The role of cognitive and affective conflict in early implementation of activity-based cost management.
It has been claimed that activity-based cost management (ABCM) can provide improved information for strategic decisions involving product planning and cost management (Cooper 1988; Kaplan 1994: Shank and Govindarajan 1995). However, evidence over the past ten years has indicated relatively low rates of adoption of ABCM (Innes and Mitchell 1995; Banerjee and Kane 1996; Evans and Ashworth 1996; Bhimani 1996; Hrisak 1996; Innes et al. 2000; Kennedy and Affleck-Graves 2001). In addition, it is apparent that many organizations have not gained promised benefits (Innes and Mitchell 1995; Bhimani 1996; Lukka and Granlund 1996; Gordon and Silvester 1999, Innes et al. 2000). It appears that while the technical characteristics of ABCM are well understood, some organizations have difficulties in implementing the systems (Selto 1995; Innes and Mitchell 1995; Lukka and Granlund 1996; Palmer and Vied 1998). Several authors have suggested that an important obstacle to successful outcomes is a lack of attention to behavioral factors during implementation (Shields and Young 1989, 1994; Cooper et al. 1992; Argyris and Kaplan 1994; Anderson 1995; Shields 1995; Foster and Swenson 1997; McGowan and Klammer 1997; Krumwiede 1998; Anderson and Young 1999; Anderson et al. 2002). However, as yet, there is very little empirical research that helps explain why attention to behavioral factors during implementation improves the likelihood of successful ABCM.
This paper reports the results of an examination into the role of conflict in implementing ABCM in a sample of firms where the applications were at an early stage. (1) That is, ABCM had been accepted and were used by nonaccounting managers but were not mature or integrated with primary financial systems. Specifically, it is argued, first, that there is a positive association between the successful implementation of ABCM and attention to behavioral factors during the implementation process. Next, it is argued that this association can be explained, in part, by the intervening role of cognitive and affective conflict generated during the early implementation process. The effects of cognitive and affective conflict were explored because of the theoretical links between behavioral factors relevant to the early stages of implementing ABCM and both cognitive and affective conflict, and the association of these aspects of conflict with successful outcomes.
The study contributes in several ways to our understanding of how behavioral issues can enhance the likelihood that ABCM will be effective. First, it provides evidence that conflict within the implementation of ABCM can be separated into two components: a cognitive and an affective element. Second, in implementing ABCM, cognitive conflict is beneficial as it intervenes between behavioral implementation factors and beneficial outcomes, whereas affective conflict has a less important role. Specifically, the results indicate that attention to behavioral implementation factors is associated with an increase in cognitive conflict, which, in turn, enhances the usefulness of ABCM for product planning and cost management. However, contrary to expectations, affective conflict did not act as an intervening variable in the relationship between behavioral implementation and ABCM outcomes. The study is exploratory in that it focuses on three ABCM behavioral implementation factors derived from the ABCM implementation literature but identified, empirically, as relevant to the study of ABCM success. These were multidimensional constructs concerning top management support, clarity of objectives, and training.
The paper is organized as follows. First, the association between behavioral implementation factors and the successful application of ABCM is considered. Next, the concepts of cognitive and affective conflict are explained to provide a theoretical basis for the proposed associations between conflict and the successful implementation of ABCM, and between behavioral implementation factors and conflict. This is followed by sections describing the research method, results, and a discussion covering conclusions and limitations of the study.
PRIOR LITERATURE AND HYPOTHESIS DEVELOPMENT
ABCM Implementation and Successful ABCM Application
Survey evidence suggests that the adoption of ABCM by firms is about 20 percent, although this is higher (up to 50 percent) in larger entities (Innes and Mitchell 1995; Banerjee and Kane 1996; Evans and Ashworth 1996; Bhimani 1996; Hrisak 1996; Krumwiede 1998; Chenhall and Langfield-Smith 1998; Kennedy and Affleck-Graves 2001; Ittner et al. 2002; Cotton et al, 2003). However, while ABCM may be seen as an improvement on traditional systems (Shields 1995; Swenson 1995, McGowan 1998), it is also apparent that for many organizations ABCM has not fulfilled expectations (Innes and Mitchell 1995; Bhimani 1996; Lukka and Granlund 1996; Innes et al. 2000). It appears that while the technical characteristics of ABCM are well understood, some organizations have difficulties in implementing the systems (Selto 1995; Innes and Mitchell 1995; Lukka and Granlund 1996).
A series of studies has demonstrated that a key factor in determining various aspects of ABCM success is concern with behavioral factors when implementing the systems (Shields 1995; Anderson 1995; McGowan and Klammer 1997; Krumwiede 1998; Anderson and Young 1999). Behavioral factors associated with successful applications include top management support, linkages to competitive strategy, adequacy of resources, nonaccounting ownership, linkages to performance evaluation and compensation, implementing training, clarity of objectives, and number of purposes for ABCM (Shields 1995: Foster and Swenson 1997; McGowan and Klammer 1997). (2) Krumwiede (1998) found, for routine, early applications, behavioral factors of importance were top management support, extent of applications, and years since adoption. For well-advanced ABCM applications, Krumwiede (1998) found that the role of nonaccounting ownership, clarity, and consensus of objectives and training were important. Anderson and Young (1999) found that behavioral factors associated with extent of use of the ABCM for recent applications were the reward environment, union support, commitment to change, adequate resources, and the likelihood of layoffs. For mature applications, top management and union support, and rewards were important.
While there is broad agreement that ABCM behavioral implementation factors are associated with successful outcomes, a difficulty exists in developing hypotheses as existing theories do not relate specific ABCM implementation factors to particular aspects of success, and empirical work varies in terms of effectiveness constructs, duration of implementation and units of analysis (Anderson and Young 1999). In this study, three key multidimensional constructs of behavioral implementation are derived from empirical analysis, based on existing dimensions considered in the literature to be associated with ABCM success. The ABCM behavioral dimensions concern top management support (top management support, resources provided to ABCM, links to competitive strategy), clarity of objectives (clear and concise objectives, consensus about objectives, non-accounting ABCM ownership), and training (training concerning implementation, designing, and using ABCM). (3)
The current study examined decision areas in which ABCM may be useful. ABCM has been acknowledged as potentially useful in product planning and cost management (Bromwich and Bhimani 1994, 77-81; Kaplan 1994; Shank and Govindarajan 1995; Palmer and Vied 1998; Innes et al. 2000; Kennedy and Affleck-Graves 2001; Ittner et al. 2002). In the current study, assessments of the usefulness of ABCM for product planning and cost management are used as proxies for success. Inevitably, there is an expectation that ABCM will improve financial outcomes (Cooper and Kaplan 1992). While it seems that effects on financial performance from early implementation of ABCM might be premature, it is possible that ABCM information that is useful for product planning and cost management may be associated with enhanced financial performance (Kennedy and Affleck-Graves 2001; Ittner et al. 2002). This study is concerned with early stages in the implementation of ABCM as it is during these stages that conflict is most likely to result from debate as to the design of ABCM and the uncertainties concerning its potential impact (Krumwiede 1998; Anderson and Young 1999).
Several arguments support the potential role of the three behavioral implementation factors in ensuring that ABCM information will be useful for product planning and cost management. Decisions in the areas of product planning and cost management tend to be important strategically as they specify organizational direction and involve significant reengineering and cost reduction programs. Successful implementation of innovations associated with these types of strategic decisions, such as ABCM, depends on acceptance of the systems by the users (Leonard-Barton 1988). Such acceptance is enhanced if the systems are legitimized by top management support (Shields 1995). Goal theory suggests that acceptance is enhanced and individuals will expend effort in trying to make systems work, if they are provided with the specific goals of the initiatives (Locke et al. 1981). Explicating the goals of ABCM is likely to be encouraged by the implementation factor of clarity of objectives. Also, it is plausible that ABCM would be accepted and more readily promoted if there is non-accounting ownership of the systems (Cooper et al. 1992). Such ownership derives from the centrality of ABCM to the individual's job and generates a proclivity to champion the cause of ABCM (Anderson 1995, 35). In addition to being accepted, for innovations such as ABCM to be useful strategically, they must be compatible with the direction of the organization (Schultz and Slevin 1975). Clarity of objectives is likely to show how ABCM aims to link operations to strategy, thereby enhancing the organizational validity of the systems. Finally, the usefulness of ABCM for product and cost management decisions will be enhanced if it is clear how ABCM can improve these types of strategic decisions (Kraemer et al. 1993). Training provides the basis to develop such understanding (Shields 1995). These arguments may be summarized in Hypotheses 1a, 1b, and 1c.
H1a: There is a significant positive association between an emphasis on the behavioral implementation factor of top management support and usefulness of ABCM for (1) product planning and (2) cost management.
H1b: There is a significant positive association between an emphasis on the behavioral implementation factor of clarity of objectives and usefulness of ABCM for (1) product planning and (2) cost management.
H1c: There is a significant positive association between an emphasis on the behavioral implementation factor of training and usefulness of ABCM for (1) product planning and (2) cost management
An association between ABCM behavioral implementation factors and financial success is not expected in this study because it seems unlikely that direct effects on financial performance would occur in early implementation of ABCM. However, to the extent that ABCM provides useful information for product planning and cost management, it might be that some improvements in financial performance would occur. In addition, for some commentators there is considerable interest in the extent to which ABCM improves financial returns (Cooper and Kaplan 1992). Hypothesis 1d presents this predicted association.
H1d: There is a significant positive association between financial success and the usefulness of ABCM for (1) product planning and (2) cost management.
Hypotheses 1a, 1b, and 1c form the basis for the main research question of this study: Is there an association between an emphasis on behavioral implementation factors and successful ABCM? The proposed model develops causal connections between behavioral implementation factors and both cognitive and affective conflict, and between these elements of conflict and ABCM outcomes. Hypotheses are developed reflecting particular causal paths involving behavioral implementation and links to the two forms of conflict and between these aspects of conflict and ABCM outcomes. Figure 1 illustrates the generic form of the casual model.
[FIGURE 1 OMITTED]
Conflict and ABCM, Constructs of Conflict
At a general level conflict is concerned with a clash between ideas and for individuals to be in opposition. It is often related to situations where scarcity of resources encourages parties to compete by attempting to block the goal achievement of others (Robbins 1989, 367). Conflict is often an important aspect of social interactions associated with business decisions, particularly those of a strategic nature where there are significant resources employed, a variety of managers participate in the decision, and the consequences to those involved and to the organization are important (Janis 1982; Schweiger et al. 1986; Mintzberg et al. 1976). The decision to adopt ABCM is strategic, as it requires considerable resource commitment, involves top management, operational management, and functional specialists including accountants, and has the potential to influence the strategic direction and performance of the organization (Kaplan 1994; Shank and Govindarajan 1995). It follows that conflict is likely to arise during implementation of ABCM (McGowan 1998, 47).
Conflict can have potentially contradictory effects on social interactions (Pinkley 1990; Jehn and Oswald 1992). Schweiger et al. (1986) assert that conflict can, on the one hand, improve decision quality, and on the other threaten decision quality by weakening the ability of individuals to work together. Conflict that has beneficial effects has been referred to as cognitive conflict, while conflict that is dysfunctional is called affective conflict (Amason and Schweiger 1994). Cognitive conflict is generally task-focused and oriented toward judgmental differences about how to achieve a common purpose. Affective conflict tends to involve emotional responses and is focused on personal incompatibilities or disputes and is often manifested as personal criticism. Theory and evidence indicate that cognitive and affective conflict are two distinct dimensions of conflict, rather than opposite ends of a conflict continuum (Amason 1996). Consequently, this study will develop theory employing cognitive and affective conflict as distinct dimensions.
Cognitive Conflict and Outcome Criteria
The beneficial effects of cognitive conflict derive from the potential for this form of conflict to provide the opportunity for dialectically styled interactions, which provide a means to debate, vigorously, opposing positions (Mitroff and Emshoff 1979; Janis 1982; Schweiger and Sandberg 1989). This can produce a way of identifying and synthesizing a variety of viewpoints into the decision process (West 1990). Cognitive conflict has been associated with the effective implementation of non-routine decisions as it encourages a task focus and draws on the perceptual diversity of managers over how best the task will achieve the objectives of the organization (Cosier 1981; Astley et al. 1982). It is these characteristics of cognitive conflict that lead to higher quality decisions in areas of non-routine decision making (Churchman 1971; Cosier 1978). ABCM has an important role in non-routine strategic decisions, such as product planning and cost management. Thus, it may be expected that cognitive conflict will lead to effective ABCM implementation resulting in strong benefits from the systems for product planning and cost management.
To ensure the success of ABCM it has been claimed that managers should contribute to the development of the systems, particularly as the details of the systems evolve throughout the early stages of implementation (Player and Keys 1995; Selto 1995). These interactions potentially generate cognitive conflict that assists in ensuring that the views of a variety of end users are considered before and throughout the implementation process.
Evidence on the association between cognitive conflict and successful implementation of accounting systems is not available. However, some studies have found favorable decision outcomes. For example, evidence supports the view that cognitive conflict encourages a thorough evaluation of the alternative underlying assumptions (Schweiger et al. 1986; Schweiger and Sandberg 1989; Putnam 1994); encourages new ideas and approaches (Baron 1991); increases learning and the accuracy of assessments of the decision situation (Fiol 1994); enhances high-quality decisions (Schwenk and Valacich 1994; Amason 1996); and increases satisfaction with the final decision (Hoffman and Maier 1961).
Thus, the theoretical arguments and supporting evidence form the basis for the expectation that cognitive conflict will elicit from those involved in designing and using ABCM a variety of abilities, perspectives, and widely based knowledge, which when synthesized, will result in the increased usefulness of the ABCM for product planning and cost management. This leads to Hypothesis 2:
H2: There is a positive association between cognitive conflict and enhanced usefulness of ABCM for (1) product planning and (2) cost management.
Affective Conflict and Outcome Criteria
There is a danger that managers may consider the introduction of ABCM and any resulting contentious information about alternative strategies and the nature of existing and proposed production processes as personal criticism (Brehmer 1976). This may have a consequence that disagreements will generate affective conflict that fosters cynicism and avoidance or counter effort that could undermine implementation of the systems (Petersen 1983; Ross 1989; Amason 1996). In such cases, the potential for ABCM to develop to a stage where it is accepted as providing information useful for product planning and cost management could be eroded or negated.
There is no evidence directly linking affective conflict to the usefulness of accounting applications. However, the potential dysfunctional effects of affective conflict have been widely recognized (Evan 1965; Bourgeois 1980; Dess 1987). The undesirable consequences of affective conflict may be summarized as a retarding of communications and cognitive processing, reduction in group cohesiveness and receptiveness to new ideas, a subordination of effort that should be devoted to working on the task to infighting between managers (Robbins 1989, 370; Pelled 1996). It may be anticipated that personal interactions involved in implementing ABCM that are characterized by affective conflict will be associated with ABCM systems that are less useful for product planning and cost management. This leads to Hypothesis 3.
H3: There is a negative association between affective conflict and enhanced usefulness of ABCM for (1) product planning and (2) cost management.
Behavioral Implementation Factors and Conflict
Behavioral factors related to implementing ABCM concern those procedures and processes that deal with people issues and may be contrasted with the technical design characteristics. In this study three dimensions of behavioral implementation are studied. These are top management support (top management support, resources provided to ABCM, links to competitive strategy), clarity of objectives (clear and concise objectives, consensus about objectives, nonaccounting ABCM ownership), and training (training concerning implementation, designing, and using ABCM). Behavioral implementation factors are considered to be most effective in ensuring successful applications of ABCM when they are in concert, as part of an integrated approach to implementation (Shields 1995).
The way in which ABCM behavioral implementation factors affect conflict can be examined by considering, first, several underlying theoretical conditions that encourage cognitive conflict and discourage affective conflict, then identifying how behavioral implementation factors can induce these conditions. Maximizing cognitive conflict and minimizing affective conflict during implementation of ABCM is likely to occur if the following conditions are apparent: (1) there is diversity of capabilities and orientations applied to the implementation; (2) commitment is encouraged; and (3) supportive relationships that enable managers to work collaboratively over time are developed (Amason 1996). Attention to ABCM behavioral implementation factors can foster effective behaviors in all three areas and, as a consequence, increase the likelihood that cognitive conflict will be maximized and affective conflict minimized.
Regarding diversity of capabilities and orientations, researchers have claimed that diversity of capabilities provides opportunities to draw upon an assorted set of talents and perspectives reflecting variety in skills, knowledge, abilities, and perspectives (Hoffman and Maier 1961; Wanous and Youtz 1986). While the existence of diverse capabilities does not guarantee that conflict will be cognitive rather than affective, it would appear to be a necessary condition to provide a pool of knowledge that can be drawn upon to generate cognitive conflict. Moreover, evidence suggests that groups of senior managers with diverse capabilities engage more often in cognitive conflict than in affective conflict making more innovative, higher quality decisions than groups with less diverse capabilities (Bantel and Jackson 1989; Murray 1989). Dimensions of the ABCM behavioral implementation factor clarity of objectives provide opportunities for a variety of individuals to bring their capabilities to the ABCM application and focus attention on agreed purposes of ABCM. First, the involvement of nonaccounting personnel in ABCM projects can ensure that a wide range of knowledge related to the operating environment is incorporated into the systems design and methods of application. Second, the effectiveness of such diversity of information on cognitive conflict can be enhanced by ensuring that during the design of ABCM there is understanding of, and focus on, the purposes of the ABCM. Such understanding and focus will be enhanced by clarifying objectives and gaining consensus about objectives. In summary, incorporating clarity of objectives into the implementation process provides opportunities and focus for end-users to agree on organizational direction and technical characteristics of the organization. This increases the possibility for cognitive conflict involving discourse focused on how the system relates to the operational situation and strategic imperatives of the organization.
A diversity of capabilities provides a broad knowledge base to engage in cognitive conflict. However, to achieve cognitive conflict and avoid affective conflict, managers will need to apply their knowledge to the task of developing ABCM and avoid negative infighting and personal recriminations (Amason 1996). Developing commitment to ABCM provides one way of ensuring a task rather than a personal-focus to the implementation (Folger 1977; Erez et al. 1985). A commitment to decisions, such as adopting ABCM, enables individual managers to engage in vigorous debate from independent viewpoints but in ways that are consistent with the ethos of ABCM (Amason 1996). Commitment can be effected by ensuring that users understand the rationale underlying the development of the systems (Wooldridge and Floyd 1989, 1990). Several behavioral implementation factors can encourage commitment thereby generating more cognitive and less affective conflict. Training in designing and using ABCM provides a mechanism for users to understand the rationale of ABCM systems and to accept them. (4) An important aspect of sustaining and resourcing effective training is the provision of top management support. Also, top management support for ABCM can signal to managers that commitment to the system is desirable (Shields 1995). The nonaccounting ownership aspect of the clarity of objectives construct can encourage commitment. This follows as managers will more readily commit to ABCM systems if authoritative nonaccountants from elsewhere in the organization also demonstrate commitment to the systems in their decisions and interactions with others in the organization (Argyris and Kaplan 1994). Since commitment involves belief in and acceptance of goals, commitment to ABCM will be enhanced if the consequences of that commitment are made clear by clarity and consensus about objectives of ABCM.
In assessing the adequacy of ABCM, after initial adoption, it is important that managers engage in reevaluating the effectiveness of the systems, in terms of the changing information needs of the organization (Bromwich and Bhimani 1994, 239). Supportive relationships are important to enable cognitive conflict to be maintained and provide information to sustain such reevaluations over time and avoid affective conflict that could be destructive to systems development (Amason 1996). As with many other administrative innovations, top management support has been identified as necessary to nurture ABCM development (Shields and Young 1989; Foster and Swenson 1997). Regular training programs can also provide a forum for nonaccountants to work collaboratively to develop these systems over time to suit the changing needs of the organization. These changing needs may require reassessing the objectives of ABCM and linkages of the systems with strategies and performance evaluation systems. Thus, cognitive conflict is encouraged and affective conflict discouraged by these implementation factors that help sustain efforts to develop ABCM over time.
In summary, concern with the three ABCM behavioral implementation factors of top management support, clarity of objectives, and training may be associated with higher levels of cognitive conflict and lower levels of affective conflict. This is achieved as clarity of objectives encourages a diversity of information relevant to the adoption of the systems and develops commitment to the systems. Both top management support and training encourage commitment and supportive relationships to ensure that system improvements are sustained through time, in ways that reflect changes in operational and strategic circumstances. These relationships are presented as Hypotheses 4 and 5.
H4: There is a positive association between cognitive conflict and attention to behavioral implementation factors concerning (1) top management support, (3) clarity of objectives, and (3) training.
H5: There is a negative association between affective conflict and attention to behavioral implementation factors concerning (1) top management support, (2) clarity of objectives, and (3) training.
The population for this study is strategic business units (SBUs) within large manufacturing companies that had recent implementations of ABCM. Manufacturing was selected, as much of the literature and theory related to ABCM has been restricted to manufacturing. The population was limited to recent applications as the role of conflict is likely to be more relevant to the early stages of adoption where the main systems design parameters are considered and the potential effects on resourcing throughout the organization are first recognized. In an attempt to select a sample that was representative of the target population, three large professional accounting companies were approached to provide lists of manufacturing organizations that had recently introduced ABCM. Eighteen large manufacturing organizations were identified that had introduced ABCM within the prior 18 months, thus ensuring they were in the recent, rather than mature, stages of implementation (Krumwiede 1998; Anderson and Young 1999). (5) While this is a convenience sample, the professional accounting companies indicated that the sample is quite representative of manufacturing firms that had introduced ABCM over the prior five years. Moreover, the sample was likely to provide sufficient variation in ABCM behavioral implementation to enable the study's hypothesized effects to be tested statistically.
Data were collected by a survey questionnaire administered to senior managers within SBUs of these 18 organizations. The 18 companies all employed more than 1,000 individuals. Initial telephone calls to the managers of SBUs within these companies identified which units had introduced ABCM and confirmed that they had started the application within the prior 18 months. From these 18 companies, 64 SBUs had recently introduced ABCM. The most senior manager involved in the ABCM implementation was contacted. The senior manager was selected, as this individual was likely to have the most comprehensive knowledge of the ABCM implementation. In some instances these were the senior financial officers, in others they were general managers or manufacturing managers. To test if function provided a bias in results, tests of differences in construct scores across different functional areas were undertaken. No significant differences were found. It is possible that the respondents held positive attitudes to the introduction of ABCM, thus biasing responses to more positive outcomes. Apart from guaranteeing confidentiality, the study has no direct controls for such bias.
Managers were sent questionnaires with a letter noting that the senior executive officer of the company encouraged participation in the study. Usable responses were received from 56 managers. A reminder letter was sent one month after the first mail out, but no additional surveys were returned. The final response rate was 87.5 percent. Given the high response rate, checks for nonresponse bias were unnecessary. The average age of respondents was 40 years, with an average length of employment in the companies of six years and in their current positions of four years. Table 1 provides information on the size, industry, and functional area of respondents.
By including multiple SBUs within firms it is possible that within-firm effects may have reduced variance between SBUs drawn from within a particular firm. At the extreme, a situation could involve no variation in variable scores between SBUs within firms. This would be equivalent to model testing with variance based on 18 scores, with each score being a composite of identical within-firm SBU scores. While this may not negate the results, it does potentially restrict the variance in the model by limiting within-firm variation. To test if there was significant variation among SBUs within firms, one-way repeated measures ANOVAs were performed on the independent variables. That is, ranked SBU scores were treated as repeated measures within firms, thereby enabling within-subject variation to be determined. To provide the opportunity to respond anonymously, it was not necessary for SBUs to identify their parent firm. Of the 56 SBUs in the sample, only eight could not be related to their parent firm. Considering the remaining 48 SBUs: five firms had four SBUs, five firms had three SBUs, five firms had two SBUs, and three firms had one SBU each. For all firms with three and four SBUs, there were significant F-statistics for the within-subject effects on all independent variables. (The Huynh-Feldt Epsilon adjustment to degrees of freedom was used when the assumption of sphericity was violated.) For firms with two SBUs the within-firm differences for top management support and training were not significant. These results indicate that, on the whole, potential problems of interpretation due to lack of significant within-firm differences are unlikely to be a major problem. Finally, an examination of the regression standardized residuals for the full sample indicated that there were no significant problems with lack of normality. An examination of the Mahalanobis distance values indicates that there are no multivariate outliers among the independent variables.
The questionnaire elicited information on Activity-Based Cost Management (ABCM) behavioral implementation factors, cognitive and affective conflict, and the success of the ABCM application. (6) While established instruments were used for all constructs except the usefulness of ABCM, pretesting was undertaken using three faculty members, four professional accountants, and three managers of firms not involved in the survey. No difficulties were encountered with the format of the questionnaire or with the language or meaning of items.
The ABCM behavioral implementation factors were measured using a 13-item instrument developed by Shields and Young (1989) and Shields (1995). The questionnaire asked managers the extent to which the items were present for their organization's ABCM initiative. Items were anchored at 1 = extremely low and 7 = extremely high. These items represented behavioral factors from a broader list of 17 items that included technical characteristics of ABCM. Using the 13 items related to behavioral factors, factor analysis with varimax rotation was used to identify underlying dimensions of the construct. Several items included in Shield's (1995) behavioral factors did not load cleanly on any factor and were deleted from the analysis. These excluded items were: links of ABCM to quality initiatives, JIT, and other speed initiatives; ownership by the accounting department; and linkages to performance evaluation and compensation. The final three dimensions are presented in Table 2 together with the results of a factor analysis on the nine items included in the study. The wording of the items included in the final constructs is provided in the Appendix. Alpha coefficients of over 0.70 provided evidence of the internal reliability of these three dimensions of ABCM implementation factors. The first factor relates to training provided to employees concerning ABCM; the second, identifies clarity in objectives about ABCM and ownership by operating departments; and the third, indicates top management support, links to competitive strategy, and resourcing of the ABCM application. Three measures of ABCM implementation, based on the factors for training, clarity of objectives, and top management support were used in the analysis.
Cognitive and affective conflict were measured with seven items from a scale developed by Jehn (1994) and used by Jehn (1994) and Amason (1996). Managers were asked to indicate the extent to which items were evident within the group implementing ABCM, anchored at 1 = none and 7 = a great deal. These scales have been found to have high levels of construct validity, measuring two distinct dimensions of conflict (Amason 1996). Consistent with prior research, factor analysis in the current study generated two factors related to cognitive and affective conflict. An item measuring the extent of anger among the group did not load cleanly on either factor and was deleted. Alpha coefficients of over 0.70 attested to high internal reliability. The factor analysis is presented in Table 2 and the wording of items in the Appendix.
In this study, successful ABCM was conceptualized as providing useful information for product planning and cost management (Bromwich and Bhimani 1994, 77-81; Kaplan 1994; Shank and Govindarajan 1995; Innes et al. 2000). These outcomes were confirmed as important to evaluating the success of ABCM in pilot work conducted as part of the survey method. A list of potential decision areas concerning product planning and cost management were derived from the literature. (7) Of these, nine items were identified, in the pilot study, as particularly important performance outcomes. Respondents were asked to rate the usefulness of ABCM to five areas concerning product planning: pricing decisions, decisions on the range of products, the output of products, new product development, and customer profitability analysis. Additionally, respondents rated usefulness related to cost management: cost reduction and modeling, reengineering and improvement processes, budgeting, and performance measurement. The seven-point scales were anchored at 1 = not at all useful and 7 = extremely useful. Factor analysis confirmed the validity of these items as measures of the related constructs. Table 2 provides details of the factor analysis and the alpha coefficients related to the factors, and the appendix provides the wording of the questions.
Hypothesis 1d specifies the expectation that usefulness of ABCM for product planning and cost management will improve financial outcomes. The wording of the item for financial success followed Shields (1995) and asks the extent to which financial benefits had been received, anchored at 1 = not at all and 7 = extensive benefits.
Descriptive statistics, based on the weighted average scores of multi-item variables, are provided in Table 3.
To test the hypotheses, the causal modeling technique of Partial Least Square (PLS) was used. PLS is used to examine structural models and is particularly suited to small sample size studies (Wold 1985). Also, it overcomes some theoretical and estimation problems in the use of more well-known structural modeling approaches such as LISREL (Hulland 1999). The technique of PLS comprises a structural model that specifies the relations among constructs and a measurement model that specifies the relations between the manifest items and the constructs that they represent. PLS enables an overall assessment of validity of constructs within the total model. Given the exploratory nature of the current study, a two-step approach was taken that involved a preliminary analysis to examine the construct validity of multi-item variables and then estimation of the structural model. This approach is recommended when theory is more tentative and measures are less well developed as it maximizes the interpretability of both measurement and structural models (Hair et al. 1998, 600). The preliminary analysis of multi-item constructs was reported above, and detailed in Table 2. The measurement model is reexamined within PLS with the generation of additional statistics to access the validity of the measurement model. Figure 2 presents the detailed structural model to be tested, with solid lines indicating significant associations.
[FIGURE 2 OMITTED]
To examine the structural model, PLS generates standardized [beta]s that are used as path coefficients within the structural model and are interpreted as in OLS regression. Bootstrapping provides a basis to evaluate parameter estimates and their confidence intervals based on multiple estimations. Bootstrapping using 500 samples with replacement was used to assess the significance of the path coefficients.
Table 4 presents the results related to the structural model. This includes the path coefficients that are also presented in Figure 2. It is inappropriate in PLS to use any overall goodness-of-fit measures, as used in covariance structure analysis modeling such as LISREL or AMOS, because PLS makes no distributional assumptions (Chin 1998). Rather, fit is evaluated by the overall incidence of significant relationships between constructs and the explained variance of the endogenous variables. [R.sup.2] values are reported in Table 4.
In the preliminary investigation of the measurement model, factor analysis and Cronbach's alpha attested to the construct validity and reliability of the measures. The PLS analysis confirmed the earlier factor analysis with all items loading over 0.70 on their respective latent variables, except two affective cognitive conflict items ("How much were personal clashes between group members evident during the implementation?" and "How much tension was there in the group during the implementation?"), which loaded 0.677 and 0.663, respectively. An additional analysis examined the discriminant validity of the measurement model by calculating the square roots of the Average Variance Extracted (AVE) and comparing with the correlations between constructs. This provides a test of the extent to which a construct shares more variance with its measures than it shares with other constructs (Fornell and Larcker 1981). Table 4 provides the AVEs that can be compared with the correlations between other constructs in Table 5. All square roots of the AVE measures are greater than the respective correlations attesting to satisfactory discriminant validity. The AVEs are all above 0.50, the conventional level to attest to convergent validity.
Hypotheses 1a, 1b, and 1c proposed that there is an association between ABCM behavioral implementation factors and usefulness of ABCM for product planning and cost management. Hypothesis 1d proposed associations between financial success and usefulness of ABCM for product planning and cost management. Before testing these hypotheses within the structural model, regression analysis was used to enable comparisons with other studies that have examined only the direct effects of behavioral implementation factors. The regressions, reported in Table 6, provide support for the associations between usefulness of ABCM for product planning and both behavioral implementation factors of clarity of objectives and training, although training is only marginally significant (p < 0.10 level). (8) Also, usefulness of ABCM for cost management was associated with implementation factors top management support and clarity of objectives, with the association involving clarity of objectives being marginal (p < 0.10 level). While not hypothesized, financial success was associated with implementation factors top management support and training, the latter being only marginal (p < 0.10 level). These results are consistent with the findings of studies that have found positive associations between ABCM behavioral implementation factors and outcomes (Shields 1995; Foster and Swenson 1997; McGowan and Klammer 1997). Finally, financial success was significantly associated with usefulness of ABCM for product planning but not with cost management.
When considering the proposed effects in H1a, H1b, and H1c within the structural model, the association between usefulness of ABCM for product planning and the implementation factor of clarity of objectives remains significant, but only marginally; the association with implementation factor training is not significant. The association between usefulness of ABCM for cost management and implementation factor top management support is marginally significant, while implementation factor clarity of objectives is not significant. The association between usefulness of ABCM for product planning and implementation factor training is not significant. (9) Hypotheses 1a and lb receive limited support in the context of this study. Hypothesis 1c is not supported.
Hypotheses 2-5 specify the indirect paths of ABCM behavioral implementation factors on ABCM outcomes, acting through conflict. Hypotheses 2 and 3 considered the direct effects of conflict on outcomes. The proposed associations between cognitive conflict and both usefulness of ABCM for product planning and cost management are strongly supported. The link between affective conflict and usefulness of ABCM for cost management is significant at conventional levels and with usefulness for product planning at the p < 0.10 level. (10) Hypotheses 2 and 3 are supported, partially, with the study confirming that conflict, particularly cognitive conflict, has an important role in determining the usefulness of ABCM.
The next stage in the model involved hypotheses proposing that concern with ABCM behavioral implementation would enhance cognitive conflict. Lack of attention to these factors was predicted to result in affective conflict. The results are mixed. Of the three implementation variables identified, clarity of objectives and training are associated with cognitive conflict. However, there is no support for the predicted associations between any of the behavioral implementation factors and affective conflict.
Finally, the study examined H1d, which stated that there are positive associations between financial success and both the usefulness of ABCM for product planning and for cost management. Only ABCM usefulness for product planning was associated with financial success. While the association with usefulness of ABCM for cost management is in the predicted direction, it is only significant at p < 0.15 level.
The objective of this study was to examine the extent to which cognitive and affective conflict are involved in the relationship between ABCM behavioral implementation factors and the usefulness of ABCM during early applications of the systems. Drawing on the work of Shields (1995), three constructs of ABCM behavioral implementation were identified: top management support, which included links to competitive strategy and resourcing of ABCM; clarity and consensus of objectives, which included nonaccounting ownership of ABCM; and training. As a first step, the study used multiple regression to examine the way in which the implementation practices were related directly to the usefulness of ABCM for product planning and cost management, and how attributes of ABCM usefulness were associated with financial success. Table 6 provides the results and enables comparisons to be drawn with earlier research that examined only direct effects.
There were positive associations between the usefulness of ABCM for product planning and implementation factors clarity of objectives (p < 0.05) and training (p < 0.10), and between usefulness of ABCM for cost management and implementation factors top management support (p < 0.05) and clarity of objectives (p < 0.10). Financial success was associated only with usefulness of ABCM for product planning (p < 0.05). While not hypothesized, there were positive associations between financial success and top management support (p < 0.05) and training (p < 0.10). The results are consistent with Shield's (1995) findings related to overall success. However, Shields (1995) also found that only nonaccounting ownership, which is part of clarity of objectives in the current study, was associated with financial success. The current study did not support this, finding that top management support and training were associated with financial benefits. Similar results to this study involving satisfaction with ABCM were found by McGowan and Klammer (1997) and Foster and Swenson (1997). Overall, the current study provides further evidence supporting these earlier studies.
In the current study, which examined early rather than mature applications, top management support was significantly associated with usefulness of ABCM for cost management, and clarity of objectives (which included nonaccounting ownership) was associated with the usefulness of ABCM for product planning. These results differ somewhat from prior research into early ABCM applications (Anderson and Young 1999; Krumwiede 1998). Differences in samples, examining usefulness rather than extent of use, and different measures of outcomes may explain, in part, these different results. However, the findings of the current study are consistent with the change management literature that emphasizes the importance of top management support, commitment of operational personnel, adequate resourcing, clarity of strategy, and training (Kanter et al. 1992).
The novel contribution of the current study is in examining the role of conflict in the relationship between behavioral factors employed in implementing ABCM and the outcomes of the applications. Results from a structural model found that the association between implementation factor top management support and usefulness of ABCM for cost management was marginally significant. This further attests to the potentially important role of top management support in early applications of ABCM. In addition, there was evidence of a weak direct association between implementation factor clarity of objectives and usefulness of ABCM for product planning. Considering the intervening role of conflict, significant paths were found between both implementation factors of training and clarity of objectives and cognitive conflict, and then between cognitive conflict and the usefulness of ABCM for both product planning and cost management. Affective conflict was negatively associated with usefulness of ABCM for cost management and with product planning, the latter at p < 0.10 level. However, none of the implementation factors was associated with affective conflict. Finally, none of the implementation factors or cognitive and affective conflict were associated with financial success.
The results of the study support the idea that cognitive conflict is important in ensuring the usefulness of ABCM and that concern with training and clarity of objectives during implementation can assist in encouraging cognitive conflict. It seems likely that training provides an important role in ensuring that individuals are skilled and confident in debating issues concerning the adoption and use of ABCM. Training may also provide an ongoing forum for discussing ABCM issues in ways whereby individuals are not disadvantaged by lack of understanding of the practice of ABCM and the nature of its application in their organizations. This may help improve capabilities and commitment to ABCM (Wooldridge and Floyd 1989, 1990) and, as a consequence, enhance cognitive conflict. Thus, the results of the structural model suggest that while training did not have a direct effect on favorable outcomes, it did have a role in enhancing cognitive conflict, which is important in developing useful ABCM systems.
The implementation factor, clarity of objectives, involves nonaccounting ownership of ABCM, and clarifying and gaining consensus about objectives. Each of these dimensions has a potential role to play in ensuring effective interactions during ABCM implementation. First, having nonaccounting personnel own ABCM provides a basis for those with process knowledge to participate in the ABCM implementation and to share knowledge relevant to the applications. It has been shown that participation of those with operational knowledge in the early stages of ABCM implementation, leads to improved discussions about ABCM and with success of the systems (Anderson 1995). Second, clarifying and gaining consensus involves processes whereby managers can focus their capabilities and knowledge about how ABCM might influence their operations and their role in the organization. Goal theory suggests that such clarification of objectives can reduce ambiguity and that this can encourage managers to expend effort to try and make the systems work (Taylor et al. 1984; Graen 1976). All of these implementation elements encourage cognitive conflict where managers bring their capabilities and knowledge about operations to the ABCM application and engage in interactions focused on the primary task of effectively implementing ABCM. The results of this study provide some support for the proposition that the implementation factor clarity of objectives has a role in ensuring benefits are received from ABCM, and importantly, that this aspect of implementation is significant in developing cognitive conflict.
Top management support had a marginally significant direct effect on cost management (p < 0.10) within the structural model. However, it was not significantly related to cognitive or affective conflict. It may be that providing adequate resourcing and formulating links to competitive strategy are somewhat macro conditions that have a role in establishing and supporting ABCM, but are not part of the ongoing interactions that potentially influence conflict. Managers of SBUs, by definition, have considerable responsibility for using ABCM to assist in formulating and implementing product decisions and cost management. Once the ABCM systems have developed momentum, top management is often not involved closely with the details of implementation.
Linking ABCM to performance measurement has been associated with success in various studies (Shields 1995; Anderson and Young 1999). In the current study, links to performance measures did not form part of any of the constructs and was excluded from the analysis. Informal feedback from several respondents who agreed to discuss the results of the survey, after the study, suggested that it was not the norm to build performance measures into early applications of ABCM. For some, plans to extend the ABCM to include performance links were to be considered after the systems had bedded down. For others, while information on nonfinancial measures had been in use for some time and were now derived from the ABCM, formal integration of performance measures with ABCM was not proposed.
It is noteworthy that while affective conflict was associated with usefulness of ABCM, associations with implementation factors within the structural model were insignificant. Apparently, lack of attention to ABCM behavioral implementation factors did not generate the levels of tension that were predicted to result in increased affective conflict. It is possible that in some organizations accounting innovations are not a strong part of the information and decision culture. Consequently, in the early stages of implementation they may be ignored or tolerated and are not taken sufficiently seriously to generate affective conflict. It may also be the case that other aspects of the implementation, not considered in this study, are important in generating affective conflict. For example, if the systems are part of broader change programs, lack of attention to behavioral factors may be taken more seriously and consequently have effects on affective conflict. Also, it is possible that ABCM implementation factors not included in the study, such as linkages to quality initiatives or JIT programs, may have been associated with affective conflict. The personality of the accounting personnel introducing the ABCM may influence the style of personal interchanges during implementation. It may, also, have been that the instrument used to measure affective conflict was not sufficiently sensitive to measure this attribute of the social processes involved in ABCM implementation.
The importance of developing ABCM information that is useful to product planning and cost management was examined in the structural model by identifying if these outcomes were significantly associated with financial success. Only usefulness of ABCM for product planning was associated with financial success. This finding should be treated with caution as only a single item was used to measure financial success. In addition, it is not clear that sustained financial benefits would be derived from early applications of ABCM. However, it is possible that effective implementation of ABCM results in some initial rapid financial gains as unprofitable products are identified and deleted from the product range. Benefits from actions to implement cost management strategies may take longer to generate a financial impact.
This study is subject to several limitations. First, the results of the analysis represent necessary but not sufficient conditions for the existence of causal relationships. The paths indicate statistical associations consistent with the theory developed in the paper. It is possible that reverse or recursive causality is possible. For example, an organization experiencing cognitive conflict may encourage the adoption of ABCM behavioral implementation, which, in turn, reinforces cognitive conflict, and so on. Alternate research methods employing experimental techniques or longitudinal cases could investigate these issues. Second, the ABCM behavioral implementation factors studied are only a subset of possible factors. This was justified to provide focus for theory development, tied to the nature of the studied organizations. While this may be satisfactory in relatively novel areas of research, more work is needed to validate dimensions of ABCM behavioral implementation and then to articulate, further, theory relating additional dimensions to particular outcomes. Third, more research in considering additional aspects of success is required. In particular, it is possible that the model may be sensitive to the type of success such as decision quality or learning (Swenson 1995; Foster and Swenson 1997). McGowan (1998) notes that the results of ABCM studies differ depending on attitudinal, perceptual factors, as well as technical and organizational dimensions. Fourth, while established instruments were used to measure conflict and ABCM implementation, outcome measures were novel. Fifth, the model considered only conflict and it is likely that other variables can add to the explanation of the association between behavioral implementation factors and successful outcomes. Factors such as the organization's culture or attitudes to accounting may indicate a proclivity to accept accounting innovations. These attitudes may affect the relationship between ABCM behavioral implementation, conflict, and organizational outcomes. Sixth, the study considered early applications of ABCM. It is likely that the role of conflict may differ in more mature applications of ABCM where managers have developed experience with the systems. Finally, a convenience sample was used in the study. While attempts were made to ensure the sample was representative of the target population, future studies could benefit from employing larger numbers selected randomly.
Notwithstanding these limitations, the results suggest that improved understanding of the study of implementing ABCM can be gained by examining the role of variables, such as conflict, that intervene between behavioral factors and the success of the application. The results of the study may be generalizable to other accounting innovations. For example, it has been suggested that management control systems, such as balanced scorecards, can assist in formulating strategy if they are used "interactively" to identify and generate discussion of strategic uncertainties (Simons 2000). Attention to behavioral implementation factors may provide a basis to ensure that cognitive conflict is generated, which may lead to more effective interactive use of the systems.
Factor 1: Training
The amount of training provided employees concerning implementing ABCM
The amount of training provided employees concerning designing ABCM
The amount of training provided employees concerning using ABCM
Factor 2: Clarity of Objectives
When ABCM initiatives began, the extent to which its objectives were clear and concise
When ABCM initiatives began, the extent of consensus about ABCM objectives
The degree of ABCM "ownership" by various operating departments (e.g., marketing, engineering, manufacturing)
Factor 3: Top Management Support
The degree to which the ABCM initiative has the support of top management
The degree to which ABCM is linked to competitive strategy
The amount of resources provided ABCM initiatives relative to the amounts of resources needed for them
The degree of linkage of the ABCM initiatives to quality incentives
The degree of linkages of the ABCM initiative to JIT and other speed initiatives
The degree of ownership by the accounting department
The degree of linkage of the ABCM to performance evaluation and compensation
Factor 1: Cognitive Conflict
How many disagreements over different ideas about implementation were there?
How many differences about content of the implementation did the group have to work through?
How many differences in opinion were there within the group over the implementation?
Factor 2: Affective Conflict
How much personal friction was there in the group during the implementation?
How much were personal clashes between group members evident during the implementation?
How much tension was there in the group during the implementation?
How much anger was there among the group over implementation?
USEFULNESS OF ABCM FOR PRODUCT PLANNING AND COST MANAGEMENT
Please indicate the extent to which you have found your organization's activity-based cost management (ABCM) system useful in assisting you in the following areas:
Factor 1: Product Planning Items
Decisions on the range of products
Decisions on the output of products
New product development and design
Customer profitability analysis
Factor 2: Cost Management
Cost reduction and modeling
Reengineering and improvement
TABLE 1 Respondents by Size of Strategic Business Units, Industry, and Functional Area Panel A: Size of Strategic Business Units Number of Employees n <50 10 51-100 8 101-200 12 201-500 14 >501 12 Panel B: Industry Category Steel fabrication 4 Automotive components 5 Electrical 10 Foodstuffs 6 Chemicals and plastics 11 Light engineering 12 Heavy engineering 8 Panel B: Functional Area Category Finance 35 Manufacturing 9 General management 12 TABLE 2 Factors for ABCM Implementation, Conflict, and ABCM Usefulness Panel A: ABCM Behavioral Implementation Factors: Training, Clarity of Objectives, Top Management Support Factor 1 Factor 2 Factor 3 Factor 1: Training ([alpha] = 0.90) Training concerning implementation 0.892 0.107 0.191 Training concerning designing 0.910 0.200 0.165 Training concerning using ABCM 0.833 0.185 0.328 Factor 2: Clarity of Objectives ([alpha] = 0.88) Clear and concise objectives 0.167 0.881 0.132 Consensus about objectives 0.219 0.992 0.059 Nonaccounting ABCM "ownership" -0.006 0.845 0.139 Factor 3: Top Management Support ([alpha] = 0.85) Top management support 0.103 0.168 0.866 Links to competitive strategy 0.228 0.098 0.860 Resources provided for ABCM 0.319 0.072 0.824 Eigenvalue 4.34 1.80 1.27 % Variance 48% 20% 14% Panel B: Conflict Factors: Affective and Cognitive Conflict Factor 1 Factor 2 Factor 1: Affective Conflict ([alpha] = 0.76) Person friction 0.823 -0.090 Personal clashes 0.873 -0.087 Tension 0.798 -0.186 Factor 2: Cognitive conflict ([alpha] = 0.78) Disagreements over different ideas -0.123 0.665 Differences about content -0.068 0.894 Differences in opinions -0.151 0.863 Eigenvalue 2.65 1.50 % Variance 44% 25% Panel C: ABCM Usefulness Factors: Product Planning and Cost Management Factor 1: Product Planning ([alpha] = 0.97) Pricing decisions 0.884 0.363 Range of products 0.910 0.340 Output of products 0.909 0.310 New product development and design 0.924 0.241 Customer profitability analysis 0.920 0.260 Factor 2: Cost Management ([alpha] = 0.74) Cost reduction and modeling 0.304 0.912 Reengineering and improvement 0.254 0.928 Budgeting 0.274 0.832 Performance measurement 0.332 0.873 Eigenvalue 6.49 1.64 % Variance 72% 19% See the Appendix for the complete wording of items. TABLE 3 Descriptive Statistics Actual Theoretical Standard Range Range Variable Mean Deviation Min Max Max Min ABCM behavioral 4.92 1.17 2.33 7.00 1.00 7.00 implementation Factor 1: Training ABCM behavioral 4.26 1.07 1.67 7.00 1.00 7.00 implementation Factor 2: Clarity of Objectives ABCM behavioral 4.14 1.22 2.33 6.67 1.00 7.00 implementation Factor 3: Top Management Support Cognitive conflict 4.12 1.16 1.33 6.33 1.00 7.00 Affective conflict 3.20 0.64 2.00 5.00 1.00 7.00 Usefulness of ABCM 4.08 1.31 1.60 6.08 1.00 7.00 for product planning Usefulness of ABCM 4.98 1.04 2.75 7.00 1.00 7.00 for cost management ABCM financial benefits 2.94 1.19 1.00 6.00 1.00 7.00 n=56 TABLE 4 Results of PLS: Path Coefficients and t-Statistics, [R.sub.2], Average Variance Extracted (AVE) Paths to Usefulness Usefulness of ABCM of ABCM Cognitive Affective for Product for Cost Conflict Conflict Planning Management Paths from ABCM 0.569 -0.212 -0.018 -0.004 implementation: (4.517) ** (-1.155) (-0.122) (-0.031) Training ABCM: 0.193 -0.096 0.153 -0.044 implementation: (2.214) ** (0.667) (1.550) * (-0.440) Clarity of Objectives ABCM: 0.032 -0.133 0.114 0.168 implementation: (0.224) (-0.919) (0.892) (1.494) * Top Management Support Cognitive 0.435 0.551 conflict (2.627) ** (4.372) ** Affective -0.176 -0.205 conflict (-1.309) * (-1.968) ** Usefulness of ABCM for product planning Usefulness of ABCM for cost management Financial Success Multiple [R.sup.2] 0.464 0.121 0.428 0.523 Financial AVE Success statistics (a) Paths from ABCM -0.008 0.852 implementation: (-0.041) Training ABCM: -0.029 0.815 implementation: (-0.200) Clarity of Objectives ABCM: 0.122 0.780 implementation: (0.859) Top Management Support Cognitive 0.031 0.675 conflict (0.120) Affective 0.143 0.557 conflict (0.792) Usefulness of ABCM (0.360) 0.833 for product (1.796) ** planning Usefulness of 0.218 0.959 ABCM for cost (1.095) management Financial (b) Success Multiple [R.sup.2] 0.307 *, ** p < 0.10, p < 0. 05, respectively. (a) Average Variance Extracted. (b) Single item measure. TABLE 5 Correlations Derived from PLS Usefulness of ABCM Product Cost Financial Planning Management Benefits Usefulness of 0.747 *** ABCM for cost management Financial 0.510 *** 0.479 *** benefits ABCM 0.434 *** 0.486 *** 0.278 ** implementation: training ABCM 0.394 *** 0.272 ** 0.184 implementation: clarity of cbjectives ABCM 0.355 *** 0.410 *** 0.297 ** implementation: top management support Cognitive 0.602 *** 0.677 *** 0.361 *** conflict Affective -0.415 *** -0.469 *** -0.145 conflict ABCM Implementation Top Clarity of Management Cognitive Training Objectives Support Conflict Usefulness of ABCM for cost management Financial benefits ABCM implementation: training ABCM 0.367 *** implementation: clarity of objectives ABCM 0.482 *** 0.281 ** implementation: top management support Cognitive 0.655 *** 0.411 *** 0.364 *** conflict Affective -0.311 ** -0.210 * -0.263 ** -0.419 *** conflict *, **, *** p < 0.10, p < 0.05, p < 0.01, respectively. TABLE 6 Multiple Regressions Results (n = 56) Panel A: Dependent Variable: Usefulness of ABCM for Product Planning by ABCM Behavioral Implementation Factors ABCM Behavioral Standard Implementation Factor [beta] Error t Probability Training 0.174 0.169 1.352 0.10 Clarity of Objectives 0.288 0.169 2.237 0.05 Top Management Support 0.162 0.169 1.255 NS adjusted [R.sup.2] = 0.090 F = 2.803, p < 0.05 Panel B: Dependent Variable: Usefulness of ABCM for Cost Management by ABCM Behavioral Implementation Factors ABCM Behavioral Standard Implementation Factor [beta] Error t Probability Training 0.147 0.135 1.131 NS Clarity of Objectives 0.189 0.135 1.453 0.10 Top Management Support 0.259 0.135 1.996 0.05 adjusted [R.sup.2] = 0.070, F = 2.479, p < 0.05 Panel C: Dependent Variable: Financial Success by Usefulness of ABCM for Product Planning and Cost Management Usefulness of ABCM for Product Planning and Cost Standard Management [beta] Error t Probability Product Planning 0.383 0.149 2.479 0.05 Cost Management 0.072 0.187 0.465 NS adjusted [R.sup.2] = 0.154, F = 5.987, p < 0.0.01 Panel D: Dependent Variable: Financial Success by ABCM Behavioral Implementation Factors ABCM Behavioral Standard Implementation Factors [beta] Error t Probability Training 0.207 0.165 1.586 0.10 Clarity of Objectives 0.117 0.165 0.894 NS Top Management Support 0.243 0.165 1.866 0.05 adjusted [R.sup.2] = 0.065, F = 2.266, p < 0.0.05
My thanks to Mike Shields for comments on an earlier version of this paper. Also thanks to David Smith, Steve Kaplan, and two anonymous reviewers for valuable suggestions and advice.
(1) It has been shown that different factors affect the success of ABCM depending on whether the implementation is early or more mature (Anderson 1995; Krumwiede 1998; Kennedy and Affleck-Graves 2001).
(2) For ease of exposition the term "behavioral" is used to cover both implementation factors focused on individuals, such as "ownership of the ABCM," and factors that may more accurately be described as organizational, such as "linkages to competitive strategy."
(3) The way dimensions of ABCM behavioral implementation combine into constructs is essentially an empirical question (Shields 1995). The precise nature of underlying constructs of ABCM implementation and their determination are elaborated in the method section.
(4) Also, training can assist in ensuring that the interrelationships between ABCM, strategy, and operations are examined by end-users and system designers, thereby providing opportunities and stimulation for the development of cognitive diversity.
(5) The average time of application in Krumwiede's (1998) study was 2.57 years and more integrated systems were associated with time of application. Anderson and Young's (1999) mature applications were sites that had first completed an ABCM system two years prior to their study. These rules place the current study's applications in the earlier phase of implementation. The average time of the applications in this study was 12 months.
(6) In this study the nature of the relationship between constructs and measures is reflective. That is, measures are believed to reflect the unobserved underlying constructs, with each construct giving rise to the observed measures. As such, it is appropriate to identify reliability and convergent validity of measures. This may be contrasted with formative measures that define or cause the construct and may or may not be correlated.
(7) The initial list, derived from the potential benefits of ABCM claimed in the literature, included pricing, product range, output of products, subcontracting, product design, customer profitability analysis, budgeting, performance measurement, cost reduction and modeling, reengineering and improving process, inventory management, improving supplier networks, improve customer service relations, and investment appraisal.
(8) Factor scores are used in the regression analysis (Tabachnick and Fidell 1996, 678).
(9) It is noteworthy that there are no significant associations between financial success and ABCM behavioral implementation factors, supporting the view that a direct effect of ABCM on financial success for early application seems unlikely.
(10) There were no significant associations between financial success and either cognitive or affective conflict.
Amason, A. C., and D. M. Schweiger. 1994. Resolving the paradox of conflict, strategic decision making and organizational performance. International Journal of Conflict Management 5: 239-253.
--. 1996. Distinguishing the effects of functional and dysfunctional conflict on strategic decision making: Resolving a paradox for top management teams. Academy of Management Journal 39: 123-148.
Anderson, S. W. 1995. A framework for assessing cost management system changes: The case of activity based costing implementation at General Motors. Journal of Management Accounting Research 7: 1-51.
--, and S. Young. 1999. The impact of contextual and process factors on the evaluation of activity-based costing systems. Accounting, Organizations and Society 24: 525-559.
--, J. W. Hesford, and S. M. Young. 2002. Factors influencing the performance of activity based costing teams: A field study of ABC model development time in the automobile industry. Accounting, Organizations and Society 27: 195-211.
Argyris, C., and R. Kaplan. 1994. Implementing new knowledge: The case of activity based costing. Accounting Horizons (September): 83-105.
Astley, G. W., R. Axelsson, J. Butler, D. J. Hickson, and D. C. Wilson. 1982. Complexity and cleavage: Dual explanations of strategic decision making. Journal of Management Studies 19: 357-375.
Banerjee, J., W. and Kane. 1996. Report on CIMA/JBA survey. Management Accounting (October): 30-37.
Bantel, K. A., and S. E. Jackson. 1989. Top management and innovations in banking: Does the composition of the top team make a difference. Strategic Management Journal 10: 107-112.
Baron, R. A. 1991. Positive effects of conflict: A cognitive perspective. Employee Responsibilities and Rights Journal 4: 25-36.
Bhimani, A., ed. 1996. Management Accounting, European Perspectives. Oxford, U.K.: Oxford University Press.
Bourgeois, L. J. 1980. Performance and consensus. Strategic Management Journal 1: 227-248.
Brehmer, B. 1976. Social judgment theory and the analysis of interpersonal conflict. Psychological Bulletin 83: 985-1003.
Bromwich, M., and A. Bhimani. 1994. Management Accounting: Pathways to Progress. London, U.K.: The Chartered Institute of Management Accountants.
Chenhall, R. H., and K. Langfield-Smith. 1998. Adoption and benefits of management accounting practices: An Australian study. Management Accounting Research (9) 1: 1-19.
Chin, W. W. 1998. The partial least square approach for structural equation modeling. In Modern Methods for Business Research, edited by G. A. Marcoulides. Mahway, NJ: Lawrence Erlbaum Associates.
Churchman, C. W. 1971. The Design of Inquiring Systems: Basic Concepts of Systems and Organizations. New York, NY: Basic Books.
Cooper, R. 1988. Cost management concepts and principles: The rise of activity-based costing--Part one: What is an activity-based cost system. Journal of Cost Management (March): 45-54.
--, R. Kaplan, L. Maisel, E. Morrissey, and R. Oehm. 1992. Implementing Activity-Based Cost Management. Montvale, NJ: Institute of Management Accountants.
--, and --. 1992. From ABC to ABM. Management Accounting (US) 74: 54-57.
Cosier, R. A. 1978. The effects of three potential aids for making strategic decisions on prediction accuracy. Organizational Behavior and Human Performance 22: 295-306.
--. 1981. Dialectical inquiry in strategic planning: A case of premature acceptance. Academy of Management Review (October): 643-648.
Cotton, W. D. J., S. M. Jackman, and R. A. Brown. 2003. Note on a New Zealand replication of the Innes et al. UK activity-based costing survey. Management Accounting Research 14 (1): 67-72.
Dess, G. G. 1987. Consensus on strategy formulation and organizational performance: Competitors in a fragmented industry. Strategic Management Journal 8: 259-277.
Erez, M., P. C. Earley, and C. L. Hulin. 1985. The impact of participation on goal acceptance and performance: A two-step model. Academy of Management Journal 28: 50-66.
Evan, W. 1965. Conflict and performance in R & D organizations. Industrial Management Review 7: 37-46.
Evans, H., and G. Ashworth. 1996. Survey conclusions: Wake up to the competition. Management Accounting (UK) (May): 16-18.
Fiol, C. M. 1994. Consensus, diversity, and learning in organizations. Organization Science 5: 403-420.
Folger, R. 1977. Distributive and procedural justice: Combined impact of "voice" and improvement of experienced inequality. Journal of Personality and Social Psychology 35: 108-119.
Fornell, C., and D. F. Larcker. 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18: 39-50.
Foster, G., and D. W. Swenson. 1997. Measuring the success of activity-based cost management and its determinants. Journal of Management Accounting Research 9: 109-141.
Gordon, L. A., and K. J. Silvester. 1999. Stock market reactions to activity-based costing adoption. Journal of Accounting and Public Policy (18) 3: 229-251.
Graen, G. 1976. Role-making processes within complex organizations. In Handbook of Industrial Organizational Psychology, edited by M. D. Dunnette. Chicago, IL: Rand McNally.
Hair, J. F. Jr., R. E. Anderson, R. L. Tatham, and W. C. Black. 1998. Multivariate Data Analysis. Upper Saddle River, NJ: Prentice Hall.
Hoffman, L. R., and N. R. F. Maier. 1961. Quality and acceptance of problem solutions by members of homogenous and heterogeneous groups. Journal of Abnormal and Social Psychology 62: 401-407.
Hrisak, D. 1996. The controller as strategist. Management Accounting (US) (December): 48-49.
Hulland, J. 1999. Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal 20: 195-204.
Innes, J., and F. Mitchell. 1995. A survey of activity-based costing: A survey of CIMA members. Management Accounting Research (June): 137-153.
--, --, and D. Sinclair. 2000. Activity-based costing in the UK's largest companies: A comparison of 1994 and 1999 survey results. Management Accounting Research 11: 349-362.
Ittner, C. D., W. N. Lanen, and D. F. Larcker. 2002. The association between activity-based costing and manufacturing performance. Journal of Accounting Research (June): 711-726.
Janis, I. L. 1982. Groupthink: Psychological Studies of Policy Decisions and Fiascoes. Boston, MA: Houghton Mifflin.
Jehn, K. A., and W. Oswald. 1992. Theory, a thesaurus, and word frequency. Cultural Anthropology Method 5: 8-10.
--. 1994. Enhancing effectiveness: An investigation of advantages and disadvantages of value-based intragroup conflict. International Journal of Conflict Management 5: 223-238.
Kanter, R. M., B. A. Stein, and T. D. Jick. 1992. The Challenge of Organizational Change. New York, NY: Free Press.
Kaplan, R. S. 1994. Management accounting (1984-1994): Development of new practices and trends of development. Management Accounting Research 5: 247-260.
Kennedy, T., and J. Affleck-Graves. 2001. The impact of activity-based costing techniques on firm performance. Journal of Management Accounting Research 13: 19-45.
Kraemer, K., J. Danziger, D. Dunkle, and J. King. 1993. The usefulness of computer based information to public managers. MIS Quarterly (June): 129-148.
Krumwiede, K. R. 1998. The implementation stages of activity based costing and the impact of contextual and organizational factors. Journal of Management Accounting Research 10: 239-277.
Leonard-Barton, D. 1988. Implementation characteristics of organizational innovation. Communications Research (15) 5: 603-631.
Locke, E. A., K. N. Shaw, L. M. Saari, and G. P. Latham. 1981. Goal setting and task performance. Psychological Bulletin 90: 125-152.
Lukka, K., and M. Granlund. 1996. Cost accounting in Finland: Current practice and trends of development. The European Accounting Review (5): 1-28.
McGowan, A. S., and T. P. Klammer. 1997. Satisfaction with activity-based cost management implementation. Journal of Management Accounting Research 9: 217-237.
--. 1998. Perceived benefits of ABCM implementation. Accounting Horizons (12) 1:31-50.
Mintzberg, H., D. Raisinghani, and A. Theoret. 1976. The structure of unstructured decision processes. Administrative Science Quarterly 21: 192-205.
Mitroff, I. I., and J. R. Emshoff. 1979. On strategic assumption making: A dialectical approach to policy and planning. Academy of Management Review (January): 1-12.
Murray, A. I. 1989. Top management group heterogeneity and firm performance. Strategic Management Journal 10: 125-141.
Palmer, R. J., and M. Vied. 1998. ABC: Could ABC threaten the survival of your company? Management Accounting (US) (November): 33-36.
Pelled, L. H. 1996. Demographic diversity, conflict and work group outcomes: An intervening process theory. Organization Science 7: 615-631.
Petersen, D. R. 1983. Conflict. In Close Relationships, edited by H. H. Kelley et al., 360-396. New York, NY: W. H. Freeman.
Pinkley, R. L. 1990. Dimensions of conflict frame: Disputant interpretations of conflict. Journal of Applied Psychology 75: 117-126.
Player, R. S., and D. E. Keys. 1995. Lessons from the ABCM battlefield: Getting off to the right start. Journal of Cost Management (Spring): 2-37.
Putnam, L. L. 1994. Productive conflict: Negotiation as implicit coordination. International Journal of Conflict Management 5: 285-299.
Robbins, S. P. 1989. Organizational Behavior Concepts, Controversies and Applications. Englewood Cliffs, NJ: Prentice Hall.
Ross, R. S. 1989. Conflict. In Small Groups in Organizational Settings, edited by R. Ross, and J. Ross, 139-178. Englewood Cliffs, NJ: Prentice Hall.
Schultz, R, and D. P. Slevin. 1975. Implementation and organizational validity. In Implementing Operations Research, Management Science, edited by R. L. Schultz, and D. P. Slevin, 153-182. New York, NY: American Elsevier.
Schweiger, D. M., W. R. Sandberg, and J. W. Ragan. 1986. Group approaches for improving strategic decision making: A comparative analysis of dialectical inquiry, devil's advocacy, and consensus. Academy of Management Journal 32: 745-772.
--, and --. 1989. The utilization of individual capabilities in group approaches to strategic decision-making. Strategic Management Journal 10: 31-43.
Schwenk, C., and J. S. Valacich. 1994. Effects of devil's advocacy and dialectical inquiry on individuals versus groups. Organizational Behavior and Human Decision Processes 59: 210-222.
Selto, F. H. 1995. Implementing activity-based management. Journal of Cost Management (Summer): 36-49.
Shank, J. K., and V. Govindarajan. 1995. Strategic Cost Analysis: The Evolution from Managerial to Strategic Accounting. Homewood, IL: Irwin.
Shields, M. D., and S. M. Young. 1989. A behavioral model for implementing cost management systems. Journal of Cost Management (Winter): 17-27.
--, and --. 1994. Behavioral and organizational issues. In Handbook of Cost Management, edited B. Brinker. New York, NY: Warren Gorham Lamont.
--. 1995. An empirical analysis of firms' implementation experiences with activity-based costing. Journal of Management Research 7: 148-166.
Simons, R. 2000. Performance Measurement and Control Systems for Implementing Strategy. Upper Saddle River, NJ: Prentice Hall.
Swenson, D. 1995. The benefits of activity-based cost management to the manufacturing industry. Journal of Management Research 7: 167-180.
Tabachnick, B. G., and L. S. Fidell. 1996. Using Multivariate Statistics. New York, NY: Harper Collins College Publishers.
Taylor, M. S., C. D. Fisher, and D. R. Ilgen. 1984. Individuals' reactions to performance feedback in organizations: A control theory perspective. Research in Personnel and Human Resource Management 2: 81-124.
Wanous, J. P., and M. A. Youtz. 1986. Solution diversity and the quality of group decisions. Academy of Management Journal 29: 149-159.
West, M. A. 1990. The social psychology of innovation in groups. In Innovation and Creativity at Work, edited by M. A. West, and J. L. Farr, 309-333. West Sussex, U.K.: Wiley & Sons.
Wold, H. 1985. Systems analysis by partial least squares. In Measuring the Unmeasureable, edited by P. Nijkamp, L. Leitner, and N. Wrigley. Dordrecht, Holland: Marinus Nijhoff.
Wooldridge, B., and S. W. Floyd. 1989. Strategic process effects on consensus. Strategic Management Journal l0: 295-302.
--, and --. 1990. The strategy process, middle management involvement, and organizational performance. Strategic Management Journal 11: 231-241.
Robert H. Chenhall
|Printer friendly Cite/link Email Feedback|
|Author:||Chenhall, Robert H.|
|Publication:||Behavioral Research in Accounting|
|Date:||Jan 1, 2004|
|Previous Article:||An investigation of the attributes of top industry audit specialists.|
|Next Article:||Audit-planning judgments and client-employee compensation contracts.|