LIST OF CONTRIBUTORS

obert H. Ashton Fuqua School of Business, Duke

Solomon Appel

Metropolitan College of New York,New York, NY, USA

R

University, Durham, NC, USA

Reza Barkhi

Pamplin College of Business, VirginiaPolytechnic Institute and StateUniversity, Blacksburg, VA, USA

Mohamed E. Bayou

School of Management, University ofMichigan-Dearborn, MI, USA

Chee W. Chow

College of Business Administration,San Diego State University, San Diego,CA, USA

Cynthia M. Daily

Department of Accounting, University ofArkansas at Little Rock, AR, USA

Harry Z. Davis

Zicklin School of Business, CUNY –Baruch College, New York, NY, USA

Nabil Elias

Belk College of Business, University ofNorth Carolina at Charlotte, NC, USA

Arron Scott Fleming

College of Business and Economics,West Virginia University, Morgantown,WV, USA

Frank G. H. Hartmann

RSM Erasmus University, Departmentof Financial Management, Rotterdam,The Netherlands

vii

LIST OF CONTRIBUTORSviii

Fred A. Jacobs

School of Accountancy, Georgia StateUniversity, Atlanta, GA, USA

Frances Kennedy

Department of Accountancy and LegalStudies, Clemson University, SC, USA

James M. Kohlmeyer, III

College of Business, East CarolinaUniversity, Greenville, NC, USA

Leslie Kren

School of Business, University ofWisconsin, Milwaukee, WI, USA

John Y. Lee

Lubin School of Business, PaceUniversity, Pleasantville, NY, USA

Michael S. Luehlfing

School of Professional Accountancy,Louisiana Tech University, LA, USA

Adam S. Maiga

School of Accounting, FloridaInternational University, Miami, FL,USA

William W. Notz

I.H. Asper School of Business, Universityof Manitoba, Winnipeg, Canada

Thomas J. Phillips, Jr.

School of Professional Accountancy,Louisiana Tech University, Ruston,LA, USA

Alan Reinstein

School of Business, Wayne StateUniversity, Detroit, MI, USA

Lydia Schleifer

Department of Accountancy and LegalStudies, Clemson University, SC, USA

Anne Wu

College of Commerce, National ChengchiUniversity, Taipei, Taiwan

EDITORIAL BOARD

Thomas L. Albright Eric G. Flamholtz

ix

University of Alabama

University of California, Los Angeles

Jacob G. Birnberg

George J. Foster University of Pittsburgh Stanford University

Germain B. Boer

Eli M. Goldratt Vanderbilt University Avraham Y. Goldratt Institute

William J. Bruns, Jr.

John Innes Harvard University University of Dundee

Peter Chalos

Larry N. Killough University of Illinois, Chicago Virginia Polytechnic Institute

Donald K. Clancy

Thomas P. Klammer Texas Tech University University of North Texas

Robin Cooper

Carol J. McNair Emory University U.S. Coast Guard Academy

Srikant M. Datar

James M. Reeve Harvard University University of Tennessee, Knoxville

Antonio Davila

Karen L. Sedatole Stanford University Michigan State University

Alan S. Dunk

George J. Staubus University of Canberra University of California, Berkeley

Nabil S. Elias

Lourdes White University of North Carolina,

Charlotte

University of Baltimore

Kenneth J. Euske

Sally K. Widener Naval Postgraduate School Rice University

STATEMENT OF PURPOSE AND

REVIEW PROCEDURES

Advances in Management Accounting (AIMA) is a professional journalwhose purpose is to meet the information needs of both practitioners andacademicians. We plan to publish thoughtful, well-developed articles on avariety of current topics in management accounting, broadly defined.

AIMA is to be an annual publication of quality-applied research inmanagement accounting. The series will examine areas of managementaccounting, including performance evaluation systems, accounting forproduct costs, behavioral impacts on management accounting, andinnovations in management accounting. Management accounting includesall systems designed to provide information for management decision-making. Research methods will include survey research, field tests,corporate case studies, and modeling. Some speculative articles and surveypieces will be included where appropriate.

AIMA welcomes all comments and encourages articles from bothpractitioners and academicians.

REVIEW PROCEDURES

AIMA intends to provide authors with timely reviews clearly indicating theacceptance status of their manuscripts. The results of initial reviewsnormally will be reported to authors within eight weeks from the date themanuscript is received. Once a manuscript is tentatively accepted, theprospects for publication are excellent. The author(s) will be accepted towork with the corresponding Editor, who will act as a liaison between theauthor(s) and the reviewers to resolve areas of concern. To ensurepublication, it is the author’s responsibility to make necessary revisions ina timely and satisfactory manner.

xi

EDITORIAL POLICY AND

MANUSCRIPT FORM GUIDELINES

1.

Manuscripts should be type written and double-spaced on 81/200 � 1100

white paper. Only one side of the paper should be used. Margins shouldbe set to facilitate editing and duplication except as noted:a. Tables, figures, and exhibits should appear on a separate page. Each

should be numbered and have a title.b. Footnote should be presented by citing the author’s name and the

year of publication in the body of the text; for example, Ferreira(1998) and Cooper and Kaplan (1998).

2.

Manuscripts should include a cover page that indicates the author’sname and affiliation.

3.

Manuscripts should include, on a separate lead page, an abstract notexceeding 200 words. The author’s name and affiliation should not ap-pear on the abstract.

4.

Topical headings and subheadings should be used. Main headings in themanuscript should be centered, secondary headings should be flush withthe left-hand margin. (As a guide to usage and style, refer to the WilliamStrunk, Jr., and E.B. White, The Elements of Style.)

5.

Manuscripts must include a list of references, which contain only thoseworks actually cited. (As a helpful guide in preparing a list of references,refer to Kate L. Turabian, AManual for Writers of Term Papers, Theses,

and Dissertations.)

6. In order to be assured of anonymous review, authors should not identify

themselves directly or indirectly. Reference to unpublished working pa-pers and dissertations should be avoided. If necessary, authors mayindicate that the reference is being withheld for the reason cited above.

7.

Manuscripts currently under review by other publications should not besubmitted. Complete reports of research presented at a national or re-gional conference of a professional association and ‘‘State of the Art’’papers are acceptable.

8.

Four copies of each manuscript should be submitted to John Y. Lee atthe address given below the Guideline 11.

xiii

EDITORIAL POLICYxiv

9.

A submission fee of $25.00, made payable to Advances in ManagementAccounting, should be included with all submissions.

10.

For additional information regarding the type of manuscripts that aredesired, see ‘‘AIMA Statement of Purpose.’’

11.

Inquires concerning Advances in Management Accounting may be di-rected to either of the editors:

Marc J. EpsteinJones Graduate School of Administration

Rice UniversityHouston, TX 77251-1892

John Y. LeeLubin School of Business

Pace UniversityPleasantville, NY 10570-2799

INTRODUCTION

This volume of Advances in Management Accounting (AIMA) begins with apaper by Ashton on various models of value creation that have been pro-posed for supporting value-based management. Balanced Scorecard, theBaldridge Quality Award Criteria, the Service-Profit Chain, and the SkandiaIntellectual Capital Model are among them. Similarities and differencesamong value-creation models are noted, their potential for guiding the iden-tification of value drivers and performance measures for value-based man-agement is assessed, and critical management issues that must be addressed ifsuch models are to contribute to long-run value creation are explored. Thesubstantial body of research evidence linking intangible value drivers to fi-nancial outcomes is reviewed, and some directions for further research areoffered. This will become a valuable source for management accountingresearchers, including doctoral students, in their research in this area.

The next paper by Chow, Kohlmeyer, and Wu addresses the issues ofinnovation and risk. Innovation is the key to competitive advantage, andattaining innovation often requires taking on higher-than-usual levels ofrisk, but managers often emphasize safe, short-term results over more risky,long-term outcomes. As a result, a major challenge to firms is increasingemployees’ willingness to adopt risky yet more profitable alternatives. Thisstudy uses an experiment to test how the level of performance standard, perse, affect employees’ propensity to take on (more) risky projects. Usingparticipants from the U.S. and Taiwan to represent higher vs. lower indi-vidualism national cultures, it also examines the effects of national cultureon employee actions. The findings are consistent with expectations fromcombining goal and prospect theories that a specific high standard motivatesgreater risk taking than a low standard. They find only limited differencebetween the U.S. and Taiwanese samples’ individualism/collectivism scores,which may help to explain the lack of significant differences between theirreactions to the performance standard treatment.

The paper by Elias and Notz tests the effects of two different organiza-tional cultures on budgetary conflict. Budgetary conflict is perceived byconflicting parties as a zero sum game, or distributive – one party’s gain isthe other party’s loss. They propose and test the effects of an empowering

xv

INTRODUCTIONxvi

organizational culture (EOC) in contrast to the traditional organizationalculture (TOC). They hypothesize that an EOC would produce more inte-grative conflict resolution than the typical TOC. Using a laboratory exper-iment they confirmed their hypotheses that the EOC produces moreintegrative budget negotiation outcomes, greater convergence, and greatersatisfaction with the outcome than TOC.

The next paper by Kren and Maiga examines subordinate–superior in-formation asymmetry as an intervening variable linking budgetary partic-ipation and slack. The results indicate two offsetting effects of participationon slack. A significant negative indirect relation between participation andslack was found to act through information asymmetry. Thus, managersreveal private information during the budget process, reducing informationasymmetry that subsequently reduces budget slack. These results provideevidence about the inability of past research to confirm a consistent directrelation between budget participation and budget slack.

The paper by Hartmann investigates whether acknowledgment of differ-ent types of uncertainty may explain the following apparently conflictingresearch findings: Research on budget-based performance evaluation tradi-tionally predicts that the use of accounting performance measures (APM) incomplex, dynamic, and uncertain situations results in dysfunctional man-agerial attitudes and behaviors. Although this suggests that such situationsrequire the use of subjective performance measures (SPM), empirical evi-dence is inconclusive, as APM, rather than SPM, have been found to alsohave a negative effect on managerial ambiguity. This suggests that APMmay be more, rather than less, appropriate than SPM in situations of highuncertainty. He develops hypotheses that predict differential interactionsbetween the environmental uncertainty and task uncertainty and APM andSPM on managerial ambiguity. These hypotheses are tested using surveydata from 250 managers in 11 organizations. Tests using moderated regres-sion analysis provide support for the existence of different interactions be-tween uncertainty and the use of performance measures, and providereconciliation for the opposing findings in the extant literature.

In the next paper, Bayou and Reinstein address the problem of the ineffec-tive pricing methods available for smaller firms. The few management ac-counting pricing methods in the management accounting literature areineffective in helping small firms use their idle capacity during lingering eco-nomic recessions, and some of these methods may even worsen this problem.

Extending the traditional break-even-cost-volume-profit model, they de-rive a more effective pricing method, the break-even-full-capacity-utilization(BEFCU) model, to handle this problem. To demonstrate its practicality,

Introduction xvii

the authors apply the BEFCU model to an actual job shop. This modelintegrates certain strategies based on built-in flexibility in commitments withsuppliers and customers and maintaining a mode of conservatism in ac-counting for plant assets.

The paper by Davis, Appel, and Lee provide the evidence that even whenMurphy’s Law is objectively untrue, because of sampling bias, people per-ceive the law as true, and this perceptual bias has a far-reaching implicationin management accounting research. A corollary to Murphy’s Law is: ‘‘Theother lane always moves faster than my lane.’’ A manager who is aware ofthis perceptual bias will try to structure her budget cutbacks and all other‘‘negative compensations’’ in such a way that her employees perceive thatthe cutback applies to everyone, not just to themselves. The findings of theirstudy support the wisdom that, whenever managers implement managerialplans that will be perceived as ‘‘negative,’’ the plan should be implementedall at once. Spreading the implementation over a period of time producesmore discontent on the part of the personnel affected. The findings lendcredence to a generalization that people’s discontent is minimized when thenumber of observations (and thus the number of chances for forming anegative perception) of undesirable events is minimized.

In the next paper, Maiga and Jacobs use structural equation modeling toinvestigate the impact of ABC implementation factors (management sup-port, clarity and consensus of ABC objectives, nonaccounting ownership,and training) on quality, cost, and cycle time improvements. Overall, theresults of the structural analyses support the theoretical model indicatingthat ABC implementation factors influence quality, cost and cycle time, andpartial support for the relations among quality, cost and cycle time im-provement and their effect on financial performance. The relationships arefurther analyzed within the context of ABC implementation stage, adoptionof advanced manufacturing practices, industry characteristics, and plant sizeto determine if these contextual factors impact the model constructs and therelationships between the variables in the theoretical model. The resultsshow that these contextual factors do not affect the model constructs.However, they affect the model relations.

The paper by Kennedy and Schleifer examines how performance meas-urement can both encourage and hinder team performance. It then proposesa team performance measurement system using ratios and activity-basedmanagement that seek to encourage innovation and empowerment whilemaintaining a system of control. In the next paper, Fleming and Barkhiexamine the influence of the psychological factor of social comparison overAPM in a compensation experiment. The results of this study are consistent

INTRODUCTIONxviii

with social comparison theory in that CEO director-subjects award greaterpay and shield the compensation of the CEO when firm accounting per-formance is below average. They also find shielding is mitigated when sub-jects are informed that the decision of the amount of compensation awardedwill be revealed to the public. In a research note, Phillips, Daily, andLuehlfing address the consistency of readability levels before and after therecent changes in professional examinations for management accountants.

We believe the 11 articles in Volume 16 represent relevant, theoreticallysound, and practical studies the discipline can greatly benefit from. Thesemanifest our commitment to providing a high level of contributions tomanagement accounting research and practice.

Marc J. EpsteinJohn Y. Lee

Editors

VALUE-CREATION MODELS FOR

VALUE-BASED MANAGEMENT:

REVIEW, ANALYSIS, AND

RESEARCH DIRECTIONS

Robert H. Ashton

ABSTRACT

Models of value creation that have been proposed for supporting value-

based management are described and analyzed, including the Balanced

Scorecard, the Baldrige Quality Award Criteria, the Deming Manage-

ment Method, the Service-Profit Chain, and the Skandia Intellectual

Capital Model. These models are compared, their potential for guiding

the identification of value drivers and performance measures for value-

based management is assessed, and management issues that must be ad-

dressed if such models are to contribute to long-run value creation are

explored. These issues include causally linking value drivers to each other

and to financial outcomes, the extent to which the models take a dynamic,

or whole-system, view of value creation, and whether multiple value driv-

ers should be explicitly weighted and combined to form a ‘‘value index.’’

Finally, the substantial body of research evidence linking intangible value

drivers to financial outcomes is reviewed, and some directions for further

research are offered.

Advances in Management Accounting, Volume 16, 1–62

Copyright r 2007 by Elsevier Ltd.

All rights of reproduction in any form reserved

ISSN: 1474-7871/doi:10.1016/S1474-7871(07)16001-9

1

ROBERT H. ASHTON2

INTRODUCTION

Adopting the perspective of value-based management, this paper analyzesseveral value-creation models that have appeared in the literature and thathave guided both the practice of management and research in managementaccounting. Value-based management focuses on defining and implementingmanagement strategies having the highest potential for creating shareholdervalue, identifying value drivers and aligning management processes thatsupport value creation, and designing performance measurement and in-centive systems that reflect value creation (Ittner & Larcker, 2001). Thegrowing interest in value-based management reflects profound changes inthe competitive business environment – involving, for example, technology,globalization, customer demands, greater attention to quality and service,and increased emphasis on business forms such as partnerships and alli-ances. These changes have led to dissatisfaction with traditional, transac-tions-based financial measurement and reporting systems for managing thefirm (e.g., Johnson & Kaplan, 1987; Dixon, Nanni, & Vollmann, 1990;Eccles, 1991; Eccles & Pyburn, 1992).

One result of the dissatisfaction with traditional measurement and re-porting has been an increased interest in identifying and measuring ‘‘intan-gible value drivers’’ such as the skills and knowledge contained within thefirm, the operational, customer-related, and innovation processes throughwhich the firm manages today and prepares for tomorrow, and the rela-tionships the firm has developed with key external stakeholders. The interestin firms’ potential, or capacity, for creating long-run shareholder value thatultimately will be realized is evidenced not only by proposals for morecomprehensive measurement systems for internal use (e.g., Kaplan &Norton, 1992), but also by proposals for expanded external disclosures (e.g.,American Institute of Certified Public Accountants (AICPA), 1994; OECD,2000; Danish Ministry of Science & Technology, 2003). This interest is alsosupported by surveys of financial analysts’ information use (e.g., Previts,Bricker, Robinson, & Young, 1994; Dempsey, Gatti, Grinnell, & Cats-Baril,1997; Low & Siesfeld, 1998) and by academic research documenting positiveassociations between non-financial disclosures and market outcomes (e.g.,Maines et al., 2002, 2003). Moreover, the impact of these developments onmanagement practice and management accounting research is being feltacross a wide range of developed and developing economies (e.g., Epstein &Manzoni, 2002, 2004; Rejc, 2005).

Because research in management accounting has tended to follow changesin management practice, much of today’s management accounting research

Value-Creation Models for Value-Based Management 3

involves a strategic focus on long-run value creation through the identifi-cation, measurement, management, and reporting of the drivers of firmvalue. A generic value-based management accounting framework involvingsix (largely sequential) steps is shown in Fig. 1. Briefly, the frameworkinvolves (1) choosing value-enhancing organizational objectives, (2) select-ing strategies consistent with those objectives, (3) identifying performancevariables, or value drivers, that create value, (4) selecting performancemeasures that reflect those value drivers, along with performance targetsand action plans, (5) evaluating managerial performance and the success of

Identify SpecificOrganizational Objectives

Develop Strategiesand Select

Organizational Design

IdentifyValue Drivers

Develop ActionPlans, Select Measures,

and Set Targets

EvaluatePerformance

Overall Objective:Increase Shareholder Value

Fig. 1. Value-Based Management Accounting Framework. Source: Ittner and

Larcker (2001).

ROBERT H. ASHTON4

action plans, and (6) continuously assessing the overall value-based man-agement framework and making modifications as needed (Ittner & Larcker,2001). Steps (3)–(6) generally involve some model of value creation thatguides the identification of value drivers and performance measures andtheir use in managing the firm.

Many value-creation models have been proposed, each with a particularperspective on the drivers and measures considered essential for long-runvalue creation. The principal value-creation models are described andanalyzed in this paper, beginning with the ‘‘quality management’’ prescrip-tions of Deming (1982) and including other quality- and customer-orientedmodels (the Baldrige Quality Award Criteria and the Service-Profit Chain),the Balanced Scorecard, and the Skandia Intellectual Capital Model.The basic features of these models are described, their potential for sup-porting value-based management is assessed, and research evidence thatbears on the link between intangible value drivers and financial outcomes isreviewed.

The paper is organized as follows. The value-creation models are de-scribed in the next two sections, where key features of each model are notedand similarities and differences among them are discussed. The followingsection examines several issues that arise in the use of such models by man-agement, including the importance of understanding causal relationshipsthat link value drivers to each other and to financial outcomes, the extent towhich the models take a dynamic, or whole-system, view of value creation,and whether the measures associated with underlying value drivers shouldbe explicitly weighted and combined to form a ‘‘value index.’’ Finally, re-search evidence that links many of the value drivers included in such modelsto financial outcomes at both the firm and market levels is reviewed, andsome directions for additional research are suggested.

VALUE-CREATION MODELS

Models that involve mainly a quality orientation or a customer orientationare described first. Much overlap exists between quality and customermodels because a principal rationale for improved quality is its positiveeffects on customer-related outcomes. This is followed by models that em-phasize intellectual capital. Finally, the balanced scorecard and related strat-

egy map are described, with emphasis on the fact that recent explications ofthe latter incorporate important aspects of both quality and customer mod-els and intellectual capital models.

Value-Creation Models for Value-Based Management 5

Quality- and Customer-Oriented Models

Deming Management Method

Many important aspects of today’s value-creation models can be traced atleast as far back as the writings of W. Edwards Deming. Among othercontributions now often known collectively as the Deming ManagementMethod (Walton, 1986), Deming developed a list of 14 Points that heconsidered ‘‘principles of transformation’’ for management practice. The14 Points, largely oriented toward business processes, are a set of interrelatedprescriptions that serve as guidelines for quality management practices. Forthe most part, they are loosely stated and contain such prescriptions as‘‘Break down barriers between departments,’’ ‘‘Eliminate the need forinspection on a mass basis by building quality into the product,’’ and ‘‘Re-move barriers that rob [management and employees] of their right to pride ofworkmanship’’ (Deming, 1982, pp. 16–17).1

Deming also described a set of cause-and-effect linkages – the Deming‘‘Chain Reaction’’ – beginning with improved quality and ending with im-provement in various organizational outcomes. The linkages in the DemingChain Reaction involved hypothesized relationships among internal proc-esses, costs, customer and market variables, and employee and organiza-tional outcomes, as shown in Fig. 2. The elements of the chain reflected astrong emphasis on internal processes and were heavily oriented towardproduct quality, consistent with much of the total quality management(TQM) focus of the 1980s (e.g., Garvin, 1987).

Anderson, Rungtusanatham, and Schroeder (1994) have articulated atheory of quality management based on concepts underlying Deming’s14 Points, the Deming Chain Reaction, and other Deming contributions.

Improvequality

Costs decrease because of lessrework, fewer mistakes, fewerdelays, snags; better use ofmachine-time and materials.

Productivityimproves

Capture the marketwith better qualityand lower price

Stay inbusiness

Provide jobsand more jobs

Fig. 2. The Deming ‘‘Chain Reaction.’’ Source: Deming (1986).

VisionaryLeadership

OrganizationalSystem

Internal andExternal

Cooperation

Learning

ProcessManagement

ProcessOutcomes

EmployeeFulfillment

ContinuousImprovement

CustomerSatisfaction

Causal Direction Feedback Mechanism

Fig. 3. Model Underlying the Deming Management Method. Source: Anderson

et al. (1994).

ROBERT H. ASHTON6

Anderson et al. analyzed the content and evolution of the DemingManagement Method, based on Deming’s writings and those of others,observations of practice, and a Delphi study involving a panel of Demingexperts from academe and practice.2 Seven concepts underlying Deming’swork were identified – (1) visionary leadership, (2) internal and externalcooperation, (3) learning, (4) process management, (5) continuous improve-ment, (6) employee fulfillment, and (7) customer satisfaction – and a theoryof causal linkages among these concepts was proposed (Fig. 3).

The breadth of the concepts and practices under the TQM umbrellaexpanded significantly from Deming’s early contributions. Juran (1989), forexample, distinguished between ‘‘Little Q’’ and ‘‘Big Q’’ quality-improvementprojects: ‘‘Little Q’’ projects focused narrowly on products, manufacturingprocesses, end purchasers of current products, and the costs associatedwith deficient products. In contrast, ‘‘Big Q’’ projects concerned servicesas well as products, support processes as well as manufacturing processes,internal as well as external customers, and a broader range of costs. As TQMcontinued to evolve, it became more oriented toward overall firm strategy.Included were customer and supplier relationships, employee empower-ment, cross-functional training and product design, commitment to a‘‘quality philosophy’’ (involving specific practices such as benchmark-ing, continuous improvement, and a lean and open organization), and anincreased focus on measurement and analysis of organizational performance

Value-Creation Models for Value-Based Management 7

(Powell, 1995; Cua, McKone, & Schroeder, 2001; Douglas & Judge, 2001).TQM came to be seen as

an integrated, systematic, organizationwide strategy for improving product and service

qualityy neither a program nor a specific tool or technique [but] a shift in both thinking

and organizational culturey (Waldman, 1994, p. 511).

The original, process-oriented perspective of Deming’s work strongly influ-enced subsequent TQM developments, especially the Baldrige QualityAward Criteria, and both Deming’s work and elements of the BaldrigeModel are reflected in later models of value creation.

Baldrige Quality Award Criteria

The Malcolm Baldrige National Quality Award was introduced in 1987 topromote quality awareness and practices and to publicize the qualityachievements of U.S. companies. A major impetus for its introduction waswidespread concern about the eroding competitive position of the U.S.Some of the major building blocks for the Baldrige Model were the qualityprescriptions of Deming and others. A new framework for quality assess-ment was created, however, ‘‘above the views of particular practitioners orprescribers of specific quality systems’’ (Bell & Keys, 1998, p. 54).

While the Baldrige Quality Award Criteria are perhaps best known fortheir role in evaluating applicants and selecting recipients of the BaldrigeQuality Award, their principal use has been as a source of informationabout achieving performance excellence (Bemowski & Stratton, 1995). Onlya few hundred companies have actually applied for the Baldrige Award, butmore than 2 million copies of the performance criteria underlying the awardwere requested during the first 10 years of its existence (Flynn & Saladin,2001). Most U.S. states and several countries have established qualityawards based closely on the Baldrige Award, extending its impact evenfurther. Thus, the Baldrige Model has evolved from an initial emphasis onpromoting and recognizing quality management practices to a comprehen-sive framework for improving organizational performance, and is often usedas a model for performance improvement (Flynn & Saladin, 2001). Oneanalysis concluded that the Baldrige Model provides

the most complete description in the world of what an organization capable of consistently

delivering superior value to customers should look like (Gale, 1994, p. 323, emphasis in

original).

The Baldrige Model incorporates ‘‘core values and concepts,’’ performancecriteria and subcriteria, and a weighting scheme for the criteria and sub-criteria. The core values and concepts, described as ‘‘embedded beliefs and

1Leadership

2StrategicPlanning

3Customer andMarket Focus

4Information and Analysis

5Human

Resource Focus

6Process

Management

7BusinessResults

Organizational Profile:Environment, Relationships, and Challenges

Fig. 4. Baldrige Criteria for Performance Excellence Model. Source: Baldrige

National Quality Program (2004).

ROBERT H. ASHTON8

behaviors found in high-performing organizations’’ (Baldrige NationalQuality Program, 2004), are: (1) visionary leadership, (2) customer-drivenexcellence, (3) organizational and personal learning, (4) valuing employeesand partners, (5) agility, (6) focus on the future, (7) managing for innova-tion, (8) management by fact, (9) public responsibility and citizenship, (10)focus on results and creating value, and (11) systems perspective. The model,shown in Fig. 4, involves seven categories of performance criteria. Note thata Leadership category is included (unlike the other models described here).In fact, the model ‘‘starts’’ with leadership – based on the view that in an‘‘assessment scheme based on cause/effect thinking, the most fundamentalplace to look for ‘cause’ is the place where vision, direction, and culture arecreated – senior leadership’’ (Bell & Keys, 1998, p. 55).

Moreover, a ‘‘leadership triad’’ – consisting of the Leadership, StrategicPlanning, and Customer and Market Focus categories – is said to drive a‘‘results triad’’ – consisting of the Human Resource Focus, Process Man-agement, and Business Results categories. The remaining category, Infor-mation and Analysis, serves as a foundation for the entire framework, whilethe Organizational Profile, which is not a part of the model per se, explicitlyrecognizes the importance to performance of the broad context in which thefirm operates.

Criteria and Subcriteria Point Values

Leadership 120Organizational Leadership 70Public Responsibility and Citizenship 50

Strategic Planning 85Strategy Development 40Strategy Deployment 45

Customer and Market Focus 85Customer and Market Knowledge 40Customer Satisfaction and Relationships 45

Information and Analysis 90Measurement and Analysis of Organizational Performance 45Information Management 45

Human Resource Focus 85Work Systems 35Employee Learning and Motivation 25Employee Well-Being and Satisfaction 25

Process Management 85Value Creation Processes 50Support Processes 35

Business Results 450Customer-Focused Results 75Product and Service Results 75Financial and Market Results 75Human Resource Results 75Organizational Effectiveness Results 75Governance and Social Responsibility Results 75

Total Points 1000

Fig. 5. Performance Criteria and Point Values for the Baldrige Quality Award.

Source: Baldrige National Quality Program (2004).

Value-Creation Models for Value-Based Management 9

The model’s seven categories of performance criteria entail 19 first-levelsubcriteria, 32 second-level subcriteria, and several dozen items at an evenmore detailed level of analysis. Importance weights, or ‘‘point values,’’ areassigned to the criteria and first-level subcriteria for the purpose of selectingwinners of the Baldrige Quality Award from each year’s pool of applicants,with 55% of the total points attached to value drivers and 45% to results(see Fig. 5).3 Interviews with Baldrige examiners reveal that companiesscoring highest on the Baldrige criteria have both widespread deployment(horizontally and vertically) and extensive alignment of quality practicesthroughout the company (Garvin, 1991), as well as a determined focus onselected practices (Brown, 2003). Similarly, focus and alignment have beenidentified by Kaplan and Norton (2001a) as the two most critical issues insuccessful implementation of the Balanced Scorecard.

ROBERT H. ASHTON10

Service-Profit Chain

Linkages among employee- and customer-related value drivers and financialoutcomes were proposed by Heskett, Jones, Loveman, Sasser, and Schlesinger(1994) in the Service-Profit Chain (see Fig. 6). The Service-Profit Chain bringstogether marketing and operations perspectives on value creation by linkingcustomer-perceived quality with operational (or internal) quality processes(Soteriou & Zenios, 1999). Heskett et al. (1994, pp. 164–165) were explicitabout the kinds of linkages envisioned:

The links in the chain (which should be regarded as propositions) are as follows: Profit

and growth are stimulated primarily by customer loyalty. Loyalty is a direct result of

customer satisfaction. Satisfaction is largely influenced by the value of services provided

to customers. Value is created by satisfied, loyal, and productive employees. Employee

satisfaction, in turn, results primarily from high-quality support services and policies

that enable employees to deliver results to customers.

The Service-Profit Chain was later expanded by Heskett, Sasser, andSchlesinger (1997) to include additional concepts – for example, employeecapability and output quality – and to introduce the ‘‘customer value equa-tion,’’ expressed as the relationship between benefits and costs (both financialand non-financial) from the customer’s perspective (see Fig. 7). Heskett et al.(1997) described linkages among elements of the Service-Profit Chain forseveral companies, including the positive association between employee sat-isfaction and customer satisfaction (called the ‘‘satisfaction mirror’’). Theyalso noted that ‘‘the elements of the service profit chain constitute a form ofthe balanced scorecard’’ (p. 210) that is especially relevant for service or-ganizations. Their notion of ‘‘customer value’’ that links internal (employee-related) variables to external (customer-related) variables – and ultimately tofinancial outcomes – provides a structured approach to understanding thedeterminants of customer profitability (Shapiro, Rangan, Moriarty, & Ross,1987; Bellis-Jones, 1989; Foster & Gupta, 1994; Foster, Gupta, & Sjoblom,1996; Kaplan & Narayanan, 2001; Zeithaml, Rust, & Lemon, 2001; Guilding& McManus, 2002), customer equity (Blattberg & Deighton, 1996; Lemon,Rust, & Zeithaml, 2001), and customer lifetime value (Rust, Lemon, &Zeithaml, 2000).

The potential usefulness of the Service-Profit Chain for managing busi-ness processes is illustrated by Rucci, Kirn, and Quinn (1998), three exec-utives at Sears, Roebuck and Company. The Sears approach, called theEmployee-Customer-Profit Chain, emphasizes three key stakeholder groups– employees, customers, and investors – reflecting the premise that Searswants to be ‘‘a compelling place to work,’’ ‘‘a compelling place to shop,’’and ‘‘a compelling place to invest.’’ Based on data collected from employees

EmployeeRetention

EmployeeProductivity

EmployeeSatisfaction

InternalService Quality

External ServiceValue

CustomerSatisfaction

CustomerLoyalty

Revenue Growth

Profitability

workplace design job designemployee selection and developmentemployee rewards and recognitiontools for serving customers

service concept:

results for customers

service designed anddelivered to meet targeted customers needs

retentionrepeat businessreferral

Operating Strategy andService Delivery System

Fig. 6. Links in the Service-Profit Chain. Source: Heskett et al. (1994).

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Operating strategy andservice delivery system

Internal

Loyalty

Satisfaction

Capability

Servicequality

Productivity&

Outputquality

Employees

Service concept

Customer Value Equation:

Value to Customer =

(Results Produced for Customer +Service Process Quality)(Price to Customer +Service Acquisition Costs)

Servicevalue

Satisfaction Loyalty

Target market

External

Revenuegrowth

Profitability

Customers

Fig. 7. Service-Profit Chain. Source: Adapted from Heskett et al. (1997).

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12

A Compelling Place to Work A Compelling Place to Shop A Compelling Place to Invest

AttitudeAbout theCompany

Employee

Behavior

EmployeeRetention

DRIVES

Service

Helpfulness

Merchandise

Value

5 unit increase inemployee attitude

CustomerRecommendations

CustomerImpression

CustomerRetention

1.3 unit increase incustomer impression

Return on Assets

Operating Margin

Revenue Growth

0.5% increase inrevenue growthDRIVES

AttitudeAbout the

Job

Fig. 8. Employee-Customer-Profit Chain at Sears. Source: Rucci et al. (1998).

Value-Creation Models for Value-Based Management 13

and customers, internal financial data, and related statistical analyses, Rucciet al. (1998) constructed a model of employee–customer–profit relationshipsthat specifies value drivers and causal linkages among them (see Fig. 8). Themodel and related analyses allow statements such as ‘‘y a 5 point im-provement in employee attitudes will drive a 1.3 point improvement in cus-tomer satisfaction, which in turn will drive a 0.5% improvement in revenuegrowth’’ (Rucci et al., 1998, p. 91). Such statements are indicative of thedeep level of knowledge claimed by the Sears executives with respect to thevalue drivers incorporated in the model:

We understand the several layers of factors that drive employee attitudes, and we know

how employee attitudes affect employee retention, how employee retention affects the

drivers of customer satisfaction, how customer satisfaction affects financials, and a great

deal more. We have also calculated the lag time between a change in any of those metrics

and a corresponding change in financial performance, so that when we see a shift in, say,

employee attitudes, we know not only how but also when it will affect results. Our

[model] makes the employee-customer-profit chain operational because we manage the

company on the basis of these indicators, with remarkably positive results (Rucci et al.,

1998, p. 84, emphasis in original).

Epstein and Westbrook (2001) describe two other companies that havedeveloped employee–customer–profit models that are similar in spirit to theSears model. The Canadian Imperial Bank of Commerce (CIBC) devel-oped models for four distinct customer groups that link leadership,employee commitment, customer loyalty, customer behavior, and profit.

ROBERT H. ASHTON14

They found that ‘‘a 5% increase in employee commitment yields a 2%increase in customer loyalty, which increases profitability by $72 millionannually’’ (p. 42). BFI, a large waste disposal company, focusing oncustomer dissatisfaction resulting from breaches in service dependability(e.g., missed pickups), modeled the path from service dependability to profit.One result: ‘‘A 2-point gain in service dependability led to a 1-point gain inoverall customer satisfaction, which led to a 1% decline in customerdefection, which produced a pretax increase in profits of $41 million’’(p. 45). The CIBC, BFI, and Sears applications demonstrate that theService-Profit Chain provides an integrative framework for understandingthe links among investments in service quality, employee actions, customerperceptions, and behaviors – and how all of these translate into financialperformance.

Skandia Intellectual Capital Model

Skandia, an insurance and financial services company with headquarters inStockholm, has devoted substantial resources to understanding, measuring,and reporting what might be considered the ultimate non-financial valuedriver – intellectual capital. A working definition of intellectual capital thatSkandia has used is:

The knowledge, skill and technologies Skandia uses to create a competitive edge. This

includes accessible knowledge and the applied experiences of all employees, and the

organizational structure, technology and professional systems within a firm. Intellectual

capital is the soft and intangible part of a company’s value (Oliver, 1996, p. 6).

Skandia was the first company to appoint a Director of Intellectual Capital(Leif Edvinsson), and a series of internal studies of the company’s ‘‘hiddenvalue’’ was begun in 1992.4 A prominent feature of the approach is theSkandia Business Navigator, shown in Fig. 9. The Navigator is depicted as ahouse. The foundation of the house is termed the renewal and development

focus, and is oriented toward the future. The walls consist of the customer

focus and the process focus, both oriented toward the present, while the roofreflects the financial focus, oriented toward the past. The human focus con-stitutes the center of the house.

The Navigator is similar in many respects to the Balanced Scorecard(described later): The financial and customer dimensions are common to thetwo frameworks, and the Navigator’s process and renewal and developmentdimensions correspond closely to the Balanced Scorecard’s internal businessprocesses and learning and growth dimensions, respectively. However, the

CUSTOMER FOCUS PROCESS FOCUS

RENEWAL & DEVELOPMENT FOCUS

HIS

TO

RY

TO

DA

YT

OM

OR

RO

W

FINANCIAL FOCUS

INT

ELL

EC

TU

AL

CA

PIT

AL

HUMANFOCUS

Fig. 9. Skandia Business Navigator. Source: Adapted from Skandia (1994).

Value-Creation Models for Value-Based Management 15

Navigator has a fifth dimension – the human focus – whereas the BalancedScorecard includes the human focus as only one of several componentswithin its learning and growth dimension. Moreover, the Skandia frameworkhighlights intellectual capital – comprising the human, customer, process, andrenewal and development dimensions – as the ultimate value driver.

The critical role of intellectual capital is developed further in a relatedframework known as the Skandia Value Scheme (Edvinsson, 1997; Edvinsson& Malone, 1997), shown in Fig. 10. It maintains that a firm’s market valueresults from its financial capital and its intellectual capital. Of course, finan-cial capital and intellectual capital cannot literally be summed to get marketvalue, and no claim is made that the difference between market value andfinancial capital is a measure of a firm’s intellectual capital at a particularpoint in time. Instead, financial capital, which generally is quantified anddisclosed in financial reports, and intellectual capital, which generally is not,are viewed from a conceptual standpoint as the two major types of valuedrivers for a firm.

The Skandia Value Scheme posits that intellectual capital is composed ofnarrower classes of value drivers, which in turn are composed of even nar-rower classes, and so on, in an effort to make the conceptual notion ofintellectual capital less abstract. First, intellectual capital is seen as resultingfrom human capital and structural capital. Human capital includes knowl-edge, skills, and experience, while structural capital includes value drivers

Marketvalue

Financialcapital

Intellectualcapital

Humancapital

Structuralcapital

Customercapital

Organizationalcapital

Innovationcapital

Processcapital

Intellectualproperty

Intangibleassets

Monetarycapital

Physicalcapital

Fig. 10. Skandia Value Scheme. Source: Adapted from Edvinsson (1997).

ROBERT H. ASHTON16

that are internal to the firm (e.g., processes, routines, databases, and ‘‘cul-ture’’) and external to the firm (e.g., relationships with customers, suppliers,and alliance partners). The two major components of structural capital arecustomer capital and organizational capital. Organizational capital, in turn,results from both organizational processes that apply existing knowledgeand innovations that generate new knowledge. Innovation capital includesintellectual property (intellectual capital that is legally protected) and intan-

gible assets that may be quantified and disclosed in financial reports.The Skandia Value Scheme has been further refined to produce the In-

tellectual Capital Distinction Tree (Roos, Roos, Edvinsson, & Dragonetti,1998) which offers a more detailed breakdown of human capital and struc-tural capital, the two principal determinants of a firm’s intellectual capital(see Fig. 11). Human capital is divided into competence, attitude, and ‘‘in-tellectual agility,’’ which together capture a wide range of variables at theindividual level. Structural capital is divided into relationships (with cus-tomers, alliance partners, and other stakeholders), organization (e.g., proc-esses and ‘‘culture’’), and renewal and development, which together capturea wide range of variables at the organizational and market levels.

The original goal of Skandia’s intellectual capital efforts was to commu-nicate to the outside world (mainly the capital markets) the company’s

Total Value

Financial Capital Intellectual Capital

Human Capital Structural Capital

Competence AttitudeIntellectual

AgilityRelationships Organization

Renewal andDevelopment

• Knowledge• Skills

• Innovation• Imitation• Adaptation• Packaging

• Customers• Suppliers• Alliances• Shareholders• Other Stakeholders

• Infrastructure• Processes• Culture

• Motivation• Behavior• Conduct

Fig. 11. Intellectual Capital Distinction Tree. Source: Roos et al. (1998).

Value-Creation Models for Value-Based Management 17

‘‘hidden value’’ by putting metrics on intellectual capital (Bartlett &Mahmood, 1996) and, toward that end, one result was the publication ofseveral Intellectual Capital Supplements to their annual and interim finan-cial reports (e.g., Skandia, 1994, 1995a, 1995b, 1996a, 1996b, 1997, 1998). Itwas soon realized, however, that the Skandia Navigator could also be usedas ‘‘a tool to steer the organization’’ (Oliver, 1996, p. 9) and to support anew business model and organizational structure that were widely viewed ashighly innovative (e.g., Goldman Sachs Global Equity Research, 2000).5

Key elements of the Skandia Intellectual Capital Model, especially the dis-tinction between human capital and structural capital, have strongly influ-enced subsequent work on value creation, including the most recentexposition of the Balanced Scorecard.

Balanced Scorecard and Strategy Map

The Balanced Scorecard was developed during a period of intense interest inTQM principles. Several authors in the late 1980s and early 1990s com-mented on the limitations of financial performance measurement systems fordecision making and control in an environment of dramatically increasedquality-consciousness. While some of the concern stemmed from issues

ROBERT H. ASHTON18

surrounding traditional standard costing systems (Berliner & Brimson,1988; Fisher, 1992; Maisel, 1992), perspectives provided by the qualitymovement were critical (Beischel & Smith, 1991; Eccles, 1991). Interest inperformance measurement systems that combined financial and non-financial (especially operational) measures led to development of theBalanced Scorecard, and the related interest in linking such measures toeach other and to firm strategy subsequently led to the Strategy Map.

Balanced Scorecard

The Balanced Scorecard (Kaplan & Norton, 1992), shown in Fig. 12, em-phasizes goals and measures within four perspectives: (1) Financial: How dowe look to shareholders? (2) Customer: How do customers see us? (3) In-ternal Business: What must we excel at? (4) Innovation and Learning: Canwe continue to improve and create value?6 Hoque (2003) argues explicitlythat a firm following TQM principles needs a performance managementsystem such as the Balanced Scorecard to achieve continuous improvement.Moreover, he develops specific linkages among TQM activities, TQM-related performance measures, and the four scorecard perspectives.7 Non-financial measures – especially in the internal business processes and

How do we look to shareholders?

Financial Perspective

Goals Measures

Goals Measures

Customer Perspective

What must we excel at?

Can we continue to improveand create value?

Measures

Goals

Innovation and Learning Perspective

Goals

Internal Business Perspective

How do customers see us?

Fig. 12. Balanced Scorecard. Source: Kaplan and Norton (1992).

Value-Creation Models for Value-Based Management 19

learning and growth perspectives – are the key components of the linkagesdeveloped.

While development of the Balanced Scorecard was influenced by theTQM focus of the 1980s, the advantages of using a balanced set of measures,including non-financial measures, to evaluate organizational performancehad been appreciated for quite some time. Drucker (1954, p. 87), forexample, maintained that management must balance the firm’s objectives:

There are few things that distinguish competent from incompetent management quite as

sharply as the performance in balancing objectivesyObjectives in the key areas are the

‘‘instrument panel’’ necessary to pilot the business enterprise.

To achieve this balance, Drucker argued that firms need performance meas-ures in eight areas: (1) market standing, (2) innovation, (3) productivity,(4) physical and financial resources, (5) profitability, (6) manager performanceand development, (7) worker performance and attitude, and (8) publicresponsibility.8 A second early example of the recognition of the importanceof using a balanced set of performance measures was the Tableau deBord (‘‘dashboard’’ or ‘‘instrument panel’’), developed in France severaldecades ago (Epstein & Manzoni, 1997, 1998; Lebas, 1994). The Tableaude Bord incorporated non-financial measures that were tailored to respon-sibility centers and/or the firm as a whole at operational, management, andstrategic levels, evolving initially from a reporting emphasis to a managerialtool (Bourguignon, Malleret, & Norreklit, 2004).9 Kaplan (1998) hasremarked that the Tableau de Bord has had little impact on managerialpractice, but a survey by Gehrke and Horvath (2002) suggests that it has – atleast in France.

The Balanced Scorecard has been developed more extensively than theother value-creation models discussed here and has been adopted by a largeand diverse set of companies, including non-profits.10 Its popularity can beexplained by at least two factors. One is the insight of its developers toembed the scorecard within the firm’s strategic management system. Theother is the recognition that the scorecard can enable a firm to communicatethe corporate vision, promote organizational alignment, and create sharedunderstanding – providing a ‘‘line of sight’’ from the activities of individualemployees to overall firm objectives and performance. Thus, the BalancedScorecard is more than a measurement framework, as Kaplan and Norton’s(1996c) first book suggests by devoting roughly equal space to ‘‘measuringstrategy’’ and ‘‘managing strategy.’’ They argue that the Balanced Scorecardcan serve as the cornerstone of a firm’s strategic management systemby supporting four management processes that ‘‘contribute to linking

ROBERT H. ASHTON20

long-term strategic objectives to short-term actions’’ (Kaplan & Norton,1996d, p. 75):

�

Translating the vision: Clarifying the vision, gaining consensus � Communicating and linking: Communicating and educating, setting goals,linking rewards to performance measures

�

Business planning: Setting targets, aligning strategic objectives, allocatingresources, establishing milestones

�

Feedback and learning: Articulating the shared vision, supplying strategicfeedback, facilitating strategy review and learning

Kaplan and Norton (2001a) describe the experiences of several earlyadopters of the Balanced Scorecard, focusing especially on its use for stra-tegic management purposes. When used effectively, it was observed, thescorecard allowed companies to describe and communicate the strategy inunderstandable and actionable ways, align all resources and activities to thestrategy, and establish organizational linkages across business units, sharedservices, and individuals. Thus, the management systems in which successfulscorecards were embedded had three distinct characteristics – strategy, fo-cus, and organization – leading Kaplan and Norton to the notion of a‘‘strategy-focused organization’’ and to the Strategy Map, a refinement andextension of the Balanced Scorecard.

Strategy Map

Strategy-focused organizations are said to embody five principles: (1) trans-lating the strategy to operational terms, (2) aligning the organization to thestrategy, (3) making strategy everyone’s everyday job, (4) making strategy acontinual process, and (5) mobilizing change through executive leadership(Kaplan & Norton, 2001a). While these principles, working together, canenable a company to effectively implement its strategy, implementationcannot be achieved without first describing and communicating the strategy.Thus, the Strategy Map was developed as a ‘‘logical and comprehensivearchitecture for describing strategy’’ (Kaplan & Norton, 2001a, p. 10).11

Strategy Maps maintain the original four perspectives of the BalancedScorecard, tailor each perspective according to the firm’s (or business unit’s)particular situation, and posit detailed cause-and-effect linkages within andacross the four perspectives – and in turn to strategic financial themes.

The generic Strategy Map depicted in Fig. 13 illustrates the broad finan-cial themes of revenue growth, cost management, and asset utilization, andit encompasses broad customer value propositions related to product lead-ership, customer intimacy, and operational excellence. Broad themes are

Improve Shareholder Value

Revenue Growth Strategy Productivity Strategy

Build the FranchiseIncrease Customer

ValueImprove Cost

StructureImprove Asset

Utilization

Shareholder ValueROCE

New Revenue Sources Customer Profitability Cost per Unit Asset Utilization

Customer Acquisition Customer Retention

Product Leadership

Customer Intimacy

Operational ExcellenceCustomer Value Proposition

Price Quality TimeFunction-

alityService

Relation-ships

Brand

Product/Service Attributes Relationship Image

“Build theFranchise”(InnovationProcesses)

“IncreaseCustomer Value”

(CustomerManagementProcesses)

“Be a GoodCorporate Citizen”

(Regulatory andEnvironmental

Processes)

A Motivated and Prepared Workforce

Strategic Competencies Strategic Technologies Climate for Action

Customer Satisfaction

Learning andGrowthPerspective

InternalPerspective

CustomerPerspective

FinancialPerspective

“AchieveOperationalExcellence”(OperationalProcesses)

Fig. 13. Generic Template for a Balanced Scorecard Strategy Map. Source: Kaplan and Norton (2001a).

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also shown for the internal perspective and the learning and growth per-spective, and hypothesized causal linkages connect all of these themes andperspectives to the ultimate objective of increasing shareholder value. Strat-egy Maps have come to occupy a central position in Kaplan and Norton’sthinking:

[W]e have learned how the Balanced Scorecard, initially proposed to improve the meas-

urement of an organization’s intangible assets, can be a powerful tool for describing and

implementing an organization’s strategyy. We now realize that the strategy map, a

visual representation of the cause-and-effect relationships among the components of an

organization’s strategy, is as big an insight to the executives as the Balanced Scorecard

itself (Kaplan & Norton, 2004b, p. 9).

They argue that Strategy Maps add a level of granularity that improvesclarity and focus, effectively illustrate the temporal dynamics of strategy,and provide a uniform and consistent way to describe strategy.

As the Strategy Map has continued to evolve, the most critical differencesbetween it and the initial formulation of the Balanced Scorecard concern theinternal perspective and the learning and growth perspective, which together‘‘drive the strategy.’’ As illustrated in Fig. 14, the key roles of three types ofinternal business processes are further developed and clarified – processesrelated to operations management, customer management, and innovation.The internal perspective also features a substantially increased emphasis ona fourth set of internal processes – regulatory and social processes related tothe environment, safety and health, employment, and the community. Ear-lier versions of the Balanced Scorecard had sometimes been criticized forwhat some saw as insufficient concern with stakeholders other than share-holders.

The learning and growth perspective has been developed even more ex-tensively than the internal perspective, especially with respect to linkingintangible assets to strategy and ultimately to financial outcomes. The de-velopment of the Balanced Scorecard’s learning and growth perspective overtime is shown in Fig. 15. The most recent version classifies intangible assetsinto three categories:

�

Human capital: Skills, knowledge, competences � Information capital: Information systems, databases, networks, technol-ogy infrastructure

�

Organization capital: Culture, leadership, alignment, teamwork

The similarity of these three categories to key elements of the SkandiaIntellectual Capital Model is apparent. The Strategy Map goes further thanthe Skandia Model, however, by considering ways of establishing bridges

Human Capital

Information Capital

Organization Capital

Culture Leadership Alignment Teamwork

Learning and

Growth

Perspective

Supply

Production

Distribution

Risk Management

Selection

Acquisition

Retention

• Opportunity ID

• R&D Portfolio

• Design/Develop

• Launch

• Environment

• Safety and Health

• Employment

• Community

Operations Management

Processes

Customer Management

ProcessesInnovation

Processes

Regulatory and Social

Processes

Price Quality Availability Selection Functionality Service Partnership Brand

Product/ Service Attributes Relationship Image

Customer Value Proposition

Improve Cost

Structure

Expand Revenue

Opportunities

Enhance

Customer Value

Long-Term

Shareholder Value

Productivity Strategy Growth Strategy

Increase Asset

Utilization

Internal

Perspective

Customer

Perspective

Financial

Perspective

Growth

Fig. 14. Balanced Scorecard Strategy Map. Source: Kaplan and Norton (2004b).

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B. Learning and Growth Perspective in 2001

Strategic Competencies Strategic Technologies Climate for Action

• Strategicskillcoverageratio

• Bestpracticesharing

• Strategic technologycoverage

• Under-standingof strategy

• GoalsalignedwithBalancedScorecard

• Averagetenure(keypositions)

• Morale(satisfaction)

• Suggestionprogram(empowerment)

C. Learning and Growth Perspective in 2004

CREATINGALIGNMENT

Strategic JobFamilies

Strategic ITPortfolio

OrganizationChange Agenda

CREATINGREADINESS

Human Capital Information Capital Organization Capital+ +• Skills• Training• Knowledge

• Systems• Databases • Networks

• Culture• Leadership

• Alignment• Teamwork

ApplicationsSkillsKnowledge

SharingInfrastructure Awareness Alignment Readiness Motivation

A. Learning and Growth Perspective in 1996

Staff Competencies Technology Infrastructure Climate for Action

Strategic skillsTraining levelsSkill leverage

Strategic technologiesStrategic databasesExperience captureProprietary softwarePatents, copyrights

Key decision cycleStrategic focusStaff empowermentPersonal alignmentMoraleTeaming

Fig. 15. Balanced Scorecard: Learning and Growth. Source: Kaplan and Norton

(1996c, 2001a, 2004b).

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from these three types of capital to the internal perspective of the scorecardand, in turn, to the customer and financial perspectives. Human, informa-tion, and organization capital are linked to the rest of the scorecard viastrategic job families, the strategic IT portfolio, and the organization changeagenda, respectively. These three bridges provide operational platforms forachieving the organizational ‘‘readiness’’ (Kaplan & Norton, 2004a) thatintangible assets can provide.

COMPARING THE MODELS

All of these models involve value drivers in three fundamental (and over-lapping) categories: people, processes, and relationships. People include

Value-Creation Models for Value-Based Management 25

employees and managers inside the firm, of course, but also customers andsuppliers outside the firm and other stakeholders as well. Processes includethose with both short- and long-run time frames, for example, quality as-surance processes, customer relationship processes, human resource proc-esses, R&D and innovation processes, and control and learning processes.Relationships include those linking the firm to both its immediate compet-itive environment (involving suppliers, distributors, end users, alliancepartners, competitors, etc.) and its broader social and natural environments(e.g., the community, regulators, and interest/pressure groups). People,processes, and relationships are viewed as sources of a firm’s ‘‘valuepotential’’ or ‘‘value capacity.’’

The function of management that is implied by these models involvesboth the transformation of intangible sources of value into value potential(via people, processes, and relationships) and the transformation of valuepotential into realized value (via transactions). Tangible sources of value(physical and monetary capital) are still considered essential for value cre-ation, but the emphasis of these models is on intangible sources of value.The models emphasize the capacity of such sources for value creation, andless attention is paid to value realization in the form of cash, profit, orshareholder return.

All of the models incorporate a combination of financial and non-financial measures, lead and lag measures, ‘‘hard’’ and ‘‘soft’’ measures, andmeasures that focus on both the short run and the long run. All ofthe models envision using such measures in the implementation, rather thanthe formulation, of firm strategy. All entail measures of value drivers thatare both internal and external to the firm. And all make clear the importanceof using measures to foster improvement instead of just control. None of themodels is a stand-alone approach to management, but must be embedded inand supported by other systems – including organizational design systems,incentive and reward systems, executive development systems, and knowl-edge management and organizational learning systems.

All of the models reflect measurement approaches, as opposed to valuationapproaches, to understanding value creation. Measurement approaches focuson underlying dimensions of performance, allowing measures to be expressedin a variety of units (e.g., dollars, employee satisfaction scores, customerretention percentages, defect rates, response times). By focusing on underlyingdimensions of performance at a disaggregate level, measurement approachesfacilitate the construction of causal chains that link fundamental value driverswith financial outcomes. Valuation approaches, in contrast, restrict measuresto a monetary unit, emphasizing aggregate financial outcomes. Several

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valuation approaches exist, including comprehensive models such as Eco-nomic Value Added (Stewart, 1991; Ehrbar, 1998) and Shareholder ValueAdded (Rappaport, 1986, 1998) that provide aggregate measures such aseconomic profit, and more specialized models that emphasize the importanceof intangibles or intellectual capital and that provide aggregate measures suchas ‘‘knowledge earnings’’ (Lev & Mintz, 1999; Mintz, 2000; Webber, 2000).12

Note that measurement approaches to value creation are more comprehensivethan valuation approaches, as the summary measures resulting from the lattercan be included among the financial measures of the former.

While many similarities exist among these value-creation models, theydiffer in the types of value drivers, and therefore the types of measures, thatare emphasized. For example, the Skandia Model takes more of an ‘‘inside-out’’ view of value creation, while the Balanced Scorecard takes more of an‘‘outside-in’’ view. The difference can be thought of in the context of theSWOT framework (strengths, weaknesses, opportunities, threats), whichplays a dominant role in much strategic thinking. Following Porter (1980),the relevance for strategy of the SWOT framework’s external elements(opportunities and threats) is often emphasized more than that of its internalelements (strengths and weaknesses), even though managers have greaterinfluence over the latter.

The Porterian view – which emphasizes the bargaining power of the firm’ssuppliers and customers, the threat of new entrants and substitute products,and the actions of competitors – can be contrasted with the more recentresource-based view of the firm, which emphasizes internal resources andcapabilities. The resource-based view maintains that resources and capabil-ities create strategic advantage to the extent they are valuable, rare, inim-itable, and non-substitutable (e.g., Wernerfelt, 1984; Barney, 1991, 1995;Foss, 1997). Thus, the Porterian, or external, view is basically outside-in,while the resource-based, or internal, view is basically inside-out.

Both the Skandia Model and the Balanced Scorecard clearly recognize theimportance of both outside-in and inside-out perspectives on value creation,but the Balanced Scorecard has somewhat more of an outside-in flavor whilethe Skandia Model clearly emphasizes inside-out. This distinction must betempered, however, by recognition that recent expositions of the BalancedScorecard include a larger role for intangibles and intellectual capital in itslearning and growth dimension. In Kaplan and Norton (2001a) this is re-flected in the learning and growth categories of strategic competencies,strategic technologies, and ‘‘climate for action,’’ and in Kaplan and Norton(2004b) it is reflected in the categories of human capital, information capital,and organization capital.

Value-Creation Models for Value-Based Management 27

Because value-creation models differ in their relative emphasis on differ-ent types of value drivers, the types of measures employed will likely differamong models as well. The Service-Profit Chain, for example, strongly em-phasizes operations- and customer-related drivers of quality products andservices, but does not emphasize research and development or learning sys-tems that promote firm-wide innovations. This has implications for thetypes of non-financial measures that a Service-Profit Chain approach tovalue creation would likely emphasize. And while the Service-Profit Chaincan be viewed as a detailed expansion of the Balanced Scorecard’s customerdimension (as well as customer-related elements of its internal businessprocesses dimension), the Balanced Scorecard includes a more comprehen-sive set of value drivers across the entire value chain (Porter, 1985), and thusentails a broader (more ‘‘balanced’’) set of non-financial measures. Simi-larly, because the Baldrige Model emphasizes leadership as a key valuedriver, measurement systems based on the Baldrige Model would likelyemphasize measures of leadership effectiveness more strongly than wouldmeasurement systems based on other models.

MANAGEMENT ISSUES

The principal goal of value-creation models such as these is to supportmanagement activities aimed at long-run shareholder value creation, in partthrough guiding the identification and measurement of tangible and intan-gible value drivers. Achieving this goal will require that management ad-dress several complex issues that arise in the formulation and use of suchmodels. This section considers several important issues including the need tounderstand causal linkages among value drivers and outcomes, the extentto which these value-creation models take a dynamic, or whole-system, viewof value creation, and whether multiple value drivers should be explicitlyweighted and combined to form a ‘‘value index.’’

Causal Linkages

Perhaps the most critical issue in using any value-creation model for man-agement purposes is the extent to which it embodies chains of cause-and-effect relationships that link measures of intangible value drivers toeach other and, ultimately, to financial outcomes such as profit, cash, andshare return. Causal relationships could be estimated statistically based oncareful empirical analysis of historical data, or they could be estimated

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subjectively based on careful reasoning by knowledgeable managers andothers. Whatever their basis, some understanding of causal linkages amongmeasures related to a model’s value drivers is required if the model is to beuseful for management purposes.

Over time, the proposed causal linkages included in value-creation modelshave become more prevalent, more comprehensive, and more empiricallybased. The model underlying the Deming Management Method, the earliestmodel reviewed here, entails hypothesized causal relationships, but they donot extend to financial outcomes. The Baldrige Model hypothesizes causallinkages that are better developed than those in the Deming Model but notas well developed as those in later models. The Skandia Intellectual CapitalModel also entails the notion of cause and effect (i.e., human capital is seenas the driver of structural capital). The Service-Profit Chain and the Bal-anced Scorecard Strategy Map strongly emphasize causal linkages. Theformer involves linkages that connect operational and service measures tocustomer measures to financial measures, and the latter involves linkagesthat connect learning and growth measures to internal business processmeasures to customer measures to financial measures. Moreover, as dem-onstrated in the next section of the paper, many of the proposed linkages inthe Service-Profit Chain and the Balanced Scorecard Strategy Map havebeen examined by empirical research.

The Strategy Map is especially strong with respect to causal linkages.Recall the earlier observation that the Service-Profit Chain can be viewed asa detailed expansion of the customer dimension of the Balanced Scorecard(and customer-related elements of the internal business processes dimen-sion). Kaplan and Norton (2001a) demonstrate that the Strategy Map canrepresent links in the Service-Profit Chain by recasting the Sears Employee-Customer-Profit Chain into the Strategy Map framework (see Fig. 16). Inaddition, the applicability of the Strategy Map to a functional area of anorganization – specifically, the human resources function – is demonstratedby Datar and Epstein’s (2001) portrayal of the GTE ‘‘HR Linkage Model’’as a Strategy Map (see Fig. 17). In this application, the operations andcustomer dimensions of the GTE model do not refer to the operations orcustomers of GTE, but to the operations and customers of GTE’s HRfunction. Nevertheless, this functional Strategy Map connects with thelarger organization’s strategy in important ways, for example, by capturingthe five key ‘‘strategic thrusts’’ that GTE identified as foundational for itsfuture financial success. Shown at the foundation of the model in Fig. 17,these are talent, strategic competencies, a performance-based culture, or-ganizational integration, and leadership (Becker, Huselid, & Ulrich, 2001).13

Revenue GrowthProfit ContributionSales per SquareFootInventory Turnover

CustomerRetention

Turnover• Customer Service• Recommend Products• Loyalty

Managers:• Business

Knowledge• Customer

ServiceOrientation

• Job Context

• Teamwork• Training

• Sears BeingFair/Ethical

• PromotionOpportunities

• Pay and Benefits

CompellingPlaceto Work

CompellingPlaceto Shop

CompellingPlaceto Invest

Value/Price MerchandiseSelection

ReturningMerchandise Image

Associates’Behavior

Attitude aboutthe Job

Attitude aboutthe Company

JobSupervision

JobStructure

JobContext

CustomerImpression

Fig. 16. Strategy Map of the Sears Employee-Customer-Profit Chain. Source:

Kaplan and Norton (2001a).

Value-Creation Models for Value-Based Management 29

Static versus Dynamic View

The second management-related issue is the extent to which value-creationmodels take a static versus a dynamic view of the process of value creation.At the risk of oversimplifying, a static view tends to regard causes as in-dependent of each other, and as one-time events each of which produces an

Contribute toCorporate

Shareholder ValueMaximize

Human CapitalMinimizeHR Costs

Business Partner(Strategic Support)

OrganizationalHealth and Competitive

Capability

Skills,Competencies, and

Leadership

Low-CostProvider

Align HRPlanning with

Business Strategy

Provide ProactiveWorkforce Solutions

Ensure a Strategy-Focused Workforce

Develop andEnhance World-Class Programs

Optimize ServiceDelivery through

Streamlined Processes

TalentCapability

(Build StrategicCompetencies)

Enable aPerformance-Based

Culture/Climate

OrganizationalIntegration

• Grow the talent pool• Select, assimilate, and

retain key talent• Organizational renewal• High potential

development• Reduce turnover

• Service delivery design• Organizational change skills

- staffing expectations- design interventions- provide reinforcement

• Relationship building• HR planning• Performance management• Workforce planning

• Culture that values:- results- customer- open communications

• Climate that exhibits:- flexibility- clarity- high standards

• External trend data- HR best practices/

breakthroughs• Internal employee data

- demographics• Organizational strategy• Industry trends• Integrated technology

infrastructure (SAP)

• Invest in leadershipgrowth

• Leadershipcompetencies

• Structure rewardsto foster leadershipbehavior

Leadership

FINANCIAL

CUSTOMER

OPERATIONS

STRATEGIC

Corporate/Business Unit Employees

Fig. 17. GTE’s HR Linkage Model. Source: Datar and Epstein (2001).

ROBERT

H.ASHTON

30

Value-Creation Models for Value-Based Management 31

effect. A dynamic view, in contrast, tends to focus on patterns in whichmultiple causes produce multiple effects via sets of causal relationships inwhich effects feed back to influence causes and causes affect other causes inan ongoing process (Richmond, 1993, 2000). This is part of the systemdynamics view of the world, where it is often maintained that ‘‘everything isconnected to everything else’’ through a network of feedback loops and timedelays (Sterman, 2000, p. 4). In essence, system dynamics takes a whole-system view of the firm, its interconnected value-creating processes, and thecomplex network of economic relationships in which it is embedded (Allee,2003; Dyer & Singh, 1998; Post, Preston, & Sachs, 2002).

While the implications of the system dynamics view for modeling causallinkages among value drivers and outcomes have occasionally been recog-nized (e.g., Kaplan & Norton, 2001a, pp. 311–313), the value-creationmodels described in this paper generally do not explicitly reflect this view.However, several aspects of system dynamics suggest its relevance in thecontext of understanding long-run value creation (Klein, 1998, Sterman,2000). For example, system dynamics clarifies that effects have multiplecauses, causes produce multiple effects, and effects are often not propor-tional to causes. In addition, system dynamics emphasizes the importance ofself-correcting and self-reinforcing feedback loops among value drivers.14

Finally, system dynamics focuses management’s attention on the sometimescounterintuitive outcomes that can result from networks of causal relation-ships, feedback loops, and the varying time delays that characterize value-creation processes. Thus, system dynamics provides a powerful structure formodeling causal linkages, and for explicitly incorporating network effectsinto the understanding of value creation.

Weighting and Combining Measures

The third management-related issue is whether the various non-financialmeasures that reflect a model’s key value drivers should be explicitlyweighted and combined to form a ‘‘value index.’’ The advantages and dis-advantages of using an index or composite measure versus a disaggregatedset of measures to assess individual or organizational performance havebeen debated for decades (e.g., Ridgway, 1956, Schmidt & Kaplan, 1971).Weighting and combining measures reflects a ‘‘middle ground’’ betweenthe measurement approach (disaggregate measures expressed in differentunits) and the valuation approach (a summary measure expressed ina monetary unit) to value creation. A value index could serve as a summary

ROBERT H. ASHTON32

measure of current performance and possibly as a predictor of futureperformance.

An index that has received attention recently is the Value Creation Indexdeveloped by Ernst & Young’s Center for Business Innovation (Low, 2000;Baum et al., 2000; Kalafut & Low, 2001; Funk, 2003). The index is based ondata from a number of internal studies, particularly the ‘‘Measures thatMatter’’ study (Ernst & Young LLP, 1997; Low & Siesfeld, 1998), as well as‘‘industry literature and conversations with business and academic research-ers’’ (Low, 2000, p. 254). The index summarizes nine categories of non-financial information: (1) innovation, (2) quality, (3) customer relations,(4) management capabilities, (5) alliances, (6) technology, (7) brand value,(8) employee relations, and (9) environmental and community issues. Thesecategories, each involving several specific measures, were statistically com-bined to form the Value Creation Index.

While such an index could be used as a management tool, Ernst andYoung has emphasized the relationship between index scores and firms’market values, finding an average correlation of 0.70. Of the nine value-driver categories, innovation (as reflected in R&D expenditures and thenumber and importance of patents) has the highest association with marketvalue, followed closely by management quality and employee relations.Alliances (both manufacturing and marketing, as well as joint ventures andother forms of partnership) are also positively associated with market value.Perhaps surprisingly, technology and customer satisfaction are unrelated tomarket value. It is possible, however, that a threshold level of technologyand customer satisfaction are required just to ‘‘be in the game’’ but that theydo not act as differentiators across companies.

The Baldrige Model, which originally was developed for the purpose ofawarding quality prizes to U.S. companies, also involves an explicit weight-ing scheme that produces composite scores (see Fig. 5). If the BaldrigeModel is to be applied consistently in evaluating applicants for qualityawards, a standard weighting scheme is needed. Of course, when theBaldrige Model is used as part of a strategic management system instead ofin quality contests, it is not necessary to use the standard weighting scheme(or any weighting scheme) to compute composite scores.

Weighting schemes could be applied, and indexes constructed, for theother models described here, but little interest has been shown in doingso. Some attention has been focused on an Intellectual Capital Index basedon the Skandia Value Scheme, but this effort appears to be in a preliminarystage (Roos et al., 1998; Roos & Jacobsen, 1999). Weighting factorsare sometimes applied to Balanced Scorecard measures, or at least to

Value-Creation Models for Value-Based Management 33

the four overall scorecard categories, but the extent of this practice isnot clear.

It is sometimes maintained, however, that Balanced Scorecard measuresshould be weighted and combined to produce a single summary measure ofperformance. Jensen (2001, p. 18) argues this point strongly, claiming that

decision makers cannot make rational choices without some overall single dimensional

objective to be maximized. Given a dozen or two dozen measures and no sense of the

tradeoffs between them, the typical manager will be unable to behave purposefully, and

the result will be confusion.

Without weighting the various measures to specify trade-offs among them,Jensen argues, there is no ‘‘balance’’ in the Balanced Scorecard and it doesnot yield a score that distinguishes winners from losers. Jensen maintainsthat a scorecard without weights and a composite score functions more likea dashboard or instrument panel, although he suggests it can still be usefulfor communicating a large amount of information within the firm.

In practice, however, managers may find value both in the general frame-work of the Balanced Scorecard and in the hypothesized causal linkages (andperhaps in the process of constructing a scorecard). Moreover, explicitlyweighting and combining Balanced Scorecard measures into a single metric –whether monetary as in Economic Value Added, or non-monetary as in theIntellectual Capital Index – would likely result in the kinds of criticismsleveled at traditional composite metrics, for example, earnings per share.

Additional Issues

There are, of course, many additional issues concerning the mapping of valuedrivers to financial outcomes that are relevant to managers – and to man-agement accounting researchers. One is the functional form of the relation-ship between value driver and outcome. Straightforward linear relationshipsare unlikely to adequately characterize all driver-outcome linkages. Ittnerand Larcker (1998), for example, found that certain threshold levels ofcustomer satisfaction must be achieved before either customer retention orrevenue increases. Similarly, Anderson and Mittal (2000), in discussing themodeling of the Service-Profit Chain, point out that asymmetric and non-linear relationships exist in many settings. Asymmetry implies that the im-pact of an increase in a causal variable differs in magnitude from that of anequivalent decrease, while non-linearity implies diminishing (or increasing)returns for adjacent changes of equivalent magnitude in a causal variable.

ROBERT H. ASHTON34

Anderson and Mittal consider this issue in some detail for the driver ofcustomer satisfaction, but it is likely to be relevant at other points along theService-Profit Chain as well, and more generally for any causal model ofvalue creation.

Shields and Shields (2005) provide an insightful discussion of this andmany other value driver/financial outcome relationships. These include theextent to which driver/outcome relationships are direct or indirect (i.e.,operating through some intermediate value driver), the extent to whichthey are additive or interactive (i.e., conditional on the effect of anothervalue driver), the timing of the relationship (contemporaneous or pros-pective), the duration of the value-enhancing effect, and the level of analysisthat drives the effect (e.g., customer, product, organization and industry).While the relevance of such issues depends on the specific modelingapplication, it is essential to recognize that they apply to both statisticalmodeling and judgmental approaches to the linking of value drivers andoutcomes.

RESEARCH ON VALUE-CREATION MODELS

The value-creation models described here are supported by a substantialamount of research that links measures of intangible value drivers to fi-nancial outcomes. Some of the research specifically addresses the manage-ment issues discussed above. Three broad streams of research are reviewedin this section. The first concerns the related categories of quality- andcustomer-oriented models, that is, research that addresses the DemingManagement Method, the Baldrige Model, and the Service-Profit Chain.The second involves the large body of research relevant to the componentsof the Skandia Intellectual Capital Model. Finally, some of the rapidly-growing body of research that investigates the adoption, use and perform-ance effects of the Balanced Scorecard is reviewed.

Research on Quality and Customer Models

Anderson et al.’s (1994) articulation of the theory underlying Deming’s workstimulated a few research studies on the Deming Management Method. Forexample, Anderson, Rungtusanatham, Schroeder, and Devaraj (1995) usedperformance data from the World-Class Manufacturing Project database(see Flynn, Schroeder, & Sakakibara, 1996; Flynn, Schroeder, Flynn,

Value-Creation Models for Value-Based Management 35

Sakakibara, & Bates, 1997) to test certain aspects of the theory. The datainvolved 41 manufacturing plants in the electronics, machinery, and trans-portation components industries. Within each industry were U.S. plants withworld-class reputations, U.S. plants selected at large, and U.S. plants withJapanese ownership – to ensure variance in the application of quality man-agement practices. Path analysis revealed that six of eight causal pathshypothesized by Anderson et al. (1994) were statistically significant, althoughthe strength of the association was sometimes low. The study was replicatedwith Italian manufacturing companies with similar results (Rungtusanatham,Forza, Filippini, & Anderson, 1998).

It was also replicated in a service context by Douglas and Fredendall(2004), using data from 193 U.S. hospitals studied earlier by Douglasand Judge (2001). Quality variables derived from the Deming Modelwere supplemented with additional variables derived from the BaldrigeHealth Care Criteria (Meyer & Collier, 2001), and the hospital’s finan-cial performance was included as a performance dimension along withcustomer (patient) satisfaction. Douglas and Fredendall (2004) notedthat the Baldrige Model is more comprehensive and includes more causalpaths than the Deming Model as articulated by Anderson et al. (1994), apoint made by other authors as well (e.g., Gale, 1994). While supportwas found for both the Deming and Baldrige perspectives, Douglasand Fredendall (2004) did find the Baldrige Model to be superior to theDeming Model in capturing the relationships among quality variables andperformance.

Other research has focused on the Baldrige Model more directly. Forexample, Flynn and Saladin (2001), using the World-Class ManufacturingProject database employed by Anderson et al. (1995), focused on 164 man-ufacturing plants in the electronics, machinery, and transportation compo-nents industries across five countries – U.S., U.K., Japan, Germany, andItaly. Path analysis revealed that 12 of 15 hypothesized causal paths werestatistically significant. One intriguing result was that Leadership had asubstantially stronger relationship with business results than any of theother Baldrige categories. Leadership has also emerged as the strongestvariable in other empirical studies of the Baldrige Model (e.g., Wilson &Collier, 2000; Meyer & Collier, 2001; Pannirselvam & Ferguson, 2001),consistent with the decision of the model’s developers to place primaryemphasis on this construct (Bell & Keys, 1998).

Research with more of a customer focus has examined the various links inthe Service-Profit Chain. These links, originally presented as ‘‘propositions’’by Heskett et al. (1994), have been strongly supported by research. Zeithaml

ROBERT H. ASHTON36

et al. (2001, p. 119) summarize a large body of research that connectsimproved service quality to increased firm profitability as follows:

�

service improvement efforts produce increased levels of customer satis-faction at the process or attribute level,

�

increased customer satisfaction at the process or attribute level leads toincreased overall customer satisfaction,

�

higher overall service quality or customer satisfaction leads to increasedbehavioral intentions, such as greater repurchase intention,

�

increased behavioral intentions lead to behavioral impact, including re-purchase or customer retention, positive word-of-mouth and increasedusage, and

�

behavioral impact then leads to improved profitability and other financialoutcomes.

The studies on which these conclusions are based have examined a singlelink (or a small number of links) in the Service-Profit Chain in a piecemealfashion.15 Loveman (1998), in contrast, appears to have studied all of theprincipal links in the model, but he examined them separately. Only recentlyhave all of the posited relationships in the Service-Profit Chain been studiedsimultaneously in a single research setting (Kamakura, Mittal, de Rosa, &Mazzon, 2002).

Kamakura et al. (2002) studied the Service-Profit Chain with data frommore than 5,000 customers of more than 500 branches of a national bank inBrazil. A strategic analysis focused on the conceptual relationships within theService-Profit Chain at the level of the bank, while an operational analysisfocused on ways of implementing the model at particular branches. The goalwas to operationalize the distinction between ‘‘strategic benchmarking’’ and‘‘efficiency benchmarking’’ made earlier by Soteriou and Zenios (1999) –essentially, the distinction between identifying the key strategic links in thechain and exploiting the identified links operationally. Kamakura et al. sup-plemented variables specified in the Service-Profit Chain with some elementsof the ‘‘Return on Quality’’ framework (Rust, Zahorik, & Keiningham, 1995),which emphasizes the financial cost of service-quality investments more ex-plicitly than does the Service-Profit Chain. In the strategic analysis, a struc-tural model with numerous hypothesized links was formulated, and the resultswere strongly supportive. Inferences were drawn concerning the relativeamount of investment the bank should make in human versus technologicalresources for enhancing service quality. In the operational analysis, the resultsshowed that particular branches must be successful at both operational effi-ciency and customer retention if they are to improve profitability.

Value-Creation Models for Value-Based Management 37

Research on Intangible (Intellectual Capital) Value Drivers

A large number of studies have investigated the association between meas-ures of intangible, or intellectual capital, value drivers and financial out-comes at both the firm and market levels. Ashton (2005) identifies almost200 such studies conducted over the past 20 years from several disciplinaryperspectives including accounting, marketing, operations, human resources,and information technology. Ashton organizes this body of research ac-cording to the four non-financial dimensions of the Skandia Business Nav-igator (see Fig. 9) – human, customer, process, and renewal anddevelopment. While none of the research was designed specifically to in-vestigate the Skandia approach to intellectual capital, the results are clearlyapplicable to the Skandia Intellectual Capital Model. Moreover, because theSkandia Model is similar in many respects to other value-creation modelsdiscussed here, the results are also relevant to other models.

This body of research examines multiple value drivers within eachdimension of the Skandia Navigator. Research on human capital examinesthe performance effects of both individual manager characteristics (such asability, experience, and certain personality traits) and systems of humanresource practices (sometimes called ‘‘high-performance work systems’’)that involve rigorous selection procedures, management training anddevelopment activities, significant commitment to employee involvement,and performance-contingent incentive systems. Research on customer

capital examines customer satisfaction (including customer retention andreferrals) and drivers of satisfaction such as brand equity (including theestablishment and extension of brand names and investments in advertis-ing). Research on process capital examines the performance effects of qual-ity-improvement initiatives (sometimes proxied by the winning of a majorquality award) as well as the performance effects of investments in infor-mation technology. Finally, research on renewal and development capital

examines R&D investments (both basic and applied) and patents (reflecting,e.g., new product development). The bottom line of the research reviewed byAshton (2005) is that measures of intangible value drivers in all of theseareas are positively associated with a wide array of firm- and market-levelfinancial outcomes.

Research on the Balanced Scorecard

A considerable amount of research on the Balanced Scorecard has ap-peared. It includes case studies of single companies that have implementedscorecards, surveys of multiple companies in which comparative data are

ROBERT H. ASHTON38

collected, analyses of archival data, and experimental studies (both lab andfield) of scorecard adoption and use.16 Some of the research focuses on theincentive function of the scorecard, that is, incorporating non-financialmeasures into managers’ incentive compensation plans to motivate in-creased overall effort, or the reallocation of effort from other tasks, to themanagement of intangible value drivers. To the extent that non-financialmeasures are valid and contain information not captured in financial meas-ures, the wisdom of including them in managers’ incentive plans is bothintuitively apparent (e.g., Kerr, 1975) and supported by formal agency-theoretic research (e.g., Feltham & Xie, 1994). Most of the research, how-ever, focuses only on the scorecard’s use as a strategic measurement systemthat provides information for decision-making beyond that provided bytraditional financial measures.

This section describes Balanced Scorecard research that addresses fourimportant issues: (1) the extent to which the Balanced Scorecard has beenadopted by companies, and its links with strategy and performance evalu-ation, (2) the use of both explicit and subjective weights for measures includedin scorecards, (3) judgment biases that arise when a mixture of financial andnon-financial measures is used in performance evaluation, and (4) whethernon-financial scorecard measures are associated with financial outcomes.

Balanced Scorecard Adoption

While several surveys estimate that between 40 and 60% of large U.S. andU.K. companies have either adopted or experimented with the BalancedScorecard for at least some of their business units (see Speckbacher, Bischof,& Pfeiffer, 2003), the adoption rate appears to be substantially lower (typ-ically below 25%) in other European countries (Malmi, 2001; Gehrke &Horvath, 2002; Speckbacher et al., 2003; Stemsrudhagen, 2004).17 Moststudies find that the measures employed reflect the standard four perspec-tives (financial, customer, internal, and learning and growth), but applica-tions involving as few as two or three perspectives (Speckbacher et al., 2003)and as many as seven perspectives (Kalagnanum, 2004) have been reported.Learning and growth measures appear much less frequently than measuresin the other three perspectives, and are weighted less heavily when they doappear (Gehrke & Horvath, 2002; Hoque & James, 2000; Ittner, Larcker, &Rajan, 1997; Malina & Selto, 2001; Maltz, Shenhar, & Reilly, 2003; Olson &Slater, 2002; Speckbacher et al., 2003; Stemsrudhagen, 2004).

In addition to the common finding that scorecard adoption is more fre-quent in larger companies, adoption is mainly at the business-unit level(profit center, division, subsidiary) instead of the corporate or departmental

Value-Creation Models for Value-Based Management 39

level (Malmi, 2001; Speckbacher et al. 2003). Adoption has been found to beassociated with the pursuit of differentiation as opposed to cost leadershipstrategies and with having won or been a finalist for a major quality awardsuch as Baldrige (Ittner et al., 1997; Said, HassabElnaby, & Wier, 2003).

Surveys also provide information about the linking of scorecard measuresto each other and to strategy via causal models, and about the inclusion ofnon-financial scorecard measures in incentive compensation contracts.Gehrke and Horvath (2002), Ittner, Larcker, and Meyer (2003a), Ittner,Larcker, and Randall (2003b), and Speckbacher et al. (2003) report thatapproximately 25% of their respondents claim to use models that causallylink drivers to outcomes. Malmi (2001, p. 210), however, in a study based onsemi-structured interviews, found that although most interviewees stated thatscorecard measures were derived from strategy and were based on cause-and-effect chains, further exploration revealed that ‘‘the claimed link bet-ween strategy and measures appeared weak for most companies.’’ Finally,about half of the scorecard users in the Malmi (2001), Gehrke and Horvath(2002), and Speckbacher et al. (2003) surveys reported that scorecardmeasures were tied to compensation, often among other measures.

Explicit versus Subjective Weights

When scorecards are linked to performance evaluation, measures that rep-resent the various value drivers must be weighted and combined, usingeither an explicit or subjective weighting scheme. Explicit weights have beenthe subject of research by Ittner et al. (1997) and Said et al. (2003), andsubjective weights have been studied by Malina and Selto (2001), Ittner et al.(2003a), and Moers (2005).

Ittner et al. (1997) studied CEO annual bonus plans involving financialand non-financial measures for 317 large companies in several industries. Allof the plans had explicit weights for the measures employed. Thirty-sixpercent (114) of the companies used non-financial measures, and the meanweight placed on non-financial measures was 37.1% for the companies usingnon-financial measures (13.4% across all 317 companies). The mean num-bers of financial and non-financial measures used were 1.7 and 2.3, respec-tively. In all, 13 types of non-financial measures were used, ranging fromcustomer satisfaction (36.8% of the 114 companies) and product/servicequality (21.0%) to leadership (5.2%) and innovation (2.6%). Significantlygreater weight was placed on non-financial measures in companies that(1) were pursing differentiation as opposed to cost leadership strategies,(2) had won or been finalists for major quality awards such as Baldrige,(3) were more subject to regulation (utilities and telecommunications

ROBERT H. ASHTON40

companies), and (4) had more noise in their financial performance measures(proxied by correlations between accounting returns and market returns andby time series variability in industry accounting returns).

Said et al. (2003) followed up Ittner et al. (1997) by comparing companiesthat use both financial and non-financial measures in their annual bonusplans with companies that use only financial measures. The two samples werematched on size, industry, and past financial performance, and each included1,441 firm-year observations over an 8-year period. Detailed information onthe mean number of non-financial measures used and the mean weightplaced on them was not reported. Consistent with the results of Ittner et al.,however, the inclusion of explicitly weighted non-financial measures in an-nual bonus plans was positively associated with pursing differentiationinstead of cost leadership strategies, winning or being a finalist for majorquality awards, and regulation. Inconsistent with the earlier study, noise infinancial performance measures was unrelated to the use of non-financialmeasures. Said et al. (2003) also examined the relation between use of non-financial measures and the length of both the product development cycle andthe product life cycle (see Bushman, Indjejikian, & Smith, 1996), finding apositive relation with the former but not the latter.

Malina and Selto (2001) described the development and use of a BalancedScorecard by a single Fortune 500 company with annual sales of more than$6 billion. Subjective weights were employed to reflect management’s viewof both the importance of the underlying drivers and the reliability of thenumerical measures, and the weights were sometimes changed to mirrorchanges in management’s view. The scorecard involved 22 measures in thefinancial (3), customer (4), internal business processes (12), and learning andgrowth (3) categories.18 The subjective weights assigned to the measureswithin each category totaled 15% for financial, 40% for customer, 41% forinternal business processes, and 4% for learning and growth. The percent-age assigned to learning and growth measures (4%) had been reduced from20% the previous year because management believed the measures wereunreliable. Weights on a few measures in other categories were also reduced,with most of the weight reassigned to customer-related measures.

Ittner et al. (2003a) studied the subjective weighting of Balanced Score-card measures in a single financial services company – a U.S. retail bank.The company had previously replaced a bonus plan for branch managersthat was based on a single financial measure (branch profitability) with aformula-based plan that also included operational and customer-basedmeasures. This second plan was later replaced by a Balanced Scorecardcontaining six categories of measures. Three of the categories involved

Value-Creation Models for Value-Based Management 41

objective (quantitative) measures, and the other three involved subjective(qualitative) measures. Within each category, measures were weighed andcombined to determine ‘‘category par scores,’’ and all six categories wereweighted and combined subjectively (in contrast to the previous, formula-based plan) to determine an ‘‘overall par score’’ on which bonuses werebased. The implicit weights placed on the various performance measuresover a 15-quarter period were estimated by regression analysis; that is, theweights were those implied by the statistical relationships among branchperformance on the scorecard measures, the par scores subjectively assignedto the branch managers, and the size of their quarterly bonuses.

Four results are of particular interest. First, the overall par scoresassigned to branch managers reflected greater weight on financial than onnon-financial measures. Second, the category par scores often did not reflectthe performance measures on which they presumably were based. Third, theentire set of scorecard measures explained only 50% of the variance inbonuses across branch managers. Finally, one scorecard measure that wascommon to all of the branches (overall customer satisfaction) was weightedvery heavily, consistent with Lipe and Salterio’s (2000) finding (describedlater) that more weight is placed on common than on unique measures.Ittner et al. (2003a, p. 725) summarized their results:

We find that the subjectivity in the scorecard plan allowed superiors to reduce the

‘‘balance’’ in bonus awards by placing most of the weight on financial measures, to

incorporate factors other than the scorecard measures in performance evaluations, to

change evaluation criteria from quarter to quarter, to ignore measures that were pre-

dictive of future financial performance, and to weight measures that were not predictive

of desired results.

Moers (2005) also studied the effects of subjectivity in the weighting ofmultiple performance measures – in the context of a single Dutch industrialfirm in the maritime industry. This firm had replaced a fixed-wage com-pensation plan that was driven mostly by seniority with an incentive plan inwhich annual bonuses were based on a combination of objective and sub-jective measures. The objective measures related to quantitative dimensionsof performance (e.g., whether a 5% reduction in absenteeism was achieved),while the subjective measures reflected qualitative assessments (e.g., whethera ‘‘good’’ use of resources or ‘‘adequate’’ planning was achieved). The linkbetween individual performance and the bonus was subjectively determinedin that direct superiors (1) allocated the size of the bonus between objectiveand subjective performance dimensions, (2) chose the number of measureswithin each dimension, and (3) provided ex post evaluations for eachdimension. It was found that slightly more subjective than objective

ROBERT H. ASHTON42

performance measures were used in determining bonuses (2.5 vs. 2.3 onaverage), a smaller average portion of the overall bonus awarded was basedon subjective measures (46 vs. 54%), and the range in the number ofperformance measures used was 1–5 (1–6) for objective (subjective) meas-ures. This latter result indicates that for some individuals only a singleperformance measure within a dimension was used.

Judgment Biases

Other Balanced Scorecard research has identified subtle but potentially im-portant judgment biases that occur when a mixture of financial and non-financial measures is used for performance evaluation. For example, theMoers (2005) study, just described, found that the use of multiple objectivemeasures (as opposed to a single objective measure) and the use of subjectivemeasures were associated with two commonly observed biases in perform-ance ratings – leniency and compression. Leniency (higher ratings) can re-sult in increased compensation costs due to higher bonuses, whilecompression (less differentiation) can make promotion decisions moreproblematic. Both biases are traceable in part to the greater discretion inperformance assessments that is allowed by multiple, and by subjective,measures.

A series of experimental studies initiated by Lipe and Salterio (2000) hasinvestigated a judgment bias – the ‘‘common measures bias’’ – that seemsespecially relevant in the Balanced Scorecard setting. Lipe and Salterio(2000) examined the effects of scorecard measures on evaluations of theperformance of divisional managers. Some of the measures were commonacross divisions, while others were unique to a particular division, and thestudy focused on the relative weights placed by the evaluators on these twotypes of measures. One of the assumed strengths of the Balanced Scorecardwhen adopted at the business-unit level concerns the inclusion of measuresthat uniquely reflect the strategy and goals of the business unit. Althougheach business unit develops its own scorecard measures, some measures arelikely to be common across all units and other measures are likely to beunique to a particular unit.

If decision makers faced with both common and unique measures placemore weight on common measures (as suggested by earlier research in thejudgment/decision making literature), managers who evaluate multiple unitsmay underuse or ignore the unique measures designed for each unit. More-over, if unique measures do not affect superiors’ ex post performance eval-uations of subordinate managers, then subordinate managers may beunlikely to use unique measures in ex ante decision making. Consequently,

Value-Creation Models for Value-Based Management 43

since unique measures are often leading and non-financial indicators whilecommon measures are often lagging and financial indicators, managers maypay insufficient attention to leading and non-financial measures, possiblydefeating a key purpose of implementing the Balanced Scorecard (i.e.,expanding the set of measures that managers use).

Participants in the study (MBA students) assumed the role of a seniorexecutive who evaluated two divisions of a hypothetical retail firm – basedon Kaplan and Norton’s (1996c) Kenyon Stores example – that used bothcommon and unique measures. They were provided a 16-measure scorecardfor each division, with each of the four scorecard categories including twocommon measures and two unique measures. As expected, the commonmeasures had much stronger effects than the unique measures on theperformance evaluation of the two divisional managers.19

Several studies have followed up the Lipe and Salterio (2000) results usingmodified versions of their task. Essentially, these studies have sought to‘‘debias’’ the common measures bias by emphasizing causal linkages amongscorecard measures (Banker, Chang, & Pizzini, 2004), highlighting account-ability for one’s performance evaluations (Libby, Salterio, & Webb, 2004),providing an assurance report on the scorecard measures (Libby et al.,2004), disaggregating the performance evaluation task into rating andweighting activities followed by mechanical aggregation (Roberts, Albright,& Hibbets, 2004), and providing training in designing scorecards (Dilla& Steinbart, 2005).

Banker et al. (2004) provided some of their participants (MBA students)with a brief narrative description of the two divisions’ strategies, while othersreceived this same description plus a simple Strategy Map (Kaplan & Norton,2004b) depicting linkages among the four scorecard categories. Both uniqueand common measures were included, and some measures of each type wereexplicitly linked with the division’s strategy while others were not. Partic-ipants receiving Strategy Maps relied more heavily on linked than on non-linked measures, whether common or unique, whereas participants receivingonly the narrative descriptions relied equally on linked and non-linked meas-ures. Further, participants receiving Strategy Maps relied more on linkedunique measures than on common non-linked measures, while there was nodifference in reliance on unique and common measures in the absence of aStrategy Map. Thus, Banker et al. (2004) demonstrated that the commonmeasures bias can be debiased when strategic linkages are made salient.

Libby et al. (2004) also found that the common measures bias can bedebiased. Relying on the notion that judgment biases typically have eithereffort-related or data-related sources (Kennedy, 1993, 1995), Libby et al.

ROBERT H. ASHTON44

investigated one potential debiaser thought to be relevant to each source. Ifthe tendency to focus on common measures and to ignore unique measuresresults from greater difficulty of evaluating unique measures (requiringgreater cognitive effort), an accountability requirement that induces greatereffort might be effective in mitigating the bias. On the other hand, if thecommon measures bias results from a perception that the measures uniqueto each division are of lower quality than common, companywide measures,an independent report that provides assurance on the relevance and reli-ability of the measures might mitigate the bias. Using Lipe and Saterio’s(2000) task and MBA students as participants, Libby et al. manipulated therequirement for participants to provide written justification for theirperformance evaluations and the provision of an assurance report on thescorecard measures. Both accountability and assurance mitigated the com-mon measures bias.

Roberts et al. (2004) modified the Lipe and Salterio (2000) task by havingMBA-student participants rate the performance of each manager on each ofthe 16 scorecard items, multiply these ratings by a set of pre-determinedweights, and sum to get a mechanically aggregated rating for each manager.Participants also provided a separate overall rating of each manager’s per-formance. (In making the overall ratings, participants were not strictlybound by their mechanically aggregated ratings, but the results showed thatoverall ratings and mechanically aggregated ratings correlated 0.74 for onedivision and 0.84 for the other.) While the pre-specified weights wereallocated equally (25%) across the four scorecard categories, 64% of thetotal weight was assigned to the eight unique measures and only 36% tothe eight common measures. Therefore, it is perhaps not surprising that thecommon measures bias was eliminated with the mechanical aggregation.

Dilla and Steinbart (2005) investigated whether training and experiencewith scorecards could debias the common measures effect, also using amodified version of Lipe and Salterio’s (2000) task. They provided class-room instruction and individual and team-based practice in designingscorecards to undergraduate students who had no previous experience withscorecards. The principal finding was that both common and unique meas-ures were used, although substantially greater emphasis was still placed oncommon measures.

Association with Financial Outcomes

A major issue in research on the Balanced Scorecard is whether scorecardadoption and use are associated with superior financial performance, eithercontemporaneously or prospectively, on key accounting- and market-based

Value-Creation Models for Value-Based Management 45

outcomes. A cross-sectional study by Said et al. (2003) and time seriesstudies by Davis and Albright (2004) and Banker, Potter, and Srinivasan(2000) have investigated this issue.

The Said et al. (2003) study, described earlier, which compared companiesthat use both financial and non-financial measures in their annual bonus planswith companies that use only financial measures, examined both accounting-based (ROA) and market-based (share return) performance measures. Bothtypes of measures were calculated, on both a contemporary (same year) andprospective (1 year later) basis, resulting in four key comparisons betweencompanies using only financial measures and those using both financial andnon-financial measures. All four comparisons were directionally consistentwith the hypothesis that companies using both types of measures would out-perform those using only financial measures, although the effect based onROA in the contemporaneous test was not statistically significant. Thus,1-year-ahead accounting-based and market-based measures, as well as thecontemporaneous market-based measure, indicated significantly better per-formance when non-financial variables were included in annual bonus plans.

Davis and Albright (2004) studied a single banking organization in theU.S. Nine of the firm’s 14 branches were included in the study, four of whichimplemented a Balanced Scorecard during the time period of the study andfive of which did not. Each branch used a set of nine key financial per-formance measures that pre-dated the introduction of the scorecard, andthese nine measures were subjectively weighted and combined by manage-ment to form a single ‘‘composite key performance measure’’ for eachbranch. Performance on the nine measures determined each branch’s annualbonus level. The results indicated that, during the 2-year period followingintroduction of the scorecard, financial performance improved significantlyfor the scorecard branches but not for the non-scorecard branches, and thatpost-scorecard-introduction performance was significantly greater for thescorecard branches than for the non-scorecard branches.

Banker et al. (2000) analyzed archival performance data for a 6-yearperiod from 18 hotels managed by a national hotel chain. During this periodcorporate management introduced a new incentive plan for key managers ateach hotel. While the previous plan had been based entirely on financialmeasures, the new plan – which was based on principles underlying theService-Profit Chain – included both financial (operating profit) and non-financial (customer satisfaction) measures. The customer satisfaction meas-ure had been reported prior to the introduction of the new incentive plan,but it had not been used as a basis for incentive compensation. Analysis ofthe time series of customer satisfaction and operating profit showed that

ROBERT H. ASHTON46

both satisfaction and future profitability increased after adoption of the newincentive plan. Further analysis revealed that the profitability effect resultedfrom increased revenues rather than lower costs, and was driven by in-creased occupancy rather than increased room rates.

Noting that the non-financial (customer satisfaction) measure was re-ported for information purposes before the new incentive plan was adopted,Banker et al. (2000, p. 89) considered the question of why the hotel managershad ignored it: ‘‘Our study raises some interesting questions. Hotel managersreceived a substantial annual bonus based on profit before the change in theincentive plan, and there was only a 6-month lag between customer satis-faction and profit. Why then did they not exert the appropriate effort toimprove customer satisfaction, and why did the incorporation of the cus-tomer-satisfaction measures in compensation lead to improvements in bothcustomer satisfaction and profit?’’ Referring to the complex interplay amongthe managers’ knowledge of customer satisfaction, the structure of theirincentive plans, and hotel performance, Banker et al. (2000, pp. 89–90) rea-soned that

although hotel managers were aware of the strategic importance of customer satisfaction

for financial performance, they did not know either the timing or the magnitude of this

relation. Without such knowledge, managers did not recognize the true benefit of

allocating more effort and resources to improve customer satisfaction, and did not do so

until the change in the compensation plan that focused their attention on improving the

customer-satisfaction measures.

They further suggested that when managers gain better knowledge of issuessuch as the timing and magnitude of the association between non-financialmeasures and financial results, the use of formal incentives based on non-financial measures may not be essential. In other words, the informationrole of non-financial measures may be sufficient to achieve improved fi-nancial results.

Further Research

While the amount of research that is relevant to the value-creation modelsdiscussed here is sizable, additional research on the use and effects of suchmodels would be valuable. Concerning the Balanced Scorecard, substantialevidence exists about the frequency of scorecard adoption and the experi-ences of specific companies with respect to implementation and use,20 butmuch less is known about how companies view causal linkages among non-financial and financial measures or about the incorporation of non-financialmeasures in incentive compensation contracts – including the circumstances

Value-Creation Models for Value-Based Management 47

under which explicit or subjective weights are employed. Concerning theother value-creation models, they often have been studied in piecemealfashion (e.g., specific links in the Service-Profit Chain) but much less often ina more comprehensive ‘‘structural equations’’ sense (e.g., the Baldrige andDeming models). And while much existing research can be interpretedwithin the framework of the Skandia Model, studies have not investigatedthis model directly – in contrast to all of the other models discussed. Therelative strengths and weaknesses of the different models, and the specificmanagerial applications for which particular models are better or worsesuited, have not been explored.

Future research might benefit from combining the perspectives on valuecreation that are characteristic of the different models. For example, theService-Profit Chain tends to emphasize customer-related drivers and service

quality, while the Baldrige and Deming models tend to emphasize process-related drivers and product quality. Research that combines the customerand quality perspectives of these models might result in a more penetratinganalysis of value creation than research that derives from a single perspec-tive. Similarly, research conducted from a Balanced Scorecard perspectivemight benefit from elaborating the customer dimension of the scorecardwith insights from the Service-Profit Chain, as well as from elaborating theinternal dimension of the scorecard with insights from the quality-orientedmodels. And the broad perspective provided by Skandia’s approach to in-tellectual capital, which is compatible with all of the other models, can likelyenhance the understanding of value creation that results from application ofthe other models. Stated differently, since the Baldrige, Deming, Skandia,Service-Profit Chain, and Balanced Scorecard models reflect somewhatdifferent (and incomplete) perspectives on value creation, gains will likelyresult from carefully combining key elements of multiple perspectives.

Research could also focus on value-creation models that have been pro-posed more recently than those analyzed here. The Action-Profit LinkageModel (Epstein, Kumar, & Westbrook, 2000), a more elaborate version ofthe Service-Profit Chain which considers employee actions, customer actions,and economic impact, relates more to customer segments than to individualcustomers. The Value Dynamics Framework (Boulton, Libert, & Samek,2000), which also emphasizes employees and customers as well as importantaspects of the Skandia Model, is oriented toward ‘‘designing a businessmodel [and] mastering risk’’ (p. 249). The Value Explorer Model (Andriessen& Tissen, 2000), while similar to the Skandia Model, emphasizes estimationof the financial contribution to firm value of the various core competenceswithin a firm. The Value Chain Scorecard (Lev, 2001) focuses on three

ROBERT H. ASHTON48

(largely sequential) stages of value creation – discovery/learning, implemen-tation, and commercialization – with a strong emphasis on technology,research and development, and intellectual property. The Performance Prism(Neely, Adams, & Kennerley, 2002), which explicitly builds on more estab-lished models including Baldrige, Balanced Scorecard, and Skandia, consid-ers what the firm wants from its stakeholders in addition to what the firmmust do for them. To what extent do models such as these, which clearlyincorporate key elements of earlier models, provide incremental insights intovalue creation? To what extent might they better guide value-based man-agement activities in specific settings, for example, for certain types of firmsor industries? Can selected elements or perspectives of ‘‘second generation’’models be profitably blended with those of more established models?

Other research could consider the potential usefulness of value-creationmodels for guiding external disclosures. Much interest has been shown inexpanding the traditional external reporting model (e.g., American Instituteof Certified Public Accountants (AICPA) (1994); FASB, 2001; Wallman,1995, 1996). A recent analysis raised the question of whether firms shouldreport non-traditional disclosures using an ‘‘integrated financial/non-financialframework’’ (Maines et al., 2002): It was observed that ‘‘an integrated frame-work could disclose specific non-financial performance measures and providea description of the firm’s business model in the context of these measures andhow these measures map into firm value’’ (Maines et al., 2002, p. 357,emphasis added). Blair and Wallman (2000) suggest that the lack of businessmodels that describe the use to which such disclosures might be put is asignificant hindrance to the development of expanded external disclosurepractices. The value-creation models described in this paper are ‘‘integratedfinancial/non-financial frameworks’’ that suggest possible linkages amongnon-financial measures and financial outcomes, and they potentiallycould serve as frameworks for expanded external disclosure of non-financialinformation. Their value in this regard is an important area for research.

Moreover, it seems apparent that the credibility of non-traditional dis-closures such as those contemplated by value-creation models would beenhanced by some form of independent third-party association. In fact, itseems likely that third-party association concerning the reliability and/orrelevance of such disclosures will be necessary if they are to be taken se-riously by investors, thus creating new opportunities for attestation andassurance services and for related research.21 Further, new attestation andassurance applications are likely to require new skills on the part of assurorsand new assurance standards for conducting and reporting the results ofsuch engagements. Finally, new audit and review methodologies are likely to

Value-Creation Models for Value-Based Management 49

be needed, as current methodologies are strongly oriented toward exami-nation of traditional financial statements and are unlikely to be adequate forexamining long-run value-creation potential. Recently, one of the Big Fourfirms, KPMG, has actively developed a new methodology, the KPMGBusiness Measurement Process, that is specifically designed for understand-ing how firms create value through the transformation of tangible andintangible resources (Bell, Marrs, Solomon, & Thomas, 1997; Bell, Peecher,& Solomon, 2002, 2005). All of these areas – assuror skills, assurancestandards and methodologies, and the expanded scope of attestation andassurance services – would likely benefit from research that reflects per-spectives of the value-creation models discussed in this paper.

CONCLUSION

Value-creation models that have been proposed for supporting value-basedmanagement are discussed. Value-based management involves defining andimplementing strategies for long-run shareholder value creation and align-ing management systems and activities, as well as financial and non-financialperformance measures, for value creation. Accordingly, management ac-counting research conducted within the value-based management paradigmfocuses on the identification, measurement, management, and reporting oftangible and intangible value drivers, and generally involves some model ofvalue creation that guides the identification and use of value drivers andrelated performance measures.

Because each value-creation model involves a particular (and incomplete)perspective on the drivers and measures considered necessary for long-runvalue creation, it is essential to understand the differences among the prin-cipal models that have been proposed, and because substantial overlapexists among models it is important to understand their similarities as well.It is also useful to recognize that measures reflecting many of the intangiblevalue drivers included in value-creation models are supported by significantstreams of research. Thus, much of this paper is concerned with describingvalue-creation models, their similarities and differences, and the researchevidence that supports them. Critical management considerations thatvalue-creation models entail are also discussed, including linking measuresof intangible value drivers to each other and to financial outcomes andwhether such measures should be explicitly weighted and combined to forma ‘‘value index.’’ The discussion of these issues is necessarily preliminary

ROBERT H. ASHTON50

given the current state of knowledge and experience in these areas, and isintended primarily to stimulate further discussion and research.

The scope of research that could usefully address the role of value-creation models in value-based management is extremely broad – both interms of topics and methods – and includes research on the formulation,validation, implementation, and effectiveness of value-creation models usingarchival and field-based approaches among others. While a substantialamount of research already exists on management aspects of value-creationmodels, the research nevertheless is relatively limited vis-a-vis the range ofintangible value drivers included in existing models and the complexity ofthe value-creation processes and relationships in which those models areembedded. The opportunities for further progress in understanding the roleof value-creation models in value-based management are enormous, at bothresearch and practical levels.

NOTES

1. Similarly, Crosby (1979) proposed a set of 14 Steps (which were orientedsomewhat more toward top management than were Deming’s 14 Points), and Juran(1989) advocated the Juran Trilogy, the three managerial processes of quality plan-ning, quality control, and quality improvement – analogous to the financial man-agement processes of financial planning, financial control, and financialimprovement. March (1986) and Ross (1999) provide comparative evaluations ofthe approaches of Deming, Juran, and Crosby.2. All of the panel members had been actively involved in the implementation of

Deming’s methods, and some had conducted research and written about qualitymanagement in general and the Deming Management Method in particular.3. Of course, the weighting scheme in Fig. 5 is not necessarily relevant when the

Baldrige Model is used as a value-creation framework.4. Skandia’s efforts are described in several publications (e.g., Edvinsson, 1997;

Edvinsson &Malone, 1997; Roos & Roos, 1997; Roos et al., 1998; Roos & Jacobsen,1999, and Roos, Bainbridge, & Jacobsen, 2001), and a sizable literature concerningthe approach has appeared (e.g., Bontis, Dragonetti, Jacobsen, & Roos, 1999; Choo& Bontis, 2002; Larsen, Bukh, & Mouritsen, 1999; Lynn, 1998a, 1998b; Mouritsen,1998; Mouritsen, Bukh, Larsen, & Johansen, 2002; Mouritsen, Johansen, Larsen,& Bukh, 2001; Mouritsen, Larsen, & Bukh, 2001; OECD, 2000; Petty & Guthrie,2000; Stewart, 1997, 2001; Sullivan, 1998, 2002; and The Conference Board, 1997).5. These developments are described in more detail by Ashton (2005).6. More recent expositions of the Balanced Scorecard refer to the internal business

perspective as the ‘‘internal business processes’’ perspective or simply the ‘‘internal’’perspective, and to the innovation and learning perspective as the ‘‘learning andgrowth’’ perspective (e.g., Kaplan & Norton, 1996c).

Value-Creation Models for Value-Based Management 51

7. The TQM activities that Hoque (2003) considers are (1) internal process im-provement and manufacturing innovation, (2) benchmarking, (3) a zero-defects cul-ture, (4) supplier relations, (5) customer relations, (6) employee training, (7) an open,less bureaucratic culture, and employee empowerment, and (8) executive commit-ment and management competence.8. A measurement system based on Drucker’s ideas was incorporated into Gen-

eral Electric’s ‘‘Measurements Project’’ in the 1950s (Greenwood, 1974).9. Bourguignon et al. (2004) describe four ways in which the Tableau de Bord

differs from the Balanced Scorecard. First, the Balanced Scorecard, building on theframework of Porter (1980, 1985), works ‘‘from the outside in’’ (from customers tointernal processes), while the Tableau de Bord works from both the outside in andthe inside out, where the latter reflects a resources/core competences perspective (e.g.,Barney, 1991; Prahalad & Hamel, 1990). Second, while the Balanced Scorecardassumes a generic cause-and-effect performance model (from Learning and Growthto Internal Processes to Customer to Financial perspectives), the Tableau de Bordassumes no systematic overall links among various perspectives. Third, the dominantapproach of the Balanced Scorecard is top-down (cascading management’s objec-tives to lower organizational levels), while deployment of the Tableau de Bordinvolves more interaction and negotiation among different organizational levels.Finally, the Balanced Scorecard emphasizes incentives, rewards, and accountability,while the Tableau de Bord focuses more on information and learning.10. Development of the Balanced Scorecard and its application in several com-

panies is described by Kaplan and Norton in three books (Kaplan & Norton, 1996c,2001a, 2004b) and a series of articles (Kaplan & Norton, 1992, 1993, 1996a, 1996b,1996d, 2000, 2001b, 2001c, 2004a).11. Oliva, Day, and DeSarbo (1987) had earlier proposed a ‘‘strategy map’’ for

describing how performance measures, competitive tactics, and competitors’ perform-ance are related. However, the application was at the industry level instead of the firmlevel, and the Oliva et al. strategy map was simply a graphical depiction in whichindividual firms were located relative to competitors on measures that were particularlyrelevant to their industry, for example, profitability, growth, and market share.12. The distinction between valuation approaches and measurement approaches is

consistent with Ijiri’s (1995) distinction between managing capital and managingresources. Ijiri maintains that because capital managers such as top executives andboard members raise capital and allocate it among projects while resource managerssuch as division heads operate projects to achieve particular financial objectives, thetwo types of managers have different orientations and different information needs.Since capital is abstract, aggregate, and homogeneous, capital managers needaggregate information that allows them to assess investment returns and risks. Sinceresources are concrete, disaggregate, and heterogeneous, resource managers needdisaggregate information that allows them to plan and execute the use of resources.Thus, in Ijiri’s framework valuation approaches are likely to better satisfy the in-formation needs of capital managers, while measurement approaches are likely tobetter satisfy the information needs of resource managers.13. This application of the Strategy Map also clarifies that HR functional man-

agers are expected to focus on more than just efficiency by linking the five strategicthrusts to the broad themes of both ‘‘minimizing HR costs’’ and ‘‘maximizing human

ROBERT H. ASHTON52

capital,’’ which are shown in the financial dimension of the scorecard as drivers ofshareholder value. More generally, it is intended to incentivize HR managers toattend to a particular set of value drivers, to view HR as a strategic asset, and to bebetter able to demonstrate to others the contributions of HR to firm performance.14. An early discussion of self-correcting and self-reinforcing feedback loops in

the context of management accounting systems is provided by Ashton (1976).15. Specific references are provided by Zeithaml (1988, 2000), Zahorik and Rust

(1992), Rust, Zahorik, and Keiningham (1994), Oliver (1997), and Zeithaml et al.(2001).16. Case studies include Ahn (2001), Braam and Nijssen (2004), Butler, Letza, and

Neale (1997), Ittner et al. (2003a), Kalagnanum (2004), Malina and Selto (2001),Moers (2005), Mooraj, Oyon, and Hostettler (1999), Papalexandris, Ioannou, andPrastacos (2004), Schneiderman (2001), and Vaivio (1999). Surveys include Gehrkeand Horvath (2002), Hoque and James (2000), Ittner et al. (2003b), Malmi (2001),Maltz et al., 2003, Olson and Slater (2002), Speckbacher et al. (2003), and Stems-rudhagen (2004). Archival studies include Ittner et al. (1997) and Said et al. (2003).Laboratory studies include Banker et al. (2004), Dilla and Steinbart (2005), Libby etal. (2004), Lipe and Salterio (2000, 2002), and Roberts et al. (2004). Field studiesinclude Banker et al. (2000) and Davis and Albright (2004).17. Countries included in these surveys are Austria, Finland, France, Germany,

Italy, Norway, and Switzerland.18. The categories actually used by the company were not precisely these four, so

Malina and Selto (2001) reclassified a few of the measures.19. Lipe and Salterio (2002) adapted their original task to examine the effects on

performance evaluation of different ways of organizing scorecard measures. Twospecific types of organization were investigated – organization via the four standardscorecard categories and an ‘‘uncategorized list’’ of measures. It was expected thatmultiple measures that are organized into each of the four standard categories wouldhave less impact on performance evaluations than the same measures distributedacross the four categories (‘‘uncategorized’’) because categorized measures would beperceived by evaluators as somewhat redundant (not independent of each other).This expectation was supported.20. Much of this evidence comes from Kaplan and Norton’s books (Kaplan

& Norton, 1996c, 2001a, 2004b), in addition to studies listed in footnote 16.21. Professional standards in the U.S. consider attestation services to include not

only the traditional audit of historical financial statements, but also examinations of,and the issuance of reports on, the reliability of other types of assertions that are theresponsibility of another party. In contrast, assurance services are independent pro-fessional services that improve the quality of information for decision makers, andthey include relevance-assurance as well as reliability-assurance services (AmericanInstitute of Certified Public Accountants (AICPA), 1997, 2001).

ACKNOWLEDGMENTS

This research was supported by KPMG under its Business MeasurementResearch Program. I am indebted to KPMG for their support, and

Value-Creation Models for Value-Based Management 53

particularly to Timothy B. Bell, Director of Assurance Research at KPMG,for support and for many valuable conversations on value creation. Thepaper has also benefited from the substantial input of Alison Ashton (DukeUniversity), Ram Menon (KPMG) and Ira Solomon (University of Illinois).

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PERFORMANCE STANDARDS AND

MANAGERS’ ADOPTION OF RISKY

PROJECTS$

Chee W. Chow, James M. Kohlmeyer, III and

Anne Wu

ABSTRACT

Innovation is the key to competitive advantage, and attaining innovation

often requires taking on higher-than-usual levels of risk. Yet, while man-

agers commonly profess support for efforts in innovation, they often

emphasize safe, short-term results over more risky, long-term outcomes.

As a result, a major challenge to firms is increasing employees’ willingness

to adopt risky yet more profitable alternatives.

This study uses an experiment to test how the level of performance

standard, per se, affect employees’ propensity to take on (more) risky

projects. Using participants from the U.S. and Taiwan to represent higher

versus lower individualism national cultures, it also examines the effects

of national culture on employee actions. The findings are consistent with

expectations from combining goal and prospect theories that a specific

high standard motivates greater risk taking than a low standard. We find

only limited difference between the U.S. and Taiwanese samples’

$Alphabetically ordering is followed in listing the authors. All authors contributed equally to

this project.

Advances in Management Accounting, Volume 16, 63–105

Copyright r 2007 by Elsevier Ltd.

All rights of reproduction in any form reserved

ISSN: 1474-7871/doi:10.1016/S1474-7871(07)16002-0

63

CHEE W. CHOW ET AL.64

individualism/collectivism scores, which may help to explain the lack

of significant differences between their reactions to the performance

standard treatment.

1. INTRODUCTION

This study explores how performance standards affect managers’ willingnessto pursue risky projects. This topic is important because in the current era ofglobalized competition, innovation has been identified as a key to firms’continued survival and success (Bouchikhi & Kimberly, 2001; Jacobson,1992; Page, 1993). In turn, attempts at innovation often require venturinginto uncharted waters, thus exposure to higher-than-usual levels of risk(Chatterjee, Wiseman, Fiegenbaum, & Devers, 2003; Mullins, Forlani, &Walker, 1999; Wan, Ong, & Lee, 2005). Yet, despite professing to supportinnovation, more often than not managers emphasize safe, short-termresults at the expense of more risky long-term opportunities (Kuczmarski,1996; Kunz & Pfaff, 2002; Stewart, Watson, & Carland, 1999; Wiseman &Gomez-Mejia, 1998).

Managers’ tendency to shun risk can be understood when one comparesthe effects of risky projects from their perspective versus that of the firm.A firm typically is comprised of many projects across numerous managers.Having a large portfolio, in turn, helps to diversify away much of theproject-specific risks. In contrast, individual managers generally haveauthority over, and are held accountable for, a far more limited set ofprojects. Consequently, they also have far less ability to diversify away therisk of individual projects (Barney & Hesterly, 1996; Fama, 1980; Jensen &Murphy, 1990). Given this divergence between them and their managers,firms are faced with the challenge of increasing the latter’s willingness topursue risky projects (Atkinson et al., 1997; Baysinger, Kosnik, & Turk,1991; Garen, 1994; Hoskisson, Hitt, & Hill, 1991; Jensen & Meckling, 1976;Lambert, 2001; Tosi & Gomez-Mejia, 1989).

An important part of firms’ tool kit for motivating managers is theperformance evaluation and reward system, and the design and effects ofperformance-based compensation schemes have long been a major area ofmanagement accounting research (Sprinkle, 2003). However, this literaturehas mostly focused on effects other than risk taking (e.g., effort, perform-ance). While a few studies have explored how performance evaluationand compensation schemes affect employees’ risk taking tendency, theyhave only considered a limited set of features or special circumstances.

Performance Standards and Managers’ Adoption of Risky Projects 65

The current study extends investigation to the level of the performancestandard. This focus is predicated on a substantial prior literature havingdemonstrated powerful effects of performance standards on employee mo-tivation (Bonner & Sprinkle, 2002; Merchant & Manzoni, 1989; Otley, 1987).

A secondary focus of this study is potential cross-cultural differences inperformance standards’ motivational effects. This aspect of the studyis motivated by the increasing globalization of economic activities. Mosttheories of behavior used to derive management implications have made theassumption that people would act opportunistically for private gain at theexpense of the collective (e.g., the firm) (Baiman, 1990; Koford & Penno,1992). While this assumption may accurately capture a major behav-ioral tendency of, say, Anglo-Americans, it may only partially, or eveninaccurately, represent the leanings of individuals from other nationalcultures (e.g., Asia; see Chow, Kato, & Merchant, 1996; Hofstede, 1984;Kachelmeier & Shehata, 1997). As such, it is important to evaluate theglobal applicability of extant theories and findings. In their review of cross-cultural management accounting research, Harrison and McKinnon (1999)specifically identified as worthwhile future research on the interplay betweennational culture and issues such as risk-taking and innovative propensities.In the current study, national culture is operationalized by comparingsubjects from the U.S. and Taiwan.

The remainder of this article is structured as follows. Section 2 providesan overview of related prior literatures as the basis for developing two hy-potheses. Then, the data collection method is explained in Section 3. Section 4presents the results, and Section 5 provides a summary and discussion.

2. LITERATURE REVIEW AND HYPOTHESIS

DEVELOPMENT

This section is divided into two subsections. The first subsection discussesprior studies on the motivational effects of standard-based compensationsystems. This review provides the basis for our first hypothesis. In thesecond subsection, prior studies on the effects of national culture are usedfor developing our second hypothesis.

2.1. The Motivational Effects of Standard-based Compensation Systems

Organizations often try to motivate employees via explicit performancestandards, such as ones relating to profit, return on investment, sales

CHEE W. CHOW ET AL.66

revenue, and production cost (Merchant, 1998). Numerous studies haveexamined the effects of such standard-based compensation systems on awide range of outcomes, including performance (Bonner, Hastie, Sprinkle, &Young, 2000; Fatseas & Hirst, 1992; Nouri & Parker, 1998; Sprinkle, 2000;Young & Lewis, 1995), effort (Awasthi & Pratt, 1990; Berg, Daley, Gigler, &Kanodia, 1990; Bonner & Sprinkle, 2002), satisfaction (Awasthi, Chow, & Wu,2001; Birnberg, Turopolec, & Young, 1983; Hopwood, 1972; Parker &Kohlmeyer, 2005), and budgetary slack (Chow, Cooper, & Waller, 1988;Fisher, Frederickson, & Peffer, 2002; Waller, 1988; Young, 1985; Young,Fisher, & Lindquist, 1993). However, few studies have examined whichincentive schemes or dimensions thereof affect managers’ risk takingbehavior (Sprinkle, 2003). Among the small number of accounting studies inthis area, Chow and Haddad (1991), later supplemented by Frederickson(1992), reported experimental results supportive of Holmstrom’s (1982)proposition that by filtering out the effects of common uncertainty, relativeperformance evaluation increases individuals’ propensity to take risk. Sayre,Rankin, and Fargher (1998) investigated compensation systems of the‘‘winner-take-all’’ type (e.g., one manager is selected out of several forpromotion). In their experiment, subjects tended to make investments ofextremely high or low risk depending on how their performance comparedwith that of the leader.

Along the same vein, Ruchala (1999) focused on performance relative tothe budget goal rather than the performance of others. She found thatsubjects assigned to a not-achieving-budget-goals condition made riskierdecisions. The highest risks were taken by those who were both not meetingbudget goals and being compensated with bonus-based compensation.However, while this study demonstrates that budget goals can affectrisk-taking behavior, it does not examine the effect of different goal levels.

A key issue in designing a standard-based compensation system is selectingthe level of the performance standard. An extensive literature on goal settinghas long maintained that specific and challenging goals (performance stand-ards) lead to higher performance than easy goals, ‘‘do your best’’ goals, orno goals at all (Brown & Latham, 2000; Latham, 2004; Locke & Latham,1990). Studies in the accounting literature (e.g., Chong & Chong, 2002;Chow, 1983; Fatseas & Hirst, 1992; Hopwood, 1974; Otley, 1987) havereported findings consistent with this view. However, a pre-condition forthe (difficult) standard to have positive motivating effects is that it has tobe accepted by employees. If a performance standard is perceived as beingunattainable, then it can lead to poorer performance because employeeswould become discouraged and give up trying for its attainment (Fatseas &

Performance Standards and Managers’ Adoption of Risky Projects 67

Hirst, 1992). This latter concern has led some authors to question thesuperiority of difficult performance standards. A field study by Merchantand Manzoni (1989), for example, has reported that profit center managersconsider it most advantageous to the firm to have performance targets thatare achievable more than 80% of the time.

For purposes of understanding how performance standards affectemployee’s risk-taking, a limitation of prior studies is that they have largelyfocused on tasks with well-defined input/output relationships. With suchtasks, superiors can infer subordinates’ effort or action choices from theiroutputs. In contrast, risk taking is an ex ante concept, where a ‘‘good’’outcome can come from a ‘‘bad’’ decision, and a ‘‘good’’ decision can giverise to a ‘‘bad’’ outcome. To the extent that superiors cannot infer subor-dinates’ decisions based on their outcomes, whether these prior studies’findings can be generalized to risk-taking tasks is an open question.

Both psychology-based and economics-based theories (e.g., expectancyand agency theories) suggest that when compensation is linked to perform-ance relative to the standard, employees can be motivated to attain higherperformance standards by increasing the rewards for standard attainment(e.g., Demski & Feltham, 1978; Holmstrom, 1979; Isaac, Zerbe, & Pitt,2001; Prendergast, 1999; Vroom, 1964). This inference is predicated on theassumption that employees are risk averse. Yet people’s choices among riskyprospects often exhibit patterns that are inconsistent with risk aversion(Fiegenbaum, 1990; Piron & Smith, 1995; Wiseman & Bromiley, 1996;Wiseman & Gomez-Mejia, 1998). For example, sometimes they may opt fora more risky alternative even if its expected value, or certainty equivalentin terms of economic theory, is lower than that of the less risky option(e.g., purchase of a lottery ticket). Prospect theory was proposed byKahneman and Tversky (1979) to explain these choices, as well as riskychoices in general. This theory suggests that people may be either risk averseor risk taking, depending on how the uncertain outcomes compare with areference point. The function representing how people value differentalternatives is envisioned as an S-shaped curve that passes through thereference point, being concave for gains and convex for losses, and alsosteeper for losses than for gains (Kahneman & Tversky, 1979). This shapeimplies that people are more concerned with avoiding loss to wealth thanattracting additional wealth (i.e., they are loss avoiders rather than wealthmaximizers) (Gomez-Mejia, Welbourne, & Wiseman, 2000; Wiseman &Gomez-Mejia, 1998). Stated another way, the aggravation that one expe-riences in losing a certain sum of money is greater than the pleasureassociated with gaining the same amount (Kahneman & Tversky, 1979).

CHEE W. CHOW ET AL.68

In general, prospect theory implies that in choosing among risky alter-natives, decision makers will act conservatively when facing potential gains,but take greater risks when facing potential losses (Sitkin & Pablo, 1992;Thaler & Johnson, 1990; Tversky & Kahneman, 1986, 1991). In the account-ing literature, Rutledge and Harrell (1993, 1994) and Sharp and Salter (1997)have provided support for prospect theory in their study of escalation-of-commitment, while Luft (1994) has demonstrated its applicability toemployees’ choice of bonus schemes.

Overall, prospect theory, in conjunction with goal theory, implies that thelevel of performance standard, per se, can affect employees’ risk takingbehavior. This inference is consistent with the argument of Payne, Laughhunn,and Crun (1980) that the performance target (standard) influences thelocation of the reference point in people’s value functions. While theexistence of any explicit performance standard will demarcate outcomes intogains and losses, a high performance standard will cause more outcomes to beframed as loss-making situations as compared with a low performance stand-ard. And given that people are more averse to a loss than they value a gain ofthe same magnitude, they should be more inclined toward risk taking to avoidfailing to meet a high performance standard than to exceed a low performancestandard. Testing this prediction is the primary objective of our study.

In focusing on the motivating effects of performance standard level, we arequick to acknowledge the implication of expectancy and agency theories thatemployees can be motivated to take greater risks if they are compensated fordoing so, such as by paying more for attaining a higher performance stand-ard. Tests of this implication also are worthwhile. But if it is found that thelevel of performance standards, independent of standard-based compensa-tion, affects employees’ risk taking behavior, this additional knowledge canfurther enrich managers’ repertoires in designing the overall control system.

H1. The level of performance standard, per se, affects employees’willingness to take risk. A higher performance standard leads to greaterrisk taking as compared with a lower performance standard.

2.2. The Nature and Effects of National Culture

National culture influences a person’s actions either by supplying the valuestoward which the actions are oriented, or by shaping a repertoire of actionstrategies in which certain patterns of action are facilitated while others arediscouraged (Erez & Earley, 1993; Hodgetts & Luthans, 1997; Triandis,1989). Across multiple taxonomies that have been proposed for

Performance Standards and Managers’ Adoption of Risky Projects 69

operationalizing the national culture construct (e.g., Hofstede, 1980, 1991;Ronen & Shenkar, 1985; Schwartz, 1994; Smith, Dugan, & Trompenaars,1996; Trompenaars, 1994), individualism/collectivism has consistently beenidentified as being a basic, or core, value that distinguishes membersof different cultural groups from one another (Harrison, 1993; Hofstede,1980, 1991; Lachman, Nedd, & Hinings, 1994; Smith, Peterson, & Schwartz,2002; Triandis, 1995; Triandis, Bontempo, Villareal, Asai, & Lucca, 1988;Sondergaard, 1994; see Earley & Gibson, 1998 for a comprehensive review).Furthermore, across a large number of studies, individualism/collectivismhas been identified as the most important dimension of national culture incomparing East and West (Triandis, 1995; Smith et al., 1996).

Individualism and its opposite, collectivism, relate to the relative emphasisthat members of a society place on their self-interests versus those of thegroup. People from a collectivist culture tend to define themselves in terms oftheir relationships with others, and they are more inclined to give up theirindividual needs when there is a conflict between them and group needs(Earley, 1993; Triandis, 1995). In contrast, members of an individualist cul-ture tend to define themselves as autonomous entities independent of groups(Hofstede, 1991; Markus & Kitayama, 1991), and are more likely to empha-size their individual needs over group needs (Redding, 1993; Wagner, 1995).

We operationalize individualism/collectivism by comparing U.S. nation-als to Chinese nationals residing in Taiwan. Based on Hofstede’s (1980)measurement scale, the former had a score of 91 on individualism/collectivism, while the latter were at the low end of the scale with a scoreof 45. More generally, much research has isolated the self-interest motive asbeing a cornerstone of American theories and practices (Bellah, Madsen,Sullivan, Swidler, & Tipton, 1987; Earley, 1993; Harris & Moran, 1987;Triandis et al., 1988). In contrast, Chinese nationals have repeatedly beencited for an emphasis on subjugating one’s own interests to those of thecollective (Bond, Leung, & Wan, 1982; Bond & Hwang, 1986; Leung &Bond, 1984). Based on the preceding characterization of individualism/collectivism, when risk taking poses a conflict between self and collectiveinterests, members of a more collectivist culture (i.e., Chinese nationals) areexpected to take on more risk as compared with members of a moreindividualistic culture (i.e., U.S. nationals), thereby placing the interests ofthe collective over their own interests. Thus, we hypothesize:

H2. Holding constant the level of performance standard, when it is in thefirm’s best interests to take on more risky projects, Chinese nationals inTaiwan will engage in greater risk taking than will their U.S. counterparts.

CHEE W. CHOW ET AL.70

3. METHOD

3.1. Design

An experiment was used to control for the effects of extraneous factors andfor its replicability. There were two between-subjects treatments: nationalculture (U.S., Chinese) and performance standard (high, low). As explainedfurther below, the dependent variable was the riskiness of projects selectedby the participants.

3.2. Task and Procedure

An experimenter and an assistant were present at all times to answer questionsand to preclude interaction among participants during the experiment. Sub-jects were asked to assume the role of a product manager for XYZ Company.Their task was to select products for the company to produce and market.

The appendix provides the experimental materials for the high perform-ance standard case. The materials for all the treatments were pre-tested withupper-level accounting major students in both the U.S. and Taiwan. Allreported that the ‘‘story’’ of the task motivated them to take the decisionmaking seriously. The experimental session lasted �75min and comprisedthe following three stages:

3.2.1. Stage One

Participants read through a description of XYZ Company and the productmanager’s role. Then they worked through several practice exercises whichfocused on the nature of the probability distributions reflecting the possibleprofit outcomes from each product, and how product profit outcomesaffected the company’s profit and the participant’s pay. Correct answerswere provided after participants were satisfied with their own answers.

The XYZ Company was described as being in the business of manufac-turing and marketing fad items that have a 1-year market life. At thebeginning of each year, the Company allotted each of its many productmanagers $1,000,000 to produce and market one product (all products cost$1,000,000 to manufacture and market).1 The product manager’s task wasto select one out of the seven new products that his/her staff proposed eachyear. Each product proposal included the staff’s estimate of the probabilitythat specific profit rates (return on the $1,000,000 invested) will be achievedover the product’s 1 year life.

Performance Standards and Managers’ Adoption of Risky Projects 71

For simplicity, the same seven probability distributions were used in eachyear. All of these distributions were symmetrical about a mean of 15%, witha range of 1–29%.2 The three distributions at the low end of riskiness werebell-shaped, with increasingly dispersed distributions about the mean. Thefourth distribution was uniform in shape (i.e., a rectangle, with all outcomesbeing equally likely). Then at the high end of riskiness were three bimodaldistributions, with the two modes leaning increasingly toward the lowestand highest possible outcomes as one moved toward the high end of the riskcontinuum. In terms of outcome variance (our measure of riskiness), thevalues for the seven distributions were 21.96, 37.3, 50.48, 70.15 (uniformdistribution), 100.54, 114.81, and 129.05.3

Participants were randomly assigned to a high performance standard(a 25% profit rate) or a low performance standard (a 5% profit rate).To maintain our focus on the level of performance standard, per se, pay wasentirely based on actual performance and not affected by performancerelative to the standard. This treatment was operationalized as follows.Participants with a high performance standard were paid a base salary of$125,000 ( ¼ 50% of profit at the 25% performance standard profit rate),plus an adjustment (+ or �) equal to 50% of the deviation between actualand standard performance. Participants with a low performance standard(5% profit rate) were paid a base salary of $25,000 ( ¼ 50% of profit at the5% profit rate), plus an adjustment (+ or �) equal to 50% of the deviationbetween actual and standard performance. Use of the parameter ‘‘50%’’ toderive all subjects’ base pay and as the bonus/penalty factor assured thatunder both performance standard levels, pay was exactly the same for anylevel of realized profit.4

Lastly, participants were told that XYZ Company has been in existencefor a number of years and on average, its products had produced profitsequal to 15% of their manufacturing and marketing costs. But because theindustry was intensely competitive and getting more so, the Companybelieved that if it was to survive and prosper, it must come out with anumber of breakthrough products that can produce significantly higherlevels of profits than the historical level. Participants were explicitly told thatbecause the Company had a large number of product managers, it was notconcerned with individual managers taking on highly risky projects.5

3.2.2. Stage Two

Participants completed three experimental periods. Each period beganwith each participant receiving the profit probability distributions for sevenproposed products. These products were labeled At, Bt,y , Gt, where the

CHEE W. CHOW ET AL.72

subscript ‘‘t’’ stood for the experimental period (thus, t ¼ 1, 2, 3). After aparticipant had written down (on a form provided for this purpose) his/herchoice among these products, he/she brought the form to the experimenterat the front of the room. The experimenter had an array of seven brownpaper bags, labeled At, Bt,y , Gt just like the seven proposed products. Theparticipant was asked to draw out a folded piece of paper from the bagcorresponding to his/her product choice, and to record the outcome (real-ized profit rate) on his/her form. After returning the folded piece of paper tothe bag, the participant returned to his/her seat to compute the Company’sprofit and his/her total pay for the period, then started the next experimentalperiod.

Participants were told that the first two experimental periods were forpractice, and that they would only be paid based on their profit performancein the third period. Since participants’ product choices may be affected byprior outcomes, they were randomly assigned to two outcome sequences forthe two practice periods (by rigging the numbers written on the folded sheetsin each brown bag). The ‘‘good-bad’’ sequence was a 22% realized profitrate for the first period, and 7% for the second period. These outcomeswere reversed in the ‘‘bad-good’’ sequence. For the ‘‘real’’ (third) period,participants drew from bags that accurately reflected their selected products’probability distributions.

3.2.3. Stage Three

After completing the third (real) experimental period, participants com-pleted an exit questionnaire. Then they were debriefed, paid, and dismissed.6

In addition to several demographic questions, the exit questionnaireasked each participant how much he/she had made his/her decisions in theexperiment as if facing a real world situation. There also were questions onthe degree to which their decision making had been influenced by each of theexperimental treatments: performance standard, relation between actualperformance and pay, and the company’s desire for breakthrough products.7

All used a 9-point response scale, with 1 ¼ ‘‘not at all’’ and 9 ¼ ‘‘completely.’’Participants who answered above ‘‘1’’ were asked to elaborate on theirnumerical answers. Finally, subjects completed three scales from Hofstede(1980), Earley (1993), and Chinese Cultural Connection (1987), respectively,on individualism/collectivism.

To avoid biases or errors due to language proficiency, the Taiwaneseportion of the experiment was conducted in Chinese. The English version ofthe instrument was first translated into Chinese by a person not affiliatedwith the study. Then two bilingual members of the research team evaluated

Performance Standards and Managers’ Adoption of Risky Projects 73

the translation against the English version for content equivalence. Onlyminor changes were deemed necessary; there were made through a consult-ative process.

3.3. Sample

A total of 40 (46) U.S. (Chinese) nationals voluntarily participated in theexperiment. All were full time Masters level business students. The U.S.participants attended a large public university in the southeast, while theTaiwanese subjects were enrolled in a large public university in Taipei. Three(9) participants were dropped from the U.S. (Taiwan) sample due toanswering 4 or below about the extent to which they had made decisions inthe experiment as if facing a situation in real life. Thus, the sample used forhypothesis testing comprised 37 U.S. nationals and 37 Chinese nationalsresiding in Taiwan. Both national samples were split 17/20 between thelow and high performance standard treatments.

On average, the U.S. participants were 26 years old (range: 21–42 years)and had 3.16 years of full time equivalent working experience (range: 0–13years). Slightly over half (51%) were male. The average Taiwanese subjectwas slightly younger (23.86 years; range: 22–27 years) and tended not tohave had significant full time working experience (mean ¼ 0.38 years; range:0–3.5 years). Slightly over half (54%) were male. Within each nationalsample, there was no statistically significant demographic difference acrossperformance standard treatments. Independent samples tests indicated thatthe U.S.–Taiwan differences in work experience and age were statisticallysignificant. However, neither factor had a significant effect when included inthe hypothesis tests.

4. RESULTS

4.1. Validation of Cultural Differences

Table 1 presents the two national samples’ scores on each item of the threeculture scales. Panel A contains six questions from Hofstede (1980, p. 220)which he identified as being significantly related to the individualism/collectivism dimension. Of the six items, individualists are expected to assigngreater importance (i.e., higher scores) to the first three, while collectivistsare expected to ascribe greater importance to the latter three. Panel A shows

Table 1. Comparison of U.S. and Taiwan Samples’ Culture Scores.

Panel A: Individualism/Collectivism Scale from Hofstede (1980)a

U.S. Taiwan

Part I: Mean scores on individual items

Have sufficient time for personal or family

life

4.13 3.97

Have considerable freedom to adopt own

approach to the job

3.35 3.73

Challenging work 3.97 3.84

Fully use skills and abilities 3.73 3.97

Good physical working conditions 3.84 3.84

Have training opportunities 3.86 4.05

Part II: Sums of subsets of items

SUM1–3 (first 3 items) 11.46 11.54

SUM4–6 (last 3 items) 11.43 11.86

Independent Samples Test: U.S. versus Taiwan

Mean Difference t df Sig. (2-tailed)

SUM1–3 – SUM4–6 0.35 0.72 72 0.47

SUM1–3/SUM4–6 0.02 0.48 72 0.63

Panel B: Individualism/Collectivism Scale from Earley (1993)b

U.S. Taiwan

Part I: Mean scores on individual items

Employees like to work in a group rather than by themselves 2.73 3.78

If a group is slowing me down, it is better to leave and work

alone

2.75 2.86

To be superior, a man must stand alone 3.70 2.86

One does better work working alone than in a group 3.25 3.49

I would rather struggle through a personal problem by myself

than discuss it with my friends

3.75 4.03

An employee should accept the group’s decision even when

personally he/she has a different opinion

2.47 3.70

Problem solving by groups gives better results than problem

solving by individuals

3.56 3.41

The needs of people close to me should take priority over my

personal needs

3.09 3.73

Part II: Summed score over all 8 items 25.30 27.86

CHEE W. CHOW ET AL.74

Independent Samples Test: U.S. versus Taiwan

Mean Difference t df Sig. (2-tailed)

Summed score over all 8 items �2.57 �3.33 72 0.00

Panel C: Integration (Individualism) Scale from Chinese Cultural Connection (1987)c

U.S. Taiwan

Part I: Mean scores on individual items

a. Tolerance of others 6.89 5.86

b. Harmony with others 6.62 7.78

c. Solidarity with others 5.78 7.58

d. Non-competitiveness 3.92 7.55

e. Trustworthiness 8.35 7.20

f. Contentedness with one’s position in life 7.65 5.65

g. Being conservative 4.50 4.11

h. A close, intimate friend 7.54 7.68

i. Filial piety 2.81 2.14

j. Patriotism 3.57 5.19

k. Chastity in women 5.70 4.62

Part II: Scores on Integration culture dimension

Integration 63.34 65.36

Independent Samples Test: U.S. versus Taiwan

Mean Difference t df Sig. (2-tailed)

Integration �2.03 �1.24 72 0.22

aResponse scale for individual items: 5 ¼ of utmost importance and 1 ¼ of little or no impor-

tance.bResponse scales for individual items: For items 1, 6, 7, and 8, 5 ¼ strongly agree and

1 ¼ strongly disagree; for items 2, 3, 4, and 5, 5 ¼ strongly disagree and 1 ¼ strongly agree.cResponse scale for individual items: 9 ¼ ‘‘Of supreme importance’’ and 1 ¼ ‘‘Of no importance

at all.’’ For items i, j, and k, the raw ratings were reversed in calculating the Integration scores.

Table 1. (Continued)

Performance Standards and Managers’ Adoption of Risky Projects 75

that the expected directional difference between the Taiwan and U.S.samples is found for four out of the six items.

Following Chow, Deng, and Ho (2000), two indices were constructed totest the significance of the Taiwan–U.S. difference on Hofstede’s scale. Onewas the difference between the summed scores for the first and second sets of

CHEE W. CHOW ET AL.76

three questions. The other was the ratio between these two summed scores.Consistent with the U.S. sample being more individualistic, it had a highernumerical value than the Taiwanese sample for both indices (0.35 and 0.02).However, neither difference was statistically significant (t ¼ 0.72, p ¼ 0.47;t ¼ 0.48, p ¼ 0.63). Panel B of Table 1 presents the 8-item individualism/collectivism scale adopted from Earley (1993). The responses are coded suchthat a higher score indicates higher collectivism. This panel indicates thatthe Taiwanese sample is higher on six out of the eight items, and the sum ofthe eight items also is higher for the Taiwanese than the U.S. sample (27.86vs. 25.30). Although the difference between the Taiwanese and U.S. samplesis numerically quite small, it nevertheless is statistically significant (t ¼ 3.33,p ¼ 0.00), and is consistent with the former being more collectivistic.

Finally, Panel C of Table 1 contains 11 items from the Chinese CulturalConnection’s (1987) ‘‘Integration’’ cultural dimension, which they found tobe positively related to Hofstede’s individualism measure. Of these 11 items,the first eight are positively, and the last three are negatively, loaded on theIntegration measure. While the sum of the 11 Integration items’ scores (withthe last three being reverse scaled) has a higher mean for the Taiwanesethan for the U.S. sample (65.36 vs. 63.34), this difference is not statisticallysignificant (t ¼ 1.24, p ¼ 0.22). Hence, across the three measurement scalesfor individualism/collectivism, there is only limited indication that the U.S.nationals in our sample were more individualistic than our sample ofChinese nationals from Taiwan.

4.2. Hypothesis Tests

Table 2 presents the means of variable Choice3, which is based on thesubjects’ product choices in the third (real) experimental period. The numericvalue of this variable was based on assigning to each of the seven possiblechoices the values 1–7, progressing from the product with the lowestvariance (product A) to that with the highest variance (product G). Thus, ahigher mean indicates that subjects in that treatment had chosen proportionally

Table 2. The Means of Choice3 within and between National Origins.

U.S. Taiwan

Low standard 3.33 3.18

High standard 4.84 4.50

Note: Choice3 is the riskiness of subjects’ product choices in the experimental period.

Performance Standards and Managers’ Adoption of Risky Projects 77

more of the higher variance products. Exhibit 1 presents these resultsvisually by plotting the mean values of Choice3 across the subjects’ nationalorigins and performance standard treatments.

H1 states that a higher performance standard would lead to greater risktaking as compared with a lower performance standard. Both Table 2 andExhibit 1 strongly suggest existence of such an effect. More formally, H1 issupported by the ANOVA results in Table 3, which indicate a significantmain effect for performance standard. Within this table, the results labeled‘‘A’’ and ‘‘B’ differ in that the latter also includes the sequence of outcomes

U.S., 4.84

U.S., 3.33

Taiwan,

4.5

Taiwan,

3.183

3.5

4

4.5

5

Low Standard High Standard

Performance Standard

The

mea

ns o

f ch

oice

3

Exhibit 1. Plot of Choice3 against Performance Standard, U.S. versus Taiwan.

Table 3. ANOVA Results for Hypothesis Testing (Dependent Variable:Choice3).

Sum of Squares df F-Value Sig.

A.

National origin 1.15 1 0.36 0.55

Perf. Std. 36.97 1 11.55 0.00

National origin � Perf. Std. 0.16 1 0.05 0.82

Error 224.00 70

B.

National origin 0.62 1 0.19 0.66

Perf. Std. 35.61 1 10.96 0.00

Good/bad 0.51 1 0.16 0.69

National origin � Perf. Std. 0.35 1 0.11 0.74

Perf. Std. � Good/bad 2.64 1 0.81 0.37

Error 221.00 68

CHEE W. CHOW ET AL.78

(good/bad vs. bad/good) in the two trial periods. Both approaches indicatethat only performance standard has a statistically significant main effect(F ¼ 11.55, 10.96; both p values areo0.001).

H2 states that when it is in the firm’s best interests to take on more riskyprojects, Chinese nationals in Taiwan will engage in greater risk-taking thantheir U.S. counterparts. The ANOVA results in Table 3 indicate no signifi-cant main or interaction effects for national origin. Thus, H2 is notsupported.

4.2.1. Additional Analyses

Recall that the exit questionnaire had asked each subject the extent that his/her product choice had been affected by the performance standard, thelinkage of actual performance to pay, and the company’s expressed desirefor managers to pursue breakthrough products. Table 4 reports the meanresponses (on a 1–9 scale) to these questions by each national sample.Table 5 presents the results of separate ANOVAs on these three metrics,with national origin, performance standard, and their interaction as factors.Performance standard level has a significant main effect on the influenceof the performance standard (F ¼ 7.92, p ¼ 0.00), while the interactionbetween national origin and performance standard level is significant for thelinkage to pay (F ¼ 5.38; p ¼ 0.02).

To gain further insights into how the performance standard level hadaffected the subjects’ product choices, we examined their open-endedresponses explaining how they had been affected by this factor. Most subjectsmerely described their decision rules (e.g., ‘‘If the performance standard ishigher, I would take more risks to select the products with high profit’’).Nevertheless, there was a discernable shared concern with the probability

Table 4. Mean Ratings of the Influence of the Performance Standard,Link between Performance and Pay, and Company’s Expressed Desire

for Breakthrough Products.

U.S. Taiwan

Perf. Std. Pay Link Co.

Breakthrough

Desire

Perf. Std. Pay Link Co.

Breakthrough

Desire

Mean 6.65 6.46 3.59 6.16 6.92 3.92

Std. Dev. 2.37 2.42 2.76 1.99 1.61 2.70

Range 1–9 1–9 1–9 1–9 3–9 1–9

Table 5. ANOVA Results for Influences of the Performance Standard,Link between Performance and Pay, and Company’s Expressed Desire

for Breakthrough Products.

Sum of Squares df F-Value Sig.

Dependent variable: Perf. Std.

National origin 4.95 1 1.12 0.29

Treatment 35.08 1 7.92 0.00

National origin � treatment 0.18 1 0.04 0.84

Error 310.18 70

Dependent variable: Pay link

National origin 4.52 1 1.17 0.28

Treatment 11.94 1 3.08 0.08

National origin � treatment 20.85 1 5.38 0.02

Error 271.06 70

Dependent variable: Co. breakthrough desire

National origin 1.68 1 0.22 0.64

Treatment 12.74 1 1.70 0.20

National origin � treatment 7.09 1 9.45 0.99

Error 524.94 70

Performance Standards and Managers’ Adoption of Risky Projects 79

that the performance standard would not be met. Among the 17 Taiwaneseresponses from the low standard treatment, 13 explicitly noted this concern(‘‘I would avoid selecting the products whose profit rate is below 5% withhigh probability;’’ ‘‘The level of performance standard would affect theprobabilities of profit rates that are higher than performance standard.Therefore, performance standard would affect my product selection’’).Seventeen of their 20 countrymen in the high standard case noted the sameconcern (‘‘Because of the existence of a performance standard, I would thinkabout the risk (probability) when I made decisions’’).

Out of 17 U.S. subjects facing a low performance standard, 10 out of the13 who had answered the open-ended question reported focusing on the riskof failing to meet the performance standard (‘‘I looked to see what was theprobability of getting at least the performance standard profit rate’’; ‘‘Basedon likelihood of greater than 5% profit and moderate risk of achieving 15%or more’’). Of the 20 U.S. subjects in the high performance standard case,12 also named this as their major concern (‘‘I wanted to evaluate the prod-ucts to see which had the greatest probability of a 25% profit’’). Amongthe Taiwanese and U.S. subjects who did not explicitly mention risk, thecommon concern was achieving the performance standard (‘‘The higher the

CHEE W. CHOW ET AL.80

performance standard, the more pressure there is to make a good decision,’’‘‘You have to select a product that exceeds or meets your performancestandard’’).

5. SUMMARY AND DISCUSSION

This experimental study has tested the effect of performance standard level,per se, on employees’ choice among risky projects. Based on goal settingtheory and prospect theory, it hypothesizes that a high performance stand-ard would induce greater risk taking by employees as compared with a lowperformance standard. It further hypothesizes that due to their purportedhigher collectivism (or, conversely, lower individualism), Chinese ascompared with U.S. nationals would voluntarily take more risk to benefitthe company at the expense of their personal interests.

Our findings strongly supported the predicted relative effects of high andlow performance standards. Across both national samples, subjects under ahigh performance standard selected significantly riskier projects than theircounterparts in the low standard treatment. Responses to open-ended ques-tions revealed that subjects facing both low and high performance standardswere very much focused on the risk of failing to attain the standard. Thiswas despite their pay being totally determined by actual outcome, ratherthan outcome relative to the standard. We interpret this finding as evidencethat the performance standard, per se, influences subjects’ risk taking.

We did not find significant differences in the risky choices of the twonational samples. While our data are insufficient for determining the reasonfor this finding, we believe that it can be attributed, at least in part, to theinteraction of two factors. First is the weakness of the treatment creating afirm–individual interest conflict, as reflected in the subjects’ lack of consid-eration for the firm’s desire for breakthrough products (thus the desirabilityof risk taking). Compounding this is the relative lack of difference in the twonational samples’ degrees of individualism. This lack of difference reinforcecalls for cross-national research to measure the participants’ cultural values(e.g., Gernon & Wallace, 1995; Harrison & McKinnon, 1999), instead ofsimply assuming that purported differences exist. In addition to the potentialfor a sample to be non-representative of the larger population, the increas-ing globalization of economic and cultural exchanges could be bringingabout a convergence of work-related values. In the case of Taiwan, its closeeconomic ties to the U.S. over the past several decades could have inducedan acculturation to the latter’s work-related values, thereby blunting the

Performance Standards and Managers’ Adoption of Risky Projects 81

effects of cultural differences in management settings (HassabElnaby &Mosebach, 2005; Venezia, 2005). Further insights into the acculturationprocess and more broadly, the nature and magnitude of cultural effects willrequire engaging subjects from more divergent national cultures.

Overall, the most important implication of our study is that performancestandards, per se, can affect employees’ risk taking behavior at work. Giventhe importance of innovation for success and survival in an increasinglycompetitive and global marketplace, this finding can inform the designof systems for supporting innovation initiatives. At the same time, it isimportant to recognize the exploratory nature of the current study, hencethe need for efforts to test the robustness of its findings. Like all laboratorystudies, this study’s findings are a function of the experimental design,including nature of the task, the subjects, the salience ascribed by subjects tothe experimental treatments, and the values of parameters (e.g., relationbetween pay and performance). There is much room for expanding andrefining the experimental design. For example, the lack of salience for thecompany’s desire for breakthrough products may be due to the fictitiousnature of the company, thus a lack of identification with its objectives.(In contrast, the performance standards may have been more salient becausethey directly affected the subjects’ personal performance evaluation.) Therealso is room for examining the nature of effects in a multi-period setting.When individuals make choices across multiple periods, they may view theoutcomes across time as forming a portfolio for risk diversification. Also,performance standards often are used in tandem with performance-basedcompensation systems, wherein total compensation is tied to performancevis-a-vis the standard. It is desirable to study how these elements jointlyaffect risk taking by employees, and both expectancy and agency theoriescan provide guidance to such undertakings. And in light of the increasinguse of teamwork in today’s organizations, it is worthwhile to examine if theeffects of performance standards differ between individual and teamsettings. Even more broadly, management systems generally have manyelements that work together as a whole, with some elements complementing,and others substituting, for one another (Chow, Kato, & Shields, 1994).By expanding the scope of analysis to include more facets of managementsystems, one can increase assurance that the findings are not subject tobiases from important omitted variables.

Finally, there is room to triangulate the investigation by using multiplemethods (e.g., field experiments, in-depth case studies, surveys, and analysisof archival records). For example, while laboratory experiments are advan-tageous for determining causal relationships, they only allow a limited

CHEE W. CHOW ET AL.82

number of variables to be examined simultaneously. In comparison, surveyspermit many more variables to be included, but they tend to precludedetailed investigation of phenomena. Case studies are particularly suited toin-depth exploration of processes as well as the ‘‘how’’ and ‘‘why’’ ofphenomena, but their generalizability tends to be limited by small feasiblesample sizes. Given that each research method has its relative strengths andweaknesses, the use of multiple methods will help to increase the reliabilityand richness of findings (Birnberg, Shields, & Young, 1990). Such effortsare warranted in view of the topic’s importance to practice.

NOTES

1. The Taiwanese experimental materials were stated in New Taiwanese dollars(NT$). Taiwanese masters graduates’ starting salaries are about one third those ofour U.S. graduates. Using the exchange rate of US$1 ¼ NT$30 approximately, themagnitudes of numbers in NT$ were ten times those in the U.S. instrument.2. Because of the discrete probability plots used in our examples and experimental

materials (for ease of understanding by the subjects), we were not able to make allseven distributions’ expected outcomes exactly 15%. The actual range of expectedvalues was 14.85–15.08%, with product B having the highest expected value. Thismay have created a slight bias in favor of this low risk product, but as will be reportedlater, we still found a significant main effect due to performance standard level.3. Our use of variance as a proxy for project risk is based on the voluminous

literature in finance dealing with decision making under risk and uncertainty. It alsois analogous to the approach of Chow and Haddad (1991) and Sayre et al. (1998),where outcome variability via operating leverage was used as the measure of projectrisk. Our original intent was to create a set of probability distributions with equallyspaced variances, centered on the variance of the uniform distribution. Unfortu-nately, we were unable to achieve this result even after exhaustive efforts. As a result,we treat the riskiness of the subjects’ product choices as an ordinal, rather than aratio scale. The hypothesis testing results were not qualitatively affected by whetherwe used the ordinal or ratio representations of project risk.4. The subjects were informed that their experimental earnings would be converted

into cash pay using a ratio of $5,000 in experimental earnings to $1.5. A reviewer has noted that with all the products having the same expected value,

a risk neutral firm would be indifferent among them. We acknowledge that it wouldhave been more realistic to ascribe higher expected values to products with higherrisk and as such, holding the expected value constant could bias our results againstfinding significant treatment effects. In this regard, our design was aimed at reducingthe effects of omitted factors. If we had conferred higher expected values to higherrisk projects, then the relation that we created between risk and return could havedriven the results depending on how much ‘‘compensation’’ we had built in forhigher risk. With the probability distributions we provided to the subjects, there stillwas a conflict of interests between individual managers and the firm. The managerswere not compensated for adopting higher-risk projects with higher expected returns,

Performance Standards and Managers’ Adoption of Risky Projects 83

yet the firm could only have increased probabilities of very high outcomes by takingon more risk. As reported in the next section, despite facing equal expected values,our subjects still adopted more risky products when they were faced with higherperformance standards.6. The U.S. subjects earned an average of $15.125 while the Taiwanese subjects’

average earning was US$6.18. This proportional relationship approximates the1:3 ratio between graduates’ beginning salaries in the two countries.7. We did not ask about product risk because all subjects faced the same set of

probability distributions. As reported in the next section, the subjects’ open-endedresponses indicated that subjects in both performance standard treatments werehighly conscious that risk was present in their product choices.

ACKNOWLEDGMENTS

An earlier version of this paper was presented at the American AccountingAssociation’s Management Accounting Section 2006 midyear conference.The authors are indebted to the discussant, David Otley, participants at theconference, and May Zhang for their helpful comments. They also thank thereviewers and editor for their guidance in improving the paper.

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APPENDIX: EXPERIMENTAL MATERIALS (HIGH

PERFORMANCE STANDARD VERSION)

An Overview Of Overall Procedures

In this business simulation, you will be asked to make decisions in the role ofa product manager working for XYZ Company. You will be paid based onthe outcomes of your decisions in the simulation. The entire simulation willhave three major stages:

Stage One:

You will be asked to read through a description of theXYZ Company, and the nature of a product manager’sjob in the company. Then, you will work throughpractice exercises to solidify your understanding of thisinformation.

Performance Standards and Managers’ Adoption of Risky Projects 89

Stage Two:

The simulation will proceed in the following manner: Youwill go through a total of three experimental periods.The first two are practice periods to help youunderstand the procedures and how your decisions willproduce outcomes. You will not be paid for these twoperiods. The third period is the real experiment and youwill earn cash pay based on the outcome of yourproduct selection in this period.

(1) At the beginning of the first period, you will be given some informationand then will be asked to select a product for manufacturing andmarketing.

(2) After you have written down your product selection on a form providedin your packet, you will come to front of the class and select a piece ofpaper from a bag. This piece of paper will indicate the actual return(profit rate) on the project you selected from the information in step (1).

(3) You will write down on the product selection form the actual return(profit rate) on the product you had selected. (This would be thenumber written on the piece of paper you had pulled from the bag.)

(4) You will return to your desk and use that profit rate to calculate yourperformance (outcome to XYZ Company) and amount of pay.

(5) The second practice period will begin. You will repeat steps (1)–(4) againfor this second trial period.

(6) After completing the second practice period, you will begin the realperiod by repeating steps (1)–(4). Your actual pay ($) will be based onyour decision in this period.

Stage Three:

After completing the real period, you will be asked tocomplete a questionnaire. When you hand in yourmaterials to the researcher, he will verify how much youhave earned in the simulation based on your decision inthe real period. Keep the participant ID number slipand give it to the researcher when you have finished thequestionnaire. Then the researcher will pay you.

Please note that your materials for the simulation will have a participant ID.The only purpose of this ID is to keep track of materials in the simulation,so that the correct amount of cash pay can be distributed to the rightindividuals. You are not asked to write down your name anywhere, andno attempt will be made to link the simulation outcomes with specificindividuals.

CHEE W. CHOW ET AL.90

Participant ID:_____

Background Information

This business simulation will ask you to make decisions as a productmanager working for XYZ Company. To prepare for the simulation, pleasecarefully read through the following description of the company and the jobof their product managers.

XYZ Company is in the business of manufacturing and marketing noveltyitems. Because of the fad nature of its products, the entire life span of thecompany’s products, from selection for manufacturing/marketing to expi-ration of all market appeal, is 1 year.

The task of selecting products to manufacture and market is delegated to anumber of product managers. Each product manager oversees his/her ownstaff of product developers, whose job is to design or seek out potential newproducts. At the beginning of each year, the product development staff foreach product manager will propose to their manager seven new products.The proposal for each product will include the staff’s estimate of the prob-ability that specific rates of return (profit rate) can be achieved by theproduct over its 1 year life.

At the beginning of each year, each product manager is allocated $1,000,000for that year. He/she then uses this allocated amount to manufacture andmarket one product out of the seven that his/her staff proposes (all productscost $1,000,000 to manufacture and market). Because of the transience oftastes, proposed products that are rejected in 1 year typically would have nomarket potential in a later year. Thus, each product manager’s staff has topropose seven new products each year.

As a product manager for the XYZ Company, you are paid an annual basesalary of $125,000 plus a profit-based ‘‘adjustment’’ in each funding cycle.This adjustment can be positive or negative, and is equal to 50% of theamount by which a product manager’s realized profit exceeds or falls shortof his performance standard.

To illustrate with some simple numbers, suppose that product manager Ahad been allocated $1,000 in a given yearly cycle, and that he had used thesefunds to manufacture and market a product. Assume that his performance

Performance Standards and Managers’ Adoption of Risky Projects 91

standard is set at a profit rate of 10% on the amount of his annual allo-cation. Further, suppose that the actual profit rate on the project is 15%.The total amount of profit from this product would be $150 ($1,000� 15%).After subtracting 10% of $1,000, or $100, the excess profit generated byproduct manager A is $50. Product manager A would be paid, on top of hisbase salary, an additional $25 ( ¼ 50% of the $50 overage). Alternately,suppose that the actual profit rate achieved on the product was 5%, suchthat the total amount of profit generated was $50. In this case, the totalprofit would be $50 short of the required 10% return on the $1,000, andmanager A would be paid his base salary minus $25 ( ¼ 50% of the $50shortfall). The Company does not impose an upper limit to the additionalpay that a product manager can earn by generating profit in excess of his/herperformance standard. It also does not limit the amount of negative profit-based adjustment that a product manager may face.

The XYZ Company has been in existence for a number of years and onaverage, its products have produced profits equal to 15% of what it costs tomanufacture and market them. But because the industry is intensely com-petitive and getting more so, the Company believes that if it is to survive andprosper, it must come out with a number of breakthrough products thatcan produce significantly higher levels of profits than this historical level.Having a number of products that stand out in the market will help toincrease the company’s name recognition among customers, increase thewillingness of retailers to stock and prominently display its products, andalso strengthen the Company’s financial ability to support development andimprovement. The Company recognizes that trying to achieve breakthroughprofit performance likely will require taking risks. But because the Companycan diversify away much of the risk of individual products due to having anumber of product managers, it is not concerned with individual managersselecting highly risky products.

� � � � � � � � � �

Illustration of Products’ Profit Rate Distributions

As a project manager, each year you will select one of seven productsproposed by your product development staff. The examples below (ProductsX, Y, and Z) illustrate the three general forms that a product’s possible

CHEE W. CHOW ET AL.92

profit rates may be distributed and each rate’s probability of being realized(in other words, a probability distribution). Product X’s probability distri-bution is often referred to as a bell-shaped curve, with one possible outcome(in the middle) being the most likely to be realized, and outcomes on boththe higher and lower sides of it being progressively less likely. Product Y’sprobability distribution is called a uniform distribution. All possible out-comes are equally likely. Finally, Product Z’s distribution of possible profitrates is called a bi-modal distribution. The values in the middle are the leastlikely to be realized, while outcomes toward the higher and lower extremesare progressively more likely to be realized. The profit rate distributions ofProducts X and Y are on the next page. The distribution for Product Z is onthe page following the next page.

As you probably already know, the total of all the possible outcomes’probabilities is 100%. In the graph for each of the products, the horizontalaxis (the line at the bottom) will be clearly labeled with each of the possibleoutcomes in terms of profit rates, only one of which will be realized. On thevertical axis (the vertical line on the left hand side) will be the probability ofeach specific profit rate being realized.

Product X

0%

1%

2%

3%

4%

5%

6%

7%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

11%

12%

13%

14%

15%

16%

17%

18%

19%

20%

21%

22%

23%

24%

25%

26%

27%

28%

29%

Profit Rates

Pro

bab

ilit

y

Performance Standards and Managers’ Adoption of Risky Projects 93

Product Y

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

1% 2% 3% 4% 5% 6% 7% 8% 9% 10%

11%

12%

13%

14%

15%

16%

17%

18%

19%

20%

21%

22%

23%

24%

25%

26%

27%

28%

29%

Profit Rates

Pro

bab

ility

Product Z

0%

1%

2%

3%

4%

5%

6%

7%

1% 2% 3% 4% 5% 6% 7% 8% 9% 10%

11%

12%

13%

14%

15%

16%

17%

18%

19%

20%

21%

22%

23%

24%

25%

26%

27%

28%

29%

Profit Rates

Pro

bab

ility

To further illustrate, suppose that you wanted to ascertain the probabilityof a particular outcome (say, a 10% profit rate) being realized by a givenproduct. If you looked at the profit rate probability distribution for Product

CHEE W. CHOW ET AL.94

X, the answer would be 4%. For Product Y, the answer would be approx-imately 3.5%, and for Product Z, the answer would be 3%. If what youwanted to know is the probability that the realized profit rate would be atleast some level (i.e., that level and above), you would add up the individualprobabilities of each profit rate from that profit rate and up. Again, let usassume that you wanted to know the probability of the realized profit ratebeing at least 10%. For Product X, the answer would be 74%. (This is thesum of the probability of the 10% profit rate, plus that of the 11% profitrate,y , plus the probability of the 29% profit rate.) For Product Y, theanswer would be approximately 68.5%, and for Product Z, the answerwould be 60.5%. On the other hand, if what you are interested in is theprobability that the realized outcome would be below some level, you wouldadd up the individual probabilities of each profit rate below (to the left of)this outcome level.

Practice Exercises

For the results of this simulation to be meaningful, it is crucial that you fullyunderstand the role of product managers in the XYZ Company, how theirdecisions affect the company, and how they are evaluated and paid. Pleasework through the following two practice examples when you feel confidentthat you fully understand all of the information provided above. Please feelfree to re-read any of the preceding materials and to take all the time thatyou need. You also may refer back to the preceding materials while workingon the practice examples.

First, to make sure that you fully understand the information that yourproduct development staff will provide you each period, please workthrough the follow practice exercises for the three types of probabilitydistributions. Please write down your answers in the spaces provided.

The Probability Distribution for Product X:

(a)

What is the probability that this product will produce an actual profitrate equal to 10%?__________

(b)

What is the probability that this product will produce an actual profitrate equal to or above 20%?__________

(c)

What is the probability that this product will produce an actual profitrate below 5%?__________

Performance Standards and Managers’ Adoption of Risky Projects 95

The Probability Distribution for Product Y:

(a)

What is the probability that this product will produce an actual profitrate equal to 10%?__________

(b)

What is the probability that this product will produce an actual profitrate equal to or above 20%?__________

(c)

What is the probability that this product will produce an actual profitrate below 5%?__________

The Probability Distribution for Product Z:

(a)

What is the probability that this product will produce an actual profitrate equal to 10%?__________

(b)

What is the probability that this product will produce an actual profitrate equal to or above 20%?__________

(c)

What is the probability that this product will produce an actual profitrate below 5%?__________

When you are satisfied that you have the correct answers to these questions,please open the next page (which is taped shut) to check the answers. If someof your answers above differ from the correct answers, please go back toexamine why this had occurred.

Correct Answers to the Practice Exercises

Product X:

(a)

4% (This is the probability indicated for the profit rate of 10%.) (b) 30% (This is the sum of the probabilities for the profit rate of 20%

as well as all the profit rates above it, i.e., to its right.)

(c) 10% (This is the sum of the probabilities of all the profit rates below 5%.)

Product Y:

(a)

3.5% (This is the probability indicated for the profit rate of 10%.) (b) 35% (This is the sum of the probabilities for the profit rate of 20%

as well as all the profit rates above it, i.e., to its right.)

(c) 14% (This is the sum of the probabilities of all the profit rates below 5%.)

Product Z:

(a)

3% (This is the probability indicated for the profit rate of 10%.) (b) 42.5% (This is the sum of the probabilities for the profit rate of 20%

as well as all the profit rates above it, i.e., to its right.)

(c) 21.5% (This is the sum of the probabilities of all the profit rates below 5%.)

CHEE W. CHOW ET AL.96

The next two practice examples focus on how your product selection de-cisions will translate into realized profits for the Company, as well as thetotal amount that you will be paid by the Company for this performance.

For both practice examples below, please assume that as a product manager,you are paid an annual base salary of $100, plus an adjustment (+ or �)equal to 50% of the amount by which your realized profit exceeds or fallsshort of your performance standard. Your performance standard is 10% ofthe amount of funds you have been allocated for the year. The magnitudesof the numbers in these examples are not the ones that will be used inthe experiment. They have been chosen to simplify the necessary calcula-tions.

Practice Example One. Suppose that you had been allocated $1,000 for agiven year. The actual profit rate you had achieved from the product youselected was 5%.

Based on this information, what would be the total profit you had generatedfor the Company for this year? $_________

How much total pay would you receive from the Company for this year?$__________________.

Practice Example Two. Suppose that you had been allocated $1,000 for agiven year. The actual profit rate you had achieved from the product youselected was 15%.

Based on this information, what would be the total profit you had generatedfor the Company for this year? $_________

How much total pay would you receive from the Company for this year?$__________.

When you are satisfied that you have the correct answers to these questions,please open the next page (which is taped shut) to check the answers. If someof your answers above differ from the correct answers, please go back toexamine why this had occurred.

Performance Standards and Managers’ Adoption of Risky Projects 97

Correct Answers to the Practice Examples

Practice Example One. Suppose that you had been allocated $1,000 for agiven year. The actual profit rate you had achieved from the product youselected was 5%.

Based on this information, what would be the total profit you had generatedfor the Company for this year?

ANSWER: $50 ( ¼ 5% times $1,000).

How much total pay would you receive from the Company for this year?ANSWER: $75.

Explanation: You would be paid your base salary of $100, minus an ad-justment equal to 50% of your profit shortfall. The amount of the shortfallis $50, which is the difference between the actual profit realized of $50 (5%times $1,000) and your performance standard of 10% of $1,000, or $100.Thus, in total, your pay would be $100 – (50% of $50 shortfall), or $75.

Practice Example Two. Suppose that you had been allocated $1,000 for agiven year. The actual profit rate you had achieved from the product youselected was 15%.

Based on this information, what would be the total profit you had generatedfor the Company for this year? $150 ( ¼ 15% times $1,000).

How much total pay would you receive from the Company for this year?ANSWER: $125.

Explanation: You would be paid your base salary of $100, plus an adjust-ment equal to 50% of your profit overage. The amount of the overage is$50, which is the difference between the actual profit realized of $150 (15%times $1,000 and your performance standard of 10% of $1,000, or $100.Thus, in total, your pay would be $100+(50% of the $50 overage), or $125.

� � � � � � � � � �

Now we are almost ready to start the simulation. Before we do that, pleasego back and carefully re-read all the background information about theCompany, your role as a product manager, how your product choices affectthe Company as well as your own pay. We will not start the simulation untilyou have done so.

� � � � � � � � � �

CHEE W. CHOW ET AL.98

Participant ID:______

Business Simulation

Please assume the role of a product manager of the XYZ Company. In eachof three periods, you will be asked to make decisions in this role. There willbe two practice periods and a ‘‘real’’ period. You will be paid cash based onthe outcome of your decision in the ‘‘real’’ period.

All of the preceding information, including product managers’ responsibil-ities, the company’s situation and desires, and how product managers areevaluated and paid, will apply. The only difference is that your base pay ineach yearly period is $125,000 rather than the $100 used in the practiceexamples. The 50% adjustments to pay for excess or deficient net income arethe same as in those examples.

At the end of the third period (‘‘real’’ period), your total pay earned in thethird period will be converted into cash pay at a rate of $5,000 in exper-imental pay equals $1. (Thus, experimental pay of $50,000 will convert intocash pay of $10.) Please permit us to emphasize that for the results of thissimulation to be meaningful, it is absolutely necessary that you act in thissimulation as if you are facing a situation in real life. Please do your best todo so. Thank you.

Information for Period One

The company has allocated $1,000,000 to you for product manufacturingand marketing in this period. Your base pay is $125,000 and your per-formance standard is a 25% profit rate.

Your product development staff has proposed seven new products forthis period. The probability distributions of profit rates for these productsare in Products A1–G1. These probability distributions are stapled in apacket named ‘‘Probability Distributions of Profit Rates for Products inPeriod One.’’

Probability

Di

-

0.0

2

0.0

4

0.0

6

0.0

8

0.1

0

0.1

2

0.1

4

Probability

Perfo

rmance

Sta

- .02

.04

.06

.08

.10

.12- .01

.02

.03

.04

.05

.06

.07

stributio

nsofProfitRates

forProducts

inPerio

dO

Pro

du

ct A

1%2%3%4%5%6%7%8%9%

10%11%12%13%14%15%16%17%18%19%20%21%22%23%24%25%26%27%28%

Pro

fit Ra

tes

ndardsandManagers’

Adoptio

nofRisk

yProjects

Pro

du

ct B

0 0 0 0 0 0

1%2%3%4%5%6%7%8%9%

10%11%12%13%14%15%16%17%18%19%20%21%22%23%24%25%26%27%28%

Pro

fit Rates

Probability

Pro

du

ct C

0 0 0 0 0 0 0

1%2%3%4%5%6%7%8%9%

10%11%12%13%14%15%16%17%18%19%20%21%22%23%24%25%26%27%28%

Pro

fit Rates

Probability

ne

29%

99

29%29%

Pro

du

ct D

-

0.01

0.02

0.03

0.04

0.05

0.061%2%3%4%5%6%7%8%9%

10%11%12%13%14%15%16%17%18%19%20%21%22%23%24%25%26%27%

Pro

fit Rates

Probability

Pro

du

ct E

0. 0. 0. 0. 0. 0. 0.

Probability

0 0 0 0 0 0 0 0 0

ProbabilityCHEEW.CH

100

- 01 02 03 04 05 06 07

1%2%3%4%5%6%7%8%9%

10%11%12%13%14%15%16%17%18%19%20%21%22%23%24%25%26%

Pro

fit Rates

Pro

du

ct F

- .01

.02

.03

.04

.05

.06

.07

.08

.09

1%2%3%4%5%6%7%8%9%

10%11%12%13%14%15%16%17%18%19%20%21%22%23%24%25%26%

Pro

fit Rates

28%29%

OW

ET

AL.

27%28%29%

27%28%29%

Performance Standards and Managers’ Adoption of Risky Projects 101

Product G

-

0.02

0.04

0.06

0.08

0.10

0.121% 2% 3% 4% 5% 6% 7% 8% 9% 10%

11%

12%

13%

14%

15%

16%

17%

18%

19%

20%

21%

22%

23%

24%

25%

26%

27%

28%

29%

Profit Rates

Pro

bab

ility

Participant ID:______

Decision and Recording Form for Period One (Same for Periods Two and

Three)

I have decided as follows (Please check the box representing the product ofyour choice):

Product I will manufacture and market:

A1 B1 C1 D1 E1 F1 G1

Now please walk to the front of the class and pull a piece of folded paperfrom the bag of your project choice. The bag for each product contains 100pieces of folded paper, with proportions corresponding to the probabilitiesprovided by your product development staff. (Thus, a given profit rate thathas a 9% probability of occurring has nine folded pieces of paper containingthis particular profit rate.)

Please open the piece of folded paper and show it to the researcher. Writedown in the space below the realized profit rate that is written on this pieceof paper.

Realized profit rate: __________%

CHEE W. CHOW ET AL.102

Then, please return to your seat and complete the rest of this form usingyour performance standard and how your pay is determined by theCompany.

The amount of actual profit my selected product has generated for the Company: $_____________________________________

The total amount I will be paid by the Company: $_____________________________________

After you have completed your calculations for this period, please proceedto the next period.

Participant ID:______

Exit Questionnaire

DIRECTIONS: Please complete the questionnaire below, answering eachquestion as accurately and honestly as you can. Remember, all answers areanonymous and cannot be traced back to you in any way.

1.

Two digit identification number (on front of brown packet) _________ 2. Gender (circle one): M/F 3. Age ________ 4. Years of full time equivalent working experience _______ years

For each of the following statements, please circle the number that bestdescribes your actions and beliefs in the experiment.

(1)

In the ‘‘real’’ period of this business simulation, when you made de-cisions in the role of a product manager in XYZ Company, to whatextent did you act as if you were facing a situation in real life? (Pleasecircle one number.)

1 2 3 4 5 6 7 8 9Not at all Completely

Performance Standards and Managers’ Adoption of Risky Projects 103

(2)

To what extent did your performance standard affect your productselection for the real period?

1 2 3 4 5 6 7 8 9Not at all Completely

(3)

If your numerical answer to (2) was a number greater than one, pleasebriefly describe how the performance standard had affected yourproduct selection decision:

______________________________________________________________________________________________________________

(4)

To what extent was your product selection for the real period influ-enced by the way that your pay depended on the actual outcome fromyour selected product?

1 2 3 4 5 6 7 8 9Not at all Completely

(5)

If your numerical answer to (4) was a number greater than one, pleasebriefly describe how your product selection decision was affected byhow the company determined your pay based on the actual outcomefrom your selected product:

______________________________________________________________________________________________________________

(6)

To what extent did your company’s desire for break through/stand outproducts affect your product selection for the real period?

1 2 3 4 5 6 7 8 9Not at all Completely

(7)

If your numerical answer to (6) was a number greater than one, pleasebriefly describe how the company’s desire for break through/stand outproducts had affected your product selection decision:

______________________________________________________________________________________________________________

CHEE W. CHOW ET AL.104

(8)

Below are six items related to the nature of one’s job. Please indicatehow important each of them is to you personally. (Please check a box

for each item.)

Of

littl

e or

no

Impo

rtan

ce

Of

littl

e Im

port

ance

Of

mod

erat

e Im

port

ance

Ver

y Im

port

ant

Of

utm

ost

Impo

rtan

ce

Have a job that leaves you sufficient time for your personal or family life. Have considerable freedom to adopt your own approach to the job. Have challenging work to do-work for which you can get a personal sense of accomplishment. Fully use your skills and abilities on the job. Have good physical working conditions (good ventilation and lighting, adequate workspace, etc.) Have training opportunities (to improve your skills or to learn new skills).

(9)

Please indicate how important each of the following is to youpersonally. (Please check a box for each item.)

Of

no

Impo

rtan

ce a

t al

l

Of

Mod

erat

e Im

port

ance

Of

Supr

eme

Impo

rtan

ce

(1) (2) (3) (4) (5) (6) (7) (8) (9) Tolerance of others Harmony with others Solidarity with others Non-competitivenessTrustworthinessContentedness with one'sposition in life Being conservativeA close, intimate friend Filial piety (Obedience to parents, respect for parents, honoring of ancestors, financial support of parents) PatriotismChastity in women

Performance Standards and Managers’ Adoption of Risky Projects 105

(10)

Please indicate your agreement or disagreement with each of thefollowing statements. (Please check a box for each item.)

Stro

ngly

D

isag

ree

Dis

agre

e

Neu

tral

Agr

ee

Stro

ngly

A

gree

Employees like to work in a group rather than by themselves. If a group is slowing me down, it is better to leave it and work alone. To be superior, a man must stand alone. One does better work working alone than in a group. I would rather struggle through a personal problem by myself than discuss it with myfriends. An employee should accept the group's decision even when personally he or she has a different opinion. Problem solving by groups gives better results than problem solving by individuals. The needs of people close to me should take priority over my personal needs.

This is the end of the questionnaire. Thank you.

THE EFFECTS OF

ORGANIZATIONAL CULTURE

ON BUDGETARY CONFLICT:

INTEGRATIVE VERSUS

DISTRIBUTIVE CONFLICT

RESOLUTION

Nabil Elias and William W. Notz

ABSTRACT

Like conflict in general, budgetary conflict is perceived by conflicting

parties as a zero-sum game or distributive: one party’s gain is the other

party’s loss. We identify an organizational culture that promotes this view

as ‘‘traditional.’’ We propose that changing certain elements of organ-

izational culture is sufficient to produce more integrative, nonzero-sum

outcomes. We call this changed organizational culture ‘‘empowering.’’

We propose and test the effects of an empowering organizational culture

(EOC) in contrast to the traditional organizational culture (TOC).

We hypothesize that an EOC would produce more integrative conflict

resolution than the typical TOC. Based on our review of the literature, we

identify two elements of the EOC that are essential in producing more

Advances in Management Accounting, Volume 16, 107–140

Copyright r 2007 by Elsevier Ltd.

All rights of reproduction in any form reserved

ISSN: 1474-7871/doi:10.1016/S1474-7871(07)16003-2

107

NABIL ELIAS AND WILLIAM W. NOTZ108

integrative solutions to budgetary conflict. The two elements that we

simultaneously manipulate are the superior’s empowering style (or lack

thereof) as reflected in encouragement to freely negotiate, and the

superior’s intervention process in failed negotiations (a process that

encourages the search for integrative solutions and avoids imposed

compromises that dampen the desire to negotiate). Using a laboratory

experiment, 84 subjects forming 42 dyads negotiated the allocation of

discretionary budgets face-to-face. The results of the experiment confirm

our hypotheses that the EOC produces more integrative budget negoti-

ation outcomes, greater convergence, and greater satisfaction with the

outcome than TOC.

1. BUDGETARY CONFLICT AND THE PRODUCTION

OF INTEGRATIVE RESOLUTION

Budgetary conflict is inherent in many organizations and has been studiedfrom different perspectives including budgetary slack in hierarchical rela-tionships (e.g., Schiff & Lewin, 1970; Chow, Cooper, & Waller, 1988), thesetting of managerial goals (Etherington & Tjosvold, 1992), the effects ofinformation asymmetry on the budgeting negotiation process and budgetslack (Fischer, Fredrickson, & Peffer, 2002a), and the effects of usingbudgets for performance evaluation (Fischer, Maines, Peffer, & Sprinkle,2002b). Negotiation research in accounting is relatively recent (Dejong,Forsythe, Kim, & Uecker, 1989; Elias, 1990; Chalos & Haka, 1990; Anctil &Dutta, 1999; Kachelmeier & Towry, 2002; Ghosh, 2000) and negotiationresearch in budgeting is fairly limited (Fischer et al., 2002a, 2002b). The costof conflict in organizations can be considerable (Slaikeu & Hasson, 1998),and most of that cost is not explicit. Like conflict in general, budgetaryconflict is perceived by conflicting parties as a zero-sum game or distrib-utive: one party’s gain is the other party’s loss. We identify an organiza-tional culture (OC) that promotes this view as ‘‘traditional.’’ We proposethat changing certain elements of OC is sufficient to produce more integra-tive, nonzero-sum outcomes. We call this changed OC ‘‘empowering.’’Managing conflict to increase integrative resolution in organizations has notreceived much attention in the literature – much less managing budgetaryconflict. Conflict does not have to be costly; on the contrary, it could stim-ulate the search for integrative agreements (Baron, 1990; Deutsch, 1990). Anintegrative agreement occurs when a resolution is found that produces high

The Effects of Organizational Culture on Budgetary Conflict 109

joint utility for the parties in conflict. We propose and test the effects of anempowering organizational culture (EOC) in contrast to the traditional or-ganizational culture (TOC). We hypothesize that an EOC would producemore integrative conflict resolution than the TOC. Based on our review ofthe literature, we identify two major elements of the EOC that are essentialin producing more integrative solutions to budgetary conflict. Compared tothe TOC, the two major elements that we simultaneously manipulate are thesuperior’s empowering style, as reflected in encouragement to freely nego-tiate, and the superior’s intervention process in failed negotiations (a processthat encourages the search for integrative solutions and avoids imposedcompromises that dampen the desire to negotiate). Integrative resolutionof budgetary conflict can decrease explicit and implicit costs of conflict andyield positive outcomes. This paper focuses on how changes in thesetwo major elements of the TOC may produce such integrative budgetarybehavior.

Organizational scholars assert that conflict in organizations is bothinevitable and prevalent (Thomas, 1992; Bazerman and Neale, 1983).Axelrod (1984) suggests that coordination between conflicting organiza-tional subunits can be achieved by allowing them to bargain directly witheach other. Accordingly, we propose that a managerial style that empowerssubunits to negotiate their conflict and expects them to resolve their conflicton their own is likely to reach more integrative solutions as in the EOCtreatment. On the other hand, in a TOC when negotiators fail to reach anagreement, they expect intervention by superiors who usually impose acompromise solution. The expectation of a compromise solution naturallydiscourages the search for integrative solutions and dampens the motivationto negotiate in the first place. We simultaneously manipulate these twoelements of OC in our experiment: superior’s managerial style (empoweringvs. traditional) and superior’s intervention process in failed negotiations,and treat them as one composite variable. We examine the effect of twoOCs: the ‘‘empowering culture’’ and the ‘‘traditional culture’’ on negotiationoutcomes of allocating discretionary budget amounts in a laboratoryexperiment setting.

The remainder of this paper is organized into five sections. Section 2reviews the development of the budgetary OC variable and provides a dis-cussion of two types of negotiation outcomes: distributive and integrative.Section 3 develops the hypotheses. Section 4 describes the experimentaldesign. Section 5 provides the results of the experiment. Finally Section 6provides a discussion of the results and conclusions, including limitations ofthe study.

NABIL ELIAS AND WILLIAM W. NOTZ110

2. THE CONFLICT ENVIRONMENT AND

NEGOTIATION OUTCOME

2.1. Organizational Culture

The conflict environment is inextricably linked to OC. Even though there isconsiderable debate about the meaning of OC (Martin, 1991; Ott, 1989;Morrill, 1995), Hofstede (2003) and Hofstede, Neuijen, Ohayv, and Sanders(1990) have demonstrated empirically that OC can be used to meaningfullydescribe differences between organizations. The OC surrounding conflictand leadership are inseparable; how leaders view the organization largelydetermines the OC surrounding conflict (Schein, 1992). We focus on aspectsof OC surrounding conflict related to leadership style and interventionprocess and hypothesize that different OCs are likely to affect the wayorganizations deal with budget conflict.

Kolb and Sheppard (1985) suggest that organizations dominated by hi-erarchical authority would likely discourage bargaining activity betweensubunits. If allowed to negotiate, parties in a traditional OC would likely feelless empowered to resolve the conflict. On the other hand, an empoweringOC would generally be characterized by norms of teamwork, decentralizedhierarchical structures, consultation, and negotiation. In such organizations,negotiation and bargaining are encouraged and desirable, reflecting seniormanagement’s belief in the strength of negotiated solutions (Schein, 1992).The empowerment would allow conflicting parties to deal directly with theirconflict rather than rely on an expected compromise by their superior.

We treat the superior’s managerial style and the superior’s interventionprocess in failed negotiations as one composite OC variable, because weanticipate that the presence of one is unlikely in the absence of the other.For example, it would be inconsistent that an OC that empowers subunitnegotiation would at the same time use an intervention process that intro-duces a predictable riskless compromise if negotiations fail.1 Our treatmentof OC as ‘‘empowering’’ or ‘‘traditional’’ is consistent with the literature onOC, in particular Hofstede et al. (1990), who describe an organization’scommunication climate as open or closed, and its internal (hierarchical)structure as loose or tight.

2.2. Negotiation Outcomes

Distributive bargaining within organizations occurs when the conflictingsubunits perceive the outcome to be negatively correlated; one party’s gain is

The Effects of Organizational Culture on Budgetary Conflict 111

the other’s loss. It implies bargaining over finite resources such as anorganization’s discretionary budget, and a zero-sum game where an increasein benefit to one subunit necessarily means a decrease in that benefit toanother. The tendency for subunits to bargain distributively will vary withthe extent to which their interests are seen as finite and with the natureand amount of costs they expect if the conflict cannot be bargained toresolution.

As opposed to the zero-sum nature of distributive bargaining, integrativebargaining includes positive-sum elements; the divergent needs of the sub-units in conflict are ‘‘integrated’’ (Follett, 1940; Walton & McKersie, 1965)and the agreements are tantamount to achieving the greatest good for thebargainers (Pruitt & Lewis, 1975). It requires recognition of a potentialcommon ground and the possibility of alternatives that are superior to asimple distributive (compromise) resolution of the conflict (Butler, 1994).The possibility that novel solutions will be produced makes conflict poten-tially valuable rather than damaging to the subunits and the organization.In other words, integrative bargaining is a behavior that unlocks the con-structive potential of conflict.

Walton and McKersie (1965) postulate that one of the conditions ofintegrative bargaining is that several issues be considered simultaneouslysuch that trade-offs can be made between bargainers. Several researcherstested this trade-off, called ‘‘logrolling,’’ including Froman and Cohen(1970), Pruitt and Lewis (1975), and Thompson and Hastie (1990). Forexample, in a classic study, Pruitt and Lewis (1975) used a logrolling meth-odology that included negotiating more than one factor having differentvalues to negotiators, thus allowing integrative resolution. While thesestudies addressed integrative behavior, none of them dealt with intra-organizational conflict or budgeting contexts.

Previous research indicates that the quality of outcomes depends onnegotiators’ perceptions of the conflict as a ‘‘fixed pie’’ problem (Lax &Sebenius, 1986; Thompson, 1990; Thompson & Lowenstein, 1992). The‘‘fixed pie’’ perception leads to inadequate search for information aboutopponent preferences and error in the interpretation of available infor-mation (Bazerman & Neale, 1992; Carnevale & Lawler, 1986; Carroll,Bazerman, & Maury, 1988; Neale & Bazerman, 1983; Pinkley, Griffith, &Northcraft, 1995; Thompson, 1990; Thompson & Hastie, 1990). As Pinkleyet al. (1995) observe, alleviating these biases does not necessarily produceintegrative agreements unless the ‘‘fixed pie’’ expectation of individualnegotiators is altered. In this study, we examine dyadic budgetary conflict inthe context of different OCs.

NABIL ELIAS AND WILLIAM W. NOTZ112

One aspect that affects negotiation outcomes is third party intervention,or threat of intervention, in the event of failed negotiations. Different thirdparty intervention processes, or threat of intervention, in the context oflabor negotiations produced different effects on settlements (Notz & Starke,1978). Elias and Ezzamel (1990) suggest that information search by nego-tiators subject to final offer arbitration intervention, where an arbitratorselects one or the other of the final offers, may be greater than informationsearch by negotiators subject to conventional arbitration intervention,where an arbitrator imposes a compromise settlement usually between thetwo positions. These studies, however, were confined to one-factor nego-tiation (e.g., wage increase) that did not allow for logrolling. As a compo-nent of the OC, we introduce the application of different types ofintervention in the context of intra-organizational budget conflict. Conven-tional intervention usually splits the difference and shields negotiators fromfailed negotiations; indeed it leads conflicting parties to take extremepositions to protect their final outcome. Final offer intervention forcesnegotiators to explore, probe, and reach an agreement to avoid the risk oftheir opponent’s final offer being selected.

In summary, we use multiple budget items of different value to eachnegotiator to allow for logrolling and integrative solutions. We simulate theOC by simultaneously varying (a) the managerial style empowering or notempowering subunit negotiation, and (b) the managerial intervention proc-ess used in the case of failed negotiations. Based on our discussion of OCand negotiation outcome, we develop our hypotheses.

3. HYPOTHESES

A traditional OC reflects a hierarchical environment that attempts to con-trol outcomes. If negotiation between subunits takes place, failed negoti-ations are virtually riskless if negotiating subunits take extreme positions, orin the words of Argyris (1973) ‘‘not owning up.’’ On the other hand, anempowering OC encourages subunit cooperation and genuine negotiation.If negotiation takes place, the risk of failed negotiations could be substantialto the negotiating subunits, which leads them, in the words of Argyris(1973), to ‘‘owning up.’’ This contrast between the two negotiating envi-ronments is summarized in Fig. 1.

We hypothesize that a TOC would produce a bargaining process char-acterized by a distributive zero-sum orientation, constrained informationexchange, low levels of trust, and a bargaining outcome that is relatively

Traditional Organizational Culture (TOC)

Empowering Organizational Culture (EOC)

Hierarchical order; controlling Riskless failed negotiation Not owning up Distributive negotiation outcome

Delegation; freedom to negotiate Substantial burden & risk of failed negotiation Owning up Integrative negotiation outcome

Fig. 1. Contrast of Two Types of Organizational Culture.

The Effects of Organizational Culture on Budgetary Conflict 113

distributive in nature. In contrast, an EOC would produce a bargainingprocess characterized by an integrative positive-sum orientation, informa-tion sharing, trust, and a bargaining outcome that is relatively integrative innature. The specific hypotheses are listed below.

3.1. Budget Negotiation Outcomes

H1. The EOC treatment will produce more integrative budget negotia-tion outcomes than the TOC treatment.2 We test this hypothesis from twoangles as follows:

H1A. The EOC (TOC) treatment will result in more (fewer) integrativeagreements and fewer (more) distributive agreements3 and arbitrated set-tlements.

H1B. The EOC (TOC) treatment will result in higher (lower) benefit tonegotiators.

3.2. Budget Negotiation Expectations

H2. The EOC (TOC) treatment will exhibit more (less) realistic expecta-tions and greater (lesser) dyadic budget convergence at different stages ofthe negotiation process. We test this hypothesis from two angles as follows:

H2A. The EOC (TOC) treatment will exhibit more (less) realistic expec-tations of budget settlement at different stages of the negotiation process.

H2B. The EOC (TOC) treatment will exhibit greater (lesser) dyadic con-vergence at different stages of the budget negotiation process.

NABIL ELIAS AND WILLIAM W. NOTZ114

3.3. Budget Negotiation Perceptual and Cognitive Variables

H3. The EOC (TOC) treatment will result in relatively more (less) pos-itive perceptions related to trust, cooperation, and information sharing.

4. EXPERIMENTAL DESIGN

We test our hypotheses with experimental data from a simulated organi-zation with two different OCs in a laboratory setting.

4.1. Subjects

Eighty-four undergraduates from a School of Business at a major publicuniversity took part in this experiment. All subjects were recruited volun-teers who received payment for their participation in the study. The 84subjects formed 42 negotiating dyads.

4.2. Task

All subjects participating in the experiment received a description of a uni-versity that had received a substantial cut in its annual funding grant alongwith instructions from the funding authority, the University Grants Com-mission (UGC), to employ a new approach of internal budget reallocation.The UGC instructed the university that it must seek a synergistic re-allocation of the budget cut by initially making a cut to mega units,consisting of two faculties (schools) that seemed to have some significantpotential for synergistic reallocation of resources between them.

Subjects were told that the university’s response to the instructions fromthe UGC was first to create mega units. The mega unit of interest consistedof the schools of Agriculture and Engineering. Each subject was assigned therole of a dean of one of these two schools by a toss of a coin. Each subjectplaying the role of one of the deans received instructions from his/her su-perior, the university vice president. Aside from forming a mega unit, theuniversity’s response to the instructions from the UGC varied as part of themanipulation of the organization’s corporate culture.

Each subject received a voucher to receive $55 and information explainingthat the amount the subject would eventually receive as pay for the

The Effects of Organizational Culture on Budgetary Conflict 115

experiment depended on bargaining performance with the other subject.Each subject received the information that the mega unit in question had toabsorb an aggregate cut of two million dollars. Each subject received anidentical schedule (Table 1) that showed the relationship between the size ofthe budget cut for the subject’s school and the amount deducted from thesubject’s voucher. We provided identical schedules to create distributive

Table 1. How Budget Settlements Affect Subjects’Pay Schedule of Budget Cuts and Personal Consequences for You.

Budget Cut for Your Faculty Dollars Deducted from Your Voucher

Column 1 Column 2 (Confidential)

$2,000,000 �$110

1,900,000 �110

1,800,000 �107

1,700,000 �104

1,600,000 �100

1,500,000 �95

1,400,000 �89

1,300,000 �82

1,200,000 �74

1,100,000 �65

1,000,000 �55

900,000 �45

800,000 �36

700,000 �28

600,000 �21

500,000 �15

400,000 �10

300,000 �6

200,000 �3

100,000 �1

0 0

Example

Amount of Budget Cut Dollars Deducted

from Your

Voucher

Value of Your

Voucher

Your Pay

More than $1,000,000 More than �$55 $55 You get nothing

$1,000,000 �$55 $55 You get nothing

$600,000 �$21 $55 You get $34

NABIL ELIAS AND WILLIAM W. NOTZ116

tension between bargainers; a gain by one subject was a loss to the other.Each subject received various items of information about the subject’sschool, such as the consequences of a substantial budget cut on that school,and about the dean’s personal situation such as age and health. The in-struction the subjects received was to reallocate the two million dollarbudget cut across the two schools.

All subjects received information prior to negotiating that:

a.

Bargaining would start with one round of negotiations, with a possibilityof a second round.

b.

Their budget consisted of four separate budget items of differing value toeach of the faculty deans but that they would receive more informationon the relative value of these four budget items if they received approvalfrom the vice president to continue negotiations in a second round.4

c.

If allowed to continue to negotiate, they would then be able to negotiateeach budget item separately.

d.

Both members of a negotiating dyad would have to request to continue tonegotiate.5 If only one made such a request, the vice president wouldinvoke the intervention process (arbitrate).

e.

They would have fifteen minutes to bargain. If at the end of fifteenminutes the subjects reached agreement, they signed the appropriateforms and filled out a post-negotiation questionnaire. If they did notreach agreement, they could either request the vice president’s interven-tion (using the applicable intervention process) or request another roundof negotiations with additional information.6 In the latter case, they hadto submit an impasse offer the vice president can use for interventionpurposes if permission to continue to negotiate was not forthcoming.7

Agreements at this stage (end of Round 1) were, by definition, distrib-utive, since no logrolling was possible and one’s gain was another’s loss.Agreements at this stage were not profitable to reach (see Table 1); anyresolution at this stage could not be synergistic or integrative and requiredvery little search.

If dyads requested a second round of negotiations, their task continued tofind an agreeable reallocation of the two million dollar budget cut betweenthe two schools. Each subject entering the second round of negotiationsreceived information about the relative value of each of the four compo-nents of their budget to the subject. The relative value of each of the fourcomponents was different to the two schools as represented by a differentcorresponding voucher deduction amount for each subject in a dyad at eachlevel of budget cut (Table 2a and 2b). This allowed for logrolling and

Table 2a. How Individual Item Budget Settlements Affect Subjects’ Pay.Schedule of Budget Cuts and Personal Consequences for You. Report from the Vice President

(Session 2) – Agriculture.

Budget Cut for

Item A

(Geotechnical

Engineers)

Dollars Deducted

from Your

Voucher

Budget Cut for

Item B (Design

Engineers)

Dollars Deducted

from Your

Voucher

Budget Cut for

Item C

(Programmers)

Dollars Deducted

from Your

Voucher

Budget Cut for

Item D (Technical

Staff)

Dollars Deducted

from Your

Voucher

Column 1 Column 2

(Confidential)

Column 3 Column 4

(Confidential)

Column 5 Column 6

(Confidential)

Column 7 Column 8

(Confidential)

$2,000,000 �$187 $2,000,000 �$101 $2,000,000 �$48 $2,000,000 �$104

1,900,000 �187 1,900,000 �101 1,900,000 �48 1,900,000 �104

1,800,000 �180 1,800,000 �98 1,800,000 �48 1,800,000 �102

1,700,000 �174 1,700,000 �94 1,700,000 �47 1,700,000 �101

1,600,000 �166 1,600,000 �90 1,600,000 �46 1,600,000 �98

1,500,000 �156 1,500,000 �84 1,500,000 �45 1,500,000 �95

1,400,000 �144 1,400,000 �78 1,400,000 �43 1,400,000 �91

1,300,000 �132 1,300,000 �70 1,300,000 �40 1,300,000 �86

1,200,000 �120 1,200,000 �60 1,200,000 �37 1,200,000 �79

1,100,000 �108 1,100,000 �52 1,100,000 �32 1,100,000 �68

1,000,000 �98 1,000,000 �46 1,000,000 �24 1,000,000 �52

900,000 �94 900,000 �45 900,000 �24 900,000 �51

800,000 �86 800,000 �42 800,000 �23 800,000 �49

700,000 �74 700,000 �37 700,000 �21 700,000 �46

600,000 �59 600,000 �31 600,000 �18 600,000 �40

500,000 �46 500,000 �25 500,000 �13 500,000 �28

400,000 �42 400,000 �22 400,000 �11 400,000 �25

300,000 �32 300,000 �16 300,000 �8 300,000 �18

200,000 �16 200,000 �8 200,000 �4 200,000 �8

100,000 �5 100,000 �2 100,000 �1 100,000 �2

0 0 0 0 0 0 0 0

Note: There is a minimum budget cut for the MEGA UNIT of $300,000 in each of items A, B, C, and D; the total cut for the MEGA UNIT

must be a minimum of $2,000,000.

TheEffects

ofOrganiza

tionalCultu

reonBudgeta

ryConflict

117

Table 2b. How Individual Item Budget Settlements Affect Subjects’ Pay.Schedule of Budget Cuts and Personal Consequences for You. Report from the Vice President

(Session 2) – Engineering.

Budget Cut for

Item A

(Geotechnical

Engineers)

Dollars

Deducted from

Your Voucher

Budget Cut for

Item B (Design

Engineers)

Dollars

Deducted from

Your Voucher

Budget Cut for

Item C

(Programmers)

Dollars

Deducted from

Your Voucher

Budget Cut for

Item D

(Technical Staff)

Dollars

Deducted from

Your Voucher

Column 1 Column 2

(Confidential)

Column 3 Column 4

(Confidential)

Column 5 Column 6

(Confidential)

Column 7 Column 8

(Confidential)

$2,000,000 �$49 $2,000,000 �$111 $2,000,000 �$190 $2,000,000 �$90

1,900,000 �49 1,900,000 �111 1,900,000 �190 1,900,000 �90

1,800,000 �48 1,800,000 �108 1,800,000 �181 1,800,000 �87

1,700,000 �47 1,700,000 �107 1,700,000 �177 1,700,000 �85

1,600,000 �46 1,600,000 �104 1,600,000 �168 1,600,000 �82

1,500,000 �44 1,500,000 �100 1,500,000 �158 1,500,000 �78

1,400,000 �42 1,400,000 �96 1,400,000 �146 1,400,000 �72

1,300,000 �40 1,300,000 �92 1,300,000 �130 1,300,000 �66

1,200,000 �37 1,200,000 �85 1,200,000 �115 1,200,000 �59

1,100,000 �32 1,100,000 �76 1,100,000 �99 1,100,000 �53

1,000,000 �24 1,000,000 �56 1,000,000 �96 1,000,000 �41

900,000 �24 900,000 �54 900,000 �93 900,000 �41

800,000 �23 800,000 �52 800,000 �87 800,000 �38

700,000 �21 700,000 �48 700,000 �77 700,000 �32

600,000 �19 600,000 �42 600,000 �60 600,000 �27

500,000 �14 500,000 �30 500,000 �45 500,000 �21

400,000 �13 400,000 �27 400,000 �41 400,000 �19

300,000 �10 300,000 �19 300,000 �30 300,000 �15

200,000 �5 200,000 �9 200,000 �15 200,000 �7

100,000 �1 100,000 �2 100,000 �5 100,000 �2

0 0 0 0 0 0 0 0

Note: There is a minimum budget cut for the MEGA UNIT of $300,000 in each of items A, B, C, and D; the total cut for the MEGA UNIT

must be a minimum of $2,000,000.

NABIL

ELIA

SAND

WIL

LIA

MW.NOTZ

118

The Effects of Organizational Culture on Budgetary Conflict 119

discovery of integrative solutions following Thompson and Hastie (1990)and Pruitt and Lewis (1975). Achieving an integrative solution requiredsubjects to use negotiations to find which is more beneficial. Table 2 isgenerally consistent with Table 1; an evenly distributed budget cut across thefour budget items in Table 2 would produce the same amount of voucherdeduction as the total budget cut would in Table 1.8

It is important to note that the two rounds of negotiations were used tosimulate one aspect of search. Further search for information resulted in thenegotiation process in the second round. Real world negotiations are typ-ically not concluded in one round unless negotiations are either easy (noconflict), or bargainers give up without searching for information or solu-tions. The two rounds of negotiations were available to all dyads who wereinformed that the budget consisted of four separate items of differing valueto each subject, that they would be able to negotiate each budget item sep-arately but that they would receive more information on the relative value ofthese four items if they chose and were approved to have a second round.

4.3. Experimental Design and Procedures

The basic design is a 1� 2 factorial design with one level of conflict and twolevels of OCs, traditional and empowering. The remainder of this subsectiondescribes the experimental procedures and manipulations.

As subjects arrived at the experimental setting, they were told that theywould be participating in a simulated budget negotiation in a university.After screening pairs of subjects to ensure they are not known to each other,dyads were randomly assigned to either the TOC or the EOC treatment, andeach subject within a dyad was randomly assigned the role of either the deanof engineering or the dean of agriculture. Each was directed to a separateroom, given general information about the university and their particularschool. Subjects were then presented with information that differed acrossthe two treatments.

In the TOC treatment, subjects received the following statement from theuniversity vice president:

The University Grants Commission has required that we allow Deans to negotiate

directly, and that is why you are negotiating. If you ask me, I think this is really a waste

of time. I can reach a superior decision on my own as I have done in the past. I think the

hierarchy of decision making in the University has to be maintained and that I have the

best idea of how the budget cuts should be made. I hope that your negotiations will lead

to a reallocation decision that is as good as the one I reached on my own. If it’s not, I will

ask you to either continue to negotiate or to provide me with your impasse offer.

NABIL ELIAS AND WILLIAM W. NOTZ120

Subjects in the TOC treatment also received information explaining that ifan impasse in negotiations occurred, the vice president’s intervention proc-ess for conflict resolution would be to examine the impasse offers of bothsubjects (deans) and decide on a settlement that was fair to both – a decisionthat would generally fall somewhere between their respective demands andimpose a compromise. Subjects also received a hypothetical illustration ofthe vice president’s impasse intervention process and answered questions toindicate they fully understood its application.

In the EOC treatment, subjects received the following statement from thevice president:

I seriously believe that the two of you can negotiate a much better agreement from the

University’s point-of-view than any solution that I can reach on my own without your

involvement or commitment. I have made these decisions in the past without your

involvement, but these solutions did not always prove to work well. On the other hand, I

have already made a tentative decision on your reallocation and I will not accept any

solution that is less advantageous from the University’s point-of-view than my own

decision. If you reach an agreement that is less beneficial from the University’s point-

of-view, you will be asked either to continue to negotiate or to provide me with your

impasse offer.

Subjects in the EOC treatment received information that the universityhoped that its new decentralized structure would produce more creative,better informed, more equitable, and substantially more acceptable budgetreallocations. The university further realized that this would demand a verysignificant effort from the deans to understand each other’s position, eachother’s dilemmas, opportunities, constraints, fears, etc. In order to encour-age and reward these behaviors, the vice president explained the supportingintervention process for dealing with impasses between deans. The vicepresident’s intervention process in the EOC treatment consisted of twocomponents. Subjects in this treatment read the following description of thefirst component:

The Vice Presidentywill first examine the impasse offers of both Deans for all issues. If

the impasse offers are judged to be reasonable, even though short of an agree-

ment,y then the Vice President will select either one or the other of the two impasse

offers in its entirety, the one which is the most fair to both deans. No compromise is

possible. The one offer the Vice-President selects will then determine the outcomes for

both you and the other dean.

Subjects also read a hypothetical example that further illustrated theoperation of this part of the intervention process. They also received in-formation about the second component of the EOC treatment interventionprocess; the vice president will examine the deans’ impasse offers and if the

The Effects of Organizational Culture on Budgetary Conflict 121

examination revealed excessive self-interest in the offers of either one orboth deans, then role reversal (in budget cut allocation) would be imposedto resolve the impasse and correct the self-interest problem. This wouldapply by switching (or reversing) the voucher deduction amount. Subjectsreceived an illustration of the budget cut assignment of role reversal with amodification of the previous example. They also answered questions to in-dicate they fully understood both aspects of this intervention process (finaloffer selection and role reversal).

Subjects in both treatments (EOC and TOC) were given the opportunityto ask questions and in one or two cases were referred back to the materialthey already had for clarification. They then completed a pre-negotiationinstrument (described later). Manipulation checks were performed throughquestions that examined each subject’s understanding of the vice president’sinstructions, the intervention process, and the negotiating situation. If anymisunderstanding of these items occurred (and this happened only rarely),subjects were referred back to the relevant material and were asked toanswer the questions again. All questions were answered correctly.

Subjects came together in another room and bargained for fifteen min-utes. They faced each other across a table with a low partition between themand, with their knowledge, were tape-recorded.9 At the conclusion of thebargaining session, subjects returned to their separate rooms.

Two outcomes were possible for each dyad: either they reached an agree-ment or they did not. Subjects in a dyad who reached agreement then com-pleted a post-negotiation questionnaire, and received information abouttheir pay. They were also prompted to provide an offer the vice presidentcould use to settle the dispute, should the agreement not receive his/heracceptance. Dyads who did not reach agreement could either request im-mediate intervention by the vice president, or permission to continue bar-gaining in a second round of negotiations. All subjects were required tosubmit an impasse offer to the vice president, whether or not they hadrequested permission to continue bargaining.10 Subjects who requested im-mediate intervention were informed of the vice president’s decision in ac-cordance with the applicable intervention process. These subjects thencompleted a post-negotiation questionnaire and received information abouttheir pay. Subjects who requested continued bargaining were also requiredto submit their impasse offer for use by the vice president if continuedbargaining was denied.11 Thus at the end of the first stage of negotiations inthe experiment, all subjects had either reached an agreement or made animpasse offer, thereby providing a useful measure of their bargainingbehavior. See Fig. 2 for the experimental sequence.

*In all cases of agreement, subjects also filled out an offer to be used by the arbitrator in case the agreement was not approved.

RandomAssignment of Subjects

Round 1 Negotiation

Agreement* (Distributive)

No Agreement

Request for Intervention and Submission of Impasse Offer

Vice President’s Intervention (Decision)

Using Applicable Intervention Procedure

Request for Continued Bargaining and Accompanying Impasse Offer

Round 2 Negotiation Agreement*

(Integrative)

Request for Intervention and Submission of Impasse Offer

Vice President’s Intervention (Decision)

Using Applicable Intervention Procedure

Role Faculty of:

1. Engineering 2. Agriculture

Experimental Treatment(Conflict Environment)

1. TOC 2. EOC

No Agreement

Or

Fig. 2. Experimental Sequence.

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The Effects of Organizational Culture on Budgetary Conflict 123

All dyads requesting continued negotiations received authorization tohave another round of negotiation. Each subject received a ‘‘Report fromthe Vice President’’ which contained the applicable decomposed budget cutlevels and corresponding voucher deduction (Table 2a or 2b). Subjects hadten minutes to prepare for the second round of negotiation. Then, theycompleted another questionnaire (similar to the pre-negotiation instru-ment), and were brought together for the second round of face-to-face ne-gotiations. Dyads had twenty minutes to negotiate at the end of which theyreturned to their separate rooms.12 If they had reached agreement, theyreceived instructions similar to those who had agreed in the previous ses-sion. Subjects in dyads who had not reached agreement completed theirimpasse offers for resolution by the vice president based on the applicableintervention process. The remaining procedures for these subjects wereidentical to the previous description.

4.4. Dependent Variable Measures

In this study, the dependent variable revolves around budget conflict bar-gaining behavior in terms of its distributive or integrative nature. Consistentwith the hypotheses, the dependent variable measures include budget ne-gotiation outcomes, expectations, and perceptions.

4.5. Budget Conflict Negotiation Outcome

The most important measures of our dependent variable relate to the out-come of budget bargaining behavior. We use one discrete variable and onecontinuous variable to measure bargaining behavior outcomes.

(1)

The first variable we use provides a relatively crude but useful measureof integrative behavior. As mentioned earlier, dyads that decided tosettle their budget conflict by intervention during the experiment or byagreements at the end of Round 1 reflect predominantly distributivebargaining behavior. Only strictly distributive agreements (or arbitratedsettlements) were possible at the end of Round 1 since logrolling was notpossible, and dyadic joint earnings would at best sum to zero (a fulldeduction of the voucher amount). Agreements in Round 2 were po-tentially integrative, since logrolling was possible only in that round.Thus, the number of negotiations settled by intervention or by agree-ment at the end of Round 1 as well as those settled by intervention at the

Table 3. Maximum Integrative Solution.

Budget Cut Amount (in $1,000) Voucher Deduction, Value, and

Earnings (in $)

Agriculture Engineering Total Agriculture Engineering Total

Budget Item A $0 $700 $700 $0 �$21 �$21

Budget Item B $200 $100 $300 �$8 �$2 �$10

Budget Item C $700 $0 $700 �$21 $0 �$21

Budget Item D $100 $200 $300 �$2 �$7 �$9

Total budget cut $1,000 $1,000 $2,000 �$31 �$30 �$61

Voucher value $55 $55 $110

Maximum

Possible

individual and

dyadic earnings

$24 $25 $49

NABIL ELIAS AND WILLIAM W. NOTZ124

end of Round 2 can serve as a proxy for distributive agreements whilethe number of agreements in Round 2 can be considered a reasonableproxy for integrative agreements.

(2)

A more refined measure of integrative behavior was the total dyadicearnings. This variable could range from strictly distributive to fullyintegrative. The maximum integrative agreement would result in a com-bined dyadic pay of $4913 as shown in Table 3.

4.6. Budget Conflict Negotiation Expectations

Several measures of expectations were taken using the responses to thequestionnaires administered before and after negotiations. These include:

(a)

Subjects’ expectations about what would constitute a good, poor, andreasonable settlement, and intended opening offer before each round ofnegotiations.

(b)

Another measure of the degree of integrative outcomes is the differencebetween the two offers of a negotiating dyad, which is an indicatorof bargaining discrepancy (Elias, 1990). Small (large) bargaining dis-crepancies reflect more (less) integrative (distributive) behavior. Putanother way, large bargaining discrepancies measure the level ofintransigency in bargaining behavior that is associated more withdistributive behavior.

The Effects of Organizational Culture on Budgetary Conflict 125

4.7. Budget Conflict Perceptual and Cognitive Variables

These variables include the following:

1.

Subjects’ satisfaction with the information sharing and the level of mu-tual trust.

2.

The extent to which the situation was framed by subjects as zero-sum orjoint problem solving.

3.

How subjects saw themselves and their negotiating counterpart as havingbehaved in a flexible and innovative manner.

4.

Subjects’ estimate of the likelihood that settlement by intervention of thevice president could be avoided.

5.

Subjects’ satisfaction with the negotiation outcome.

The next section provides the results of the effects of OC on budgetconflict negotiating outcomes and behavior.

5. RESULTS

5.1. Budgeting Conflict Negotiation Outcome

As described in the previous subsection, the first relatively crude measure ofbudget conflict bargaining outcome is the number of dyads in a cross clas-sification of distributive or integrative outcomes that is the outcome of theEOC and TOC. As described earlier, the outcomes of dyads who reachedagreement at the end of the first round of negotiations or who requestedsettlement through intervention (by the vice president) were classifiedas distributive, and those of dyads who reached agreement at the end of thesecond round of negotiations were classified as integrative. Dyads whosettled or asked for intervention at the end of Round 1 knew there was apotential Round 2 with more information on individual budget items.Table 4 reports these data for the TOC and EOC treatments.

Whereas bargaining under the TOC produced 16 distributive (11 inRound 1 and 5 in Round 2) and 5 integrative outcomes (Round 2), the EOCtreatment produced a nearly symmetric reversal of 7 distributive outcomes(all in Round 1) and 14 integrative (all in Round 2). The result wasstatistically significant, supporting hypothesis H1A.

An analysis of the subjects’ earnings in the experiment provides a moresensitive analysis of distributive vs. integrative outcomes. Table 5 providesan analysis of individual and dyadic earnings produced by the TOC and

Table 4. Distributive and Integrative Outcomes by BudgetaryEnvironment.

Traditional

Organizational

Culture

Empowering

Organizational

Culture

w2 Value P-Value

(1-Tail)

Distributive outcomes

Dyads settled through

intervention in Rounds

1 and 2 or agreements

in Round 1

16a 7b

Integrative outcomes

Dyads settled through

agreements in Round 2

5 14

Total number of dyads 21 21 7.78490 0.00264

aThis number consists of 1 agreement and 10 interventions in Round 1; and 5 interventions in

Round 2.bThis number consists of no agreements and 7 interventions in Round 1; no interventions in

Round 2.

Table 5. Dyadic and Individual Earnings and Pay Variables byOrganizational Culture.

Traditional

Organizational

Culture Mean

(n ¼ 21 Dyads;

42 Subjects)

Empowering

Organizational

Culture Mean

(n ¼ 21 Dyads;

42 Subjects)

F-Ratio F-Prob. (1-Tail)

Dyadic earnings

(negative earnings

included)

$15.24 $29.38 4.6050 0.0190

Individual earnings

(negative earnings

included)

$7.61 $14.69 3.7409 0.0283

Dyadic pay (negative

earnings converted

to $0)

$24.00 $34.86 4.7776 0.0174

Individual pay

(negative earnings

converted to $0)

$12.00 $17.43 5.2881 0.0120

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The Effects of Organizational Culture on Budgetary Conflict 127

EOC treatments. While dyads earned an average of $15.24 (31% of the $49maximum possible dyadic earnings) in the TOC treatment, the EOC treat-ment produced more integrative outcomes of nearly twice as much, $29.38(60% of the maximum possible dyadic earnings). Since negative earningswere possible but negative pay to subjects was not, we examined dyadic payafter converting negative earnings to reflect zero pay. On that basis, averagedyadic pay was $24.00 in the TOC treatment as compared to $34.86 in theEOC treatment. In both cases, the results were statistically significant andthe same was true for individual earnings and individual pay. Thus, subjectswho bargained under the EOC treatment produced significantly more in-tegrative agreements and enjoyed substantially greater pay than their coun-terparts who bargained under the TOC treatment, thus supportinghypothesis H1B.

5.2. Budgeting Conflict Negotiation Expectations

A second set of variables describes subjects’ initial states, expectations, andplans. Subjects answered questions about what they considered a reason-able, good, or poor settlement. In addition, they also provided their in-tended opening offer prior to the beginning of negotiations. Subjectsprovided this data before each of the two rounds of negotiations began.Panel A of Table 6 shows that before the first round of negotiation began,subjects’ pre-negotiation expectations reflect a pattern consistent with H2A.Subjects in the EOC treatment generally expected greater budget cuts andlower personal earnings than did their counterparts in the TOC treatment(see the difference column). Differences in subjects’ intended opening offerswere statistically significant. Differences in subjects’ perceptions of a rea-sonable, good, or poor settlement were in the expected direction though notstatistically significant. Thus, prior to the beginning of negotiations, there isonly partial support for H2A.

On the other hand, as Panel B of Table 6 shows, the patterns describedabove did not only persist but also became more pronounced and were allstatistically significant at the end of the first round of negotiations (before thesecond round of negotiations began), lending support to H2A. This was soeven in view of the smaller sample size that participated in the second roundof negotiations.

Table 7 shows the mean of the impasse offers subjects submitted as theirrequest for intervention if they reached an impasse or as prompted in theexperiment if they requested to continue to negotiate with a decomposed

Table 6. Individual Expectation Variables Regarding Budget Cuts.

Traditional

Organizational

Culture (n ¼ 42)

Empowering

Organizational

Culture (n ¼ 42)

Difference F-Ratio F-Prob. (1-Tail)

Panel A: Pre-negotiation – Round 1 Mean budget cut (in $000)

Reasonable (fair) settlement 814 888 74 3.5064 0.0647

Good settlement 560 648 88 3.0195 0.0860

Poor settlement 1,082 1,105 23 0.1067 0.7448

Intended opening offer 374 565 191 11.0814 0.0013

Mean individual earnings (in $)

Reasonable (fair) settlement 15.57 10.17 5.40 2.7490 0.0506

Good settlement 33.40 28.52 4.88 2.7400 0.0509

Poor settlement �4.93 �8.21 3.28 0.5195 0.2366

Intended opening offer 42.13 33.70 8.43 5.9252 0.0086

Traditional

Organizational

Culture (n ¼ 20)

Empowering

Organizational

Culture (n ¼ 28)

Difference F-Ratio F-Prob. (1-Tail)

Panel B: Pre-negotiation – Round 2

Reasonable (fair) settlement 725 904 179 8.1022 0.0066

Good settlement 598 811 213 16.7199 0.0002

Poor settlement 910 1,079 169 6.5588 0.0138

Next offer 526 (n ¼ 19) 796 270 20.1266 0.0000

Mean individual earnings (in $)

Reasonable (fair) settlement 21.00 8.84 12.16 6.8740 0.0059

Good settlement 31.62 17.32 15.30 15.3961 0.0002

Poor settlement 6.10 �7.07 13.17 7.0407 0.0055

Intended next offer 35.37 (n ¼ 19) 18.36 17.01 15.1946 0.0002

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Table 7. Individual Impasse Offers Regarding Budget Cuts to SubjectEnd of Round 1.

Mean Budget Cuts (in 000’s) F-Ratio F-Prob.

(1-Tail)Traditional

Organizational

Culture

(N ¼ 42)a

Empowering

Organizational

Culture

(n ¼ 42)

Difference

Impasse offers

submitted for

intervention or

offers to be

considered in case

of intervention

343 770b 427 66.2542 0.0000

Mean individual earnings (in $)

Impasse offer

submitted for

intervention or

offer to be

considered in case

of intervention

44.30 20.31c 23.99 75.8400 0.0000

aThis includes one dyad who reached agreement in Round 1.bThe EOC mean budget cut offer is nearly 2.25 times the TOC mean offer.cThe EOC individual earnings mean associated with the budget cut is nearly 46% of the TOC

mean.

The Effects of Organizational Culture on Budgetary Conflict 129

budget.14 Consistent with H2B, the budget cut offer was substantially higherin the EOC treatment than in the TOC treatment (nearly 2.25 times) and thecorresponding payment associated with the budget cut offer was substan-tially lower (46%), and these differences were statistically significant.

5.2.1. Budget Conflict Expectations at the Dyadic Level

Another test of H2B is to examine budget expectations at the dyadic level. Animportant aspect of the bargaining was the subjects’ cooperation in absorbingthe budget cut. We measured this variable by comparing the required twomillion dollar budget cut with the sum of the cuts proposed by the two mem-bers of each negotiating dyad. Since we expect the EOC treatment to produceintegrative outcomes, we would also expect it to produce smaller differencesbetween the two million dollar cut and the dyadic proposals. As Table 8shows, the pre-negotiation closeness to the target budget cut was considerablysmaller in the EOC than in the TOC treatment. This pattern was generally

Table 8. Dyadic Closeness to Target Budget Cut of $2 Million by Organizational Culture.

Traditional

Organizational

Culture (in $000)�

Empowering

Organizational

Culture (in $000)�

Difference (in $000)�

EOCoTOC

F-Ratio F-Prob. (1-Tail)

Panel A: Pre-negotiation – Round 1 (n ¼ 21 dyads) (n ¼ 21 dyads)

Reasonable (fair) settlement 371 224 147 4.7154 0.0180

Good settlement 881 705 176 3.3076 0.0383

Poor settlement �164 �210 46 0.1372 0.3565

Intended opening offer 1,252 869 383 11.8920 0.0007

Panel B: Pre-negotiation – Round 2 (n ¼ 10 dyads) (n ¼ 14 dyads)

Reasonable (fair) settlement 550 193 357 6.3463 0.0098

Good settlement 805 379 426 14.5323 0.0005

Poor settlement 180 �157 337 4.4605 0.0232

Intended opening offer 956 (n ¼ 9) 407 549 29.5346 0.0000

Panel C: End of Round 2

For agreements reached only; offers

submitted for possible arbitration

360 (n ¼ 5) 80 (n ¼ 14) 280 4.5419 0.0264

For combined agreements reached

and impasse offers; offers

submitted for possible arbitration

and impasse offers submitted for

arbitration

485 (n ¼ 10) 80 (n ¼ 14) 405 5.4149 0.0159

�Values rounded to the nearest $1,000.

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The Effects of Organizational Culture on Budgetary Conflict 131

consistent from pre-negotiation dyadic expectations and intended openingoffers (Panel A) to the end of the first round of negotiation (Panel B). Inaddition to having a zero divergence for all 14 EOC and only 5 TOC dyadswho reached agreement at the end of the second round of negotiations, wemeasured dyadic divergence as the difference between the total requiredbudget cut and the offers to be used in case agreements were not approved.The EOC treatment produced greater convergence as measured by the averagecloseness to the target budget cut of $80,000 while the TOC treatment pro-duced lesser convergence as evidenced by the average closeness of $360,000.When we consider dyadic divergence of all dyads participating in the secondround of negotiations (including dyads who did not reach agreement) thisdifference is even greater; the EOC treatment produced an average closeness tothe target budget cut of $80,000 as compared to the TOC average of $485,000,which is six times larger. These differences were statistically significant (PanelC), lending support to hypothesis H2B.

5.3. Budget Conflict Perceptual and Cognitive Variables

Table 9 provides a comparison by conflict environment treatment of the per-ceptions of subjects regarding their trust of the other subject, being trusted bythe other subject, holding the view that one’s gain is the other’s loss, the needfor cooperation, their plans to share information with the other subject, andtheir expectation that the other subject would share information. This analysiscovered pre-negotiation responses conducted prior to each of the two nego-tiation sessions. In addition, the post-negotiation questionnaire also includedrelevant perceptual and cognitive variables. Panel A of Table 9 shows thatbefore the beginning of negotiations all perceptual and cognitive variableswere in the predicted direction; that is, there was a perception of greatermutual trust, greater desire for cooperation, and more plans for informationsharing in the EOC than in the TOC treatment. The differences in perceptionin all of these variables were statistically significant. While the view that one’sgain is the other’s loss was also in the predicted direction, the difference wasnot statistically significant. Likewise, Panel B of Table 9 shows that all Round2 pre-negotiation variables were in the predicted direction, but not all werestatistically significant. This, only in part, may be due to the substantialshrinkage in sample size.15

At the end of negotiations, subjects in the EOC treatment reported thattheir behavior and their opponent’s behavior were less ‘‘rigid and uncom-promising.’’ These subjects also perceived that there had been more mutual

Table 9. Individual Perceptual and Cognitive Variables by Organizational Culture.

Panel A: Pre-Negotiation – Round 1 Traditional

Organizational

Culture Mean

(n ¼ 42)

Empowering

Organizational

Culture Mean

(n ¼ 42)

F-Ratio F-Prob. (1-Tail)

(1 not at all; 9 completely)

Extent to which subject is prepared to trust other subject 4.67 5.77 8.8671 0.0019

Extent that other subject is prepared to trust subject 4.60 5.24 2.7827 0.0496

View of one’s gain being the other’s loss 6.81 6.36 1.1512 0.1432

View of situation involving joint problems requiring cooperation 6.43 7.07 2.8803 0.0468

(1 everything; 9 nothing)

Extent to which subject plans to share information about budget

situation

4.52 3.71 4.9702 0.0143

Expectation that other subject will share information about

budget situation

4.93 3.83 10.7239 0.0008

Panel B: Pre-Negotiation – Round 2 Mean (n ¼ 20) Mean (n ¼ 28)

(1 not at all; 9 completely)

Extent to which subject is prepared to trust other subject 4.85 5.46 1.1408 0.1456

Extent that other subject is prepared to trust subject 4.60 5.57 2.9503 0.0463

View of one’s gain being the other’s loss 7.25 6.29 2.5271 0.0594

View of situation involving joint problems requiring cooperation 6.35 7.00 1.2286 0.1367

(1 everything; 9 nothing)

Extent to which subject plans to share information about budget

situation

4.30 3.57 1.5910 0.1068

Expectation that other subject will share information about

budget situation

4.65 4.21 0.6165 0.2182

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Panel C: Post-Negotiation Mean

(n ¼ 42)

Mean

(n ¼ 42)

F-Ratio F-Prob. (1-Tail)

(1 not at all; 9 completely)

Extent of subject’s own behavior as rigid and uncompromising 4.50� 3.42� 5.0090 0.0141

Extent of other subject’s behavior as rigid and uncompromising 4.37� 3.53� 2.8462 0.0479

View of one’s gain being the other’s loss 6.95 5.67 6.5705 0.0061

View of situation involving joint problems requiring cooperation 6.24 6.60 0.5233 0.2358

Extent to which subject provided distorted information 2.97� 2.89� 0.0249 0.4376

Extent of subject’s own behavior as flexible and innovative 5.18� 6.16� 4.0793 0.0235

Extent of other subject’s behavior as flexible and innovative 5.11� 5.61� 1.0140 0.1586

Satisfaction with exchange of information during negotiation 5.24� 5.92� 1.5990 0.1050

(1 everything; 9 nothing)

Extent to which subject shared information about budget situation 4.88 3.88 5.9214 0.0086

Extent to which other subject shared information about budget situation 5.21 4.45 3.0713 0.0417

(1 extremely dissatisfied;

9 extremely satisfied)

Satisfaction with the outcome 4.87� 6.50�� 12.0789 0.0005

�n ¼ 38 (four missing values).��n ¼ 36 (six missing values).

TheEffects

ofOrganiza

tionalCultu

reonBudgeta

ryConflict

133

NABIL ELIAS AND WILLIAM W. NOTZ134

sharing of information, and they had a weaker belief in the zero-sum natureof the bargaining than subjects in the TOC treatment. As Panel C shows,these differences were statistically significant. However, other variables,while in the predicted direction, were not statistically significant, for exam-ple, need for cooperation, and other subject’s flexible and innovative be-havior. In general, subjects in the EOC treatment had different perceptionsof themselves, their negotiating counterparts, and the bargaining process.We conclude that there was some support for H3.

5.3.1. Satisfaction with Budget Negotiation Outcome

Table 9, Panel C shows greater satisfaction with the negotiation outcome bysubjects in the EOC than in the TOC treatment. Although satisfaction withthe outcome can, at least in part, be attributed to the expectation of higherpay in the EOC treatment, it is nonetheless important to report the dramaticdifference in satisfaction with the outcome, in favor of the EOC. Whilesatisfaction with the exchange of information during negotiation was alsohigher in the EOC treatment, this result was not statistically significant.

6. DISCUSSION AND CONCLUSIONS

Budget conflict received little explicit attention in the literature, and partic-ularly with respect to OC and managerial style. Budgetary conflict can becostly to organizations. We proposed changes in two elements in the TOC asa means to enhance the propensity to produce more integrative budgetaryconflict resolution: an empowering leadership style and an interventionprocess (in failed negotiations) that encourages the parties to resolve theirown conflict. The results of this study supported our primary hypothesis thatthe TOC treatment produced budget conflict outcomes that were relativelydistributive. On the other hand, the EOC treatment produced significantlymore integrative budget conflict outcomes. The most dramatic evidence ofthis is dyadic earnings (which represent subunit utility); dyads in the EOCtreatment earned nearly twice as much as dyads in the TOC treatment.

The study also found that differences in budget bargaining outcomes wereconsistent with differences in subject and dyadic expectations. Bargainers inthe EOC treatment held more reasonable expectations than those held bytheir counterparts in the TOC treatment before negotiations began, andthose differences became greater as negotiations proceeded. Subjects in theEOC treatment expected greater budget cuts and lower personal earningsthan did their counterparts in the TOC treatment; and this was persistent

The Effects of Organizational Culture on Budgetary Conflict 135

throughout the negotiation process. Dyadic expectations in the EOC treat-ment showed greater convergence towards budget goals than their coun-terparts in the TOC treatment.

Finally, subjects in the EOC treatment saw themselves and their opponentas less ‘‘rigid and uncompromising,’’ more sharing of information, and lesslikely to define bargaining as a zero-sum conflict than their counterparts inthe TOC treatment.

Although the experimental results support our primary hypothesis thatthe EOC treatment would produce more integrative budgetary negotiationbehavior and outcomes than the TOC treatment, this research cannot ex-plain the complex causal chain that must have taken place during negoti-ations. For example, Galinsky and Mussweiler (2001) provide evidence thatwhoever makes the first offer (a buyer or seller) provides an anchor forsubsequent negotiations. Although we collected data about intended open-ing offers at each stage of budget negotiations, we did not collect data as towho made the first offer and the role this may have had on outcome.

There is a threat to the external validity of our findings. Subjects bargainingin a laboratory simulation are clearly quite different from managers bargain-ing in a real organization. Similarly, relationships between negotiators in or-ganizations are relatively long term as compared to those in the laboratory,constituencies are present in real organizations but not in the laboratory, andthe stakes of bargainers are much greater and more complex. However, stud-ying real organizations and implementing the proposed changes in what wecalled the TOC can be costly without experimentation. The rich context oforganizational settings hampers theory development (internal validity). Ulti-mately, the study findings must find their true test in real organizations.

Our independent variable, OC, is subject to wide interpretation. Our focuswas on two factors that if simultaneously changed would produce what wecalled an ‘‘empowering’’ OC. The construct we used consisted of the cou-pling of two factors that we did not independently manipulate as two sep-arate independent variables: managerial style and managerial intervention.Such coupling was made for pragmatic and practical reasons: (a) consist-ency: it is more likely to see an empowering managerial style with an in-tervention process that expects the parties to resolve their conflict on theirown, or a traditional managerial style that does not expect parties to resolveconflict on their own and applies a compromise instead, than the other twoinconsistent and less likely combinations (see Note 1); (b) feasibility: if allpossible combinations of these two factors were used we would have re-quired four different experimental treatments (instead of two), doubling thesample size, and doubling the cost of the experiment. Nonetheless, future

NABIL ELIAS AND WILLIAM W. NOTZ136

research can determine which of these two factors, if any single factor, ispowerful in producing integrative budgetary conflict resolution.

Our study did not consider the way in which negotiators framed the issues.There is a good deal of evidence that the framing of issues in a negotiationaffects bargaining strategies and outcomes (Bazerman, Magliozzi, & Neale,1985; Bottom & Studt, 1993; Neale, Huber, & Northcraft, 1987). Moreover,since framing affects changes in risk attitude, it follows that bargaining overlosses (burdens) or gains (benefits) will affect bargaining behavior and out-comes (Sondak, Neale, & Pinkley, 1995). Our experiment framed the subjectof negotiation as a budget cut, and the corresponding parallel effect onsubjects as a deduction from their voucher which determined their pay (aloss). Quite possibly, if the experiment framed the subject of negotiation as abudget increase instead of a budget cut and the corresponding parallel effecton subjects as an increase in their pay (a gain), the results could be different.In this connection, it is worth noting that Sondak et al. (1995) found thenegotiators allocating burdens (similar to budget cuts in our study) weremore competitive and less effective in finding integrative solutions than weretheir counterparts who were allocating benefits. If so, one implication fromtheir findings is that the effects of our conflict environment manipulationmight be rather robust. These and other variations of framing suggest in-teresting interaction effects, which will require further research.

NOTES

1. It is possible to treat each of the components of our OC independent variable asa separate variable. If so, we would have four cells of possible combinations asfollows:

Intervention Process

Managerial Style

Traditional

Empowering

Compromise

Cell 1 (TOC) Cell 2a

Final offer

Cell 3a Cell 4 (EOC)

aCombination is less likely to exist.

We conclude that cells 2 and 3 are unlikely and focus on cells 1 and 4. Such focuswould not enable us to determine the separate effects of each of the two componentsof the OC variable on conflict resolution. There is also a practical dimension to thischoice. The number of negotiating dyads would have to be doubled and so would the

The Effects of Organizational Culture on Budgetary Conflict 137

cost of conducting the experiment. While the separation of the two componentscould provide us with useful insights, the combination of cost and the apparentinconsistency within cells 2 and 3 resulted in our decision to focus on cells 1 and 4.Future research can focus on other combinations.2. The word ‘‘treatment’’ could be substituted with ‘‘situation,’’ ‘‘environment,’’

or ‘‘condition.’’ Since we manipulated this variable in the experiment, we use ‘‘treat-ment’’ in that sense.3. Agreement does not necessarily result in an integrative resolution if both parties

treated the problem as a fixed-pie conflict and did not probe or search for infor-mation to reach an integrative resolution.4. The two-rounds of negotiations were used to simulate one aspect of search for

information.5. This is typical in real life. Both parties have to agree to negotiate before ne-

gotiation takes place.6. Here again, subjects who search for information would opt for the second

round if they were searching for integrative resolution.7. All requests to continue negotiation received approval. The purpose of re-

questing an impasse offer was a means of obtaining a reading of where the subjectswere at the end of the first round of negotiation.8. At realistic budget cut levels, voucher deductions corresponding to equal

budget cuts across the four budget items in Table 2 were equal to the voucherdeduction resulting from a corresponding total budget cut in Table 1. For example, abudget cut of $200,000 in each of the four budget items would produce a voucherdeduction of $36 (Table 2), which is equivalent to the voucher deduction corre-sponding to a total budget cut of $800,000 (Table 1).9. Tape-recording was an added measure to discourage any attempt at collusion.10. This provided the researchers with data at the end of the first session to gauge

how close or far apart subjects were.11. This was the justification to obtain a measure of the discrepancy between the

two bargainers at the end of Round 1.12. Subjects were not allowed to share their voucher deduction information in

Table 2 with their negotiating counterpart.13. The $49 amount is the maximum dyadic pay and is the amount remaining

from the combined dyadic voucher value of $110 after subtracting the minimumpossible combined voucher deduction of $61. See Table 3.14. Subjects received instructions that they must submit an impasse offer that

would be used by the university vice president if they did not receive approval tocontinue to negotiate. See Footnote 1.15. From n ¼ 42 in each treatment who completed the round one pre-negotiation

questionnaire to n ¼ 20 and n ¼ 28 who completed the pre-negotiation questionnairefor round two in the TOC and EOC treatments, respectively.

ACKNOWLEDGMENT

The authors acknowledge with gratitude stimulating discussions on thistopic with Professor John Atwell. We benefited from information provided

NABIL ELIAS AND WILLIAM W. NOTZ138

by Professor Terence Hogan, former vice president, University of Manitoba.We would also like to acknowledge the research assistance of Natalie Braunand Lucy Guest, comments of anonymous CAAA and AAA conferencereviewers, Professor Ralph Greenberg’s helpful comments as AAA confer-ence discussant of our paper, comments made by audiences at both con-ferences, and comments made by colleagues at research workshops at theUniversity of Manitoba and UNC Charlotte.

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THE INTERVENING EFFECT OF

INFORMATION ASYMMETRY ON

BUDGET PARTICIPATION AND

SEGMENT SLACK

Leslie Kren and Adam S. Maiga

ABSTRACT

The objective of this study was to extend prior research by examining

subordinate–superior information asymmetry as an intervening variable

linking budgetary participation and slack. The results indicate two off-

setting effects of participation on slack. A significant negative indirectrelation between participation and slack was found to act through infor-

mation asymmetry. Thus, managers reveal private information during the

budget process, reducing information asymmetry which subsequently re-

duces budget slack. These results provide evidence about the inability of

past research to confirm a consistent direct relation between budget par-

ticipation and budget slack.

The problem of budgetary slack has been extensively studied by researchersin accounting. Budget slack, defined as overstated expenses, understatedrevenues, or underestimated performance capabilities, allows managers toobtain excess resources and to shirk more effectively. It can also be used as a

Advances in Management Accounting, Volume 16, 141–157

Copyright r 2007 by Elsevier Ltd.

All rights of reproduction in any form reserved

ISSN: 1474-7871/doi:10.1016/S1474-7871(07)16004-4

141

LESLIE KREN AND ADAM S. MAIGA142

hedge against uncertainties that affect outcomes (Fisher, Maines, Peffer, &Sprinkle, 2002; Kren, 1997). From the organization’s perspective, however,slack budgets do not represent managers’ best estimates of expected resultsso they hinder planning and control, resource allocation, and coordinationof business unit activities (Baiman, 1982; Covaleski, Evans, Luft, & Shields,2003; Choudhury, 1985). Prior field-based research attempting to linkbudget participation to budget slack has provided inconsistent results andwhether budgetary slack is a likely outcome in participatively set budgets isa matter of conjecture (Hansen, Otley, & Van der Stede, 2003).

The objective of this study is to extend prior research by examining sub-ordinate–superior information asymmetry as an intervening variable linkingbudgetary participation and slack. The hypotheses developed below suggestthat participation is not linked directly to a reduction in segment slack butindirectly, through information asymmetry, such that more budget partic-ipation fosters a lower information asymmetry which, in turn, decreasessegment slack. The consideration of information asymmetry therefore in themodel adds to the existing literature on budgeting by offering insight intobudget slack. Such insight is useful as it is based on reviews of the budgetingliterature that consistently emphasize the need for richer theoretical modelsto better explain when and how participation is effective (Covaleski et al.,2003; Dunk & Nouri, 1998; Shields & Shields, 1998; Greenberg, Greenberg,& Nouri, 1994; Shields & Young, 1993).

Based on a sample of segment managers in S&P 500 firms, the resultsshow a negative indirect relation between participation and segment slack.As managers participate, they reduce information asymmetry with theirsuperiors and subsequently lower superior–subordinate information asym-metry leading to lower budget slack.

The next section provides the literature review and develops the hypoth-eses. Subsequent sections contain a description of the research method, ananalysis of the results, and a summary and conclusion.

LITERATURE REVIEW AND HYPOTHESES

DEVELOPMENT

Participation and Information Asymmetry

Information asymmetry arises when the subordinate has information rel-evant to the decision process associated with budgeting that is unavailable to

Information Asymmetry on Budgetary Participation and Segment Slack 143

the superior (Evans, Hannan, Krishnan, & Moser, 2001; Kren & Liao,1988). Subordinates’ participation in the budget-setting process gives supe-riors the opportunity to gain access to local information if subordinatescommunicate or reveal some of their private information (Baiman & Evans,1983; Covaleski et al., 2003). Therefore, participation is an organizationalsolution to the information asymmetry, whereby the higher the participationin budget setting, the lower the information asymmetry is expected to be.

Budget participation is expected to be negatively linked to informationasymmetry because participation provides a mechanism by which superiorscan learn subordinate manager’s private information. As participation in-creases, information becomes more valuable to the superior who increasesits use. This discussion is summarized as link P21 in the research modelshown in Fig. 1. Stated as a hypothesis:

H1. There is a negative link between budget participation and superior–subordinate information asymmetry.

Information Asymmetry and Budgetary Slack

In the second link in the model (P32), information asymmetry is positivelylinked to budget slack. When information asymmetry is lower, superiorshave better information about a budgeting manager’s performance capa-bility allowing superiors to improve ex ante inferences about the level ofbudget slack. Thus, as argued by Young (1985), lower superior–subordinateinformation asymmetry results in reduced budget slack because the budg-eting manager is then aware that the superior can directly evaluate the levelof slack in the budget. Merchant (1985), for example, reported that slack isnegatively related to subordinate managers’ perception of their superiors’

budgetparticipation

(Z1)

informationasymmetry

(Z2)

budgetslack(Z3)

P21 P32

P31

Fig. 1. Theoretical Model.

LESLIE KREN AND ADAM S. MAIGA144

ability to detect slack. Kren (1993) later reported that formal control tools,such as pre-action reviews, contact with superiors, and variances, whichpresumably enhanced the ability to detect slack (reduce information asym-metry), is negatively related to slack. Chalos and Haka (1990) and Evanset al. (2001), for example, demonstrated that gains to the organizationcan result from a reduction of information asymmetry through reducedbudget slack.

H2. There is a positive relation between superior–subordinate informa-tion asymmetry and budget slack.

Intervening Effect

In many organizational settings, a subordinate has more accurate informa-tion than his or her superior about factors influencing performance (Evanset al., 2001; Waller & Chow, 1985). However, the theoretical research ineconomics (e.g., Baiman & Evans, 1983) and psychology (e.g., Locke &Schweiger, 1979; Locke & Latham, 1990) assumes that participative budg-eting exists to share information between a superior and subordinate. An-alytical research has demonstrated that resource allocations can beimproved by the communication of the subordinate’s private information(Magee, 1989; Christensen, 1982; Baiman & Evans, 1983). Budget partic-ipation serves an informational function whereby subordinates can gather,exchange, and disseminate job-relevant information to facilitate theirdecision-making process and to communicate their private information toorganizational decision makers (Davis, DeZoort, & Kopp, 2006; Earley &Kanfer, 1985; Campbell & Gingrich, 1986; Nouri & Parker, 1998). Thisprivate information may be incorporated into the standards or budgetsagainst which subordinate’s performance would be assessed (Baiman &Evans, 1983). Therefore, participation provides subordinates with the op-portunity to reveal private information which allows central management toimprove resource allocation. This information results in more realistic plansand more accurate budgets. Magner, Welker, and Campbell (1996) forexample, found that budget participation ‘‘allows subordinates to introduceprivate information into the budgetary process, thereby enhancingthe budget’s quality.’’ This was consistent with several accounting studies(e.g., Merchant, 1981; Chow, Cooper, & Waller, 1988; Murray, 1990; Kren,1992; Magner et al., 1996; Nouri & Parker, 1998). This perspective suggeststhat participation is not linked directly to slack reduction but rather acts

Information Asymmetry on Budgetary Participation and Segment Slack 145

through information asymmetry. Thus, as depicted in the theoretical model(Fig. 1), we offer the following hypothesis:

H3. The relationship between budgetary participation and segment slackwill be explained by an indirect effect whereby participation decreasesinformation asymmetry, and information asymmetry is positively asso-ciated with segment slack.

METHODOLOGY

Sample

The objective of the sample selection procedures was to identify executive-level profit center managers for whom objective, archival financial infor-mation to measure segment slack was available at the profit-center level. Tothis end, the titles of managers listed in corporate proxy statements for allS&P 500 firms were cross-referenced with each firm’s segment-level disclo-sures in the Compustat Industry Segment data file. Managers were retainedin the initial sample if they could be identified from their job titles as man-agers of profit centers that clearly corresponded to segments listed in theCompustat segment disclosures. By this procedure, an initial sample wasdeveloped of 111 managers in 70 companies who were unambiguously profitcenter managers of reportable segments.

Segment data is disclosed in accordance with FAS 14 (AICPA, 1976),which requires separate reporting for any segment which accounts for morethan 10% of consolidated sales, profits, or assets. The Compustat data fileincludes segment sales, capital expenditures, depreciation, employee head-count, research and development, assets, and operating profit. Each segmentis assigned a four-digit SIC code by Standard & Poors.

A cover letter and a questionnaire were mailed to each manager in theinitial sample. A follow-up letter and another copy of the questionnaire weresent after approximately three weeks. All remaining non-respondents werelater contacted by telephone and another questionnaire was mailed to thespecific attention of the personal assistant that worked with the respondent.

Follow-ups of the original 111 managers revealed that 19 had retired,left the company, or had changed to new positions. Of the remaining92 potential respondents, 49 usable responses were received (a responserate of 53.3%). Forty-four different companies were represented. Therewere two respondents for each of five companies. Respondents were

LESLIE KREN AND ADAM S. MAIGA146

promised anonymity, but organizational affiliation was tracked to allowmeasurement of segment slack, as described below. Based on segment-levelSIC codes, 3 of the 49 managers in the final sample were in miningand construction (SIC 0-1799), 37 were in manufacturing (SIC 1999-3999), 1was a utility (SIC 4800-4992), 4 were in wholesale–retail (SIC 5000-5999),3 were in banking (SIC 6000-6399), and 1 was in miscellaneous services(SIC 6400-9999).

Measurement of Variables

The appendix contains an abbreviated copy of the research questionnaireused to measure the self-reported variables in this study. The reliabil-ity coefficient (Cronbach’s Alpha) for each of the self-reported scalesexceeded 0.80.

Budget participation was measured using the Milani (1975) six-itemmeasure. The validity of this scale has been assessed several times in priorresearch, including Brownell (1983). Factor analysis confirmed the single-factor structure of the scale. Only one factor was present with an eigenvaluegreater than 1. For subsequent analysis, the six items were summed.

Information asymmetry was measured using a six-item scale in Dunk(1993) and Jaworski and Young (1992). Factor analysis revealed that onlyone factor was present with an eigenvalue greater than 1. For subsequentanalysis, the six items were summed.

A precise measure of budget slack is not possible because slack manifests ina variety of ways. Moreover, different types of slack allow managers more orless discretion, which determines the amount of slack needed to pursue per-sonal goals. Sharfman, Wolf, Chase, and Tansik (1988), for example, iden-tified a range of resources providing varying managerial flexibility, beginningwith cash, which provides the most flexibility to managers. Thus, empiricalefforts to objectively measure slack have focused on determining conditionsunder which slack is more likely to be present by using financial variableswhich are presumably antecedent indicators of budget slack (Nohria &Gulati, 1996). As proposed by Leavins, Omer, and Vilitus (1995), investiga-tion of slack behavior can be accomplished by identifying a set of measurablevariables whose pattern of behavior is expected to parallel slack behavior.

To this end, researchers have operationalized slack along two, non-mutuallyexclusive, dimensions. One dimension focuses on investments made bymanagers. Greater investment signals greater availability of slack resources.Investment can take the form of capital expenditures for highly flexible

Information Asymmetry on Budgetary Participation and Segment Slack 147

machine capacity and research and development expenditures (Sharfmanet al., 1988; Greenley & Oktemgil, 1998). For example, Bourgeois (1981),proposed that slack occurs when managers have resources available to re-tain in their operations, rather than distribute to shareholders or their su-periors. Thus, he argued that greater investment by managers signals greateravailability of slack resources.

The other dimension of objective slack measures in the literature hasfocused on the level of expenditures authorized by managers. Here, slack ispresumed to be related to the level of expenditures in cost of goods sold,selling and administrative, research and development, and inventories be-cause slack can accumulate in these accounts (Bourgeois, 1981). Schiff andLewin (1970) provided confirming evidence when they reported that theseexpenditures decline following organizational efforts to reduce slack.

Prior empirical research has focused exclusively on organizational slack.We are not aware of previous attempts in the literature to objectively meas-ure budget slack at the sub-unit level.

Following this prior literature, our operational measure of slack wasbased on financial accounting measures of investments and expendituresauthorized by managers. Thus, we use the sum of capital expenditures andresearch and development as antecedent indicators of slack. These tendto be non-repetitive transactions, which according to Leavins et al. (1995),are an appropriate ‘barometer’ of slack. The sum of capital expendituresplus research and development expenditures was divided by segmentsales and averaged over the three-year observation period. Data availabil-ity was a constraint since few financial accounting measures are disclosedat the segment level in the Compustat Industry Segment database or in SECfilings.

To compare each segment’s available resources with other similarsegments, an average of the sum of capital expenditures plus researchand development expenditures divided by segment sales over the three-yearperiod 1995–1997 was calculated for each industry reported in the Com-

pustat Industry Segment data file. Industry was defined as all other segmentslisted on Compustat in the same four-digit SIC as each sample segment.When less than three other firms were in the same four-digit SIC as a samplefirm, the three-digit SIC average was used, and if fewer than three firms wereavailable in the three-digit SIC, then the two-digit SIC average was used. Asample segment was excluded in the calculation of its corresponding indus-try mean. In calculating the industry mean, outlier segments with three-yearaverage capital expenditures plus research and development expendituresexceeding twice segment sales were deleted. The slack measure for each

LESLIE KREN AND ADAM S. MAIGA148

sample firm i, was then defined as follows:

Slacki ¼ industry resources� segment resourcesi.

Empirical Procedures

The hypotheses are examined using the path analysis model summarized inFig. 1. Path analysis is appropriate for estimating the relations between aseries of interrelated variables (Wonnacott & Wonnacott, 1981). For thisstudy, it allows analysis of the direct link between participation and slackand the indirect link through information asymmetry. The path coefficients,Pij, in Fig. 1, indicate the impact of variable j in explaining the variance invariable i in units of standard deviation.

A series of regressions are used to estimate the path coefficients, accordingto the following:

Information asymmetryðZ2Þ ¼ P21ðparticipationÞ (1)

SlackðZ3Þ ¼ P31ðparticipationÞ þ P32ðinformation asymmetryÞ (2)

The path coefficients can be used to decompose the total relation betweentwo variables (i.e., slack and participation) into direct and indirect effects(i.e., through information asymmetry). The total relation is measured withthe zero-order correlation coefficient, rij. Thus,

r12 ¼ P21 (3)

r23 ¼ P32 þ P31r12 (4)

r13 ¼ P31 þ P32r12 (5)

The subscripts 1, 2, and 3 refer to participation, information asymmetry,and slack, respectively (Fig. 1). Model (4) allows decomposition of thetotal relation between information asymmetry and slack (r23) into adirect effect (P32) and a spurious effect (P31r12). The spurious effect resultsfrom participation, which is a common antecedent of both informationasymmetry and slack. Model (5) allows decomposition of the total relationbetween participation and slack (r13) into a direct effect (P31) and the in-direct effect through information asymmetry (P32r12). H3 posits that theindirect effects of participation, through information asymmetry, will pre-dominate.

Information Asymmetry on Budgetary Participation and Segment Slack 149

RESULTS

Table 1 provides descriptive statistics for measured variables in the study.The objective slack measure, denoted segment slack, is negative for 11(22.4%) of sample firms. Thus, on average, most of the sample firms have agreater level of resources available than the mean resources available toother industry segments listed on Compustat. The distribution for segmentslack is positively skewed, although both the mean and median fall withinthe quartiles. Eliminating the two most extreme observations for segmentslack brought the mean close to the median but had no substantive effect onthe results reported later.

In addition to repeating the analysis after dropping the two observationswith large segment slack measures, two other sensitivity tests were per-formed. The analyses were repeated after dropping the two segments in thebanking industry and the segment in the utility industry since control systemdifferences may exist due to regulatory effects. In addition, the analyses wererepeated after including the natural log of sales as a control for segment size(or unmeasured variables correlated with size). These additional sensitivitytests had no substantive effect on reported results. We also controlled forindustry affiliation (based on SIC code). Results (not reported) indicate thatindustry does not have an effect on the variables. Therefore, given degrees offreedom required when the control variable was included, we removed theindustry variable in order to provide additional power for the hypothesestests.

Variable correlations are shown in Table 2. The significant negative cor-relation between participation and information asymmetry (�0.570;

Table 1. Descriptive Statistics for Propensity to Create Slack, SegmentSlack, Information Asymmetry, and Participation for 49 Sample

Segmentsa.

Theoretical

Range/Actual

Range

Mean/

Median

SD Quartile 1 Quartile 3

Segment slack na/2.6 0.16/0.05 0.39 0.002 0.20

Information asymmetry 42/36 29.0/36.0 12.4 14.0 40.0

Participation 42/35 25.4/28.0 14.4 10.0 40.0

aSegment slack is measured as the sum of capital expenditures plus research and development

expenditures divided by segment sales, averaged over the three-year period 1995–1997. Other

variables are self-reported scales as described in the paper.

Table 2. Correlations for Propensity to Create Slack, Segment Slack,Information Asymmetry, and Participation for 49 Sample Segments

(Decimals Omitted)a.

2 3

1. Segment slack 0.350�� 0.003

2. Information asymmetry – �0.570���

3. Participation – 1.00

aSegment slack is measured as the sum of capital expenditures plus research and development

expenditures divided by segment sales, averaged over the three-year period 1995–1997. Other

variables are self-reported scales as described in the paper.��po0.05.���po0.01.

LESLIE KREN AND ADAM S. MAIGA150

po0.01) is consistent with H1. The positive correlation between informationasymmetry and segment slack (0.350; po0.05) is also consistent with H2.These relations will be explained more clearly as the path analysis results arediscussed next.

The results of estimating the research models (1) and (2) are shown inTable 3 and Fig. 2. As observed in Table 2, the link between informationasymmetry and budget participation (P21), is significant and the sign isnegative as expected (model 1, Table 3, and Fig. 2). The negative signindicates that as participation increases, information asymmetry decreasesand suggests that managers reveal information to their superiors duringbudget participation, reducing the level of information asymmetry. Thisresult is consistent with H1. This result obtained appears to be consistentwith prior research results that suggest that even though managers canbenefit from budget slack, the nature of the budgeting process may in factprovide incentives for (self-interested) managers to reveal their private in-formation and reduce budget slack (Kren, 1997). First, managers may findthat obtaining adequate resources for their area of responsibility requiresthat they disclose some of their private information and reduce budget slack.For example, Nouri and Parker (1998) propose that participative budgetingis an important means by which managers can influence the resourcesthey receive. Hopwood (1974) and Fisher, Frederickson, and Peffer (2000)similarly suggested that budgeting is a ‘bargaining’ process. As they bargainfor resources, budgeting managers will need to reveal some of theirprivate information. Managers are also likely to disclose information andreduce budget slack while they attempt to secure job-relevant information

Informationasymmetry

(Z2)

Budgetparticipation

(Z1)

Segmentslack (Z3)

P31 = .288

P32 = .499P21 = -.570

Fig. 2. Research Model Estimates.

Table 3. Path Analysis Results for 49 Sample Segments.

Model 1 : Information asymmetry ðZ2Þ ¼ P21ðparticipationÞ þ �.

Model 2 : Slack ðZ3Þ ¼ P31ðparticipationÞ þ P32ðinformation asymmetryÞ þ �.

Dependent Variable (t-Statistics in Parentheses)

Model 1 Model 2

Information Asymmetry Segment Slack

Participation �0.570 (�4.76���) 0.288 (1.86�)Information asymmetry 0.499 (3.22��)R-square 0.33 0.18

F-statistics 22.7��� 5.18��

Note: Segment slack is measured as the sum of capital expenditures plus research and devel-

opment expenditures divided by segment sales, averaged over the three-year period 1995–1997.

Other variables are self-reported scales as described in the paper.�po0.10.��po0.05.���po0.01.

Information Asymmetry on Budgetary Participation and Segment Slack 151

to evaluate alternative budget goals (Campbell & Gingrich, 1986; Early,Wojnaroski, & Prest, 1987; Lawrence & Lorsch, 1967; Kren, 1992). As theirinformation search activities proceed, some of the manager’s own privateinformation becomes incorporated into the budget because social normsrequire an information exchange as managers communicate with superiors(Fisher et al., 2000; Hopwood, 1974; Simons, 1987).

To evaluate the model linkages, the research model is decomposed, asshown in Table 4. The direct path relations are also shown in Fig. 2.

Table 4. Decomposition of Path Analysis Relations for 49 SampleSegments.

Model 3 : r12 ¼ P21,

Model 4 : r23 ¼ P32 þ P31r12,

Model 5 : r13 ¼ P31 þ P32r12,

where, Z1 is participation, Z2 information asymmetry, Z3 propensity to create slack or segment

slack.

Dependent Variable/Link

to

Total Effect rij Direct Effect

Pij

Indirect

Effect

Spurious

Effect

Model 3: Information

asymmetry/participation

�0.570��� �0.570��� – –

Model 4: Segment slack/

information asymmetry

0.350�� 0.499�� – �0.149

Model 5: Segment slack/

participation

0.003 0.288� �0.285� –

Note: Segment slack is measured as the sum of capital expenditures plus research and devel-

opment expenditures divided by segment sales, averaged over the three-year period 1995–1997.

Other variables are self-reported scales as described in the paper.�po0.10.��po0.05.���po0.01.

LESLIE KREN AND ADAM S. MAIGA152

For the slack measure, denoted segment slack (Fig. 2; Table 4), the resultindicates a significant direct path from information asymmetry (P32) tosegment slack. Only a small portion of the link is spurious (0.149) relative tothe direct effect (0.499). This result is consistent with H2 and supports theargument that lower superior–subordinate information asymmetry results inreduced budget slack, presumably because the superior can directly evaluatethe level of slack in the manager’s budget.

Combined with the negative link between budget participation and in-formation asymmetry, the positive link between information asymmetryand segment slack result in a significant negative indirect relation (�0.285)between segment slack and budget participation. As shown in model 5,Table 4, for every standard deviation increase in participation, informationasymmetry decreases by �0.570 standard deviation (model 3), and for everystandard deviation decrease in information asymmetry, segment slack de-creases by 0.499 standard deviation (model 4). Thus, the total indirect effectof every standard deviation increase in participation, is a decrease in seg-ment slack of �0.285 standard deviation through information asymmetry(model 5). This negative indirect effect is the sign predicted by H3,

Information Asymmetry on Budgetary Participation and Segment Slack 153

i.e., managers have incentives to reveal private information during thebudget process and concomitantly segment slack is reduced.

In contrast to H3, the result also indicate a significant direct path tosegment slack (P31 ¼ 0.288). This direct effect indicates that for everystandard deviation increase in participation, segment slack increases by0.288 standard deviation (model 5). This result is consistent with agencyarguments that budgeting managers are motivated to bias their budget es-timates for self-interest and that participation provides the mechanism formanagers to insert slack into their budgets.

The findings imply that, for superior to reduce segment slack in a par-ticipating budget setting, reduction in information asymmetry may be usedas a control mechanism to achieve this end.

SUMMARY AND CONCLUSION

The objective of this study was to examine subordinate–superior informa-tion asymmetry as an intervening variable linking budgetary participation tobudget slack. Predictions were that participation would be negatively relatedto information asymmetry which would subsequently be positively relatedto budget slack.

Using path analysis, the evidence supports the negative link betweenbudget participation and information asymmetry. This suggests that par-ticipating managers reduce information asymmetry with their superiors. Thesubsequent positive link between segment slack and information asymmetrywas also supported. This finding is consistent with the argument that lowersuperior–subordinate information asymmetry leads to lower budget slackbecause the superior can directly evaluate the level of slack in the budget.The indirect effect was sizable. For every standard deviation increase inparticipation, segment slack decreases by 0.285 standard deviation. How-ever, the result for segment slack indicates a positive significant direct effectof participation on segment slack. Overall, this is consistent with the ar-gument that participation, through information asymmetry, does not pro-vide the vehicle for managers to insert slack into their budgets.

Limitations of this study should be mentioned. First, segment slack wasmeasured within data availability constraints that represented resourcesmost directly controllable by the current segment manager. We suggest thatfuture research include additional objective slack measures. Second, ofcourse, is the size of the sample. This is a sample of only 49 business seg-ments. Although that is encouraging in that it provides a very conservative

LESLIE KREN AND ADAM S. MAIGA154

test of our model and suggests that the effects we see are quite strong, wemust be especially cautious in generalizing. Third, the paper only focused onone intervening variable designed to reduce budget slack. Attempt to reducebudget slack may be achieved through other variables (e.g., manager’s in-centive, commitment, perception of fairness). Consequently, future researchshould concentrate on developing intervening variables designed to reducebudget slack. These intervening variables would be more likely help under-stand the conflicting results based on the direct link between budget par-ticipation and budget slack.

Even with these limitations, however, the findings are both intuitively andpractically significant because they demonstrate the process by which budg-etary participation translates into lower budget slack. Our understanding isthus enhanced by the recognition of information asymmetry as interveningvariable between the level of budget participation and budget slack. Fur-thermore, the findings provide practical guidance to managers involved inresource allocation decisions. These results provide some evidence about theinability of past research to confirm a consistent direct relation betweenbudget participation and budget slack.

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Information Asymmetry on Budgetary Participation and Segment Slack 157

APPENDIX: ABBREVIATED RESEARCH

QUESTIONNAIRE

Participation

Response anchors: 1 ¼ strongly disagree, 7 ¼ strongly agree.

Q1.

I am involved in setting all of my budget. Q2. My superior clearly explains budget revisions. Q3. I have frequent budget-related discussions with my superior. Q4. I have a great deal of influence on my final budget. Q5. My contribution to the budget is very important. Q6. My superior initiates frequent budget discussions when the budget is

being prepared.

Information Asymmetry

Response anchors: 1 ¼ strongly disagree, 7 ¼ strongly agree.

Q1.

In comparison to my superior, I have better information regarding theactivities in my area of responsibility.

Q2.

In comparison to my superior, I am more familiar with the input–output relations in my area of responsibility.

Q3.

In comparison to my superior, I am more familiar with the performancepotential of my area of responsibility.

Q4.

In comparison to my superior, I am more familiar technically with myarea of responsibility.

Q5.

In comparison to my superior, I am better able to assess the impact ofexternal factors on my area of responsibility.

Q6.

In comparison to my superior, I have a better understanding of whatcan be achieved in my area of responsibility.

DO ACCOUNTING PERFORMANCE

MEASURES INDEED REDUCE

MANAGERIAL AMBIGUITY

UNDER UNCERTAINTY?

Frank G. H. Hartmann

ABSTRACT

Research on budget-based performance evaluation traditionally predicts

that the use of accounting performance measures (APM) in complex,

dynamic, and uncertain situations results in dysfunctional managerial at-

titudes and behaviors. Although this suggests that such situations require

the use of subjective performance measures (SPM), empirical evidence is

inconclusive, as APM, rather than SPM, have been found to also have a

negative effect on managerial ambiguity. This suggests that APM may be

more, rather than less, appropriate than SPM in situations of high un-

certainty. This paper explores whether acknowledgement of different

types of uncertainty may explain these apparently conflicting research

findings. It develops hypotheses that predict differential interactions be-

tween the environmental uncertainty and task uncertainty and APM and

SPM on managerial ambiguity. These hypotheses are tested using survey

data from 250 managers in 11 organizations. Tests using moderated re-

gression analysis provide support for the existence of different inter-

actions between uncertainty and the use of performance measures, and

provide reconciliation for the opposing findings in the extant literature.

Advances in Management Accounting, Volume 16, 159–180

Copyright r 2007 by Elsevier Ltd.

All rights of reproduction in any form reserved

ISSN: 1474-7871/doi:10.1016/S1474-7871(07)16005-6

159

FRANK G. H. HARTMANN160

1. INTRODUCTION

The negative effects of using budgets for managerial performance evalua-tion forms a central theme in the accounting literature (Briers & Hirst,1990). Empirical studies generally seek to confirm the expectation thatbudget-based accounting performance measures (APM) cause dysfunctionalmanagerial attitudes and behaviors, particularly in situations of high or-ganizational uncertainty and complexity (Hartmann, 2000). In such situa-tions the lack of controllability and completeness generally associated withbudget-based performance metrics is expected to become especially harmful,and the implication is that supervisors should rely on subjective perform-ance measures (SPM) instead (Hopwood, 1972; Moers, 2005). Empiricalevidence is inconclusive however. Recently, Marginson and Ogden (2005)therefore criticized the research emphasis on the negative sides of budget-based performance metrics, and blame it for our current poor understandingof the more positive consequences of using APM. In contrast with standardpredictions, they argue and show that budget-based performance metricsmay be especially useful in uncertain and complex organizational contexts,as budget-based control may provide individual managers with the pathsand goals to cope with such contexts (House, 1971; Marginson & Ogden,2005, p. 436). In particular, budgets will serve as an antidote for managerialambiguity (MANAAMBI), which is a lack of clarity managers in decen-tralized organizations may have about their role, their job objectives, andthe scope of their responsibilities (e.g., Vancil, 1979; Sawyer, 1992). Thearguments and findings of Marginson and Ogden (2005) are important sincethey allow better explanations of the continuing importance of budgets incontemporary organizations than studies that merely address the negativesides of budgeting (cf. Umapathy, 1987; Fisher, Maines, Peffer, & Sprinkle,2002). They are problematic at the same time, however, as they contradictsome earlier findings in the APM literature, without attempting theirreconciliation. Indeed, Marginson and Ogden (2005, p. 435) are merelyconcluding ‘‘budgets [y] may be as useful to the individual as they areproblematic’’. The purpose of the present paper is to provide a theoreticaland empirical reconciliation of the positive and negative effects of the use ofAPM and SPM under uncertainty. It argues that not uncertainty per se, butrather the type of uncertainty determines whether APM or SPM havedesirable or undesirable effects. In particular, it distinguishes between taskuncertainty (TU) and environmental uncertainty (EU), and argues thatthese different types of uncertainty have a different effect on the appropri-ateness of APM and SPM to reduce MANAAMBI. The remainder of the

Do Accounting Performance Measures Indeed Reduce Managerial Ambiguity 161

paper is structured as follows. Section 2 provides a brief overview of theextant literature, resulting in the formulation of hypotheses about the effectsof APM and SPM on MANAAMBI under task and EU. Section 3 presentsthe method of the empirical survey study conducted to gather data. Section4 reports the tests of the hypotheses. Section 5 concludes this paper with adiscussion of the findings and their implications.

2. LITERATURE REVIEW AND DEVELOPMENT OF

HYPOTHESES

Overviews of the empirical literature that investigates the contextual ap-propriateness of APM suggest that predictions about its adverse effects arecontingent on managers perceiving the organizational context as uncertainty(Chapman, 1997; Hartmann, 2000; Covaleski, Evens, Luft, & Shields, 2003).Perceived uncertainty in general reflects the beliefs of human actors abouttheir inability to predict future states of the world, including the outcomesof their actions (cf. Chapman, 1997; Hartmann, 2000). Uncertainty is animportant source of MANAAMBI as it concerns the impact of context onthe manager’s performance, and the required managerial actions andresponses (cf. Vancil, 1979; Milliken, 1987; Marginson & Ogden, 2005;Chun & Rainey, 2005). In an early study, Hirst (1983) thus suggested andfound that the use of APM under high uncertainty resulted in managersexperiencing high job-related tension. Building on earlier budgetary studieswhose contradictory evidence suggested that any effects of APM would becontext specific rather than universal (Hopwood, 1972; Otley, 1978), Hirstargued that contextual uncertainty would negatively affect managers’ beliefsabout the controllability, the completeness, and the relevance of budgetaryperformance targets (cf. Hopwood, 1972). Under high uncertainty, stressingsuch targets through the use of APM would create tension between the ‘true’and the budgetary job requirements, which would result in job-relatedtension. Subsequent studies that attempted to replicate Hirst’s finding wereonly partially successful, as they found high uncertainty to have no effects(e.g., Govindarajan, 1984; Imoisili, 1989; Ross, 1995), and even positiveeffects (e.g., Ezzamel, 1990), rather than the expected negative effects(e.g., Brownell, 1985). Ross (1995, p. 9) accounted for his failure to replicatethe negative effect of APM under uncertainty by suggesting that replacingAPM with SPM could have negative consequences as well. High uncertaintywould intensify the lack of clarity and objectivity associated with subjectiveevaluations, which could enhance MANAAMBI about both the required

FRANK G. H. HARTMANN162

and achieved performance levels (cf. Marginson & Ogden, 2005). Althoughnot broadly recognized in the APM literature, this explanation of positiveeffects of using APM under uncertainty seems consistent with the goal-theoretic explanations on the effect of goal specificity on individuals’motivation and task performance (Locke & Latham, 1990). Reviews of thegoal-theoretic literature done by Locke and Latham (1990) and Rodgersand Hunter (1991) provide firm evidence that specific goals, rather than‘do-your-best goals’, increase job satisfaction and performance, and de-crease job-related tension and MANAAMBI. Clearly, the budgeting systemis an important source of such goals as is indeed emphasized throughout thenormative goal-theoretic literature (e.g., McConkie, 1979), which presentsbudget-based responsibility accounting systems as a specific example ofapplied goal-setting (see, e.g., Schuler, Beutell, & Youngblood, 1989).Earlier, Tosi (1975, p. 150), summarizing available goal-setting evidence,even suggested ‘‘y the motivating effect of the budget derives from simplythe fact that it is a statement of explicit goals’’. Also Hopwood (1972, p. 173)acknowledges this potential function of budgets, stating ‘‘y one purpose ofthe budget is to clearly set out the objectives for a cost center. [y] andthereby add an important element of structure and clarity to the jobenvironment’’, but he did not study how this function would work outunder uncertainty. Although the use of budgets for performance evaluationunder uncertainty may be thus indeed be both useful and problematic(cf. Marginson & Ogden, 2005), little knowledge is gained beyond budgets’potential effects unless we study the exact conditions under which budgetsenhance (cf. Hirst, 1983) or reduce (cf. Ross, 1995) MANAAMBI.

It is very likely that part of the required reconciliation of these oppositefindings may come from a further understanding of the nature of uncer-tainty that affects the appropriateness of APM (and SPM). As Hartmann(2000) observed in his review of this area of the empirical literature, em-pirical studies have used two uncertainty constructs interchangeably, EUand TU, with apparent disregard for their differential nature and their po-tentially rather different implications for the appropriateness of budget-based performance evaluation (cf. Chapman, 1997; Tymon, Stout, & Shaw,1998; Hartmann, 2000, p. 445). These two sorts of uncertainty are intro-duced now, after which their implications for the appropriateness of bothAPM and SPM will be discussed.

The distinction between EU and TU originates in the work of early con-tingency theorists such as Thompson (1967). Analyzing uncertainty asso-ciated with managerial work, Thompson (1967, Chapters 5, 6) distinguishesbetween uncertainty stemming from sources outside the manager’s area of

Do Accounting Performance Measures Indeed Reduce Managerial Ambiguity 163

responsibility (i.e., EU), and uncertainty originating in the technologicalnature of the managerial tasks proper (i.e., TU). Regarding the latter type ofuncertainty, the two determinants of this type of uncertainty are the lack ofrepetitiveness and the lack of programmability of tasks, as these factors bothobstruct complete knowledge about cause–effect relationships when per-forming the task (e.g., Hirst, 1987; Campbell, 1988). When TU is high,managers have difficulty predicting the effects of their actions, which causesambiguity concerning the courses of action that should be taken in order toobtain desired results, and performance feedback (Luckett & Eggleton,1991). EU, instead, refers to factors – people, things, laws, and regulations –that make up the manager’s external task environment (Downey, Hellriegel,& Slocum, 1975; Ewusi-Mensah, 1981; Tymon et al., 1998). When EU ishigh, the managerial challenge is to define what desirable results are towhich effort should be directed, given the complex and changing demandsand pressures from the context on the managerial task portfolio. High EUwill, therefore, cause ambiguity concerning the nature of the results to beachieved by the manager, and their prioritization. The implications of thesetwo types of uncertainty for the appropriateness of APM and SPM areanalyzed separately below, starting with the case for EU.

Despite the mentioned common believe that uncertainty generally reducesthe usefulness of budgets and APM, there is considerable evidence thatsuggests the importance of formal planning systems in general in situationscharacterized by EU. Horngren, Sundem, and Stratton (1996, p. 257), ex-plaining the role of budgeting in organizations, for example note: Skepticalmanagers have claimed, ‘‘I face too many uncertainties and complications tomake budgeting worthwhile for me’’. Be wary of such claims. Planning andbudgeting are especially important in uncertain environments. A budgetallows systematic rather than chaotic reaction to change. This normative,textbook, argument has received considerable empirical support over thelast two decades. Merchant (1989) illustrates that EU is not merely anexogenous factor affecting (budget) plans after preparation, but also anendogenous factor providing input for the (budget) planning process, whichforces organizational participants to clarify necessary actions. Simons (1987,1995) argues that EU enhances the roles of formal controls, such as APM,as he argues ‘‘y uncertain environments require more information sharingand personal exchange. The sharing and exchange process, however, doesnot occur in the absence of formal control procedures’’ (Simons, 1987,p. 359). Formal controls may in fact stimulate the interactions requiredbetween organizational participants in situations of external dynamism, asthey are used in an interactive fashion (cf. Simons, 1995). This explains that

FRANK G. H. HARTMANN164

EU has been found to enhance the usefulness of formal cost controls(Khandwalla, 1972), and of budgeting systems in general (Abernethy &Brownell, 1999). At the individual managerial level, formal (budgetary)control systems take on an important additional role, which has been de-scribed as the ‘buffering function’ of budgets. Merchant (1984, p. 293) forexample notes that, in the context of EU stemming from unpredictablemarket developments ‘‘y the budget can be used for buffering by treatingit as a fixed performance commitment, regardless of how the market ischanging’’. Such a buffering function of control systems has been docu-mented in the wider organizational and administrative literatures as well(e.g., Thompson, 1967; Hayes, 1977; Olson & Rombach, 1996), and isdeemed important to reduce MANAAMBI by shielding the manager’simmediate working environment from its external context (cf. Olson &Rombach, 1996; Chun & Rainey, 2005). For the individual manager, thesepositive effects of budget-based APM will result in less MANAAMBI, andmore clarity about their managerial goals, roles, and responsibilities. Thisseems consistent with the recent arguments put forward by Marginson andOgden (2005). In situations of high EU, APM serve to communicate alimited set of clear, fixed, and relatively objective performance standards tosubordinate managers (Galbraith, 1977; Gordon & Narayanan, 1984;Ezzamel, 1990). The corollary of this argument is that the use of APMreduces the ambiguity associated with subjectivity performance evaluations,which have been argued to be especially harmful under high EU (see, e.g.,Rizzo, House, & Lirtzmann, 1970; Ezzamel, 1990; Ross, 1995). While ex-pecting a negative moderating effect of EU on the relationship between theuse of APM and MANAAMBI, we therefore expect a positive moderatingeffect of EU on the relationship between the use of SPM and MANAAMBI.The following two hypotheses will be tested accordingly.

H1. The effect of APM on MANAAMBI is more negative for higherlevels of EU.

H2. The effect of SPM on MANAAMBI is more positive for higherlevels of EU.

For the impact of TU on the relationship between the use of APMand SPM, and MANAAMBI the argumentation is different than for EU.Earlier, TU was defined as uncertainty caused by the inherent complexityand diversity of the managerial portfolio of tasks (cf. Hirst, 1983; Wood,

Do Accounting Performance Measures Indeed Reduce Managerial Ambiguity 165

Mento, & Locke, 1987; Campbell, 1988). Although the positive effectsof formal control systems may appear just as desirable in situations of highTU as they are in situations of high EU (Hirst, 1983; Early, Prest, &Wojnaroski, 1987; Ross, 1995), the extent to which APM and SPM mayprovide such clarity and structure is fundamentally different. TU is aninherent job characteristic, as it relates to knowledge about cause–effectrelationships that is limited because of the complexity and diversity ofmanagerial task portfolio. Although managers may have certain goals fortheir job that are specific and ‘objectively’ clear, high TU will generally causemanagers to feel undecided about the actions needed to attain those goals(Early, 1985). Locke and Latham (1990, p. 260) thus observe that theeffectiveness of goal setting will be lower under high TU, as under suchconditions effort does not necessarily pay off directly, and managers will feelambiguous about where and how to allocate their efforts (cf. Early, 1985;Hirst, 1987). Managers confronted with high TU will thus suffer from whatMilliken (1987) has labeled ‘response uncertainty’, which reflects theambiguity managers feel about the way in which a certain objective shouldbe achieved given the lack of precise cause–effect knowledge. Therefore, it isexpected that formal systems that dictate the attainment of a set of clear andobjective performance standards may in fact lead to confusion, and notclarity, over the way in which high performance may be attained (cf. Hirst,1987; Early, 1985; Wood et al., 1987; Sawyer, 1992). Instead, high TU mayprovide a rationale for more subjective ways of evaluating managerialperformance (Hopwood, 1972; Hirst, 1983, 1987). The use of subjectiveelements in their evaluation allows superiors to use their discretion whenassessing managerial efforts, which allows them to provide feedback oneffort and performance in cases where the mere attainment of preset goals isnot informative because of the said lack of cause–effect knowledge (see, e.g.,Locke & Latham, 1990; Luckett & Eggleton, 1991). Overall, therefore, thisleads to the expectation that TU has a positive moderating effect on therelationship between the use of APM and MANAAMBI, and a negativemoderating effect on the relationship between the use of SPM andMANAAMBI. The following two hypotheses will be tested accordingly.

H3. The effect of APM on MANAAMBI is more positive for higherlevels of TU.

H4. The effect of SPM on MANAAMBI is more negative for higherlevels of TU.

FRANK G. H. HARTMANN166

3. RESEARCH METHOD

To test the hypotheses, data were gathered through a questionnaire survey.The choice for the survey method was made to allow comparison with theempirical studies on which this study builds, and which it attempts to rec-oncile, and to enable the assessment of the use subjective performancemeasurement, as this is not typically documented (Ittner, Larcker, & Meyer,2003, p. 8). The survey was conducted in the Netherlands following a sam-pling method that had been successful in previous studies in this field (e.g.,Merchant, 1984; Ross, 1995). First a sample of organizations was selectedand asked for participation. Then, a sample of managers within those or-ganizations was approached. This stratification method provides an accept-able proxy to random sampling where such is not economically feasible,or would likely result in large non-response bias (e.g., Brownell, 1995;Pedhazur & Pedhazur-Schmelkin, 1991). Random sampling, which requiresthe a priori establishment of the population from which a sample is drawn,and to which inferences are made, was not possible as detailed and completedata on managers with budget responsibility are simply not available pub-licly (cf. Alreck & Settle, 1985; Oppenheim, 1992). Moreover, the methodapplied ensures variation in the key independent variables as organizationswere selected from various industries (Emory & Cooper, 1991, p. 275), andreduces the risk of drawing inferences from potential idiosyncrasies ofsingle organizations (Alreck & Settle, 1985, p. 45; Pedhazur & Pedhazur-Schmelkin, 1991, p. 320). Out of the 12 organizations initially approached,11 organizations agreed to take part in the study. In each organizationinterviews were conducted with one or more senior management officials,which served two purposes. A first purpose was to learn about the organ-izations’ budgeting and performance evaluation systems to assess the ap-plicability of the research topic for each organization. A second purpose wasto ask for formal organizational support for conducting the research in theorganization. To maintain anonymity and avoid selection bias, in each or-ganization one official was asked to select respondents (cf. Brownell, 1995)after being instructed to select a broad and large sample of responsibilitycenter managers, across functional areas and positions in the organizationalhierarchy, including line and staff managers, and of a single (Dutch) na-tionality. These managers would be in charge of a distinct area of respon-sibility, would have at least one functional subordinate (cf. Hirst, 1983;Ross, 1995), a separate budget, and would have experienced at least onebudgeting and performance evaluation cycle. In total, 250 managers wereselected. The sample size per organization ranged from 9 to 56, reflecting the

Table 1. Descriptives of 11 Participating Organizations.

Organization Main Activity Sample Usable Response

1. Chemicals Production 10 7 (70%)

2. Consumer electronics Production 34 29 (85%)

3. Consumer electronics Retail 27 19 (70%)

4. Automotive Production 15 11 (73%)

5. Food and drink Production 25 21 (88%)

6. Food and drink Production 9 6 (67%)

7. Electronic office equipment Production 24 17 (68%)

8. City development Project development 25 19 (76%)

9. City administration Governmental services 15 12 (80%)

10. National administration Legal services 56 48 (86%)

11. National government Defense 10 7 (70%)

Total 250 196 (78.4%)

Do Accounting Performance Measures Indeed Reduce Managerial Ambiguity 167

organization’s size and responsibility center structure. Table 1 below con-tains descriptive statistics on the sample of these 11 participating organi-zations.

3.1. Variable Measurement

EU was measured with a scale previously used in Govindarajan (1984) andMerchant (1984). The scale contains five attributes of the respondent’sorganizational environment, which are the behavior of (1) customers,(2) competitors, and (3) suppliers, (4) the rate of technological change inwork area, and (5) the rate of legal and political developments. For each ofthese five attributes respondents were asked to indicate on six-point, fullyverbally anchored Likert scales, both the perceived predictability and theperceived impact on work and performance (cf. Khandwalla, 1972), whichresulted in a ten-item overall instrument. Subsequent factor analysisrevealed that the ten items loaded on three factors.1 Although this appar-ent multidimensionality has been interpreted as support for the multidi-mensional nature of EU in some studies (e.g., Downey et al., 1975; Buchko,1994), scores for this study were based on the six items that loaded on thefirst factor since the specific aim of this study is to sharply delineate EUfrom other types of uncertainty. These six items related to customers, com-petitors and technological developments. Cronbach’s a for the reduced scalewas a satisfactory 0.77, exceeding scores obtained in previous occasions

FRANK G. H. HARTMANN168

(Govindarajan, 1984; Merchant, 1990). Variable scores for EU were calcu-lated using the average value of the scores on the six items.

TU was measured with an instrument developed by Withey, Daft, andCooper (1983) that asks respondents to indicate on five-point fully anchoredscales their level of agreement with nine statements related to the complexityand the variety of their tasks. The instrument was used in Brownell andHirst (1986) showing high reliability. The Cronbach’s a for the nine-itemscale was a satisfactory 0.87. Subsequent factor analysis revealed two factorsthat, however, did not correspond to the dimensions of variety and com-plexity that Brownell and Hirst (1986) distinguished. Because of the highand maximal Cronbach’s a, the overall score for TU was therefore calcu-lated as the mean of the scores on all nine items.

The use of APM and SPM was measured with the scale developed andused by Hofstede (1967) and Hopwood (1972), which has become the mostfrequently used instrument in extant research (cf. Hartmann, 2000; Vaigneur& Peiperl, 2000). This instrument contains three items related to APM andfive items related to SPM. Factor analysis attested to the two-factor struc-ture of the instrument, as Table 2 displays. The three items related toAPM loaded on one factor that explained 30.5% of variance, and had aCronbach’s a of 0.72. The five items related to SPM loaded on the secondfactor that explained 25.0% of variance, and had a Cronbach’s a of 0.73.The average scores of the respective sets of items were used to form variablescores for APM and SPM.

Table 2. Factor Analysis of Items for APM and SPM Scales (VarimaxRotation).

Q. Wording (Abbreviated) Scale Factor 1 Factor 2

1.a Concern with costs APM 0.198 0.834

1.b My (budgetary) results APM 0.009 0.844

1.c Whether I (always) meet my budget APM 0.021 0.726

2.a Cooperation with colleagues SPM 0.710 �0.075

2.b Effort put into work SPM 0.597 �0.011

2.c Concern with quality SPM 0.658 0.165

2.d Attitude toward work and organization SPM 0.771 0.133

2.e Ability to handle my employees SPM 0.714 0.126

Eigenvalue 2.703 1.737

Percentage of

variance explained

30.463 25.036

Table 3. Factor Analysis of Items for MANAAMBI (VarimaxRotation).

Q. Wording (Abbreviated) – R: Reversed Item Scale Factor 1 Factor 2

1.a Work-objectives are very clear and specific GC 0173 0.864

1.b Understand which objectives are more

important

GC 0.696 0.240

1.c Work objectives are ambiguous and vague

(R)

GC 0.469 0.612

1.d Clear, planned goals, and objectives exist for

job

RA 0.667 0.082

1.e Certain about the amount of authority RA 0.607 0.543

1.f Know what is expected RA 0.511 0.367

1.g Know how to divide time over different tasks

in job

RA 0.800 0.133

1.h Know what responsibilities are RA 0.118 0.895

Eigenvalue 3.837 1.061

Percentage of

variance explained

30.785 30.439

Do Accounting Performance Measures Indeed Reduce Managerial Ambiguity 169

MANAAMBI was measured using a scale that combined three itemsfrom Steers’ (1976) task-goal attributes questionnaire concerning goal-clarity (cf. Kenis, 1979), and five items from the instrument for role-ambiguity by Rizzo et al. (1970) and House (1971). Combining elementsfrom these two existing instrument was expected to have greater validitythan each of the two sets of items would have had individually, since thelarger eight item scale addresses both goal- and role-related characteristics.Factor analysis revealed two factors explaining 61.2% of variance in total.These factors did not, however, correspond to the original sub-scales,as Table 3 displays. For this reason, and because of a high and maximalCronbach’s a of 0.84 for the whole eight-item scale, the item scoreswere subsequently averaged to obtain a variable score for MANAAMBI.However, to allow assessing the robustness of the analyses based on thiscombined scales, two additional variables were constructed based on theoriginal items. Goal-ambiguity (GOALAMBI) signifies the lack of goal-clarity. It was measured by taking the (reversed) average scores on the threegoal-clarity items. ROLEAMBI signifies role-ambiguity. It was measured bytaking the average scores on the five ROLEAMBI items. Cronbach’s a was0.71 and 0.73 for these scales respectively. Table 4 provides an overview ofdescriptive statistics of the variables measured.

Table 4. Overview of Descriptive Statistics of the Variables Measured.

Variables Mean SD Theoretical Range Actual Range a

Panel A: Descriptives of variables (n ¼ 196)

EU 2.475 1.025 0.000–5.000 0.009–4.167 0.766

TU 2.541 0.671 1.000–5.000 1.889–4.889 0.869

APM 3.479 0.808 1.000–5.000 1.000–5.000 0.721

SPM 4.138 0.493 1.000–5.000 1.800–5.000 0.726

MANAAMBI 2.191 0.556 1.000–5.000 1.000–4.125 0.836

GOALAMBI 2.109 0.643 1.000–5.000 1.000–4.000 0.705

ROLEAMBI 2.240 0.566 1.000–5.000 1.000–4.200 0.728

Variables Mean SD Actual Range

Panel B: Descriptives of variables (n ¼ 196)

Age 46.201 6.890 31–61

Tenure in company 17.891 10.487 1–41

Tenure in job 5.766 5.710 1–42

FRANK G. H. HARTMANN170

3.2. Organization, Response Rates, and Demographics

The questionnaire was pre-tested with 5 faculty colleagues, 4 external re-viewers in senior management positions, and all 11 coordinating officials inthe participating organizations, after which it was field-tested with fourpotential respondents in two firms. Minor alterations were made in eachstep. Several measures from Dillman (2000) and Podsakoff, MacKenzie,Lee, and Podsakoff (2003) were used to optimize the response rate andreduce common method variance. These measures included the guarantee ofstrict anonymity, the use of high-quality printing with hand-written signa-tures on all correspondence, the use of pre-stamped envelopes and separatecards to request the study’s results, and the inclusion of a pen as token andfor convenience. Overall, this may have contributed to the usable responserate of 78.4% (see also Table 1), which compares favorably to earlier andsimilar studies (cf. Dillman, 2000). A test for potential non-response biaswas however conducted, comparing mean scores on variables for early andlate responders (cf. Oppenheim, 1992; Brownell, 1995). This test was usedthe promise of anonymity made it impossible to obtain additional infor-mation about non-respondents for comparison with respondents (cf. Op-penheim, 1992; Brownell, 1995). The results of this analysis showed noevidence of systematic bias from non-response above chance. To test forcommon method variance, we performed Harman’s one factor test as ad-vocated by Podsakoff and Organ (1986, p. 536). This procedure showed no

Table 5. Correlation Statistics (Pearson, Two-Tailed Significance,n ¼ 196).

Variables 1. 2. 3. 4. 5. 6.

1. EU

2. TU �0.126

(0.079)

3. APM 0.324 �0.169

(0.000) (0.018)

4. SPM 0.063 �0.070 0.183

(0.379) (0.330) (0.010)

5. MANAAMBI �0.101 0.275 �0.361 �0.277

(0.158) (0.000) (0.000) (0.000)

6. GOALAMBI �0.979 0.260 �0.342 �0.198 0.903

(0.172) (0.000) (0.000) (0.005) (0.000)

7. ROLEAMBI �0.925 0.275 �0.334 �0.301 0.956 0.739

(0.197) (0.000) (0.000) (0.000) (0.000) (0.000)

Do Accounting Performance Measures Indeed Reduce Managerial Ambiguity 171

sign of any dominant single factor, as eight factors were extracted thatexplained between 6.53% (minimal) and 10.50% (maximal) of variance.Sample descriptives on managerial tenure are reported in Table 4 as well.The average respondent’s age was 46.2 years. Respondents had, on average,worked with their present employers for 18.1 years, and had been, on av-erage, for 5.7 years in their present positions. The average number of em-ployees in the respondents’ area of responsibility, which includes both therespondent’s department and sub-departments, was 79.0. On average, therespondents’ span of control, as measured by the number of employeesunder direct supervision, was 8.8. The results of the correlation analyses thatwere performed are reported in Table 5.

4. RESULTS

The hypotheses were tested using moderated regression analysis using theprocedure recommended in Hartmann and Moers (1999). For each of thefour hypotheses, regressions were run according to the following equation

Y ¼ b0 þ b1X 1 þ b2X 2 þ b3X 1X 2 þ e (1)

In Eq. (1) Y denotes MANAAMBI, X1 denotes the use of APM or the use ofSPM, and X2 denotes EU or TU. Table 6 (panels A through D) reports onthe outcomes of these tests for the four hypotheses.

Table 6. Effects of APM and SPM on MANAAMBI.

Variable Coefficient Value SE t p

Panel A: Results of Hypothesis 1� (MANAAMBI is dependent variable)

Constant b0 �2.420 0.368 6.586 0.000

APM b1 �0.057 0.113 �0.506 0.613

EU b2 0.275 0.144 1.905 0.058

APM�EU b3 �0.080 0.042 �1.908 0.058

Panel B: Results of Hypothesis 2�� (MANAAMBI is dependent variable)

Constant b0 3.584 0.899 3.985 0.008

SPM b1 �0.309 �0.216 �1.434 0.153

EU b2 �0.051 �0.342 �0.148 0.883

SPM�EU b3 0.001 0.082 0.015 0.988

Panel C: Results of Hypothesis 3��� (MANAAMBI is dependent variable)

Constant b0 2.050 0.629 3.260 0.000

APM b1 �0.096 0.171 �0.560 0.576

TU b2 0.364 0.242 1.503 0.134

APM�TU b3 �0.052 0.067 �0.769 0.443

Panel D: Results of Hypothesis 4���� (MANAAMBI is dependent variable)

Constant b0 �0.497 1.151 �0.432 0.666

SPM b1 0.487 0.273 1.785 0.076

TU b2 1.648 0.452 3.649 0.000

SPM�TU b3 �0.334 0.107 �3.128 0.002

�Adjusted R2¼ 13.3%; F3,192 ¼ 10.982; p ¼ 0.000; n ¼ 196.

��Adjusted R2¼ 7.0%; F3,192 ¼ 5.858; p ¼ 0.001; n ¼ 196.

���Adjusted R2¼ 16.7%; F3,192 ¼ 14.021; p ¼ 0.000; n ¼ 196.

����Adjusted R2¼ 19.2%; F3,192 ¼ 16.452; p ¼ 0.000; n ¼ 196.

FRANK G. H. HARTMANN172

Regarding the EU hypotheses, the results suggest the existence of a neg-ative moderating effect of uncertainty on the relationship between the use ofAPM and MANAAMBI, as was predicted in Hypothesis 1 (see Table 6,panel A). Hypothesis 1 indicates that the relationship between the use ofAPM and MANAAMBI is more negative for higher values of EU, whichcorresponds with the negative and significant effects found for the interac-tion term’s effect on MANAAMBI. In contrast, there is no positive inter-active effect of the use of SPM and EU (Table 6, panel B), which contradictsHypothesis 2. This suggests that an increasing use of SPM under high un-certainty does not result in more MANAAMBI.

Regarding the TU hypotheses, the analyses show that Hypothesis 3should be rejected. We do not find the expected positive interactive effect ofthe use of APM and TU on MANAAMBI (Table 6, panel C). We do find,

Do Accounting Performance Measures Indeed Reduce Managerial Ambiguity 173

however, support for Hypothesis 4 that predicted a negative effect of TU onthe relationship between SPM and MANAAMBI (Table 6, panel D). Thissuggests that the use of SPM reduces MANAAMBI more under higherlevels of TU.

In addition to the straightforward analysis of the four hypotheses, twoadditional analyses were performed to assess the robustness of our findings.First, we reanalyzed Hypotheses 1 and 4 replacing the dependent variableMANAAMBI with two variables derived from the scores on the three itemsrelated to GOALAMBI and the five items related to ROLEAMBI. Resultsof these analyses are reported in Table 7 (panels A through D). These resultsshow that the negative interactive effect of EU and APM is attributable tothe enhanced clarity of goals, rather than to a reduction in role ambiguity(Table 7, panels A and B). In contrast, the negative interactive effect of TU

Table 7. Effects of APM and SPM on GOALAMBI and ROLEAMBI.

Variable Coefficient Value SE t p

Panel A: Additional analysis of Hypothesis 1� (GOALAMBI is dependent variable)

Constant b0 3.886 0.425 4.972 0.000

APM b1 0.015 0.131 0.116 0.907

EU b2 0.405 0.167 2.429 0.016

APM�EU b3 �0.120 0.049 �2.463 0.015

Panel B: Additional analysis of Hypothesis 1�� (ROLEAMBI is dependent variable)

Constant b0 2.604 0.380 6.852 0.000

APM b1 0.101 0.117 0.862 0.390

EU b2 0.196 0.149 1.317 0.189

APM�EU b3 �0.056 0.043 �1.298 0.196

Panel C: Additional analysis of Hypothesis 4��� (GOALAMBI is dependent variable)

Constant b0 0.584 1.395 0.419 0.676

SPM b1 0.200 0.331 0.604 0.547

TU b2 1.086 0.548 1.983 0.049

SPM�TU b3 �0.196 0.129 �1.512 0.132

Panel D: Additional analysis of Hypothesis 4���� (ROLEAMBI is dependent variable)

Constant b0 �1.146 1.154 �0.993 0.322

SPM b1 0.660 0.274 2.411 0.017

TU b2 1.986 0.453 4.384 0.000

SPM�TU b3 �0.417 0.107 �3.894 0.000

�Adjusted R2¼ 13.1%; F3,192 ¼ 10.784; p ¼ 0.000; n ¼ 196.

��Adjusted R2¼ 10.6%; F3,192 ¼ 8.676; p ¼ 0.000; n ¼ 196.

���Adjusted R2¼ 11.1%; F3,192 ¼ 9.138; p ¼ 0.000; n ¼ 196.

����Adjusted R2¼ 21.6%; F3,192 ¼ 18.920; p ¼ 0.000; n ¼ 196.

FRANK G. H. HARTMANN174

and APM seems attributable to the reduction in role ambiguity, rather thanan increase in the clarity of goals (Table 7, panels C and D). The substantiveimplications of these more detailed findings will be discussed in the finalsection of this paper. Results from rerunning these regressions with dum-mies for each of the organizations suggest that these findings cannot beattributed to systematic organizational differences.

Finally, it was established that the reported results are not driven byobvious spurious factors, such as the age and experience of the managers.For this aim, all regressions were rerun including control variables thatmeasured managers’ age, tenure in the company and tenure in their presentjobs. This did not lead to substantial changes in the results, suggesting thatthe described effects of uncertainty are not driven by factors related to ageand work experience.

5. CONCLUSIONS AND IMPLICATIONS

This paper attempted to explain the appropriateness of APM and SPMunder uncertainty, which is an unresolved issue in the extant literature. Forthis purpose, a distinction was made between TU and EU in an attempt toreconcile findings in previous studies of positive and negative effects of usingAPM under uncertainty. The conclusions and implications of this study areas follows.

For EU, the results support a positive effect of APM in terms of reducingMANAAMBI. As predicted, the use of APM reduces MANAAMBI morefor higher levels of EU (H1). For TU, the analysis shows no such interactiveeffect, which suggests that the use of APM does not have the greater (neg-ative) effect on MANAAMBI that was expected (H3). Instead, the use ofSPM appeared to affect to have a more negative effect on MANAAMBI forhigher levels of TU (H4). No effect was found of the interaction between EUand SPM (H2). Additional analyses revealed that the negative effects ofAPM and SPM under, respectively, EU and TU may regard different de-pendent variables as appeared when the original construct of MANAAMBIwas decomposed into GOALAMBI and ROLEAMBI. Although the resultsof this further analysis should be interpreted with care because the factorstructure of the original construct does not suggest their full independence(Table 3), it confirms that the interactive effects of the two types of per-formance metrics and the two types of uncertainty act along two ratherdifferent paths toward diminishing MANAAMBI. APM are especially asource of clear goals in, otherwise, turbulent external environments, as they

Do Accounting Performance Measures Indeed Reduce Managerial Ambiguity 175

inform the manager of the goals to be achieved. SPM, in contrast, reduceMANAAMBI by providing more immediate feedback on the actions thatmanagers takes, and thus seems related to a control style that supports themanager in the actions that his role demands, rather than emphasizing theconsequences of such actions which may be hard to predict. Note that theseconclusions are based on interactions, rather than direct effects of uncer-tainty or the indicators. Some further understanding of the relevant issuesmay be gained, however, from observing direct (selection) effects of uncer-tainty on the use of performance indicators as demonstrated by the cor-relation statistics. Indeed, EU and TU appear to have opposite effects on theuse of APM in the first place (Table 5). This provides some indication thatsuperiors do adapt their use of APM to the context, in line with the pre-dictions about the contextual appropriateness of APM, since the correla-tions between EU and TU and APM are significantly positive and negative,respectively. For SPM, no such effects are apparent. Regarding the directeffects of the use of performance measure use of MANAAMBI, both APMand SPM are able to reduce MANAAMBI. The results confirm the role ofperformance evaluation as a feedback device in the supervision of subor-dinate managers. Indeed, the intensity of such evaluations seems to be agood predictor for the direct effects of performance evaluation on subse-quent MANAAMBI.

Based on these findings, the following conclusions are drawn. Overall, theresults seem to support the relevance of knowing the origin of uncertaintywhen assessing the appropriateness of APM and SPM. In particular, theresults support the basic prediction that EU and TU do not affect theappropriateness of APM and SPM in the same way. When interpreting thedirection of the effects, the findings suggest that the use of APM may bemore appropriate under conditions of high EU. This finding is in line withthe results reported by Simons (1987) and Ezzamel (1990), and appears toconfirm the role of APM in structuring and ‘buffering’ the manager’s job byproviding specific and clear goals (e.g., Hopwood, 1972; Hirst, 1983; Ross,1995; Fisher et al., 2002). This finding is also in line with earlier results inbudgetary control studies that addressed budgeting under uncertainty (e.g.,Simons, 1987, 1995; Abernethy & Brownell, 1999), and extends these resultsto the larger body of APM literature. For TU, we find positive effects ofincreased use of SPM. This supports the general importance of subjectivecriteria (cf. Ross, 1995), but as the use of SPM does not seem to be directlyaffected by the level of uncertainty, this variable may reflect a personal traitof the superior, rather than contextually determined behavior. With thesefindings, some of the contradictions in the extant literature may be

FRANK G. H. HARTMANN176

reconciled. Studies failing to find negative effects of APM under uncertaintymay in fact have measured EU rather than uncertainty as such. For somestudies this is clear from the scale they report to have used (e.g., Ross, 1995).Also Marginson and Ogden (2005) the source of initial ambiguity may havebeen environmental rather than task related.

The study is not immune to traditional weaknesses associated with thesurvey method used, regarding internal and external validity. However, thecare taken in the design of the empirical study, in obtaining the sample ofrespondents, in the design and pretests of the questionnaire, and in thefollow-up procedures may have provided effective controls against manyreliability and validity threats normally associated with survey research (cf.Young, 1996; Podsakoff et al., 2003), and may have realized the relativeadvantages of the survey method for studying a real-world phenomenon in across-sectional analysis of real-world subjects (cf. Lipe & Salterio, 2000)without publicly available data. In addition, the high response rate, theoverall high reliability of the measurement instruments, and the size of thesample all compare favorably to previous accounting studies using a similarmethod. The fact that the sample has not been strictly randomly selectedrequires care when drawing inferences from this study’s results, although thesampling method was carefully designed to eliminate any obvious bias. Fi-nally, the cross-sectional nature of the study requires care when interpretingthe statistical associations as causal relationships.

From the evaluation of results and implications of this study, and itsstrengths and weaknesses, the following directions for further research seemworth exploring. First, in this study, the use and usefulness of APM wasexamined in terms of subordinate’s attitudes and responses. An alternativelevel of analysis would be the superior, and a related question would bewhether superiors’ evaluative behaviors can be explained in terms of con-textual appropriateness of APM. A constructive replication of the findingsin this study may explore the role of managers’ personality, in terms of theirpreference for uncertainty, or tolerance for ambiguity (e.g., McGhee,Shields, & Birnberg, 1978), which would complement the two types of un-certainty explored here. Second, future studies may attempt to integrate thefindings from this and similar psychology-based studies with agency models,to enrich economic explanations of performance evaluation practices (cf.Prendergast, 2002; Ittner et al., 2003). This would enable this literature torecognize the wider or intermediate managerial and economic roles of per-formance evaluation, for example as a provider of clear feedback informa-tion (Luckett & Eggleton, 1991), and by recognizing the potential impact ofdifferent types of uncertainty (cf. Prendergast, 2002, p. S117; MacLeod,

Do Accounting Performance Measures Indeed Reduce Managerial Ambiguity 177

2003). Methodologically, this integration may imply complementing re-searching managerial perceptions about the use and quality of performancemetrics, with archival and experimental evidence (cf. Merchant, Van DerStede, & Zheng, 2003).

NOTE

1. All factor analyses are performed using PCA extraction, extraction factors witheigenvalues greater than 1. Interpretation of factors is done after Varimax rotation.

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CAPACITY UTILIZATION AND THE

BEFCU MODEL: A FIELD STUDY

Mohamed E. Bayou and Alan Reinstein

ABSTRACT

The few management accounting pricing methods in the management

accounting literature are ineffective in helping small firms use their idle

capacity during lingering economic recessions, and some of these methods

may even worsen this problem.

Extending the traditional break-even-cost-volume-profit model, we derive

a more effective pricing method, the break-even-full-capacity-utilization

(BEFCU) model, to handle this problem. Seeking full capacity utilization,

the BEFCU model has two characteristics: (a) highlighting the importance

of the exigent fixed cost category for utilizing idle capacity and (b) using a

functional cost structure that focuses on a hierarchy of value drivers in the

firm’s value creation process. Accordingly, under the BEFCU model,

management has an instrumental pricing continuum extending from the

minimum acceptable BEFCU sale price to the regular sale price.

To demonstrate its practicality, the authors apply the BEFCU model to

an actual job shop. This model integrates certain strategies based on built-

in flexibility in commitments with suppliers and customers and maintaining

a mode of conservatism in accounting for plant assets. The model can also

help small tooling companies currently seeking entrance into China; it may

take a while for these companies to gain a foothold in this new market

because copyrights and other legalities are rarely enforced (Bunkley,

2004).

Advances in Management Accounting, Volume 16, 181–203

Copyright r 2007 by Elsevier Ltd.

All rights of reproduction in any form reserved

ISSN: 1474-7871/doi:10.1016/S1474-7871(07)16006-8

181

MOHAMED E. BAYOU AND ALAN REINSTEIN182

In a competitive economy, idle capacity costs can significantly affect anorganization’s performance (Klammer, 1996; Dodd, Lavelle, & Margolis,2002). In economic recessions, however, utilization of idle capacity is oftenthe key to survival. This is especially true for such small firms as job shopsand medium-size firms since they often do not have enough resources toenable them endure lingering economic recessions and they often depend on afew large customers who themselves may suffer decreasing market shares andhuge losses. During the current decade’s economic recession, many such firmswent out of business (Mulligan, 2004). The key to their survival is effectivecapacity management (McNair, 1994; Klammer, 1996; Paranko, 1996).

Unfortunately, traditional measures of capacity in management accountingliterature do little to highlight idle capacity (Klammer, 1996, p. 28). McNair(1994) contends that capacity management is one of the most complex andtroublesome areas in management practice. A review of many researchstudies in this literature reveals that capacity management is a twofoldproblem. First, no generally accepted definition of capacity and capacitymanagement currently exists. Klammer (1996, p. 3) discusses several types ofidle capacity. Second, traditional costing systems fail to measure the costsof unused capacity (Dodd et al., 2002; Buchheit, 2001; Klammer, 1996).Consequently, as Braucch and Taylor (1997) found, very few of their sampledfirms seek to measure the extent or costs of unused productive capacity.Using an experimental methodology, Buchheit (2001) concludes that explic-itly reporting idle capacity costs can lead to biased performance evaluation.

This paper focuses on the pricing decision’s effect on utilizing idlecapacity. Management accounting literature includes several pricingmethods for capacity utilization. However, due to their rigid structuresand lack of insight into the idle capacity’s twofold problem, these methodsdo not offer much help to solve small firms’ idle capacity problem. Manypricing methods in the management accounting literature are inadequateto help organizations utilize their idle capacity. They usually focus on eitherthe short- or long-run decisions (Paranko, 1996; most managerial costaccounting textbooks). In the short-run idle capacity case, the literatureis replete with discussions of the special-offer pricing method where theminimum acceptable special price covers the variable costs and any incre-mental fixed cost. In the long-run case, the sale price either covers the fullcost of the product or the idle capacity is recommended for elimination(downsizing). But these two cases do not deal with situations wheneconomic recessions are expected to last more than one year and the firmresists shrinking its capacity. In this case neither of the short-run nor thelong-run case is applicable.

Capacity Utilization and the BEFCU Model: A Field Study 183

This paper focuses on small firms’ idle capacity utilization during an eco-nomic recession that lasts a few years, as many small firms have experiencedsince the start of this decade. After this introduction, the first section brieflyreviews several pricing methods and shows their weaknesses in helpingutilization of idle capacity. The second section shows how the pricingdecision can help implement this goal. The third section explains how tomodify a traditional cost-volume-profit (CVP) model, thus mitigating weak-nesses of traditional pricing methods. The fourth section applies this CVPmethod to a job shop using information gathered by interviewing a jobshop’s top manager. Finally, a summary and conclusions are presented.

PRICING METHODS TO HELP UTILIZE

IDLE CAPACITY

The management accounting literature offers few techniques to helpcompanies deal with idle capacity issues during economic slowdowns andrecessions, explained as follows.

Cost-Plus Pricing

US firms often use cost-plus pricing, whose popularity stems mainly frommanagement’s concern that a product’s sale price covers its cost base togenerate a target markup (Saccomano, 1998; Shim & Sudit, 1995). Eq. 1shows the general form of this method.

Sale price ¼ Cost baseþMarkup (1)

Thus, if the cost base of manufacturing a product is $100 per unit andthe target markup is 150% of the cost base, the sale price is $250($100+[$100� 150%]). The markup covers two elements: costs excludedfrom the cost base and a target profit.

The cost-plus method has several basic weaknesses (Saccomano, 1998;Rutledge, 1996). If the cost base includes a fixed-cost element, thendecreased demand and consequent decreased production result in increasedcost base per unit. A vicious circle arises when increases in the cost base raisethe sales price per unit, which, in turn, depresses the weakening demand andso on until the firm goes out of business, a dynamic sometimes called the‘‘death spiral.’’ Some job shops are especially vulnerable to this spiraldilemma since much of their costs are fixed. In short, the cost-plus techniquecan augment – rather than reduce – the idle-capacity problem.

MOHAMED E. BAYOU AND ALAN REINSTEIN184

Contribution-Margin Pricing

Contribution margin, the excess of sales revenues over variable expensesas computed by Eq. 2, provides a different instrument to utilize idlecapacity.

Contribution margin per unit ¼ Sale price per unit� Variable cost per unit

(2)

The minimum acceptable sale price is determined when it produces a zerocontribution margin (Eq. 3).

Minimum acceptable sale price per unit ¼ Variable expenses per unit (3)

With a short-run focus, this instrument appears appropriate to handle theidle-capacity problem. Idle capacity and cost behavior (i.e., for variable,fixed or mixed costs) normally exist only in the short-run because in the longrun all costs tend to become variable and idle capacity is either reduced oreliminated by either downsizing or full utilization. Heavily automated, manyjob shops’ costs escape the contribution margin caption. For example, asale price set near variable expenses per unit hardly contributes toward thecoverage of any nonvariable cost, which offers an illusive solution to idlecapacity problems during a chronic economic recession. Moreover, the jobshop company we discuss later in this paper uses an activity-based costing(ABC) system that may prove ineffective since much of the fixed manufac-turing costs are facility-sustaining costs, which the ABC system does notallocate to output units. ABC also does not include the cost of idle capacityin product costing (Garrison, Noreen, & Brewer, 2006).

Capital-Based Pricing

Rutledge (1996, p. 50) asserts: ‘‘Don’t use cost-based pricing. Use capital-based pricing.’’ This strong opposition stems from reasoning that ratherthan being merely an operating decision, bidding on projects requiresa capital investment. Thus, he argues, ‘‘A low-margin project can be terrificif it uses very little capital. A high-margin project can be a loser if it requiresa lot of capital. You can’t tell the difference until you do the work toestimate the balance sheet impact of the project and estimate the resultingreturns.’’ He adds that a cost-plus price brings in ‘‘a mediocre return oncapital while giving all of the value of any competitive advantage to the

Capacity Utilization and the BEFCU Model: A Field Study 185

customer without charge.’’ Eq. 4 shows the general form of capital-basedpricing method.

Sale price of a project ¼ f ðIncremental investment in the project;

Incremental operating costs; Cost of capitalÞ ð4Þ

Rutledge’s (1996) argument has two problems. First, contrary to his claimthat the cost-plus price formula ignores the cost of investment, the markup inthis formula typically includes a return on capital (Anthony & Govindarajan[A & G], 2004, p. 249). This markup component arises by multiplying the netinvestment (capital) of the firm by its required return on investment (ROI) toproduce the total earnings required on the investment. Technically, thisrequired return considers the firm’s capital structure. Dividing this amount ofearnings by the number of units to be produced (or by such capacity inputmeasures as total machine hours or total direct labor hours) determines theprofit required per unit (or per hour) in the markup in the cost-plus priceformula. Second, in pursuing accounting for capital structure, the capital-based pricing method ignores the firm’s cost structure. Different firms mayallocate their total investments differently among unit-level, batch-level,product-sustaining and facility-sustaining costs. Since the risk characteristicsof these cost-structure elements differ, they may require different ROI rates.

Revenue Management

Revenue management links three functions: (1) demand forecasting capac-ity, (2) utilization and (3) adjusting prices to best meet and influence demand(Kroll, 1999, p. 26). Applied first in the late 1970s by airline companies,revenue management has gained wide acceptance in the service industry andhas attracted strong attention in the manufacturing industry. Eq. 5 showsthe general form of the revenue management method.

Sale price ¼ f ðCustomer mix; Demand forecasting; SupplyÞ (5)

Kroll (1999, p. 26) summarizes the core concepts of revenue managementas follows:

(1)

the focus is on price rather than on costs to balance supply and demand, (2) market-based pricing should replace cost-plus pricing, (3) the method is more appropriate for sales to segmented micro-markets

than to mass markets,

(4) products should be saved for the most valuable customers, (5) decision making should be based on knowledge instead of supposition,

MOHAMED E. BAYOU AND ALAN REINSTEIN186

(6)

the firm should exploit each product’s value cycle, and (7) the firm should continually evaluate its opportunities.

Revenue management is useful when the following conditions are met(Kroll, 1999, p. 28):

(1)

short-term capacity is limited, (2) marginal costs are low, (3) demand varies over time, (4) pricing is flexible, (5) management can control supply allocation, and (6) data on historical demand is obtainable.

Revenue management has several weaknesses. First, its benefits disappearif demand forecasts are uncertain. Second, charging different prices for whatappears to be the same product can raise legal and customer problems.Third, revenue management is ‘‘not gouging’’ (Kroll, 1999) since aggressiverevenue management can backfire and cause customers to seek alternativesources to satisfy their needs. Finally, by focusing on revenues and ignoringcosts, the method’s mechanism becomes unable to adjust prices for differentcost structures. In short, revenue management is inapplicable to such a smallfirm as a job shop that has very few large manufacturing customers andfaces an extended period of weak or uncertain demand.

Analytical Modeling

Analytical modeling considers capacity, pricing and costing concepts. Forexample, Balakrishnan and Sivaramakrishnan (2002) discuss how combininganalytical and numerical methods can consider capacity-planning and prod-uct-pricing problems to clarify full costing decisions, suggesting that futureresearch explore optimal time revisions and consider the costs associatedwith price and capacity revisions. Banker and Hansen (2002) develop a full-cost-based pricing heuristic model to simulate how a service firm determineseach period’s amount of capacity, a price and a price discount. Their modelfinds that accuracy is more important for capacity than for pricing decisions.Banker, Hwang, and Mishra (2002) next develop a model to analyze prod-uct-costing and pricing decisions in a dynamic information environment un-der long-term capacity commitment, finding that the average expectedoptimal charge for an activity resource equals its expected full costs. Gox(2002) analyzes a capacity-planning and pricing problem of a monopolistfacing uncertain demand. He finds that different types of capacity constraints

Capacity Utilization and the BEFCU Model: A Field Study 187

affect the firms’ obtained capacity, but not how it sets prices. However, noneof these models fully accounts for cost hierarchies, which are critical formany different types of business.

Segment-Margin Pricing

Segment-margin pricing, as Eq. 6 shows, stresses more cost coverage thanthe contribution-margin method because the former covers fixed costs.

Segment margin ¼ Sales revenues

� ðVariable expensesþDirect fixed expensesÞ ð6Þ

Thus,

Sales revenues ¼ Variable expensesþDirect fixed expenses

þ Segment margin ð7Þ

Direct fixed expenses can be easily and economically traced to the seg-ments. The minimum acceptable sale price per unit produces a zero segmentmargin in Eq. 7.

But the segment-margin pricing method, as commonly presented in themanagement accounting literature, rarely differentiates between committedand discretionary fixed costs, as explained below. Also, when presented ona unit basis, it suffers from the death spiral dilemma as explained above. Thefollowing section explains the weaknesses of these pricing methods.

LIMITATIONS OF THE TRADITIONAL PRICING

METHODS TO MITIGATE THE IDLE

CAPACITY PROBLEM

The six pricing approaches contain two limitations that impair their miti-gating the idle capacity problem, explained as follows.

Ignoring Urgent Costs for Utilization of Idle Capacity

Fixed cost and idle capacity concepts are interrelated so that one can hardlyexist without the other. The management accounting literature often clas-sifies fixed cost into committed and discretionary fixed costs, presenting themas a dichotomy: current year’s fixed cost incurrence is either unavoidable(committed) or avoidable (discretionary). But many fixed costs are neither

MOHAMED E. BAYOU AND ALAN REINSTEIN188

committed nor discretionary. A prudent cost manager would regard them asnecessary or essential although they are not bound by contracts or by law.For example, maintenance costs, usually considered discretionary, containparts that may be deemed necessary and wisely unpostponable. Similarly,some insurance coverage on factory facilities is not committed, but urgent –as are minimum advertising, necessary employee training and some softwareand hardware updating. For a lack of a better term, we label these portionsof costs ‘‘exigent fixed costs.’’ Thus, during an economic recession, the min-imum sale price should not be set below the sum of variable expenses andexigent fixed expenses.

Ignoring the Hierarchy of Value Drivers in the Value Creation Process

Most firms’ manufacturing costs (direct material, direct labor, variable andfixed manufacturing overhead) are hierarchical, where some costs dominateor rule over others from their significance and functional role in the valuecreation process. For example, in such service firms as CPAs, lawyers orconsultants, direct labor occupies a higher level of priority than raw materials,if any, and overhead in the cost hierarchy. In these businesses, direct labordetermines the type, quantity and quality of raw materials and overhead inthe production process. On the other hand, in a jewelry-manufacturer, directmaterials (e.g., diamonds and gold) may have the highest priority in the costhierarchy. In many high-tech manufacturing firms, factory overhead plays adominant role since advanced robotic assets and high-technological systemsoften determine the type of labor skills and the form of prefabricated rawmaterials needed for the manufacturing process. Thus, in these firms factoryoverhead occupies the highest functional hierarchy in their manufacturingcost structure.

The significance of the functional hierarchy of resources for idle capacityutilization purposes can be explained as follows: when the dominant resourcebecomes active in the value creating process, other resources become activeand profitable. The dynamics of this functional hierarchy follow a ‘‘dynamoprinciple’’: when the dominant resource becomes idle, other related resourcesin the hierarchy become idle also. The key to reach full utilization of idlecapacity is to keep the dominant resource active. A complete shutdown of amajor resource also creates another problem: the cost of bringing back thisresource to an operating level can be substantial.

The following section presents a full-capacity utilization-pricing modelthat overcomes these limitations.

Capacity Utilization and the BEFCU Model: A Field Study 189

COST-VOLUME-PROFIT BREAK-EVEN MODELS

Many advances in management accounting have occurred since Jaedickeand Robichek (1964) developed a CVP model to incorporate uncertainty,including Jarrett (1973), Hilliard and Leitch (1975) and Yunker andSchofield (2005). Yunker and Yunker (2003) recently developed aneconomic demand function relating the expected sales level to price,making price the entity’s basic decision variable, and Yunker and Schofield(2005) apply this similar model to determine enrollment fees for training anddevelopment programs.

Several extensions of the CVP model to incorporate production variablesinclude Hayes and Wheelwright’s (1984) product life cycle as a key variablefor products in later or ‘‘mature’’ stages of production. Hanna and Newman(1993) develop the cost volume flexibility break-even analysis (CVBA) toconsider both economies of scale and economies of scope to compareamong equipment alternatives. Their model considers that increased fixedcosts could result in both lower variable costs and lower setup costs,suggesting that future research consider broader sets of parameters formanufacturing systems to adopt. Kortge (1984) argues that since break-evenanalysis forms a basis to compare various price alternatives (rather thana tool to make industrial price decisions), such analysis should includesuch variables as optimum sales forecasts (to help ascertain properquantities) and the proper sequence of items through the productionprocess. Our model considers and improves upon these approaches,explained as follows.

THE BREAK-EVEN FULL-CAPACITY-UTILIZATION

(BEFCU) PRICING METHOD

The essential form of this BEFCU pricing method is developed as follows:

X f ¼Ef

ðPf � bÞ(8)

Where Xf, full capacity utilization in input units, e.g., machine hours, atthe annual capacity level; Ef, total amount of exigent fixed cost at the annualcapacity level; Pf, sale price per unit of input at the full-capacity utilizationlevel; b, variable cost per unit of input.

MOHAMED E. BAYOU AND ALAN REINSTEIN190

Eq. 8 is a modified version of the familiar break-even point formula.Solving for Pf yields:

Pf ¼ bþEf

X f

� �(9)

Simplifying Eq. 9 yields:

Pf ¼ bþ ef (10)

Where ef, exigent fixed cost per unit of input at the full capacity level.Eq. 10 shows that to fully utilize an asset’s capacity, the lowest acceptable

price, Pf, in a bidding context during an economic recession is equal to thevariable cost per unit plus the exigent fixed cost per unit.1 Pf represents anextreme point on a continuum whose other extreme is the regular sale price, P.

BEFCU Price Regular Price

Pf P

This proposed method has three advantages. First, it explicitly accountsfor the exigent costs, i.e., urgent and necessary costs that cannot be reducedto zero for establishing the minimum price for full-capacity utilization.Second, the modified CVP pricing method recognizes the hierarchy of valuecreation. Finally the BEFCU’s continuum points to the fuzzy nature of thepricing decision when some capacity is idle due to economic recessions, asdemonstrated in the following application.

APPLICATION OF THE BREAK-EVEN

FULL-CAPACITY UTILIZATION

PRICING METHOD

The Custom Tool and Die (CTD) Company, a small job shop, has sufferedsignificant losses due to their 60% idle capacity. Highly automated, much oftheir manufacturing costs are fixed. Management recently considered twooptions: (1) keeping several, specialized expensive machines completely idlefor one year, i.e., until the current economic recession is over or (2) keepingthese machines working with the hope the losses can somehow be sustained.Management chose not to dispose of any of these machines, expecting therecession not to last for several years. While some discretionary fixed costsmay be decreased, management feels that finding an appropriate sellingprice is a key factor in selecting between Options One and Two. However,

Capacity Utilization and the BEFCU Model: A Field Study 191

the company is unsure of the appropriate price for deciding between the twooptions.

With annual sales of approximately $40 million before the currenteconomic recession, CTD operated through several plants in various U.S.locations; it produces customized tools and dies for automakers and a fewother manufacturing companies. Contracts with these customers aregenerated mainly through price bidding practices. CTD needs assistancein utilizing its idle capacity and improving its pricing mechanism.

During our recent tour of the company’s main plant and from lengthyinterviews of its chief financial officer, we noted that high-tech machinerycosts have the highest priority in their cost hierarchy. Their machining costcategory alone is larger than direct-material and direct-labor categoriescombined. They customize most of their products to customer specificationsusing a high-tech job-order costing system, where manufacturing overheadplays a major role in the value creation process. Hence, when their machinesbecome active to produce customers’ orders, their labor, materials andadministrative resources also become active, and vice versa – when theirmachines are idle, nearly every other resource in the firm is idle (Smith,2002, p. A1). The key then is to trigger the working of equipment in a jobshop infected with idle capacity.

The Idle-Capacity Problem

Since CTD decided not to dispose of its machines, the key question becomes:what pricing method should it use to decide whether to keep sophisticatedmachines idle or working? CTD bought several high-tech machines to serve afew large customers, entailing significant amounts of committed and discre-tionary annual fixed costs. Who should bear these costs? A and G (2004,p. 250) suggest that the company charge these fixed costs to these customers.But this is inapplicable to CTD. A & G’s suggestion applies to transactionsbetween sellers and buyers in the same company. The CTD officials told usthat if they charge these fixed costs to their key customers, the latter wouldswitch to competition; external buyers do not have the same obligations tobuy from an independent job shop as they feel toward internal sellers in theircorporations. In short, finding an appropriate pricing method becomesessential in deciding between the two options outlined above.

We helped management choose between options One and Two by clas-sifying the cost-pricing techniques reviewed above in one group and settingour modified BEFCU pricing method as another group. Using several

MOHAMED E. BAYOU AND ALAN REINSTEIN192

profitability measures and capacity utilization levels, we compare the twopricing groups’ effects on the two options in a matrix such as the following:

Option 1: KeepMachines Idle

Option 2: KeepMachines Working

Cost-based pricingmethods

Contribution margin;discretionary segmentmargin; total segmentmargin

Contribution margin;discretionarysegment margin; totalsegment margin

Break-even-full-capacity-utilizationmethod

Contribution margin;discretionary segmentmargin; total segmentmargin

Contribution margin;discretionarysegment margin; totalsegment margin

Estimates of the Key Capacity Costs

The job shop uses high-tech manufacturing operations where several largemachines provide the highest level in the firm’s hierarchical capacity. Tosimplify the presentation, we use only five machines (Machines 1–5) to rep-resent this hierarchical level. Machine 3 is specialized and sophisticated witha regular sale price of $24 per hour of work on this machine. Table 1, PanelA shows that idle capacity is 60% (line (f)). Two major cost elements,manufacturing maintenance and rent, are allocated to these five machines asfollows.

Allocating Maintenance Costs

Table 1 shows the maintenance cost allocation for the five machines.Variable maintenance costs of $1.80 per machine hour multiplied by thebudgeted capacity on line (c) of Panel A provide the total variable main-tenance cost of each machine in Panel B. Thus, variable maintenance costsof Machines 1–5 are $6,453, $4,500, $567, $2,880 and $1,800, respectively, atotal of $16,200. The total discretionary maintenance fixed cost per year isnormally $62,550. Dividing this amount by the full capacity of 22,500 hyields line (h) in Table 1. However, of the $62,550 amount, managementdeems only $27,000 is necessary, i.e., exigent, as explained above. Accord-ingly, the exigent fixed maintenance cost per hour is $1.20 ($27,000/22,500 h). Multiplying the maintenance exigent fixed cost of $1.20 permachine hour by the full capacity (line (a)) of Machines 1–5 (Table 1) yields

Table 1. Allocating Maintenance Costs.

Total Machine 1 Machine 2 Machine 3 Machine 4 Machine 5

Panel A: Long-term and short-term capacity levels

Long-term capacity

(machine hours)

a 22,500 8,100 5,400 3,150 4,500 1,350

Weights b 100% 36% 24% 14% 20% 6%

Master-budget

(machine hours)

c 9,000 3,585 2,500 315 1,600 1,000

Weights d 100% 40% 28% 4% 18% 11%

Idle capacity: a–c e 13,500 4,515 2,900 2,835 2,900 350

Idle capacity

percentage: e/a

f 60% 56% 54% 90% 64% 26%

Panel B: Maintenance costs

Variable cost ($1.80

per hour)� c

g $16,200 $6,453 $4,500 $567 $2,880 $1,800

Discretionary fixed

costs�h $62,550 $22,518 $15,012 $8,757 $12,510 $3,753

Exigent fixed costs� i $27,000 $9,720 $6,480 $3,780 $5,400 $1,620

�The total amount of discretionary maintenance fixed cost is $62,550 per year, of which only

$27,000 is considered necessary, i.e., exigent and may not be postponed.

Capacity Utilization and the BEFCU Model: A Field Study 193

the allocated maintenance cost ($9,720, $6,480, $3,780, $5,400 and $1,620,respectively, a total of $27,000).

Allocating Manufacturing Rent Cost

Table 2 shows the allocation of manufacturing rent costs to the fivemachines. The $100,000 annual rent is split into two parts, 90% used by themanufacturing function and 10% used by the administrative and generalfunctions. Allocating the $90,000 amount to the five machines depends onthe size of the area each machine needs to operate. CTD’s actual practicefollows. How can we determine this area?

The four panels of Fig. 1 show how to ascertain this area for one of themachines, as explained at the bottom of Fig. 1. Since CTD’s Machines differin shape, design, function and space needed for operations and operators’safety, we allocate the machines’ factory floor space using the equipment’scenter of gravity, which, in turn, forms the focal point to determine theneeded space to be occupied. After determining the area of each machine,the factory rent of $90,000 is allocated to the five machines based on theareas they occupy (Table 2). The rent cost rate of $67 per sq. ft. multipliedby each machine’s area shows the allocated costs of $21,184, $13,561,

Table 2. Allocating Manufacturing Rent Cost.

Annual rent (based on ten-year lease) $100,000

Percentage allocated to the manufacturing area 90% $90,000

Percentage allocated to the administrative and other areas 10% $10,000

Total Machine 1 Machine 2 Machine 3 Machine 4 Machine 5

Machine boundary

(sq. ft.)

1,334 314 201 452 254 113

Rent cost (see above) $90,000

Rent rate per sq. ft.a $67

Allocated rent $90,000 $21,184 $13,561 $30,495 $17,136 $7,624

aComputed as follows: $90,000/1,334 sq. ft. ¼ $67.

MOHAMED E. BAYOU AND ALAN REINSTEIN194

$30,495, $17,136 and $7,624 to Machines 1–5, respectively. Given these andother cost elements, the following section produces a profitability report forCTD Company.

A Segment Report of the First Hierarchical Capacity Level

Table 3 shows a proforma income statement arranged by segments, wherethe five machines represent five segments. This report shows three profit-ability levels: contribution margin, discretionary segment margin and totalsegment margin.

The income statement shows negative total segment margins (losses) forall five machines ($71,142, $37,713, $78,742, $47,646, $14,287 for Machines1–5, respectively, a total of $249,530) (Table 3). This result is due mainly tothe large idle capacity of 60% (Table 1). Using the BEFCU method, Table 4shows a pro forma segment income statement for Machine 3 only, the focusof Options One and Two.

Table 4 is based on the following decisions:

(1)

The company decides to spend only the exigent fixed cost for the followingitems: fixed maintenance, $27,000 (Table 1); insurance, $45,000 and up-dating, $31,500, a total of $103,500. Machine 3’s share of these costs are:maintenance, $3,780; insurance, $6,300 and updating, $4,410 (Table 4).

(2)

The BEFCU price, Pf, of $8.60 is determined as follows:

Given the total variable cost per machine hour of $4.00, the total exigentfixed cost is $103,500 and Eq. 10, the BEFCU price per hour of work on

. .4

Panel (a): A Bird’s Eye View of a Machine’s Footprint

Panel (b): Drawing the Machine BoundaryA Rotating Machine

Panel (c): A Stationary Machine: A Rectangular

Panel (d): A Stationary Machine: Odd Shape

Center of Gravity

. .

.

.2

Fig. 1. Allocation of Rent Cost Among Factory Machinery. Note: The marked

square inside the machine represents the location of the machine’s center of gravity.

Panel (a) is a bird’s eye view of the machine footprint. Panel (b) shows two circles

around the machine that rotates around its center of gravity during operations.

Determining the inner circle requires making the machine’s center of gravity the

center of a circle whose radius, the length of the line connecting this center of gravity

measures r, and the furthest point on the machine’s footprint. Panel (b) shows that r

equals 4 ft. Adding 2 ft. to r, an additional space for safety purposes, gives a radius of

6 ft. for the outer circle in Panel (b). Hence, the area of the outer circle around the

machine is 113.097 sq. ft. For a stationary machine, as in Panel (c), its circle area can

be converted into a rectangular (Panel (c)), a square, a triangle or an odd shape

(Panel (d)) depending on the active side(s) of the machine that requires more space

for machine and labor activities.

Capacity Utilization and the BEFCU Model: A Field Study 195

Machine 3, Pf3, is computed as follows:

Pf3 ¼ bþ ef3

¼ $4:00þ$103; 500

22; 500

¼ 4:00þ 4:60 ¼ $8:60 ð11Þ

Table 3. Proforma Segmented Income Statement for the Year Ending12/31/2004.

Total Machine 1 Machine 2 Machine 3 Machine 4 Machine 5

Master budget hours 9,000 3,585 2,500 315 1,600 1,000

Regular sale price per

hour

$16 $17 $24 $16 $15

Sales $148,020 $57,360 $42,500 $7,560 $25,600 $15,000

Variable expenses

Maintenance ($1.80/

h) (Table 1)

$16,200 $6,453 $4,500 $567 $2,880 $1,800

Power ($2.0/h) 18,000 7,170 5,000 630 3,200 2,000

Cleanup ($0.20/h) 1,800 717 500 63 320 200

Total variable cost

($4.00 per hour)

$36,000 $14,340 $10,000 $1,260 $6,400 $4,000

Contribution margin $112,020 $43,020 $32,500 $6,300 $19,200 $11,000

Discretionary fixed

expensesa

Maintenance

(Table 1)

$62,550 $22,518 $15,012 $8,757 $12,510 $3,753

Insurance 90,000 32,400 21,600 12,600 18,000 5,400

Updating 58,500 21,060 14,040 8,190 11,700 3,510

Total direct

discretionary

fixed expenses

$211,050 $75,978 $50,652 $29,547 $42,210 $12,663

Discretionary segment

margin

$99,030 $32,958 $18,152 $23,247 $23,010 $1,663

Direct committed

fixed expenses

Depreciation $60,500 $17,000 $6,000 $25,000 $7,500 $5,000

Rent (Table 2) 90,000 21,184 13,561 30,495 17,136 7,624

Total direct

committed fixed

expenses

$150,500 $38,184 $19,561 $55,495 $24,636 $12,624

Total segment margin $249,530 $71,142 $37,713 $78,742 $47,646 $14,287

aThe CTD company decides to spend the full amount of discretionary fixed cost of each of these

items.

MOHAMED E. BAYOU AND ALAN REINSTEIN196

Therefore, the pricing continuum for Machine 3 extends from a minimumof $8.60 to a maximum of $24 per hour.

BEFCU Price Regular Price

Pf3 = $8.60 P = $24.00

Table 4. Proforma Segmented Income Statement for Machine 3 for theYear Ending 12/31/2004.

Machine 3

Full capacity in hours 3,150

BEFCU sale price per hour $8.60

Sales $27,090

Variable expenses

Maintenance ($1.80/h) (Table 1) $5,670

Power ($2.0/h.) 6,300

Cleanup ($20/h) 630

Total variable cost ($4.00/h) $12,600

Contribution margin $14,490

Exigent fixed expensesa

Maintenance (Table 1) $3,780

Insurance 6,300

Updating 4,410

Total direct discretionary fixed expenses $14,490

Exigent segment margin –

Direct committed fixed expenses

Depreciation $25,000

Rent (Table 2) 30,495

Total direct committed fixed expenses $55,495

Total segment margin $55,495

aThe allocation of each exigent fixed expense to the five machines is based on the exigent fixed

cost multiplied by each machine’s full capacity hours of activity as shown in Line (b) in Table 1.

Thus, maintenance’s exigent cost per hour is $1.20 ($27,000 divided by 22,500 h); insurance’s

exigent cost per hour is $2.00 ($45,000 divided by 22,500 h); updating expenses’ exigent cost per

hour is $1.40 ($31,500 divided by 22,500 h).

Capacity Utilization and the BEFCU Model: A Field Study 197

ANALYSIS OF RESULTS AND IMPLICATIONS

Tables 5 and 6 show the contribution margin, discretionary (or exigent)segment margin and total segment margin in total and per hour for each ofOption One (keep Machine 3 idle) and Option Two (keep Machine 3working). All pricing methods favor keeping Machine 3 working sincethe three profitability criteria are better under Option Two than underOption One. However, the BEFCU pricing method shows stronger valuesfor the three criteria; hence, the method provides more confidence formanagement choice.

Table 5. Implications of the Cost-Based Pricing Methods for CapacityOptions One and Two of Machine 3 (Regular Sale Price Per Unit is

$24.00).

Profitability Measures Total Per Hourf

Panel A: Contribution margin

Option One (keep Machine 3 idle) $– $–

Option Two (keep Machine 3 working) 6,300a 20.00

Panel B: Discretionary segment margin

Option One (keep Machine 3 idle) $29,547b Undefined

Option Two (keep Machine 3 working) 23,247c 74.44

Panel C: Total segment margin

Option One (keep Machine 3 idle) $85,042d Undefined

Option Two (keep Machine 3 working) 78,742e 249.97

aThe contribution margin as shown in Table 3.bThis amount is Machine 3’s share of the discretionary fixed costs (Table 3).cFrom Table 3.dThis amount is the sum of discretionary ($29,547) and committed ($55,495) fixed costs of

Machine 3 (Table 3).eThis amount is Machine 3’s contribution margin ($6,300) less its discretionary ($29,547) and

committed ($55,495) fixed cost.fThe denominator for Machine 3 under Option Two is 315 h (Table 1).

Table 6. implications of the Cost-Based Pricing Methods for CapacityOptions One and Two of Machine 3 (The BEFUC Price Per Unit is

$8.60).

Profitability Measures Total Per Hourf

Panel A: Contribution margin

Option One (keep Machine 3 idle) 0 0.00

Option Two (keep Machine 3 working) $14,490a 4.60

Panel B: Discretionary segment margin

Option One (keep Machine 3 idle) $14,490b Undefined

Option Two (keep Machine 3 working) 0c 0.00

Panel C: Total segment margin

Option One (keep Machine 3 idle) $69,985d Undefined

Option Two (keep Machine 3 working) 55,495e 17.62

aThe contribution margin as shown in Table 4.bThis amount is Machine 3’s share of the exigent fixed costs (Table 4).cFrom Table 4dThis amount is the sum of exigent and committed fixed costs (Table 4).eThis amount is Machine 3’s contribution margin ($14,490) less its exigent and committed fixed

cost (Table 4).fThe denominator under Option Two is Machine 3’s full capacity of 3,150 h (Table 1).

MOHAMED E. BAYOU AND ALAN REINSTEIN198

Capacity Utilization and the BEFCU Model: A Field Study 199

While using an ABC system, CTD’s management found that the systemsolves little of its idle capacity problem. As Yang and Wu (1996, p. 34)explain, ABC assumes full (100%) capacity utilization when the systemcalculates unit cost of resources, and that ‘‘[c]apacity ABC is unrealisticallyoptimistic in pricing application’’ because ‘‘Fixed and variable expense ratesusually increase an operation approaches full capacity due to over-time,maintenance, or fatigue.’’ This argument further supports including exigentcosts in the BEFCU model.

Generally, ABC systems are inapplicable to solve idle capacity problemsbecause as Cooper and Kaplan (1992, p. 12) explain, ‘‘activity-based costsystems are not models of how expenses or spending vary in the short-run.’’However, idle capacity is often a short-run and rarely a medium-run problembecause as Klammer (1996, p. 16) explains, in the long-run, idle capacitybecomes an ‘‘idle not-marketable’’ capacity and is target for abandonment.

When selecting Option Two, CTD may reduce its capacity and/or stressflexibility in its contracts with its suppliers and customers as follows:

1.

Contingent commitments. Certain discretionary fixed costs such as main-tenance, training and system updating costs may not be incurred orcommitted until customers sign, or are about to sign, order contracts.Costs are made contingent upon receiving customers’ orders. CTDcan use this strategy if it makes discretionary fixed costs conditional onreceiving bidding offers.

2.

‘‘Total package’’ commitment. CTD can benefit from such a total pack-age deal as that compressor manufacturer Atlas Copco offers. Baker(2004, p. 24) explains that this offer is a flexible range of funding pack-ages that can cover equipment acquisition, planned maintenance and thesupply of replacement parts, which contain three major benefits:� to help facilitate financial planning, costs are guaranteed for the periodof the contract, with no hidden extras;

� prices include all services according to the manufacturer’s recommen-dations; and

� depending on the nature of the contracts, the equipment need notappear as an asset on the job shop’s balance sheet.

3.

Flexible commitments. Leasing instead of owning plant assets with theoption to sublease these assets may allow a company to utilize its idlecapacity to cover some of its committed fixed costs, which assumes anexisting demand for the subleasable space. The increasing trends of leas-ing and hiring employees on part-time rather than full-time basis areconsistent with flexible commitment strategies.

MOHAMED E. BAYOU AND ALAN REINSTEIN200

4.

Order now, pay later. A job shop may encourage its ‘‘illiquid’’ customersto place orders during the economic recession without paying for theorders until after the recession ends. The job shop can then discount theaccounts receivable at a bank with or without recourse. Such retail storesas furniture companies have followed this strategy for years.

5.

Switching emphasis on resources. Through price adjustments, a companymay encourage customers to switch their orders from production on theless sophisticated equipment to the more sophisticated ones. For exam-ple, increasing the price per hour of Machine 1 and decreasing the price ofMachine 3 may help increase utilization of Machine 3.

6.

Full-cost recovery. When significant idle capacity risks are imminent,CTD may use the full-cost recovery approach for such specializedresources as Machine 3. Under this conservative approach, the firmrecognizes no profit until it recovers fully its invested cost. It can laterdecrease the price on this asset when depreciation charges no longer arise.

LIMITATIONS OF THE BEFCU METHOD

The BEFCU method has several limitations.

(1)

The paper’s assumption that price adjustments can help increase capacityutilization may be untenable during chronic economic recessions. Somesmall firms have solved this problem by selling some of their products inChina. Bunkley (2004, p. 1C) observes that China is ‘‘giving many smallfirms across Michigan a huge opportunity to expand. y The smallengineering and development firm [Managed Programs LLC] for thetooling industry has made approximately $1.5 million in China since2002.’’ He quickly notices the risks a small firm may face when doingbusiness in China: ‘‘getting a foothold in China is not easy and requiressomething of a gamble y The Chinese government does little to protectintellectual property rights, and business is usually conducted moreslowly. Costs can mount quickly as prospective deals drag on, and in-novative designs can fall into the hands of greedy, unethical competitors.’’

(2)

The line demarcating committed and discretionary fixed costs can bea fine one for some cost elements, e.g., software registration fees andinsurance.

(3)

Large manufacturing customers may play favorites among competingjob shops. A job shop may suffer from these maneuvers regardless of thepricing method used.

Capacity Utilization and the BEFCU Model: A Field Study 201

(4)

During an economic recession, a small job shop may be at the mercy oflarge manufacturing customers who use the ‘‘buyers’ market’’ mentalityto their advantage. The pricing methods used may become irrelevant inthis case.

SUMMARY AND CONCLUSIONS

During economic recessions, many job shops have several characteristicsthat can lead to huge losses from idle capacity: using high-tech operations,relying on a few large manufacturing customers and raising most of theirrevenues through price-bidding practices. After reviewing and critiquingseveral pricing methods in the management accounting literature, we findthese methods containing two limitations that impair their mitigating theidle capacity utilization problem. By classifying fixed costs into committedand discretionary categories, they ignore a cost element that can be impor-tant for idle capacity utilization decisions. Exigent fixed costs, e.g., theminimum maintenance, insurance and technology updating, are necessaryand urgent, but neither committed nor totally discretionary. The pricingmethods also ignore the cost structure and hierarchy of value drivers in thevalue creation process of the firm.

We derive a new pricing technique – the ‘‘break-even-full-capacity-utilization’’ (BEFCU) method, which does not contain these limitations, tohelp job shops to cope with idle capacity dilemmas. The BEFCU methodhas two characteristics: (a) highlighting the importance of the fixed exigentcost for idle-capacity utilization and (b) using a hierarchy of value drivers inthe value creation process. These characteristics help to develop an instru-mental pricing continuum whose end points are the minimum acceptable(BEFCU) sale price and the regular sale price.

We applied the BEFCU method to an actual job shop needing helpto improve its pricing mechanism and capacity utilization during aneconomic recession. Illustrating this model, we pose two options: OptionOne: keep factory machinery idle, and Option Two: keep the factorymachinery active. Using hypothetical data to compare the performanceof the traditional pricing method on the one hand and the BEFCU pricingmethod on the other, the paper suggests that the BEFCU pricing methodworks better, especially if the firm follows certain capacity strategieswith built-in flexibility and is conservative in the face of the uncertaintyof idle capacity problems. China provides substantial opportunities forsmall companies, which may find the BEFCU method a useful pricing tool

MOHAMED E. BAYOU AND ALAN REINSTEIN202

since a small firm usually needs time to establish a strong foothold in thishuge market.

Further research can incorporate several iterations for the values of theBEFCU method into a simulation program, thus providing more realisticresults for making informed decisions. To enrich Klammer’s (1996) CAM-Icapacity model, we recommend adding the above explained hierarchy of valuedrivers, as a new dimension to the three kinds of idle capacity (marketable,not-marketable and idle off-limit),2 thus making the sources of capacity idle-ness more visible to management. For example, a utilization strategy forutilizing idle-marketable capacity depends on whether the primary value driveis direct materials, direct labor or manufacturing overhead.

NOTES

1. The management accounting literature usually presents variable cost per unit asa constant within the relevant range, and the total fixed cost is a constant subject tomodification. For example, if the annual capacity utilization is 100,000 machinehours and total exigent fixed cost is $200,000, exigent fixed cost per hour, ef, is $2.00.If capacity utilization is expected to be 80,000 h (i.e., 80% of full capacity), exigentfixed cost per hour, e80%, would be $2.50 ($2.00/0.8). Similarly, at 40% capacityutilization, e40%, becomes $5.00 ($2.00/0.4) per hour.2. Klammer (1996, p. 16) explains that idle capacity includes:

�

idle marketable: a market exists but capacity is idle because of competitor’s marketshare, product substitutes or distribution constraints;

�

idle not marketable: a market does not exist or management decides not to par-ticipate in the market; and

�

idle off-limit: capacity unavailable because of holidays, contracts or management’spolicies or strategies.

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THE APPLICATION OF

PERCEPTUAL BIAS TO NEGATIVE

COMPENSATION SITUATIONS IN

MANAGEMENT ACCOUNTING

RESEARCH

Harry Z. Davis, Solomon Appel and John Y. Lee

ABSTRACT

In this article, we provide evidence that even when Murphy’s Law is

objectively untrue, because of sampling bias, people perceive the law as

true, and this perceptual bias has far-reaching implications in manage-

ment accounting research. A corollary to Murphy’s Law is: ‘‘The other

lane always moves faster than my lane.’’ A manager who is aware of this

perceptual bias will try to structure her budget cutbacks and all other

‘‘negative compensations’’ in such a way that her employees perceive that

the cutback applies to everyone, not just to themselves.

The findings of our study support the wisdom that, whenever managers

must implement managerial plans that will be perceived as ‘‘negative,’’ the

plans should be implemented all at once. Spreading the implementation

over a period of time produces more discontent on the part of the

personnel affected. The findings lend credence to a generalization that

peoples’ discontent is minimized when the number of observations

Advances in Management Accounting, Volume 16, 205–215

Copyright r 2007 by Elsevier Ltd.

All rights of reproduction in any form reserved

ISSN: 1474-7871/doi:10.1016/S1474-7871(07)16007-X

205

HARRY Z. DAVIS ET AL.206

(and thus the number of chances for forming a negative perception) of

undesirable events is minimized.

INTRODUCTION

The original formulation of Murphy’s Law in 1949 was: ‘‘If there is a wrongway to do something, then someone will do it.’’ The original formulationwas a principle in designing machines to make them idiot-proof and thushelp prevent accidents. If a machine is designed so that a connecting cablecan be inserted either correctly or incorrectly, someone somewhere some-time will insert the cable incorrectly. The machine is thus poorly designed. Ina well-designed machine, it is impossible to insert the cable incorrectly(Forward, 1983).

Over time the law morphed into its classical formulation, which is apessimistic Weltanschauung of a malevolent nature: ‘‘If something can gowrong, it will.’’ There are many corollaries to the classical formulation.1

Some of the corollaries have been validated in scholarly articles (Matthews,1997c). ‘‘If a location can lie in an awkward part of the map, it will’’ hasbeen explained using geometry and probability theory (Matthews, 1997b).‘‘Toast falls off the kitchen table buttered side down’’ was validated byanalyzing the gravitational torque of gravity and table heights (Matthews,1995, 2001). ‘‘The notorious ubiquity of unpaired – ‘odd’ – socks’’ has beendemonstrated using combinatorics (Matthews, 1996). ‘‘If a rope can becomeknotted, it will’’ has been validated using a recently discovered theorem intopology (Matthews, 1997a).

One set of corollaries is of the form: ‘‘I always suffer the worst of allpossible outcomes.’’ An example of this corollary law is: ‘‘The other lanealways moves faster than my lane.’’ Regardless of the lane I switch into, mylane is always the slowest.

In this paperwe analyze this corollary in twoways: one, the objective reality,and two, the subjective perception of reality. We show that because of sta-tistical sampling biases, people perceive that the law is valid, even when it hasno basis in objective reality. Finally, we present the managerial implications.

THE MODEL

Assume a highway with two lanes, I, the lane I am in, and O, the ‘‘otherlane,’’ both traveling in the same direction. Define Gj as the percentage of

Management Accounting Research 207

time that Lanej is ‘‘Going,’’ and define Sj as 1–Gj, i.e., the percentage of timethat Lanej is ‘‘Stopped.’’ Further, assume GI ¼ GO, both lanes go (and stop)the same percentage of time. This allows us to drop the subscript, and justwrite G as the percentage of time the lanes are moving, and S as the per-centage of time the lanes are stopped. Thus, by assumption, neither lane isslower or faster than the other.

Further, assume that at any given point in time, there is no correlationbetween the two lanes – that is, at any given point in time, the conditionalprobability that I is stopped if O is stopped equals the conditionalprobability that I is stopped if O is going.2

At any given point in time there are four possible states of the world. One,both lanes are stopped, ISOS, which happens S2. Two, both lanes are goingIGOG, which happens G2. Three, I is going and O is stopped, IGOS, whichhappens G�S. Four, I is stopped and O is going, ISOG, which happensS�G. Table 1 describes all states of the world from S ¼ 0% to S ¼ 100% inincrements of 10%.

In states ISOS and IGOG, the two lanes are equal. Thus, they provide noevidence for or against Murphy’s Law.3 For a driver in I, the state IGOS is arefutation of Murphy’s Law, since the other lane is the worse lane. ISOG issupport for Murphy’s Law, since the other lane is the better lane. Define M,the Murphy Index (see Appendix A):

M ¼ðISOG � IGOSÞ

ðISOG þ IGOSÞ(1)

When ISOG ¼ IGOS, M ¼ 0%, there is no evidence for or against Murphy’sLaw. When ISOG ¼ 100%, IGOS ¼ 0% so that M ¼ 100% and all thedata support Murphy’s Law. When IGOS ¼ 100%, ISOG ¼ 0% so thatM ¼ –100% and all the data refute Murphy’s Law.

Objective Reality

For a driver randomly sampling the state of the two lanes, M ¼ 0% forall values 0%oSo100%. This follows because, by construction, at anylevel of S, ISOG ¼ IGOS. At the extreme values S ¼ 0% and S ¼ 100%,M is not defined, since there are no observations supporting or re-futing Murphy’s Law – both lanes are always in the same state. Thus,objectively, for all values of S, there is no support or refutation forMurphy’s Law.

Table 1. Joint Traffic Probabilities (Real).

S ISOS IGOG IGOS ISOG M

Traffic

Stoppage

Both Lanes

Stopped

Both Lanes

Going

I (My Lane)

Going

I (My Lane)

Stopped

Murphy’s Index

O (Other Lane)

Stopped

O (Other Lane)

Going

S (%) S2 (%) (1–S)2 (%) S(1–S) (%) S(1–S) (%) (ISOG–IGOS)/

(ISOG+IGOS) (%)

0 0 100 0 0 Undefined

10 1 81 9 9 0

20 4 64 16 16 0

30 9 49 21 21 0

40 16 36 24 24 0

50 25 25 25 25 0

60 36 16 24 24 0

70 49 9 21 21 0

80 64 4 16 16 0

90 81 1 9 9 0

100 100 0 0 0 Undefined

HARRY Z. DAVIS ET AL.208

Happiness: Absolute or Relative

In the standard economic model of consumer behavior, each consumer’sutility depends only on the quantity of goods the consumer possesses. Bydefinition, one consumer’s utility is independent of the quantity of goodsthat any other consumer possesses. Each consumer calculates his or herutility function without comparing himself or herself to any other consumer.

Psychologists and even casual observers of human behavior are familiarwith concepts such as envy and jealousy. ‘‘Misery seeks company’’ impliesthat a person who is unhappy is comforted (and therefore less unhappy) inknowing that other people are also unhappy. If other people are also doingbadly, knowing that relative to others they are not doing badly comfortsthem. If they observe that others are doing well, they conclude that not onlyare they themselves doing badly in absolute terms, but also they are doingbadly in relative terms.

The emotion of a person saying: ‘‘The other lane always moves faster thanmy lane’’ implies that the person’s happiness depends not only on the statethey are in, but also on their state relative to the state of others. Further-more, if there are only two states in the world, good and bad, the statement

Management Accounting Research 209

can be recast in the following form: ‘‘When my state is bad, the state ofother people is good.’’ The statement says nothing about what happenswhen the state of the speaker is good. It is plausible that the underlyingassumption is that when people are content, they tend not to comparethemselves to other people. However, when they are discontent, they look athow other people are doing. We, therefore, allow for the possibility that adriver is more likely to sample how others are doing when the driver himselfis stopped than when the driver himself is going.

Subjective Perception

First, consider a driver who only samples the state of the lanes when he isstopped, IS. Such a driver will only observe two states of the world: ISOS

and ISOG. This effectively eliminates columns IGOS and IGOG from thesample, because sampling does not occur in those situations. Table 2 de-scribes all states of the world from S ¼ 0% to S ¼ 100% in incrementsof 10%.

For a driver randomly sampling the state of the two lanes, M ¼ 100% forall values 0%oSo100%. This follows because at any level of S, the driversometimes observes ISOG, but the driver never observes IGOS. Thus, sub-jectively for all values of S, there is 100% support for Murphy’s Law.4

Table 2. Joint Traffic Probabilities (Biased).

S ISOS ISOG M

Traffic Stoppage Both Lanes Stopped I (My Lane) Stopped Murphy’s Index

O (Other Lane) Going

S (%) S (%) 1–S (%) ISOG/ISOG (%)

0 0 0 Undefined

10 10 90 100

20 20 80 100

30 30 70 100

40 40 60 100

50 50 50 100

60 60 40 100

70 70 30 100

80 80 20 100

90 90 10 100

100 100 0 Undefined

HARRY Z. DAVIS ET AL.210

Biased Perception: Partially Objective, Partially Subjective

Define b (0%pbp100%), the bias level of the driver, as the percentagethat the driver samples from Table 2 and not from Table 1. A driverwith bias level 0% samples only from Table 1; a driver with a bias level of100% samples only from Table 2. A driver with bias level 0%obo100%samples b from Table 2, and 1–b from Table 1. For such a driver(see Appendix B):

M ¼b

ðbþ 2S � 2SbÞ(2)

Table 3 calculates M for b ¼ 25% to b ¼ 75% at 25% intervals. Table 4 is asummary of the three panels in Table 3, and M for b ¼ 0% and b ¼ 100%,which are calculated in Tables 1 and 2, respectively.

Results

There are two interesting results in Table 4. One, as expected, when thesampling bias increases, the Murphy Index increases (@M/@b>0, seeAppendix C). This result is intuitively obvious, because the greater thebias, the less observations of data which contradict Murphy’s Law.

Two, as S increases, the Murphy Index decreases (@M/@So0, seeAppendix D). This result is somewhat surprising. Since Murphy’s Lawdeals with a person in stopped traffic, why would the increase in thepercentage of time the driver is stopped result in a lower M? The answer liesin the fact that M decreases only if the driver observes OS, conversely M

increases only if the driver observes OG. As S increases, it becomes less likelythat the driver will observe OG. Thus, paradoxically, as the objective realityof the world improves (S decreases), people with 0%obo100% becomemore convinced that Murphy’s Law is valid.

MANAGERIAL IMPLICATIONS

Assume a manager expects to make cutbacks in all her departments. Themanager has the option of spreading the cutbacks over a year, or making allthe cutbacks simultaneously. If the manager spreads the cutbacks over thewhole year, each time a department is cut back, they will observe that otherdepartments are not cut back (the equivalent of my lane stopped and the

Table 3. Joint Traffic Probabilities (Partial Bias).

S ISOS IGOG IGOS ISOG M

Traffic

Stoppage

Both Lanes

Stopped

Both Lanes

Going

I (My Lane)

Going

I (My Lane)

Stopped

Murphy’s

Index

O (Other Lane)

Stopped

O (Other Lane)

Going

S (%) bS+(1–b)S2

(%)

(1–b)(1–S)2

(%)

(1–b)S(1–S)

(%)

b(1–S)+(1–

b)S(1–S) (%)

b/(b+2S–

2Sb) (%)

Panel A: Bias (b) ¼ 25%

10 3 61 7 29 63

20 8 48 12 32 45

30 14 37 16 33 36

40 22 27 18 33 29

50 31 19 19 31 25

60 42 12 18 28 22

70 54 7 16 23 19

80 68 3 12 17 17

90 83 1 7 9 16

Panel B: Bias (b) ¼ 50%

10 6 41 5 50 83

20 12 32 8 48 71

30 20 25 11 46 63

40 28 18 12 42 56

50 38 13 13 38 50

60 48 8 12 32 45

70 60 5 11 26 42

80 72 2 8 18 38

90 86 1 5 10 36

Panel C: Bias (b) ¼ 75%

10 8 20 2 70 94

20 16 16 4 64 88

30 25 12 5 58 83

40 34 9 6 51 79

50 44 6 6 44 75

60 54 4 6 36 71

70 65 2 5 28 68

80 76 1 4 19 65

90 88 0 2 10 63

Management Accounting Research 211

Table 4. Summary of Murphy’s Index.

S b (Sampling Bias) (%)

Traffic Stoppage (%) 0% 25% 50% 75% 100%

10 0 63 83 94 100

20 0 45 71 88 100

30 0 36 63 83 100

40 0 29 56 79 100

50 0 25 50 75 100

60 0 22 45 71 100

70 0 19 42 68 100

80 0 17 38 65 100

90 0 16 36 63 100

HARRY Z. DAVIS ET AL.212

other lane going). When a different department is cut back and this de-partment is not cut back (the equivalent of my lane going and the other lanestopped), it makes much less of an impression. Thus, because of the per-ceptual bias, each department believes that it has been singled out for cut-backs more often than others. The manager is thus better off making all hercutbacks at once (the equivalent of my lane stopped and the other lanestopped) so that each one of her departments will not feel that the otherdepartments always get fewer cutbacks.

The managerial implications of our findings are that, whenever managershave ‘‘bad news’’ to relay or must implement managerial plans that will beperceived as ‘‘negative,’’ the news should be delivered or the plans imple-mented all at once. Spreading the delivery or implementation over a periodof time produces more discontent on the part of the personnel affected. Thefindings lend credence to a generalization that peoples’ discontent is mini-

mized when the number of observations (and thus the number of chances for

forming a negative perception) of undesirable events is minimized.

Our findings have wide-reaching managerial implications. Managementmust deal with expected discontent of organizational personnel wheneveractions involving organizations’ limited resources affect peoples’ perception.Based on our results, researchers and practitioners can focus on the numberof times people observe undesirable announcements and/or events ratherthan just on the amount of resources reduced. This can have far-reachingimplications in various management related disciplines.

Management Accounting Research 213

CONCLUSION

In our simple model, we show that due to a sampling bias, people perceivethat there is evidence supporting Murphy’s Law even when there is nosuch evidence in objective reality. The model applies to many situations.As long as people compare themselves to others more often when they arein a losing situation than when they are in a winning situation, even ifthere is no objective basis for Murphy’s Law, people will perceive thatMurphy’s Law is valid. This would be true if two friends are taking anexam and one fails, he will then want to know how his friend did. If thefriend also fails, he will be less upset. However, if his friend passes, he willperceive that Murphy’s Law is working against him. As people go throughlife, there will be many situations in which the biased perception will seemto provide evidence for Murphy’s Law. As they accumulate, the personwill be more and more convinced that Murphy’s Law is valid. The im-plication for a manager is to try to schedule cutbacks across departmentsimultaneously, so that each department does not feel that it is sufferingthe most.

Based on our findings we conclude that people’s discontent is minimizedwhen the number of observations of undesirable events is minimized, be-cause it reduces the number of chances for forming a negative perception(bias). The results of our study provide a basis for future research involvingthe number of times people observe undesirable announcements and/orevents versus the amount of resource reduction managers must deal with.The application of our findings is not limited to a single social-science dis-cipline.

NOTES

1. An internet search yields over a quarter million sites. Two sites with a long listof corollaries are: Murphy’s Laws and Murphy Laws Site.2. Relaxing this assumption by allowing for a correlation between the states of the

two lanes changes the ratio of (ISOS+IGOG)/(IGOS+ISOG). Since the evidence for oragainst Murphy’s Law is the ratio IGOS/ISOG, the results of the paper remain un-changed.3. Alternatively, a state in which the two lanes are equal could be considered

evidence against Murphy’s Law.4. At the extreme values S ¼ 0% and S ¼ 100%, M is not defined, since there are

no observations supporting or refuting Murphy’s Law.

HARRY Z. DAVIS ET AL.214

REFERENCES

Forward, R. L. (1983). Murphy lives. PPC-UCI News, March/April, 30

Matthews, R. A. J. (1995). Tumbling toast, Murphy’s Law and the fundamental constants.

European Journal of Physics, 16, 172–176.

Matthews, R. A. J. (1996). Odd socks: A combinatoric example of Murphy’s Law. Mathematics

Today, March-April, 39–41.

Matthews, R. A. J. (1997a). Knotted rope: A topological example of Murphy’s Law. Math-

ematics Today, 33, 82–84.

Matthews, R. A. J. (1997b). Murphy’s Law of maps. Teaching Statistics, 19, 34–35.

Matthews, R. A. J. (1997c). The science of Murphy’s Law. Scientific American, April, 72–75.

Matthews, R. A. J. (2001). Testing Murphy’s Law: Urban myth as a source of school science

projects. School Science Review, 83, 23–28.

APPENDIX A. THE MURPHY INDEX

The basic index is: ISOG/(ISOG+IGOS), since ISOG measures the data sup-porting Murphy’s Law, and IGOS measures the data contradicting Murphy’sLaw.

To refine the measure so that it is always between 100% and �100%,subtract a constant (50%) and multiply by a constant (2):

M ¼ 2�ISOG

ðISOG þ IGOSÞ� 50%

� �(A.1)

Simple algebraic manipulation yields:

M ¼ðISOG � IGOSÞ

ðISOG þ IGOSÞ(A.2)

APPENDIX B. THE MURPHY INDEX WITH BIAS

Since

ISOG ¼ bð1� SÞ þ ð1� bÞSð1� SÞ (B.1)

and

IGOS ¼ ð1� bÞSð1� SÞ (B.2)

M ¼½bð1� SÞ þ ð1� bÞSð1� SÞ � ð1� bÞSð1� SÞ�

½bð1� SÞ þ ð1� bÞSð1� SÞ þ ð1� bÞSð1� SÞ�(B.3)

Management Accounting Research 215

M ¼b

ðbþ 2ð1� bÞSÞ(B.4)

M ¼b

ðbþ 2S � 2SbÞ(B.5)

APPENDIX C. PARTIAL DERIVATIVE OF MURPHY

INDEX WITH RESPECT TO BIAS

M ¼b

ðbþ 2S � 2SbÞ(C.1)

@M

@b¼

½ðbþ 2S � 2SbÞ � bð1� 2SÞ�

ðbþ 2S � 2SbÞ2(C.2)

@M

@b¼

2S

ðbþ 2S � 2SbÞ2(C.3)

The denominator is always positive, and for S>0 the numerator is alsopositive, so for S>0,

@M

@b40 (C.4)

APPENDIX D. PARTIAL DERIVATIVE OF MURPHY

INDEX WITH RESPECT TO TRAFFIC STOPPAGE

M ¼b

ðbþ 2S � 2SbÞ(D.1)

@M

@S¼

½�bð2� 2bÞ�

ðbþ 2S � 2SbÞ2(D.2)

@M

@S¼

2bðb� 1Þ

ðbþ 2S � 2SbÞ2(D.3)

The denominator is always positive, and for 0>b>1 the numerator is neg-ative, so for 0>b>1, @M/@So0.

ACTIVITY-BASED COST

MANAGEMENT AND

MANUFACTURING,

OPERATIONAL AND FINANCIAL

PERFORMANCE: A STRUCTURAL

EQUATION MODELING APPROACH

Adam S. Maiga and Fred A. Jacobs

ABSTRACT

This study uses structural equation modeling to investigate the impact of

ABC implementation factors (management support, clarity and con-sensus of ABC objectives, non-accounting ownership, and training) onquality, cost, and cycle time improvements, the relations among quality,

cost, and cycle time improvements and, the influence of quality, cost, and

cycle time improvement on financial performance at the business unit

level. Overall, the results of the structural analyses support the theoretical

model indicating that ABC implementation factors influence quality, cost,

and cycle time, and partial support for the relations among quality, cost,

and cycle time improvement and their effect on financial performance.

When these relationships are further analyzed within the context of ABCimplementation stage, adoption of advanced manufacturing practices,industry characteristics and plant size to determine if these contextual

Advances in Management Accounting, Volume 16, 217–260

r 2007 Published by Elsevier Ltd.

ISSN: 1474-7871/doi:10.1016/S1474-7871(07)16008-1

217

ADAM S. MAIGA AND FRED A. JACOBS218

factors impact the model constructs and the relationships between the

variables in the theoretical model, the results show that these contextual

factors do not affect the model constructs, however, they affect the model

relations.

1. INTRODUCTION

The ability of a manufacturing company to compete effectively in the globalmarket is determined to a large extent by the cost and quality of its products(Gunasekaran et al., 1994) and getting the product to market muchmore quickly (Milligan, 1999). To this end, more and more companies areimplementing activity-based costing (ABC) (Kaplan & Norton, 1992, 1993,1996; Kennedy & Affleck-Graves, 2001; Krumwiede, 1998; Shields, 1995;Shields & Young, 1994) in response to the new global competitive environ-ment, and advocates argue that ABC provides cost data needed to makeappropriate key decisions (Cooper & Kaplan, 1991). However, reservationshave been expressed regarding the benefits of ABC (Innes et al., 2000;Malmi, 1997; Morrow & Connolly, 1994). In some cases firms have failed tocomplete their ABC projects and in others they have failed to gain benefitsexpected from the ABC systems they have installed (Lyne & Friedman,1996). For example, Ittner, Lanen, and Larcker (2002) find modest evidencethat ABC use is positively associated with manufacturing performance.However, on average, they find that extensive ABC use is positively asso-ciated with higher quality levels, greater decreases in cycle time, and largerincreases in first pass quality. Additionally, their path analysis also indicatesthat ABC use has a positive indirect association with manufacturing costreductions through improvements in quality and cycle time. Despite theevidence of association between ABC use and certain improvements inmanufacturing processes, Ittner et al. (2002) find that, on average, extensiveABC use has no significant association with return on assets (ROA). In-stead, they find some evidence that the relation between ABC and profitsvaries with the extent to which the decision to use ABC ‘‘matches’’ theplant’s operational characteristics. These findings, and particularly thestrength of the relationships, may have been impacted by the choice ofmeasurement metrics and method of analysis.

Shields (1995) suggests that since ABC is embedded in a behavioraland organizational context that defines the programs and innovationsthat are implemented and succeed and fail, it is important that an ABCimplementation strategy be focused on these behavioral and organizational

Activity-Based Cost Management 219

variables. However, very little empirical information exists about firms’use of these behavioral and organizational variables and how they impactperformance. Such information could be useful in helping to identifyhow companies benefit from ABC implementation. Additionally, contextualfactors may be relevant in ABC implementation and success (Krumwiede,1998). Hence, more empirical studies are needed as the population of ABCadopters increases to provide and test alternative empirical studies for ABCsuccess.

The first contribution of this study is to re-examine the findings of Ittneret al. (2002) and to further investigate whether there is an association be-tween ABC use and manufacturing operational and financial performanceusing different measurement metrics and analyses methods in order to testthe robustness of their findings. Thus, this study differs from Ittner et al.(2002) in several important ways. First, Ittner et al. (2002) measure ABCbased on responses to a question asking whether ABC is extensively used inthe plant (1 ¼ yes and 0 ¼ no), while this study focuses on ABC imple-mentation factors, i.e., top management support, clarity and consensusof ABC objectives, non-accounting ownership, and training. Second, in ad-dition to ROA used by Ittner et al. (2002) as the financial performancemeasure, this study also uses return on sales (ROS) as ABC adoption mayaffect both income and assets. Consequently, we use improvements in aplant’s ROA and ROS to evaluate the effect on financial performance.Third, Ittner et al. (2002) use ordinary least squares regression and averagestandardized responses of the performance variables to assess the associ-ation between the use of ABC and manufacturing plant performance,while this study uses structural equation modeling (SEM) (1) to estimate theimpact of ABC implementation factors on process performance variables,(2) to examine the relations among the process performance variables,and (3) to assess the impact of process performance variables on financialperformance. SEM incorporates all of the variables in the model to aid ourunderstanding of their linkages. SEM allows the examination of the entiremodel simultaneously, rather than one relation at a time (Kline, 1998). SEMalso avoids the potential for simultaneous equation bias, which arises whenendogenous explanatory variables in the system of equations are correlatedwith disturbance terms with the result that classic ordinary least squareestimators are not consistent (Gujarati, 1995).

Recent research has examined the association between contextual factorsand innovation diffusion process. For example, research has suggestedthat the benefits of ABC are more readily realized under contextualfactors (Krumwiede, 1998; Cagwin & Bouwman, 2002). Hence, the second

ADAM S. MAIGA AND FRED A. JACOBS220

contribution of this study is to investigate the model constructs and modelrelationships within the context of ABC implementation stages, adoption ofmanufacturing practices, industry, and size. To our knowledge, no priorstudy has assessed these relationships within the context of these variables.

Overall, the results indicate support for the theoretical framework pos-ited. ABC implementation factors significantly influence both quality andcost. However, their impact on cycle time improvement is rather tenuous.Quality improvement has a negative effect on both cost improvement andcycle time improvement which is found to positively impact cost improve-ment. Both quality improvement and cost improvement have significantpositive effects on financial performance, while the impact of cycle timeimprovement on financial performance is not significant. Further analysisshows that the direct relations between ABC implementation factorsand business unit financial performance are not significant, indicating thatmanufacturing performance measures (i.e., quality, cost, and cycle time) areintervening variables that mediate the relationship between ABC imple-mentation factors and financial performance. Next, tests of contextualeffects of ABC implementation stage, adoption of manufacturing practices,industry characteristics, and plant size on the construct levels and relation-ships were carried out. Results show that although these contextual factorsdo not affect the model constructs, they affect the model relationships.Therefore, this study adds to the literature by indicating contexts underwhich ABC implementation may be beneficial.

The paper is organized as follows. First, the literature review is discussed.Next, a discussion of the research methods is conducted. After the empiricalresults are reported, a summary and discussion are presented.

2. LITERATURE REVIEW AND HYPOTHESES

DEVELOPMENT

Anderson and Young (1999) argue that, ‘‘while there is broad agreementthat ABC implementation factors are associated with successful outcomes, adifficulty exists in developing hypotheses because existing theories do notrelate specific ABC implementation factors to particular aspects of success,and empirical studies vary in terms of effectiveness constructs, duration ofimplementation and units of analysis.’’ Furthermore, Shields and Young(1989) argue that, ‘‘consistent with other administrative innovations, thesuccessful implementation of a cost management system does not depend on

Activity-Based Cost Management 221

technical resources, such as whether or what type of software is used,whether the system is integrated with other accounting systems or standsalone, or whether external consultants are used.’’ In this study, four keymultidimensional constructs of implementation are derived from empiricalanalysis, based on existing dimensions considered in the literature to beassociated with ABC success. The ABC implementation factors concerntop management support, training, non-accounting ownership, and clarityof objectives. Prior research supports the potential role of these four im-plementation factors (e.g., Krumwiede, 1998) in ensuring that ABC will beuseful for quality, cost, and cycle time improvement. Management decisionsin the areas of product quality improvement, cost management, and cycletime improvement tend to be important strategically as they specify organ-izational direction and involve significant reengineering and cost reductionprograms (Chenhall, 2004). Fig. 1 depicts the model under study.

Top management must send clear signals to various parts of the organ-ization about the importance of a project (McGowan & Madey, 1998).Transformational leadership theory suggests that senior managementcan encourage the pursuit of change by formulating and communicating avision for the future and reinforcing values that support the vision (Tichy &Devanna, 1986). This suggests that top management support will help focusefforts toward the realization of organizational benefits and lend credibilityto functional managers responsible for a project implementation and use(Doll, 1985). This support is necessary to change the culture of the organ-ization if a work environment conducive to employee involvement is to becreated (Burack et al., 1994; Daft, 1998; Hamlin, Reidy, & Stewart, 1997;Snell & Dean, 1992). Also, employees are more apt to work harder andcontribute ideas to change process if management establishes a culturethat supports employees (Burack et al., 1994). According to Shields (1995),support from top management provides the vehicle through which resourcesare controlled, goals are set and monitored, and political forces are gen-erated to support the innovation. In addition top management can instituteABC performance-based incentive and procedures for employees. Compen-sation and rewards that fit with the improvement strategy will encourageemployees to work toward the organization’s goals (Bonito, 1990).

Empirical studies indicate that top management support can encou-rage practices and behaviors that lead to superior quality performance(Anderson, Rungtusanathan, Schroeder, & Dearaj,1995; Flynn, Schroeder,& Sakakibara, 1995; Saraph, Benson, & Schroeder, 1989), cost management(Chenhall, 2004), and cycle time improvement (Simers & Priest, 1989;Schilling & Hill, 1998).

NonaccountingOwnership

Clarity ofObjectives

ManagementSupport

TrainingCycleTime

Improvement

CostImprovement

QualityImprovement

FinancialPerformanceImprovement

Fig. 1. Theoretical Framework.

ADAM

S.MAIG

AAND

FRED

A.JA

COBS

222

Activity-Based Cost Management 223

This study suggests that with management support and visible signalingof their commitment to quality, cost, and cycle time improvement, the ABCimplementation is likely to be successful. Hence,

H1. ABC implementation factor of top management support has a sig-nificant impact on (1) quality improvement, (2) cost improvement, and (3)cycle time improvement.

Goal theory suggests that acceptance is enhanced and individuals willexpend effort in trying to make systems work, if they are provided with thespecific goals of the initiatives (Locke, Shaw, Saari, & Latham, 1981). Thissuggests that before embarking upon an ABC initiative, it is imperative thatorganizations develop clear objectives of the system adoption. Clarity ofobjectives may enhance understanding of, and focus on, the purposes ofABC, and is likely to show how ABC aims to link operations to strategy,thereby enhancing the organizational validity of the systems (Chenhall,2004). Contained therein should be the expected benefits that will resultfrom the ABC adoption and steps for achieving these benefits (Jeffery &Morrison, 2000). Management should also formally communicate the im-plementation objectives to employees and help everyone understand theircontribution to the process as well as implications their decisions haveon the value of the organization (Bradford & Roberts, 2001). This impliesthat companies that embark upon an ABC adoption with clear and conciseexpectations of what the package will do for them will arguably realizegreater organizational performance. Hence, by providing clarity of objec-tives that lends to understanding the process, ABC systems are said toencourage employers and manager toward more innovative problem solvingtechniques leading to changes in the cost, quality of production, and waste(McGowan, 1994).

In summary, we argue that incorporating clarity of objectives into theABC implementation process provides opportunities and focus for end-users to agree on organizational direction and technical characteristics ofthe organization. Therefore, the usefulness of ABC for quality, cost, andcycle time improvement will be enhanced if it is clear how ABC can improvethese types of strategic decisions. Therefore,

H2. ABC implementation factor of clarity of objectives has a significantimpact on (1) quality improvement, (2) cost improvement, and (3) cycletime improvement.

Training refers to the process of teaching job-related skills and knowledgeto the employees in an organization and emerges as a crucial element of

ADAM S. MAIGA AND FRED A. JACOBS224

workforce management (Mintzberg, 1979). Training, in addition to provid-ing a stepping stone toward the efficient use of the information providedby the system, is vital in promoting acclamation to the system (Tait &Vessey, 1988). Training in designing, implementing, and using ABC is animportant way to interrelate ABC among strategy, performance evaluation,and ABC objectives (McGowan, 1994). Choi (1995) suggests that to enablecontinuous improvement in an organization, workforce training intechniques necessary for improving process should be an ongoing activity.An ongoing application training based on the results of the ABC analysisis necessary for employees to use the ABC in changing operations andmanaging the business, using such tools as performance measurement(Shields, 1995; Sharman, 1993; Chenhall, 2004). Training also allows em-ployees to more effectively undertake the goals that have been established,facilitates a better understanding of the detailed information produced bythese ABC systems, allows employees to track activities involved in eachprocess, and more clearly identify sources of waste (Choi, 1995). The in-fluences of training on ABC implementation success are captured in thefollowing hypothesis:

H3. ABC implementation factor of training has a significant impact on(1) quality improvement, (2) cost improvement, and (3) cycle time im-provement.

Designating a cross-functional team, developing and enforcing guidelinesregarding access to ABC data and authority to update ABC systems helpsavoid confusion, misuse of ABC, and turf problems (Tatikonda, 2003). It isalso plausible that ABC would be accepted and more readily promoted ifthere is non-accounting ownership of the systems (Cooper, Kaplan, Maisel,Morrissey, & Oehm, 1992). Such ownership derives from the centrality ofABC to the individuals’ jobs and generates a proclivity to champion thecause of ABC (Anderson, 1995), and to demonstrate commitment to theABC system in their decisions and interactions with others in the organ-ization (Chenhall, 2004). Therefore, this study suggests that non-accountingownership may be associated with improved information about activitiesand cost drivers that is expected to enhance improvement in quality,cost, and cycle time by identifying non-value added activities, waste, andactivities caused by poor quality and the drivers of these problems. Hence,

H4. ABC implementation factor of non-accounting ownership has asignificant impact on (1) quality improvement, (2) cost improvement, and(3) cycle time improvement.

Activity-Based Cost Management 225

Existing literature disagrees as to the compatibility of product quality andcost position. One view predicts inherent trade-offs between quality and costposition; another suggests that no inherent trade-offs exist and that thepursuit of high quality strategy may actually have synergistic effects oncost position. For example, both Juran (1988) and Crosby (1979) haveconsistently argued that better quality practices can reduce cost. The moreobvious cost reductions from higher quality are achieved through increasedoutput of defect-free products and lower expenditure on scrap, rework,inspection, and warranty and repair (Ittner, 1994; Kaynak, 2003). Perhapseven more significant are the less obvious indirect cost-reduction effects,such as fewer disruptions in operations due to out-of-conformance pur-chases and production, elimination of buffer inventories held to compensatefor poor quality, improved machine utilization, and reductions in quality-related schedule changes, congestion, and downtime (Ittner, 1994).

However, it has also been widely believed that higher quality entails theadoption of more expensive production technology including machinery,labor, and materials. Furthermore, achieving high quality position may re-quire higher expenditures in other areas beyond the direct costs of man-ufacturing or distribution. Therefore, higher quality is viewed as beingincompatible with lower per-unit manufacturing costs. Under this view, itwould be difficult, if not impossible, for a plant to simultaneously pursuehigh quality levels for its products and low manufacturing costs.

Based on the above competing arguments, we develop the following hy-pothesis in the null form:

H5. Quality improvement does not have a significant impact on costimprovement.

The relationship between quality and cycle-time has been viewed fromtwo different perspectives. One view is that cycle time must be traded offagainst improvements in quality. That is, quality-improvement processescan be implemented only at the expense of longer cycle-times. However, analternate view is that quality improvement and faster cycle-time can besimultaneously attained by reducing defects and rework (Crosby, 1979;Deming, 1986; Nandakumar, Datar, & Akella, 1993). Also, the empiricalresearch on the impact of quality on cycle time is inconclusive. For example,in a study of information technology firms, Harter et al. (2000) find thathigher quality products exhibit significant reductions in cycle-time; whileIttner et al. (2002) find that quality improvement significantly and positivelyimpacts cycle-time.

ADAM S. MAIGA AND FRED A. JACOBS226

Given the above conflicting relationship between quality and cycle-time,we develop the following hypothesis in null form:

H6. Quality improvement does not have a significant impact on cycletime improvement.

Achieving cost efficiency is increasingly critical in globally competitivemarkets (Young & Selto, 1993). Decreasing cycle time means eventuallydecreasing non-value added time and inventory, thereby lowering productcost (e.g., reduced overhead) (Ittner et al., 2002). Assuming that even withreduced capacity product volume remains constant, lowering overall oper-ating costs by reducing cycle time would reduce unit product costs (Campell,1995). We contend that that cycle improvement will exhibit a positive re-lationship with cost improvement. This leads to the following hypothesis:

H7. Cycle time improvement has a significant impact on cost improve-ment.

One of the challenges associated with making strategic decisions aboutquality is that its conceptualization varies by discipline. In marketing, qual-ity tends to mean quality as perceived by the customer (e.g., Bolton & Drew,1991; Parasuraman et al., 1985). In operations and quality management,quality tends to mean the efficiency and reliability of internal processes (e.g.,Crosby, 1979; Deming, 1986), even if those processes are invisible to thecustomer (Ramaswamy, 1996). Therefore, depending on how quality is de-fined, different kinds of quality improvement efforts are likely to be appro-priate, and most important, they are likely to have different pathways toprofitability (Rust et al., 2002). Our conceptualization is based on the latterviewpoint.

However, the direct impact of quality on financial performance has beeninconclusive. For example, while Tatikonda and Montoya-Weiss (2001)found that technical product quality defined from operational perspectivedoes, in fact, translate into financial performance, other studies in opera-tions management (Dale & Lightburn, 1992; Madu & Kuei, 1995; Voss,Blackmon, Hanson, & Oak, 1995) that examined the impact of qualityperformance on overall business performance and have reported mixed re-sults (Gale, 1994; Powell, 1995).

Consequently, we develop and test the following hypothesis in null form:

H8. Quality improvement does not have a significant impact on financialperformance.

Activity-Based Cost Management 227

Prior literature strongly endorses the view that improved manufacturingperformance will translate into higher profits (Garvin, 1988; Hayes et al.,1988). Roth and Borthick (1989), e.g., support the view that manufacturingperformance, i.e., product cost, is an important key to improved businessperformance. Low cost is linked to competitive strategy because having alow-cost position yields the firm above-average returns relative to compet-itors by achieving lower relative direct costs (Phillips et al., 1983; Porter,1980). Cost reduction programs transfer their savings to the bottom linedirectly (Rust et al., 2002). Gatignon and Xuereb (1997) suggest that thelower the cost, the greater the potential for profits, either by setting highermargin or by penetrating the market with lower price. This is in support ofprior studies that suggest that businesses which primarily compete with thelow-cost approach tend to achieve high market share through the offering oflow prices, made possible by scale economies (Hambrick, 1983; Henderson& Henderson, 1979; Porter, 1980, 1985). Tatikonda and Montoya-Weiss(2001) found that cost does not have a significant effect on customersatisfaction, but does have a significant direct effect on relative sales.Therefore, in this study, we investigate the direct link between cost im-provement and plant financial performance by developing and testing thefollowing hypothesis.

H9. Cost improvement has a significant impact on financial performance.

Manufacturers are under pressure to produce and get the product tomarket much more quickly (Milligan, 1999). Thus, cycle time is increasinglybecoming a critical variable in many business decisions (Stalk, 1988; Stalk &Hout, 1990; Meyer, 1993). This confirms the importance of cycle time inhelping firms to not only compete effectively but also attain a competitiveadvantage because the focus on cycle time translates into bottom-line profits(Sharland, Eltantawy, & Giunipero, 2003). However, Stalk and Hout (1990)found that in industries where most companies follow a strategy of rapidproduct development, and rapid production and/or delivery, firms may in-cur the costs of accelerated cycle-time without the corresponding financialbenefits.

Research studies in the literature dealing with the effect of manufacturingcycle time on financial performance are inconclusive. Thus, there is a need tofurther investigate this relationship. Therefore, the following hypothesis isdeveloped and tested in null form:

H10. Cycle time does not have a significant impact on financial per-formance.

ADAM S. MAIGA AND FRED A. JACOBS228

3. RESEARCH DESIGN

3.1. Sample

To address the hypotheses, we used a survey to collect data from a cross-section of U.S. manufacturing plants that have adopted ABC. Our primarysource is the Industry Week series on manufacturing excellence. Additionalsources include The Wall Street Journal, Journal of Cost Management,

Management Accounting, Harvard Business Review, various industrial engi-neering journals, and periodical indices for articles in any journal that mightproduce a case report or other information to determine if ABC is adopted.A total of 2,317 manufacturing units1 were randomly selected from theabove sources and the names of managers were gathered using Dun &Bradstreet. The process resulted in 611 responses of which 5972 are usable or25.77% is the usable response rate.3

3.2. Activity-Based Costing Implementation Factors

ABC factors found significant in past studies include top management sup-port, linkage to competitive strategies, adequacy of resources, non-accountingownership, linkage to performance evaluation and compensation, implemen-tation training, clarity of objectives, and number of purposes for ABC(Foster & Swenson, 1997; McGowan & Klammer, 1997; Shields, 1995;Krumwiede, 1998). However, none of these studies used SEM to linkABC implementation factors to performance outcomes. The compositeABC factors (see appendix) are measured using questions adapted fromKrumwiede (1998).4

3.3. Plant Performance Measures

We use four measures to assess plant performance based on Ittner et al.(2002) and Gatignon and Xuereb (1997) and relate to changes in quality,costs, cycle time, and financial performance over the last 5 years. The firstvariable, quality improvement, is captured using responses to two questionson product quality (‘‘finished product first pass quality yield in percentageterms’’ and ‘‘scrap and rework costs as a percentage of sales’’). The secondvariable, cost improvement, is captured using three questions borrowedfrom Gatignon and Xuereb (1997) and modified for the purpose of this

Activity-Based Cost Management 229

study (‘‘manufacturing/operations,’’ ‘‘research and development,’’ and‘‘marketing costs’’).5 The third variable, cycle time improvement, is meas-ured using responses to two questions (‘‘manufacturing cycle from start ofproduction to completion of product in hours’’ and ‘‘standard lead-timefrom order entry to shipment in days’’). Two measures capture improve-ment in plant financial performance: ROA and ROS.6 These performancemeasures relate to changes (improvement) over the last 5 years with highervalues for the four change variables that represent greater improvement.7

3.4. Contextual Factors

The specification of the theoretical framework (see Fig. 1) consists of a set ofhypotheses suggesting structural relationships among variables in the frame-work. Although ABC factors affect implementation, contextual factors maystill play a part (McGowan & Klammer, 1997). Below, we identify somerelevant contextual factors and their potential impact on the levels of theconstructs relationships in the model as well as on the model relationships.In this study, we are interested in six contextual factors.

The first contextual factor is Stage of ABC Implementation. For thisstudy, the three stages of the Cooper and Zmud (1990) model (acceptance,routinization, and infusion) are employed (Foster & Swenson, 1997). Ac-ceptance is achieved when ABC is used at least somewhat by non-account-ing management for decision making (Anderson, 1995). Routinization isachieved when ABC is commonly used by non-accounting management fordecision making and considered a normal part of the information system.Infusion is defined as not only using ABC extensively but also integrating itwith the primary financial system (Reeve, 1996; Kaplan, 1990).

The second contextual factor is Advanced Manufacturing Practices (de-noted AMP where, 1 ¼ if advanced manufacturing and 0 ¼ if no advancedmanufacturing) such as just-in-time, total quality management, MRP, ERP.Studies indicate that these elements interact to improve performance, sug-gesting that advanced manufacturing is a total system solution rather than aset of independent techniques (Milgrom & Roberts, 1995; MacDuffie, 1995;Chenhall & Langfield-Smith, 1998).8

The third contextual factor is Industry. Type of industry could havemoderating effects on the model relationships for several reasons. First,industries differ in terms of types of products and production processes.For example, the chemical industry primarily uses batch and continuousmanufacturing processes whereas the automotive or computer industry

ADAM S. MAIGA AND FRED A. JACOBS230

relies heavily on modular assembly line production. Second, adoption andimplementation rigor of technological and managerial innovations has beenlinked to industry structural characteristics such as domestic and globalcompetitive environment (Porter, 1980). The higher the volatility and globalcompetitive challenges of an industry, the higher the incentive for imple-menting such initiatives. For example, the automotive and computer in-dustries have adopted contemporary operations improvement strategiessuch as TQM to a great extent as compared with more stable industries suchas pulp and paper or clay and glass (Dreyfus, Ahire, & Ebrhimpour, 2004).Schmenner (1986) classified industry SIC groups into three categories basedon the logistical complexity of their production processes (measured bynumber of steps in the production process). He concluded that the inherentnature of processes could impact the ability of various industries to imple-ment individual elements of productivity improvement techniques. Funk(1995) further used Schmenner’s classification and argued that the logisticalcomplexity of a production system will affect the relevance of various op-erations improvement techniques. For example, teamwork and cooperationare of greater significance in logistically complex production systems suchas automobile and computer assembly than in low logistical complexityproduction processes such as chemicals or food processing (Funk, 1995).Schmenner (1986) and Funk (1995) classification coded SIC groups 20through 33 as low logistical complexity industries and SIC 34 through 38 ashigh logistical complexity industries.

Coincidentally, the high logistical complexity industries (electrical ma-chinery, fabricated metal products, industrial machinery, transportationequipment, electronics, and instrumentation) have also experienced fiercerglobal competition and use of advanced manufacturing technologies ascompared with the low logistical complexity industries (Kotha & Vadlamani,1995). Moreover, these categories are synonymous with classification ofprocess-type production industries (SIC 20 through 33) versus discrete-typeproduction industries (SIC 34 through 38) (Swamidas & Kotha, 1998).Hence, we test the impact of the differing intensity of competition, differinglogistical complexity of production, and differing production processesacross industries on our model.

The fourth factor is Size. Size measures the number of employees at theplant. Plant size9 is an important contingency factor for several reasons.First, larger plants have more market clout, capital resources, and profes-sional managerial expertise (Finch, 1986), and are likely to adopt ABC(Innes & Mitchell, 1995). On the other hand, smaller plants have flatterorganizational structures and more informal communication channels

Activity-Based Cost Management 231

(Sirinopolis, 1994). Thus, because smaller factories are more manageable,researchers have associated the smaller size and informal organizati-onal structures with their abilities to encourage and implement innovation(Sirinopolis, 1994).

3.5. Analysis and Results

In this section we first present the descriptive statistics. Next, the structuralequation model, which consists of two parts, is discussed: the measurementmodel and the structural model. The measurement model considers the ad-equacy of the various measures used for theoretical constructs employed inthe study, while the structural model specifies the relationships between thevarious constructs.

Strength of structural equation analysis is that multiple indicators areused to represent each unobserved latent construct and that it provides anefficient technique for estimating interrelated dependence relationships, suchas those proposed in this study. The contribution of each scale item isincorporated into the estimation of the independent and dependent rela-tionships of the model. This procedure is similar to performing a factoranalysis of the scale items and using the factor scores in a regression anal-ysis. Finally, the results of hypotheses testing are presented.

3.6. Descriptive Statistics

The descriptive statistics in Table 1, Panel A provide the profile of theresponding companies, showing that they constitute a broad spectrum ofmanufacturers as defined by the two-digit SIC code. The sample compo-sition has the largest representation in electronic and electrical and other(16.080%), followed by industrial equipment (11.223%), primary metals(10.385%), motor vehicles (8.878%), and instruments and related products(8.543%). Additional information on respondents’ characteristics is pro-vided in Table 2, Panel B. The length of ABC implementation has a mean of8.56 years. The respondents to the question regarding the number of yearswith the manufacturing plant have a mean of 12.34 years in their currentposition. To the number-of-years-in-management question, respondents in-dicated a mean of 18.92 years. While the number of employees range fromless than 100 to greater than 350. It appears from their positions and tenurethat the respondents are knowledgeable and experienced with access to

Table 1. Descriptive Statistics.

Panel A: Distribution of Two-Digit Industry Classifications

SIC Industry

Code

Organization Type Number of Business

Units Used in the Study

%

20 Food and kindred 39 6.533

22 Textile mill products 23 3.853

23 Apparel and other fabricated

textile products

24 4.020

24 Lumber and wood products 13 2.178

25 Furniture 12 2.010

26 Paper and allied products 13 2.178

27 Printing and publishing 15 2.513

28 Chemical and allied products 23 3.853

29 Petroleum and coal products 17 2.848

30 Rubber and plastics 15 2.513

31 Leather and leather products 14 2.345

32 Stone, clay and glass products 21 3.518

33 Primary metals 62 10.385

34 Fabricated metals 39 6.533

35 Industrial equipment 67 11.223

36 Electronic and other electric

equipment

96 16.080

37 Motor vehicles 53 8.878

38 Instruments and related products 51 8.543

597

Panel B: Other Characteristics of Respondents

Minimum Maximum Mean Standard Deviation

Length of ABC implementation (years) 7 11 8.56 1.93

Length at present position (years) 9 13 12.34 3.32

Length in management (years) 15 25 18.92 3.73

Number of employees

o100 ¼ 24

101–150 ¼ 49

151–200 ¼ 23

201–250 ¼ 105

251–300 ¼ 95

301–350 ¼ 97

>350 ¼ 204

ADAM S. MAIGA AND FRED A. JACOBS232

Table 2. Respondents’ Characteristics.

Panel A: Results of Factor Analysis and Measurement Characteristics of the ABC

Implementation Variables

Factor 1 Factor 2 Factor 3 Factor 4

Eigenvalues 2.520 2.410 2.343 1.542

Percent variance explained (80.138%) 22.911 21.909 21.301 14.017

Cronbach alpha .892 .861 .850 .701

Item loadings

ABC receives strong active support

from top management

.877 .092 �.144 .084

Upper management has provided

adequate resources to the ABC

implementation effort

.910 .034 .032 �.048

ABC has been closely tied to the

competitive strategies of the

business unit

.916 �.016 �.038 .101

Adequate training was provided for

designing ABC

.038 .768 �.123 �.026

Adequate training was provided for

implementing ABC

.032 .951 .036 .091

Adequate training was provided for

using ABC

.031 .929 �.017 .075

Departments outside accounting (e.g.,

manufacturing, marketing, etc.)

have shown personal ownership for

ABC’s success

�.158 .013 .880 �.003

The ABC implementation team was

(is) truly cross-functional

.057 .010 .876 .085

ABC has been linked to performance

evaluations of non-accounting

personnel

�.044 �.146 .861 .057

When the ABC initiative began, there

was consensus about its specific

objectives

�.078 .143 �.011 .882

When the ABC initiative began, its

purpose was clear and concise

.207 �.034 .141 .848

Activity-Based Cost Management 233

Panel B: Measurement Characteristics of Plant Performance

Item

Loading

Variance

Extracted

Reliability

(Cronbach

Alpha)

Quality improvement 72.249% .711

Finished product first pass quality yield in

percentage terms

.849

Scrap and rework costs as a percentage of

sales

.851

Cost improvement 58.243 .709

Manufacturing/operations costs .701

Research and development costs .795

Marketing costs .796

Cycle time improvement 83.584 .827

Manufacturing cycle time from start of

production to completion of product in

hours

.924

Standard lead time from order entry to

shipment in days

.904

Financial performance 78.312 .819

Return on asset .889

Return on sales .880

Table 2. (Continued )

ADAM S. MAIGA AND FRED A. JACOBS234

information upon which to provide reliable perceptions and otherwise wellqualified to provide the information required.

3.7. Analysis of Measurement Model

We assessed the measurement model using SPSS. The measurement modeldescribes the relation between the latent variables or constructs identified inFig. 1 and the indicator variables (i.e., scale items).

We first checked for sampling adequacy for the benchmarking measuresusing the Bartlett Test of Sphericity (w2 ¼ 3,659.552, significance ¼ .000)and the Kaiser–Meyer–Olkin (KMO) Measure of Sampling Adequacy(.672). Next, to examine the extent to which these categories are interrelated,we used factor analysis with varimax rotation to determine whether the

Activity-Based Cost Management 235

ABC implementation items used in this study can be grouped according toKrumwiede (1998).

Four factors with eigenvalues greater than one emerged from the analysis,with the varimax rotation factor solution retaining 80.138% of the totalvariance in the data. Three measures vary with top management support:‘‘ABC receives strong active support from top management,’’ ‘‘Upper man-agement has provided adequate resources to the ABC implementationeffort,’’ and ‘‘ABC has been closely tied to the competitive strategies of thebusiness unit.’’ Three measures capture training: ‘‘Adequate training wasprovided for designing ABC,’’ ‘‘Adequate training was provided for imple-menting ABC,’’ and ‘‘Adequate training was provided for using ABC.’’Three measures are associated with non-accounting ownership: ‘‘Depart-ments outside accounting (e.g., manufacturing, marketing, etc.) have shownpersonal ownership for ABC’s success,’’ ‘‘The ABC implementation teamwas (is) truly cross-functional,’’ and ‘‘ABC has been linked to performanceevaluations of non-accounting personnel.’’ Finally, two measures vary withclarity of objectives: ‘‘When the ABC initiative began, there was consensusabout its specific objectives,’’ and ‘‘When the ABC initiative began, its pur-pose was clear and concise.’’ Factor loadings, eigenvalues, and correspond-ing Cronbach alphas are provided in Table 2, Panel B. The factor solutionsfor the defined ABC implementation constructs support the construct va-lidity of the survey instrument. Convergent validity is demonstrated by eachfactor having multiple-question loadings in excess of .50. In addition, dis-criminant validity is supported, since none of the questions in the factoranalysis have loadings in excess of .40 on more than one factor.

No item was dropped because each loading correlated highly with itsrespective factor, indicating that each item was well reflective of the under-lying construct. The factor patterns are consistent with the four factorsidentified by Krumwiede (1998).

Next, in order to assess the consistency or reliability of responses acrossthe endogenous and exogenous items, composite reliability was calculated.Composite reliability measures the internal consistency of the construct’sindicators, similar to coefficient alpha (Fornell & Larcker, 1984). This co-efficient is based on the correlations among the responses comprising ascale. Coefficients were quite high for each of the factors. For the endog-enous variables, the alpha coefficients were .891 for management support,.862 for training, .850 for non-accounting ownership, and .711 for clarityof objectives. For the exogenous variables, the alpha coefficients were .701for quality improvement, .735 for cost improvement, .828 for cycle timeimprovement, and .732 for financial performance. Hence, all measures

ADAM S. MAIGA AND FRED A. JACOBS236

demonstrated acceptable reliabilities, with coefficients above .70. Nunnally(1967) among others have noted that this is an acceptable standard for thereliability of measures. The Cronbach alphas for the variables and theirmeasurement characteristics are provided in Table 2. Overall, these testssupport the validity of the measures representing the constructs used in thisstudy.

3.8. Analysis of the Structural Model

Our analysis focused on understanding the nature of the relationshipsamong the constructs under study. Through SEM, we tested our specifiedframework (see Fig. 1 for a diagram of the model and the testable paths).We evaluated our measurement model and considered the relationship be-tween the observed measures and the latent constructs for both the overallsample and the sub-groups.

In this section, we first assess the measures of fit (see Table 3). This fit isexpressed using measures of Goodness-of-Fit (GFI). At present, there is noconsensus on a single or even on a set of measures of fit (Maruyana, 1998).Thus, it is standard practice to report several measures. We outline belowsome of the most common measures used in the literature and in this study.

(1)

The ratio w2 test statistic over the degrees of freedom (w2/df). Goodfitting models evidence a ratio of 3.0 or less (Wheaton, Muthen, Alwin,& Summers, 1977).

(2)

GFI (Bentler & Bonnt, 1980) is based on a w2 likelihood test of thehypothesized model with a null model (no relationships among con-structs). Typically, GFI numbers greater than .9 indicate a good fit.

(3)

Comparative Fit Index (CFI) and Normed Fit Index (NFI) (Bentler &Bonnet, 1980). Both these measures compare the research model spec-ified with the null model (no relationships). The NFI can be viewed as apercent improvement over the null model but does not adjust for thenumber of parameters in the model. The CFI is based on the w2 dis-tribution. Both NFI and CFI range from 0 to 1 with values exceeding .9considered good.

(4)

Root Mean Square Error of Approximation (RMSEA) is computedas the difference between the residuals in the estimated and specifiedmodels (Steiger, 1990). Although RMSEA is sensitive to model com-plexity, it is one of the most informative criteria as to an absolute fit(Byrne, 1998). A value less than .1 is considered a good fit, and a valueless than .05 is considered a very good fit of the data to the research

Table 3. Overall Fit Summary.

Statistical Tests Proposed (Tested) Model Acceptable Fit Standard

w2 266.089 N/A

df 94 N/A

w2/df 2.831 o3.0

Fit indices

GFI .960 >.90

CFI .969 >.90

NFI .954 >.90

Residual analysis

RMSEA .055 o.10

Activity-Based Cost Management 237

model. For the overall sample measurement model analysis, the overallfit statistics in Table 4 reveal that the proposed (tested) model fits rea-sonably well the data from all respondents. First, the w2 test statisticassociated with the null hypothesis that the proposed model can effec-tively reproduce the observed covariance is 266.089 with 94 degrees offreedom resulting in a ratio of 2.831. Good fitting is achieved since theratio is less than 3.0 (Wheaton et al., 1977). Second, the various meas-ures of relative and absolute fit index (ranging from 0 to 1, with 0implying poor fit and 1 indicating perfect fit) including the GFI, the CFI,and the NFI exceed .9 without any exceptions. Noting that differentfit indices have different strengths and weaknesses, this consistentevidence of exceeding the target value of .9 for good-fitting models isencouraging. Third, Table 3 indicates that the difference between re-produced and observed covariances is rather small as evidenced by theRMSEA of .055.

Also, to ensure that specification error is not biasing the results, we rerunthe factor analysis to allow the errors (i.e., ds) of the measures to covary.According to Hughes et al. (1986), one would expect that if an unobservableerror biases the data, a common error variance would be generated betweenitems actually measured. The absence of a significant improvement in over-all model fit when these constraints are released would demonstrate theabsence of such a bias. We find no significant difference between the fit ofthe new and original factor analysis models at a confidence level of a ¼ .05.Thus, the proposition that omitted variables are generating biases at theoverall model level is rejected at a ¼ .05.

Table 4. Overall Standardized Path Coefficients and Significance.

Hypotheses Tests Standardized

Coefficient

Significance

H1: Management support

Quality improvement .120 .021

Cost improvement .208 .000

Cycle time improvement �.070 .139

H2: Training

Quality improvement .210 .000

Cost improvement .103 .005

Cycle time improvement �.489 .068

H3: Non-accounting ownership

Quality improvement .305 .000

Cost improvement .166 .001

Cycle time improvement �.151 .264

H4: Clarity of objectives

Quality improvement .337 .000

Cost improvement �.168 .020

Cycle time improvement .738 .048

H5: Quality improvement and cost improvement �.273 .002

H6: Quality improvement and cycle time improvement �.315 .000

H7: Cycle time improvement and cost improvement .152 .011

H8: Quality improvement and financial performance .210 .075

H9: Cost improvement and financial performance .727 .028

H10: Cycle time improvement and financial improvement .104 .875

Explained variances

R2 for quality improvement .275

R2 for cost improvement .196

R2 for cycle time improvement .381

R2 for financial performance .174

ADAM S. MAIGA AND FRED A. JACOBS238

3.9. Structural Model Results

Before testing the specified hypotheses, we first confirmed the overall modelby calculating w2 difference tests to identify any statistically significant pathsthat are not in the original conceptual model. This procedure has beenrecommended by Bollen (1989) and others (e.g., Hayduk, 1987; Joreskog &Sorbom, 1993; Medsker, Williams, & Holahan, 1994). None of the w2

difference tests is significant at the .05 level. Therefore, we do not includeany additional paths in fully saturated models. Hence, all the hypothesized

Activity-Based Cost Management 239

paths are confirmed, and there are no significant paths between the variablesthat were not originally identified that would further explain the impacts onfinancial performance.10

Next, to test the hypotheses, we rely on the standardized parameter es-timates for the measurement model (see Table 4 and Fig. 2). The effects ofABC implementation factors on manufacturing performance (quality, cost,and cycle time improvement) show that management support is significantlyrelated to quality improvement (z ¼ .120, p ¼ .021) and cost improvement(z ¼ .208, p ¼ .000), but it is not significantly related to cycle time improve-ment (z ¼ �.070, p ¼ .139). Training significantly impacts both quality im-provement (z ¼ .210, p ¼ .000) and cost improvement (z ¼ .103, p ¼ .005),however training negatively impacts cycle time improvement (z ¼ �.489,p ¼ .068). Non-accounting ownership significantly impacts both qualityimprovement (z ¼ .305, p ¼ .000) and cost improvement (z ¼ .166,p ¼ .001), but does not significantly affect cycle time improvement(z ¼ �.151, p ¼ .264). Clarity of objectives has significant positive impacton both quality improvement (z ¼ .337, p ¼ .000) and cycle time improve-ment (z ¼ .738, p ¼ .048); however, its impact on cost improvement is sig-nificantly negative (z ¼ �.168, p ¼ .020). The results also show that qualityimprovement has a significant negative impact on both cost improvement(z ¼ �.273, p ¼ .002) and cycle time improvement (z ¼ �.315, p ¼ .000).Cycle time improvement has a significant positive impact on cost improve-ment (z ¼ .152, p ¼ .011). Finally, both quality improvement and cost im-provement have a significant positive impact on financial performance(z ¼ .210, p ¼ .075; z ¼ .727, p ¼ .028, respectively), while cycle time im-provement is not significantly related to financial performance (z ¼ .104,p ¼ .875). Also, the squared multiple correlations (R2) indicate that themodel explains (27.50%) variance in quality improvement, 19.60% in costimprovement, 38.10% in cycle time improvement, and 17.40% in financialperformance.

Further analyses (see Table 5) show that the direct relations between ABCimplementation factors and financial performance are not significant, indi-cating the mediating effects of manufacturing performance between ABCimplementation factors and financial performance.

3.10. Contextual Factors

Before testing the model relationships for each contextual factor, it is nec-essary to evaluate its fit to the sub-group samples and its invariance across

NonaccountingOwnership

Clarity ofObjectives

ManagementSupport

TrainingCycleTime

Improvement

CostImprovement

QualityImprovement

.120*

.208*

-.070

.210*

.103*

-.489*

.305*

.166*

-.151

.337*

-.168*

.738*

-.273*

-.315*

.152*

.210*

.725*

.104Financial

PerformanceImprovement

Fig. 2. Model Path Significance Results and Explained Variances.

ADAM

S.MAIG

AAND

FRED

A.JA

COBS

240

Table 5. Analyses of Non-Hypothesized Paths.

w2 (df ¼ 93) w2 Difference P-value

Management support - financial performance 265.442 .647 .754

Training - financial performance 265.431 .658 .810

Non-accounting ownership - financial performance 265.598 .491 .521

Clarity of objectives - financial performance 264.871 1.218 .237

Activity-Based Cost Management 241

sub-groups (Marsh, 1987; Bollen, 1989). Following the recommendations ofDoll, Hendrickson, and Deng (1998), we examined the adequacy of thebaseline measurement model for each of the sub-groups as follows: themeasurement model was executed and the ratio w2/degree of freedom, GFI,CFI, NFI, and RMSEA values are used to assess the model fit for each sub-group.

The fit statistics in Table 6 reveal that the proposed model fits reasonablywell the data of each sub-group. First, the ratio ‘‘Chi-square test statistic/degree of freedom’’ for each sub-group results in a ratio less than 3.0, in-dicating good fitting (Wheaton et al., 1977). Second, the measures of relativeand absolute fit indices exceed .90, and the RMSEA for each sub-group isless than .10. These results demonstrate the overall adequacy of the baselinemeasurement model for the sub-groups.

The results for the sub-group construct model support further analyses ofthe structural path model. Hence, a LISREL analysis was conducted on thesub-group samples corresponding to each contextual factor.11 The within-group, completely standardized, coefficient estimates were reviewed for eachsub-group of the corresponding contingency models. The within-groupcompletely standardized path coefficient estimates can be used to comparethe relative magnitudes of various direct effects within each sub-group. Re-sults of the sub-group structural model analysis show the standardized pathcoefficient estimates for each sub-group.

As in the measurement model, the estimates for each sub-group wereobtained in SEM by standardizing the latent variables within a sub-group tounit variance for each sub-group, and are similar to standardized regressioncoefficients in multiple regression. First, we investigate whether ABC im-plementation stages affect the model relationships. As shown in Table 7,management support has a significant impact on quality improvement onlyin the infusion stage, a significant impact on cost improvement in bothroutinization and infusion stage. Training has a significant negative impacton quality improvement in the acceptance stage, while it has a significantpositive impact on quality improvement in both the routinization and

Table 6. Sub-Group Model Fit Analyses.

Sub-Groups w2 df w2/df GFI CFI NFI RMSEA

Acceptance stage (n ¼ 120) 75.177 94 .800 .944 1.000 1.000 .000

Routinization stage (n ¼ 206) 94.318 94 1.003 .959 1.000 1.000 .004

Infusion stage (n ¼ 271) 161.922 94 1.723 .947 .974 .942 .052

ABC with AMP (n ¼ 365) 175.311 94 1.865 .956 .976 .950 .049

ABC without AMP (n ¼ 232) 109.382 94 1.164 .958 .993 .953 .027

Industry (n ¼ 291) (SIC 20 through 33) 135.111 94 1.437 .959 .984 .950 .039

Industry (n ¼ 306) (SIC 34 through 38) 156.295 94 1.663 .954 .979 .951 .047

Large plants (n ¼ 396) 178.675 94 1.901 .960 .977 .953 .048

Small plants (n ¼ 201) 87.953 94 .936 .961 1.000 .955 .000

ADAM S. MAIGA AND FRED A. JACOBS242

infusion stages, significant positive impact on cost improvement in bothroutinization and infusion stages. Non-accounting ownership has a signifi-cant positive impact on both quality improvement and cost improvement atall stages. Clarity of objectives is significant related to quality improvementat all stage, and cycle time improvement only at the acceptance stage. Theimpact of quality improvement on cost improvement is negative in allstages. The association between quality improvement and cycle time is neg-ative at both the acceptance stage and the routinization stage. Cycletime improvement is positively related to cost improvement at the infusionstage. Quality improvement has a significant positive impact on financialperformance at the infusion stage, while cost improvement significantlyaffects financial performance at both the routinization stage and the infu-sion stage.

Second, the results for the model relations under adoption of advanced

manufacturing practices context in Table 8 show that management supporthas a significant positive impact on both quality improvement and costimprovement for the sub-group adopting advanced manufacturing prac-tices, and impact on cost improvement in the sub-group not adopting ad-vanced manufacturing practices. Training has significant impact on bothquality improvement and cost improvement for the sub-group adoptingadvanced manufacturing, and significant impact on quality improvementfor sub-group not adopting advanced manufacturing practices. Non-accounting ownership has significant impact on both quality improvementand cost improvement in both sub-groups although the impact is morepronounced for the sub-group adopting advanced manufacturing practices.Clarity of objectives is significantly related to quality improvement in both

Table 7. Standardized Path Coefficients and Significance for ABCImplementation Stages Sub-Groups.

Acceptance Stage

(n ¼ 120)

Routinization

Stage (n ¼ 206)

Infusion Stage

(n ¼ 271)

Stand.

Coef.aSign.b Stand.

Coef.

Sign. Stand.

Coef.

Sign.

H1: Management support

Quality improvement .092 .368 .107 .224 .218 .011

Cost improvement .052 .690 .237 .011 .213 .086

Cycle time improvement �.045 .647 �.065 .410 �.063 .359

H2: Training

Quality improvement �.231 .018 .181 .024 .211 .009

Cost improvement .048 .612 .130 .052 .158 .070

Cycle time improvement �.165 .359 �.528 .235 �.893 .242

H3: Non-accounting ownership

Quality improvement .226 .023 .309 .001 .371 .000

Cost improvement .252 .083 .195 .026 .203 .087

Cycle time improvement .282 .367 �.185 .431 .960 .454

H4: Clarity of objectives

Quality improvement .346 .006 .350 .002 .321 .006

Cost improvement �.387 .096 �.079 .392 .061 .405

Cycle time improvement .266 .046 .195 .309 .431 .480

H5: Quality improvement and cost improvement �.380 .075 �.308 .039 �.347 .099

H6: Quality improvement and cycle time improvement �.534 .002 �.305 .017 �.060 .577

H7: Cycle time improvement and cost improvement .116 .568 .167 .103 .422 .025

H8: Quality improvement and financial performance .024 .892 .358 .125 .709 .004

H9: Cost improvement and financial performance .362 .242 .095 .092 .937 .042

H10: Cycle time improvement and financial

improvement

�.258 .152 .031 .889 .038 .582

Explained variances

R2 for quality improvement .253 .271 .340

R2 for cost improvement .446 .585 .635

R2 for cycle time improvement .217 .203 .297

R2 for financial performance .103 .506 .446

aStandardized coefficient.bSignificance.

Activity-Based Cost Management 243

sub-groups, while negatively related to cost improvement in the sub-groupnot adopting advanced manufacturing practices. The impact of qualityof improvement on cost improvement and quality improvement on cycletime is significant but negative for both sub-groups. Cycle time improve-ment is significantly related to cost improvement in adopting advancedmanufacturing practices sub-group. Both quality improvement and cost

Table 8. Standardized Path Coefficients and Significance for ABC Sub-Groups with and without Advanced Manufacturing Practices (AMP).

With AMP

(n ¼ 365)

Without AMP

(n ¼ 232)

Stand.

Coef.

Sign. Stand.

Coef.

Sign.

H1: Management support

Quality improvement .162 .020 .081 .308

Cost improvement .229 .001 .182 .022

Cycle time improvement �.041 .514 �.082 .257

H2: Training

Quality improvement .183 .004 .243 .001

Cost improvement .115 .017 .090 .120

Cycle time improvement �.465 .224 �.587 .153

H3: Non-accounting ownership

Quality improvement .350 .000 .264 .001

Cost improvement .137 .012 .183 .025

Cycle time improvement �.317 .072 �.031 .891

H4: Clarity of objectives

Quality improvement .364 .000 .327 .002

Cost improvement .006 .894 �.238 .064

Cycle time improvement .143 .493 .516 .259

H5: Quality improvement and cost improvement �.321 .005 �.298 .045

H6: Quality improvement and cycle time improvement �.210 .035 �.464 .000

H7: Cycle time improvement and cost improvement .157 .059 .148 .142

H8: Quality improvement and financial performance .610 .008 .129 .404

H9: Cost improvement and financial performance .652 .027 .558 .260

H10: Cycle time improvement and financial

improvement

.161 .471 �.064 .595

Explained variances

R2 for quality improvement .319 .256

R2 for cost improvement .575 .699

R2 for cycle time improvement .154 .254

R2 for financial performance .106 .148

ADAM S. MAIGA AND FRED A. JACOBS244

improvement are significantly related to financial performance for sub-groups adopting advanced manufacturing practices.

Third, Table 9 reports the model relations for industry sub-groups.Management support has significant impact on quality improvement forSIC 34-38 sub-group, and significant impact on cost improvement for both

Table 9. Standardized Path Coefficients and Significance for IndustrySub-Groups.

SIC 20-33

(n ¼ 291)

SIC 34-38

(n ¼ 306)

Stand.

Coef.

Sign. Stand.

Coef.

Sign.

H1: Management support

Quality improvement .098 .161 .186 .018

Cost improvement .189 .019 .249 .064

Cycle time improvement �.013 .834 �.127 .057

H2: Training

Quality improvement .237 .000 .194 .009

Cost improvement .105 .075 .151 .074

Cycle time improvement �.392 .195 �.715 .253

H3: Non-accounting ownership

Quality improvement .330 .000 .289 .000

Cost improvement .160 .038 .217 .076

Cycle time improvement �.135 .381 .787 .470

H4: Clarity of objectives

Quality improvement .306 .000 .383 .000

Cost improvement �.188 .076 .017 .831

Cycle time improvement .462 .218 .108 .500

H5: Quality improvement and cost improvement �.194 .107 �.405 .089

H6: Quality improvement and cycle time improvement �.412 .000 �.160 .140

H7: Cycle time improvement and cost improvement .095 .233 .448 .016

H8: Quality improvement and financial performance .076 .400 .866 .003

H9: Cost improvement and financial performance .444 .249 .931 .045

H10: Cycle time improvement and financial improvement .001 .988 .052 .520

Explained variances

R2 for quality improvement .277

R2 for cost improvement .507

R2 for cycle time improvement .142

R2 for financial performance .143

Activity-Based Cost Management 245

sub-groups. Training significantly impacts both quality improvement andcost improvement for both sub-groups. Non-accounting ownership has asignificant impact on both quality improvement and cost improvement forboth sub-groups. Clarity of objectives significantly impacts quality im-provement for both sub-groups, and negatively impacts cost improvementfor SIC 20-33 sub-group. Quality improvement is negatively related to cost

ADAM S. MAIGA AND FRED A. JACOBS246

improvement for SIC 34-38 sub-group, and negatively related to cycle timeimprovement for SIC 20-33 sub-group. Cycle time improvement is positivelyrelated to cost improvement for SIC sub-group. Quality improvement andcost improvement are both significantly related to financial performance.

Fourth, the analysis of the model within the context of size shows inTable 10 that management support affects quality improvement for large

Table 10. Standardized Path Coefficients and Significance Plant SizeSub-Groups.

Large Plants

(n ¼ 396)

Small Plants

(n ¼ 201)

Stand.

Coef.

Sign. Stand.

Coef.

Stand.

H1: Management support

Quality improvement .114 .075 .131 .139

Cost improvement .204 .000 .216 .027

Cycle time improvement �.067 .250 �.075 .357

H2: Training

Quality improvement .210 .000 .210 .012

Cost improvement �.104 .023 .103 .108

Cycle time improvement �.447 .159 �.563 .251

H3: Non-accounting ownership

Quality improvement .306 .000 .303 .001

Cost improvement .168 .005 .162 .052

Cycle time improvement �.121 .473 �.202 .376

H4: Clarity of objectives

Quality improvement .339 .000 .336 .003

Cost improvement �.171 .058 �.160 .189

Cycle time improvement .865 .107 .539 .249

H5: Quality improvement and cost improvement �.272 .013 �.278 .076

H6: Quality improvement and cycle time improvement �.305 .001 �.331 .011

H7: Cycle time improvement and cost improvement .148 .044 .159 .124

H8: Quality improvement and financial performance .208 .141 .220 .289

H9: Cost improvement and financial performance .733 .071 .737 .203

H10: Cycle time improvement and financial improvement .018 .871 .006 .972

Explained variances

R2 for quality improvement .275 .277

R2 for cost improvement .196 .197

R2 for cycle time improvement .795 .839

R2 for financial performance .074 .097

Activity-Based Cost Management 247

plants, and has significant impact on cost improvement for both sub-groups.Training significantly impacts quality improvement for both plants, but hasa negative impact on cost improvement for large plants. Non-accountingownership significantly affects both quality improvement and cost improve-ment for both sub-groups. Clarity of objectives is significantly related toquality improvement for both sub-groups, while it is negatively related tocost improvement for large plants. Quality improvement is negatively re-lated to cost improvement for both sub-groups. Cycle time improvementis also negatively related to cycle time improvement for both sub-groups.Cycle time improvement is significantly related to cost improvement forlarge plants. Finally, cost improvement has a significant effect on financialperformance for large plants.

In summary, the results show that the model relations vary within thecontext of analysis. Revisiting Table 7, we note that the ABC plants atthe infusion stage performed better then those in the routinization stage,followed by those in the acceptance stage. Also, ABC plants accompaniedby advanced manufacturing practices seem to have outperformed those thatdid not adopt advanced manufacturing practices. Also, SIC 34-38 sub-groups seem to have performed better than SIC 20-33. Finally, the analysisof the model within the context of size shows that large plants, overall,outperform small plants. Thus, the results provide support for the notionthat ABC implementation is affected by contextual factors.

4. SUMMARY AND DISCUSSION

Ittner et al. (2002) find that extensive ABC use is positively associated withhigher quality levels, greater decreases in cycle time, and larger increases infirst pass quality. Additionally, their path analysis also indicates that ABCuse has a positive indirect association with manufacturing cost reductionsthrough improvements in quality and cycle time. However, they find that,on average, extensive ABC use has no significant association with ROA,rather the relation between ABC and profits varies with the extent to whichthe decision to use ABC ‘‘matches’’ the plant’s operational characteristics.Our study uses SEM and seeks to provide empirical evidence about therelations between ABC implementation factors and business unit manufac-turing performance measures (quality improvement, cost improvement, andcycle time improvement), the relations among manufacturing performancemeasures, and the impact of manufacturing performance measures onfinancial performance. The measurement model is adequate for the entire

ADAM S. MAIGA AND FRED A. JACOBS248

sample as well as for the sub-groups based on four contextual factors (ABCimplementation stage, adoption of advanced manufacturing practices, in-dustry, and size). Overall, except for cycle improvement, the hypotheses aresupported. However, although the results demonstrate adequacy of thebaseline measurement model and sub-group constructs, the results influencethe strength of path relations using the contextual factors, indicating thatthe model relations vary within the context of analysis.

These findings are of particular interest to manufacturing units. The im-portance of ABC implementation in achieving high product quality whilereducing product costs and improving cycle time in order to achieve favor-able financial outcome becomes critical to production managers. Thus, thisstudy contributes significantly to the literature by improving our under-standing of the relationships among factors leading to improved financialperformance in strategic business units. This is an important finding as priorstudies relating ABC implementation to business financial performance havebeen mixed. Thus, this study provides strong evidence to suggest that theimpact of ABC implementation factors on business unit financial perform-ance could be mediated by manufacturing performance.

The study also provides evidence of the importance of contextual factorsin the success of ABC implementation. Prior to this research there has beenno comprehensive explanation of the plant-specific conditions under whichABC is associated with positive performance results. For example, our re-sults show that when ABC is used concurrently with advanced manufac-turing practices, plants have a net improvement in financial performancegreater than that obtained from those without use of advanced manufac-turing practices. This study provides needed empirical evidence to supportanalytical and theoretical research regarding the conditions favorable toobtaining benefits from ABC (MacDuffie, 1995; Milgrom & Roberts, 1995;Chenhall & Langfield-Smith, 1998; Krumwiede, 1998).

However, there are a number of limitations in this study. For example thisstudy only sampled ABC implementers. Further studies should includefirms which have attempted to implement ABC but failed. These studiescan then look for differences in firm characteristics, or other factorsto explain success or failure with ABC. Also, an interesting approachfor empirical research would be to use a research design that captures thelongitudinal aspects of the design, implementation, and use of cost man-agement systems. Related, since all research methods have strengths andweaknesses, future research should use multiple methods between studiesand within a study (Birnberg, Shields, & Young, 1990). Also, we suggestfurther study using a more complex model that would include other

Activity-Based Cost Management 249

variables such as firm’s efficiency and effectiveness, customer satisfaction,customer loyalty, and price.

Despite the limitations, the results of this study have several implicationsfor managers and researchers. The evidence strongly suggests that the pathanalytical model offers a useful way for managers to approach ABC success.In particular, given the contextual factor(s), ABC implementation should beincorporated into development of quality improvement, cost improvementand cycle time improvement, and the justification of attaining higher fi-nancial performance. The results of this study should enhance practitioners’confidence in their implementation of ABC and improvement efforts asenablers of financial performance.

NOTES

1. Because of budget constraint, only 2,317 manufacturing units were randomlyselected.2. Of the 14 non-usable responses, 5 business units have mentioned that they have

abandoned ABC, 3 responses were returned blank, and 6 were incomplete.3. To investigate the possibility of non-response bias in the data, the surveys were

tested for statistically significant differences in the responses between the early andlate waves of returned surveys, with the last wave of surveys received considered tobe representative of non-respondents (Armstrong & Overton, 1977). t-tests wereperformed to compare the mean scores of the early and late responses. The t-testsyielded no statistically significant differences among the survey items, suggesting thatnon-response bias was not a problem in this study.4. Linkage to competitive strategies and adequacy of resources are assumed to be

closely related to top management support and have been combined as part of topmanagement support. Because non-accountants may be more likely to take own-ership for ABC if it is linked to their personal welfare, the linkage of ABC toperformance evaluation is combined with the non-accounting ownership factor.Clarity and consensus includes both clarity of purpose and consensus for the ob-jectives of ABC. Training reflects the level of training relating to the design, im-plementation, and usage of ABC. These three training phases have been combineddue to their high correlation with each other and with ABC success in Shields’ (1995)study.5. Consistent with the literature, the commercial performance of a product can

be measured based on multiple items Moenaert, Souder, De Meyer, and Deschool-meester (1994).6. Higher values for the four change variables represent greater improvement.7. See appendix for an abbreviated copy of the research questionnaire used to

measure the self-reported variables in this study.8. For example, Steimer (1990) has suggested that ABC is ideally suited to TQM

because it encourages a better analysis of activities through which non-value addedactivities can be reduced or even eliminated completely.

ADAM S. MAIGA AND FRED A. JACOBS250

9. To our knowledge, only Ahire and Golhar (1996) and Ahire and Dreyfus (2000)have compared TQM implementation in large firms (more than 250 employees) andsmall firms (250 or fewer employees).10. The detailed results of the analyses are available from the authors.11. Significant effects (positive or negative) are discussed (all p-values o.10 are

considered significant). Please see appropriate Tables for other path coefficients.

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APPENDIX: QUESTIONNAIRE

Part I

Please provide the extent to which the following items are present for your business unit’s ABC implementation.

1 ¼ ExtremelyLow

7 ¼ ExtremelyHigh

ABC receives strong activesupport from topmanagement

1 2 3 4 5 6 7

Upper management hasprovided adequate resourcesto the ABC implementationeffort

1 2 3 4 5 6 7

ABC has been closely tied to thecompetitive strategies of thebusiness unit

1 2 3 4 5 6 7

Adequate training was providedfor designing ABC

1 2 3 4 5 6 7

Adequate training was providedfor implementing ABC

1 2 3 4 5 6 7

ADAM

S.MAIG

AAND

FRED

A.JA

COBS

256

Adequate training was providedfor using ABC

1 2 3 4 5 6 7

Departments outside accounting(e.g., manufacturing,marketing, etc.) have shownpersonal ownership for ABC’ssuccess

1 2 3 4 5 6 7

The ABC implementation teamwas (is) truly cross-functional

1 2 3 4 5 6 7

ABC has been linked toperformance evaluations ofnon-accounting personnel

1 2 3 4 5 6 7

When the ABC initiative began,there was consensus about itsspecific objectives

1 2 3 4 5 6 7

When the ABC initiative began,its purpose was clear andconcise

1 2 3 4 5 6 7

Activity

-Based

Cost

Managem

ent

257

Part II

Please indicate the extent to which the following performance measures have improved over the last 5 years.

1 ¼ ExtremelyLow

Improvement

7 ¼ ExtremelyHigh

Improvement

Finished product first pass qualityyield in percentage terms

1 2 3 4 5 6 7

Scrap and rework costs as apercentage of sales

1 2 3 4 5 6 7

Manufacturing/operations costs 1 2 3 4 5 6 7Research and development costs 1 2 3 4 5 6 7Marketing costs 1 2 3 4 5 6 7Manufacturing cycle time from

start of production tocompletion of product in hours

1 2 3 4 5 6 7

Standard lead time from orderentry to shipment in days

1 2 3 4 5 6 7

Return on assets 1 2 3 4 5 6 7Return on sales 1 2 3 4 5 6 7

APPENDIX (Continued )ADAM

S.MAIG

AAND

FRED

A.JA

COBS

258

Activity-Based Cost Management 259

Part III

The following statements are used to classify your business units into one ofthree ABC implementation stages. Please check mark below the stage towhich your business unit belongs.

A.

Acceptance is achieved when ABC is used at least somewhat by non-accounting management for decision making.

B.

Routinization is achieved when ABC is commonly used by non-accounting management for decision making and considered a normalpart of the information system.

C.

Infusion is defined as not only using ABC extensively but also integrat-ing it with the primary financial system.

A. _

__________ B. _ __________ C. _ ___________ Other(s): (please specify) _ _________________________________

Part IV

Regarding advanced manufacturing practices, please indicate which of thefollowing is used in your business unit (please check mark all that apply).

Balanced Scorecard

______ Benchmarking ______ Computer Integrated Manufacturing (CIM) ______ Computer Aided Engineering (CAE) ______ Computer Aided Design (CAD) ______ Flexible Manufacturing Systems (FMS) ______ Just-in-Time ______ Total Quality Management ______ Other(s): (please specify) ______

ADAM S. MAIGA AND FRED A. JACOBS260

Part V

Please answer the following:

1. How long have you implemented ABC? _

_________ 2. What is your business two-digit SIC code? _ _________ 3. What is the number of employees at your company?

Please check below:_

_________

o100 _

___________ 101–150 _ ___________ 151–200 _ ___________ 201–250 _ ___________ 251–300 _ ___________ 301–350 _ ___________ >350 _ ___________ 4. Number of years at this position? _ __________ 5. Number of years in management? _ __________

TEAM PERFORMANCE

MEASUREMENT: A SYSTEM TO

BALANCE INNOVATION AND

EMPOWERMENT WITH CONTROL

Frances Kennedy and Lydia Schleifer

ABSTRACT

A current highly competitive and rapidly changing business environment

requires companies to continually innovate to survive. An increasing

number of companies are using teams to leverage the knowledge and

experience of their employees in order to improve quality, reduce costs

and ‘delight’ the customer. The growing prevalence of teams signals the

need to examine the adequacy of management accounting information

and its use in performance measurement and control systems.

Some research has examined the impact of team empowerment on

creativity and innovation, while other research discusses the sometimes-

hampering role of performance measures in team environments. This pa-

per contributes to this research, with two major goals. First, it discusses

innovation and empowerment and examines how performance measure-

ment can both encourage and hinder team performance. The second pur-

pose is to propose a team performance measurement system using ratios

based on activity-based management that seeks to encourage innovation

and empowerment while maintaining a system of control.

Advances in Management Accounting, Volume 16, 261–285

Copyright r 2007 by Elsevier Ltd.

All rights of reproduction in any form reserved

ISSN: 1474-7871/doi:10.1016/S1474-7871(07)16009-3

261

FRANCES KENNEDY AND LYDIA SCHLEIFER262

INTRODUCTION

In a competitive, ever changing world, the success and survival of organizations depends on

innovation (Mumford & Licuanan, 2004, p. 163).

Changing an organization to compete in a highly volatile business environ-ment usually requires multiple and continuous innovations (Damanpour,1991). Innovation has been defined as new approaches to methods or tech-nologies (Schroeder, Scudder, & Elm, 1989) that help to meet organizationalobjectives (Drake, Haka, & Ravenscroft, 1998). It begins as a creative ideathat has been implemented within an organization (Amabile, 1996). Teamshelp companies to compete by improving quality, reducing costs and de-veloping new products (Alper, Tjosvold, & Law, 2000). The premise is thatthrough the pooling of ideas, expertise and experiences, teams will morequickly develop innovations (Scott & Tiessen, 1999) that will better positionthe company to compete in this challenging business environment.

Achieving flexibility requires moving decision-making authority to lowerlevels (Simons, 1995). As companies are changing their work structures toinvolve teams, teams are being empowered to make decisions within thescope of the team mission. Sim and Carey (2003, p. 112) define empow-erment as ‘‘y a means of giving the authority to make decisions to that levelor people in the organization which, by virtue of available knowledge andcloseness to the activity concerned, is most able to make a correct, quick,and effective decision.’’ It is now fairly widely recognized and accepted thatempowerment can be associated with effectiveness of managers and corpo-rations (Bennis & Nanus, 1985; McClelland, 1975), and can lead to highercommitment to a project and a greater chance to meet team goals(McDonough, 2000). Empowered teams contribute to achieving success inimplementing such new management philosophies as total quality manage-ment (TQM), just in time (JIT) (Cua, McKone, & Schroeder, 2001) andworld class manufacturing (WCM) (Lind, 2001).

The growing prevalence of teams in organizations signals the need toexamine the adequacy of management accounting information. A recentsurvey found that 81% of Fortune 500 companies are building, at leastpartially, team-based organizations and 77% use temporary project teamsto perform core work (Lawler, Mohrman, & Benson, 2001). Work struc-tures that involve teams are often more efficient and effective than indi-vidual work (Banker, Potter, & Schroeder, 1993). Birnberg (1999) arguesthat coalitions evolve in order to facilitate the pooling of information.This concept suggests that team structures develop to make information

Team Performance Measurement 263

more available and decisions more transparent. In their overview of man-agement accounting research, Atkinson et al. (1997, p. 80) state that,‘‘Changing [team] structures imply changes in the information needed,and the way information is used to measure and motivate performance.’’Indeed, Scott and Tiessen (1999) found that teams are a response to com-plexity and use financial and nonfinancial measures to adequately captureperformance.

Simons (1995, p. 110) suggests that empowerment requires greater con-trol: ‘‘The control systems used, however, must balance empowerment andcontrol in such a way that empowerment does not lead to a control failure,and correspondingly, control does not lead to an empowerment failure.’’Control systems that include performance measures have traditionally fo-cused on results-based measures (Merchant, 1985). McNair and Carr (1994)argue that the traditional view of control uses outcome measures largelybased on a pre-determined historical performance standard and is illequipped to add value to modern organizations that increasingly emphasizeachieving continuous improvement goals. Current measurement systemsfocus on monitoring and controlling behavior when what is needed with newwork structures are systems that support process improvement – the new‘mantra’ of the age of world class manufacturing (WCM) (Ghalayini,Noble, & Crowe, 1997). The dilemma becomes how to measure and monitorthe progress of teams while providing the encouragement and environmentnecessary to align team behavior with the organizational goal of innovation.

This paper has two goals. It first examines possible influences of per-formance measurement on innovation by exploring their effects on fiveconditions for team creativity (Amabile et al., 1996) and four dimensions ofteam empowerment (Kirkman & Rosen, 1999). The second goal is to pro-pose a team measurement system that seeks to balance innovation and em-powerment with control. This measurement system was developed withinput from representatives of five diverse companies in both service andmanufacturing industries. The goal of this development team was to define asystem of measurement that assessed team performance as well as measuredthe financial impact of team structures on overall performance.

The next section describes challenges in current performance measureswith regard to team measurement. This is followed by a discussion of per-formance measurement effects on innovation and the enabling conditionsthat promote creativity, and on four dimensions of team empowerment.Then, a team performance measurement system (TPMS) is proposed. TheTPMS is then critically assessed according to the models of innovation andempowerment. Finally, limitations and future research are addressed.

FRANCES KENNEDY AND LYDIA SCHLEIFER264

TEAM PERFORMANCE MEASUREMENT

CHALLENGE

Performance Measurement and Control

Rapidly changing customer demands require a high degree of customization(Maskell & Baggaley, 2004) that can only be met with flexible work systems(Kalagnanam & Lindsay, 1998). This trend requires changes in organizationstructures – both in production processes and in measurement systems.Ansari, Bell, Klammer, and Lawrence (2004, p. 4) define management ac-counting as ‘‘y a system of measuring and providing operational and fi-nancial information that guides managerial action, motivates behaviors, andsupports and creates the cultural values necessary to achieve an organiza-tion’s strategic objectives.’’ This broad definition not only allows manage-ment accountants to consider appropriate measurement techniques forcontemporary flexible systems, but also demands that they strive to providethe right balance of information to mitigate actions contrary to organiza-tional objectives.

Control involves focusing on human behavior in an effort to influencepeople to strive towards meeting organizational goals. Merchant’s (1985)control framework includes results, action and personnel controls. Resultscontrols involve rewarding people or holding them accountable for certainresults. Action controls concern how work is done and involve providingperiodic feedback that decision-makers can use to assess progress and alteractions. Personnel controls involve managerial actions taken to ensure hav-ing the right people through hiring, training, team assignment and moti-vational practices. Skills are developed through correction andreinforcement with feedback information (or action controls) (Brannick &Prince, 1997). Action controls are, therefore, critical for both changingcourse and for skill development.

Nanni, Dixon, and Vollman (1990) assert that management accountantshave tended to provide performance measures with a product-orientedrather than a process-oriented focus. McNair and Carr (1994) suggest theremay be a shift from results to action controls in order to support flexibilityand speed. In their review of performance measurement systems, Ghalayiniet al. (1997) argue that current systems focus on monitoring and controllingwhen what is needed are systems that support process improvement. Processmeasurement is an action control that provides feedback mechanisms thatsupport continuous improvement goals and a rapidly changing environ-ment. Traditional measurement, on the other hand, is largely designed to

Team Performance Measurement 265

encourage boundaries and maintain the status quo through systems of ac-countability.

Simons (1995) argues that with decision-making authority (empower-ment) comes accountability for decisions, and accountability can be per-ceived as a great motivator (Epstein & Birchard, 2000). Tetlock’s (1985)behavioral theory of accountability maintains that simply the knowledgethat one will be held accountable affects behavior. Further, when the di-mensions upon which individuals will be held accountable are clearly de-fined, they will organize themselves around those dimensions. Therefore,establishing a system that clearly makes managers and teams accountablewill make them more aware of the need to react to information cues(Birnberg, 1999). Birnberg suggests that high levels of accountability may beinconsistent with high levels of innovation. As the responsibility for deci-sions descends to lower levels, there is greater need for mechanisms thatconsider the dispersion of decision authority.

Failure to make appropriate adjustments to traditional measurement canlessen team effectiveness, undermine new management practices, such as JITor WCM (McNair & Carr, 1994), and cause members to forget team goalsand revert to prior work patterns (Meyer, 1994). In addition to technicalconsiderations of what to measure and how, it is critical to consider thebehavioral effects of the measures on the teams. The next section describesmodels of innovation and empowerment, and discusses how performancemeasures can both positively and negatively influence team behavior inthese areas.

Behavioral Implications of Team Measurement

Previous discussion highlighted innovation and empowerment as criticalfactors in successful teaming organizations as well as the need for action andresults controls to provide appropriate control mechanisms. A performancemeasurement system is an essential ingredient in a control system. The fol-lowing discussion describes the potential influences of team performancemeasurements on the dimensions of Amabile et al.’s (1996) and Kirkmanand Rosen’s (1999) models of innovation and empowerment.

Innovation

Schroeder et al. (1989) define innovation as new approaches or technologiesthat help to meet organizational objectives (Drake et al., 1998) and is es-sential to the future success of the organization (Brennan & Dooley, 2005).

FRANCES KENNEDY AND LYDIA SCHLEIFER266

Argrell and Gustafson (1996) portray innovation as an interactive process.Teams intentionally attempt ‘‘to arrive at [through their own efforts] an-ticipated benefits for the individual, team, organization or society as op-posed to top-down change’’ (Drach-Zahavy & Somech, 2001, p. 111).Managing motivation is about managing an entire system of innovationrather than about doing any one thing really well, like research and devel-opment (Bessant, Lamming, Noke, & Phillips, 2005).

Motivating innovation is complex and some research supports that tra-ditional external motivators, such as rewards, may diminish creativity. Thegreatest creativity emerges when employees are intrinsically motivatedthrough enjoyment, interest or challenge (Amabile et al., 1996). A com-pany’s ability to develop and exploit creative abilities is critical to the even-tual development of innovations. A primary consideration when developinga culture that is conducive to creativity is the importance of perception.Employee or team perception of reality is more important than whether thatreality actually exists (Amabile et al., 1996; Spreitzer, 1996) because per-ceptions shape interpretations (Thomas & Velthouse, 1990). These inter-pretations then become a stronger influence than actual occurrences.Developing and nurturing a culture that is perceived by employees as en-couraging creativity and valuing innovation should be the goal of innovativeorganizations.

Amabile et al. (1996) focused on identifying conditions that must exist tofoster creativity and implement ideas into innovations. The result was amodel of five dimensions that promote (or stifle) creativity. (1) Encourage-

ment of creativity comes from several sources: the organization, the super-visor and the work group itself. By setting goals and providing performanceevaluations, the organization can encourage or inhibit risk-taking and ideageneration. If creating new ideas is explicitly included as part of their job,employees will be more likely to engage in that type of activity. The ex-pectation of a fair performance evaluation can motivate teams; however, ifthe expectation is threatening, employees are less likely to take chances andtry something new. Supervisors can encourage creativity by being activelyinvolved with the team, communicating clear goals to the team and sup-porting the team’s ideas. These actions communicate that the team’s role ismeaningful to the organization. The group itself can also provide encour-agement. The members of a team can encourage each other by exchangingideas, being openly supportive and demonstrating a shared commitment.(2) Autonomy originates from a sense of ownership over decisions andprocesses. When team members feel a sense of ownership, they are morelikely to seek information and make decisions concerning their processes.

Team Performance Measurement 267

The team’s creativity is assumed to increase as choice increases (Amabileet al., 1996). (3) The allocation of resources to team projects signals to theteam that its work is important. When the team perceives that its contri-bution positively impacts the organization, there is likely to be even morecreativity and innovation. The perception that resources will support inno-vation will further enhance the team’s creativity. (4) Pressures interact withthe creative processes in two potentially opposite ways. Excessive ‘workload’pressures can suppress creativity. On the other hand, the pressures of a good‘challenge’ can stretch ideas and promote creativity. (5) Organizational

impediments include formal management structures, conflict and conserv-atism. These are external influences that send mixed signals and detract fromidea generation and creative processes. If team members perceive that suchimpediments provide the extrinsic motivation, or pressure, to accomplishtasks, then there may be a decrease in the intrinsic motivation, like satis-faction, that actually inspires creativity (Amabile et al., 1996).

As outlined in Exhibit 1, performance measurement can both encourageand hinder creativity. On one hand, capturing team innovations in a meas-urement system enables the team to be recognized and rewarded. On theother hand, performance reviews that are critical and punishing on the basisof this measure can result in the employee or team not being willing to trynew ideas and suppressing the flow of innovations. As teams begin to makemore decisions, they also experience a heightened accountability for thosedecisions. Measuring the impact of decisions can help a team to think aboutlong-term implications as well as the scope of their decisions. However, ifteams are held accountable for decisions or outcomes over which they havelittle or no control, their motivation to continue to participate in the creativeprocess is hampered. Teams, particularly newly formed teams, are oftenchallenged to prioritize projects. They look to the cues in the organization tohelp them get started. A performance measurement system can help themaccomplish this task and relieve some of the initial confusion and pressure.In addition, having a clear vision of expectations and targets helps the teamto adjust their activities and can enhance the challenge, intrinsically mo-tivating team members. Finally, performance measurement systems that arenot aligned with team goals or that are used as a ‘club’ to oversee teamprocesses can be perceived negatively by teams and can promote behaviornot conducive to the creative process.

Amabile et al.’s (1996) model of conditions that foster creativity high-lights the intricacies involved in maintaining an environment conduciveto creative ideas and subsequent innovation. This creativity is critical forprocess innovation (Sim & Carey, 2003) and requires fostering an

Exhibit 1. Team Performance Measurement Influence on Dimensionsof Creativity and Empowerment.

Dimension Description Influence of Team Performance

Measurement

Creativity (Amabile, Conti, Coon, Lazenby, & Herron, 1996

Encouragement of

creativity

Includes (1) fostering an

environment that promotes

risk-taking and idea

generation, (2) supporting

new ideas, (3) rewarding and

recognizing creativity and (4)

exposure to ideas across the

organization.

� Critical reviews and

consequences can repress

risk-taking.� Capturing team successes

makes the accomplishment

visible and able to be

rewarded.

Autonomy Provides sense of ownership

and control over tasks.

� Performance measurement

provides autonomy

through accountability.

Resources Signals to the team that its

work is important.

� Monitoring progress signals

to managers when

resources are needed.

Pressures Includes workload pressure and

challenge.

� Performance measurement

communicates priorities,

relieving negative

pressures.� Performance measurement

offers targets and

benchmarks and enhances

challenge.

Organizational

impediments

Sends mixed signals, detracting

from the creative processes;

includes formal management

structures, conflict and

conservatism.

� Performance measurement

can be negatively

perceived, providing

extrinsic motivation that

undermines the intrinsic

motivation that fosters

creativity.

Empowerment (Kirkman & Rosen, 1999)

Potency Collective belief of the team

that it can be effective.

� Measurement used as

feedback reinforces a

team’s confidence through

either confirming the

team’s direction or

signaling a necessary

change.

Meaningfulness Team members’ shared

perception of how to value

the task and whether it is

worthwhile.

� Measurement places a value

on an innovation.

FRANCES KENNEDY AND LYDIA SCHLEIFER268

Exhibit 1. (Continued )

Dimension Description Influence of Team Performance

Measurement

Autonomy Degree of decision-making

authority

� Performance measurement

provides autonomy

through accountability.

Impact Perception of the value of their

work to the organization.

� Measurement systems

communicate to the team

the value of their work.

Team Performance Measurement 269

environment that encourages the development and sharing of creative ideas.It implies that when teams have ownership over their processes and whenthey receive support and reinforcement, the probability that they will gen-erate creative ideas is greater.

Empowerment

Inherent in the discussion of creative processes and innovation is the idea ofempowering teams to develop new mechanisms and make decisions con-cerning their process. Bowen and Lawler (1992) assert that employees areempowered by providing information and resources that enable them tomake appropriate decisions. Instructing teams to improve their process orsolve a problem, and then not providing the encouragement or resources todo so, sends the conflicting message that the team is not really empowered atall. This will certainly reduce motivation to fully participate in the creativeprocess.

Kirkman and Rosen (1999) describe four dimensions of team empower-ment: potency, meaningfulness, autonomy and impact (Exhibit 1). Potency

is the collective belief of a team that it can be effective (Guzzo, Yost,Campbell, & Shea, 1993). Team potency is influenced by the team’s per-ceptions of available resources and the skills and abilities of its team mem-bers. Meaningfulness at the team level is a shared perception of how to valuethe team’s task and whether the team has a worthwhile impact on theorganization. Autonomy is the degree of decision-making authority a teamexercises in its task environment. Team autonomy reflects freedom and in-dependence in choosing actions as a team. The last dimension is impact,

which measures the team’s perception about the value of its contributions tothe organization. Teams can assess their impact by gathering feedback fromoutside the team (e.g., from customers). These four dimensions of empow-erment are distinct, yet they are interrelated and mutually reinforcing(Kirkman & Rosen, 1999).

FRANCES KENNEDY AND LYDIA SCHLEIFER270

Management accounting information and performance measurementare key to providing the information base teams need to succeed (Scott &Tiessen, 1999). Performance measures have a strong influence over the fourdimensions of empowerment described by Kirkman and Rosen. As the ac-complishments of teams are tracked and recognized, team efforts are re-inforced, giving them a stronger sense of potency or confidence that they cancontinue to meet their goals. The manner in which the organization meas-ures the effects of team innovations helps the team and others in the or-ganization to value the team’s contribution. If the team perceives the impactof their efforts, they experience an increase in their feeling of worth and,consequently, derive more satisfaction and are intrinsically motivated tocontinue to innovate. Conversely, if the organization makes no effort toquantify or recognize team accomplishments, the team is not able to seewhere their contributions fit into operations nor do they experience anyincrease in either intrinsic or extrinsic motivation.

The prior discussion of creativity/innovation and empowerment presentsclosely aligned constructs that are necessary to foster innovative environ-ments. The two models, innovation and empowerment, contain overlappingand complimentary dimensions, and both are influenced by performancemeasurement. This discussion underscores the importance of developing aTPMS with great care and with consideration of the motivational and be-havioral impact of those measures. Brennan and Dooley (2005, p. 1392)summarized the influence of organizations on employee empowerment:‘‘From an organizational perspective, barriers to creativity include: intol-erance of differences, overly rational thinking, inappropriate incentives andexcessive bureaucracy. Meanwhile, employee participation and empower-ment can contribute significantly to the increase and ultimately the level oforganization innovation.’’

The next section proposes a TPMS that strives to provide both processand output measures while encouraging an environment that will empowerteams and be conducive to the development of innovations.

Team Performance Measurement System

Current performance measurement systems use a mix of financial and op-erational measures to guide organizational goals. Therefore, the team meas-urement system should contain a mix of measures that fully reflect strategy ifan organization is to achieve its long-term objectives. Along with traditionalfinancial measures, the system should include nonfinancial metrics that are

Team Performance Measurement 271

specific to the team’s purpose (e.g., reduction in scrap rate, decrease in cus-tomer complaints, reduction in cycle time), and are critical for teams to focustheir activities and monitor their progress towards organizational goals.

The TPMS described below was developed with input from representa-tives of five diverse companies: one bank services organization, one petro-chemical company, two defense contractors and a commercial productmanufacturer. This team met using conference calls at regular two-weekintervals with the purpose of developing a team measurement system dy-namic enough to use in each of their various industries and that satisfiedtheir information needs. Once the system was developed, each companyallowed access to teams to further consider these measures from the teamperspective. Subsequently, participating teams summarized their recentprojects using the proposed measures. The examples used in this paperoriginate from these teams and their own ex post application of the TPMS.

The TPMS contains four measurement categories: financial, operational,effectiveness and innovation measures. When viewed in concert, these meas-ures provide a broad perspective of team activity, progress and successes. Inaddition to the range of measurement categories, the TPMS provides amechanism for consolidation that facilitates assessment information at thefacility level.

Exhibit 2 illustrates the TPMS for one team (called The Wheelies Team)that completed three projects over a two-year period. This example is usedto illustrate the TPMS in the subsequent discussion of each measurementcategory and to demonstrate how this team’s performance is consolidated aspart of the facility performance. Projects and data reflect real team inno-vations.

Components of the Measurement System

Financial Measures

It usually becomes difficult for companies to quantify in financial terms theimpact of team performance. When teams tackle a problem or a process,they normally utilize various tools in their analysis. Flowcharting, story-boarding, fishbone diagrams and brainstorming are some examples ofactivity analysis techniques used by teams. Regardless of the tools used, theprocess of outlining the tasks performed in an activity for the purposeof making improvements is commonly referred to as activity-based man-agement (ABM) (Ansari et al., 2004). ABM provides the team with a com-mon understanding of all the tasks and resources included in an activity.

Exhibit 2. Illustration: Project and Team Effectiveness Indexes.

PANEL A

Project Effectiveness Index (PEI) Calculation

The Wheelies Team (Warehouse)

Project No. 1

Incremental savings

Labor: (1/2 h/shift� 3 shifts� 30 forklifts� 325 days� $20/h) $292,500

Extended battery life: (30 forklifts� $5,000 ¼ $150,000; $150,000/3

years) – ($150,000/4 years)

12,500

Total incremental savings $305,000

Incremental costs

Equipment: Purchase and installation of 30 monitors $54,000

Total incremental costs $54,000

Net annualized savings $251,000

Project effectiveness index (PEI) ($305,000 savings/$54,000 costs) 5.6

PANEL B

Team Effectiveness Index

The Wheelies Team

Project Annualized Savings Annualized Costs PEI TEI

No. 1 $305,000 $54,000 5.6 –

No. 2 $300,000 $40,000 7.5 –

No. 3 $26,000 $– 20.0 –

Total $631,000 $94,000 – 6.7

PANEL C

Grassroots Facility

Multiple Teams’ Performance Summary

Team Number of Innovations Net Project Savings Overall Index

The Wheelies 3 $537,000 6.7

Sweepers 2 49,000 9.2

Shippers 0 0 0.0

Number crunchers 1 16,000 20.0

Total 6 $602,000 7.0

Note: The TEI (Panel B) and overall index (Panel C) are recalculated using the total annualized

savings and costs

FRANCES KENNEDY AND LYDIA SCHLEIFER272

Team Performance Measurement 273

This process analysis helps teams to identify unnecessary or redundantsteps, thereby identifying wasted resources. Activity-based costing (ABC)techniques facilitate the assignment of dollars to tasks and activities in-volved in a process and helps teams quantify, or dollarize, resources savedin a process change.

Exhibit 2 (Panel A) calculates the net annualized savings for The WheeliesTeam’s project no. 1. The Wheelies Team is a work team of forklift driversresponsible for collecting finished product from manufacturing, storing it inthe warehouse and subsequently retrieving it for shipment. Each forkliftdriver was responsible for keeping the battery in his lift charged. It hadbecome customary for the forklift drivers to congregate in the batterycharging area at the beginning of the shift in order to be charged up and,therefore, ready to move product during the rest of the shift. The WheeliesTeam noted that drivers’ average wait time was between 20 and 40minutesdue to limited charging resources, and it was often necessary to work over-time to complete shipping requirements. After collecting data and analyzingthe charging process, the team recommended that battery monitors be in-stalled on each lift and required that drivers bring the lift in for chargingonly when the monitor showed the charge had dropped below a specifiedlevel. During their investigation, the team also discovered that charging thebatteries prematurely had shortened the total life. Four-year batteries werebeing replaced in three years. It was anticipated that the new procedure ofcharging batteries only when necessary would extend the useful life of thebatteries to at least four years. The annualized savings for the project, in-cluding savings in labor and in battery life, totaled $305,000. Incrementalcosts included the purchase and installation of monitors on all forklifts.Exhibit 2 (Panel A) details the calculations. The net annualized savings forthis project is $251,000.

Why use annualized savings and costs? At one time it would have madesense to use discounted cash flow techniques and project savings over the lifeof the process change. In the current competitive environment, however,change is continuous and an innovation this year becomes next year’s statusquo. Shortening the horizon to one year and eliminating the need for usingdiscounting methods increases the validity of the savings estimate and sim-plifies the measure enabling team members to calculate with a minimum oftraining.

Using financial measures in a balanced team measurement system is im-portant because it is like speaking a universal language. Everyone can de-termine the relevance and importance of results expressed in terms ofsavings, costs and net savings. Furthermore, financial measures can always

FRANCES KENNEDY AND LYDIA SCHLEIFER274

be accumulated over time and added across entities, teams or projects. Thekey that enables teams to quantify the impact of their projects has a solidfoundation in basic ABM concepts. Appropriate training in ABM conceptsprovides the resources necessary to perform the analysis. This is an oppor-tunity for management accounting to become involved by supporting teamswith education and in providing necessary information (e.g., labor rates,material prices).

Operational Measures

As previously discussed, teams are created for a specific purpose, such asimproving receiving report accuracy, can easily track their progress with theabsolute number of errors or as a percentage of reporting errors to totalreports. This clearly communicates the progress made toward the team’sgoals. Operational measures are an important aspect of a balanced ap-proach to team measurement because such measures may be closest to howteam members are used to formulating and thinking about their tasks.Exhibit 2 (Panel A) uses the number of labor hours saved and 33% increasein battery life to estimate their project savings.

Problems can arise with regard to operational measures in that teams mayhave a narrow vision for themselves if they were formed for a particularpurpose. Improvements made to boost one metric may be at the expense ofanother. However, once they have experienced success within their statedpurpose, teams can be encouraged to brainstorm or think outside of the boxand formulate stretch goals and new innovations or new missions for them-selves.

Effectiveness Indices

The magnitude of project savings in the preceding financial measure is oneway of quantifying contributions. However, it does not provide assurancethat the project is the best use of operating dollars. Ansari et al. (2004)discuss the benefits of viewing costs from different perspectives. Anotherway to frame the estimated results of an innovation is to calculate a simpleindex. A ratio represents a measure of productivity in that it quantifies thenumber of outputs produced to the physical inputs consumed and can be auseful supplement to other financial and nonfinancial information (Kaplan,1983). Ratios also have the benefit of scaling for volume and increase theopportunity for comparisons across time and units. Indeed, ratios aremeaningless except in comparison across reporting units or periods (Banker,Datar, & Kaplan, 1989). Similar ratios are used in other analyses. Forexample, a present value factor (Horngren, Datar, & Foster, 2003) is often

Team Performance Measurement 275

used to evaluate capital investment opportunities. A project effectivenessindex (PEI) is calculated by dividing annualized savings by annualized costs.Exhibit 2 (Panel B) illustrates this calculation. An index of 5.6 indicates,very simply, that every dollar expended to maintain a solution is estimatedto yield $5.60.

Interviews with teams and managers revealed that many teams implementcontinuous improvement innovations that do not require an investment infunds nor do they require any ongoing cost to maintain – perhaps merely achange in the process. This is exactly what companies desire – continuousimprovement without additional investment – a company’s optimal solu-tion! As a result, however, their innovations were not documented becausethey did not need to request funding and no system was in place to capturethese savings. Since the PEI cannot be calculated without a denominator, areview of projects’ PEIs in the pilot study that did have a cost ran as high as16.9. Considering these results, a default PEI of 20.0 was assigned toprojects not requiring any cost to implement and maintain. This is illus-trated in Exhibit 2 (Panel B) for project innovation no. 3.

Teams can also use the PEI to compare alternative solutions, as well as tocommunicate their successes and innovations to managers. It is simple touse and understood by team members in all different types of teams. Atarget hurdle index could also be incorporated into goals and objectives.

The PEI calculates the benefits of one project. The same method can beapplied to the team. Considering that each innovation could take as little asone week or as long as 18 months, it is important to consolidate all theinnovations for a team for a given time period. Consensus among partic-ipating companies during development was to use a rolling two years as anappropriate time frame for calculating the team effectiveness index (TEI).

Exhibit 2 (Panel B) illustrates how the TEI is calculated for The WheeliesTeam. Annualized project savings for all projects implemented during thetime period is divided by the total annualized project costs, resulting in aneffectiveness index of 6.7. This recalculates a TEI to reflect all team inno-vations. The TEI can be used to compare dissimilar teams and may con-tribute to reward and recognition programs.

Innovation

Two problems arise with respect to managing innovation. The first is thatteams have varying abilities and talents that influence their capacity to in-novate. For example, a team with an engineer or two has greater potentialfor recommending a leap innovation that will result in large savings. An-other team consisting of machine operators may not have the same potential

FRANCES KENNEDY AND LYDIA SCHLEIFER276

for large improvements, but can initiate several smaller incremental inno-vations. For this reason, the magnitude of savings (though certainly im-portant) from a particular innovation is not necessarily the best metric tocapture both efforts and impact. Monitoring the frequency of innovationsshows a team that is meeting regularly and consistently making changes –however small – to their process. Motivating this consistent activity indi-cates a culture that positively encourages innovative thinking.

The second problem with managing innovations concerns the team thathas a major success – and then stops and basks in the acknowledgment. Thisteam may not jump in and work on another idea very quickly, either be-cause the team members do not know how to top themselves or they simplywant to ‘hold on’ to the last one. Interviews with company employees duringthe research phase of the project confirm this phenomenon. A human re-source manager with a petrochemical company recounted the first success ofa cross-functional team. The team estimated (and subsequently docu-mented) annual savings of $1.2 million dollars by changing a formula mix.There was no cost to implement the change – only benefit. This team re-ceived accolades and toured other facilities within their company tellingtheir story. When asked about subsequent projects the team initiated, thehuman resource manager answered, ‘‘Welly that project was almost 2years ago. And since then, the team hasn’t made any new recommendations.And, actually, I am just not really sure they are making any progress either.’’

Tracking the number and frequency of innovations (recommendations forchange) will help to identify and encourage teams of all types that areconsistently developing and implementing new ideas. Therefore, measuringresults and/or progress with regard to innovations is a major aspect of abalanced approach to measuring team performance. Exhibit 2 (Panel C)shows that The Wheelies Team implemented three innovations over the lasttwo years, showing consistent progress and effort.

Assessing Team Performance on a Plant-wide Basis

The TPMS offers two complimentary methods to aggregate the perform-ance of multiple teams into a facility summary. One of these is a roll-upsummary of the four categories of team measures, while the other provides aplant-wide financial impact summary. Exhibit 2 (Panel C) offers the sum-marized results of four teams over a two-year period. It shows how thenumber of innovations, net project savings and the TEI can be used tomonitor dissimilar teams’ performance, as well as provide an overview of

Exhibit 3. Summary of Team Financial Impact on Facility.

PANEL A

Grassroots Facility

Annualized Team Support Costs

Type of Cost Cost

Training (internal) $25,000

Meeting time (all members) 38,000

Celebration costs 13,000

Team system managers 20,000

Conferences, dues 12,000

Materials, supplies 5,500

Refreshments 1,500

Travel 10,000

Total support costs $125,000

PANEL B

Grassroots Facility

Team Savings Net of Support Costs

Team Net Team Savings

The Wheelies $537,000

Sweepers 49,000

Shippers 0

Number crunchers 16,000

Total incremental savings $602,000

Less: Total support costs $125,000

Net team savings retained $477,000

Percent team savings retained 79.2%

Team Performance Measurement 277

the entire facility’s team activity. From the number of innovations, a man-ager can quickly discern which teams appear most active (The Wheelies andSweepers) and which may be faltering and needing support (Shippers).Comparing the results of the Sweepers team and The Wheelies illustrateswhy having multiple measures is critical. The Wheelies saved $537,000, whilethe Sweepers saved $49,000. Looking at only this information implies that

FRANCES KENNEDY AND LYDIA SCHLEIFER278

the Wheelies performed at a higher level. Further investigation, however,shows that the Sweepers’ TEI was 9.2 compared to the Wheelies 6.7, in-dicating that their project results in a higher return on operating dollars. Inother words, both teams are engaging in innovative behavior and no singlemeasure tells the whole story.

The second method of assessing plant-wide performance provides a fi-nancial impact summary that incorporates both team innovations and thecosts of supporting the team system. Up to this point, all four measurespreviously described (number of innovations, operational measures, finan-cial measures and effectiveness indices) focus on what teams control – theirown improvement recommendations, including both the savings and costsof those improvements. There are, however, significant costs incurred in ateam-based organization that are necessary both to implement teams and tomaintain an environment conducive to their success. These costs are notcontrolled by teams, but by managers. Such costs include team training,meeting time, supplies, celebration costs, consultants, team system manag-ers, refreshment, travel related to team events and information systems.Generally, these types of costs are embedded in different cost centers withinthe accounting system. For example, the costs of meeting time for produc-tion teams may be captured in the manufacturing cost center while the costsof meeting facilities are a small part of a larger overhead allocation. Train-ing and celebration costs may be captured in the human resource depart-ment. Upper management is interested in knowing not just whether theteams are performing to expectations but whether continued investment inthe entire team system is warranted. Basically, management needs to know:Are they realizing a return on the total team support system costs? And isthis return sufficient to justify future investment?

Exhibit 3 (Panel A) presents a summary of annualized team support coststotaling $125,000. The majority of these costs can be captured throughappropriate account codes in the general ledger, but may also be calculatedusing solid assumptions (e.g., number of hours of training times the numberteam members trained). Panel B of Exhibit 3 summarizes the net financialimpact of team innovations net of team support costs. The result is $477,000or 79.2% of team net savings retained after consideration of support costs.

Evaluation of the Team Performance Measurement System

In any new measurement system, there are questions about whether themeasurements are relevant to decisions and whether they encourage the

Team Performance Measurement 279

decisions needed to accomplish the established goals. With a team meas-urement system, identifying relevant and effective measures depends onknowing what the team is expected to accomplish. The main roles of teammembers relate to identifying problems, improving processes and recom-mending solutions (Kennedy, 2003). As part of this aspect of decision-making, teams have to decide what information is needed in order toconsider their options. Having a well-defined system of metrics can aid theteam in identifying and gathering the necessary information. Knowing whatis being measured aids in knowing what is valued and in knowing what totarget. Teams generate multiple solutions and identify the best or the mostfeasible solutions. Metrics can aid team members in identifying what is likelyto be the best solutions. Simply providing a system, such as the TPMS thatis easily accessed by teams, enables teams to communicate all their successesthat previously went unnoticed.

Theoretical Support for the TPMS

The TPMS considers the various dimensions of innovation and empower-ment. It can be used to encourage creativity by setting team goals andrewarding teams for their performance. TPMS enables the team to makedecisions and encourage a sense of ownership over decisions by providingfeedback to help change direction as needed. In addition, implementing theTPMS will send a message that the team’s work is important enough toallocate resources to it. Proper implementation should involve enough pres-sure to challenge and motivate team members but not so much pressure thatit overwhelms and discourages teams from doing their best work. There isno guarantee that there will not be organizational impediments to the use ofthe TPMS, but an awareness of possible impediments should help to mit-igate their impact. The use of TPMS can engender potency by leading ateam to believe that it can be effective since it will show tangible evidence ofthe team’s performance and success. The use of TPMS will enable teams toobserve how they impact the organization and therefore be able to assess themeaningfulness of their contribution to the organization. Autonomy wouldbe enhanced by the use of TPMS since measuring the team’s performancewould enable the team members to make independent decisions and takeindependent actions. Exhibit 4 summarizes how the measurement compo-nents impact the models of creativity and empowerment presented earlier.

According to Beyerlein and Harris (2003, p. 135), team accountabilitymeans ‘‘how to enable others to act responsibly.’’ Management has to em-power teams by giving them authority in areas for which they will be heldaccountable. Then those team members can be rewarded for living up to

Exhibit 4. Assessment of TPMS Impact on Innovation andEmpowerment.

Measurement Goal Impact of TPMS Components

Creativity (Amabile, Conti, Coon, Lazenby, & Herron, 1996)� Organizational

encouragement

� Innovations: Tracking the number of innovations

encourages the generation of multiple new ideas.� Autonomy � Effectiveness indices provide tools for teams to assess

and compare alternative solutions and enable

decision-making.� Resources � All four components taken together provide managers

with an overview of activity and progress for teams.� Pressures � Effectiveness indices help teams prioritize solutions

and provide a vehicle for communicating the

successes, relieving excess work pressures. These

metrics can also be used to set stretch targets to

challenge teams.� Organization impediments � Having a well-defined team performance

measurement system eliminates mixed signals and

subjective judgments concerning team performance.

Empowerment (Kirkman & Rosen, 1999)� Potency � All four components provide feedback to the team

signaling necessary change and reinforcing

successes.� Meaningfulness � Operational and financial measures place values on the

gains from the solution.� Autonomy � Effectiveness indices provide tools for teams to assess

and compare alternative solutions and enable

decision-making.� Impact � Financial and effectiveness indices assesses the

organizational impact of the team’s contribution.

FRANCES KENNEDY AND LYDIA SCHLEIFER280

expectations. It is useful to be able to assess when teams have responded toexpectations in an accountable way. The TPMS enables that assessment anddoes it in a way that allows teams to demonstrate several areas of com-petence and success.

Practical Considerations for the TPMS

Teaming and collaboration depend on acceptance by the participants.Beyerlein and Harris (2003) assert that inefficiency results when not allresources are being used and that the two most critical under-utilizedresources are the hearts and minds of the individual employees and thesynergies that emerge from effective collaboration. Of all the characteristics

Team Performance Measurement 281

needed in a good measurement system, the ones that probably do the mostto win the hearts and minds of employees are the attributes of beingunderstandable and easy to use. In the TPMS, tracking the number andfrequency of innovations is a simple activity, and team members would beable to understand the value of such a measurement. Operational measures,like number of man-hours, can or should be more understandable the moreclosely they relate to an activity. Financial measures might be a little out ofthe realm of what many employees normally use, but with training and theuse of straightforward analytical tools that show how time and resourcesare utilized, employees will begin to understand and use such financialmeasures. The PEI is simplicity itself in that it focuses on annual costs andsavings without the need to discount future cash flows. In addition, Hinrichsand Ricke (2003, p. 295) point out that ‘‘employees need to gain a holisticview of their business.’’ Their work with a manufacturing company dem-onstrated to them that employees performed better when they understoodthe whole system, the business processes, the language of their business andthe data about their business.

According to Estrin and Kanter (1998), subjectivity can introduce biasthat serves political purposes and leads to a perceived lack of fairness. Thenumber and frequency of innovations is a measure for which any teammember or manager would come up with the same information as any othermember (Estrin & Kanter, 1998). Operational measures, depending on theparticular ones chosen (e.g., number of errors in inventory reports), will alsobe more or less objective and verifiable. Financial measures and the PEI areinformed estimates calculated with information from historical records andaccounting information.

It is a truism that more timely information is probably better than lesstimely information. According to Estrin and Kanter (1998), historical in-formation may be acceptable for strategic decision making, but it is lesslikely to be useful for managerial control decisions and operational deci-sions. The information produced by the TPMS can be produced on a real-time basis. The technology is available and the environment of JIT and leanmanufacturing has created a more spontaneous and flexible work culture.

LIMITATIONS

There are limitations to the use of TPMS. The system is designed to measureincremental improvements over current performance and may not be ap-propriate for all types of teams, such as new product development teams and

FRANCES KENNEDY AND LYDIA SCHLEIFER282

management teams. Management teams are typically evaluated on theoverall financial improvements of a facility and measured with traditionalfinancial benchmarks, such as return on assets (ROA) and return on in-vestment (ROI). Also, the use of TPMS may not be appropriate for certainprocesses, like major initiative implementations. A system like TPMS will beuseful for teams that can establish a baseline of performance for a process orproject. However, until there is enough information to establish a baseline,teams using TPMS may experience some uncertainty regarding whether theyare implementing it correctly or gaining useful information from it.

The implementation of a performance measurement system, like the onedescribed here, could face the same challenges and pitfalls experienced inprevious implementations of a balanced scorecard approach or, in general,any combination of financial and nonfinancial measures. A multidimen-sional team measure seems intuitively preferable. Caution should be exer-cised when implementing the TPMS as it may be appropriate for some typesof teams, but may not be appropriate for other types of teams (e.g., productdevelopment teams). In the same way, the system described here may beappropriate for some contexts (like decision-making, training, developingstrategic plans or evaluating the achievement of organizational objectives)and may not be appropriate for other contexts (like compensating managersand employees).

CONCLUSION

Strategic performance measures align strategic goals with operationsthrough well-defined metrics. The primary goals of teams include develop-ing and implementing innovative solutions. To achieve these goals, teammembers must be empowered to seek information and make decisions, aswell as encouraged to try new ideas. Simons (1995) suggests that empow-erment requires greater control. This implies that measures are needed toensure that decisions are made to further organizational goals without cre-ating unacceptable risk. A single metric, therefore, cannot adequately cap-ture the efforts and results of teams. Current strategic performancemeasurement systems include a mix of relevant financial and nonfinancialmeasures. It follows that a comprehensive set of team measures should alsoinclude well-chosen metrics that are informative and help guide team andmanager actions.

The four categories of metrics in the TPMS framework together provide abroad view of team performance. Measuring the number of innovations

Team Performance Measurement 283

over time communicates the activity level of the team, while the impact ofthe changes is represented in the operational and financial measures. Op-erational (or nonfinancial) measures align team goals with strategy andeffectively monitor progress towards those goals. Financial measures, rep-resented as annualized net savings, estimate the magnitude of the processchange.

Finally, the effective use of operating dollars is expressed using effective-ness indices. The PEI helps teams to manage their solutions and comparealternatives, as well as give assurance to managers that additional operatingdollars are used optimally. The TEI provides a mechanism to summarize allof a team’s activities, and can be used as a means of recognizing highperforming teams. Used together, the TPMS offers a well-rounded per-spective of team activity and innovation and provides a simple method oftying team recommendations to bottom-line impact.

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AN EXPERIMENT OF GROUP

ASSOCIATION, FIRM

PERFORMANCE, AND DECISION

DISSEMINATION INFLUENCES ON

COMPENSATION

Arron Scott Fleming and Reza Barkhi

ABSTRACT

Reports citing excessive CEO compensation continue to make the news

with evidence of peer relationships between the CEO and the compen-

sation committee often the center of debate. The compensation committee

of the board of directors determines CEO pay and is often comprises

CEOs from other companies as well as non-CEOs such as academic,

exgovernment, and professional individuals. This study examines the in-

fluence of the psychological factor of social comparison over accounting

performance measures in a compensation experiment with 176 subjects.

The results of this study are consistent with social comparison theory in

that CEO director-subjects award greater pay and shield the compensa-

tion of the CEO when firm accounting performance is below average.

Additionally, we find shielding is mitigated when subjects are informed

that the decision of the amount of compensation awarded will be revealed

to the public.

Advances in Management Accounting, Volume 16, 287–309

Copyright r 2007 by Elsevier Ltd.

All rights of reproduction in any form reserved

ISSN: 1474-7871/doi:10.1016/S1474-7871(07)16010-X

287

ARRON SCOTT FLEMING AND REZA BARKHI288

INTRODUCTION

The board of directors provides guidance and oversight to management andacts as the key body for representing shareholders and investors. Properoversight or governance by a board is a cornerstone element to our capitalmarkets. This governance is often conducted within sub-committees of theboard, such as the compensation committee. The task of determining thecompensation of the CEO falls to this committee and it represents a sig-nificant fiduciary duty to the board, shareholders, and investors alike. Oneof the nation’s largest pension funds expressed the significance of this com-mittee and the compensation process as a critically important and highly

visible responsibility of the board of directors of a corporation. In a real sense,

it represents a window through which the effectiveness of the board may be

viewed (TIAA-CREF, 2002). Our primary research interest is in compen-sation committee ineffectiveness, where the decision-making outcome maynot be in the best interest of the board or shareholders.

We build from associative findings of compensation shielding by the com-pensation committee for unfavorably performing CEOs (Dechow, Huson, &Sloan, 1994; Gaver & Gaver, 1998; Duru, Iyengar, & Thevaranjan, 2002;Adut, Cready, & Lopez, 2003). Shielding occurs when the compensationcommittee minimizes reductions in executive compensation in the face ofreduced firm performance. The compensation committee effectively limitsthe downward exposure of compensation to the executive in times ofreduced performance. In this study, we test whether subjects role-playing asCEO directors on the compensation committee shield or protect the paylevel of the chief executive officer when firm performance is below theindustry average. Further, we test to see if potential publicity of CEOdirector’s decisions mitigate this shielding effect. Our experimental findingsprovide evidence for shielding by showing CEO director-subjects awardgreater compensation than non-CEO director-subjects when firm perform-ance is below the industry average. Additionally, we find potential publicitysurrounding individual decisions by CEO director-subjects reduces theshielding effect. In conducting this research, we expand the causal under-standing of the influence of connections to peer groups within individualdecision making. This extends the current body of literature in that it ex-amines individual decision factors found within the executive compensationsetting process through an experimental methodology.

Motivation for continued compensation research stems from the relativeimportance of the topic in the business, investment, and political commu-nity. Disclosures regarding executive pay, such as the NYSE chief

CEO Compensation 289

executive’s pay package of roughly $140 million and subsequent resignationin 2003, highlight the repercussions and agency costs of governance processbreakdowns. While the primary media focus was on the magnitude of com-pensation, much less attention was applied to the board of directors and themake-up of the compensation committee that awarded such a package. Inthe NYSE case, most of the committee members have titles of president,CEO, or chairman. Given the excessive CEO pay package and a lack oflinkage to pay and firm performance, political and social pressures appear tohave forced a change in the governance and compensation setting structureof the NYSE. The direct result is a change in board of director and com-pensation committee membership, and a possible return of excess-awardedcompensation.1

The potential cause of the high pay package may be attributed to thenature and composition of compensation committee within the board ofdirectors where alliances and interactions may compromise rational deci-sionmaking (Perel, 2003). The board of directors and the compensationcommittee is often comprises CEOs of other companies, academicians, re-tired military or government officials, and professional directors. It is thecoterie of CEO directors within the compensation committee that may affectthe compensation setting process, thus representing an agency problem inmanagerial incentives between the owner’s of the firm and those in controlof the firm (Fama, 1980). Our research attempts to experimentally determineif CEO directors look out for their own, particularly when firm performanceis below average.

Agency problems, where management elevates their personal interestsover the interests of the shareholders (Fama, 1980), result in various form ofagency costs within an organization. CEO compensation setting processesare no exception, and mechanisms or structures that unnecessarily elevateCEO compensation are agency costs. In this area of concern, researchershave examined the board using inside or outside director categorization(O’Reilly, Main, & Crystal, 1988; Daily, Johnson, Ellstrand, & Dalton,1998; Newman & Mozes, 1999; Bhagat & Black, 2002). Additionally,though, a contributing factor relating to CEO compensation may be thenumber of outside directors who are also CEOs. Nell Minow, editor of theCorporate Library, indicates that the best predictor of CEO overpay is the

number of CEOs on a compensation committee (Burns, 2003, p. R6). Whilethe boardroom is comprised of inside management such as the domicileCEO and outside directors, it is the outside director who is also a CEO thatidentifies most with the domicile CEO. This identification or social com-parison to another individual or group (Festinger, 1954) forms the basis for

ARRON SCOTT FLEMING AND REZA BARKHI290

agency costs within the compensation setting process. The objective of thisstudy is to experimentally examine the aspect of social comparison as anagency cost. This is accomplished by studying subjects role-playing as CEOdirectors in the compensation setting process.

Ideally, the compensation committee considers firm performance whensetting CEO compensation, but this may not always be the case. While theWall Street Journal/Mercer Human Resource Consulting (2003) notes apronounced positive relationship between CEO annual pay and perform-ance for 2001 and 2002, the Economic Research Institute found executivecompensation grew faster than firm revenues in 2002.2 This occurred duringa time when stock prices continued to decline, suggesting that compensationand performance does not always run in parallel.

As the information and details of compensation packages become public,the compensation to performance incongruity has led shareholders to moreclosely scrutinize the CEO compensation award decision of the boards andfile resolutions with the SEC. According to the Investor ResponsibilityResearch Center, shareholder resolutions filed with the SEC in 2003 aimedat curbing CEO compensation have risen 200% over the previous year –General Electric’s CEO Jeff Immelt was subjected to 26 compensation-re-lated resolutions (Ulick, 2003b). Even CEOs who meet or exceed expecta-tions, such as Jeff Immelt’s predecessor Jack Welch, face investor criticismwhen pay and retirement packages become public. Welch returned significantportions of his post-employment compensation perquisites ($2.5 million/year) when details of the retirement package became public during divorceproceedings in 2002 (Naughton, 2002). Publicity regarding excessive com-pensation carries negative consequences for both CEOs and directors. In thecase of NYSE, not only was excess compensation ordered returned, but alsothe CEO was ousted along with certain board members supporting the paypackage. Taken together, this suggests that public scrutiny of pay packagesmay by increasing and may have an impact on compensation awards.

We conjecture that CEO peers on the compensation committee positivelyaffect CEO pay. Further, we conjecture that publicity of excessive pay neg-atively affects CEO pay. It is the interplay of the number of CEO memberson the compensation committee and the publicity of their decision of thepay package that is the focus of this study.

In this paper we report the results of a 2� 2� 2 between-subjectsexperimental study. The three factors as illustrated in Fig. 1 are: groupassociation of director type (CEO director-subjects versus non-CEO direc-tor-subjects), firm performance (above or below industry average), andcompensation decision dissemination (public or private).

(CEO, non-CEO Director)

Performance

(above, below average)

Decision Dissemination

(pubic, private)

Compensation

Model of Factor Association To Compensation

+ -

-

-

+

+

Association

Fig. 1. Model of Factor Association to Compensation.

CEO Compensation 291

The remaining paper is organized as follows: Section 2 provides a briefliterature review, develops the hypotheses, and explains the model; Section3 explains the methodology; Section 4 presents the results; and Section 5discusses the implications, limitations, and direction for future research.

HYPOTHESES DEVELOPMENT

As a proxy for shareholders and acting on their behalf, the board of di-rectors monitors, hires, fires, and guides the direction of the professionalmanagers within the firm. The compensation committee, a sub-group to theboard, determines the compensation of the CEO. The significance of ex-ecutive compensation is emphasized in the following statement: The gov-

ernance of the executive compensation process is a critically important and

highly visible responsibility of the board of directors of a corporation. In a real

sense, it represents a window through which the effectiveness of the board may

be viewed (TIAA-CREF, 2002).Although the board is purported to represent the shareholders, agency

problems with the CEO can become an issue. Top management may electexpropriation of wealth as opposed to competition once having gained

ARRON SCOTT FLEMING AND REZA BARKHI292

control of a board (Fama, 1980). Overt agency problems are manifestthrough financial fraud, large perquisites, and excessive compensation, butwe cannot discount the possibility of agency problems through a more sub-tle control by the CEO over the board or compensation committee via peerascendancy. While the board of directors represents the shareholders, CEOdirectors more closely resemble the CEO from a social and professionalstandpoint. This peer association can create an effect of control or influenceconsistent with the representatives in the group.

Research on groups has given attention to the natural formation of groups,uniformity within groups, and a normalization of behavior (Greenberg,Solomon, & Pyszczynski, 1997; Baumeister & Leary, 1995; Festinger, 1950).Similar observations appear in business contexts. Corporations are hierar-chical while the board of directors is a collegial group working toward con-sensus (Bainbridge, 2002). In choosing an outside successor, the board tendsto pick someone demographically similar to their own profiles (Zajac &Westphal, 1996), and the compensation committee is influenced by the de-mographic similarities to the CEO (Young & Buchholtz, 2002). Social capital(social status and network ties) of the CEO is associated to higher compen-sation (Belliveau, O’Reilly, & Wade, 1996) as is the compensation level of theoutside director on the compensation committee (O’Reilly et al., 1988). Theseresults can be explained by Social Comparison Theory. The theory suggeststhat individuals make comparisons to those they perceive as similar andassociate with those having similar characteristics (Festinger, 1954). Exam-ples of associations include status, position, and wealth. Hence, CEO direc-tors associate more with the CEO, thus their compensation award decision islikely to be biased and positively influenced. We hypothesize that subjectsrole-playing as a CEO director will award greater pay than subjects role-playing as non-CEO directors when evaluating a CEO and awarding com-pensation. We suggest the following hypothesis:

H1. CEO director-subjects will award greater compensation than non-CEO director-subjects.

From prior research we expect compensation to be positively associatedto firm performance3 (Sloan, 1993; Natarajan, 1996; Gaver & Gaver, 1998;Duru & Iyengar, 1999; Tosi, Werner, Katz, & Gomez-Mejia, 2000; Sheik-holeslami, 2001; Lambert & Larcker, 1987). In the absence of all otherfactors, we expect performance to be positively associated with pay. Hence,we present the following hypothesis:

CEO Compensation 293

H2. Director-subjects will award greater compensation when firm per-formance is above industry average as compared to below industryaverage.

Previous archival research has also shown that the makeup of the com-pensation committee mediates the pay to performance ratio for under-performing firms. That is, CEOs of firms that are poor or unfavorableperformers may have their compensation levels or package protected by thecompensation committee (Dechow et al., 1994; Gaver & Gaver, 1998; Duruet al., 2002; Adut et al., 2003). Further, director type affects the extent ofsuch compensation shielding (Newman & Mozes, 1999). We conjecture theCEO director, a more closely associated member of the coterie, exacerbatesthis protection or shielding of CEO compensation. Through this socialcomparison or group association, the CEO director-subject will awardgreater compensation than the non-CEO director-subject when performanceis below average. We propose the following hypothesis:

H3. CEO director-subjects will award greater compensation than non-CEO director-subjects when performance is below the industry average.

In addition to group association and firm performance, we study theimpact of individual decision dissemination. Research has shown decisionsof groups involve greater levels of risk-taking than individuals and canexacerbate or escalate decision trends (e.g., Stoner, 1961; Argote, Seabright,& Dyer, 1986; Whyte, 1993). Given the CEO director-subject is a member ofa group or coterie within the compensation committee, the publication ofthe decision makes salient the individuality of the subject and breaks themental association to the group. Without individual decision publicity, in-dividual decision makers may be prone to the more risk-taking attitude of agroup. If the individual decision is public, though, then the dynamics of thegroup association and decision escalation is less likely to materialize. There-fore, the publicity of the individual decision can mitigate the compensationshielding effects of the group and lessen the agency costs. Specifically, wehypothesize that the CEO director-subject will award lower levels of com-pensation when the individual subject’s decision is noted to be made publicas compared to being kept private. Hence, we present the following hy-pothesis:

H4. When performance is below the industry average, CEO director-subjects will award lower compensation when the individual decision isnoted to be made public as compared to being kept private.

ARRON SCOTT FLEMING AND REZA BARKHI294

Fig. 1 summarizes the main components of the model that describe thestudy reported in this paper. The association of the director type, the per-formance of the firm, and the decision dissemination potential are individ-ually and jointly affecting compensation levels determined by thecompensation committee member.

METHOD

Sample

We conducted this research at a large American university. We used subjectsenrolled in the second of two principle accounting courses. A total of 115men and 61 women participated in the study. Subjects were on average 20.4years old (SD ¼ 1.3) with an average of 0.7 years (SD ¼ 1.3) of full-timework experience (see Table 1). The subjects were primarily first- and second-year undergraduate students enrolled in the college of business. Althoughthe use of student subjects in behavioral accounting research is not unusual,we acknowledge that it is not ideal but may be a practical solution givenlimited accessibility to CEO and board of director subjects. Following Sedorand Kadous (2004), student subject are appropriate since this study employstheories centered on characteristics not dependent on the professional pop-ulation (Peecher & Solomon, 2001; Libby, Bloomfield, & Nelson, 2002).Evidence in student surrogate studies examining attitudes show there is adivergence between students and other subjects, while in studies examiningdecision making, considerable similarities exist4 (Ashton & Kramer, 1980).Since our experiment is centered on decision making and the subjects wereimmersed in their roles,5 we believe the results obtained from using studentsubjects provide strong internal validity and reasonable external validity asapplied to decision makers composing boards of directors in general. Whileit may be argued that undergraduate subjects are unlikely surrogates for

Table 1. Subject Descriptives.

Mean Standard Deviation

Age 20.4 1.3

Full-time work experience 0.7 1.3

Males Females

Gender 115 61

CEO Compensation 295

CEO directors, it may also be argued that given the pragmatic distance ofreality from such subject to such population, any evidence obtained in sucha weak manipulation indicates the presence of a stronger bona fide effect.

Variables

The dependent measure in our study is compensation. To reduce potentialsubject anchoring confounds and biases we elected not to use dollars butrather a non-bounded artificial currency we labeled as ‘‘Qwert’’. This fol-lows from accounting and economic literature where researchers in lieu ofdirectly employing dollars use points (e.g., Kachelmeier & Shehata, 1997) orother artificial denominations (e.g., Friedman, 1967; Forsythe, Palfrey,& Plott, 1982; Plott & Sunder, 1982; Forsythe & Lundholm, 1990). Inde-pendent variables include director type, performance, and decision dissem-ination. Within each vignette subjects were assigned to the role as a CEOdirector or non-CEO director on the compensation committee. The per-formance of the subject firm was either above or below the industry average.This was indicated primarily in two ways: (1) it was shown numerically asa comparative growth rate and through earnings per share data, and(2) through a verbal statement stating the company’s operating margins andnet income levels were above or below the industry average.6 Lastly, withineach vignette, the compensation decision for each director was noted aseither a private and confidential decision or one that would be made public.

Procedures

Student subjects were given a one-page overview on corporate governance(Appendix A). The subjects were asked to participate in an in-class exper-iment for the following week. Participation was voluntary and those whochose to participate received either extra-credit or a waiver of one home-work grade, equal to 3 points out of 550 total points for the class. Subjectswere given a pre-numbered cover sheet and demographic questionnaire(Appendix B) to complete. After signing the cover sheet we collected andgave them to the instructor for credit purposes. Subjects at this point weretracked only via the pre-numbered forms.

The pre-numbered demographic forms were collected and the subjectswere introduced to an individual who played the role of the CEO. Thesubjects were given an overview of the experiment and told that they wererole-playing as compensation committee members of the board of directorsand would determine the compensation of the CEO who was being eval-uated. The subjects were told that roughly half were role-playing as CEOdirectors from other companies, one-quarter were role-playing as retired

ARRON SCOTT FLEMING AND REZA BARKHI296

public servants, and one-quarter were role-playing as academicians. Subjectswere then given name tags with their title as either CEOs of fictitious com-panies (randomized three-letter abbreviated names), or the titles of eitherretired senator or business school dean.

A pre-numbered vignette (see sample in Appendix C) was given to eachsubject. This is a 2� 2� 2 study with subjects assigned to one of eightvariations. Within each vignette is information describing the compensationcommittee and their role, their compensation in their own profession, theaccounting performance of the fictitious CEO’s company as compared tothe industry average, and the industry average compensation level. Addi-tionally, each subject was informed within the vignette whether or not theircompensation decision is to be kept private and confidential or made public.From this information the subject determined the compensation of the fic-titious CEO.

On completion of the task the vignettes were collected and pre-numberedpost-experimental surveys were distributed (Appendix D), completed, andcollected.

RESULTS

A 2� 2� 2 (director� performance�decision) ANOVA is presented inTable 2 with cell descriptives in Table 3. Overall results indicate significantmain effects for director type and performance (decision was not significant),no significant two-way interactions, but a significant three-way interaction.Hypothesis H1, CEO director-subjects will award greater compensationthan non-CEO director-subjects, is supported. The mean award for a CEOdirector-subject is 71.1886 and the mean for a non-CEO director-subject is68.9494 (p ¼ 0.007). Hypothesis H2, director-subjects will award greatercompensation when firm performance is above average as compared to be-low industry average, is also supported. The above industry average per-formance compensation mean is 74.3250 versus the below industry averageof 67.1049 (po0.001). Hence, the experimental results suggest that bothdirector type and performance have significant influence on CEO compen-sation. Results are represented graphically in Figs. 2 and 3 for hypothesesH1 and H2, respectively. Fig. 4 illustrates the combined results to helpvisually compare CEO award as a result of performance (above or belowaverage) and as decided by board member type (CEO or non-CEO).

The lack of two-way interactions is not surprising given that we expectdirector differences only when performance is below industry average.

Table 2. 2� 2� 2 ANOVA Table: Tests of Between-Subjects Effects.

Source Type III Sum

of Squares

df Mean

Square

F Significance

Corrected model 2865.52� 7 409.36 8.23 0.000

Intercept 796452.61 1 796452.61 16010.49 0.000

Directora 373.90 1 373.90 7.52 0.007

Performb 2112.18 1 2112.18 42.46 0.000

Decisionc 1.94 1 1.94 0.04 0.844

Director� perform 40.16 1 40.16 0.81 0.370

Director� decision 0.79 1 0.79 0.02 0.900

Perform� decision 6.93 1 6.93 0.14 0.709

Director� perform� decision 238.24 1 238.24 4.79 0.030

Error 8307.53 167 49.75

Total 870522.04 175

Corrected total 11173.04 174

�R squared ¼ 0.256 (adjusted R squared ¼ 0.225).a‘‘Director’’ is CEO director/non-CEO director categorization.b‘‘Perform’’ is firm performance above/below the industry average.c‘‘Decision’’ is public/private individual decision dissemination.

CEO Compensation 297

Therefore, to test hypothesis H3 we conducted a 2� 2 (director type�decision) ANOVA restricted by below average performance (Table 4).Results indicate significant main effects for director type (CEO or non-CEO)but not for decision (public or private). Therefore, hypothesis H3, CEOdirector-subjects will award greater compensation (mean ¼ 68.88) thannon-CEO director-subjects (mean ¼ 65.07) when performance is below theindustry average, is supported (F ¼ 9.480, p ¼ 0.003). This finding indicatesa significant interaction of director type and performance in the negativeperformance domain.7 This result further supports the compensation-shielding phenomenon.

The significant three-way interaction (F ¼ 4.789, p ¼ 0.030) leads to theinvestigation of hypothesis H4, that when performance is below the industryaverage the CEO director-subject will award lower compensation when theindividual decision is noted to be made public as compared to being keptprivate. Table 5 presents the results of a one-way ANOVA to testing hy-pothesis H4. When we restrict the data to the below industry average per-formance and CEO directors, the results moderately support H4. The meancompensation awarded by CEO director-subjects when performance is be-low the industry average and the decision is private is 70.0280 versus 67.9233when the decision is made public (F ¼ 2.023, one-tail p-value ¼ 0.081).This indicates that CEO directors no longer shield the chief executive’s

Table 3. Cell Descriptives.

Value Label N

Director

0 CEO 88

1 Non-CEO 87

Perform

0 Below average

growth

103

1 Above average

growth

72

Decision

0 Private 92

1 Public 83

Director Perform Decision Mean Standard Deviation N

CEO Below average growth Private 70.03 3.08 25

Public 67.92 6.84 30

Total 68.88 5.52 55

Above average growth Private 74.27 3.39 20

Public 76.22 3.67 13

Total 75.04 3.58 33

Total Private 71.91 3.83 45

Public 70.43 7.14 43

Total 71.19 5.71 88

Non-CEO Below average growth Private 63.67 7.64 21

Public 66.16 7.87 27

Total 65.07 7.79 48

Above average growth Private 74.80 4.45 26

Public 71.58 16.00 13

Total 73.72 9.81 39

Total Private 69.82 8.21 47

Public 67.92 11.25 40

Total 68.95 9.72 87

Total Below average growth Private 67.12 6.42 46

Public 67.09 7.33 57

Total 67.10 6.91 103

Above average growth Private 74.57 3.99 46

Public 73.90 11.62 26

Total 74.32 7.60 72

Total Private 70.85 6.50 92

Public 69.22 9.38 83

Total 70.08 8.01 175

Variables are defined in Table 2.

ARRON SCOTT FLEMING AND REZA BARKHI298

68

70

72

CEO non-CEO

Subject Type

71.19 68.94 p = 0.007

Fig. 2. Compensation Award by Subject Type.

68

70

72

Above BelowFirm Performance

74.32 67.10 p < 0.001

Fig. 3. Compensation Award by Performance Realm.

64

68

72

76

CEO non-CEOSubject Type

“Above”avg. = 74.32

“Below”avg. = 67.10

Above

Below Below

Above

75.04

68.88

65.07

73.72

Fig. 4. Compensation Award by Subject Type and Performance Realm.

CEO Compensation 299

Table 4. 2� 2 ANOVA Table: Tests of Between-Subjects Effects.

Source Type III Sum of Squares df Mean Square F Significance

Corrected model 505.91� 3 168.64 3.83 0.012

Intercept 453869.92 1 453869.92 10305.45 0.000

Director 417.51 1 417.51 9.48 0.003

Decision 0.97 1 0.97 0.02 0.882

Director�decision 133.99 1 133.99 3.04 0.084

Error 4360.13 99 44.04

Total 468681.38 103

Corrected total 4866.05 102

Variables are defined in Table 2.�R squared ¼ 0.104 (adjusted R squared ¼ 0.077).

Table 5. One-Way ANOVA: Public versus Private Comparison forCEO Directors in the Below Industry Average Domain.

Sum of Squares df Mean Square F Significance�

Between groups 60.40 1 60.40 2.02 0.081

Within groups 1582.50 53 29.86

Total 1642.91 54

�Reported p-value is one-tail given the directional nature of H4.

6

69

71

Private PublicDecision Type

70.03 67.92 p = 0.081

Fig. 5. CEO Director-Subject Compensation Award by Decision Type.

ARRON SCOTT FLEMING AND REZA BARKHI300

CEO Compensation 301

compensation when the decision is noted as public. A graphical represen-tation is shown in Fig. 5.

CONCLUSION

Agency problems manifest themselves in various forms within an organi-zation, and the executive compensation setting process is no exception. Ourstudy experimentally tests the influence of three factors: (1) director-subjects(CEO and non-CEO), (2) accounting performance (below average or aboveaverage), and (3) decision dissemination (public or private). A contributionof our study is that it shows how these three factors elevate awarded com-pensation. We find results consistent with previous compensation shieldingliterature.

A limitation to this study is the subject pool. While our convenient sampleprovided internal validity, these subjects are not perfect substitutes for thebusiness leaders, and thus this potentially limits our external validity. Futurestudies should build on this research to address the limitations of this studyand examine the anchoring effects and other environmental factors thathave been empirically shown to influence CEO compensation.

Our experimental results indicate that director type influences the com-pensation setting process, particularly when firm results are below the in-dustry average. CEO director-subjects award greater compensation ingeneral and award significantly greater compensation as compared to non-CEO director-subjects when performance is below average. A further in-fluencing factor presents itself when the individual decision of the director isnoted as being kept either private or made public. In our study we findevidence of further shielding by CEO director-subjects when performance isbelow average and when the decisions are private, as compared to when thedecisions are public. Thus, while director type mediates the influence ofperformance on pay, decision dissemination also mitigates the relation be-tween performance and pay.

NOTES

1. Kelly, Craig, and Dugan (2003). Further, on October 19, 2006 the New Yorkstate Supreme Court in Manhattan ordered Mr. Grasso to forfeit a portion of hispay package (Lucchetti & Lublin, 2006).2. Cash compensation increases of 5.9% versus revenue increases of 0.89% in

2002 (Ulick, 2003a).

ARRON SCOTT FLEMING AND REZA BARKHI302

3. Iyengar (2003) finds higher compensation levels for perennially loss-making firms with negative retained earnings. Given the uniqueness of theirsample, we feel the results do not apply to the general population in regards toperformance to pay association. Firms within the sample may represent companieswilling to hire and compensate executives at higher levels for potential turnaroundperformance.4. In a later, yet unpublished experiment (Fleming) that utilized 71 undergrad-

uate, 63 graduate accounting, and 95 executive MBA subjects in the determination ofcompensation, results shows that the subjects were qualitatively similar in their de-cision outcomes (F ¼ 0.975, p ¼ 0.379).5. We believe the students were fully immersed in their roles and confirm this

through post-experimental questions on scenario role and group association. Allsubjects correctly answered the question of role (CEO director versus non-CEOdirector). Additionally, from post-experimental survey questions asking subjects torate their association to a particular group (CEO, non-CEO, and board of directorgroups) where 1 is weak and 10 is strong, we find that CEO director-subjects sig-nificantly associated to the CEO group (mean ¼ 8.75; F ¼ 158.969; po0.001); non-CEO director-subjects significantly associated to the non-CEO group (mean ¼ 8.20;F ¼ 149.765; po0.001); and both CEO and non-CEO director-subjects associatedsimilarly to the board of directors (CEO mean ¼ 7.69; non-CEO mean ¼ 7.29;F ¼ 1.495; p ¼ 0.223).6. Executive compensation is often based on targets and goals set forth by the

board of directors via a specific employment contract. In this research we do notmake available explicit targets or goals but rather we provide the subjects withperformance measures that indicate the subjects firm’s performance to a benchmark,such as previous year’s performance or performance to the industry.7. As a further analysis, the same test was performed in the above industry av-

erage domain without significant results. The CEO director mean of 75.0364 versusthe non-CEO director mean of 73.7231 proved non-significant (F ¼ 1.020,p ¼ 0.277).

ACKNOWLEDGMENTS

The authors wish to acknowledge the assistance of Richard Brooks, JohnBrozovsky, William Kerler III, John Maher, and Christian Schaupp. Inaddition, we wish to acknowledge the constructive comments of John Leeand two anonymous reviewers.

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APPENDIX A. CORPORATE GOVERNANCE

OVERVIEW

Corporate Governance

A Very Short Overview

What is Corporate Governance?

‘‘Corporate governance is a hefty-sounding phrase that really just means oversight of a

company’s management – making sure the business is run well and investors are treated

fairly’’ (Burns, 2003).

Publicly traded companies are those whose stock is traded in a public forum,usually over the New York Stock Exchange (NYSE), the American Ex-change (AMEX), National Association of Securities Dealers AutomatedQuotation System (NASDAQ), or other regional exchanges such asPhiladelphia or San Francisco. As such, any company can literally havethousands of ‘‘owners’’.

It is difficult for a company to be managed simultaneously by potentiallythousands of different owners; therefore, the owners or stockholders elect aboard of directors as their representatives. The board sizes vary with anaverage of 9–11 members.

The board of directors hires management, such as the chief executiveofficer (CEO), chief financial officer (CFO), and other vice-presidents to runthe company – the board oversees their activities. This oversight is oftenconducted within a sub-committee of the board, such as the audit commit-tee, the compensation committee, or the nominating committee. As an ex-ample, selected members of the board may be on the compensationcommittee – their job is to determine the compensation for the CEO and is asignificant fiduciary duty as a board member.

‘‘The board’s most important job is hiring, firing, and setting compensation for a com-

pany’s chief executive, who runs the company day-to-day’’ (Burns, 2003).

ARRON SCOTT FLEMING AND REZA BARKHI306

The membership of the board is often comprised of the CEO or other firminsiders, CEO’s from other industries, bankers, retired politicians, acade-micians, and professional directors (often representing mutual or retirementfunds). Note: Although often on the board of directors, the CEO cannot be a

member of his own compensation committee. Board member provide not onlyoversight but also expertise and advice, often meeting three to five times ayear in addition to the (usual) legally required once a year meeting.

APPENDIX B. DEMOGRAPHIC QUESTIONNAIRE

Name: __________________________________

_________

When you turn in the survey to your instructor, remove this first page. It willbe used to record your participation.

Instructions:Please read the following and the attached.

The board of directors is the governing body for a publicly held corpo-ration. The board represents the shareholders, decides the major investmentand social policies for a company, and hires and determines the compen-sation of the executive management.

In this case you serve on the board of directors of PUTT Company – thisis not your full-time employment – please read the details of the attached fora description of your occupation. One of your duties while serving on theboard of directors is to serve on the compensation committee.

TIAA-CREF, a major retirement pension fund in the United States,describes the importance of this function in their 2002 policy statementas such:

‘‘The governance of the executive compensation process is a criticallyimportant and highly visible responsibility of the board of directors of acorporation. In a real sense, it represents a window through which theeffectiveness of the board may be viewed’’ TIAA-CREF (2002).

Please answer all the questions on the following page and the question atthe bottom of the case.

Thank you for your time and assistance.

Demographics_

_____________

CEO Compensation 307

This section captures basic information related to you, the survey partic-

ipant.

1.

Age in years______________

2.

Gendera. Maleb. Female

3.

Are you currently a student?a. Yesb. No

4.

Number of years of full-time employment (excludes time as a student)______________

5.

Number of Accounting courses completed (no ranges please)______________

6.

Number of Finance courses completed (no ranges please)______________

7.

Number of Management courses completed (no ranges please)______________

APPENDIX C. SAMPLE VIGNETTE

You are the Chief Executive Officer (CEO) of FHN Corporation. You arealso on the board of directors of PUTT Company, an industrial companythat manufactures golf equipment. Within the board of directors, one com-mittee for which you serve is the compensation committee.

Your company, FHN Corporation, does not perform any services forPUTT, nor does it anticipate doing so. You serve on the compensationcommittee of the board of directors for PUTT as an independent director.Serving with you on the compensation committee are five other members:Three are CEO’s of other companies, one is a dean of a business school, andone is a retired U.S. senator.

Your compensation as CEO of FHN Corporation is in QWERTs, a non-denominational monetary unit.

You currently make 70 Qwerts as CEO.The industry of PUTT has a CEO average compensation of 70 QWERTs.

ARRON SCOTT FLEMING AND REZA BARKHI308

The average compensation of all CEOs in all industries is 70 QWERTs.

�

The golf equipment industry grew 10% this past year. � PUTT Company grew at a 6% pace. � Last year’s earnings per share for PUTT was $1.00. This year’s earningsper share for PUTT is $1.06.

�

PUTT’s closest competitor’s earnings per share numbers are $1.10 for thecurrent year. The size of PUTT is comparable to the industry average, asis the total sales volume, and the number of shares of common stockoutstanding.

�

PUTT’s operating margins and net income levels are below industry av-erages.

The compensation committee of the board of PUTT Company performsan annual compensation review of the chief executive officer. Your task as amember of the compensation committee is to set the compensation level ofthe CEO in QWERTS.

The compensation level you decide will be kept private and confidential.

Based on the information provided, what compensation in QWERTs will you

award to the CEO of PUTT Company?

___________________Qwerts

CEO Compensation 309

APPENDIX D. POST-EXPERIMENTAL SURVEY

Post Case Questions1. Describe your role in this case

a. A chief executive officer (CEO) serving on a board of directorsb. A dean of a business school serving on a board of directorsc. A retired senator serving on a board of directors

2. On a scale of 1 to 7 rate PUTT Company’s performance

1below average above

3. In this case, is your compensation decision a. private and confidential b. public and disclosed

Based on your role in the case, rate your association or connection to thefollowing group(s) by circling a number.

4. Chief Executive Officers (CEOs)

1 2 3 4 5 6 7 8 9 10

1 2 3 4 5 6 7 8 9 10

1 2 3 4 5 6 7 8 9 10

weak strong

weak strong

weak strong

5. Non-CEOs (e.g., retired senators or business school deans)

6. Board of Directors

7. Please rate the difficulty in determining the compensation level.

difficult easy

difficult easy

8. Please rate the difficulty in assessing the information provided

2 3 4 5 6 7

1 2 3 4 5 6 7

1 2 3 4 5 6 7

A NOTE ON THE READABILITY OF

PROFESSIONAL MATERIALS FOR

MANAGEMENT ACCOUNTANTS

Thomas J. Phillips Jr., Cynthia M. Daily and

Michael S. Luehlfing

ABSTRACT

Recent changes in professional examinations have generated much debate

concerning various issues. One specific debate relates to the consistency of

readability levels before and after the changes. While no significant dif-

ferences in examination readability were found with respect to consistency

across the entire time horizon of the study, comparisons with respect to

the readability of other professional materials generate questions on

whether the exam is testing at an appropriate level and whether other

materials such as those produced for continuing education are written at a

level commensurate to practice.

Intuitively, the readability level at which professional materials for man-agement accountants are written should reflect the readability level requireddaily in the business world. From time to time it seems appropriate toquestion whether such materials adequately prepare management account-ants for the expectations placed on professionals. For example, ‘‘friendly’’debates have emanated regarding the appropriateness of recent changes in

Advances in Management Accounting, Volume 16, 311–318

Copyright r 2007 by Elsevier Ltd.

All rights of reproduction in any form reserved

ISSN: 1474-7871/doi:10.1016/S1474-7871(07)16011-1

311

THOMAS J. PHILLIPS JR. ET AL.312

professional examinations and if readability levels have remained consistentbefore and after these changes. The results of this study suggest that such adebate might rather focus on whether professional examinations test at ap-propriate levels of readability. Moreover, the broader issue might concernthe extent that other professional materials assist management accountantsin business.

BACKGROUND

There have been numerous studies on the readability of accounting liter-ature including textbooks, authoritative pronouncements, audit reports,and financial statements (Courtis, 1986; Adelberg & Razek, 1984; Raabe,Stevens, & Stevens, 1984; Adelberg, 1982; James, Lewis, & Wallschutzky,1981; Lewis & James, 1981; Pound, 1981). These studies used the clozeprocedure, the Flesch Reading Ease Formula and the Gunning’s Fog Indexin evaluating readability. The cloze procedure was one of the first methodsdeveloped to analyze readability (Taylor, 1953). In this procedure, words aredeleted from selected passages of writing and the subject is asked to fill in theblanks. Replacement with the exact word implies that the reader compre-hends the content of the text. Although the cloze procedure is widely used, itcan be difficult to administer and is often criticized as being a better as-sessment of the reader’s abilities than readability. The Flesch Reading EaseFormula (Flesch, 1948) uses the average sentence length and the averagenumber of syllables per word to calculate a score. The score can range from0, for extremely difficult reading, to 100, for very easy reading. Gunning’sFog Index (Gunning, 1952) is similar to Flesch’s Reading Ease formula.However, instead of counting syllables Gunning requires counting words ofthree or more syllables, referred to as ‘‘hard words.’’ The formula then relieson two calculations, average sentence length and percentage of words hav-ing three or more syllables, to find the grade level of the passage.

To evaluate the readability level of the various exams, textbooks, pro-fessional pronouncements, rules and regulations included in the analysis, wechoose to use three standard readability tests – the Flesch Reading Ease,Gunnings Fog Index (both described above), and the Flesch–Kincaid GradeLevel formula. The Flesch–Kincaid Grade Level formula converts theFlesch Reading Ease score to a grade-level. Both the Flesch Reading Easescore and the Flesch–Kincaid Grade Level can be calculated using Micro-soft Word. These tests were chosen because they are easy to use, objective,and their validity has been proven in earlier studies.

A Note on the Readability of Professional Materials 313

Criticism of the tests used here revolves around the fact that these pro-cedures sometimes tend to inflate the calculated readability level when deal-ing with highly technical information. However, this is not a problem whencomparing the readability of technically comparable passages, as in thiscase. While some argue that the cloze procedure more accurately reflectsgrade level, cloze is not feasible to use when determining readabilityinvolving large amounts of material. Before reporting the initial results ofour analysis, we will first overview the exam format changes.

FORMAT CHANGES

The IMA instituted an exam format change in December 1990 (referred tohereinafter as the first change) and again in July 2004 (referred to hereinafteras the second change). While the number of exam parts was reduced fromfive to four with the first change in the exam, and the second change in theexam resulted in the same number of parts, albeit different, the body ofknowledge to be tested essentially has not changed. For example, droppingethics and taxes from the section titles did not eliminate those subjects fromthe exam. In essence, the first changes were intended primarily to be arearrangement of topic coverage. However, the most recent change, whileprimarily reorganization of the content, does include some additional ma-terial. According to the IMA, Strategic Marketing has been added, as wellas Strategic Planning with more emphasis on manufacturing paradigms andbusiness process performance. Given that the content of the exam remainedprimarily intact after the first change, there seems to be no reason why thesechanges should instigate any change in the readability level of the exam.

Although the exam remains primarily unchanged, it should be noted thatthere have been changes in the requirements for the exam. In the firstchange, the educational requirements were amended slightly, allowing col-lege students to take the Certified Management Accountant (CMA) exam intheir senior year. However, successful candidates are still required to possessa baccalaureate degree before they can use the CMA designation. The mostrecent set of exam changes included a new requirement before completion ofthe computerized exam itself. Candidates must complete the first three partsof the exam: Business Analysis, Management Accounting and Reporting,and Strategic Management (receiving immediate performance feedback onthose sections) before attempting the fourth part, Business Applications.The subject matter in the first three parts is tested in the objective format,while Business Applications tests the same subject matter, but includes

Table 1. Comparison of Exam Contents.

Exam before December 1990 Exam from December

1990 to 2004Exam after 2004

Managerial economics and

business finance

Economics, finance, and

management

Business analysis

Organization and behavior,

including ethical

considerations

Financial accounting and

reporting

Management accounting

and reporting

Public reporting standards,

auditing, and taxes

Management reporting,

analysis, and

behavioral issues

Strategic management

Periodic reporting for internal

and external purposes

Decision analysis and

information systems

Business applications

Decision analysis and

information systems

THOMAS J. PHILLIPS JR. ET AL.314

problems and essays. The CMA Board requires that the first three parts besuccessfully completed in order to demonstrate a degree of competence be-fore allowing the candidate to apply their knowledge in the business-oriented situations of the fourth part (Table 1).

RESULTS

To evaluate the readability level of the various CMA exams included in theanalysis, we selected all of the written problems and essay questions (non-objective format) from each section of each CMA exam during the periodJune 1988–June 1992. This included five CMA exams before and five CMAexams after the 1990 exam format changes. The latest exam format changehas resulted in a computer-based exam, with each candidate receiving adifferent version of the exam. Because the exam is not disclosed and can-didates are required to keep the questions and answers confidential, actualexam questions are not available for comparison.

Each section was then analyzed using the three previously addressed read-ability tests. The results of our analysis suggest that, while the format changed,the level of readability of the selected questions remained consistent (at thecollege graduate level), on a statistically significant basis, for all sections of allCMA exams for the entire time horizon of the analysis of the first change.

While these results are laudable, the results do not speak to the appro-priateness of the readability level of the CMA exam. Accordingly, wesearched for benchmarks to gain insights regarding this issue. In this regard,

A Note on the Readability of Professional Materials 315

we noted that the CMA exam is written at a college graduate level versus theWall Street Journal which is written at a college student level (Hart, 1993;Colossi, 1991). While this benchmark provides insight with respect to thebusiness community, this benchmark is rather general in nature. To enhancecomparability, the level of readability of the Certified Public Accountants(CPA) exam was analyzed for the same time horizon.

To evaluate the readability level of the various CPA exams included in theanalysis, we selected questions from each section of each CPA exam forthe period May 1988–May 1992, using the same selection procedure as withthe CMA exam. The CPA exam has also been revised since 1992 and is nowa computer-based exam, with each candidate receiving a different version ofthe exam. Because the exam is not disclosed and candidates are required tokeep the questions and answers confidential, exam questions are not avail-able for comparison.

Similarly to the analysis of the CMA exam, each section of the CPA exam(from the period May 1988 to May 1992) was analyzed using the threereadability tests. The results disclose that the level of readability was sta-tistically higher for the CMA exam than for the CPA exam (i.e., the read-ability level of the CPA exam was primarily at the college student level)(Table 2).

To assess the significance of these results, we searched for a commonbenchmark for comparison. Since both exams draw heavily from the pro-fessional literature regarding financial accounting, Financial AccountingStandards Board (FASB) statements were selected as such a benchmark.

We analyzed the readability level of the first 122 FASB statements, eachone in its entirety, using the same three readability tests. The analysis wasperformed on FASB Statement Nos. 1–122. FASB Statement No. 1 wasissued well before June 1988 and FASB Statement No. 122 was issued wellafter December 1992. For convenience, we discontinued our analysis afterFASB Statement No. 122. We have no reason to believe that the resultswould have changed on a statistically significant basis had our analysis beenextended to the most recent FASB statement.

Table 2. Readability Differences between CMA and CPA Exams :Mann–Whitney U Probabilities.

Flesch–Kincaid .0000

Flesch Reading Ease .0000

Gunnings Fog Index .0000

Note: Significant probabilities in bold.

THOMAS J. PHILLIPS JR. ET AL.316

There were two significant results of this analysis. First, the results in-dicate that the level of readability is consistent (on a statistically significantbasis) across all FASB statements before, during, and after the time horizonof the analysis. Second, the results show that the readability level of theFASB statements is at the doctoral level. Accordingly, the readability levelof the FASB statements is considerably higher than the CPA exam, but onlysomewhat higher than the CMA exam.

DISCUSSION

This article presents the preliminary results of a long-term project. Since theunderlying analysis relates to a very specific time horizon (i.e., five examsbefore and five exams after the date of the December 1990 CMA examformat change), this limits the generalizability of the results reported in thisarticle to exams administered outside of this time horizon. However, wehave no reason to believe that significant differences exist between thereadability level of the exams included in our analysis and the exams ex-cluded in our analysis. Since the readability analysis was centered on onlythe written problems and essay questions of the CMA exam, the results ofthe analysis may not be generalizable to the multiple choice questions.However, we have no reason to believe that significant differences existbetween the readability level of the written problems/essay questions and themultiple choice questions.

The results regarding the consistent readability level of the CMA examare comforting. In contrast, the results of the remainder of the analysis are,at worst, less comforting, and at best, thought provoking. Given the prac-tical nature of both the CMA exam and the CPA exam, we would haveexpected a somewhat lower readability level when compared with the tech-nically oriented FASB statements. However, the question must be asked –how much lower? Financial statement footnotes, for instance, have beenfound previously to be written at the college graduate level. We have in-cluded a reference chart comparing readability grade levels of the CMAExamination with other materials.

Readability levels in Table 3 are shown using Gunning’s Fog Index. TheCMA exam, CPA exam, and FASB Statement scores were computed in thisstudy. Other Fog Index approximations came from the referenced articles.The Fog scores for Sales Training Manuals (Kaminski & Clark, 1987) werederived using manuals in four industries, including insurance service, officeproducts, steel products, and industrial cutting tools. The scores shown are

Table 3. Readability Grade Levels using Gunning’s Fog Index.

Benchmark Level Source

FASB Statements 22 This study

New York Times 21 Colossi (1991)

Financial Statement Footnotes 18 Worthington (1978)

CMA Examination 17 This study

CPA Examination 16 This study

Wall Street Journal 15 Colossi (1991)

MS Windows Manual 14 Colossi (1991)

Sales Training Manual 13 Kaminski and Clark (1987)

A Note on the Readability of Professional Materials 317

all above the high school level, with the lowest 13 (college freshman) and thehighest 22 (Ph.D. level).

While some might argue that language should not be a barrier to per-formance, others might argue that professional exams should be written atthe level at which people function. Although the CMA exam’s readability isat a lower level than FASB statements, CMA questions are at a higherreadability level than CPA questions. Accounting professionals may notspend time examining FASB statements as part of their daily routine, but acertain amount of time must be spent reading technical information. Fur-thermore, hardly anyone would argue with the accounting profession’s em-phasis on the need to improve writing and other communication skills ofgraduating students to meet the complexity of today’s business environment.

One testing expert argues that professional certification examinationsshould consider items such as written material and memos appropriate toexpected job performance, state statutes and regulations, standard referenceworks required in practice, and other materials typical to the work envi-ronment (Plake, 1988). While the results of our analysis do not directlyaddress these arguments, some might suggest that the results do providesupport for the old adage that ‘‘professional exams evaluate the minimumlevel of acceptable competence.’’ In this regard, future research could focuson post-professional examination activities such as continuing educationmaterials and examinations as well as specialty certification examinationsand related materials.

ACKNOWLEDGEMENT

The authors would like to acknowledge their appreciation for the valuableassistance they received from the Institute of Certified Management

THOMAS J. PHILLIPS JR. ET AL.318

Accountants, the American Institute of Certified Public Accountants, andthe Financial Accounting Standards Board.

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