the impact of contextual and process factors on the ... · the impact of contextual and process...

The impact of contextual and process factors on theevaluation of activity-based costing systems$

Shannon W. Anderson a,*, S. Mark Young b

aAccounting Department, University of Michigan Business School, Ann Arbor, MI 48109, USAbLeventhal School of Accounting, University of Southern California, Los Angeles, CA 90089, USA

Abstract

This paper investigates associations between evaluations of activity based costing (ABC) systems, contextual factors,and factors related to the ABC implementation process using interview and survey data from 21 ®eld research sites of

two ®rms. Structural equation modeling is used to investigate the ®t of a model of organizational change with the data.The results support the proposed model; however, the signi®cance of speci®c factors is sensitive to the evaluation cri-terion. The model is stable across ®rms and respondents, but is sensitive to the maturity of the ABC system. # 1999

Elsevier Science Ltd. All rights reserved.

1. Introduction

Early proponents of ABC claimed superiorityover traditional costing methods that stemmedfrom employing causally related ``cost drivers'' toassign common costs to business activities, pro-ducts and services (Cooper, 1988; Cooper &Kaplan, 1988). Later studies argued that a judi-ciously designed ABC system provides e�ectivebehavioral control (Cooper & Kaplan, 1991;Cooper & Turney, 1990; Foster & Gupta, 1990.Evidence of ABC implementation failures1 hascaused researchers to suggest that achieving eitherobjective depends critically on organizational andtechnical factors (Anderson, 1995; Malmi, 1997)and recent empirical evidence supports this view

(Chenhall & Lang®eld-Smith, 1998; Foster &Swenson, 1997; Gosselin, 1997; Innes & Mitchell,1995; Krumwiede, 1998; McGowan & Klammer,1997; Shields, 1995). This paper combines theresults of previous studies with theory on organi-zational change to propose a structural model ofthe relation between evaluations of ABC systems,contextual factors and factors related to the ABCimplementation process.We evaluate the model's descriptive validity

using survey data from managers and systemdevelopers associated with 21 implementationprojects in two automobile manufacturers. Struc-tural equation modeling (SEM) is used to investi-gate the in¯uence of the contextual environmentand the implementation process on evaluations of

0361-3682/99/$ - see front matter # 1999 Elsevier Science Ltd. All rights reserved.

PI I : S0361-3682(99 )00018-5

Accounting, Organizations and Society 24 (1999) 525±559

www.elsevier.com/locate/aos

$ The authors are not permitted to redistribute the data of

this study without permission of the participating ®rms.

* Corresponding author. Tel.: +1-734-647-3308; fax: +1-

734-936-8716.

E-mail address: [email protected] (S.W. Anderson)

1 By some estimates only 10% of ®rms that adopt ABC

continue to use it (Ness & Cucuzza, 1995). In reviews of inter-

national studies of ABC adoption, Innes and Mitchell (1995);

and Chenhall and Lang®eld-Smith (1998) ®nd adoption rates

generally less than 14%.

the ABC system and the in¯uence of the con-textual environment on the ABC implementationprocess. The data are consistent with the proposedstructural model. After establishing a plausiblemodel structure, we investigate its applicability todi�erent evaluation criteria and its stability acrossdi�erent sub-groups in our sample. Thus, theresearch contributes a uni®ed investigation ofthree aspects of ABC implementation: modelstructure; variable de®nition and measurement; and,model stability.We test a structural relation between contextual

variables, process variables and ABC system eva-luations that is hypothesized in ``process theories''of ABC implementation (Anderson, 1995; Argyris& Kaplan, 1994; Kaplan, 1990; Shields & Young,1989). By separating the in¯uence of the con-textual environment on the ABC implementationproject from its in¯uence on the evaluation of theABC system, we extend previous studies thatdocument correlation between ABC project out-comes and contextual and process factors. In acase study of an ABC project that did not sur-vive, Malmi (1997) posits that implementationfailures are related more to exogenous contextualfactors than to the process of implementation Ðthat even good implementation processes fail onbarren ground. Our results support this observa-tion and point to speci®c exogenous factors thatmake ABC less suitable and that are unlikely tobe remedied by improving the implementationprocess.The study contributes to the area of variable

de®nition and measurement through ®eld researchand the use of multiple data collection methods.Multiple modes of data collection (e.g. surveysand personal interviews) provide the opportunityto address the question: What is meant by ``suc-cess'' in ABC implementation? A danger of askingmanagers to rate ABC implementation successwithout specifying the de®nition of success is fail-ure to detect cases in which individuals hold dif-ferent views on the de®nition of success but shareviews on attainment of a particular dimension ofsuccess. In light of evidence that success in ABCimplementation is multi-dimensional (Cooper,Kaplan, Maisel, Morrissey & Oehm, 1992) witheach dimension having somewhat di�erent corre-

lates (Foster & Swenson, 1997), it is appropriateto ask what criteria respondents use in evaluatingABC. Content analysis of interviews conductedwith survey respondents reveals two dominantviews of what de®nes an e�ective ABC system:whether ABC data are used in product costreduction or process improvement; and, whetherABC data are more accurate than data from thetraditional cost system. Further investigationindicates that the respondent's job is a predictor ofwhich view is held. Previous studies explore deter-minants of di�erent evaluation measures (Foster &Swenson, 1997) and document respondent-e�ectson evaluation levels (McGowan & Klammer, 1997).This study investigates whether di�erent evaluationmeasures correspond to di�erent views on appro-priate evaluation criteria. Then, using survey datawe estimate simultaneously the contextual andprocess determinants of respondents' evaluationsof the ABC systems according to the operativecriteria. Simultaneous estimation methods allowus to consider whether the factors that in¯uencedi�erent evaluation measures are common Ð(suggesting that all criteria may be achieved) Ð ormutually exclusive Ð (suggesting that successalong one dimension is achieved at the expense ofanother).A ®nal contribution of this research is investi-

gation of the stability of the relation between eva-luations of ABC implementation and factorsrelated to context and the implementation process.The research design and sampling plan permitinvestigation of three potential sources of modelinstability: company e�ects, respondent e�ectsand e�ects of ABC system maturity. Companye�ects are examined in the spirit of sensitivityanalysis, to explore the degree to which the resultsgeneralize. We can not address the degree to whichresults generalize to di�erent industry settings;however, we ®nd few di�erences between the ®rmsand reject the hypothesis that a ®rm-speci®c modelis warranted. The exploration of respondente�ects, speci®cally of di�erences between man-agers and ABC system developers, is a uniquecontribution of this study.2 Previous research reliesalmost exclusively on data taken from accountingprofessionals. These people may hold di�erent viewson the ABC system as a result of proximity to or

526 S.W. Anderson, S.M. Young / Accounting, Organizations and Society 24 (1999) 525±559

responsibility for ABC implementation, or as aresult of the degree to which ABC threatens or rein-forces their professional standing. The results indi-cate di�erences in what determines evaluations ofthe ABC system betweenmanagers andABC systemdevelopers; however, statistical tests reject thehypothesis that a model that distinguishes betweenrespondent types improves model ®t. Exploration ofe�ects of ABC system maturity on model stabilitycontinues the investigation of ``stages'' of ABCimplementation in Anderson (1995) and Krum-wiede (1998). The results indicate signi®cant di�er-ences in determinants of respondents' evaluation ofABC as a function of ABC system maturity. Thus,the maturity-speci®c model ®ts the data better thana model that omits this factor.Section 2 reviews the literature and develops the

research questions. Section 3 describes theresearch sites and data collection methods.Variable measures and descriptive statistics arepresented in Section 4. Evidence on the relationbetween ABC system evaluations and contextualand process variables is presented in Section 5.The stability of the model between di�erentcompanies, respondents and ABC systems ofdi�ering maturity is investigated in Section 6.Section 7 summarizes the contributions of theresearch and discusses avenues for future investi-gation.

2. Associations between context, process andevaluations of ABC systems

2.1. Summary of prior research

Practitioner accounts of ABC projects and casestudy research on determinants of project outcomeshave long associated technical and behavioral factorswith ABC implementation success (Beaujon &Singhal, 1990; Foster & Gupta, 1990; Cooper,

1990; Cooper et al., 1992; Drumheller, 1993; Eiler& Campi, 1990; Foster & Gupta, 1990; Haedicke& Feil, 1991; Jones, 1991; Kleinsorge & Tanner,1991; Richards, 1987; Shields & Young, 1989;Stokes & Lawrimore, 1989). Anderson (1995) sur-veys the literature on ABC and information tech-nology implementation and compiles ®vecategories of 22 variables that are implicated inABC project outcomes. More recent empiricalstudies provide evidence on the correlationbetween these factors and ABC implementatione�ectiveness and introduce ®ve new variables(Chenhall & Lang®eld-Smith, 1998; Foster &Swenson, 1997; Gosselin, 1997; Innes & Mitchell,1995; Krumwiede, 1998; Malmi, 1997; McGowan& Klammer, 1997; Shields, 1995). Table 1 updatesAnderson's (1995) list of variables hypothesized toin¯uence ABC system evaluations (column 1) andsummarizes the statistical relations documented inthese studies (column 3).A di�culty in specifying the hypothesized e�ect

of each factor on evaluations of ABC systems isthat each study is di�erent in ways that draw intoquestion comparability of results. For example,Anderson (1995), Gosselin (1997) and Krumwiede(1998) distinguish di�erent stages of ABC imple-mentation and ®nd evidence that di�erent factorsin¯uence ``success'' at each stage. Foster andSwenson (1997), McGowan and Klammer (1997)and Malmi (1997) study speci®c ABC imple-mentation projects, while other studies gather dataat the ®rm level. Moreover, Foster and Swensondocument somewhat di�erent correlates for fourdi�erent measures of ABC implementation suc-cess. Finally, McGowan and Klammer collectdata from di�erent informants at an ABC imple-mentation site and ®nd evidence of a shift in themean level of evaluation that is associated withrespondents' involvement with the ABC project.Other studies rely almost exclusively on informantsfrom accounting functions (of the ®rm or the ABCsite). In sum, comparing empirical studies of thedeterminants of ABC implementation e�ectivenessrequires strong assumptions about invariance ofthese relations across time, respondent groups,measures of e�ectiveness and units of analysis.In spite of caveats concerning comparability of

prior studies, the research ®ndings are remarkably

2 McGowan and Klammer (1997) document a signi®cant

di�erence in the level of satisfaction with ABC between those

who developed the ABC data and those who use the data. They

assume a common (stable) model for both populations with

di�erences re¯ected only in a shift in the mean level of satis-

faction.

S.W. Anderson, S.M. Young / Accounting, Organizations and Society 24 (1999) 525±559 527

Table

1

Candidate

variablesforanalysisofdeterminants

ofevaluationsofABC

implementation:contextualfactors

andprocess

factors

identi®ed

intheresearchliterature

Candidate

variables

Literature

sources(s)a

Hypothesized

e�ecton

evaluationof

ABCb

Contextualfactors

Process

factors

Research

designor

variable

mappingd

Individual

factors

Organizational

factors

ABC

project

managem

ent

Team

process

c

Individualcharacteristics

Disposedto

change

A,C,E,F

+,+

,+,+

X±

±X

CHANGE

Productionprocess

knowledge

A+

X±

±X

interviews

Role

involvem

ent

A,E

+,+

,X

±±

XCOMMIT

,

VALUES,AND

Mvs

D

Individualreceived

ABC

training

G0

X±

±±

Mvs

D

Organizationalfactors

Centralization

A,D

+,+

±X

±X

C1vs

C2

Functionalspecialization

A,B

ÿ,0

±X

±X

C1vs

C2

Form

alization/jobstandardization

D+

±X

±X

C1vs

C2

Verticaldi�erentiation

D+

±X

±C1vs

C2

Form

alsupport

inaccountingfunction

B,C

0,+

XX

C1vs

C2

Support

A,B,C,E,F,G

,H+

,+,+

,+,+

,0,+

±±

XX

�Topmanagem

entsupport

±±

±±

±MSUPPORT,

�Localmanagem

entsupport

±±

±±

±MIN

VOLVE,

�Localunionsupport

±±

±±

±USUPPORT

Internalcommunications

A+

±X

±X

C1vs

C2

Extrinsicreward

system

sA,B,E,G

+,+

,+,+

XX

REWARD

ABC

traininginvestm

ents

A,B,E,G

,H+

,+,+

,+,+

±±

XX

Team

Technologicalfactors

Complexityforusers

A,C

±,±

±±

±X

Team

Compatibilitywithexistingsystem

sA,B,C,I

+,0,+

,+±

X±

XN/A

Relativeim

provem

ents

over

existing

system

(accuracy

andtimeliness)

A,C,H

+,+

,0±

X±

XIN

FOQUAL

Relevance

tomanagers'decisionsand

compatibilitywith®rm

strategy

A,B,C,E,H

,I+

,+,+

,+,+

±X

±X

IMPCOST

Task

characteristics

Uncertainity/lack

ofgoalclarity

A,B,E,H

ÿ,0,ÿ

,0±

±±

XTeam

Variety

A+

±±

XTeam

Worker

autonomy

A+

±±

±X

Team

Worker

responsibility/personalrisk

Aÿ

±±

±X

Team

Resourceadequacy

B,C,E

+,+

,+±

±X

±RESOURCES

AvailabilityofABC

software

B,C

0,+

±±

X±

N/A


Externalenvironment

Heterogeneity

ofdem

ands

A,C,D

,H,I

+,+

,+,+

,0±

X±

±TURB

Competition

A,C,H

+,+

,+±

X±

±COMPETE

Environmentaluncertainity

A,C,D

,F,I,

�,ÿ

,+,+

,+±

X±

±

�Likelihoodoflayo�s

±±

±±

±LAYOFF

�Growth

opportunities

±±

±±

±NOGROW

�Laborrelations

±±

±±

±LABOR

�Importance

ofsite

tocompany

±±

±±

±IM

PPLT

Externalcommunications/external

experts

A,B,

+,0

±X

±X

C1vs

C2and

Team

aSourcelegend:A:Anderson(1995).Andersonconstructsalist

offactors

previouslyim

plicatedin

ABC

implementationoutcomes

from

theresearchliterature

and

from

practitioner

accounts

ofim

plementationsandprovides

evidence

onhow

thesefactors

in¯uence

one®rm

'sadoptionofABC.Recentem

piricalresearch:B:Shields

(1995);C:Innes

andMitchell(1995);D:Gosselin

(1997);E:Foster

andSwenson(1997);F:Malm

i(1997);G:McG

owanandKlammer

(1997);H:Krumwiede(1998);I:

ChenhallandLang®eld-Smith(1998)

bFoster

andSwenson(1997),

McG

owanandKlammer

(1997)andMalm

i(1997)studyspeci®cABC

implementationsitesrather

than®rm

-level

implementation.

Anderson(1995),Krumwiede(1998)andGosselin

(1997)distinguishcorrelatesofdi�erentstages

ofim

plementation.Because

westudyplants

thatim

plementedABC

after

theform

alcorporate

adoptionofABC,wefocusonfactors

thatstage-speci®cstudies®ndto

in¯uence

the`àdoption''and`àdaptation''stages.Forstudiesthatdo

notdistinguishstage-speci®ce�

ects

orthatem

ploymultiplemeasuresofABCproject

outcomes,wereportthesignofthecorrelationbetweenofthevariableandoverall

evaluationsoftheABC

project.

cVariablesassociatedwithintrateam

processes

oftheABC

designteam

(e.g.groupcohesion,team

leadership)are

notexamined

inthisstudy.

dVariabletreatm

entlegend:C1vs

C2indicatesavariablethatdi�ersbetweenbutnotwithin

companies.M

vsD

indicatesavariablethatdi�ersbetweenmanagersand

ABCdevelopersbutnotwithin

each

group.ThedesignationN/A

indicatesthatthevariableisnotexamined

inthisstudybecause

thereisneither

within

®rm

norbetween

®rm

variation(e.g.allsitesuse

acommonsoftware

packageandnosite

integratedABC

withother

inform

ationsystem

spriorto

theendofthestudy).Team

indicatesa

variable

thatre¯ects

intrateam

processes

oftheABC

developmentteam.Thee�

ects

ofintrateam

processes

onABC

system

evaluationsare

notconsidered

inthispaper

(see

Andersonet

al.,1999).Wordsin

UPPER

CASEare

variablenames

offactors

thatare

examined

inthispaper.`Ìnterviews''designatesanassociationthatisexamined

inthispaper

usinginterview

data

butnotsurvey

data.


consistent. Although some studies fail to docu-ment a statistically signi®cant e�ect (denoted ``0''),in only one case (environmental uncertainty) dostudies ®nd signi®cant but con¯icting e�ects. Inthis case, Innes and Mitchell (1995) investigateuncertainty associated with the likelihood of ABCdata threatening respondents' employment (e.g.cost reduction through layo�s), while other studiesfocus on the potential for ABC data mitigatinginformational uncertainties and promoting betterdecision-making. Thus, this is a case of di�erentde®nitions of uncertainty rather than substantivedisagreement between the studies' results. In sum-mary, there is widespread agreement in the aca-demic literature on broad correlates of ABCimplementation e�ectiveness. Sources of instabilityin the relation are less well understood. Theseobservations are the departure point for this study,which tests a structural framework among corre-lates of ABC system e�ectiveness and examines thestability of the structural model.

2.2. The proposed structural framework

Although the empirical literature has progressedfrom case studies and anecdotes to systematicevidence on correlates of ABC project outcomes,

there is little correspondence between empiricalstudies and studies that propose process theoriesof ABC implementation (Anderson, 1995; Argyris& Kaplan, 1994; Kaplan, 1990; Shields &Young,1989). Process theories hypothesize thatproject outcomes depend critically on the imple-mentation process and on contextual factors relatedto the external environment. Process theories ofABC implementation are similar to Rogers' (1962,1983) model of organizational change and innova-tion. In Rogers' model, managers' consideration ofan innovation is motivated or constrained by cir-cumstances in the ®rm's external and internal envir-onment and by characteristics of the individualevaluating the innovation Ð what we refer to col-lectively as contextual factors. Subsequent evalua-tions of the innovation are in¯uenced bycomparison between the innovation, the status quo,and alternative innovations, and by factors relatedto the innovation experience Ð what we term pro-cess factors. Contextual factors in¯uence the processof implementation and the evaluation of the result-ing ABC system. Process factors only in¯uence eva-luations of the ABC system. This ``structure'' thatprocess theories hypothesize is depicted by arrowsthat connect the boxes in Fig. 1 (ignore for nowrelations among process factors).

Fig.1. Structural model of ABC implementation.


Process theories distinguish from the list of cor-relates in Table 1 those that relate to the context inwhich the evaluation of ABC is conducted Ðincluding factors related to organizational contextand factors related to the individual asked to ren-der an evaluation Ð and those that relate to theprocess of implementing ABC. This distinction isre¯ected in columns 4±7 of Table 1. The categoriesare not perfectly separableÐfor example, organi-zational norms with respect to functional speciali-zation may be mirrored in how the ABC project ismanaged (e.g. as an accounting project or as amulti-disciplinary project). Nonetheless, theseparation is reasonably straightforward. Con-textual factors include those related to the organi-zation (column 4) and those related to theindividual asked to evaluate the ABC system (col-umn 5). Implementation process factors are alsodivided into two types: those that re¯ect interac-tions between the ABC project team and theorganization (column 6), and those that re¯ect theinternal functioning of the ABC project team(column 7, e.g. communications and goal clarityamong team members). The organizational litera-ture discusses the relation between project outcomesand internal team processes (e.g. Bettenhausen,1991; Cohen, 1993; Cohen & Bailey, 1997). How-ever, because these research questions are not clo-sely related to the accounting literature that weextend, are motivated by di�erent organizationaltheories and demand a di�erent unit of analysis,we consider the variables in column 7 outside thescope of this paper.3 We focus instead on thestructure and stability of relationships between thevariables in columns 4±6 and evaluations of ABCsystems.

2.3. Research questions

Two sets of related research questions are con-sidered. The ®rst research questions are related to theobjective of testing the descriptive validity of processtheories of ABC implementation. Speci®cally, weinvestigate whether there are associations between:

1.1 evaluations of ABC systems and contextualvariables that represent individual andorganizational circumstances;

1.2 evaluations of ABC systems and the imple-mentation process; and,

1.3 the ABC implementation process and con-textual variables that represent individualand organizational circumstances.

These research questions are depicted in Fig. 1as arrows between three groups of variables: con-textual and process factors and implementationoutcomes. Questions 1.1 and 1.2 have been con-sidered in previous empirical research on corre-lates of ABC implementation e�ectiveness.Question 1.3, which links process theories of ABCimplementation and theories of organizationchange to the existing empirical literature, is aunique contribution of this study.A second set of research questions is motivated

by the earlier observation that the third column ofTable 1 can not be constructed without assumingvery strong forms of model stability. We explorethe validity of these assumptions in two ways.First, we use ®eld research to explore criteria thatour respondents employ in evaluating ABC sys-tems. We then investigate how the model of ABCevaluation is a�ected by the evaluation criterion.Second, our research design and sample selectionpermits us to test for forms of model stabilitysuggested by previous studies. Thus, we explore:

2.1 what criteria are used by managers andABC system developers to evaluate ABCimplementation e�ectiveness;

2.2 do the associations examined in researchquestions 1.1±1.3 di�er for di�erent evalua-tion criteria; and,

2.3 are the associations examined in researchquestions 1.1±1.3 stable across ®rms, acrossrespondents with di�ering involvement inthe ABC project, and across sites with ABCsystems that are of di�erent maturity?

In summary, this paper attempts to link empiricalstudies of correlates of ABC implementation withprocess theories of ABC implementation (questions1.1±1.3) and provides evidence on model stabilityacross a number of dimensions (questions 2.1±2.3).

3 We investigate questions related to intrateam processes of

designing and maintaining ABC systems in a second paper

from this study (Anderson, Hesford & Young, 1999).


3. Research design

The research design is de®ned by three choices:selection of ®rms and speci®c ABC implementationprojects for study and selection of quali®ed respon-dents for each project. Evaluating process models ofABC implementation necessitates an understandingof the ®rm's approach to implementing ABC andaccess to several ABC implementation projects ateach ®rm. A research design that employs ®eld-based research o�ers this level of understanding andaccess; however, costs of ®eld research and the timeto develop relationships with corporate partnerslimit the sample size. We study two automobilemanufacturing ®rms, both with mature corporateABC programs and many ABC implementationsites. In the sections that follow, we discuss the®rms' programs for implementing ABC and ourapproach for selecting sites for study.

3.1. ABC implementation in two automobilemanufacturers

The US auto industry is an appropriate settingfor studying the applicability of models of organi-zational change to ABC implementation because®rm-wide implementation demands developingABC models for many of remote locations andbecause in 1995 ABC was a mature technology fortwo of the ®rms. Previous research that examinesABC adoption by the ®rm ®nds that large organi-zations with hierarchical structures, centralizeddecision-making and signi®cant job standardiza-tion are more likely to adopt ABC (Gosselin,1997). Moreover, ABC is attractive to ®rms incompetitive environments that demand con-tinuous cost reduction (Chenhall & Lang®eld-Smith, 1998), particularly when existing cost sys-tems fail to support decisions related to costreduction. In the early 1980s, the then ``Big 3'' USauto manufacturers ®t this characterization. Con-sequently, it is not surprising that when ABCbecame visible in the practitioner literature (e.g.Cooper, 1988), at least two ®rms began experi-menting with it and adopted it by 1991. Anderson(1995) investigates the correspondence of a modelof innovation with one company's eight yearexperience of moving from ``problem awareness'', to

experimentation and evaluation of alternative costsystems, and ®nally, to adoption of ABC.4 Thispaper continues the exploration, adding a second®rm that adopted ABC shortly after the ®rst ®rm,and shifting the unit of analysis to individualsinvolved in 21 ABC implementation projects.After the ®rms adopted ABC as a corporate

initiative, corporate ABC groups were charged withsupporting implementation at all manufacturingsites. Nonmanufacturing sites were to follow, as thecorporate group gained implementation expertise.The process of implementing ABC at a site di�erssomewhat between the two companies. Althoughboth companies espouse a theory of activity basedmanagement Ð in which process or activity costsare as important as product costsÐ Company 2 hasmade this more central to the objective of ABCimplementation than has Company 1. Neither com-pany used ABC in budgeting or performance eva-luation at the time of our study, although both wereexperimenting with these possibilities. Company 2used an outside consulting ®rm to oversee site-levelABC implementation projects and augmented siteteams with corporate ABC group members. Com-pany 1 relied solely on employees at the site,although divisional liaisons were available to assistthe team. Assistance most often took the form ofcomputer software technical support.In spite of these di�erences, there are striking

similarities in the ®rms' approach to ABC imple-mentation. Both ®rms had a corporate mandate toimplement ABC that allowed plants to implementABC within a three to ®ve year window of localmanagement's choosing. Both ®rms used whatLindquist and Mauriel (1989) term a ``depthstrategy'' of implementing ABC fully in a few sitesand adding sites over time, rather than a ``breadthstrategy'' of simultaneously implementing a morelimited version of ABC across all sites. Neither®rm used ABC data for inventory valuation or inperformance evaluations of managers (e.g. calcu-lating ABC-based cost variances) at the close ofour study. Indeed both ®rms were advised by their(di�erent) external auditors to delay using ABC

4 Anderson (1995) uses the ®rm as the unit of observation

and limits consideration of speci®c ABC projects to prototype

projects that were critical to the ®rm-level adoption decision.


for inventory valuation until all sites installed ABCsystems. Both ®rms standardized development andmaintenance of ABC models over time and used thesame PC-based software. Both ®rms managed ABCimplementation from a corporate group that reportsthrough the ®nance function, but attempted to gainsupport from the operations function. Both ®rmsasked that the local ABC team be ``multi-dis-ciplinary''; however, most teams included at leastone accountant or budget analyst and the teamalways reported to the plant controller. Finally,both ®rms introduce the ABC project with anexecutive awareness session for functional managersat the site, training for ABC developers, and sub-sequent management reviews of project milestones.

3.2. Selection of ABC implementation sites andcritical informants

Anderson (1995) observed that, after the ®rm'sdecision to adopt ABC, continued corporate sup-port of the ABC initiative did not depend uponsuccess of speci®c ABC projects. Rather, followinga period of controlled experimentation when pro-ject success was critical, the corporate initiativeacquired a life of its own. This pattern is con-sistent with research on innovation adoption andillustrates a critical distinction between individualand organizational adoption of innovations:

Because organizations are complex hierarchicalsystems, contradictory part-whole relations areoften produced when system-wide innovationsare introduced. An organization-wide innova-tion of change developed by one organizationalunit often represents an externally imposedmandate to adopt the innovation to other,often lower-level, organizational units. Thus . . .top management . . . may express euphoriaabout the innovation it developed for the entireorganization, while frustrations and opposi-tions to that same innovation are expressed bythe a�ected organizational units (Van de Ven,1993, p. 285).

Studies of ABC implementation that rely on asingle senior manager's evaluation of a ®rm's ABCsystem may re¯ect an average assessment of

widely di�ering project outcomes or biases of topmanagement. To address these concerns, wegather data from several ABC projects within each®rm and use local informants for each project.ABC sites are selected using three criteria. First,

we select sites that initiated ABC development afterthe corporate decision to implement ABC but dur-ing di�erent periods of the ®rm's ABC implementa-tion history. We exclude experimental or prototypeprojects to avoid confounding routine implementa-tions with those that were linked to the organiza-tional decision to adopt ABC.5 Inclusion of projectsfrom di�erent periods permits investigation of tem-poral in¯uences on ABC system evaluations.A second factor in site selection is an attempt to

include all auto manufacturing production pro-cesses. Approximately half of the sites from each®rm produce components that are unlikely to beoutsourced (termed ``core'' components), including:major metal stampings, foundry castings, engines,and transmissions. The remaining sites produceperipheral components and face external competi-tion. Selecting core and peripheral components sitesmaximizes variation in the external environmentfactors of Table 1 (subject to the limitation that thestudy occurs within a single industry), allowinginvestigation of the relation between these con-textual factors and ABC system evaluations. Inaddition to traditional manufacturing sites, the ser-vice parts distribution groups of both ®rms areincluded. We were unable to match a contiguousstamping and assembly plant that was included forone ®rm; thus, the sample includes eleven sites fromone ®rm and ten sites from the other ®rm.A ®nal factor in selecting sites is the perceived

success of the ABC project. We wish to study sitesthat represent the full range of implementationoutcomes. One ®rm adopted ABC in 1991 andconsequently had fewer ABC projects from whichto choose. Meeting the ®rst two criteria for siteselection virtually exhausted the population ofABC sites; thus, it is unlikely that we were directedtoward exceptional projects. The second ®rmadopted ABC in 1989 and had over 150 ABCmodels at the inception of our study. To guard

5 Anderson (1995) provides evidence of the highly charged

political environment of prototype projects.


against the ®rm directing us to relatively successfulprojects, we examined an independent assessmentof ABC project success. In a related study weconducted a survey of eight division-level ABCmanagers; assessments of 50 ABC implementationprojects. Seven of the proposed sites were coveredby the survey. Responses to questions related toABC project outcomes indicate that the seven sitesspan the full range of evaluations.Selection of quali®ed informants about local ABC

implementation projects is guided by the literatureon organizational change and evidence on ``respon-dent e�ects'' in ABC system evaluation. McGowanand Klammer's (1997) ®nding that managerscharged with using the ABC system hold di�erentopinions about the system compared to those whodevelop the ABC data suggests two groups ofrespondents. The organizational literature goes fur-ther, arguing that organizational change is a processof changing the beliefs and behaviors of individuals(Marcus & Weber, 1989). Although key individualsmay play a disproportionate role in convincing oth-ers to adopt an innovation, it is rare that a singleindividual can unilaterally adopt innovations onbehalf of an organization. Moreover, the beliefs andbehaviors of individuals toward a particular inno-vation are shaped by their unique, individual cir-cumstances within the organization.6 We attempt toobtain full participation from two populations ofcritical informants: ABC system developers and thesite's management team (e.g. the plant manager andfunctional managers who report to the plant man-ager), and measure ``individual characteristics''(Table 1) that are hypothesized to in¯uence theserespondents evaluations of ABC7

3.3. Data collection

Each research site was visited for two daysbetween March and November of 1995. Surveyswere mailed to the site one week before the visitand respondents brought the completed survey tothe interview, or, if they were not available for aninterview, mailed it to the researchers. Similarsurveys were administered to ABC developers andmanagers. Survey questions about individualmotivation, organizational circumstances and theABC project were developed from establishedscales in the organizational and information sys-tems literatures (e.g. Davis, 1989; Davis, Bagozzi& Warshaw, 1989; Jaworski & Young, 1992;McLennan, 1989; Robinson, Shaver & Wrights-man, 1991; Seashore, Lawler, Mirvis & Cammann,1983; Van de Ven & Ferry, 1980). At times wordingchanges were necessary to ®t the organizationalcontext and the speci®cs of ABC system develop-ment. Questions dealing with the implementationprocess were based on information gathered in the®rm-level study of ABC implementation and, whereapplicable, by using question structures that aresimilar to related scales (e.g. for involvement in sys-tem design, scales for ``task involvement'' wereused). A survey pre-test administered to ten corpo-rate and divisional ABC employees at each ®rm, allwith experience implementing ABC, was used torevise the survey questions. We received 265 surveysÐ 176 management surveys and 89 ABC developersurveys (Table 2).8

The pro®les of the respondents do not di�ervisibly between ®rms. Company 2 uses slightly lessexperienced people as ABC system developers

6 See Pedhazur and Schmelkin (1991) for a discussion of

respondent e�ects associated with factors such as age, gender,

and educational attainment, as well as a variety of personality

traits. Weick (1995) describes individual processes of interpret-

ing, or ``making sense'' of the environment.7 Although some have argued that e�ective use of ABC

requires involvement of employees at the lowest level of the

organization, the ®rms of this study have not disseminated

ABC data to this audience. Thus we have no basis for investi-

gating the associations between workers' attitudes and ABC

system outcomes and were discouraged from doing so by both

®rms. Production workers are occasionally included in the sur-

vey population as ABC system developers when they served on

development teams.

8 To our knowledge 15 people (all managers) who were tar-

geted to complete a survey failed to do so. Of these, twelve were

managers who claimed to be unfamiliar with ABC either by

virtue of recently joining the plant or because their job respon-

sibilities did not cause them to use the system (e.g. ®ve were

Personnel department managers). Three plant managers who

appeared quali®ed to complete the survey refused to do so

because of the time involved; however, even these managers

agreed to be interviewed and the interviews suggested that this

group included both advocates and opponents of the ABC

approach. The non-respondents were scattered across thirteen

of the 21 sites. In sum, we do not believe that this aspect of

non-response has induced signi®cant bias in the data.


than does Company 1 Ð evidenced by fewer yearson average with the company and in the depart-ment from which they joined the ABC project.There are no di�erences between the ®rms in edu-cational attainment or ABC training Ð developerstypically receive 30 hours of training in ABC andmanagers typically receive no ABC training.Managers have longer tenure with the ®rms thando ABC developers; however, by virtue of pro-motions and company transfers, tenure at the siteis not appreciably di�erent between managers andABC developers.Interviews were conducted with 236 of the 265

survey respondents. Although most intervieweesreturned their surveys at the start of the interview,they did not provide their names in the body ofthe survey and speci®c survey responses were notdiscussed during the interview. Interviews fol-lowed a loose structure aimed at supplementingthe survey data. On average 10 interviews wereconducted at each plant, ranging in duration fromapproximately 30 minutes, for functional man-agers with little awareness of the ABC project, to 4hours for system developers with years of systemdesign and maintenance experience. With twoexceptions, all interviews were taped and transcribedfor content analysis.

4. Measurement of variables and process structure

4.1. Contextual variables

A limitation of having only two ®rms is that``organizational factors'' that re¯ect ®rm character-istics (e.g. centralization, functional specialization)are confounded (e.g. Table 1, column 8, ``C1 vsC2''). We investigate the stability of our modelacross ®rms; however, the research design doesnot permit us to distinguish among alternativeexplanations for ®rm e�ects. Studies that use alarge number of ®rms, each providing data onseveral implementation sites are needed to investi-gate the e�ect of ®rm characteristics on imple-mentation project outcomes. This study focuses onorganizational factors that are more ``local'' Ð asa result of di�erent products, processes and peopleat the sites.To explore research question 1.1, we examine

the signi®cance of direct associations betweentwelve contextual factors (Table 1, column 8).Three variables hypothesized to in¯uence indivi-duals' evaluations of ABC are considered: (1) theextent to which the individual believes that changeis warranted (CHANGE) Ð what the organiza-tional psychology literature terms ``felt need for

Table 2

Distribution of survey respondents by site and company

Number of respondents Number of respondents

Plant processes Total ABC developers Plant management

Company 1 Company 2 Company 1 Company 2

Core manufacturing

Major stampings 22 3 2 10 7

Contiguous stamping and assembly 14 0 4 0 10

Foundry 28 4 2 10 12

Engine manufacture 31 5 9 9 8

Engine manufacture 24 2 5 10 7

Transmission assembly 40 7 5 15 13

Secondary manufacturing

Parts assembly 19 3 2 7 7

Parts machining and assembly 19 4 4 5 6

Parts machining and assembly 18 2 3 8 5

Electronics assembly 27 6 2 12 7

Other: service parts distribution 23 10 5 5 3

Total 265 46 43 91 85


change''; (2) individual commitment to the orga-nization (COMMIT); and (3) the extent to whichthe individual identi®es with the values of theorganization (VALUES). Of the four variablesidenti®ed as individual contextual factors in Table1, two are captured in these measures. A thirdfactor, ABC training received, was measured;however, because most managers receive no train-ing and most developers receive 30±40 hours oftraining, this ``variable'' is confounded with otherfactors that di�er between managers and ABCdevelopers (e.g. role involvement in the ABC sys-tem). A fourth factor Ð the individual's knowl-edge of production processes and job experience,was explored in interviews only.Eight organizational contextual factors are

hypothesized to in¯uence evaluations of the ABCsystem and management involvement in theimplementation process: (1) the extent to whichindividual performance is linked to rewards(REWARD)9 (2) the competitive environment(COMPETE); (3) the quality of existing informa-tion systems (INFOQUAL); (4) environmentalturbulence (TURB); (5) the likelihood of employeelayo�s (LAYOFF); (6) impediments to plantgrowth (NOGROW); (7) the perceived importanceof the plant to the company (IMPPLT); and (8)the perceived importance of cost reduction to theplant (IMPCOST). Advocates of ABC claim thatnew cost data are most valuable when competitionor limited growth prospects cause ®rms to focuson cost reduction. COMPETE, NOGROW andIMPCOST measure these motivations for adopt-ing ABC. Cost reduction e�orts often producereductions in employment (Innes and Mitchell,1995). In a unionized environment with con-tractual employment guarantees, managers areoften reluctant or unable to reduce employeeheadcount. As a result, many of the prospectivebene®ts of ABC systems may be unrealizable. Ameasure of the likelihood of layo�s (LAYOFF) is

used to capture this potential deterrent to ABCimplementation. In the same vein, the quality ofhistorical management±labor relations (LABOR)is a ninth contextual factor that is hypothesized toin¯uence the degree to which the local union sup-ports the ABC implementation project (but notmanagers' evaluations of the ABC system). Evenin ®rms that face heightened competition, someoperations are less threatened than others. Selec-tion of core and peripheral component plantsmaximizes the observed range of competitionwithin each ®rm. The perceived importance of thesite (IMPPLT) to the ®rm is included as a poten-tial mitigating factor to external competition.Organizational theorists argue that there are limitsto the amount of change that an organization canabsorb. Site-speci®c turbulence (TURB) is a mea-sure of concomitant changes that compete withABC for management attention. Finally, within a®rm, sites often have diverse ``legacy'' informationsystems that are more or less e�ective in meetingmanagers' information needs. Adoption of newinformation technology such as ABC systemsdepends upon ®t with and incremental improve-ment upon existing systems (Kwon & Zmud,1987). A measure of respondents' beliefs about thequality of existing information systems (INFOQ-UAL) is used to examine how the current infor-mation environment in¯uences managers'commitment to or evaluation of the ABC system.

4.2. Process variables related to ABCimplementation

Appropriate process variables for analysis andthe relation between process variables dependupon ®rm-speci®c ABC implementation strategies.Previous studies that consider the relation betweenprocess variables and ABC project outcomes testfor correlation between a wide range of possibleprocess variables without knowledge ex ante of®rms' implementation processes. As a result, teststhat pool ®rms with di�erent implementationstrategies can not distinguish between relevantprocess factors that are not statistically correlatedwith ABC outcomes and irrelevant process fac-tors. The ABC implementation process variablesthat we consider are those identi®ed in Table 1,

9 It is important to note that REWARD does not measure

the extent to which individuals believe that they will be rewar-

ded if they implement ABC or use ABC data. Rather,

REWARD measures general reward expectancy, because man-

agement control practices and incentives were not changed to

promote ABC use in any of the sites.


column 6. The hypothesized structure between theprocess variables (Fig. 1) was developed prior todata collection based on interviews at the corporatelevel and is described below.The general structure of the ®rms' ABC imple-

mentation process is depicted inside of the boxlabeled ``ABC Implementation Process Factors'' inFig. 1. Speci®cally, ®rm-level managers committedthe company to ABC implementation prior to theABC project at the research sites. Respondents'opinions about the strength of this commitmentare measured by the variable, MSUPPORT. Themanagement awareness sessions that each ®rmused to introduce ABC to local managers wasintended to increase managers' knowledge of ABCand to secure their involvement in the project.Respondents' beliefs about whether local commit-ment was achieved are measured by the variable,MINVOLVE. The proposed relation betweenMSUPPORT and MINVOLVE re¯ects the direc-tion of in¯uence described above. Both ®rmsassigned responsibility for implementing ABC tolocal management. Although corporate resourceswere available to augment or support the team,team members were selected and freed from otherresponsibilities (or not) by local managers. Localmanagers were also given discretion in the deci-sion to involve local union leaders in the project.The adequacy of resources committed to the ABCproject is measured with the variable, RESOUR-CES. The degree to which the union was aware ofand involved in the ABC project is measured bythe variable, USUPPORT. The proposed relationbetween MINVOLVE and both RESOURCESand USUPPORT re¯ects the gatekeeper role thatlocal managers are assigned. The proposed rela-tion between MSUPPORT and USUPPORTre¯ects a possible ¯ow of in¯uence from top man-agers of the ®rm, to the local union. Although topmanagers did not intervene directly in local uniona�airs, at least one ®rm noted that the ®rm'sdecision to implement ABC had become a nego-tiating point with labor at the national level.The proposed relation between MSUPPORTand RESOURCES re¯ects the support that thecorporate ABC group provided developmentteams after local management initiated an ABCproject.

The relationships described above re¯ect ®rm-speci®c implementation processes that have beenignored in previous research. However, ®ndingthat these relations hold is simply evidence of facevalidity of the data rather than evidence on theproposed research questions. To explore researchquestion 1.2 we examine the e�ects of each processvariable on evaluations of the ABC system.Because implementing ABC is a local managementdecision, we explore research question 1.3 byexamining the association between contextual fac-tors and local managers' involvement in the ABCproject (MINVOLVE).

4.3. Evaluation measures of the ABC system

As in previous studies we employ an overallevaluation of the ABC system (OVERALL) thatallows respondents to self-de®ne the evaluationcriteria. However, as research question 2.1 indi-cates, this raises the question: ``What criteria areembedded in overall assessments of ABC sys-tems?'' Content analysis of 236 taped interviews isused to explore this question.10 Full text tran-scripts of the 236 interviews were searched for keywords related to ABC system evaluations. Searchresults yielded 129 respondents'' opinions.11 The129 respondents represent 20 of the 21 sites andare split in approximately the same proportion asthe survey respondents (Table 2) between the ®rmsand between ABC developers and managers. Afteridentifying discussions of ABC system evaluation,the transcripts were read by a researcher and aresearch assistant. Three evaluation criteriaemerged: (1) use of ABC data for cost reduction;(2) use of ABC data for process improvements;and, (3) improved accuracy of product cost infor-mation relative to the traditional cost system. The

10 An analysis software package designed for non-numerical,

unstructured data (NU.DIST1) was used. The researcher codes

interview passages related to constructs of interest and attri-

butes of the interviewee. The software uses these codes to create

a structured index that is useful for investigating systematic

response patterns.11 Some interviewees did not believe that they had su�cient

knowledge about ABC systems to o�er an opinion about sys-

tem e�ectiveness, while others were not asked to discuss the

issue.


researcher and research assistant independentlycoded the responses as belonging primarily to oneof the three categories. The codes were comparedand di�erences were discussed and resolved.Although respondents occasionally combined the®rst and second criteria in their discussion, veryfew respondents discussed the third criterion inconjunction with either of the others. Conse-quently, we combine the ®rst and second groupsto form two groups who di�er in the way that theyde®ne an e�ective ABC system. In the ®rst groupare 67 respondents who de®ne e�ectiveness as useof ABC data in cost reduction or process improve-ments. In the second group are 47 respondents whobelieve that increased cost accuracy de®nes ane�ective ABC system.12

Contingency tables are used to evaluate theassociation between opinions about evaluationcriteria and: the respondents' company; whetherthe respondent worked in a plant that faced sig-ni®cant external competition; whether the respon-dent was an ABC system developer; whether therespondent claimed to have received training inABC; and the respondent's job title. Small samplesizes cause some contingency tables to haveobserved and expected counts that violate asymp-totic assumptions for normal chi-square estima-tions. Consequently, we use exact test proceduresthat do not impose distributional assumptions.Comparing di�erences between respondents whoevaluated ABC system e�ectiveness based on usein cost reduction or process improvement withthose who base their opinion on improved accu-racy of cost data, respondents' job title is the onlysigni®cant (p <0.01) determinant of which view isheld. The same result is obtained in a multivariateanalysis. When a logit model is estimated for theprobability of holding the ``accuracy'' view, therespondent's job title is the only signi®cant expla-natory variable.13 Respondents with jobs that arelinked to production (e.g. engineering, materials

management, production operations, quality con-trol) are signi®cantly more likely to evaluate theABC system based on whether it provides moreaccurate costs than are respondents in adminis-trative roles (e.g. plant manager, ®nance andaccounting, information systems, human resourcemanagement). Di�erences in opinions are unre-lated to the respondent's ®rm, whether therespondent works in a site that faces competition,whether the respondent is a system developer orwhether the respondent received ABC training.The content analysis is consistent with evidence

(Cooper et al., 1992) that ®rms typically adoptABC with one of two objectives: increased pro-duct cost accuracy (e.g. for inventory valuation) oruse in process improvement (e.g. activity basedmanagement). Cooper et al. hypothesize that dif-ferent ABC project objectives require di�erentimplementation processes. Foster and Swenson(1997) ®nd that di�erent evaluation measures arecorrelated with di�erent implementation processvariables. We explore these issues (research ques-tion 2.2), by substituting for the OVERALL mea-sure of success in Fig. 1, measures of ABC dataaccuracy (ACCURACY) and use (USE) developedfrom the survey data. Simultaneous estimation ofboth aspects of ABC system e�ectiveness allow usto provide evidence on whether the two objectivesare compatible or are mutually exclusive outcomes.

4.4. Descriptive statistics

Responses to 61 survey questions are used asindicators of 16 latent contextual (12) and imple-mentation process (4) variables and of measures ofABC implementation e�ectiveness (3). Table 3provides the full text of the survey questionsdeveloped for each latent variable, as well assummary descriptive statistics and Cronbach's(1951) measure of construct validity. Responses to58 of the survey items generated responses thatspanned the ®ve point Likert-type scale. With twoexceptions the constructs are adequately identi-®ed, with Cronbach's alpha exceeding the 0.6 levelused in exploratory research. Item-level responserates from the 265 respondents ranges from 190 to265. There are typically two reasons for non-response: (1) inadequate knowledge for forming

12 Ten respondents (7.8%) o�ered opinions that were su�-

ciently unusual that they were discarded. Five respondents

o�ered valid but very di�erent responses and thus are not

included in subsequent analysis.13 The logit model correctly classi®es 73% of the respon-

dents, an improvement over the naõÈ ve model which correctly

classi®es 58% of the respondents.


Table 3

Summary statistics for manifest variables and latent constructsa

Latent construct Survey items Item Item Std. Cronbach's

(R=reverse coded item) 5=strongly agree, 1=strongly disagree N Mean Dev. std. alpha

Measures of ABC implementation success

OVERALL: overall value of ABC 0.93

1 Despite the implementation challenges, I am convinced that ABC is the

right tool for helping us manage costs in this company

245 3.8 0.75

2 Overall, the bene®ts of ABC data outweigh the costs of installing a new

system

220 3.6 0.87

3 Supporting ABC is the right thing to do in this company 240 3.9 0.79

4 If I were asked to decide whether this company should continue

implementing ABC, I would vote to continue

240 3.9 0.92

5 In general ABC is a good thing for this company 244 4.0 0.70

ACCURACY: perceived accuracy of ABC data 0.65

1 (R) The ABC costs do not seem reasonable to me based on what I know about

this plant

223 3.8 0.81

2 The results from the ABC model matched my intuition about costs of

production

233 3.6 0.77

3 Data from the ABC model provides an accurate assessment of costs in

this plant

240 3.7 0.72

USE: perceived use of ABC data 0.70

1 Information from the ABC model has had a noticeable positive impact

on this plant

228 2.9 0.83

2 (R) I am reluctant to use ABC data in place of costs from the traditional

cost system

232 3.4 0.96

3 (R) The ABC model has not been used and has been `gathering dust' since it

was completed

233 3.5 0.97

4 Data from the ABC model are used for special costs studies 229 3.2 1.0

ABC implementation process variables

MSUPPORT: top management support 0.77

1 This company's top managers have provided visible support for the ABC

initiative

252 3.4 1.1

2 Support for implementing ABC in this company comes from both the

manufacturing operations and ®nance groups

254 3.2 1.0

3 Support for implementing ABC in this company is widespread 250 2.9 1.0

MINVOLVE: local management knowledge of and involvement in ABC 0.72

1 The managers of this plant are knowledgeable about the theory of ABC 243 3.4 0.80

2 Most managers of this plant are capable of using ABC data to reduce costs 238 3.1 1.0

(continued on next page)

S.W

.Anderso

n,S.M

.Young/Acco

unting

,Organiza

tionsandSociety

24(1999)525±559

539

Table

3(continued)

Latentconstruct

Survey

item

sItem

Item

Std.

Cronbach's

(R=

reverse

coded

item

)5=

strongly

agree,

1=

strongly

disagree

NMean

Dev.

std.alpha

3Most

managersoftheplantwereinvolved

indetermininghow

thei

departmentalexpenseswereallocatedto

activitiesandproducts

228

3.5

1.0

4When

thedevelopersoftheABC

model

met

withlocalmanagers,

they

received

suggestionsfrom

themanagers

205

3.4

0.91

USUPPORT:unionsupport

0.77

1Thelocalunionisreceptiveto

theconceptofABC

190

2.9

0.87

2Thelocalunionwasinvolved

indecisionsthata�ectedtheABC

model

192

2.6.

0.99

RESOURCES:adequacy

ofresources

fortheABC

developmentproject

0.63

1Thepeople

whodeveloped

theABC

model

hadtheequipmentand

materialsneeded

todotheirjob

214

3.9

0.63

2Thepeople

whodeveloped

theABC

model

hadaccessto

thepeople

from

whom

they

needed

toget

inform

ation

224

4.0

0.60

3TheABC

developmentproject

wasadequately

sta�ed

toinsure

completionofthetask

inthetimeallotted

210

3.3

0.90

Contextualvariables

COMPETE:competitiveenvironment

0.54

1Future

dem

andfortheproductsthatthisplantproducesisuncertain

264

2.6

1.0

2Competitivepressurescould

cause

thisplantto

close

264

3.3

1.0

3Thisplanthashadalotofmanagem

entturnover

inrecentyears

263

3.3

0.98

4Thisplantfacescompetitionfrom

other

plants

inthiscompanyforbusiness

241

3.3

1.2

5Thisplantfacessti�

competitionfrom

outsidecompaniesforbusiness

264

3.8

0.97

INFOQUAL:quality

ofother

inform

ationsystem

s0.76

1Most

ofthedata

required

foragoodABC

model

are

readilyavailable

inthisplant

222

3.0

1.1

2Theplant'sinform

ationsystem

sgenerallyprovidedata

thatare

accurate

andupto

date

246

3.0

1.0

3(R

)Theinform

ationsystem

softhisplantcontain

manydata

errors

238

2.8

1.0

TURB:environmentalturbulence

0.61

1Theworkingenvironmentatthisplantchanges

constantly

264

2.9

0.88

2Manufacturingprocesses

atthisplantchangeallthetime

261

3.0

0.96

3New

managem

entprogramsare

introducedallthetimein

thisplant

265

3.5

0.82

LAYOFF:history

ofem

ployee

layo�s

0.70

1(R

)Thethreatoflayo�sorcutbacksto

hourlyworkersislow

264

2.4

0.99

2Thisplanthashadmajorcutbacksandlayo�sin

recentyears

264

2.3

1.1

NOGROW:im

pedim

ents

toplantgrowth

0.71

1Gettingauthorizationto

hirenew

employeesforthisplantisdi�

cult

263

4.0

0.93


2Wehavedi�

cultygettingauthorizationto

hirereplacements

for

employeeswhoretire

orleavethisplant

263

3.6

1.1

LABOR:quality

oflaborrelations

0.87

1Atthisplanttheunionandmanagem

enthavesimilargoals

261

2.9

1.1

2(R

)Relationsbetweenlaborandmanagem

entneedto

beim

proved

atthisplant

261

2.4

0.92

3Laborandmanagem

entin

thisplantwork

welltogether

262

3.2

0.98

IMPPLT:im

portance

oftheplantto

company

0.78

1Thisplantisoneofthemost

importantmanufacturingsitesofthiscompany

241

4.0

0.87

2Thisplantproducesproductsthathaveamajorin¯uence

onthiscompany's

pro®tability

264

4.4

0.70

3Thisplantiscriticalforthesuccessofthiscompany

262

4.0

1.0

IMPCOST:im

portance

ofcost

reductionto

plant

0.40

1Thisplant'scost

reductione�

ortsare

importantto

thecompany

264

4.3

0.53

2Cost

reductionisthemost

importantobjectivein

thisplant

264

3.0

0.94

3(R

)Cost

reductionisnotamajorconcern

inthisplant

263

4.2

0.82

CHANGE:feltneedforchange

0.58

1Changes

inthewaywework

inthisplantare

needed

265

3.8

0.85

2(R

)Thereisnoneedforthisplantto

changethewayitdoes

things

265

4.2

0.73

3Iwould

liketo

seechanges

inplantpolicies

andprocedures

264

3.6

0.71

COMMIT

:commitmentto

theorganization

0.73

1Iam

proudto

work

forthiscompany

264

4.3

0.65

2(R

)Ifeel

verylittle

loyaltyto

thiscompany

264

4.2

1.0

3Iam

proudto

work

forthisplant

264

4.2

0.77

4(R

)Ifeel

verylittle

loyaltyto

thisplant

265

4.1

1.1

VALUES:sharedorganizationalvalues

0.75

1Myvalues

andthevalues

ofthiscompanyare

quitesimilar

264

3.7

0.69

2Myvalues

andthevalues

atthisplantare

quitesimilar

263

3.6

0.82

REWARD:reward

expectancy

0.84

1In

thisplant,highquality

work

increasesmychancesforaraise,

abonus,orapromotion

264

3.6

1.0

2In

thisplant,®nancialrewardsare

tied

directlyto

perform

ance

264

3.1

1.0

3Theabilityto

reduce

costsisrewarded

inthisplant

264

3.3

0.95

4In

thisplant,highperform

ance

isrecognized

andreward

263

3.3

0.92

aThistable

provides

thetextofthesurvey

item

sanddescriptivestatisticsforeach

oftheitem

sthatcomprise

thelatentconstructsusedin

subsequentanalysis.Item

response

indicatesthenumber

ofrespondents

thatcompletedthequestionoutofapossible

sample

of265respondents.Descriptivestatisticsandscale

reliability(C

ron-

bach'salpha)are

reported

forthereducedsub-sample

of112respondents

whocompletedallquestions(e.g.withlistwisedeletionofmissingobservations).


an assessment, and (2) apprehension aboutrespondent anonymity. Understandably, itemswith the highest non-response are those related tomanagement-union relations, a politically sensitivetopic. Higher response rates for environmentalcontext variables, which depend on general plantknowledge alone, compared to ABC implementa-tion process variables, which require knowledge ofthe ABC project, suggests that inadequate knowl-edge may also explain di�erential response rates.14

Earlier we argued that individuals are theappropriate unit of analysis for modeling thedeterminants of evaluations of ABC systemsbecause organizational theory suggests signi®cantwithin-site variation in perceptions of contextualand process factors likely to in¯uence theseassessments. The counter argument is that surveyscompleted by individuals who observe the sameABC system should be identical and that variationsimply re¯ects measurement error. Table 4 pre-sents evidence on the existence of respondente�ects. Analysis of variance is used to estimate a®xed e�ects, hierarchical linear model with ®rm,site, and respondent e�ects (Bryk & Raudenbush,1992). The hierarchical model nests respondentswithin sites and sites within ®rms. Joint F-tests on

Table 4

Analysis of company, plant and respondent e�ectsa

Variable p-Value on F-statistic on

®rm e�ects

p-Value of F-statistic on

plant e�ects within ®rms

p-Value of F-statistic on

respondent e�ects within

plants, within ®rms

Model

adjusted (R2)

OVERALL 0.000 0.000 0.000 0.67

ACCURACY 0.016 0.000 0.000 0.45

USE 0.000 0.000 0.000 0.33

MSUPPORT 0.000 0.000 0.000 0.49

MINVOLVE 0.000 0.000 0.000 0.39

USUPPORT 0.844 0.000 0.000 0.65

RESOURCES 0.016 0.000 0.000 0.20

COMPETE 0.001 0.000 0.043 0.10

INFOQUAL 0.017 0.000 0.000 0.46

TURB 0.263 0.002 0.000 0.21

LAYOFF 0.124 0.000 0.000 0.49

NOGROW 0.407 0.000 0.000 0.48

LABOR 0.000 0.000 0.000 0.48

IMPPLT 0.423 0.000 0.000 0.53

IMPCOST 0.637 0.018 0.887 0.00

CHANGE 0.101 0.000 0.000 0.27

COMMIT 0.693 0.223 0.000 0.38

VALUES 0.005 0.000 0.000 0.67

REWARD 0.559 0.000 0.000 0.52

a For each dependent and independent variable, the p-values of F-statistics from an ANOVA model of nested ®rm, site and

respondent e�ects are reported. The estimated ®xed e�ects model treats respondents as nested within sites, which are in turn nested

within ®rms. The model estimated for responses to items that comprise each construct (X) is:

X � intercept� �1 Firm� � � �2 Site Firm� �� 3 Respondent Site Firm� �� "

The results indicate signi®cant respondent e�ects for all but one variable. This supports the claim that evaluation of ABC systems is

signi®cantly related to individual perceptions and that individuals are an appropriate unit of analysis (Bryk & Raudenbush, 1992;

Goldstein, 1987; Hannan, 1991).

14 The analysis treats as missing both items with no response

and selection of the ``Not Applicable'' response. ``Not Applic-

able'' was o�ered selectively as an option for some questions

related to ABC where we anticipated problems of inadequate

knowledge and wanted to guard against forcing a ``neutral''

response of ``3''.


the signi®cance of each e�ect indicates signi®cant®rm, plant and respondent e�ects for all three mea-sures of ABC implementation e�ectiveness. Simi-larly, three of the four process variables di�ersigni®cantly at all three levels. F-tests of union sup-port evince plant and respondent e�ects, but not®rm e�ects. Eleven of the 12 contextual variablesshow strong evidence of respondent e�ects. There issigni®cant agreement about the importance of cost(IMPCOST) within a site; however, the importanceof cost di�ers signi®cantly between sites of a ®rm.After site e�ects are included, there is no di�erencebetween the ®rms in the importance attributed tocost. The overwhelming signi®cance of respondente�ects for these constructs is consistent with orga-nizational research that documents the importanceof unique, individual circumstances in shapingindividual beliefs about and behaviors toward aparticular innovation. The data support using indi-viduals as the unit of observation and argue againstdata reduction (e.g. averaging survey responses of asite), which would be appropriate if random mea-surement error was the primary source of with-insite disagreement (Goldstein, 1987; Hannan, 1991;Seidler, 1974; Rousseau, 1985).

5. The relation between evaluations of ABCsystems and contextual and process variables

Structural equation modeling (SEM) is used toinvestigate the correspondence of the data with Fig.1. SEM is amethod of assessing the correspondenceof a proposed set of associations and implied var-iance and covariance relationships with observedsample variances and covariances (Bollen, 1989). Amaximum likelihood ®tting function is the basis forassessing goodness of ®t.15 Maximum likelihoodestimates are presented for the coe�cients of the

measurement model, which relates the survey itemsto latent variables, and the structural model, whichrelates latent variables to one another. The relationbetween a latent variable, �i, and a survey item thatcomprises it, xj, is: xj � lij�i � �ij, where lij is theloading, or degree of association between the latentvariable and the manifest variable, and �ij is mea-surement error associated with the survey item. Thelatent variable approach mitigates measurementerror in individual survey items and provides betterestimates of the relation between latent variables.None of the con®dence intervals for correlations ofthe latent variables with one another includes thevalue of one; a necessary but insu�cient conditionfor the constructs to be distinct from one another(discriminate validity).

5.1. The relation between overall evaluations ofABC systems and process and contextual variables

Sample size limitations preclude estimating thefull model depicted in Fig. 1 simultaneously.Consequently, the analysis proceeds in three steps.In the ®rst step the relation between contextualvariables and overall evaluation of the ABC sys-tem (OVERALL) is examined. Untabulatedresults indicate that the only contextual variablesthat exhibit signi®cant (p <0.05) direct associa-tions with OVERALL are the reward environment(REWARD) of the site and the quality of existinginformation systems (INFOQUAL). Beliefs thatindividual performance is rewarded at the site(high reward expectancy) are positively associatedwith evaluations of the ABC system. Beliefs thatexisting information systems provide adequate,accurate information are negatively associatedwith evaluations of the ABC system, suggestingthat ABC adds little in environments with goodinformation systems. In the second step of analysisthe relation between contextual variables and localmanagement involvement (MINVOLVE) in theABC project is examined. The only contextualvariable that exhibits a signi®cant (p <0.05) rela-tion with MINVOLVE is the reward environment;high reward expectancy is positively related to localmanagement involvement in the ABC project.In the ®nal step, the ®rst two steps are com-

bined, retaining only those contextual variables

15 Although the categorical data of this study fail tests of

normality, that are presumed in maximum likelihood (ML)

methods, di�erences in estimated coe�cients between ML and

unweighted least squares (ULS) methods, which do not make

distributional assumptions are often small. This is particularly

true if skewness and kurtosis are not serious problems (Bollen,

1989). Olsson (1979) discusses the robustness of ML methods.

The results that follow are qualitatively unchanged when ULS

estimation is employed.


that are associated with evaluations of the ABCsystem or local managers' involvement in the ABCproject in earlier steps (INFOQUAL andREWARD). An additional contextual variable, thequality of historical labor-management relations(LABOR), is considered a potential antecedent ofunion support of the ABC project.16 Table 5 pre-sents coe�cient estimates of the measurementmodel (Panel A) and structural equation model(Panel B) for the relation between ABC imple-mentation process variables, the limited set ofcontextual variables and respondents' overall eva-luation of the ABC system. Reported results arebased on analysis of the covariance matrix for thesurvey items and a sample in which observationswith missing values are excluded (listwise dele-tion). Alternative treatments of missing values(e.g. pairwise deletion or imputation of missingvalues) do not alter the qualitative results. Boot-strapping methods (500 samples with replacement)are used to generate approximate p-values for thesigni®cance of total e�ects of the contextual andprocess variables on overall evaluations of theABC system (Stine, 1989).The standardized estimated loadings of survey

items on latent constructs in Panel A are sig-ni®cant at the p <0.01 level (two tailed test), andare large enough (greater than 0.4) to providecon®dence that they measure common latent con-structs. Overall model ®t is excellent, as indicatedby two goodness of ®t measures: the comparative®t index (CFI) developed in Bentler (1989), andthe root mean square error of approximation(RMSEA) with its 90% con®dence interval.17

Considering sub-model ®t, 56% of the variation inrespondents' overall evaluation of the ABC systemis explained by process variables. The remainingendogenous process variables are also wellexplained, suggesting that the survey data arevalid representations of the ®rms' implementationprocess.

The results indicate that three process variablespositively in¯uence respondents' overall evalua-tion of the ABC system: top management andunion support of the ABC project and the ade-quacy of resources devoted to the project. Thesigni®cant associations of the reward environmentand the quality of existing information systems onoverall evaluations that were discovered in the ®rststep of the analysis persist. However, when totale�ects are considered, the positive indirect e�ect ofhigh quality information systems acting throughlocal managers' involvement in the ABC projecto�sets the negative direct e�ect on the overall eva-luation of the ABC system. Also, the relationbetween local management involvement and thereward environment (step 2, above) disappearswhen the direct in¯uence of the reward environmenton system evaluations is estimated simultaneously.Local management involvement is positively asso-ciated with top management support and with thequality of existing information systems. Union sup-port of the ABC project is associated positively withlocal management support and with the quality ofhistorical labor relations. The adequacy of resourcesdevoted to the ABC project appears to be in¯uencedmore by top management support than by localmanagement support of the ABC project. We mightexpect this result to di�er between companies, sinceone company systematically assisted the sites insta�ng ABC projects with consultants and corpo-rate employees; however, as we will see later, theresult holds for both companies.The results provide face validity of the proposed

structure of the ®rms' ABC implementation pro-cess. Top management support is directly asso-ciated with local management awareness of andinvolvement in the ABC implementation project.Local management plays a gatekeeper role inassuring local union support and there is no evi-dence that top management intervenes in thisrelation. In contrast, local managers' knowledgeof and involvement with the ABC project has littlerelation to whether the development team is pro-vided adequate resources. Whether the project isbelieved to have an adequate resource endowmentis related to top management support.Comparing previous research results (Table 1)

to the total e�ects of Table 5, the sign of coe�-

16 Recall from Section 4.1 that historical labor relations

(LABOR) are not hypothesized to in¯uence management

involvement in ABC implementation.17 CFI ranges from 0 to 1 and values greater than 0.9 are

generally interpreted as indicating good model ®t (Hatcher,

1994). RMSEA values less than 0.08 indicate adequate ®t and

values less than 0.06 indicate very good model ®t.


Table

5

Maxim

um

likelihoodestimationoftherelationbetweencontextualandprocess

variablesandoverallABC

implementationsuccess

Panel

A:standardized

coe�

cients

forthemeasurementmodelsa

Survey

item

OVERALL

MSUPPORT

MIN

VOLVE

USUPPORT

RESOURCES

INFOQUAL

REWARD

LABOR

10.90

0.91

0.64

0.72

0.61

0.57

0.80

0.79

20.76

0.64

0.60

0.88

0.71

0.81

0.67

0.74

30.88

0.68

0.60

0.46

0.79

0.66

0.95

40.90

0.69

0.89

50.86

Panel

B:maxim

um

likelihoodestimatesofthecoe�

cients

ofthestructuralmodelt-statistics(inparentheses)b

Estim

ateddirecte�

ects

Totale�

ects

(approxim

ate

p-value)

OVERALL

MIN

VOLVE

USUPPORT

RESOURCES

Predictede�

ect

Estim

atede�

ect

Independentvariables

MSUPPORT

0.20

0.30

ÿ0.19

0.17

+0.29

(1.68)*

(4.02)***

(1.63)

(2.33)**

(0.012)**

MIN

VOLVE

ÿ0.35

±0.86

0.21

+0.10

(1.21)

±(3.08)***

(1.47)

(0.735)

USUPPORT

0.34

±±

±+

0.34

(2.50)**

±±

±(0.016)**

RESOURCES

0.76

±±

±+

0.76

(2.58)***

(0.042)**

INFOQUAL

ÿ0.25

0.18

±±

ÿÿ0

.23

(2.08)**

(1.92)*

±±

(0.132)

REWARD

0.19

0.07

±±

+0.19

(1.96)**

(0.87)

(0.042)**

LABOR

±±

0.17

±0.06

±±

(2.00)**

±(0.030)**

Model

®tstatistics

R2=

0.56

R2=

0.55

R2=

0.39

R2=

0.44

CFI=

0.932RMSEA=

0.054,90%

C.I.=

[0.039,0.068]

aAllloadingsare

signi®cantat0.01(two-tail)level.

bThesample

(N=

112)includes

only

observationswithnomissingvalues.Theresultsare

qualitativelysimilarwhen

data

imputationmethodsare

employed.Two

measuresofoverallmodel

®tare

provided:Bentler's

(1989)ComparativeFitIndex

(CFI)

andtheRootMeanSquare

ErrorofApproxim

ation(R

MSEA)withits90

percentcon®dence

interval.Bootstrappingmethods(500sampleswithreplacement)are

usedto

generate

approxim

ate

p-values

(two-tail)forthesigni®cance

ofthetotal

e�ects

ofcontextualandproceduralvariablesonOVERALL.Thep-values

are

approxim

ationsbecause

bootstrappingmethodsdonotmakedistributionalassumptions.

***,**,*

Statisticallysigni®cantatthep<

0.01,0.05or0.10(two-tail)level.


cients is consistent with previous studies; however,many contextual variables found to be in¯uentialin previous studies are not in¯uential. Finding thattop management support and adequate projectresources are signi®cantly related to evaluations ofABC systems replicates previous results (Table 1).The distinction between the importance of topmanagement support (MSUPPORT) and theseeming irrelevance of local management support(MINVOLVE) is a new ®nding, as is the impor-tance of employee-level involvement (USUP-PORT). Foster and Swenson (1997) report thatline workers rarely use ABC data, and indeed, wefound no evidence that the companies in ourstudy disseminated ABC data to line workers.Thus, our ®nding of a signi®cant associationbetween ABC system evaluations and union sup-port may re¯ect unique aspects of deploying ABCin a union setting.

5.2. The association between measures of ABCsystem use and accuracy and process andcontextual variables

Section 4 documents two criteria used by surveyrespondents for evaluating ABC systems: per-ceived system accuracy (ACCURACY) and evi-dence of use (USE) in cost reduction e�orts. Thissection investigates whether di�erent contextualand process variables in¯uence these componentsof overall ABC system evaluations. Although weare unable to link all of the individual surveys tointerviews, the survey data are consistent with theproposition that the measures, USE and ACCU-RACY, are components of OVERALL. In a sim-ple model (results untabulated) in which USE andACCURACY are predicted to in¯uence OVER-ALL, the coe�cient estimates are signi®cant forboth variables.18 The estimated coe�cient relating

ACCURACY and OVERALL is 0.40 (p <0.01),the estimated coe�cient relating USE andOVERALL is 0.47 (p <0.01), R2 � 0:66 forOVERALL and model ®t is good (CFI=0.96,RMSEA=0.06). Based on these results, we repeatthe ®rst analysis step Ð relating contextual factorsto ABC project outcomes Ð substituting ACCU-RACY and USE for OVERALL (results untabu-lated). Compared with the overall evaluation ofthe ABC system, a larger set of contextual vari-ables are associated with respondents' beliefsabout the use and accuracy of ABC data andmany of the new explanatory variables are indivi-dual rather than organizational factors. Con-textual variables associated with at least one of themeasures are: the reward environment(REWARD), the quality of existing informationsystems (INFOQUAL), commitment of therespondent to the organization (COMMIT), therespondent's attitude toward change (CHANGE),the perceived importance of the site (IMPPLT),and the likelihood of employee layo�s at the site(LAYOFF).Table 6 repeats the analysis, substituting

ACCURACY and USE for OVERALL andincluding contextual variables found to directlyin¯uence either variable. Panel A presents coe�-cient estimates of the measurement model andPanel B presents coe�cient estimates of the struc-tural model relating contextual and process vari-ables to ACCURACY and USE. An increase inthe number of estimated parameters and the asso-ciated sample size requirements preclude listwisedeletion of missing observations. Reported resultsare based on imputing missing data for cases withno more than one missing value for each latentvariable. Missing values are imputed as the aver-age response of the respondent to other questionsthat comprise the latent variable. Model ®t is goodand measures of sub-model ®t indicate that 50 and84% of the variation in ACCURACY and USE,respectively, is explained by contextual and pro-cess variables. Relationships between the processvariables are consistent with those reported inTable 5, although the relation between local man-agers' involvement and the reward environment isnow marginally signi®cant. The results are con-sistentwithFoster and Swenson's (1997) proposition

18 We estimate a covariance rather than a causal path

between ACCURACY and USE. Although some people argue

that perceived accuracy of the ABC data logically precedes use,

we ®nd little evidence of this in¯uence in the interview data.

Moreover, Weick (1995) argues that an individual's actions

often in¯uence their interpretations. In the context of ABC,

although beliefs about accuracy of ABC data may in¯uence use

of the data, it is equally likely that use of the data alters man-

agers' beliefs about data accuracy.


Table

6

Maxim

um

likelihoodestimationoftherelationbetweencontextualandprocess

variablesandtw

ocomponents

ofoverallABC

implementationsuccess

Panel

A:standardized

coe�

cients

forthemeasurementmodelsa

Survey

item

USE

ACCURACY

MSUPPORT

MIN

VOLVE

USUPPORT

RESOURCES

INFOQUAL

COMMIT

CHANGE

IMPPLT

LAYOFF

REWARD

LABOR

10.64

0.77

0.84

0.64

0.86

0.68

0.57

0.75

0.58

0.84

0.93

0.79

0.72

20.53

0.54

0.65

0.61

0.79

0.54

0.79

0.37

0.58

0.68

0.53

0.65

0.69

30.70

0.72

0.68

0.60

0.52

0.72

0.86

0.55

0.75

0.64

0.92

40.55

0.70

0.48

0.89

5 Panel

B:maxim

um

likelihoodestimatesofthecoe�

cients

ofthestructuralmodel

andt-statistics(inparentheses)b

Estim

ateddirect

e�ects

Estim

atedtotal

e�ects

(approxim

ate

p-values)

ACCURACY

USE

MIN

VOLVE

USUPPORT

RESOURCES

ACCURACY

USE

Independentvariables

MSUPPORT

0.07

0.14

0.34

0.04

0.23

0.18

0.32

(0.69)

(1.76)*

(4.91)***

(0.41)

(3.19)***

(0.125)

(0.009)***

MIN

VOLVE

0.04

0.06

0.68

0.08

0.15

0.22

(0.21)

(0.49)

(3.40)***

(0.69)

(0.399)

(0.315)

USUPPORT

0.06

0.19

0.06

0.19

(0.78)

(3.08)***

(0.409)

(0.033)**

RESOURCES

0.85

0.40

0.85

0.40

(4.08)***

(3.07)***

(0.008)***

(0.028)**

INFOQUAL

ÿ0.04

0.03

0.16

0.02

0.06

(0.31)

(1.90)*

(0.294)

(0.595)

COMMIT

0.03

ÿ0.30

0.03

ÿ0.30

(.17)

(2.31)**

(0.977)

(0.076)*

CHANGE

0.47

0.25

0.47

0.25

(2.97)***

(2.22)**

(0.034)**

(0.156)

IMPPLT

ÿ0.05

0.04

ÿ0.05

0.04

(0.60)

(0.69)

(0.604)

(0.586)

LAYOFF

0.00

ÿ0.09

0.00

ÿ0.10

(0.06)

(1.87)*

(0.845)

(0.092)*

REWARD

0.00

0.25

0.11

0.02

0.27

(0.00)

(3.27)***

(1.66)*

(0.861)

(0.015)**

LABOR

0.16

0.01

0.03

(1.78)*

(0.286)

(0.061)*

Model

®tstatistics

R2=

0.50

R2=

0.84

R2=

0.50

R2=

0.31

R2=

0.31

CFI=

0.900RMSEA=

0.044,90%

C.I.=

[0.037,0.050]

aAllloadingsare

signi®cantat0.01(two-tail)level.

bThesample(N

=199)includes

allobservationsforwhichnomore

thanoneitem

per

construct

ismissingandmissingvalues

are

imputedastheaveragevalueofremainingitem

sin

thescale.Two

measuresofoverallmodel

®tare

provided:Bentler's

(1989)ComparativeFitIndex

(CFI)

andtheRootMeanSquare

ErrorofApproxim

ation(R

MSEA)withits90%

con®dence

interval.Boot-

strappingmethods(500sampleswithreplacement)are

usedto

generate

approxim

ate

p-values

(two-tail)forthesigni®cance

ofthetotale�

ectsofcontextualandproceduralvariablesonACCURACY

andUSE.Thep-values

are

approxim

ationsbecause

bootstrappingmethodsdonotmakedistributionalassumptions

***,**,*

Statisticallysigni®cantatthep<

0.01,0.05or0.10(two-tail)level.

&


that di�erent factors in¯uence di�erent criterionfor evaluating ABC systems. The only processvariable that in¯uences ACCURACY is adequacyof resources devoted to the ABC project(RESOURCES). Notably absent are signi®cantdirect or indirect e�ects of top managers, localmanagers or the union. This suggests a mechanicalrelation, like a production function, linking pro-ject inputs to the quality of data outputs. The onlycontextual variable associated with perceiveddata accuracy is the respondent's felt need forchange in local work practices (CHANGE). Themore that an individual agrees that changes areneeded the more likely they are to ascribeincreased accuracy to ABC data. Notably absentis a signi®cant relation between the quality ofexisting information systems and accuracy ofABC data. Combined with the results of Table 5this suggests that while high quality informationsystems may substitute for ABC systems inmeeting managers' information needs, they arenot necessarily complementary in the sense ofproviding better data from which to design ABCsystems.In contrast to ACCURACY, USE of ABC data

is related to top management and union supportof the ABC project and adequacy of resourcesdevoted to the project. Consistent with the resultsof Table 5, local management involvement in theABC project appears to play no role in whetherdata from the ABC system are used at the site.Compared to ACCURACY, the attainment ofwhich is mechanistically related to project inputs,USE of ABC data depends on both social andtechnical factors. Contextual variables that in¯u-ence USE include the respondent's commitment tothe organization and felt need for change in theorganization, the likelihood of employee layo�sand the reward environment. Together these vari-ables suggest that use of ABC data imposes a largepersonal cost on employees. Employees will sus-tain these costs if they are committed to the orga-nization, if they believe that the organization iscommitted to them (low layo� prospects) and isprepared to reward individual performance, and ifthey believe that there are opportunities forenacting changes that will improve performance atthe site.

6. Analysis of model stability

A common concern in case study research isgeneralizability of results to di�erent organiza-tions. McGowan and Klammer (1997) suggestthat researchers should be equally concernedabout generalizability of results across informantswith di�erent levels of involvement in the ABCproject, and Anderson (1995) and Krumwiede(1998) ®nd that correlates of ABC system evalua-tions di�er over time. This section investigates thestability of the estimated model across companies,respondent types, and ABC systems of di�erentmaturity.Small sub-sample sizes preclude estimating a

latent variable model; consequently, responses tothe items hypothesized to re¯ect latent variablesare summed for each respondent to create man-ifest indicator variables and a path model is esti-mated (Bagozzi & Heatherton, 1994). The ®rstcolumn of Tables 7±9 (Model 1) repeats the ana-lysis of Table 6 substituting summated scales forlatent variables. Although the squared multiplecorrelations for all of the endogenous variablesdeclines, overall model ®t remains good, and thepattern of signi®cant variables is consistent withthose for the latent variable model. Model 1 is thebasis for assessing whether distinguishing thecompany, the respondent type or the ABC sys-tem's maturity leads to a statistically di�erentmodel of ABC implementation. The sub-samplemodels are estimated simultaneously, assumingcommon model structure across sub-samples butallowing all parameter estimates (path coe�cients,variances, covariances) to di�er.

6.1. Model stability between companies

Table 7 presents results of simultaneously esti-mating models for each company (Model 2)allowing the estimated parameters to di�erbetween the two ®rms. Overall model ®t remainsgood, and tests of nested models indicate thatthere is no signi®cant loss of ®t between Model 1and Model 2 (change in model chi-squared statis-tic=25.3 with 22 degrees of freedom) (Arbuckle,1997; Bagozzi & Yi, 1988). Thus, relaxing theconstraints that sub-groups have common model


parameters does not signi®cantly improve model®t. Path coe�cients between contextual and pro-cess variables and USE and ACCURACY that

were signi®cant in Model 1 and which remain sig-ni®cant for both sub-sample models include posi-tive relations between:

Table 7

Stability of the relation between contextual and process variables and two components of overall ABC implementation success:

company e�ectsa

Dependent variable

(summated scale)

Independent

variables

Model 1: full sample

(N=199)

Model 2: split by company

Company 1 (N=85) Company 2 (N=114)

ACCURACY R2=0.19 R2=0.26 R2=0.24

MSUPPORT 0.02 (0.32) 0.03 (0.33) 0.01 (0.106)

MINVOLVE 0.05 (0.84) 0.00 (0.03) 0.09 (1.31)

USUPPORT 0.01 (0.10) 0.02 (0.20) 0.03 (0.32)

RESOURCES 0.35 (4.55)*** 0.31 (2.80)*** 0.41 (3.88)***

INFOQUAL ÿ0.01 (0.15) 0.11 (1.14) ÿ0.08 (1.32)

COMMIT 0.04 (0.78) 0.10 (1.09) 0.01 (0.24)

CHANGE 0.22 (2.92)*** 0.40 (2.98)*** 0.18 (2.05)**

IMPPLT ÿ0.01 (0.16) ÿ0.27 (2.30)** 0.09 (1.48)

LAYOFF ÿ0.03 (0.44) 0.07 (0.65) ÿ0.11 (1.34)

REWARD 0.02 (0.35) 0.04 (0.57) ÿ0.03 (0.51)

USE R2=0.46 R2=0.54 R2=0.42

MSUPPORT 0.24 (3.25)*** 0.26(2.60)** 0.17 (1.48)

MINVOLVE 0.12 (1.82)* 0.05 (0.49) 0.18 (1.95)*

USUPPORT 0.30 (3.27)*** 0.27 (1.96)* 0.31 (2.55)**

RESOURCES 0.11 (1.96)** 0.14 (1.08) 0.32 (2.35)**

INFOQUAL 0.00 (0.01) 0.01 (0.09) 0.01 (0.10)

COMMIT ÿ0.10 (1.57) ÿ0.17 (1.65) ÿ0.03 (0.33)

CHANGE 0.20 (2.17)** 0.19 (1.21) 0.23 (1.98)**

IMPPLT 0.05 (0.68) ÿ0.07 (0.54) 0.13 (1.56)

LAYOFF ÿ0.23 (2.75)*** ÿ0.18 (1.40) ÿ0.29 (2.63)***

REWARD 0.23 (4.21)*** 0.35 (4.44)*** 0.15 (1.67)*

MINVOLVE R2=0.32 R2=0.35 R2=0.26

MSUPPORT 0.47 (6.57)*** 0.50 (4.81)*** 0.31 (2.81)***

REWARD 0.14 (2.41)** 0.14 (1.48) 0.18 (2.35)**

INFOQUAL 0.17 (2.49)** 0.07 (0.57) 0.23 (2.87)***

USUPPORT R2=0.22 R2=0.21 R2=0.32

LABOR 0.09 (1.95)* 0.01 (0.099) 0.20 (3.01)***

MINVOLVE 0.23 (4.91)*** 0.13 (1.92)* 0.30 (5.03)***

MSUPPORT 0.08 (1.53) 0.17 (2.42)** 0.05 (0.62)

RESOURCES R2=0.18 R2=0.19 R2=0.15

MSUPPORT 0.23 (4.80)*** 0.19 (2.51)** 0.26 (3.99)***

MINVOLVE 0.06 (1.42) 0.12 (1.63) 0.02 (0.28)

Model ®t statistics CFI 0.951 0.948

RMSEA, 90% C.I. 0.074,[0.44,0.100] 0.056,[0.030,0.079]

a Maximum likelihood estimates of the coe�cients (t-statistics in parentheses) of the direct e�ects of contextual and process vari-

ables on ABC system accuracy and use. To permit the estimation of company and respondent-type sub-models, summated scales

replace the latent variables used in earlier analyses. The full sample (N=199) includes all observations for which no more than one

item per construct is missing and missing values are imputed as the average value of remaining items in a scale. The ®rst model repeats

the analysis of Table 6 using summated scales. Model 2 examines the sensitivity of the results to company e�ects.

***,**,* Statistically signi®cant at the p<0.01, 0.05 or 0.10 (two-tail) level.


. perceived ABC data accuracy and adequacyof project resources;

. perceived ABC data accuracy and respon-dents' attitude toward change;

. use of ABC data and union support of theABC project; and,

. use of ABC data and the perceived rewardenvironment at the site.

Relationships that were signi®cant in the pooledsample, but which appear to be company-speci®cinclude relations between:19

. use of ABC data and top management sup-port, which is con®ned to company 1; and,

. use of ABC data and local managementinvolvement, adequacy of project resources,respondents' attitude toward change, and theprobability of employee layo�s, which arecon®ned to company 2.

Most of the path coe�cients for the portion ofthe model relating context and process variables(lower half of Table 7) remain signi®cant acrosssub-samples. Speci®cally top management supportis positively related to local management involve-ment, local management involvement is positivelyrelated to union support, and top managementsupport is positively related to whether adequateresources are devoted to the ABC project. Rela-tionships that were signi®cant in the pooled sam-ple, but which appear to be company-speci®cinclude positive relations between:

. the reward environment and managementinvolvement;

. the quality of existing information systemsand local management involvement; and,

. historical management-labor relations andunion support of the ABC project; all ofwhich are con®ned to company 2.

In summary, the structure of the model provesto be stable across the two ®rms of our study;

however, the magnitude of particular coe�cientsvaries somewhat by ®rm. Of course these resultsprovide limited evidence on the question of gen-eralizability of the model to other ®rms and noevidence on whether the model would apply inother industry settings.

6.2. Model stability between managers and ABCsystem developers

Table 8 presents results of simultaneously esti-mating the model for each company (Model 3).Overall model ®t is good; however, again testsindicate that there is no signi®cant loss of ®tbetween Model 1 and Model 3 (change in modelchi-squared statistic=21.1 with 22 degrees offreedom) (Arbuckle, 1997; Bagozzi & Yi, 1988).Thus, relaxing the constraint that sub-groups havecommon model parameters does not yieldimproved model ®t. Path coe�cients betweencontextual and process variables and USE andACCURACY that were signi®cant in Model 1and which remain signi®cant are:

. perceived ABC data accuracy and respon-dents' attitude toward change;

. use of ABC data and top management sup-port of the ABC project:

. use of ABC data and union support of theABC project;

. use of ABC data and respondents attitudetoward change; and,

. use of ABC data and the perceived rewardenvironment at the site.

Relationships that were signi®cant in the pooledsample, but which appear to be respondent type-speci®c include relations between:

. accuracy of ABC data and adequacy ofresources provided to the ABC project,which is con®ned to managers; and,

. use of ABC data and adequacy of projectresources and the probability of employeelayo�s, which are con®ned to ABC systemdevelopers.

In the case of ACCURACY, it is likely thatABC project developers have an incentive to uni-formly claim inadequacy of resources since data

19 Although our knowledge of the companies suggests why

some of these relations might exist, we are not at liberty to

share these casual explanations because they would identify the

companies to a reader with industry knowledge. We have

agreed to attempt to preserve the anonymity of both companies

in all published research results.


accuracy may be viewed as re¯ecting the quality ofABC system development work. Similarly, man-agers, who are typically responsible for using ABCdata, have an incentive to attribute their failure to

use the data to inadequacies in the project devel-opment. In summary, in appears that opinionsabout resource adequacy for the project may beshaped by respondents' incentives to shift blame

Table 8

Stability of the relation between contextual and process variables and two components of overall ABC implementation success:

manager and ABC developer e�ectsa

Dependent variable

(summated scale)

Independent

variables


(N=199)

Model 3: split by respondent

Managers (N=123) ABC developers (N=760

ACCURACY R2=0.19 R2=0.30 R2=0.23

MSUPPORT 0.02 (0.32) ÿ0.06 (0.83) 0.03 (0.30)

MINVOLVE 0.05 (0.84) 0.15 (2.06)** ÿ0.02 (0.23)

USUPPORT 0.01 (0.10) 0.00 (0.03) 0.03 (0.26)

RESOURCES 0.35 (4.55)*** 0.37 (3.30)*** 0.14 (1.41)

INFOQUAL ÿ0.01 (0.15) 0.17 (2.37)** ÿ0.11 (1.52)

COMMIT 0.04 (0.78) 0.00 (0.02) 0.14 (1.61)

CHANGE 0.22 (2.92)*** 0.28 (2.98)*** 0.25 (2.32)**

IMPPLT ÿ0.01 (0.16) ÿ0.01 (0.08) 0.01 (0.13)

LAYOFF ÿ0.03 (0.44) ÿ0.13 (1.48) ÿ0.06 (0.58)

REWARD 0.02 (0.35) 0.04 (0.65) 0.05 (0.69)

USE R2=0.46 R2=0.38 R2=0.67

MSUPPORT 0.24 (3.25)*** 0.17 (1.70)* 0.37 (3.47)***

MINVOLVE 0.12 (1.82)* 0.10 (1.05) 0.14 (1.62)

USUPPORT 0.30 (3.27)*** 0.34 (2.63)*** 0.27 (2.46)**

RESOURCES 0.11 (1.96)** 0.05 (0.36) 0.20 (1.77)*

INFOQUAL 0.00 (0.01) 0.19 (1.96)* ÿ0.13 (1.58)

COMMIT ÿ0.10 (1.57) ÿ0.07 (0.86) ÿ0.06 (0.67)

CHANGE 0.20 (2.17)** 0.21 (1.73)* 0.30 (2.48)**

IMPPLT 0.05 (0.68) 0.06 (0.68) ÿ0.09 (0.80)

LAYOFF ÿ0.23 (2.75)*** ÿ0.12 (0.99) ÿ0.45 (4.21)***

REWARD 0.23 (4.21)*** 0.22 (3.07)*** 0.18 (2.27)**

MINVOLVE R2=0.32 R2=0.30 R2=0.36

MSUPPORT 0.47 (6.57)*** 0.48 (5.51)*** 0.53 (4.47)***

REWARD 0.14 (2.41)** 0.07 (0.99) 0.12 (1.17)

INFOQUAL 0.17 (2.49)** 0.16 (1.64)* 0.08 (0.78)

USUPPORT R2=0.22 R2=0.25 R2=0.18

LABOR 0.09 (1.95)* 0.13 (2.61)*** ÿ0.08 (0.75)

MINVOLVE 0.23 (4.91)*** 0.20 (3.37)*** 0.22 (2.63)***

MSUPPORT 0.08 (1.53) 0.09 (1.47) 0.09 (1.05)

RESOURCES R2=0.18 R2=0.25 R2=0.18

MSUPPORT 0.23 (4.80)*** 0.14 (2.60)*** 0.31 (3.51)***

MINVOLVE 0.06 (1.42) 0.19 (3.65)*** ÿ0.03 (0.39)


RMSEA, 90% C.I. 0.074, [0.044,0.100] 0.052, [0.024,0.075]

a Maximum likelihood estimates of the coe�cients (t-statistics in parentheses) of the direct e�ects of contextual and process variables

on ABC system accuracy and use. To permit the estimation of company and respondent-type sub-models, summated scales replace the

latent variables used in earlier analyses. The full sample (N=199) includes all observations for which no more than one item per con-

struct is missing and missing values are imputed as the average value of remaining items in a scale. The ®rst model repeats the analysis

of Table 6 using summated scales. Model 3 examines the sensitivity of the results to company e�ects.



Table 9

Stability of the relation between contextual and process variables and two components of overall ABC implementation success: e�ects

of ABC system maturitya

Dependent variable

(summated scale)

Independent

variables


(N=199)

Model 4: split by model maturity

Mature models (N=97) Recent models (N=102)

ACCURACY R3=0.19 R3=0.21 R2=0.23

MSUPPORT 0.02 (0.32) 0.02 (0.22) 0.01 (0.07)

MINVOLVE 0.05 (0.84) 0.06 (0.79) 0.03 (0.35)

USUPPORT 0.01 (0.10) 0.11 (1.12) ÿ0.13 (1.21)

RESOURCES 0.35 (4.55)*** 0.34 (3.21)*** 0.35 (3.13)***

INFOQUAL ÿ0.01 (0.15) ÿ0.03 (0.39) ÿ0.03 (0.40)

COMMIT 0.04 (0.78) 0.10 (1.13) 0.04 (0.57)

CHANGE 0.22 (2.92)*** 0.23 (2.22)** 0.26 (2.44)**

IMPPLT ÿ0.01 (0.16) ÿ0.14 (1.53) 0.05 (0.66)

LAYOFF ÿ0.03 (0.44) ÿ0.01 (0.04) ÿ0.09 (1.04)

REWARD 0.02 (0.35) ÿ0.00 (0.04) 0.04 (0.51)

USE R2=0.46 R2=0.56 R2=0.45

MSUPPORT 0.24 (3.25)*** 0.35 (3.87)*** 0.11 (0.93)

MINVOLVE 0.12 (1.82)* 0.13 (1.39) 0.08 (0.86)

USUPPORT 0.30 (3.27)*** 0.29 (2.53)** 0.35 (2.52)**

RESOURCES 0.11 (1.96)** 0.02 (0.16) 0.46 (3.29)***

INFOQUAL 0.00 (0.01) ÿ0.07 (0.86) 0.16 (1.63)

COMMIT ÿ0.10 (1.57) ÿ0.09 (0.94) ÿ0.12 (1.42)

CHANGE 0.20 (2.17)** 0.17 (1.47) 0.14 (1.98)**

IMPPLT 0.05 (0.68) 0.15 (1.40) ÿ0.05 (0.54)

LAYOFF ÿ0.23 (2.75)*** ÿ0.22 (1.47) ÿ0.28 (2.54)***

REWARD 0.23 (4.21)*** 0.25 (3.57)*** 0.14 (1.67)*

MINVOLVE R2=0.32 R2=0.21 R2=0.42

MSUPPORT 0.47 (6.57)*** 0.31 (3.14)*** 0.64 (6.39)***

REWARD 0.14 (2.41)** 0.16 (2.02)** 0.09 (0.98)

INFOQUAL 0.17 (2.49)** 0.07 (0.75) 0.30 (2.90)***

USUPPORT R2=0.22 R2=0.26 R2=0.17

LABOR 0.09 (1.95)* 0.08 (0.98) 0.09 (1.27)

MINVOLVE 0.23 (4.91)*** 0.32 (4.54)*** 0.14 (2.24)***

MSUPPORT 0.08 (1.53) 0.06 (0.80) 0.11 (1.53)

RESOURCES R2=0.18 R2=0.11 R2=0.27

MSUPPORT 0.23 (4.80)*** 0.20 (3.01)*** 0.28 (3.88)***

MINVOLVE 0.06 (1.42) 0.02 (0.24) 0.09 (1.55)


RMSEA, 90% C.I. 0.074, [0.044, 0.100] 0.068, [0.045,0.090]

a Maximum likelihood estimates of the coe�cients (t-statistics in parentheses) of the direct e�ects of contextual and process vari-

ables on ABC system accuracy and use. To permit the estimation of two sub-models, summated scales replace the latent variables used

in earlier analyses. The full sample (N=199) includes all observations for which no more than one item per construct is missing and

missing values are imputed as the average value of remaining items in a scale. The ®rst model repeats the analysis of Table 6 using

summated scales. Model 4 examine the durability of relations estimated in the model by segmenting the data into two roughly equal

sized groups based on the date when the ®rst ABC model was completed. Ten sites where ABC was implemented prior to May 1993

are termed ``mature'' implementations, while those that completed ABC implementation during the latter part of 1993 and 1994 are

termed ``recent'' implementations relative to the 1995 data collection period.



for failure to obtain more accurate data from theABC system or failure to use ABC data after thesystem is developed.Most of the path coe�cients for the portion of

the model relating context and process variables(lower half of Table 8) remain signi®cant acrosssub-samples. Speci®cally top management supportis positively related to local management involve-ment, local management involvement is positivelyrelated to union support, and top managementsupport is positively related to whether adequateresources are devoted to the ABC project. Sig-ni®cant relationships in the pooled sample thatappear to be respondent speci®c include positiverelations between:

. the quality of existing information systemsand local management involvement; and,

. historical management-labor relations andunion support of the ABC project;

both of which are con®ned to managers. Man-agers have a di�erent perspective on the qualityof existing information systems and historicalmanagement±labor relations. ABC developersmay be inclined to evaluate information systemsbased on whether they contain data that are usefulin ABC system design and are unlikely to beinformed about management-union relations. In afew cases developers are union members them-selves. In sum, ABC developers are either unin-formed or from a su�ciently di�erent populationof respondents to draw into question the compar-ability of their responses to these survey items andthose of managers.

6.3. Model stability between older and more recentABC implementations

Assessing the e�ects of maturity of the ABCsystem on respondents' evaluations of the systemsrequires separating the ABC sites into di�erent``generations''. The model of six ``stages'' of ABCimplementation developed in Anderson (1995) andtested by Krumwiede (1998) suggests separatingsites based on which stage is attained by the timeof data collection. Since we study only sites that®rst implemented ABC after ABC was adopted asa ®rm initiative and since we conducted the study

before either ®rm completed implementation andintegrated ABC with other information systems,the sites are in either the ``adaptation'' stage or the``acceptance'' stage. As its name suggests, adapta-tion is the period following completion of an ABCsystem when the site becomes familiar with thetechnology and adapts the ABC system to localcircumstances and information needs. In theacceptance stage, the structure of the ABC systemis stable and the system is maintained and updatedas becomes necessary.For purposes of testing the e�ect of system

maturity on the model, we split the sample intosites that ®rst completed an ABC system prior toMay 1993 (what we term ``mature'' ABC systems)Ð and those that completed an ABC system there-after Ð (``recent'' implementations). This splitresults in 10 sites (®ve from each ®rm) being desig-nated ``mature''. The choice of mid-1993 as thedividing point is guided by two facts. First, mostsites use ``templates'' of an ABC system (e.g. costcenters and cost drivers) from a similar plant toguide the development of the ®rst ABC model.After ABC data are obtained and studied by man-agers, it is common for the structure of the systemto be modi®ed somewhat in the ®rst major systemupdate. Thereafter, the structure of the modeltends to be changed only when major physicalchanges in the plant or its product mix necessitateit. Second, the sites develop ABC models thatassign costs from a particular ®scal year to pro-ducts. Those who begin implementing ABC late ina year typically assign the current year budgetedcosts, (perhaps modi®ed to re¯ect year-to-dateactual costs) to the planned product mix. Thosewho begin implementing ABC early in the yeartypically assign the previous year's actual costs toactual output. Consequently, sites that completedevelopment of the ®rst ABC system after May1993 updated their system one time prior to ourdata collection in 1995 while sites that completeddevelopment of an ABC system between 1990 andmid-1993 did several system updates. We believethat most changes to system structure (adapta-tions) occur in the ®rst system update, thus thisdivision of sites is a proxy for whether sites are inthe acceptance (e.g. ``mature'') or adaptation (e.g.``recent'') stage of implementation.


Clearly, model maturity also proxies for di�er-ences in the opportunity that managers from thetwo sub-samples have had to act on the ABC data.If the costs of developing ABC systems represent acertain, signi®cant outlay that is concentratedduring system development while bene®ts of ABCdata are uncertain and dispersed over an inde-terminate future, respondents from sites withmature ABC systems will have more accurateinformation about the bene®ts of ABC imple-mentation.20 Model maturity also confounds anypriority basis that the ®rms may use to target sitesfor implementation. Although sites were givensome freedom to implement ABC at a convenienttime, both ®rms implemented ABC ®rst in largeplants with complex products and processes. Weare unable to separate the confounding e�ects ofwhat the respondent knows about costs and bene-®ts of ABC implementation, the ®rm's priorityscheme for implementing sites, and the ``stage'' ofmodel development. Some of these issues could befruitfully explored in future research using anexperimental setting.Table 9 presents the coe�cients for simulta-

neously estimating the model for mature andrecent implementations of ABC (Model 4). Unlikethe previous cases, tests indicate that there is sig-ni®cant loss of ®t between Model 1 and Model 4(change in model chi-squared statistic=33.9 with22 degrees of freedom) (Arbuckle, 1997; Bagozzi& Yi, 1988). Consequently constraining the mod-els of evaluation of mature and recent imple-mentations to be identical is inappropriate. Themajor di�erences are in the portion of the modelthat relates contextual and process factors to USEof the ABC data for cost reduction. The portionsof the model related to ACCURACY are remark-ably stable, as are the portions of the model thatrelate contextual factors to the process of ABCimplementation (lower half of Table 9).Stable determinants of ACCURACY suggest

that assessment of accuracy is a natural artifact ofinstalling any ABC system and neither subsequent

revisions to the system nor a respondent's re-eva-luation of accuracy dramatically change theunderlying model. In short, the model of ACCU-RACY is ``durable'' Ð una�ected by di�erentstages of implementation, by ®rms priority forimplementing at the site or by changes in respon-dents' information about costs or bene®ts ofimplementation. Similarly, the relation betweencontextual factors and the process of ABC imple-mentation appears to be quite stable. The rewardenvironment appears to have played a moreimportant role in local management involvementduring early ABC implementations, while thequality of existing information systems played amore important role in local management invol-vement in later ABC implementations. This seemsto suggest di�erences between the types of plantsthat were chosen or that volunteered to implementABC early rather than late; however, as notedearlier we can not unambiguously rule out otherexplanations.Turning to determinants of USE, the model

di�ers substantially between mature and recentimplementation sites. Since USE is something thatemerges (or doesn't) over time after completion ofthe ABC system, it is reasonable that this measureof ABC implementation e�ectiveness is vulnerableto model instability over time. For sites withmature implementations, respondents' assessmentof USE is related signi®cantly to top managementsupport, union support and the reward environ-ment. For sites with more recent implementations,USE is also related to union support and to thereward environment; however, it is not related totop management support and the reward environ-ment has a smaller in¯uence than for mature sites.In addition to these factors, adequacy of resour-ces, respondents' commitment to change, and thelikelihood of layo�s are determinants of USE inthis setting. Di�erences in the role of top manage-ment are consistent with top management takingpains to promote the ABC program early in itsexistence but becoming less visible as implementa-tion proceeds. These di�erences are also consistentwith mature sites experiencing pressure to use thedata that sites with more recent implementationshave not yet experiencedÐ a shift in top managers'expectations that occurs with model maturity. The

20 Of course time and employee turnover may also attenuate

respondents' perceptions, so respondents from mature sites

may have less accurate information about the costs of imple-

mentation.


former explanation points to real di�erences in theimplementation experience of early versus lateadopters while the latter explanation points todi�erences that stem from the stage of imple-mentation of the site. The data do not permit us todistinguish between these explanations.Among the other determinants of USE that dif-

fer between the two sub-samples, only the e�ect ofRESOURCES is quite di�erent in magnitude andsigni®cance. (Commitment to change and thelikelihood of layo�s are marginally signi®canteven for the mature sites and the magnitude of thepath coe�cients is similar.) Since RESOURCESrefers to the project resources associated withABC system development, this result seems tosuggest that mature sites no longer depend on theABC development team (or maintenance team) todrive the use of the ABC data. This interpretationis consistent with the hypothesis advanced byCooper et al. (1992), that moving from 'imple-mentation to action' requires a di�erent group ofpeople to become involved in using ABC datathan was involved in creating the data.

A more fundamental cause of the delays intaking action may have been inadequate pre-paration of the organization for changes inthinking and decision making. . . The mostsuccessful projects occurred when a speci®ctarget for change was identi®ed early in theproject . . . The target was the person or groupwhose decisions were expected to change as aconsequence of the information (p. 8).

That recent implementations rely heavily onABC developers to promote use of the data hintsat a transition period during which those who arefamiliar with the system help users to identifyproblems to which the system lends itself.In summary, stability of the model of determi-

nants of ABC system e�ectiveness over timedepends upon the measure of e�ectiveness thatone considers. The relations between contextualand process factors and between these factors andACCURACY are quite stable. The relationbetween these factors and USE is not stable. Wecan not unambiguously distinguish betweenexplanations related to real di�erences in the

implementations of early versus late adopters, dif-ferences in the implementation stages that earlyand late adopters are in at the time of our datacollection, and di�erences in the knowledge thatrespondents have about ABC implementation andits subsequent outcomes. All of these explanationsare plausible and have distinct implications formanaging ABC implementation. Consequently,future research aimed at disentangling these factorsis warranted.

7. Conclusion

This study extends a large body of empiricalresearch that has examined factors that promoteadoption and satisfaction with activity basedcosting. The primary research contributions areproposing and testing a model that links previousresearch to ``process theories'' of ABC imple-mentation and examining the stability of themodel across several settings. Process theorieshypothesize that, in additional to contextual fac-tors related to the environment and to character-istics of the individual rendering the assessment,evaluations of ABC systems are related to theprocess by which the organization develops andgains employee support for the systems. We sepa-rate factors previously found to be correlated withABC system evaluations into two categories Ðthose that re¯ect the contextual setting in whichthe evaluation is performed and those that re¯ect theprocess of managing the ABC implementation pro-ject. These two categories are used to explore a pre-mise of process theories; speci®cally, that contextualand process factors in¯uence ABC system evalua-tions and that contextual factors also in¯uence theprocess of ABC project management.We ®nd that although the process of imple-

mentation clearly in¯uences the outcomes of anABC implementation, both the process and theoutcomes are directly in¯uenced by the contextualsetting. For example, managers are more likely tosupport the ABC implementation process whengood performance is viewed as likely to yieldrewards (e.g. high reward expectancy). Independentof involvement in the process, evaluators are alsomore likely to evaluate the ABC system positively in


environments with high reward expectancy. In theface of ABC implementation failures, practitionershave focused on improving the process by whichABC is implemented. As the above example illus-trates, in many cases the impediment may be thecontextual environment. Improving the imple-mentation process may attenuate the indirecte�ect of the contextual environment actingthrough the implementation process; however, itwill not a�ect the direct association between thecontextual environment and evaluations of theABC system. As Malmi (1997) argues, in somesettings ABC is simply unlikely to thrive regard-less of how skillfully the implementation is mana-ged. After establishing the process theory model asa plausible description of the underlying data-generating process, we turn to issues of modelstability across alternative evaluation measuresand di�erent subsamples.Foster and Swenson (1997) document di�erent

correlates of di�erent measures of ABC systeme�ectiveness. We provide evidence that respon-dents' use di�erent criterion to evaluate ABC sys-tems. Content analysis of interview transcriptsreveals two widely held views Ð that ABC systemsare valuable if they provide more accurate costdata than the traditional cost system or if ABCdata are used for cost reduction or processimprovement. The respondent's job title is theonly signi®cant factor in explaining di�erences inopinions. Respondents with jobs that are closelylinked to production operations are more likely toevaluate the ABC system against a criterion ofdata accuracy, while respondents in support func-tions are more likely to require evidence of use incost reduction.Examining the stability of the model of ABC

evaluation relative to these two evaluation criteria,we ®nd that evaluations of data accuracy and useare associated with di�erent contextual and pro-cess factors. Perceived accuracy of ABC data isrelated positively to the adequacy of resourcesdevoted to system development and beliefs of therespondent that change is needed. Use of ABCdata is in¯uenced by a wider array of contextualand process variables, including: top managementand union support of the ABC project, adequacyof project resources, the respondent's commitment

to the organization, the likelihood of layo�s, and thedegree to which good performance is expected to berewarded. Achieving increased cost accuracydepends on a relationship that resembles a produc-tion function Ð accuracy of data outputs is relatedto the quality of project inputs. In contrast, use ofABC data depends upon a wider array of contextualand process factors. In sum, because both evalua-tion criteria share one common determinant Ðresource adequacy of the ABC project Ð which ispositively related to both outcomes, we concludethat these evaluation criteria are neither mutuallyexclusive (e.g. requiring di�erent processes or con-texts) nor perfectly correlated. Although ABC sys-tems that are used for cost reduction are likely to beviewed as accurate, the attainment of accuracy is nota su�cient condition to insure use.A ®nal contribution of the paper is investigation

of three forms of model instability: ®rm e�ects,respondent e�ects, and e�ects of ABC systemmaturity. Although the coe�cients of particularvariables di�er somewhat between ®rms andrespondents, we reject the hypothesis that ®rm orrespondent speci®c models better ®t the data. Sta-ted di�erently, removing the constraint that themodel coe�cients are identical across subsamplesdoes not improve model ®t more than could beexpected by chance. In contrast, separating oursample into mature and recent implementations ofABC leads to signi®cantly di�erent models; how-ever, the di�erences are concentrated in the por-tion of the model related to use of ABC data. Theportion of the model that relates contextual fac-tors to the implementation process and that whichrelates both types of variables to accuracy of theABC data are stable for both groups. Di�erencesin the model of ABC system use appear related tothe role of the ABC development team. Formature ABC systems, use depends on manage-ment support and the reward environment. Forrecent implementations, use is more closely tied tothe ABC development team.In summary, this paper unites a theoretical lit-

erature that portrays ABC implementation as anorganizational change with an empirical literaturethat documents correlates of ABC implementationoutcomes but remains agnostic about the cause ofthese relations. The design of the research program


distinguishes this study from previous empiricalwork and provides the opportunity to exploremore fully issues raised in prior studies. None-theless, important limitations remain and, as inthe prior studies, the analysis raises new questions.Obvious limitations include studying two ®rmsfrom a single industry, assuming that the datare¯ect opinions and attitudes of respondents whoare informed and truthful, and relying exclusivelyon attitudinal data that are not calibrated againstobjective data. We provide evidence on therobustness of our results across company andrespondent-type subgroups; nonetheless, theempirical results may not generalize to other ®rms,other industry settings or other informants.Although we identify respondents who arebelieved to be informed and use methods aimed atreducing random responses and mitigating mea-surement error, we remain vulnerable to systema-tic response bias. Limitations associated withattitudinal data re¯ect widespread disagreementamong researchers about how to measure perfor-mance of cost systems as well as the fact thatmany of the variables are fundamentally latent(unobserved). Although objective measures shouldbe used at every opportunity to augment attitu-dinal data, we are as skeptical about the measure-ment properties of ``objective'' measures as we areof any single survey question.Three opportunities for future research emerge

from this study. The management accounting lit-erature on ABC implementation employs a macroview of the implementation process. Since we usethe organizational literature to frame the study ofABC implementation, it is natural to draw on arelated literature, the organizational literature onproject management, to examine how the work ofABC development teams a�ects project outcomes.An unchallenged assertion of many consultants isthat ABC development should be done by a multi-disciplinary team. It is time for researchers toinvestigate the development process to determinewhether dysfunctional aspects of team productionarise during the development of ABC systems andwhether on balance, multi-disciplinary teams yielddi�erent, better outcomes.A second issue follows from our discovery that

models of ABC system evaluation vary with time

and our inability to distinguish between severalexplanations for this. Speci®cally, the researchdesign did not permit us to distinguish whether dif-ferences are caused by a change in respondents'uncertainty about the costs and bene®ts of ABCimplementation, by real unmeasured di�erencesbetween sites that self-selected into the early imple-mentation group, or by di�erences associated withthe maturing process. An experimental researchapproach might be able to disentangle these e�ects.Finally, two opportunities exist for the

researcher who can enlarge the research sample byeither including more ®rms while retaining multi-ple sites at each ®rm, or by including a broadercross-section of employees at each site. The ®rstapproach would make possible a more thoroughinvestigation of the in¯uence of ®rm-speci®c con-textual variables on dispersed implementationsites. The second approach would allow us to dis-entangle the e�ects of role involvement in theABC project and the evaluator's level in the orga-nizational hierarchy. Our study, which uses infor-mants that di�er along both of these dimensions,confounds these explanations for why respondentshold di�erent views of the ABC system.

Acknowledgements

The Foundation for Applied Research of theInstitute of Management Accountants provided®nancial support for this project and J. Freedman,Director of Research of IMA, provided projectassistance. The authors acknowledge researchsupport from their universities and the ®rst authoracknowledges ®nancial support from the Interna-tional Motor Vehicle Program at M.I.T. and fromArthur Andersen. We are grateful to managementat both companies for providing access to data.We also acknowledge contributions of ourresearch assistants: S. Abraham, T. Colony, D.Daly, J. Hesford, and S. Rice. Finally, we thank S.Ansari, R. Bagozzi, J. Core, J. Deng, M. Gupta,R. Kaplan, W. Lanen, J. Luft, T. Shevlin, M.Shields, N. Soderstrom and workshop participantsat Harvard Business School, University of Roche-ster, Washington University, University of South-ern California, Michigan State University,


University of Washington, Memphis University,and the June 1997EIASMconference, for commentson the paper.

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