgeting process in which the subordinate’s input to the budget is being ignored (Argyris, 1952; see also Pasewark & Welker, 1990). Pasewark and Welker (1990) suggest pseudo-participation can have a de-motivating effect on subordinates. 6. We tested for non-response bias using the approach suggested by Oppenheim (1966, p. 34). No statistically significant differences in the mean scores between the early and late responses were found. 7. The seven responses considered as outliers were from individuals in companies that employed substantially more people than the other firms. These seven firms employed a range of 10,000–140,000 employees. We conducted a univariate assessment of the values of the standardized scores, which revealed that all these seven responses exceeded the recommended threshold standardized values (Z scores) range from 73 to 74 (Hair, Anderson, Tatham, & Black. 1998, p. 65). The structural equation modeling (SEM) analyses were repeated before the exclusion of the seven outliers. The results revealed that there were no differences between SEM results based on 77 (before exclusion of the seven responses) and those based on 70 responses. This implies that our results are relatively robust to variations in the size (i.e. number of employees) of the sample.

ACKNOWLEDGEMENTS The authors appreciate the helpful comments and suggestions of Vicky Arnold (Editor), the Associate Editor, two anonymous reviewers, and seminar participants at York University, Toronto on the earlier drafts of this chapter. An earlier version of this paper was presented at the 2001

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Asian-Pacific Conference on International Accounting Issues, Rio de Janeiro, Brazil.

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