Gordon and Silvester

Stock market reactions to activity-basedcosting adoptions q

Lawrence A. Gordon a,*,1, Katherine J. Silvester b

a Department of Accounting, Room 4471, Robert H. Smith School of Business, University of

Maryland ± College Park, College Park, MD 20742, USAb Lally School of Management and Technology, Rensselaer Polytechnic Institute, USA

Abstract

The use of activity-based costing (ABC) has been steadily, if not rapidly, spreading

on an international level. This fact notwithstanding, the economic bene®t associated

with adopting ABC is suspect, at best. In an e�ort to shed additional light on this

apparent dilemma, this paper empirically investigates the stock market e�ects of an-

nouncing the adoption of an ABC system. The research methodology includes both

parametric and non-parametric tests for excess market returns from a seemingly unre-

lated regressions model with a matched pairs sample of ®rms. The analysis indicates that

the installation of an ABC system in the United States is not associated with a signi-

®cant (either positive or negative) stock market reaction. Ó 1999 Elsevier Science Inc.

All rights reserved.

1. Introduction

Ever since the 1980s, it has been widely argued that cost management sys-tems are in need of major change (e.g., Johnson and Kaplan, 1987, pp. 1±18).

Journal of Accounting and Public Policy 18 (1999) 229±251

q The authors wish to thank Wolfgang Bessler, Kwok Leung, Eric Noreen, Albert Paulson, Steve

Loeb, Kimberly Smith, Krishnamoorthy Surysekar, Jerold Zimmerman, and the participants at the

research workshops of The University of Essex, London Business School, University of

Manchester, The University of Toronto, and Rensselaer Polytechnic Institute for their comments

on earlier drafts of this paper.* Corresponding author. Tel.: +1-301-405-2255; fax: +1-301-405-0359; e-mail: lgor-

[email protected] Ernst & Young Alumni Professor of Managerial Accounting.

0278-4254/99/$ ± see front matter Ó 1999 Elsevier Science Inc. All rights reserved.

PII: S0 2 78 -4 2 54 (9 9 )0 00 0 9- 5

One change, which has received widespread attention, concerns the allocationof indirect costs via activity-based costing (ABC). In fact, ABC has become anissue of increasing and fundamental concern to numerous researchers andpractitioners around the world. In response to this concern, numerous ®rmshave either implemented, or are considering implementing, ABC.

For example, a 1997 survey by the Cost Management Group (1998, p. 1) ofthe Institute of Management Accountants notes that 39% of its member or-ganizations have ``at least approved ABC implementation.'' A survey of UK®rms (Innes and Mitchell, 1995, p. 141) shows that almost 19.5% of the re-spondents were using ABC and that 27.1% were considering its adoption. Asurvey of 134 Finnish manufacturing units (Lukka and Granlund, 1996, p. 17)found that 5% were in the process of implementing ABC and another 24% wereconsidering its use. Although ABC has only been sparsely used by Japanese®rms in the past, there are many ®rms in Japan that are seriously consideringintroducing ABC systems, with a particular emphasis on activity-based man-agement concepts (Sakurai, 1995, p. 26). Hence, while US companies may havetaken the initial lead, there is clearly a signi®cant international movement to-ward adopting ABC.

Despite this movement toward adopting ABC, its bene®ts have been as-serted largely through anecdotal, self-reported survey data, and case studyevidence (see the next section). Accordingly, it is not surprising that manyresearchers question the inherent value of an ABC system. For example,Bromwich and Bhimani (1989, p. 3) note that the evidence does not suggestthat the adoption of ABC will improve pro®ts, while Dopuch (1993, p. 617)observes that the payo� from an ABC system might not justify its imple-mentation costs. Shields (1995, pp. 159±161) shows that the bene®ts derivedfrom implementing and using ABC vary greatly among ®rms and depend uponseveral behavioral and organizational dimensions. Innes and Mitchell (1995,p. 150) argue that it may be di�cult to separate the real bene®ts of ABC fromthe claimed bene®ts, since those claiming the bene®ts are often the individualsresponsible for the adoption and development of the ABC systems within theirown companies (i.e., a vested interest issue is present). Some have even gone sofar as to challenge the basic logic underlying the use of ABC (e.g., Piper andWalley, 1991, p. 42; Piper and Walley, 1990, p. 54) and ABC's relevance tooperational decisions (Johnson, 1992, p. 141). In a recent survey-based em-pirical study by Foster and Swenson (1997, p. 136), it was concluded that theway ABC's success is measured has an e�ect on studies regarding the derivedbene®ts of ABC.

As indicated by the above, we seem to be on the horns of the followingdilemma. On the one hand, the number of ®rms adopting ABC seems to bespreading rapidly into a multitude of countries. On the other hand, there isgood reason to doubt whether the adoption of an ABC system results in aneconomic bene®t to a ®rm. In fact, mounting evidence suggests that many of

230 L.A. Gordon, K.J. Silvester/Journal of Accounting and Public Policy 18 (1999) 229±251

the ABC systems implemented in the late 1980s and early 1990s were failures(e.g., Roberts and Silvester, 1996, p. 23; Malmi, 1997, p. 460). Accordingly, thepurpose of our study is to empirically investigate the performance e�ects as-sociated with the adoption of an ABC system. Our study di�ers signi®cantly,however, from other work published in the area in that the stock market e�ectof announcing the installation of an ABC system is assessed. Thus, our mea-sure of success is based on publicly available stock market data. Further, theidenti®cation of when a ®rm introduced ABC is also based on publicly avail-able information.

The results of our study indicate that, in general, the announcement of theadoption of an ABC system among US-based ®rms did not have a signi®cant(either positive or negative) stock market e�ect. These ®ndings are particularlyrelevant to organizations that are considering, but have not yet implemented,an ABC system.

The remainder of our paper will proceed as follows. In Section 2, the mainhypothesis of the empirical study is developed. Section 3 describes the empiricalstudy conducted to test this hypothesis, including methodological concerns.The results and implications of the study are discussed in Section 4 of thepaper. Section 5 o�ers some concluding comments.

2. Hypothesis development

Conventional costing systems often allocate indirect costs using a singlebase, with direct labor hours or direct labor dollars being the most commonchoice of an allocation base (Gordon, 1998, p. 63). In contrast, ABC systemsutilize the equivalent of a multiple base approach to allocating indirect costsvia a two-stage process. In stage 1, the signi®cant activities that cause the or-ganization to incur indirect costs are identi®ed and the costs of these activitiesare assigned to homogeneous cost pools. In stage 2, a quanti®able cost driver isidenti®ed for each activity, which is then used to allocate the costs of the givenactivity to the organization's products (or any other cost objective) (Gordon,1998, p. 137). Where the number of activities are numerous, an optimal (on acost/bene®t basis) number of cost drivers may be derived, as discussed byBabad and Balachandran (1993, p. 565). Other characteristics of a typical ABCsystem include the fact that the costs of many activities are associated withnon-volume related cost drivers, and non-manufacturing indirect costs areoften included in the allocation scheme for deriving product costs (for mana-gerial rather than ®nancial reporting purposes). 2

2 Conceptually, an ABC system could aid in shifting indirect costs to direct cost categories (where

the cost object is a product's cost by uncovering previously unidenti®ed relationships between the

factors of production and the products being produced).

L.A. Gordon, K.J. Silvester/Journal of Accounting and Public Policy 18 (1999) 229±251 231

ABC systems have many potential bene®ts, including: (1) helping to identifynon-value added activities, (2) improving the ability of managers to makepricing, production, and investment decisions through the provision of moreaccurate product and process costs, and (3) improving the integration of thestrategic and operating/production processes of the organization (Gordon,1998, p. 142). Case studies supporting these claimed bene®ts are plentiful (e.g.,Bhimani and Pigott, 1992; Cooper et al., 1992a,b; Cooper and Turney, 1989;Kaplan, 1990; Innes and Mitchell, 1990, 1991; Turney and Anderson, 1989).

Despite the above, empirical evidence on the value of ABC is limited. Fosterand Gupta (1990, p. 327), for example, found that non-volume cost drivers addlittle explanatory power to the behavior of indirect costs, 3 while Banker andJohnston (1993, p. 587) show both volume-based and operations-based costdrivers to be statistically signi®cant within the US airline industry. Spicer(1990, p. 143) notes that ``we do not as yet have any real systematic evidencethat relate the use of ABC systems to improved internal and external perfor-mance measures.'' Bromwich and Bhimani (1989, p. 3), while noting that ``. . .activity costing changes product costing substantially'', also argue that there``is little to suggest that it enhances pro®ts.'' Noreen (1991, p. 165) also pointsout that ``. . . the widespread and rapid adoption of ABC systems is an inter-esting phenomenon in and of itself ± particularly since it is not obvious that onbalance ABC systems as implemented provide greater bene®ts relative to coststhan any other possible costing systems.'' Shields (1995, p. 159), in a survey of143 ®rms, found a wide variation across ®rms in terms of the bene®ts derivedfrom ABC. Innes and Mitchell (1995, p. 150), while noting that ``thoseadopting ABC considered its application had been successful,'' pointed outthat the ``survey respondents had a vested interest in their ®rm's ABC appli-cation.''

Innes and Mitchell (1995, p. 151) argue that there is a need for more em-pirical (and objective) research on the topic. Young and Selto (1991, p. 296),Anderson (1995, p. 48), and Shields (1995, p. 154) also call for further empiricalresearch on the performance e�ects of ABC. Shank (1989, p. 47) points outthat there is nothing conceptually new in activity-based accounting. Hence, thequestion regarding the net value of ABC remains unresolved, in large part dueto the limited systematic and objective empirical evidence regarding its per-formance e�ects. Nevertheless, as noted earlier, the use of ABC systems issteadily, if not rapidly, spreading on an international level.

In constructing an objective empirical analysis of the net bene®ts of an ABCimplementation, the methodology should be based upon a conceptual frame-

3 Although only volume-related cost drivers were signi®cant in their study, Foster and Gupta

(1990, pp. 335,336) warn against generalizing from their results due to data limitations (i.e., all of

their data were from 37 facilities of a single electronics company).


work that links such systems to organizational performance. The economicanalysis approach of Gonedes and Dopuch (1979, pp. 386±391) to accountingtechniques provides such a framework. Their (Gonedes and Dopuch, 1979,p. 390) basic argument is that ``accounting techniques are, in general, amongthe determinants of accounting numbers' systematic properties, both becausethe techniques constitute accounting numbers' computational speci®cationsand because there may be connections between the techniques and manage-ments' operating and ®nancing decisions.'' Accordingly, to the extent thataccounting techniques, of which ABC is one, a�ect the observed values ofaccounting numbers and the ®rm's decision-making processes, such techniquescan potentially have an e�ect on the ®rm's value. 4 A pictorial representationof these e�ects is provided in Fig. 1.

Fig. 1. Accounting techniques and ®rm value.

4 In a related study, Nair (1979, pp. 239±241) has empirically shown that accounting techniques

can have an e�ect on the relative ranking of economic investment models.


As illustrated in Fig. 1, accounting techniques do not solely a�ect the ob-served values of accounting numbers. Accounting techniques can also a�ect thecash ¯ows and, thus, the net present value (NPV) of a ®rm via their interactivee�ects with the various operating, investment and ®nancing decisions of the®rm.

As noted earlier, the adoption of an ABC system may materially changeproduct costs, which in turn may cause changes in a ®rm's decisions related topricing, project or product selection, and process improvement. 5 Therefore, tothe extent that ABC reduces the noise in the information environment andreveals potential areas for operational and productive e�ciencies, a ®rm hasthe opportunity to eliminate waste and, thereby, to reduce the economic costsof production. Ceteris paribus, cash out¯ow can be reduced for a given level ofproduction. Such changes could yield improved ®rm performance and, thus,increased ®rm value.

Of course, the improved performance (value) argument noted above takesthe position of ABC proponents (e.g., Kaplan, 1992, pp. 58±60). In contrast,opponents argue that ABC is one of those accounting techniques that will haveeither no performance e�ects (i.e., the cost of the system implementation will beo�set by the cost savings derived from the improved information system) or, inthe worst case scenario, have a negative e�ect due to the additional costs ofimplementing the technique (e.g., Bromwich and Bhimani, 1989, p. 3; Dopuch,1993, p. 617). At the ®rm level, these contrasting views regarding the expectedbene®ts from an ABC implementation could be objectively investigated bytesting the e�ects of adopting an ABC system on the security returns of the®rm. That is, if a ®rm's adoption of an ABC system could be publicly identi-®ed, the following general null hypothesis could then be tested.

Hypothesis H0: The adoption of an ABC system is not associated with a se-curity market reaction.

3. Empirical study

3.1. Sample identi®cation

In order to test the above hypothesis, it is necessary to identify a sample of®rms that have publicly announced the adoption of ABC. Unfortunately, as ageneral course of business, ®rms do not make public announcements about the

5 One of the primary alleged bene®ts of ABC is its explicit di�erentiation of process/activity costs.

Such recognition of the cost of production processes can play a crucial role in the elimination of

non-value added activities or ine�ciencies (Kelly, 1991, p. 43).


adoption of internal cost accounting methods. A search of standard databaseservices con®rmed this fact. However, one notable exception was a BusinessWeek article entitled ``The Productivity Paradox'' by Port et al. (6 June 1988,pp. 100±114). The major focus of Port et al., 1988 was on explaining the reasonwhy investments that were intended to increase productivity in US corpora-tions do not appear to be performing as well as expected. Port et al. (1988,p. 100) argued that the more corporations try to become productive, the moredi�cult it is for them to achieve such productivity. The key explanation o�eredfor this paradox was that conventional cost accounting systems provide mis-leading information (Port et al., 1988, p. 108). Although the article referred tomany cost and operations management programs, it emphasized the virtues ofnew cost management systems, with speci®c reference to ABC, to capture andfacilitate the bene®ts of investments in new technologies and productivityprograms (Port et al., 1988, p. 108, 112). The article went on to mention 16European and US ®rms that either had already adopted or were planning toadopt ABC. Of the 16 ®rms, 14 had already adopted an ABC system, one wasutilizing improved cost management techniques (which included ABC), and theother hoped to adopt ABC (Port et al., 1988, p. 108).

3.2. Link between capital markets and ABC announcements

Theoretically, the ability of an announcement to in¯uence market prices isheavily dependent upon the assumption that the news content is both infor-mative and generally available to, and understood by, the market. Althoughthere are admittedly no absolute distinctions among the various levels ofmarket e�ciency, the inferences drawn regarding the information content of aBusiness Week article seem to depend upon an implicit assumption of a semi-strong type of market e�ciency (i.e., that the market e�ciently impounds theinformation content of all obviously publicly available information).

Market power of non-traditional announcements. Market e�ciency should bede®ned with respect to a speci®c information set. In this regard, Foster (1979,p. 271) states that ``there is no clear-cut distinction between `publicly available'and `non-publicly available' information sets.'' Although public announce-ments in periodicals such as The Wall Street Journal have been stressed in mostmarket studies, this pervasive dependence upon such sources does not precludethe use of public announcements from other sources. Indeed, previous researchevidence does identify signi®cant e�ects of non-traditional periodicals uponmarket forces. In a study of the impact of Abraham Brilo�'s e�ect upon thestock market, Foster (1979) utilized articles in Barron's, Commercial and Fi-nancial Chronicle, and the New York Magazine to isolate an immediate andpermanent decline of approximately 8% in the market price of the a�ectedstocks. A more recent study (Dos Santos et al., 1993), concentrates upon themarket impact of announcements concerning investments in new information


technology. The Dos Santos et al. (1993) study isolates a positive and signi®-cant market reaction to announcements concerning investments in innovativetechnologies, by utilizing both traditional sources (The Wall Street Journal andPR Newswire and non-traditional sources (American Banker, American MetalMarkets, Minneapolis Star, and Greenville News).

Interestingly, the Securities and Exchange Commission (SEC), itself, deemsit necessary to closely monitor the timing and e�ect of the release of suchperiodicals because of their impact upon the market. Under Section 10(b) ofthe US Securities Exchange Act of 1934 (US Congress, 1934 ), the SEC hashistorically sought to (among other goals) ``guarantee that no single group oftraders enjoys unfair informational advantages over the rest of the market''(Harpaz, 1985, p. 1036). Although originally targeted at scalping by journal-ists, 6 the SEC has been fairly aggressive in attempting to extend the scope ofSection 10(b) to include a general duty by ®nancial reporters to the readership(Harpaz, 1985, pp. 1035±1038).

Business Week has a policy that precludes selling advance copies of theirperiodical before the o�cial release date for each issue (see footnote 9). Thispolicy regarding no early releases, combined with SEC's timing concerns, priorimpact of similar periodical articles, and our veri®cation that the ABC adop-tions were not previously revealed through another news medium, providesubstantiation that the Business Week article released new and potentiallyvaluable information to the market on the day of the announcement.

Analysis of reactions in the information market. Intuitively, it seems rea-sonable to argue that movements in security markets should be a re¯ection ofthe activity in the information markets. Indeed, as noted in Foster (1979, pp.273±274), the existence of ``superior insight'' in analyzing public informationdoes not preclude an e�cient market hypothesis. Therefore, one would expectthat the Business Week article (if informative to ®nancial markets) would alsohave generated an immediate and corresponding amount of interest in thegeneral manufacturing and business community. The novelty of this article andits impact have also been noted by Campi (1992, p. 5).

Furthermore, as noted by Beaver (1981, p. 32), ``...empirical studies ofchange in accounting methods are viewed as testing market e�ciency withrespect to more information than merely the knowledge that a change tookplace.'' Therefore, to the extent that the market did not already fully under-stand the implications of the adoption of ABC, the extensive Business Weekarticle would have conveyed valuable information to the market. Accordingly,it is crucial to emphasize that the Business Week article by Port et al. (1988) not

6 Scalping is generally considered to occur when a journalist uses private advance information

about the content of forthcoming articles to personally take advantageous positions in the stock

market.


only revealed the ABC adopting ®rms but also provided substantial discussionregarding the initiation, utilization, and purported bene®ts of such costinginnovations. Ceteris paribus, it seems reasonable to argue that the marketimpact of this extensive and substantive Business Week article concerning ABCmay actually be stronger than the announcements found in the traditionallyutilized release formats (e.g., The Wall Street Journal, corporate proxy state-ments, etc.). In addition, the Business Week article reports on the adoption ofABC by ®rms during the period when the costing methodology was receiving agreat deal of attention. Hence, if the market was ever to be sensitive to theadoption of ABC by ®rms, the mid-to-late 1980s would have surely been thetime period for this to happen.

3.3. Validity of market study to assess ABC performance

In arguing the legitimacy and usefulness of the case study methodology,many researchers have rejected the market methodology for ABC analysis(e.g., Kaplan et al., 1990, pp. 30±32). However, rejection of this technique is (1)a rejection of the market e�ciency hypothesis and prior similar research (e.g.,Foster, 1979, pp. 271±273; Dos Santos et al., 1993, pp. 1±4) and/or (2) astatement that the market and its analysts and investors are dismissing, notinterested in, or are ignorant regarding potential improvements that ¯ow fromnew management techniques, such as ABC. Again, this latter position, in es-sence, takes the stance of rejecting the market e�ciency hypothesis. An alter-nate form of our same argument would be to consider whether the market, itsanalysts, and investors are ignoring ABC and not translating potential pro®tenhancements to price increases in the stocks of the innovative adopting ®rms.This position also supports recent anecdotal and case evidence regarding theprevalence of ABC implementation failures (Roberts and Silvester, 1996, p. 23;Cooper et al., 1992b).

3.4. Matching procedure

Most empirical event studies achieve a reasonable level of control by virtueof the fact that multiple announcements are made during di�erent time periodsfor di�erent ®rms. However, our study has only a single announcement timeperiod for all ®rms (i.e., the Port et al., 1988 article released on 30 May 1988).Therefore, to strengthen the reliability of the statistical analysis of the event, amatched pairs analysis was also undertaken (Larcker, 1983, pp. 12±14; Haka etal., 1985, pp. 665,666). Accordingly, the announcing ®rms were match-pairedwith a sample of nonannouncing ®rms, with the former being referred to as theexperimental ®rms and the latter as the control ®rms.

Firms were matched on the Standard Industrial Code (SIC) from the 1988Compustat Data Tapes to control for industry e�ects and on Total Net Assets


to control for size. 7 The matching procedure consisted of choosing the control®rm within the same 3-digit or 4-digit SIC code as the experimental ®rm withthe closest match on Total Net Assets. There were actually 16 ®rms named inthe Port et al. (1988, pp. 108, 112) article. However, four of these ®rms were notcontained in the 1988 and 1989 CRSP tapes for the time period around theevent under consideration. Of the 12 usable experimental ®rms, matches wereobtainable for 10 of the ®rms. 8 A list of both the experimental ®rms andmatched control ®rms is provided in Table 1. Also included in Table 1 is astatistical assessment of the quality of the matches. Based upon a WilcoxonSigned Rank Test, the experimental and control groups of ®rms do not di�ersigni®cantly (at a � 0:10, two-tailed) when matched on Total Net Assets as acontrol for size.

3.5. Event and estimation period

The release date of the Port et al. (1988) article, for purposes of our study,was Monday, 30 May 1988 (i.e., the day the 6 June 1988 issue of Business Weekwas released to the general public via placement on the news stands). 9 The 31stof May is the ®rst trading day following the release of Port et al. (1988) an-nouncement on the 30 May holiday. Hence, we refer to 30 May as the releasedate and 31 May as the announcement date. Since Business Week was releasedto the general news stands while the market was closed on 30 May, the marketactually had two days (both 30 and 31 May) to assess the informationalcontent before impounding the value of the information in stock prices on 31May.

This Business Week issue may also have been released to a limited numberof selected news stands on Friday, 27 May 1988 (see footnote 9). To control forthis possibility, Friday, 27 May 1988 is also included in the event period.

Therefore, given the nature of the Business Week release, the event periodwas constructed to contain four separate and consecutive trading days (i.e., onetrading day before, the trading day of the announcement, and two trading days

7 As an additional control, a review of the 1988 Wall Street Journal Index was made to check for

announcements concerning the ®rms. The review indicated that no sales or earnings announce-

ments were made by any of the experimental or control ®rms during the event period (27 May

1988±2 June 1988)8 Of the 12 experimental ®rms, nine were matched on a 4-digit SIC code, one was matched on a 3-

digit SIC code, and two ®rms were unable to be matched. One unmatched ®rm did not have a

match at the 3-digit SIC code level, and the 2-digit SIC code was deemed inadequate as an industry

match. The second unmatched ®rm did not have a match even at a 2-digit level. In general, the size

of these two unmatched ®rms (as measured by Total Net Assets) far over-shadowed the size of any

potential industry-matched control ®rm.9 This information was obtained from Business Week.


after the announcement): 27 May, 31 May, 1 June, and 2 June 1988 (28 and 29May were weekend days, and 30 May was Memorial Day). The estimationperiod used in the study contains 271 daily returns and covers the period 2 May1988±31 May 1989. 10

10 Our study utilizes a forward-looking estimation period with the event period placed at the

beginning of the estimation period. Such a design allows us to abstract from the chaotic e�ects of

the 19 October 1987 stock market crash when the Dow dropped 508 points (22.6%) in a single day.

This forward-looking approach is not new, and it has previously been utilized in the SUR-based

event study literature (Binder, 1985b, p. 178).

Table 1

Experimental and control ®rms

Experimental ®rms Control ®rms

Firm number

and name

SIC

code

Total net

assetsa

Firm number

and name

SIC

code

Total net

assets

($000) ($000)

E1: Westing-

house Corp.

3600 16,937,305 C1: Philips

N.V.

3600 26,398,008

E2: Eaton Corp. 3714 3,033,800 C2: Dana

Corp.

3714 4,786,379

E3: Northern

Telecom

3661 5,878,199 C3: Harris

Corp.

3663 1,643,719

E4: Northrop

Corp.

3721 3,139,200 C4: Grumman

Corp.

3721 2,565,984

E5: Unisys

Corp.

3570 11,534,602 C5: Digital

Equipment

Corp.

3570 10,111,500

E6: General

Motors Corp.

3711 164,063,000 C6: Ford

Motor Corp.

3711 143,366,000

E7: United

Technologies

3724 12,748,301 C7: Allied

Signal Corp.

3724 10,005,000

E8: Parker-

Hanni®n Corp.

3728 1,741,802 C8: Sunstrand

Corp.

3728 1,567,030

E9: General

Dynamics Corp.

3721 6,118,098 C9: McDonnell

Douglas Corp.

3721 11,885,000

E10: Honeywell 3882 5,089,098 C10: Johnson

Controls

3822 2,013,099

a Mean di�erence (the mean di�erences were computed by subtracting the total net assets of the

control ®rm from the total net assets of the experimental ®rm; the ®gures for the total net assets

were obtained from the 1988 CRSP tapes and represent the end-of-year numbers for that year).

Mean di�erence in the total net assets: $ 1,594,149, median di�erence in the total net assets:

$ 2,909,550, Wilcoxon Signed Rank Statistic: T� 34, p-value of the two-tailed Wilcoxon statistic:

0.5560.


3.6. Validation of information release date

One of the key issues in evaluating the robustness of a study of this type isvalidation of the actual dates of information release and ensuring that no earlypre-release of information took place. Therefore, we purposefully undertookan extremely careful approach to these matters. First, we performed an ex-haustive search of the existing literature (including The Wall Street Journal,stock analysts reports, SEC reports, etc.) to ensure that once of the informationregarding ABC adoption or planned adoption was previously publicly released.Second, we veri®ed that no confounding events occurred during the eventwindow (see footnote 7). Third, we veri®ed the actual date of the BusinessWeek release to the general public and obtained written con®rmation of therelease date from Business Week (see footnote 9). Fourth, we personally spokewith representatives of ®rms active in ABC practice and in professionallyoriented ABC research and conferences to see if they knew of any previousreleases of the information. Our research indicated that the news released inPort et al. (1988) article was, indeed, new to the market. Finally, we also havemade every attempt possible to statistically control for any possible event-dateconfounding by de®ning the event period to consist of four separate andconsecutive trading dates.

3.7. Methodology

Accounting-related event studies have traditionally used the cumulativeabnormal returns (CAR) methodology to study the impact of new information(such as earnings announcements) on stock market returns (Ball and Brown,1968; Fama et al., 1969). The standard CAR methodology, which generallyuses Ordinary Least Squares (OLS), estimates regression parameters over apre-event time period utilizing one of the many return generating models, suchas the one-factor Capital Asset Pricing Model (CAPM) (Brown and Warner,1980, p. 208). The abnormal returns are then calculated and tested as theprediction errors (residuals) from the model. OLS assumes that the error termsfrom the regressions are independent and identically distributed, have a meanvalue of zero, and are homoscedastic (i.e., E�~eit� � 0; E�~eit; ~ei;tÿ1� � 0;E�~eit; ~ejt� � 0; for all i 6� j; and E�~eit�2 � r2). In an event study of well-diver-si®ed announcement dates, calendar times and event times are not the sameand, therefore, the above assumptions concerning the error terms may be valid.

However, the traditional CAR methodology and its assumptions are notappropriate for our study because of the calendar clustering of ABC an-nouncement dates, the potential industry e�ects, and the possibility of di�eringlevels of abnormal returns across ®rms as a result of di�erential impacts ofABC adoption across ®rms. More speci®cally, in our study, event time andcalendar time are the same because of our reliance on the single announcement


in Business Week. This severe form of calendar clustering lessens the proba-bility that the residuals will be independent because of their contemporaneouscross-sectional correlation. In addition, independence of the residuals is alsothought to be lessened by industry e�ects within the sample. That is, many ofthe sample ®rms operate in similar industries and, therefore, it is reasonable toassume that their calendar-based residuals might be related to one another.Finally, it is also possible that ABC may have di�erential impacts across ®rmsdepending upon certain ®rm-speci®c factors. Therefore, the standard OLSassumption of homoscedasticity may also be inappropriate.

In light of the foregoing and due to the methodological need for joint hy-pothesis testing, a seemingly unrelated regressions (SUR) model will be used inour study (Binder, 1985a, pp. 370±372; Binder, 1985b, pp. 170±171; Smirlockand Kaufold, 1987, pp. 352±354). 11 The SUR model allows for contempora-neous correlation of the error terms across ®rms, as well as non-constantvariance (heteroscedasticity) of the disturbance terms across ®rms. Utilizingthis approach, one can separate the basic market model into a system ofseemingly unrelated sets of ®rm regressions, which are (in reality) related viacontemporaneous correlation in their error terms.

The SUR model which is used to test the hypothesis underlying our study ispresented below.

~R1t � a10 � a11DSt � b10~Rmt � b11

~RmtDSt

� c11D1 � c12D2 � c13D3 � c14D4 � ~e1t;

~R2t � a20 � a21DSt � b20~Rmt � b21

~RmtDSt

� c21D1 � c22D2 � c23D3 � c24D4 � ~e2t;

..

.

~RNt � aN0 � aN1DSt � bN0~Rmt � bN1

~RmtDSt

� cN1D1 � cN2D2 � cN3D3 � cN4D4 � ~eNt;

where ~Rit is the return for a security for ®rm i, in period t, net of the risk-freerate, i � 1; . . . ;N ; t � 1; . . . ; T ; ~Rmt is the return for an equally weighted marketportfolio in period t, net of the risk-free rate; ~eit is the random error term; DSt isthe announcement shift dummy variable; contains a 1 for every observation

11 Other SUR studies have utilized di�ering approaches to the control issue in calendar based

event studies. Binder (1985a,b), Rose (1985), and Schipper and Thompson (1983) used the existence

of multiple legislative announcements across all ®rms for their control mechanism. In a study of the

e�ect of the Mexican debt crises, Smirlock and Kaufold (1987) examined two separate and

di�erently sized portfolios of exposed and non-exposed banks. To the best of our knowledge, our

study is the ®rst matched pairs analysis performed within a SUR methodology. As indicated

previously, our methodology is driven by the ®nite number of ABC adopting ®rms, the single

adoption announcement, and the need to perform joint hypothesis testing.


between the ®rst day of the event period and the last day in the estimationperiod inclusive; zero otherwise; cia is the abnormal returns (event) coe�cientfor ®rm i on day a of the event period, a � 1; . . . ; 4 corresponding to 5/27, 5/31,6/1, 6/2; Da is the abnormal returns (event) dummy variable; contains a single1 during day a of the event period, a � 1; . . . ; 4 corresponding to 5/27, 5/31, 6/1,6/2; zero otherwise; ai0 is the intercept for ®rm i; ai1 is the intercept shift coef-®cient for ®rm i; bi0 is the slope coe�cient on the equally weighted marketportfolio return, Rmt, for ®rm i; bi1 is the slope shift coe�cient for ®rm i.

Daily ®rm returns were gathered from the University of Chicago Center forResearch in Security Prices (CRSP) tape. The equally weighted daily marketindex from the CRSP tape is used for ~Rmt, as recommended by Brown andWarner (1980, p. 239). Risk-free daily rates were obtained from the monthlyFederal Reserve Statistical Publication G.13 (1987±1988) for 3-month Trea-sury Bills. In essence, the approach described above will test whether or not theABC announcement was associated with a statistically signi®cant stock marketreaction on any one (or more) of the four trading days in the announcementperiod under consideration. Out estimation and testing of daily event coe�-cients follows the example of Smirlock and Kaufold (1987, pp. 352±354) intheir study of the Mexican debt crisis. This type of approach is appropriatewhen the information release date can be exactly identi®ed; accordingly, itallows for a more exact identi®cation and detailed analysis of the impact ofinformation on market returns than an analysis of cumulative abnormal re-turns averaged over multiple event days.

3.8. Hypothesis testing

In the case where ®rms show stock market return reactions (i.e., have cia

coe�cients on a given day a) of the opposite sign, a joint hypothesis test shouldbe more powerful than an average hypothesis test in detecting market reac-tions. However, when all or most of the ®rms show reactions of the same sign(on a given day a), an average hypothesis test may be more powerful than thejoint test. Accordingly, since it is not known ex ante whether the market wouldimpound the value of ABC adoptions to all ®rms in a similar manner, bothaverage and joint hypothesis testing are undertaken. Therefore, the main hy-pothesis underlying the empirical study will be tested via the two operationalhypotheses noted below. In each case, the alternate hypothesis is the logicalalternative.

Null joint Hypothesis 1A. All event coe�cient �cia� � 0 for day a � 27 and 31May, 1 and 2 June; for all i, i � 1; . . . ;N .

Null average Hypothesis 1B. The average event coe�cient �1=NP

i cia� � 0 forday a, a� 27 and 31 May, 1 and 2 June; i � 1; . . . ;N .


The test statistic initially used in testing the joint and average hypotheses 1Aand 1B will be Theil's F statistic. 12 This test statistic allows for contempora-neous correlation in the disturbance terms and has been used extensively inprior calendar-based SUR event studies for testing of similar joint and averagehypotheses (Binder, 1985a, pp. 370±372; Binder, 1985b, pp. 171,172).

4. Results and implications

The results of estimating the four separate daily event parameters for each ofthe control and experimental ®rms are presented in Table 2. 13 14 15 None of

12 Theil's (Theil, 1971, pp. 314,402) F statistic can be stated as follows.

NT ÿPNi�1 Ki

q� �cÿ CB̂�0�C�X 0�R̂ÿ1 I�X �ÿ1C0�ÿ1�cÿ CB̂�

�Rÿ X B̂�0�R̂ÿ1 I��Rÿ X B̂� ;

where cÿ CB̂ the vector of linear constraints being tested, c the vector of dimension �q� 1�,C of full row rank and is of dimension �q� �Pi Kixi��,B the estimated coefficient vector of dimension �Pi Kixi�,Ki the number of parameters estimated in equation i,N the total number of equations in the regression system,

T the number of observations in time,

q the number of restrictions tested,

X the matrix of independent variables of dimension �NT � �Pi Ki��,R the vector of dependent variables of dimension �NT � 1�,R̂ the sample covariance matrix of disturbances of dimension �N � N �,I the identity matrix of dimension �T � T �: Theil's statistic is exactly distributed, when the null

hypothesis is stated in terms of average market abnormal returns (Binder, 1985b, p. 173). In

general, Theil's F is asymptotically distributed F �q;NT ÿPi Ki�.13 Although not separately reported, the intercept, intercept shift, slope, and slope shift

coe�cients were also analyzed for signi®cance at the a � 0:10 level (two-tailed). For the

experimental ®rms, none of the intercepts, intercept shifts, or slope shift coe�cients were

signi®cant. For the control ®rms, none of the intercepts or intercept shift coe�cients

were signi®cant, but two of the slope shift coe�cients were signi®cant. Eight of ten slope

coe�cients were signi®cant for the experimental ®rms, and eight of ten slope coe�cients were

signi®cant for the control ®rms. These results on the signi®cance of the coe�cients resemble those

previously reported under a similar methodology (Smirlock and Kaufold, 1987, pp. 352±360)14 The general non-signi®cance of the shift variables led us to re-estimate the models without the

intercept shifts and slope shifts. Doing so resulted in no material changes to any of the event

coe�cients, their signi®cance levels, or the hypothesis testing. However, it did cause swings in the

signi®cance levels of some of the basic slope and intercept estimates. Therefore, for control

purposes, the shift variables were retained in the regression system.15 As is apparent from the SUR model structure, the independent variables are the same on the

right side of each regression equation. Under these circumstances, the parameter estimates and

standard deviations obtained under this system of equations are essentially identical to those

obtained via OLS. However, the use of SURs is still advantageous in that it allows for joint

hypothesis testing, which OLS would not allow.


the estimated event parameters on 27 May are signi®cant for either the ex-perimental or the control ®rms. Similarly, only 1 out of the 20 estimated eventparameters on 2 June is signi®cant (a � 0:10, two-tailed). However, on the dayof the general announcement (31 May) and the day after the announcement (1June), we see that a number of the estimated event parameters are signi®cantfor both the experimental and the control ®rms. Of particular note is the factthat 9 of the 10 event coe�cients for the experimental ®rms are positive on theday of the general announcement (31 May); this pattern is not repeated for thecontrol ®rms on 31 May. However, perhaps of more import is the fact that allof the signi®cant coe�cients for both the control and the experimental ®rmsare positive on 31 May, and all of the signi®cant coe�cients for both thecontrol and the experimental ®rms are negative on 1 June. This sign and sig-ni®cance pattern naturally raises the question of whether any real di�erences

Table 2

Estimated event parameters (cia)a

Firms 27/5/88 31/5/88 1/6/88 2/6/88

Experimental ®rm

E1: Westinghouse Corp. 0.0045 0.0241�� ÿ0.0176� ÿ0.0082

E2: Eaton Corp. ÿ0.0020 ÿ0.0063 0.0003 0.0070

E3: Northern Telecom 0.0030 0.0205 ÿ0.0167 0.0047

E4: Northrop ÿ0.0064 0.0110 ÿ0.0191 ÿ0.0095

E5: Unisys 0.0001 0.0014 0.0125 ÿ0.0054

E6: General Motors ÿ0.0002 0.0169 0.0078 ÿ0.0056

E7: United Technologies ÿ0.0162 0.0226� ÿ0.0055 0.0021

E8: Parker-Hanni®n ÿ0.0149 0.0141 ÿ0.0152 0.0046

E9: General Dynamics 0.0012 0.0087 ÿ0.0285�� ÿ0.0050

E10: Honeywell ÿ0.0024 0.0040 ÿ0.0033 0.0058

Average ÿ0.0033 0.0117�� ÿ0.0085� ÿ0.0010

Control ®rms

C1: Phillips N.V. ÿ0.0070 0.0226 ÿ0.0125 ÿ0.0136

C2: Dana Corp. 0.0121 ÿ0.0273�� ÿ0.0015 0.0157�

C3: Harris Corp. ÿ0.0114 0.0063 ÿ0.0260�� 0.0123

C4: Grumman ÿ0.0170 ÿ0.0176 ÿ0.0111 0.0026

C5: Digital Equipment ÿ0.0062 0.0243� 0.0014 0.0012

C6: Ford Motor ÿ0.0055 0.0278�� ÿ0.0080 0.0091

C7: Allied Signal 0.0017 0.0317�� ÿ0.0043 ÿ0.0047

C8: Sunstrand 0.0079 ÿ0.0120 0.0105 ÿ0.0030

C9: McDonnell Douglas 0.0008 ÿ0.0150 ÿ0.0106 ÿ0.0001

C10: Johnson Controls 0.0098 0.0004 ÿ0.0150 0.0148

Average ÿ0.0015 0.0096� ÿ0.0077 0.0034

a Signi®cant at (two-tailed): *0.10 level, **0.05 level, ***0.01 level; number of observations: t� 271;

system weighted R-square� 0.1288.


exist between the experimental and control ®rms on the two days under dis-cussion.

The results of testing the average and joint hypotheses on both theexperimental and the control groups are contained in Table 3. Forthe experimental group, the Average Hypothesis 1B is rejected on 31 May atthe a � 0:05 (two-tailed) and on 1 June at the a � 0:10 (two-tailed); theJoint Hypothesis 1A is rejected on 1 June at the a � 0:05 (two-tailed). Forthe control group, the Average Hypothesis 1B is rejected on 31 Mayat the a � 0:10 (two-tailed) and is not rejected on 1 June at the a � 0:10(two-tailed); the Joint Hypothesis 1A is rejected on 31 May at the a � 0:01(two-tailed). Therefore, both the experimental and control groupsexperienced signi®cant abnormal returns on the day of and the dayfollowing the Business Week announcement. In particular, both thecontrol and experimental groups experienced positive and signi®cant(at a � 0:10, two-tailed) average excess returns on the day of the

Table 3

Tests of joint and average Hypotheses 1A and 1Ba

27/5/88 31/5/88 1/6/88 2/6/88

Null hypothesis F value F value F value F value

Pr > F Pr > F Pr > F Pr > FD.F.b D.F. D.F. D.F.

Experimental group

Hypothesis 1A 0:4390 1:2335 1:9021 0:3754

All cia � 0 0:9280 0:2636 0:0403�� 0:9577

for day given 10 10 10 10

5260 5260 5260 5260

Hypothesis 1B 0:4317 5:1952 2:7550 0:0351

Average cia � 0 0:5112 0:227�� 0:0970� 0:8515


5260 5260 5260 5260

Control group

Hypothesis 1A 0:6856 3:0732 0:8176 0:6179

All cia � 0 0:7389 0:0007 0:6117 0:7999


5260 5260 5260 5260

Hypothesis 1B 0:1148 3:8278 2:4902 0:4976

Average cia � 0 0:7347 0:0505� 0:1146� 0:4806


5260 5260 5260 5260

a Signi®cant at (two-tailed): *0.10 level; **0.05 level; ***0.01 level.b Degrees of freedom are presented with the numerator degrees of freedom followed vertically by

the denominator degrees of freedom.


announcement. 16 However, given the matching process underlying theexperimental design, the question now arises as to whether the experimentalgroup signi®cantly outperformed the control group on the day of the an-nouncement. 17 It is to this more focussed question that we now turn in ouranalysis.

4.1. Matched pairs analysis

Table 4 recaps the average daily estimated event parameter for each groupand reports the di�erence between the average estimated event parameters(experimental average ± control average) for each day of the four day eventperiod. The table indicates that on the day before the Business Week an-nouncement and the two days after the announcement, the control group av-erage event coe�cient exceeds the experimental group average event coe�cient.In other words, the control group appears to outperform the experimentalgroup (on average) on the day before and the two days Business Week an-nouncement. However, on the day of the announcement (31 May), the ex-perimental group average coe�cient value exceeds the control group averagecoe�cient ± i.e., the experimental group appears to outperform the controlgroup. Although the signs are consistent with an instantaneous and positivee�ect from the 31 May announcement, the di�erences are not statisticallysigni®cant at conventional levels for the 10 pairs. 18

In order to further investigate whether or not the abnormal excess returns forthe experimental ABC ®rms exceed the abnormal excess returns for non-ABCmatched control ®rms on a pair-by-pair rather than on an average basis, a non-parametric one-tailed Wilcoxon Signed Rank Test was performed. The

16 In order to test for the potential and di�ering impact of information leakage e�ects versus

announcement e�ects, the model was also estimated with a di�erent speci®cation of the event

periods. Instead of four separate daily parameters, two event periods were structured for the

Business Week announcement. The ®rst period was 2-day information leakage period that included

26 and 27 May (the two trading days before the 31 May announcement). The second period was a

3-day announcement period that included 31 May, 1 and 2 June. None of the F statistics associated

with the hypothesis testing regarding the information leakage event parameter or the announce-

ment period event parameter were signi®cant at the a � 0:10 level (two-tailed) under this

speci®cation. This is not surprising given the opposite signs of the average reactions of the two

groups on 31 May and 1 June.17 The potential for the market to completely impound the value of the ABC announcement on a

single trading day is strengthened by the fact that the information was physically on the market for

an additional non-trading day before the 31 May announcement date in our study.18 Sign tests were not performed to assess the signi®cance of the trend because such tests are

reliant upon the assumption of statistical independence. This assumption is violated by the

contemporaneous correlation created by the calendar and industry clustering extant in the data set.


Wilcoxon Signed Rank Test Statistics 19 and p-values for the di�erences in theestimated parameter values between the pairs of ®rms for each of the four days ofthe event period are presented in Table 5. Although the p-value of the Wilcoxonstatistic on 31 May is substantially lower than on any of the other days of theevent period, the pairs comparison does not indicate a statistically signi®cantdi�erence between the experimental and control groups on any of the 4 days. 20

Although our ®ndings indicate no signi®cant market e�ect for the group of®rms as a whole, that does not preclude the possibility that individual ®rmsmay have gained from the new ABC system. Indeed, given the recent ®ndingsby Shields (1995, p. 159), there is strong reason to believe that the bene®ts ofABC are contingent upon various behavioral and organizational factors.Further, anecdotal and case study evidence indicates that a great variety ofABC systems are in existence and that ®rms are experiencing signi®cant

19 The Wilcoxon Signed Rank Test is superior in this situation to a general sign test, because the

Wilcoxon utilizes both the signs of the di�erences between the pairs as well as size of the di�erences.

A general sign test (such as the Binomial Test) utilizes only the sign of the di�erences. While the

Wilcoxon is a distribution free test, it does assume that the true underlying distribution of

di�erences is symmetric about its median (see, e.g., Van Matre and Gilbreath, 1983, pp. 514±518).20 Sensitivity analysis was also performed on the sample by conducting the Wilcoxon Signed

Rank Test on all 9-pair subsets chosen from the 10 pairs. None of these combinations indicated

signi®cantly higher experimental excess returns (as compared to the control excess returns) on any

of the four event days.

Table 4

Test of the di�erences between the experimental and control group average estimated parametersa

Firm 27/5/88 31/5/88 1/6/88 2/6/88

Average experimental coe�cient ÿ0.0033 0.0117�� ÿ0.0085� ÿ0.0010

Average control coe�cient ÿ0.0015 0.0096� ÿ0.0077 0.0034

Di�erence (Experimental less

control)b

ÿ0.0018 0.0021 ÿ0.0008 ÿ0.0044

p-valuec (one-tailed) 0.3598 0.3405 0.4382 0.1986

a Coe�cient averages are drawn from Table 2.b A folded F test of H0 : r2

Experimental � r2Control. The null hypothesis of the equality of the variances

of the two groups could not be rejected at the a � 0:10 level (two-tailed). In addition, t-tests of the

equality of the mean portfolio returns of the control and the experimental groups indicate that the

mean portfolio returns were not statistically di�erent during the estimation period (at the a � 0:10

level, two-tailed).c The p-value refers to a one-tailed t-test of the hypothesis that the average di�erence (experimental

coe�cient less control coe�cient) exceeds zero. Such OLS based t-tests are appropriate tests of

average coe�cients (even in the presence of contemporaneous correlation) when all independent

variables in the regressions are the same. As discussed previously, these OLS determined coe�cient

values are identical to the SUR determined values. However, the OLS t-test allows for the more

appropriate one-tailed test of the extant hypothesis, which a two-tailed F -test does not allow.


challenges in attempting to implement full-blown ABC systems (see, e.g.,Roberts and Silvester, 1996, p. 23).

5. Concluding comments

The use of ABC is spreading on an international level. Nevertheless, theevidence to date regarding the net bene®t of ABC has been mixed, at best. In ane�ort to help resolve this issue, we have undertaken an empirical analysis of thesecurity market e�ect of announcing the installation of an ABC system byseveral ®rms. The time when the announcement took place was the late 1980s,which was a peak period for the advocation of ABC in the US. Thus, if asecurity market reaction to the adoption of ABC was ever to occur, this wouldseem to be the likely time period for it to happen. In other words, this timeperiod would seem to be biased in favor of ®nding a signi®cant market e�ect.Nevertheless, the analysis indicates that the installation of an ABC informationsystem was not associated with a signi®cant stock market reaction (eitherpositive or negative). This ®nding should, at a minimum, caution organizationscurrently considering the adoption of ABC to carefully consider the cost/bene®t aspects of implementing such a system.

We recognize that an assessment of the ABC-®rm performance link viastock market returns has limitations. Nevertheless, we believe that examiningthe issue from a capital markets research perspective provides an importantcomplement to the other research in the area. Indeed, if a positive market e�ectcannot ultimately be established for pro®t oriented organizations, the ABCexercise needs to be seriously questioned. Viewed in this light, we hope that the®ndings reported in this paper will become a catalyst for future market studiesconcerning the value of cost management techniques.

References

Anderson, S.W., 1995. A framework for assessing cost management system changes: the case of

activity based costing implementation at General Motors, 1986±1993. Journal of Management

Accounting Research 7, 1±51.

Table 5

Wilcoxon Signed Rank Test for di�erences in the event coe�cients between experimental and

control groups on a pair-by-pair basis

27/5 31/5 1/6 1l2/6

T value T value T value T value

p-value p-value p-value p-value

10 pairs 23 32 28 12

All ®rms 0.652 0.348 0.500 0.935


Babad, Y.M., Balachandran, B.V., 1993. Cost driver optimization in activity-based costing. The

Accounting Review 68 (3), 563±575.

Ball, R., Brown, P., 1968. An empirical evaluation of accounting numbers. Journals of Accounting

Research 6 (2), 103±126.

Banker, R.D., Johnston, H.H., 1993. An empirical study of cost drivers in the US airline industry.

The Accounting Review 68 (3), 576±601.

Beaver, W.H., 1981. Market e�ciency. The Accounting Review 56 (1), 23±37.

Bhimani, A., Pigott, D., 1992. Implementing ABC: a case study of organizational behavioural

consequences. Management Accounting Research 3 (2), 119±130.

Binder, J.J., 1985a. On the use of the multivariate regression model in event studies. Journal of

Accounting Research 23 (1), 370±383.

Binder, J.J., 1985b. Measuring the e�ects of regulation with stock price data. Rand Journal of

Economics 16 (2), 167±183.

Bromwich, M., Bhimani, A., 1989. Management accounting: evolution not revolution. Chartered

Institute of Management Accountants, London.

Brown, S.J., Warner, J.B., 1980. Measuring security price performance. Journal of Financial

Economics 8 (3), 205±258.

Campi, J.P., 1992. It's not as easy as ABC. Journal of Cost Management 6 (2), 5±11.

Cooper, R., Turney, P.B.B., 1989. Hewlett-Packard: Roseville Networks Division. Harvard

Business School, Case 9-187-117.

Cooper, R., Kaplan, R.S., Maisel, L.S., Morrissey, E., Oehm, R.M., 1992a. From ABC to ABM.

Management Accounting 70 (10), 54±57.

Cooper, R., Kaplan, R.S., Maisel, L.S., Morrissey, E., Oehm, R.M., 1992b. Implementing Activity-

Based Cost Management: Moving from Analysis to Action. Institute of Management

Accountants, Montvale, NJ.

Cost Management Group of the Institute of Management Accountants, 1998. Cost Management

Update (81).

Dopuch, N., 1993. A perspective on cost drivers. The Accounting Review 68 (3), 615±620.

Dos Santos, L.B., Pe�ers, K., Mauer, D.C., 1993. The impact of information technology

investment announcements on the market value of the ®rm. Information Systems Research 4

(1), 1±23.

Fama, E., Fisher, L., Jensen, M., Roll, R., 1969. The adjustment of stock prices to new

information. International Economic Review 10 (1), 1±21.

Federal Reserve Statistical Publication G.13, 1987±1988. Open market money dates and bond

prices ± selected interest dates. Monthly release dates of 1 September 1987±9 September

1988.

Foster, G., 1979. Brilo� and the capital market. Journal of Accounting Research 17 (1), 262±274.

Foster, G., Gupta, M., 1990. Manufacturing overhead cost driver analysis. Journal of Accounting

and Economics 12 (1±3), 309±337.

Foster, G., Swenson, D., 1997. Measuring the success of activity based cost management and its

determinants. Journal of Management Accounting Research 9, 109±141.

Gonedes, N.J., Dopuch, N., 1979. Economic analyses and accounting techniques: perspective and

proposals. Journal of Accounting Research 17 (2), 384±410.

Gordon, L.A., 1998. Managerial Accounting: Concepts and Empirical Evidence, 3rd edu.

McGraw-Hill, New York.

Haka, S.F., Gordon, L.A., Pinches, G.E., 1985. Sophisticated capital budgeting selection

techniques and ®rm performance. The Accounting Review 60 (4), 651±669.

Harpaz, T., 1985. Financial reporters, the securities laws and the ®rst amendment: where to draw

the line. Fordham Law Review 8 (3), 1035±1062.

Innes, J., Mitchell, F., 1995. A survey of activity-based costing in the UK's largest companies.

Management Accounting Research 6 (2), 137±153.


Innes, J., Mitchell, F., 1990. Activity Based Costing: A Review with Case Studies. Chartered

Institute of Management Accountants, London.

Innes, J., Mitchell, F., 1991. Activity Based Costing: A Case Study of Development and

Implementation. Chartered Institute of Management Accountants, London.

Johnson, H.T., 1992. Relevance Regained. The Free Press, New York.

Johnson, H.T., Kaplan, R.S., 1987. Relevance Lost: The Rise and Fall of Management

Accounting. Harvard Business School Press, Boston, MA.

Kaplan, R.S., 1992. In defense of activity-based cost management. Management Accounting 74 (5),

58±63.

Kaplan, R.S., Shank, J.K., Horngren, C.T., Boer, G., Ferrara, W.L., Robinson, M.A., 1990.

Contribution margin analysis: no longer relevant/strategic cost management: the new

paradigm. Journal of Management Accounting Research 2, 1±32.

Kelly, K., 1991. A bean-counter's best friend. In: Business Week Special 1991 Bonus Issue: The

Quality Imperative, pp. 42, 43.

Larcker, D.F., 1983. The association between performance plan adoption and corporate capital

investment. Journal of Accounting and Economics 5 (1), 3±30.

Lukka, K., Granlund, M., 1996. Cost accounting in Finland: current practice and trends of

development. The European Accounting Review 5 (1), 1±28.

Malmi, T., 1997. Towards explaining activity-based costing failure: accounting and control in a

decentralized organization. Management Accounting Research 8 (4), 459±480.

Mitchell, F., 1994. A commentary on the applications of activity based costing. Management


Nair, R., 1979. Economic analyses and accounting techniques: an empirical study. Journal of


Noreen, E., 1991. Conditions under which activity-based cost systems provide relevant costs.

Journal of Management Accounting Research 3, 159±168.

Piper, J.A., Walley, P., 1990. Testing ABC logic. Management Accounting (British) 68 (8), 37, 42.

Piper, J.A., Walley, P., 1991. ABC relevance not found. Management Accounting (British) 69 (3),

42, 44, 54.

Port, O., King, R., Hampton, W.J., 1988. The productivity paradox. Business Week, 100±114.

Roberts, M.W., Silvester, K.J., 1996. Why ABC failed and how it may yet succeed. Journal of Cost

Management 10 (4), 23±35.

Rose, N.L., 1985. The incidence of regulatory rents in the motor carrier industry. Rand Journal of

Economics 16 (3), 299±318.

SAS Institute Inc., 1982. SAS/ETS User's Guide: 1982 Edition and SAS User's Guide: Statistics,

version 5 edition. SAS Institute, Cary, NC.

Sakurai, M., 1995. The past and future of Japanese management accounting. Journal of Cost

Management 9 (3), 21±30.

Schipper, K., Thompson, R., 1983. The impact of merger-related regulations using exact

distributions of test statistics. Journal of Accounting Research 23 (1), 408±415.

Shank, J.K., 1989. Strategic cost management: new wine, or just new bottles?. Journal of

Management Accounting Research 1, 47±65.

Shields, M.D., 1995. An empirical analysis of ®rms' implementation experiences with activity-based

costing. Journal of Management Accounting Research 7, 148±166.

Smirlock, M., Kaufold, H., 1987. Bank foreign lending, mandatory disclosure rules, and the

reaction of bank stock prices to the Mexican debt crisis. Journal of Business 60 (3), 347±364.

Spicer, B.H., 1990. New directions in management accounting practice and research. Management

Accounting Research 2, 139±146.

Theil, H., 1971. Principles of Econometrics. Wiley, New York.

Turney, P., Anderson, B., 1989. Accounting for continuous improvement. Sloan Management

Review 30 (2), 37±47.


US Congress, 1934. Securities Exchange Act of 1934. 15 US Code.

Van Matre, J.G., Gilbreath, G.H., 1983. Statistics for Business and Economics, revised edition.

Business Publications, Plano, TX.

Young, S.M., Selto, F.H., 1991. New manufacturing practices and cost management: a review of

the literature and directions for research. Journal of Accounting Literature 10, 265±298.

Zellner, A., 1962. An e�cient model of estimating seemingly unrelated regressions and tests for

aggregation bias. Journal of the American Statistical Association 57 (298), 348±368.


Gordon and Silvester

Documents

Transcript of Gordon and Silvester