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International Journal of Management Vol. 27 No. 1 April 2010 12�

The Impact of Business Unit Strategy, Structure and Technical Innovativeness on Change in Management Accounting and Control Systems at the Business Unit Level: An Empirical AnalysisFrank H.M. VerbeetenRotterdam School of Management, Erasmus University, Netherlands

This study tests the hypothesis that business unit strategy and business unit structure affect change in a business unit’s Management Accounting & Control System (MACS). Business units are parts of a larger ‘whole’ organization, such as departments, teams, strategic groups or divisions. Change in MACS has been assessed by asking managers to estimate the number of changes that has taken place in their business unit’s MACS over a two-year time period. Using data from a survey amongst 61 business unit managers in the Netherlands, the study suggests that the costing & transfer pricing and reward system are relatively ‘resistant to change’. The results also indicate that the administrative capacity of a business unit is the main driver of change in MACS. Finally, business unit strategy and business unit structure affect change in specific components of MACS at the business unit level, apparently depending on whether the change in MACS facilitates or influences managerial decisions.

IntroductionDespite several recent studies (eg. Emsley, 2005; Bouwens & Abernethy, 2005), our collective knowledge on the factors that drive change in management accounting & control systems (MACS) at the business unit level is limited. This is rather surprising, considering the fact that business units tend to vary considerably with regard to the accounting changes that they adopt and implement (cf. Bouwens & Abernethy, 2005; Kasurinen, 2002). Several studies have empirically investigated the impact of contextual and structural variables at the firm level in different countries (cf. Williams & Seaman, 2001; Libby & Waterhouse, 1996); however, general management literature suggests that theoretical models on change may not be transferable to other organizational levels or social-economic environments (see Pettigrew et al, 2001; Armenakis & Bedeian, 1999). Other empirical studies on change in MACS at the business unit level have focused on the relation between structure and the adoption of specific MACS-innovations (for example, Activity Based Costing, ABC; see Gosselin, 1997). However, structural arrangements may drive the adoption of a specific change in MACS yet may not drive change in MACS more generally (Emsley, 2005). In addition, studies that focus on the adoption of specific MACS-innovations may not provide insight in the factors that drive important changes in MACS; the importance of change in MACS is explicitly considered in this study.

The objective of this study is to investigate whether strategy and structure drive (major) change in MACS at the business unit level. The model that is developed in the theoretical section is subsequently tested using survey data from 61 business unit managers in the

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Netherlands. The results indicate that, consistent with the results from studies at the corporate level (cf. Williams & Seaman, 2001; Libby & Waterhouse, 1996), the planning, controlling and reporting components of the MACS are changed more frequently than the costing and reward system components of MACS. The administrative capacity of the business unit appears to be the main driver of change in MACS; in addition, strategy and structure drive change in specific components of MACS. Finally, industry and size indirectly affect change in MACS through their impact on the administrative capacity of the business unit. Overall, change in MACS appears to be related to the function of the MACS-component; i.e., organic business units appear to initiate (major) change in the decision-making components of MACS while more mechanistic business units seem to initiate (major) change in the decision-influencing components of MACS.

The remainder of this paper is organized as follows. The next section provides a review of the literature on change in MACS. Next, the research method and design are described, including the data collection methods and the sample selection. The fourth section provides the results of this study, as well as a discussion of the findings. Finally, some implications for theory and practice are discussed.

Literature ReviewChange in MACSThe focus of this research project is on change in the management accounting & control system (MACS). Many studies focus on change (see Pettigrew et al, 2001; Armenakis & Bedeian, 1999; and Damanpour, 1991; for reviews), yet what constitutes change is often taken for granted and its definition is often avoided (Quattrone & Hopper, 2001). According to Quattrone & Hopper (2001, p. 408), ‘… organizations ‘change’ when they transform their structure and operations; or MACS ‘change’ when a new information system is implemented or cost allocation bases are redefined…’. Whether the change is an innovation (i.e., ‘an idea, practice or object that is perceived as new by an individual or other unit of adoption’; cf. Gosselin, 1997; Rogers, 1995), whether it is actually implemented (cf. Damanpour, 1991) or whether the adoption of the innovation is driven by a rational process are issues that are not explicitly considered in this research project. In other words, the dependent variable should be considered as a measure of (an important) change (as opposed to stability) in MACS (see Granlund, 2001, p. 143).

Organizational characteristics and changeIn the general management literature, there are two dominant perspectives on how characteristics of organizations affect change: the techno-economic and the structural perspective (Collins et al, 1988, p. 513). The techno-economic perspective focuses on external pressures for change, such as changes in competition; the structural perspective focuses on the internal mechanisms in organizations that enable change. The structural perspective on change suggests that different organizational strategies and structures vary in their capacity for change in MACS because of the patterns of social action that those structures encourage (cf Collins et al, 1988). The focus of this study is on the structural perspective as it is generally considered most relevant for change in MACS (cf. Gosselin, 1997; Collins et al, 1988).


Strategy plays a key role in the change process (cf. Gosselin, 1997). Several generic taxonomies are recognized in the literature (see Langfield-Smith, 1997, for a review on strategy and MACS); for the purposes of this research project, I focus on the prospectors-analyzers-defenders taxonomy (Miles & Snow, 1978) as it has been employed in previous studies in this area (eg. Gosselin, 1997). The fundamental difference among prospectors, analyzers and defenders is the rate of change in the organizational domain (cf. Gosselin, 1997). Previous research suggests that strategies characterized by a prospector orientation are linked to flexible, decentralized structures and processes (Chenhall, 2003).

In addition, such prospective strategies are characterized by their dynamism in seeking market opportunities, their capability to develop and produce new products to meet customer needs, and their investments related to research and development (cf. Miles & Snow, 1978). In other words, prospector strategies tend to be associated with a higher degree of technical innovativeness (cf. Song et al, 2008).

Prospector business units also tend to have information needs (i.e., a higher administrative capacity) that cover a much broader range that competitors that focus on a defender strategy; this is mainly due to their quest for product market opportunities (see Chenhall, 2003). At first sight, this would suggest that prospector business units are also more likely to adapt their MACS to these new structures, i.e., are more likely to change their MACS (Gosselin, 1997). On the other hand, the power in prospector business units is more likely to be at the output functions (marketing, product development; Hambrick, 1981). This suggests that change of MACS is less likely to be a priority in these firms. The previous review results in the following hypothesis:

H1: A prospector strategy is positively associated with (a) decentralization, (b) technical innovativeness, and (c) administrative capacity yet not associated with (d) (major) change in MACS.

Another key structural dimension is decentralization; decentralization refers to the (locus of) authority and responsibility for making particular decisions that affect the organization (Kaplan & Atkinson, 1998, p. 288). Previous literature has argued and found empirically that decentralization is an important variable in explaining technical innovativeness (cf. Damanpour, 1991; Daft, 1978); the concentration of decision-making authority (i.e., centralization) prevents innovative solutions and experimenting with new technologies.

In addition, decentralization has been found to be associated with administrative capacity (cf. Libby & Waterhouse, 1996; Gordon & Narayanan, 1984). One reason is that managers in decentralized organizations need more detailed information to make informed decisions; in addition, firms use result controls in order to provide incentives to lower-level managers.

The relation between decentralization and change in MACS is more debatable. One stream of literature argues that decentralization is positively related to change in MACS (e.g. Abernethy & Bouwens, 2005); decentralization provides business unit


managers the awareness and opportunity to initiate and influence change in MACS as well as the legitimacy and ability to bring about change in the business unit (i.e., use the information from the change in MACS to improve business unit performance). However, another stream of literature argues that decentralization is negatively associated with change in MACS. For example, Daft (1978) argues that top managers are concerned with administrative problems and will be tuned to new developments that apply to these problems while business unit managers may be less likely to see the big picture administratively. Empirical evidence on the relation between decentralization and change in MACS is mixed (see Abernethy & Bouwens, 2005; Williams & Seaman, 2001; Gosselin, 1997; Libby & Waterhouse, 1996; Kimberly & Evanisko, 1980; Daft, 1978).

The previous review results in the following hypothesis:

H2: Decentralization is positively associated with (a) technical innovation and (b) administrative capacity yet not related to (c) (major) change in MACS.

Administrative capacity is defined as the use of administrative systems in a business unit (cf Libby & Waterhouse, 1996). Administrative capacity refers to the reservoir of current administrative expertise and experience that can be transformed into change or into the adoption of new systems and tools (Williams & Seaman, 2001). The presence of administrative expertise in the organization can serve to increase top management support for change in MACS, instill new knowledge among managers, and bolster internal commitment to the benefits of the new system (Williams & Seaman, 2001). In other words, possessing expertise in (specific areas) of MACS not only provides the ability to utilize that expertise, but also contributes to the organization’s absorptive capacity in that area (Libby & Waterhouse, 1996; Cohen & Levinthal, 1990). Empirical results by Libby & Waterhouse (1996) are consistent with the notion that administrative capacity is positively associated with change in MACS; however, results by Williams & Seaman (2001) are mixed.

The relation between administrative capacity and technical innovativeness is less clear. One stream of literature suggests that a high administrative capacity provides business unit managers information for problem solving and integration as well as with opportunities to initiate and monitor technical change (cf. Chenhall, 2003). However, another stream of literature suggests that a high administrative capacity may limit the ability of managers to experiment and search for new opportunities (cf. Chenhall, 2003). The previous review results in the following hypothesis:

H3: Administrative capacity is not associated with (a) technical innovativeness, yet positively associated with (b) (major) change in MACS.

Finally, I focus on the relation between technical innovativeness and change in MACS; again, two perspectives prevail (cf. Damanpour, 1991). Some researchers argue that change in MACS will lag technical innovations (Granlund, 2001; Gosselin, 1997); in that case, change in MACS is likely to be associated with technical innovativeness albeit with

International Journal of Management Vol. 27 No. 1 April 2010 127

some time lag. Others argue that technical innovativeness and change in MACS imply different decision-making processes (cf Daft, 1978); as a result, different structural factors are relevant. Empirical results (Damanpour, 1991) suggests that technical innovativeness and change in MACS may be related, yet driven by other structural factors. This results in my last hypothesis:

H4: Technical innovativeness is not associated with (major) change in MACS.

Figure 1 presents my theoretical model.

Materials and MethodsSampleThe level of analysis is specified at the business unit level. Student teams have been used to request business unit managers to cooperate in this research project and, once these managers agreed, to administer the questionnaire. Since the student teams have met with the respondent, potential problems with respondent identification (i.e., a junior employee rather than a business unit manager responds to the survey) and respondent understanding of the questions (teams have been instructed to clarify questions if asked, without giving guidance to obtain ‘desirable results’) are mitigated. The final sample includes the responses of 61 business unit managers.

Figure 1: Theoretical Model

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Figure 1: Theoretical model


The results indicate that the respondents work on average more than 6 years in the business unit (median: 4 years), and about 5 years in their current position (median: 4 years). The average business unit has more than 500 employees (median: 203), annual sales of approximately 250 mln euro (median: 65 mln euro) and total assets of about 296 mln euro (median: 69.5 mln euro). A profile of the industries where the respondents operate is provided in table 1.

Table 1 indicates that the distribution of the sample is somewhat biased towards business units in the service industry.

Measurement instruments

Survey instruments are based on previous research in this area, yet sometimes purposefully adjusted for this research project. Measurement instruments are validated by comparing the responses of managers to the responses of controllers of the same business unit; this correlation is generally positive and significant (p<0.05). In addition, the Cronbach alpha of all measurement instruments is above conventional levels of 0.7 (Hair et al, 1998).

Change in MACS is measured using an adapted version of the instrument developed by Libby & Waterhouse (1996) which has also been used by Williams & Seaman (2001), and Sulaiman and Mitchell (2005). Respondents have been asked to indicate the number of MACS that are currently used in their business unit in specific categories (planning, performance measures, costing, reward and reporting systems). In addition, respondents have been requested to identify the number of changes that have occurred in each of the categories of the MACS of their business unit during a specific time period (two years; May 2002-May 2004), regardless of the extent to which the changes have been integrated into daily operations. Finally, respondents have been asked to rank the impact of change in a specific component of MACS for their business unit. Change in MACS (CHMACS) is defined as the sum of the total number of changes in MACS (respectively the sum of the number of changes for each category i). Analysis of the distribution of this variable indicates that it is skewed to the right; therefore, the variable is winsorized. In addition, a variable (labeled CHMACSIMP) that proxies for the impact of change of each component of MACS (where 1=highest impact, 0= second or lower impact) is created.

Table 1: Profile of sample business units

Industry

# employeesManufacturing Service Public sector Total

<100 fte 6 10 4 20

100-250 fte 2 6 6 14

250-500 fte 3 5 4 12

>500 fte 5 4 6 15

Total 16 25 20 61


Independent variablesThe measure for decentralization (labeled DEC) is based on the instrument used by Gordon and Narayanan (1984). Respondents have been asked to indicate to what extent they can make (i) Strategic decisions, (ii) Investment decisions, (iii) Marketing decisions, (iv) Decisions regarding internal processes, and (v) Human resources decisions either independently, or whether decisions in these areas are out of their control (anchor: 1= I take the decision without discussing it with my superior, 5= I cannot influence the decision made). The measure for strategy is based on the instrument developed by Snow & Hrebiniak (1980); a similar instrument has also been used by Gosselin (1997). Respondents have been asked to indicate which description most adequately describes their business unit’s strategy: an innovative strategy, a stable strategy, a partially innovative, partially stable strategy, a reactive strategy or a divestment strategy (see Appendix A). Three dummy variables (labeled PROSPSTR, respectively DEFSTR and ANASTR) are created to indicate whether the business unit is oriented towards a prospector strategy respectively a defender or an analyzer strategy. Technical innovativeness (TECHINN) is based on the instrument used by Merchant (1990) and asks respondents to what extent their business unit is innovative in the following categories: (i) the choice and use of technology to produce products and/or services, (ii) the process design to produce the product or service, (iii) the design of the product or service itself, and (iv) the knowledge that is necessary for the product or service (anchors: 1=not at all, 5=to a very large extent). The measure for administrative capacity (AC) is based on Libby & Waterhouse (1996) and Williams & Seaman (2001) and is a summary measure for all systems in place (ACT), respectively a summary measure for all systems in each category (ACi). These measures are winsorized in order to prevent statistical problems.

The analysis includes dummy variables for the manufacturing (MANUFACT) and public sector (PUBLSEC) since previous literature suggests that accounting change may differ amongst manufacturing and service firms, as well as amongst for-profit and non-for-profit organizations (Damanpour, 1991). Finally, the logarithm for the total number of employees (labeled SIZE) has been used as a measure for size (which may proxy for the availability of resources; cf. Rogers, 1995).

Results of StudyThis section has three subsections. Descriptive statistics for all variables are reported in the first subsection. The regression results for the determinants of change in MACS are reported in the third subsection. Finally, the last subsection reports the results for some additional analysis and robustness checks.

Descriptive statisticsThe total number of changes in MACS, the subdivision of change in MACS in the five components of MACS and the importance of change in specific components of MACS is presented in table 2.

Table 2 indicates that the number of changes in MACS in the sample appears to be somewhat higher than the number of changes in MACS in the studies at the firm level

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(Sulaiman & Mitchell, 2005; Williams & Seaman, 2001; Libby & Waterhouse, 1996). One reason for the difference may be related to how change in MACS is measured in this study (i.e., count the total number of changes rather than ask whether the system has changed). Another (more speculative) reason may be that change in MACS appears more prevalent at the business unit level; i.e., business unit managers change their MACS in addition to the changes that are initiated at the top level of the firm. Finally, change in MACS may be affected by differences in national cultural (cf. Williams & Seaman, 2001) which may affect manager’s willingness to initiate change in MACS.

The planning (38%), performance measures (27%) and reporting (18%) components of the management accounting system are changed most often, while the costing & transfer pricing (10%) and reward system (8%) components of the MACS are relatively ‘resistant to change’. The ranking of change in components is similar to the ‘firm level studies’ in this area (eg. Sulaiman & Mitchell, 2005; Williams & Seaman, 2001; Libby & Waterhouse, 1996). In addition, all business unit managers indicate that their MACS has changed over the past 2 years; this defies claims in literature that MACS are rather stable (Granlund, 2001). The ranking of ‘change versus stability’ in the components, respectively the most important change of the MACS, is similar to the ranking for the average number of changes in MACS. However, interesting is that while the reporting system accounts for 18% in the number of changes, these changes are considered to be

Table 2: Matrix of Change in MACS by component1 2 3 4 5

Change in MACS PlanningPerform.

measuresCosting

Reward systems

Reporting system

Average number of changes in MACS (#)

4.80 1.80 1.31 0.46 0.38 0.85

Distribution of change (in %) 100% 38% 27% 10% 8% 18%

Percentage of BU’s indicating that MACS has changed

100% 74% 66% 39% 33% 64%

Most important change in subsystem

33% 21% 8% 5% 31%

Planning: planning systems (eg. budgeting, operations planning); Perform. measures: performance measures (eg. Individual or team-based performance measurement, measurement of performance in terms of quality)Costing & transfer pricing: costing & transfer pricing (eg. direct allocation of manufacturing overhead, marketing costs or other overhead)Reward systems: reward systems (eg. bonuses, pay-for-performance plans);Reporting system: reporting system (eg. information reported more frequently, use of more nonfinancial measures). PMS: performance measurement system, a summary measure for of column 2 (perform. measures) and 5 (reporting system)

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the most important by 33% of the respondents. On the contrary, changes in the planning and controlling components are considered (somewhat) less important relative to the number of changes in that system.

The means, standard deviations and actual and theoretical ranges for one of the dependent variable (CHMACS) and the independent variables are presented in table 3. The table reveals that there is sufficient variation in the variables.

Table 4 provides the Pearson correlation for the relevant variables. The correlation matrix reveals that change in MACS is not associated with strategy and decentralization.

Table �: Descriptive statistics

N Mean Std.dev Actual range Theoretical range

CHMACSTOT *) 61 4.80 4.15 0.00-18.00 0.00-?

PROSPSTR 61 0.30 0.46 0.00-1.00 0.00-1.00

DEFSTR 61 0.20 0.40 0.00-1.00 0.00-1.00

ANASTR 61 0.49 0.50 0.00-1.00 0.00-1.00

DEC 61 17.67 3.91 5.00-25.00 5.00-25.00

ACT *) 61 9.93 5.65 1.00-25.00 0.00-?

TECHINN 61 13.00 3.32 4.00-20.00 4.00-20.00

SIZE 61 2.34 0.54 1.51-3.64 0.00-?

MANUFACT 61 0.26 0.44 0.00-1.00 0.00-1.00

SERVICE 61 0.41 0.50 0.00-1.00 0.00-1.00

PUBLSECT 61 0.33 0.47 0.00-1.00 0.00-1.00

*): variables are winsorized

CHMACSTOT = absolute number of changes in MACS (total);

PROSPSTR = prospector strategy; DEFSTR = defender strategy; ANASTR = analyzer strategy;

DEC = Decentralization (a high score indicates a high degree of decentralization),

ACT = administrative capacity (total); TECHINN = technical innovativeness of BU; SIZE = log of number of employees (in full time equivalents), MANUFACT = dummy for manufacturing organizations

SERVICE = dummy for service organizations, PUBLSECT= dummy for public sector organizations.


Consistent with expectations is the finding that ACT is positively associated with change in MACS (p<0.01). Additional analysis (non-tabulated) indicates that change in MACS is positively correlated with change (versus stability) in the individual components (p<0.01). This suggests that there are business units who are more likely to change their MACS, while others aren’t. Overall, the correlations between the independent variables are such that multicorrelation is unlikely to be problematic in the regression equations. Standardized measures for the previous variables have been used in the remainder of the analysis.

Table �: Correlation matrix (N=�1)

1 2 3 4 5 6 7 8 9 10

1. CHMACSTOT

2. PROSPSTR -.056

3. DEFSTR .044 -.320*

4. ANASTR .047 -.636** -.487**

5. DEC .013 .277* -.064 -.094

6. ACT .468** .008 .212 -.123 .099

7. TECHINN -.185 .426** -.288* -.109 .361** .041

8. SIZE .056 -.135 .177 -.041 -.181 .274* -.205

9. MANUFACT .038 -.059 .080 .010 .281* .173 .181 -.011

10. SERVICE .299* .046 .091 -.086 -.144 .117 -.202 -.130 -.497**

11. PUBLSECT -.348** .008 -.170 .081 -.112 -.285* .042 .146 -.416** -.582**

*, ** = correlation is significant at the 0.05, respectively 0.01 level

CHMACSTOT = absolute number of changes in MACS (total);

PROSPSTR = prospector strategy; DEFSTR = defender strategy; ANASTR = analyzer strategy;

DEC = Decentralization (a high score indicates a high degree of decentralization),

ACT = administrative capacity (total); TECHINN = technical innovativeness of BU; SIZE = log of number of employees (in full time equivalents), MANUFACT = dummy for manufacturing organizations

SERVICE = dummy for service organizations, PUBLSECT= dummy for public sector organizations.

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Regression results for Changes in MACSOrdinary least squares, respectively binary logistics regressions is used to investigate the hypothesized relations between the independent variables, the (potentially) mediating variables, and, ultimately, changes in MACS:

CHMACSij = α

0 + α

1PROSPSTR

i + α

2ANASTR

i + α

3DEC

i + α

4ACj

i + α

5TECHINN

i

+ α6SIZE

i + α

7MANUFACT

i + α

8PUBLSECT

i + e

i

CHMACSi = change in MACS (i.e., CHMACST, total number of changes in MACS;

CHMACSi, change in one of the components of MACS; respectively CHMACSIM, a major change in one of the components of MACS);

PROSPSTR = prospector strategy (dummy);ANASTR = analyzer strategy (dummy);DEC = decentralization;

Table �a: Regression results for predictors of change in MACS Dependent variables:

DEC ACT TECHINN CHMACST

CONSTANT 0.00 0.00 0.00 0.00

PROSPSTR H1a + 0.35* H1c + -0.04 H1b + 0.51*** H1d 0 0.14

ANASTR ? 0.12 ? -0.10 ? 0.22** ? 0.19

DEC H2b + 0.13 H2a + 0.16* H2c 0 0.02

ACT H3a 0 0.10 H3b + 0.45***

TECHINN H4 0 -0.23

SIZE ? -0.13 + 0.33** ? -0.15 ? -0.05

MANUFACT ? 0.31*** ? 0.01 ? 0.22 ? -0.11

PUBLSECT ? 0.02 ? -0.31** ? 0.18 ? -0.26**

R2 0.19 0.20 0.33 0.34

Adj. R2 0.12 0.12 0.25 0.24

Prob(F-stat) 0.03 0.05 <0.01 <0.01

N 61 61 61 61

*, **, *** = significant at 0.10, 0.05 and 0.01 respectivelyDependent variable:CHMACST = total number of changes in MACS;Independent variables: PROSPSTR = prospector strategy; ANASTR = analyzer strategy;DEC = decentralization (a high score indicates a high degree of decentralization), TECHINN = technical innovativeness; ACi = administrative capacity for subcategories of MACS (i.e., planning, performance measures, costing & transfer pricing, reward systems and reporting systems), SIZE = log of number of employees in full time equivalentsMANUFACT = dummy for manufacturing organizations, PUBLSECT= dummy for public sector organizations.

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ACi = administrative capacity (i.e., ACT, the total number of systems; respectively ACi, the number of systems in each category for the specific components of MACS);

TECHINN = technical innovativeness;

MANUFACT = manufacturing organizations (dummy);

PUBLSECT= public sector organizations (dummy);

SIZE = log of number of employees (in full time equivalents),

e = error term.

To correct for potential problems with heteroskedasticity and autocorrelation, I use Newey-West standard errors respectively Huber/White robust covariances (Verbeek 2004). Table 5 provides the results.

Table �b: Regression results for specific components of MACS

Dependent variables:

CHMACSi Planning

CHMACSi Perform.

measures

CHMACSi Costing & transfer pricing

CHMACSi Reward systems

CHMACSi Reporting

system

CONSTANT 0.61** 0.74*** 0.11 0.04 0.27

PROSPSTR H1d 0 0.17 0.10 0.19 0.20* -0.03

ANASTR ? 0.47 0.11 0.15 0.10 -0.06

DEC H2c 0 0.28 -0.05 0.05 -0.12 -0.08

ACi

H3b + 0.33*** 0.21* 0.45*** 0.36** 0.31***

TECHINN H4 0 -0.39 -0.22 -0.11 -0.14 -0.10

SIZE ? -0.08 0.01 0.11 -0.11 -0.05

MANUFACT ? -0.21 -0.27 0.02 -0.07 0.06

PUBLSECT ? -0.44* -0.39** -0.15** -0.10 -0.06

R2 0.34 0.23 0.45 0.32 0.36

Adj. R2 0.24 0.11 0.37 0.23 0.27

ProbF-stat) <0.01 0.07 <0.01 <0.01 <0.01

N 61 61 61 61 61

Dependent variable: change in specific component (planning, performance measurement, etc) of MACS; Independent variables: PROSPSTR = prospector strategy; ANASTR = analyzer strategy;DEC = decentralization (a high score indicates a high degree of decentralization), TECHINN = technical innovativeness; ACi = administrative capacity for subcategories of MACS (i.e., planning, performance measures, costing & transfer pricing, reward systems and reporting systems), SIZE = log of number of employees in full time equivalentsMANUFACT = dummy for manufacturing organizations, PUBLSECT= dummy for public sector organizations.Dependent variable: Major change =1, versus 0= no major change) in specific components planning, performance measurement, etc) of MACS;


Table 5a suggests that, consistent with hypotheses 1a and 1b, structure (specifically: prospector strategy) is positively associated with decentralization and technical innovativeness. Consistent with hypothesis 2a is that decentralization is positively

Table �c: Regression results for major changes in components of MACS

Binary Logistics Major change in component of MACS (CHMACSIM):

Dependent variable:

CHMACSIM Planning

CHMACSIM Perform.

measures

CHMACSIM Costing & transfer pricing

CHMACSIM Reward systems

CHMACSIM Reporting

system

CONSTANT -1.46*** -0.86** -2.95*** a) -1.12***

PROSPSTR H1d 0 0.33 0.12 0.60* 0.32

ANASTR ? 0.79** -0.30 0.25

DEC H2c 0 0.02 -0.23 0.40 -0.55**

TECHINN H4 0 -0.51** -0.27 -0.32 0.11

ACi

H3b + 0.23** -0.06 1.05* 0.28*

SIZE ? -0.43* 0.45** 0.15 -0.18

MANUFACT ? 0.33 -0.28 0.50 0.23

PUBLSECT ? 0.39 -0.44* 0.33 0.00

McFadden R2 0.28 0.22 0.32 0.16

Prob (LR-stat) <0.01 0.08 0.14 0.13

N 61 61 61 61

*, **, *** = significant at 0.10, 0.05 and 0.01 respectivelya) The results cannot be computed due to a limited number of observations i.e., few respondents ranked the change in the reward system component of MACS as the most important change)

Dependent variable: Major change =1, versus 0= no major change) in specific components planning, performance measurement, etc) of MACS;

Independent variables:

PROSPSTR = prospector strategy; ANASTR = analyzer strategy;

DEC = decentralization (a high score indicates a high degree of decentralization),

TECHINN = technical innovativeness;

ACi = administrative capacity for subcategories of MACS (i.e., planning, performance measures, costing & transfer pricing, reward systems and reporting systems),

SIZE = log of number of employees in full time equivalents

MANUFACT = dummy for manufacturing organizations, PUBLSECT= dummy for public sector organizations.


associated with technical innovativeness. However, strategy and structure are not associated with the administrative capacity, which is inconsistent with hypotheses 1c and 2b. The administrative capacity of a business unit appears to be determined mainly by the control variables size and industry. Specifically, larger business units tend to have a larger administrative capacity (which is consistent with Chenhall, 2003); in addition, business units in the public sector tend to have a lower administrative capacity than their counterparts in the manufacturing and service industry (which is consistent with Damanpour, 1991). Hypotheses 3a and 4 are confirmed: administrative capacity does not affect technical innovativeness and technical innovativeness does not affect change in MACS. Finally, consistent with hypothesis 3b as well as findings from previous research (Libby & Waterhouse, 1996) is that administrative capacity is the main driver of change in MACS.

Additional analysis of the individual changes in MACS (in each component) confirms the previous results; that is, change in specific components of MACS is driven by the specific administrative capacity of the business unit. For example, changes in the planning system are driven by the number of planning systems that are already used in the business unit. However, strategy, structure, size and industry appear to drive change in specific components of MACS. For example, business units with a prospector strategy are more likely to change the costing & transfer pricing component of MACS while decentralization is negatively associated with change in the reward system component of MACS.

Finally, table 5c provides some insight in the determinants of important changes in MACS. The results suggest that major changes in the planning and costing & transfer pricing components are driven by the administrative capacity in that area as well as by the business unit strategy (i.e., analyzer respectively prospector strategy). In addition, technical innovativeness as well as size are negatively associated with a major change in the planning system. On the other hand, major changes in the reward and reporting components of MACS appear to be negatively associated with decentralization, as well as positively associated with the administrative capacity in that area (for reporting components).

Robustness checks

A number of robustness checks have been performed. For example, a binary variable for decentralization has been created as it could be argued that the measure for decentralization is non-continuous (i.e., the first three items indicate joint decision making while the last two items refer to a lack of decision rights). In addition, the previous analyses are repeated using solely the prospector strategy respectively the defender strategy variable (as these are the two extremes) rather than the two strategy variables (prospector and analyzer) jointly. Finally, the measure for change in (components of MACS) has been replaced by a binary measure (‘change versus no change’). All the previous analyses have been repeated; the results remain qualitatively similar to the results presented before.


Discussion and ConclusionThe objective of this study is to investigate the impact of structural variables on change in MACS at the business unit level. The literature review suggests that business unit strategy and structure affect the administrative capacity (AC) of the business unit; in addition, AC may affect change in MACS directly as well as through technical innovativeness.

The empirical results of this study indicate that the administrative capacity of the business unit is the main driver of change in MACS. Administrative capacity is associated with size (larger organizations tend to have a higher administrative capacity) as well as industry (public sector organizations tend to have lower administrative capacity). Business unit strategy and decentralization appear to affect technical innovativeness yet appear not to be related to the administrative capacity nor to change in MACS in general. Analysis of specific components of MACS provide some additional insights: strategy and structure appear to affect change in specific components in MACS (planning, costing & transfer pricing and reward system) rather than change in MACS in general.

Considering the definition of the components, it appears that the first three components of MACS support the decision-making function, while the last three components are associated with the decision-influencing role of MACS (cf. Davila & Foster, 2005). This suggests that major changes in MACS are affected by strategy as well as structure. Specifically, prospector and analyzer strategies appear to be positively associated with major changes in the decision-making components of MACS while decentralization is negatively associated with major changes in the decision-influencing components of MACS. The previous result suggest that the (perceived) role of the change in MACS (decision-making versus decision-influencing) should be considered when studying change in MACS. Overall, the results from my study are mostly consistent with the ‘dual-core model’ (cf. Daft, 1978): prospector (and analyzer) strategies and decentralization (characteristics of organic organizations) affect technical innovativeness as well as (major) change in the decision-making components of MACS, while centralization (a characteristic of mechanistic organizations) is associated with major change in the decision-influencing components of MACS.

While this study provides additional insights in the determinants of change in MACS, it is not without limitations. First of all, the previous results are based on a survey of managers in business units in the Netherlands; as such, the limitations of any survey research project apply. Second, the economic circumstances may have affected the number of changes in MACS. Considering the economic situation (i.e., a recession) during the period that this study covers, respondents may have reported a different number of changes in (components of) MACS than they would have in other time frames with a more prosperous economic situation. Third, the strong impact of the industry dummy suggests that change in MACS may be moderated by industry (cf. Williams & Seaman, 2001; Damanpour, 1991). Finally, respondents may have suffered from hindsight bias or forgetfulness; this problem is reduced by interviewing both managers and controllers from the same business unit.


Notwithstanding these limitations, the results from this study have extended our knowledge about the determinants of change in MACS at the business unit level. Future research may investigate the impact of a change in specific contextual and/or structural factors rather than evaluate the impact of the level of these factors. In addition, future research may investigate who initiated a change and for what purpose (decision-facilitating, decision-influencing) the change has been used. Finally, it may be interesting to investigate the impact of a change in MACS on attitudes of managers and controllers (i.e., individual factors) towards that change.

Footnotes

Assistance for this project was provided by Pentascope

Acknowledgments: Previous versions of this paper have benefited from comments from Arnick Boons, Frank Hartmann, Laurence van Lent and Roland Speklé. In addition, comments from workshop participants at ARNN at Erasmus University and the EAA 2006 in Dublin are appreciated.

Appendix A: Survey

(a) The next questions deal with the Management Accounting & Control Systems (MACS) that may be used in your business unit.

You are asked to indicate the number of systems that is currently used in your business unit (please provide the relevant number of systems in each cell; this may be an estimate). For example, if you use a budgeting, an operations planning and 3 different investment systems you enter a 5 in the first cell of the first column.

Please provide the number of changes in the past two years for each of the different system categories in the second column (again, this may be an estimate).

Please indicate in the last column which change(s) had the most impact on your business unit (BU). Please use the following ranking: ‘1’ =change with the largest impact, ‘2’ = change with the second largest impact, etc. If there have been no changes in one or more categories, you may leave the last column empty.


Name of the system

A. Number of systems currently

used in your business unit?

B. Number of changes

in the system in the past 2 years?

C. Impact of change on BU

(1=most important, etc)

MeanStd. dev

MeanStd.dev

% indicating that this is the most important

change

Planning systems: eg. budgeting, operations planning, capital budgeting, strategic planning, other planning systems

3.64 2.54 1.80 1.88 .33

Performance measures: eg. Individual or team-based performance measurement, organizational performance measurement, measurement of performance in terms of quality, measurement of performance in terms of customer satisfaction, other performance measures

2.72 2.10 1.31 1.43 .21

Costing & transfer pricing: eg. Direct allocation of manufacturing overhead, marketing costs or other overhead, internal product transfers, other costing systems

.79 .69 .46 .62 .08

Reward systems: eg. Reward systems –bonuses, reward systems- pay-for-performance plans, other reward systems

.93 .66 .38 .58 .05

Reporting: information reported more frequently, use of more nonfinancial measures, information reported more broadly, other changes to reporting systems

1.85 1.35 .85 .75 .31

Other (changes to) systems that do not appear on this list:

NR NR NR NR NR

Total (ACT, respectively CHMACST) 9.93 5.65 4.80 4.15

Decentralization

In this section we would like you to rank order the importance of each of the following decision areas in terms of how they affect the performance of your organizational unit. Consider each of these categories of decisions.


i. Strategic decisions (e.g., development of new products/services; entering, developing or exiting new markets; your unit’s strategy).

ii. Investment decisions (e.g., acquiring new assets and financing investment projects; purchase of information systems; expansion of existing capacity)

iii. Marketing decisions (e.g., marketing or advertising campaigns; pricing decisions; changes in inventory levels)

iv. Decisions regarding internal processes (setting production/sales priorities; inputs used and/or processes employed to fill orders; contracting input suppliers and/or consultants).

v.Human resources decisions (e.g., hiring/firing; compensation and setting career paths for the personnel employed within your unit; determining the bonus or promotion steps of employees; reorganizing project staffing)

Please indicate in the first table how much decision rights you and/or other members in the organization currently have with regard to the previously mentioned decisions.

Explanation:

(1) My decision = I (or one of my subordinates) takes the decision without discussing it with my superior

(2) Two person decision = I, jointly with my superior, take the decision

(3) Multi person decision = my superior and me discuss the issue with other operational managers and/or financial responsible persons and, jointly with them, come to a decision (broader in the organization)

(4) Advice = I am asked for advice, but do not have much influence on the decision regarding this issue

(5) Others= I cannot influence the decision made (higher in the organization or outside the organization), I am not asked for input or advice with respect to the consequences for my business unit

How are the decision rights within your business unit currently distributed?

Mean Std.dev

Strategic decisions 2.69 1.025

Investment decisions 2.54 .976

Marketing decisions 2.97 1.402

Decisions regarding internal processes 2.05 1.056

Human resources decisions 2.08 1.115

Cronbach alpha 0.73

Score for decentralization (DEC) is calculated as the sum of the reversed scores to the individual items


Technical innovativeness (TECHINN)

Please indicate to what extent your business unit is innovative in a number of categories. (1= not at all, 2=hardly, 3= average, 4=to a large extent, 5=to a very large extent)

Please indicate to what extent your business unit is innovative in each of the following categories

Mean Std.dev.

The choice and use of technology to produce products and/or services 3.25 1.15

The process design to produce the product or service 3.25 1.03

The design of the product of service itself 3.13 1.13

The knowledge that is necessary for the product or service 3.38 1.05

Cronbach alpha 0.81

Strategy

The next table provides a number of descriptions of business unit strategies. Please indicate which description most adequately describes the strategy of your business unit:

Which description most adequately describes the strategy of your business unit:

Mean Std.dev.

Innovative (PROSPSTR): Regular innovation of product and services. Flexible, fast reaction to changes in market circumstances. Motivation to be the best in the market. Regular innovations in technology, processes or structure of business unit. Emphasis on product, services or market innovations.

.30 .46

Stable (DEFSTR): Stable products or services. Limited changes to technology, processes or structure. Emphasis on improving the efficiency of current business processes.

.20 .40

Partially innovative, partially stable (ANASTR): Stable products or services. Scanning of the environment for new, promising developments. Focus on cost control, with deliberate risk taking.

.49 .50

Reactive: No clear cut strategy. Environment is unpredictable, business unit is focused on survival.

.02 .00

Divestment: business unit is withdrawing from this industry/activity. .00 .00

Industry

Measured by annual sales (or budget depletion), what is your business unit’s main industry? (A list of industries is provided in Appendix 1 of the survey).

Code

Mea Std.dev.

Manufacturing (MANUFACT) .26 .44

Service (SERVICE) .41 .50

Public sector (PUBLSECT) .33 .47


Size

Next, we have some questions on your business unit.

Mean Std.dev.

What was the number of employees in your business unit at the end of 2002 (fte) (SIZE)?

513 845

What were annual sales (or total budget) of your business unit at the end of 2003 (euro’s)?

257 mln 686 mln

What were total assets of your business unit at the end of 2003 (euro’s)?

296 mln 565 mln

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Contact email address: [email protected]

International Journal of Management Vol. 27 No. 1 April 2010 201

Frank H.M. VerbeetenRSM Erasmus UniversityBurg. Oudlaan 50, Room T8-333062 PA RotterdamNetherlands

Wei-Ning ChenYa-Hui HsuehYu-Ta ChenChingliang ChangSchool of BusinessKainan University1 Kainan St, TuchuTaoyuan County 33857Taiwan

Lara K. GardnerSang H. Lee Department of Business AdministrationSLU-10813Southeastern Louisiana UniversityHammondLA 70402 USA

Harry HoweSchool of BusinessState University of New York GeneseoGeneseoNY 14454 USA

Jeffrey LippittSchool of BusinessIthaca CollegeIthaca NY 14850 USA

Yaonan LinDepartment of Business AdministrationFu Jen University510 Chung-cheng RdHsinchuangTaipei County 242Taiwan

Ching Yi LaiDepartment of PsychologyFu Jen University510 Chung-cheng RdHsinchuangTaipei County 242Taiwan

Prin BoonkanitAthakorn KengpolDepartment of Industrial EngineeringFaculty of EngineeringKing Mongkut’s University of Technology North BangkokBangsueBangkok 10800Thailand

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