ساختار سازمانی، رقابت و سیستم های اندازه گیری عملکرد و اثر مشترک آنها بر عملکرد
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|382||2011||21 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Management Accounting Research, Volume 22, Issue 2, June 2011, Pages 84–104
This study examines the effect of organization structure and competition on the design of performance measurement systems (PMSs) and their joint effects on performance. The design of performance measurement systems is investigated using two dimensions: the use of integrated measures related to the four perspectives of the balanced scorecard (BSC) and the stage of development of PMSs. The data for this study were collected from 168 valid responses (25.19%) of Taiwanese firms listed on the Taiwan Stock Exchange. The results indicate that organization structure is significantly associated with the design of PMSs. Compared to mechanistic organizations, organic organizations make greater use of integrated measures and the higher developmental stages of PMSs. The findings also partly support the presence of joint effects on performance involving organization structure, competition, and the use of PMSs. Specifically, the results show that when there is greater competition among firms, a positive relationship between the stages of PMS development and performance is of higher significance. Another conclusion derived from this study is that the use of integrated measures is more relevant with respect to organizational performance in mechanistic organizations than in organic ones.
In relation to the appropriate use of performance measurement systems (PMSs) and their effects, contingency theory suggests that the fit between contextual factors and the design of management control systems (MCSs) is relevant to superior organizational performance (Chenhall, 2003, Ittner and Larcker, 1997, Langfield-Smith, 1997 and Luft and Shields, 2003). However, the results of the impact of PMSs and their organizational context are mixed (Davis and Albright, 2004, Hoque and James, 2000, Ittner and Larcker, 1998a, Ittner et al., 2003 and Malina and Selto, 2001). The role of PMSs can be seen as allocating responsibilities and decision rights, setting performance targets, and rewarding outcomes (Merchant and Van der Stede, 2007). This role is consistent with aspects of organization structure, which is a formal control framework that encompasses reporting relationships, interactions between employees, information flows, and authority distribution with regard to carrying out activities within the organization (Burns and Stalker, 1961, Galbraith, 1973, Germain, 1996 and Hall, 1987). The literature suggests important links between organization structure and performance measurement, which have been argued to be two of the most important design decisions made by managers (Abernethy et al., 2004, Langfield-Smith, 1997 and Luft and Shields, 2003). The literature on PMSs emphasizes the linkages between strategy and such measures (Chenhall, 2008, Chenhall, 2005, Ittner et al., 2003, Kaplan and Norton, 1996, Kaplan and Norton, 2001 and Otley, 1999), which aims to provide integrated approaches to linking operations with strategy and objectives to achieve the firm's goals in competitive markets. It is thus worthwhile to examine how PMSs can provide integrated information for better decision making and communication of strategic goals. This study addresses this issue by seeking to understand how organization structure and competition affect the design of PMSs, and how the contingent relationship of these two variables with such measurement systems affects organizational performance. According to Gerdin and Greve (2004), this study can be classified into two categories, including congruence and contingency type of fit.1 The first research question is to test the congruence of fit, which considers the strength of the relationship between organization structure and the use of integrated measures/PMSs. The second research question tests the contingency form of fit to examine whether the effect of the use of PMSs on performance differs across various levels of context variables, such as organization structures and competition. In line with previous research, this study considers the design of PMSs as having two dimensions. One is the use of integrated performance measures related to the four perspectives of the Balanced Scorecard (BSC). The other is the stages of PMS development. Prior empirical research related to the use of performance measurements has generally considered only a number of financial and nonfinancial performance measures in the organization's PMS or BSC (HassabElnaby et al., 2005, Hoque and James, 2000, Said et al., 2003 and Van der Stede et al., 2006). In contrast, this paper considers the stage of PMS development as manifested in its links to strategy and incentives. In other words, besides a simple count or presence of measures in any or all of the four dimensions of the BSC, this paper also adopts the notion of measurement system linked to strategy and incentives, based on recent calls for viewing a PMS in a “causal model” or “strategy map” context (Kaplan and Norton, 2001 and Ittner and Larcker, 2003). Given the causal links between performance measures, objectives and strategy contained in a PMS, achieving objectives by managers is dependent on the presence of incentives tied to the attainment of these goals (Ittner and Larcker, 2003 and Webb, 2004). PMSs with cause-effect links between strategy, objectives, and incentives that are perceived to be strong will increase the attractiveness of achieving these aims. Considering these features of PMS, we follow Speckbacher et al. (2003) and classify the stage of development of such systems in this study into three types. (1) Minimum-standard PMSs, i.e. those that only contain integrated performance measures in any or all of the four BSC dimensions. (2) Cause-and-effect PMSs, i.e. those that link to strategy by using cause-and-effect relationships. (3) Fully-developed PMSs, i.e. those that contain linkages to strategy and incentives as well as integrated performance measures related to the four perspectives of the BSC and encompassing cause-and-effect relationships between measures and strategy. This study seeks to contribute to the management accounting literature in two main respects. First, it integrates organization structure, competition and the design of PMSs to explore the fit of the MCSs in organizations. In the congruence type of MCS research, the main purpose is to test if the MCS design is associated with context variables (see e.g. Frucot and Shearon, 1991 and Kaplan and Mackey, 1992). This study thus meets previous calls in the literature for attention to be paid to the congruence type of MCS research concerning the relationship between PMS design and the context of organization structure and competition (Abernethy et al., 2004, Berry et al., 2009, Bromwich, 1990, Hoque et al., 2001, Langfield-Smith, 1997 and Luft and Shields, 2003). More specifically, this study examines the effectiveness of the use of PMSs in competitive markets, as well as in different organization structures. This work also meets the need for the contingency type of MCS research to explore the consequences of different MCS designs across different context levels (Abernethy and Lillis, 1995, Bisbe and Otley, 2004 and Brownell and Merchant, 1990). Second, this study extends the literature on the usage of financial and nonfinancial performance measures by considering the linkage of strategies, incentive schemes and performance measurements, as prior research has primarily discussed the effects of the use of nonfinancial measures on performance (e.g., Banker et al., 2000, HassabElnaby et al., 2005, Hoque and James, 2000 and Said et al., 2003). The next section reviews the related literature and develops the research hypotheses. The third section presents the research and survey design. The fourth section presents the empirical results. The fifth section discusses the findings, conclusion and limitations of the study, and suggests directions for future research.
نتیجه گیری انگلیسی
4.1. Descriptive statistics Table 4 Panel A, presents descriptive statistics for the variables that have been examined. The average score of the integrated performance measure was 3.58. The average scores of organization structure and market competition were 3.55 and 3.85, respectively, showing that the sample firms mostly had organic structures and faced competitive market environments. The mean of organizational performance was 3.50, which indicated that the perceived performance of the sample firms was higher than that of their competitors.Table 4 Panel B, shows that the highest correlation coefficient is 0.55, and thus there is no problem with multicollinearity. The correlation results suggest that both Integrated_PMSU and PMS stage are related to organization structure, market competition and organizational performance. 4.2. The impact of organization structure and market competition on performance measurement systems OLS regression in Table 5 shows the associations between organization structure, market competition, and the use of PMS. Hypothesis 1a posits that organic organizations will rely more on integrated performance measures than mechanistic ones. To further analyze the relationships predicted in Hypothesis 1a, multiple regressions were additionally run, in which the analysis employed the use of measures for each of the four BSC perspectives. Thus, there are a total of five independent variables of Integrated_PMSU, F_PMSU, C_PMSU, IP_PMSU, and ILG_PMSU used in the regressions. All five regressions represent that organization structure is significant and positively associated with the use of integrated performance measures (Integrated_PMSU), as well as separately with the use of financial perspective measures (F_PMSU), customer perspective measures (C_PMSU), internal process perspective measures (IP_PMSU), and innovation and learning growth perspective measures (ILG_PMSU). Therefore, the results indicate that the degree of organic structure increases the use of integrated performance measures, supporting H1a.The industry dummy is statistically significant in explaining the use of integrated performance measures. The high-tech industries tend to emphasize the use of integrated performance measures, with relatively low usage in non-high-tech industries. Notably, a significant and positive relation (p < 0.01) between the industry dummy and the use of internal process perspective measures is found. The results show that high-tech firms need tighter internal monitoring processes as compared to firms in non-high-tech industries. In terms of organization size, SIZE has no significant influence on the use of integrated performance measures. H1b posits that organic organizations will rely more on PMSs which include causal models and the linkages between incentives and strategy. This study uses order and binary logit to identify the associations between organization structure, market competition and the stages of PMS development. Table 6 gives the ordered logit results, which indicate that organization structure significantly affects the use of different stages of PMS development. This suggests that organic structures allow the possibility of moving from no use of an integrated PMS to a minimum-standard PMS; a minimum-standard PMS to a cause-and-effect PMS; and a cause-and-effect PMS to a fully-developed PMS.The management accounting literature suggests that a causal relationship between measures and strategy in a PMS should be established. It is thus worthwhile to use this feature to separate firms into two categories, one of firms that go through the stage of establishing causal models, and the other firms that bypass this stage. Firms using cause-and-effect PMS or fully-developed PMS belong to the first category. Firms that use minimum-standard PMS or do not use any of the aforementioned PMS are assigned to the other group in the stage of not using a causal model. Binary logit analysis is used to compare the effect of organization structure and market competition on these two groups. The binary logit is expressed in the following model: View the MathML sourceLnProb(Y=1)1−Prob(Y=1)=α0+α1ORG+α2COMP+α3SIZE+α4IND Prob(Y = 1) is the probability of using a PMS to establish causal models. The binary logit results are shown in Table 6. The results of the ordered and binary logit are the same. Firms leaning towards organic structures rely more on the higher developmental stages of PMS which include causal models. The results of the ordered logit and the binary logit thus support H1b. To test H2a, it can be noted in Table 5 that the use of integrated PMSs is not significantly associated with the intensity of market competition. Moreover, the use of each of the four BSC perspective measures is not significantly related to the intensity of market competition. These results do not provide support for H2a. This suggests that the competition faced by the firm does not influence the use of integrated performance measures, and this result is consistent with the findings of Hoque and James (2000). The results from ordered logit and binary logit analysis in Table 6 show that the intensity of competition does not significantly affect the stages of PMS development in the firms, and thus, H2b is not supported. An interesting finding in the ordered logit and binary logit analysis is that both coefficients of SIZE are significant and positive (p < 0.1). This suggests that larger firms are more likely to establish PMSs that include the linkages between strategy, measures, and incentives. The rationale is that the more employees a firm has, the greater the use of appropriate systems such as PMSs in order to communicate firm strategy to employees. 4.3. The interaction effect of organization structure, market competition and performance measurement systems on organizational performance Hypothesis 3a posits that the positive effect of the use of integrated measures on organizational performance is stronger in mechanistic organizations than in organic ones. Table 7 presents the results of regression testing the above prediction according to the interaction terms obtained by crossing organic structure with use of integrated performance measures. These regressions also include the main effects as well as control variables for firm capital and firm strategy.In terms of the main effect, Table 7 column (1) shows a positive and significant association between the use of integrated performance measures and organizational performance (p < 0.01). The significant positive relation between the use of integrated performance measures and organizational performance is an indication that the use of integrated PMSs does provide relevant information and helps firms to obtain competitive advantages. The coefficient of market competition is negative and significant (coefficient = −0.182, p < 0.05), indicating that firms facing more intensive competition have lower performance. Column (2) in Table 7 indicates that the interaction term of the degree of the organic structure with the use of integrated performance measures is significantly and negatively associated with firm performance. It appears that the positive relation between the use of integrated performance measures and organizational performance is lower in organic structures compared to in mechanistic ones, thus supporting H3a. As shown in column (3) of Table 7, there is a positive and significant association between the stages of PMS development and firm performance (p < 0.1). This suggests that firms making greater uses of PMSs with established linkages between strategy, measures and incentives have higher performance. Organization structure is significantly and positively associated with organizational performance (p < 0.01). The contextual variable of market competition is negatively and significantly associated with organizational performance (p < 0.05). H3b predicts that the use of PMSs including causal models and providing linkages to incentives is positively associated with organizational performance in organic organizations. The results presented in column (4) of Table 7 indicate that the coefficient on the interaction of stages of PMS development and organic level of organizations is not significantly positive, and thus does not support H3b. H4a can be tested by the interaction term of the intensity of market competition and the use of integrated performance measures in column (2) of Table 7. The result indicates that firms with greater usage of integrated performance measures do not achieve higher performance when market competition gets more intense, which does not support H4a, but is consistent with Hoque and James (2000). Notably, the results in column (4) of Table 7 also show a significant and positive effect on performance (p < 0.1) from the interaction of intensity of market competition and the stages of PMS development. With the increased use of PMSs encompassing the causal model and providing linkages to incentives, this study finds that firms have superior performance as competition becomes more intense, supporting H4b. In terms of control variables, the coefficients of cost strategy are significant and negative in the four regressions. This phenomenon is consistent with the idea that firms must have excellent abilities to take advantage of technological changes through innovation to survive in global competition, and not simply depend on cost savings and operation efficiency (Nanni et al., 1992). The regressions also control for the capital of the firms, which is not significant in any of these results.