Linking Balanced Scorecard Measures to Size and Market Factors: Impact on Organizational Performance.
In recent years, scholars and practitioners have expressed concerns with traditional performance measures that focus solely on financial metrics such as return on investment or net earnings (Atkinson et al. 1997; Ittner et al. 1997; Kaplan and Norton 1996; Lynch and Cross 1991; Shields 1997). The balanced scorecard (BSC) approach to management (Kaplan and Norton 1992, 1993, 1996) has gained prominence in management accounting research as a way of integrating financial and nonfinancial performance measures (for reviews, see Atkinson et al. 1997; Ruhl 1997; Shields 1997; Simons 2000). The BSC views organizational performance from four dimensions: financial (or shareholders), customers, internal business processes, and learning and growth. Atkinson et al. (1997, 93-94) suggest that BSC has the potential to provide planners with a way of expressing and testing a sophisticated model of cause-and-effect in the organization--a model that provides managers with a basis on which to manage the drivers of desired outcom es.
While work of Kaplan and Norton has added to our knowledge of how a BSC can provide managers with an integrative framework to manage organizational activities, little evidence is available outside the U.S. on current practice in the area (Hoque et al. 1997; Creelman 1998).  Atkinson et al. (1997, 94) note that "the balanced scorecard is among the most significant developments in management accounting and thus, deserves intense research attention." This paper is an attempt to contribute to the limited body of knowledge in this area.
The purpose of this research is to search for a relationship between BSC usage and (1) organization size, (2) product life-cycle stage, and (3) strength of market position. It also looks for a contingent relationship between organizational performance (the outcome variable) and the match between BSC usage and the three contextual variables described above. The framework for the research is illustrated in Figure 1.
The next section of the paper briefly reviews the relevant literature and develops the research hypotheses. The research method applied is described in section three. Section four presents our results. The final section concludes the paper.
VARIABLES AND RESEARCH HYPOTHESES
Atkinson et al. (1997, 94) suggest that management accounting research can explore BSC practices using a range of methods such as case studies, behavioral experiments, and analytical and empirical approaches. Taking a contingency-theoretic perspective  in this study, we consider the size of organizations, product life-cycle stage, and market position as potential contextual factors of BSC usage and explore how organizational performance is affected by different uses of BSC in different settings. Each of these variables will now be considered in turn.
Balanced Scorecard (BSC)
A BSC looks to the following four key perspectives:
Financial perspective--includes profitability measures such as operating income, return-on-capital-employed, sales growth, generation of cash flow, or economic value-added;
Customer perspective--encompasses such measures as customer satisfaction, customer retention, new customer acquisition, customer response time, market share, and customer profitability;
Internal-business-processes perspective--the key measures include product design, product development, post-sale service, manufacturing efficiency, quality, etc.; and Learning and growth perspective--measures the ability of employees, information systems, and organizational procedures to manage the business and adapt to change.
The use of a BSC does not mean just using more measures"; it means putting a handful of strategically critical measures together in a single report, in a way that makes cause-and-effect relations transparent and keeps managers from suboptimizing by improving one measure at the expense of others.  To achieve a balance among the four dimensions of the BSC, a company should pay attention to all of them.
Contingency theories of organizations developed by Bums and Stalker (1961), Lawrence and Lorsch (1967), and Woodward (1965) suggest that size may affect the way organizations design and use management systems. Numerous accounting studies have drawn on this theoretical framework. Merchant (1981, 1984) claims that organizational growth poses increased communication and control problems. Bruns and Waterhouse (1975), Ezzamel (1990), and Libby and Waterhouse (1996) suggest that as firm size increases, accounting and control processes tend to become more specialized and sophisticated.
Others in the organizational literature (e.g., Burns and Stalker 1961; Chandler 1962; Pugh et al. 1969) claim that size is related to greater decentralization and structuring of activities because of information processing constraints upon senior management. Furthermore, the need to stimulate effective communication flows becomes more apparent in larger organizations where the behavioral orientation characterizing management controls in small organizations become unworkable. As a consequence, in large business enterprises, a broader set of information and measurement issues arises (Kaplan and Atkinson 1998). Small companies frequently do not require elaborate performance evaluation techniques, as the strategy setters, usually the owners, are close to the "action." Based on this a priori reasoning, it is proposed here that larger organizations are likely to place a greater reliance on a BSC approach to management than are smaller organizations.
Product Life-Cycle Stage
The organizational strategy literature classifies "product life-cycle stage" into four categories. These groupings comprise the emerging, growing, maturing, and declining stages of a product's life. At the emerging stage, the product is launched and sales are low with high prices. Sales begin to rise rapidly at the growth stage because of introductory promotions and growing customer awareness. At the mature stage, there is no more sales growth as the potential for new customers is exhausted. At this point, sales have peaked while prices remain low. Sales reduce at the declining stage as the product is gradually replaced with either innovations or an enhanced version (for details, see Drury 1994; Sizer 1989; Wilson 1991).
Merchant (1984) suggests that organizations with products in the early product life-cycle stages tend to make less use of traditional financial control tools such as budgeting, compared to organizations with products in the latter stages. This rationale can be extended by considering the potential of a relationship between product life-cycle stage and BSC usage.
Kaplan and Norton (1996, 21) propose that the BSC retains financial measurement as the outcome measure of managerial and business performance, but it also includes a more general and integrated set of measurements that link current customer, internal process, employee, and system performance to long-term financial success. It follows, then, that financial controls tools alone are less useful in early than in late stages of the product life cycle, perhaps because financial outcomes are less certain at early stages and therefore are noisier measures of whether the firm is doing the right thing." In addition, financial outcomes from important decisions may be too far in the future when the firm is in an early stage, and nonfinancial indicators like new product development and customer-response measures give earlier indications of whether appropriate decisions have been made. 
Market position in this study refers to a company's revenue share in relation to its competitors in a particular market. Following the Galbraith (1977) argument, Merchant (1984) suggests that for firms in a weak market position, the demand for adaptability and creativity would be greater than the demand for internal communication, while for firms in a strong market position, the demand for internal communication would be greater. He argues that when an organization has a strong market position, the use of budgets for internal controls would be more prominent than for a firm in a weak market position.
A BSC can satisfy companies' greater internal communication needs, as it facilitates decisions and actions that support strategies based on the needs of stakeholders, internal and external customers, regulatory bodies, managers, and employees and requires involvement by all levels of the organization (Kaplan and Norton 1996; Atkinson et al. 1997). We propose that organizations with a strong market position have a greater demand for internal communication, and thus are likely to place greater emphasis on the use of a BSC. Conversely, a weak position in a particular market may create a lesser demand for internal communication. A lesser demand for internal communication suggests a lower deployment of sophisticated management systems such as the BSC.
The preceding discussion can be summarized with the following hypothesis:
H1: Balanced scorecard usage is positively associated with: (a) larger organization size; (b) businesses with products at the early/growth stage; and (c) businesses with a strong market position.
Contextual Factors, BSC Usage, and Organizational Performance
Based on prior research examining the effects of contextual variables on the design and use of control systems and performance,  we seek a contingent relationship between organizational performance as the dependent variable and organization characteristics and BSC usage as the independent variables described above. The "fit" hypotheses in this study relate to the motivation that greater reliance on the BSC results in increased performance of organizations that are large, early in the product life cycle, or strong in the market. 
The first "fit" hypothesis proposes that because large organizations depend more on sophisticated information and control systems using diverse measures, they will derive more benefit from reliance on the BSC than will small organizations.
H2: The effect of BSC reliance on organizational performance will be more beneficial for large organizations than for small organizations.
Likewise, the second "fit" hypothesis derives from the motivation that since organizations at early stages of their product life cycle depend more on sophisticated information and control systems using diverse measures, they will receive more benefits from the BSC than will organizations in a mature phase.
H3: The effect of BSC reliance on organizational performance will be more beneficial for organizations with products at the early life-cycle stage than for organizations with products at the mature stage.
The third "fit" hypothesis is concerned with the expectation that because organizations in a strong market position depend more on sophisticated internal communication and control systems using diverse measures, they will derive more benefits from reliance on the BSC than will organizations in a weak market position.
H4: The effect of BSC reliance on organizational performance will be more beneficial for organizations with a strong market position than for organizations with a weak market position.
A questionnaire  with a cover letter and a postage-paid, self-addressed envelope was mailed to the chief financial controllers of 188 Australian manufacturing firms in July 1997. These companies were randomly chosen from the Business Who's Who of Australia (Dun & Bradstreet 1997). Forty-three of the 188 questionnaires sent out in the first mailing were returned. A second mailing resulted in a further 27 returned questionnaires. Four of the 70 respondents failed to complete the questionnaire, citing reasons such as contravening company policy and staffing constraints. Consequently, the adjusted usable response rate is 35.1 percent.
A sample of nonrespondents was contacted by telephone to investigate reasons for nonresponse. The reasons given for nonresponse were consistent with those who had returned the questionnaire without completing them, that is, either due to staffing constraints or contravening company policy. To test for the existence of possible response bias, t-tests for two independent samples were undertaken by testing first and second mailing returns as suggested by Oppenheim (1966, 34). No statistically significant differences in the mean scores on the firm size, industry, or performance indices between the early and late responses were noted, suggesting the absence of response bias. The average experience of the participants in the company was 6.5 years, with a range of four months to 25 years. Table 1 provides a profile of the responding firms. In this study the responding firms were independent business units, not the head offices.
Measurement of Variables Organization Size
Organization size was measured using three measures: sales turnover, total assets, and number of employees. These three measures were highly correlated (p [less than] .001). We conducted our analyses using each of these measures as a proxy for size, one at a time, but the results were similar throughout. Consistent with previous studies (Bruns and Waterhouse 1975; Merchant 1981, 1984; Ezzamel 1990; Libby and Waterhouse 1996), only those results obtained when the number of employees was used as a proxy for size are reported. Due to the nonnormality of "size,"  it was transformed using the square root of the variable for use in regression analysis. Table 2 presents the descriptive statistics for this variable in its original and transformed form.
Product Life-Cycle Stage
Product life-cycle stage was measured using an instrument adapted from Merchant (1984). The following question was posed to the respondents of this study to measure the product's maturity:
Given below are descriptions of four alternative stages of the product life cycle. Considering all the products of your firm, please indicate below the percentage of products that are at the following stages:
Emerging (a new product has recently been launched on the market; currently sales are low and prices are relatively high) ___ %
Growth (a product that has increasing sales due to increasing demand) ___ %
Mature (a product that provides stable income, neither increasing or declining sales while prices remain low) ___ %
Declining (profits and sales are declining due to declining interest by consumers) ___ %
These percentages were grouped into one variable, namely "product life-cycle stage," for each firm by summing the emerging and growth percentages and subtracting from that the sum of the mature and declining percentages.  Table 2 presents descriptive statistics for the measure.
Market position was measured using the instrument developed by Merchant (1984). It asked respondents to indicate on a five-point Likert scale their company's revenue share, ranging from 1 ("Your company's market (revenue) share is small and insignificant in comparison with that of the leading firms") to 5 ("Your company is the dominant firm in your segment of the market"). Table 3 presents descriptive statistics for this measure.
BSC usage was measured using a 20-item scale similar to that developed by Hoque et al. (1997). The instrument comprised items that incorporate Kaplan and Norton's (1992) four dimensions of the BSC. It asked respondents to indicate the extent to which each item was used to assess their organization's performance on a fully anchored, five-point Likert scale ranging from 1 (not at all) to 5 (to a great extent). A reliability check for the measure in this study produced a Cronbach alpha (Cronbach 1951) of 0.81, which is considered to be well above the lower limits of normal acceptability (Nunnally 1967). It should be noted that our BSC measure might not pick up the strategic linkages of a real BSC usage; it does pick up firms' tendency to use quantitative measures (frequency and extent of reporting) of several kinds in assessing performance. This construct shares with the BSC construct the idea that financial measures alone are insufficient.
Appendix A presents the descriptive statistics for each item of the scale. A principal components analysis (PCA) with varimax rotation was performed to determine whether the measures used in the survey can be grouped according to the BSC's four perspectives. The results of the factor analysis, which also appear in Appendix A, are roughly consistent with the four perspectives identified by Kaplan and Norton (1992).
A mean score was calculated for each of the four perspectives. The primary hypothesis tests were performed using an average of these four perspective means to represent overall BSC usage. Additional tests were performed using each of the perspective means separately. Table 2 presents descriptive statistics for the measure.
Organizational performance was measured by appraising five dimensions of performance: return on investment, margin on sales, capacity utilization, customer satisfaction, and product quality. The instrument is conceptually consistent with Kaplan and Norton's (1992) BSC theorizing. Following the procedure used by others (e.g., Merchant 1984; Abernethy and Lillis 1995), respondents were asked to indicate their organization's performance compared to their competitors along the above five dimensions on a scale from 1 = below average to 5 = above average. Although not presented here, it is noted that the five dimensions in the performance instrument were positively and significantly related to each other (p[less than]0.05). A PCA of the five items revealed that all items loaded on to a single factor with eigenvalue 2.52. The factor score was saved for use in the regression analyses. The Cronbach alpha for this scale was 0.75, indicating satisfactory internal reliability of the scale. Table 2 presents descriptive s tatistics for the measure.
Table 2 provides descriptive statistics for all variables. Pearson correlation coefficients appear in Table 3. As expected, BSC usage is positively and significantly correlated with organization size (r = 0.25, p [less than] 0.10), product life-cycle stage (r = 0.26, p [less than] 0.05), and organizational performance (r = 0.46, p [less than] 0.01). Although strength of market position is positively correlated with BSC usage (r = 0.18), it is generally not statistically significant (p-value [less than] 0.10). The contextual variables, organization size, product life-cycle stage, and strength of market position are not significantly related to each other, suggesting that multicollinearity is unlikely. Tests of multicolinearity via tolerance and variance inflation factor (VIF) presented in Table 4 revealed that multicollinearity does not pose a problem in interpreting these results. Furthermore, tests of nonlinearity and heteroskedasticity of the data indicated no major problem for regression analysis.
Hypothesis 1: Regression Analysis
The following regression model was run to test the relationship between the dependent and independent variables, as stated in hypothesis 1:
[X.sub.4] = [[alpha].sub.0] + [[beta.sub.1][X.sub.1] + [[beta.sub.2][X.sub.2] + [[beta].sub.3][X.sub.3] + e (1)
where [X.sub.4] = BSC usage; [X.sub.1] organizational size; [X.sub.2] = product life-cycle stage; [X.sub.3] = strength of market position; and e = error term. Table 4 reports the results of the regression analysis. The overall regression model for the three contextual variables explained 19.6 percent (adjusted [R.sup.2]) of the variance in the dependent variable (F = 5.714, p = 0.018). The data indicate that the standardized beta coefficients [[beta].sub.1] (size) and [[beta].sub.2] (product life-cycle stage) are both positive and significant ([[beta].sub.1] = 0.29, p = 0.005, one-tailed; [[beta].sub.2] = 1.30, p = 0.000, one-tailed). Although the coefficient [[beta].sub.3]. (market position) is negative, it is not statistically significant (p = 0.325, one-tailed). These results support the hypothesis that greater BSC usage is associated with larger organization size and businesses with products at the early/growth stage. They do not support the hypothesis that greater BSC usage is associated with businesses with a strong market position.
To further explore the relationships predicted in the regression model 1, multiple regression analysis was also run using each of the four BSC perspectives. The results of this analysis are presented in Appendix B and indicate that organization size is significantly associated with all of the BSC perspectives in the expected direction (p-values [less than] 0.10, one-tailed). Product life-cycle stage is associated with the innovation perspective of the BSC only ([beta].sub.2] = 1.79, t = 2.79, p = 0.005, one-tailed). Overall, the results of F-tests for both the financial and innovation perspectives are positive and significant (F = 3.50 1, p = 0.01, one tailed; F = 3.068, p = 0.018, one-tailed), with the contextual variables explaining 12.4 percent (adjusted [R.sup.2]) and 10.1 percent (adjusted [R.sub.2]) of the variance respectively.
Hypotheses 2, 3, and 4: ANOVA
To test H2, H3, and H4, the two-way ANOVA procedure was used using SPSS8.0. ANOVA was used because the data were of insufficient quality (due to the small sample size) to warrant sophisticated Moderated Regression Analysis (Bryman and Cramer 1995; Tabachnick and Fidell 1996; Hartmann and Moers 1999).
The results of ANOVA presented in Table 5 (Panels A, B and C) provide support for the main effect of BSC usage on firm performance (the F-values in each of the cases are significant). However, the two-way interaction between BSC usage and each of the predictor variables (size, product life-cycle stage and market position) performance is not significant. These results thereby provide no support for H2, H3, and H4.
DISCUSSION, CONCLUSIONS, AND LIMITATIONS
The paper reports a significant association between size and BSC usage; as hypothesized, larger organizations are likely to make more use of a BSC. This result suggests that as size increases, organizations find it more practical and useful to place greater emphasis on the BSC that supports their strategic decision making, as the BSC incorporates much broader measures of the performance of organizations. This evidence confirms others' findings with respect to the effect of size on accounting and budgetary control practices (see Swieringa and Moncur 1974: Bruns and Waterhouse 1975; Merchant 1984; Miller and Friesen 1984, Giroux et al. 1986; Ezzamel 1990).
In general, the regression analysis shows the positive association between early product life-cycle stage and a greater reliance on BSC. However, additional analysis using each perspective of the BSC individually indicates that firms that have a higher proportion of new products have a greater tendency to make use of measures related to new products.
The results provide no support for the positive association between a strong market position and a greater reliance on BSC. An implication of this result is that firms with a weak market position may also be motivated to change their strategy, and therefore will need a BSC more to communicate and implement their new strategy. However, it should be noted that our unexpected results in this regard could have been affected by the instruments used or by other factors such as sample selection or the use of limited control variables. More research, in different settings using a different sample or different measurement, is needed to confirm the results.
Contrary to expectations, the results relating to H2-H4 suggest that greater BSC usage is associated with increased organizational performance, but this relationship does not significantly depend on organizational size, product life cycle, or market position. An implication of these results is that an appropriate "fit" between the degree of BSC usage and organizational characteristics have less practical significance relative to the direct effects as stated above. However, these results should be evaluated in the light of the following interpretations.
Hypothesis 1, which predicts firms' reliance on the BSC, assumes that most firms will make the choice that is more beneficial to them. There may have a substantial number of firms whose behavior is not predicted by Hi, however. There are two possible reasons for this.
One possible reason that firms fall in the off-diagonal cells is that these firms are not making the right choice with respect to BSC usage. The motivation for H2- H4 assumes that this is the case: that is, firms that should rely on the BSC but do not will be worse performers. If the firms in the off-diagonal cells are indeed making incorrect choices about BSC reliance, then our H2-H4 results suggest that having the right fit between BSC reliance and firm characteristics does not matter very much to performance.
However, there is another possible reason why some firms do not make the predicted BSC-usage choices, and why H2-H4 are not supported. It may be that the theory behind Hi does not classify many of these firms correctly. For example, perhaps some of the large firms should not be BSC users, because of some other organizational characteristics they possess--if they do not rely on the BSC, then it is not really a wrong "fit." According to this line of argument, the mean performance of firms in the "low-fit" cells is about the same as the mean performance of the firms in the "high-fit" cells, because many firms are misclassified.
We close this paper with a discussion of what we can infer from this study. The findings reported in this paper suggest that large firms make more use of the measures in the questionnaire used (Appendix A) than do small firms. They do not suggest whether that is because large firms get (or expect to get) more benefit from these measures, or because they can spread the fixed cost of information systems over larger output and therefore find additional measures more affordable, or because they are more likely to pursue performance-measurement fads (or are more likely targets for consultants, more concerned about their image as up-to-date management because public image matters more to them, etc.).
The results also suggest that firms with more new products make more use of new-product-development measures; however, we do not know which way causation runs here. Emphasizing these measures might lead to more new product development, either in reality or because people are gaming the measure. Or conversely, a firm's proportion of new products might be exogenously given; if it is large, then the firm sees a greater need to measure performance in new-product development and launch.
Limitations and Future Research
This study has several limitations, notably the measurement of the extent of use of the BSC. This instrument has failed to pick up the strategic linkages of a real BSC. Using a diversity of measures is a necessary but not sufficient condition of BSC use, which also requires that the measures be chosen for strategic focus and causal linkage. Future studies may wish to add to the body of knowledge in this area by extending the instrument used in this study. A second limitation of the study is that it is confined to manufacturing firms only. Therefore, generalizing the results reported in this paper to other situations (such as service industries) should be done cautiously. A third limitation pertains to the study's small sample size. Using alternative approaches (e.g., case-study research), one may also attempt to investigate why and how companies implement BSC, pitfalls in implementing it, and its success in achieving intended goals, and additionally, whether BSC adoption is designed to improve performance or to give the external appearance of being modern, rational, efficient, and legitimate, consistent with Meyer and Rowan (1977), DiMaggio and Powell (1983), Scott (1994), Hoque and Alam (1999), and Moll and Hoque (2000).
(1.) For information about worldwide applications of the Balanced Scorecard approach to management, interested readers may wish to visit the following web sites: http://www.bscol.com/, http://www.business-intelligence.co.uk/, http://www.balancedscorecard.org, and http://www.pr.doe.gov/bsc001.htm.
(2.) For an up-to-date review of the contingency literature, see Chapman (1997).
(3.) We thank the referees of JMAR for their insightful comments on this issue.
(4.) We acknowledge that the BSC could be used by companies in a mature phase of their product life cycle to adopt a new strategy (such as customer intimacy) in order to avoid being caught in a commodity-price-driven strategy.
(5.) See, for example, Brownell (1981), Merchant (1984), Mia (1989). Lau et al. (1995) and Chenhall and Langfield-Smith (1998). For an up-to-date review of this literature, see Hartmann and Moers (1999).
(6.) It does not necessarily follow from this that organizations with the opposite properties will see reductions in ROI, product quality, customer satisfaction, and so on, when they use a BSC. If the benefits of the BSC to smaller, weaker, or mature-product organizations are slight, then the cost of Implementing the BSC might result in a net effect negative.
(7.) A copy of the questionnaire is available from the authors.
(8.) We have tested normality of the data through histograms, skewness, kurtosis, and normal plot,
(9.) In this study, five firms had 50:50 ratios for emerging/growth stage and mature/decline stage. These cases were excluded from analyses because it is not possible to claim that they were firms with products either in the emerging/growth stage or mature/declining stage.
Abernethy, M. A., and A. M. Lillis. 1995. The impact of manufacturing flexibility on management control system design. Accounting, Organizations and Society 20: 241-258.
Atkinson, A. A., R Balakrishnan, P. Booth. J. M. Cote, T. Grout, T. Mali, H. Roberts, E. Ulan, and A. Wu. 1997. New directions in management accounting research. Journal of Management Accounting Research 9: 80-108.
Brownell, P. 1981. Participation in budgeting, locus of control and organizational effectiveness. The Accounting Review (October): 844-860.
Bryman, A., anti D. Cramer. 1995. Quantitative Data Analysis for Social Scientists. Revised edition. London, U.K. and New York, NY: Routledge.
Bruns, W. J., Jr., and J. H. Waterhouse. 1975. Budgetary control and organization structure. Journal of Accounting Research (Autumn): 177-203.
Burns, T., and G. M. Stalker. 1961. The Management of Innovation. London, U.K.: Lavistock.
Chapman, C. S. 1997. Reflections on a contingent view of accounting. Accounting, Organizations and Society 22 (2): 189-205.
Chandler, A. D., Jr. 1962. Strategy and Structure: Chapters in the History of the Industrial Enterprise. Cambridge, MA: MIT Press.
Chenhall, R., and K. Langfield-Smith. 1998. The relationship between strategic priorities, management techniques and management accounting: An empirical investigation using a systems approach. Accounting, Organizations and Society 23 (3): 243-264.
Creelman, J. 1998. Building and Implementing a Balanced Scorecard. London, U.K.: Business Intelligence.
Cronbach, L. J. 1951. Coefficient alpha and the internal structure of test. Psychometrika September: 297-334.
DiMaggio, P. J., and W. W. Powell. 1983. The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review. 48 (April): 147-160.
Drury, C. 1994. Cost and Management Accounting. 3rd edition. London, U.K.: Chapman & Hall.
Dun & Bradstreet. 1997. The Business Who's Who of Australia. 31st edition, Vol. 1. Sydney, Australia: Dun & Bradstreet.
Ezzamel, M. 1990. The impact of environmental uncertainty, managerial autonomy and size on budget characteristics. Management Accounting Research 1:181-197.
Galbraith, J. R. 1977. Organization Design. Reading, MA: Addison-Wesley.
Giroux, G. A., A. G. Mayper, and R L. Daft. 1986. Organization size, budget cycle, and budget related influence in city governments: An empirical study. Accounting, Organizations and Society 11(6): 499-519.
Hartmann, F. G. H., and F. Moers. 1999. Testing contingency hypotheses in budgetary research: An evaluation of the use of moderated regressional analysis. Accounting. Organizations and Society 24: 291-315.
Hoque, Z., L. Mia, and M. Alam. 1997. Competition, new manufacturing practices, changes in MAS and managerial choice of the "balanced scorecard" approach to performance measures: An empirical investigation. Paper presented at the 1997 European Accounting Association Annual Congress in Graz, April.
-----, and M. Alam. 1999. TQM adoption, institutionalism and changes in management accounting systems: A case study. Accounting and Business Research 29 (3): 199-210.
Ittner, C. D., D. F. Larcker, and M. V. Rajan. 1997. The choice of performance measures in annual bonus contracts. The Accounting Review 72 (2): 231-255.
Kaplan, R. S., and D. P. Norton. 1992. The balanced scorecard-Measures that drive performance. Harvard Business Review (January-February): 71-79.
-----, and -----. 1993. Putting the balanced scorecard to work. Harvard Business Review (September-October): 134-147.
-----, and -----. 1996. The Balanced Scorecard: Translating Strategy into Action. Boston, MA: Harvard Business School Press.
-----, and A. A. Atkinson. 1998. Advanced Management Accounting. Englewood Cliffs, NJ: Prentice Hall.
Lau, C. M., L. C. Low, and I. R. C. Eggleton. 1995. The impact of reliance on accounting performance measures on job-related tension and managerial performance: Additional evidence. Accounting, Organizations and Society 20 (5): 359-381.
Lawrence. P. R., and J. Lorsch. 1967. Organization and Environment. Boston, MA: Harvard Business School, Division of Research.
Libby, T., and J. H. Waterhouse. 1996. Predicting change in management accounting systems, Journal of Management Accounting Research 8: 137-150.
Lynch, R. L., and K. F. Cross. 1991. Measure Up! London, U.K.: Blackwell Publishers.
Merchant, K. A. 1981. The design of the corporate budgeting system: Influences on managerial behavior and performance. The Accounting Review 56: 813-829.
-----. 1984. Influences on departmental budgeting: An empirical examination of a contingency model. Accounting, Organizations and Society 9 (3/4): 291-307.
Meyer, J. W., and B. Rowan. 1977. Institutionalized organizations: Formal structure as myth and ceremony. American Journal of Sociology 83 (2): 340-363.
Mia, L. 1989. The impact of participation in budgeting and job difficulty on managerial performance and work motivation: A research note. Accounting, Organizations and Society 14 (4): 347-357.
Miller, D., and P. H. Friesen. 1984. Organizations: A Quantum View. Englewood Cliffs, NJ: Prentice Hall.
Moll, J., and Hoque, Z. 2000. Rationality, new public management and changes in management control systems: A case study of managing change in an Australian local government setting. Paper presented at the Interdisciplinary Perspectives on Accounting Conference, Manchester, U.K., July 2000.
Nunnally, J. C. 1967. Psychometric Theory. London, U.K.: McGraw-Hill.
Oppenheim, A. N. 1966. Questionnaire Design and Attitude Measurement. New York, NY: Basic Books.
Pugh, D. S., D. J. Hickson, and C. R. Hinnings. 1969. An empirical taxonomy of structures of work organizations. Administrative Science Quarterly March: 115-126.
Ruhl, J. M. 1997. The balanced scorecard and benchmarking videos--Reviews. Journal of Cost Management Winter: 52-56.
Scott, W. R. 1994. Organisational Sociology. London, U.K.: Aldershot.
Shields, M.D. 1997. Research in management accounting by North Americans in the 1990s. Journal of Management Accounting Research 9: 3-62.
Simons, R. 2000. Performance Measurement and Control Systems for Implementing Strategy--Text and Cases. Englewood Cliffs, NJ: Prentice Hall.
Sizer, J. 1989. An Insight into Management Accounting. London, U.K.: Penguin.
Swieringa, R., and R Moncur. 1974. Some Effects of Participative Budgeting on Managerial Behaviour. New York, NY: National Association of Accountants.
Tabachnick, B. G., and L. S. Fidell. 1996. Using Multivariate Statistics. 3rd edition. New York, NY: Harper Collins.
Wilson, R. M. S. 1991. Strategic management accounting. In Issues in Management Accounting, edited by Ashton, Hopper, and Scapens. Englewood Cliffs, NJ: Prentice Hall.
Woodward, J. 1965. Industrial Organization--Theory and Practice. London, U.K.: Oxford University Press.
Profile of Responding Companies (n = 66) Number of Employees n Organization Type [a] 0-149 26 Textile, Clothing, Footwear & Leather Wood & Paper Product Printing, Publishing & Recorded Media Petroleum, Coal, Chemical & Associated Product Metal Product Machinery & Equipment 150-299 10 Food, Beverage & Tobacco Textile, Clothing, Footwear & Leather Wood & Paper Product Printing, Publishing & Recorded Media Petroleum, Coal, Chemical & Associated Product Non-Metallic Mineral Product Metal Product Machinery & Equipment 300-449 5 Textile, Clothing, Footwear & Leather Printing, Publishing & Recorded Media Metal Product Machinery & Equipment 450-999 7 Textile, Clothing, Footwear & Leather Printing, Publishing & Recorded Media Petroleum, Coal, Chemical & Associated Product Metal Product Machinery & Equipment 1,000 or greater 18 Food, Beverage & Tobacco Textile, Clothing, Footwear & Leather Metal Product Machinery & Equipment (a.)Industry classification was based on the Australia and New Zealand Standard Industry Classification code. Descriptive Statistics (n = 66) Standard Mean Deviation Organization Size 1697 5178 (26.74) [*] (31.60) [*] Product Life-Cycle Stage 29.50% 0.26 Market Position 3.71 0.92 Overall BSC Usage 3.10 0.71 Financial Perspective [a] 3.94 0.76 Internal Business Perspective [a] 3.04 0.75 Innovation and Learning Perspective [a] 2.40 1.01 Customer Perspective 3.29 1.30 Organizational Performance 3.55 0.68 Theoretical Actual Range Range Organization Size NA 11-38, 148 Product Life-Cycle Stage 0%-100% 0%-100% Market Position 1-5 1-5 Overall BSC Usage 1-5 2.03-5 Financial Perspective [a] 1-5 1.92-5 Internal Business Perspective [a] 1-5 1-4.5 Innovation and Learning Perspective [a] 1-5 1-4.33 Customer Perspective 1-5 1.38-4.63 Organizational Performance 1-5 1.6-5 (*.)Transformed statisitics. (n.)Used in additional regression analysis reported in Appendix B. NA = not available. Correlation Matrix (Pearson Coefficients) (n = 66) Organization Product Lift- Market Overall BSC Variable Size Cycle Stage Position Usage Organization Size 1.00 -0.13 0.09 0.25 [*] Product Life-Cycle Stage 1.00 0.15 0.26 [*] Market Position 1.00 0.18 Overall BSC Usage 1.00 Organizational Performance Organizational Variable Performace Organization Size 0.01 Product Life-Cycle Stage 0.32 [**] Market Position 0.29 [**] Overall BSC Usage 0.46 [***] Organizational Performance 1.00
(*.)Significant at the 10 percent,
(***.)and 1 percent level, respectively.
Regression Results (H1) [X.sub.4] (BSC Usage) = [[alpha].sub.0] + [[beta].sub.1][X.sub.1] (Size) + [[beta].sub.2][X.sub.2] (Product Life Cycle Stage) + [[beta].sub.3][X.sub.3] (Market Position) + e (n = 66) Coefficient (predicted sign Variable in brackets Estimate [[alpha].sub.0] Intercept [[alpha].sub.0] 2.21 [X.sub.1] Organization Size [[beta].sub.1] (+) 0.29 [X.sub.2] Product Life-Cycle Stage [[beta].sub.2] (+) 1.30 [X.sub.3] Market Position [[beta].sub.3] (+) -0.04 Variable t-value p Tolerance VIF [[alpha].sub.0] Intercept 5.84 0.000 NA NA [X.sub.1] Organization Size 2.07 0.005 0.85 1.18 [X.sub.2] Product Life-Cycle Stage 3.56 0.000 0.92 1.09 [X.sub.3] Market Position -0.04 0.325 0.85 1.18 Adjusted [R.sup.2] = 0.196, F[3,55] = 5.714, p = 0.018. NA = Not available. Analysis of Variance (ANOVA): Two-Way Interaction Effects (H2-H4) Sum of Mean Source of Variation Squares DF Square F Panel A: Organizational Performance by BSC Usage and Organizational Size (H2) BSC usage (a) 2.51 1 2.51 6.56 Organizational Size (b) 0.05 1 0.05 0.13 Two-way interaction (a x b) 0.22 1 0.22 0.56 Explained 2.65 3 0.88 2.31 Residual 19.93 52 0.38 Panel B: Organizational Performance by BSC Usage and Product Life-Cycle Stage (H3) BSC usage (a) 1.35 1 1.35 3.43 Product Life Cycle (c) 1.74 1 1.74 4.42 Two-way interaction (a x c) 0.01 1 0.01 0.02 Explained 4.18 3 1.39 3.54 Residual 22.07 56 0.39 Panel C: Organizational Performance by BSC Usage and Market Position (H4) BSC usage (a) 1.73 1 1.73 3.91 Market Position (d) 0.16 1 0.16 0.35 Two-way interaction (a x d) 0.45 1 0.45 1.03 Explained 2.57 3 0.86 1.94 Residual 25.19 57 0.44 Source of Variation Sig. of F Panel A: Organizational Performance by BSC Usage and Organizational Size (H2) BSC usage (a) 0.01 Organizational Size (b) 0.73 Two-way interaction (a x b) 0.46 Explained 0.09 Residual Panel B: Organizational Performance by BSC Usage and Product Life-Cycle Stage (H3) BSC usage (a) 0.07 Product Life Cycle (c) 0.04 Two-way interaction (a x c) 0.88 Explained 0.02 Residual Panel C: Organizational Performance by BSC Usage and Market Position (H4) BSC usage (a) 0.05 Market Position (d) 0.51 Two-way interaction (a x d) 0.32 Explained 0.13 Residual Descriptive Statistics and Principal Components Analysis of the BSC Items [a] (n = 66) Descriptive Statistics Actual Item Range Mean Eigenvalues Percent Variance Explained Cronbach Alpha Operating income 1-5 4.26 Sales growth 1-5 3.92 Return on investment 1-5 3.65 Labor efficiency variance 1-5 3.31 Rate of material scrap loss 1-5 2.90 Material efficiency variance 1-5 2.85 Manufacturing lead time 1-5 3.02 Ratio of good output to total output 1-5 2.84 Percent defective products shipped 1-5 2.95 Number of new product launches [b] 1-5 2.26 Number of new patents [b] 1-5 1.62 Time to market new products 1-5 2.31 Survey of customer satisfaction 1-5 2.92 Number of customer complaints 1-5 3.67 Market share 1-5 3.72 Percent shipments returned due 1-5 3.18 to poor quality On-time delivery 1-5 4.13 Warranty repair cost 1-5 2.07 Customer response time 1-5 3.23 Cycle time from order to delivery 1-5 3.13 Standard Item Deviation Eigenvalues Percent Variance Explained Cronbach Alpha Operating income 0.83 Sales growth 1.01 Return on investment 1.25 Labor efficiency variance 1.16 Rate of material scrap loss 1.16 Material efficiency variance 1.24 Manufacturing lead time 1.26 Ratio of good output to total output 1.32 Percent defective products shipped 1.20 Number of new product launches [b] 1.37 Number of new patents [b] 0.97 Time to market new products 1.35 Survey of customer satisfaction 1.27 Number of customer complaints 1.08 Market share 1.14 Percent shipments returned due 1.27 to poor quality On-time delivery 0.88 Warranty repair cost 1.18 Customer response time 1.23 Cycle time from order to delivery 1.00 Factor Loadings after Varimax Orthogonal Rotation Factor 1 Innovation and Learning Item Perspective Eigenvalues 1.99 Percent Variance Explained 66.6 Cronbach Alpha 0.75 Operating income -0.07 Sales growth 0.08 Return on investment 0.01 Labor efficiency variance -0.01 Rate of material scrap loss -0.14 Material efficiency variance -0.06 Manufacturing lead time 0.02 Ratio of good output to total output 0.04 Percent defective products shipped -0.07 Number of new product launches [b] 0.87 Number of new patents [b] 0.86 Time to market new products 0.72 Survey of customer satisfaction 0.01 Number of customer complaints -0.02 Market share 0.04 Percent shipments returned due -0.07 to poor quality On-time delivery -0.11 Warranty repair cost 0.24 Customer response time 0.07 Cycle time from order to delivery 0.12 Factor 3 Factor 2 Internal Customer Business Item Perspective Perspective Eigenvalues 3.06 2.35 Percent Variance Explained 38.3 39.2 Cronbach Alpha 0.76 0.67 Operating income 0.01 -0.02 Sales growth -0.11 0.01 Return on investment -0.06 -0.04 Labor efficiency variance -0.04 0.79 Rate of material scrap loss -0.03 0.73 Material efficiency variance -0.13 0.63 Manufacturing lead time -0.05 0.59 Ratio of good output to total output 0.05 0.58 Percent defective products shipped -0.06 0.44 Number of new product launches [b] -0.08 -0.06 Number of new patents [b] -0.09 -0.03 Time to market new products -0.06 -0.12 Survey of customer satisfaction 0.73 0.12 Number of customer complaints 0.71 -0.07 Market share 0.68 0.04 Percent shipments returned due 0.67 -0.06 to poor quality On-time delivery 0.60 0.08 Warranty repair cost 0.55 -0.01 Customer response time 0.49 0.09 Cycle time from order to delivery 0.47 -0.02 Factor 4 Financial Item Perspective Eigenvalues 1.75 Percent Variance Explained 58.2 Cronbach Alpha 0.62 Operating income 0.85 Sales growth 0.72 Return on investment 0.71 Labor efficiency variance -0.03 Rate of material scrap loss -0.06 Material efficiency variance 0.12 Manufacturing lead time -0.17 Ratio of good output to total output 0.03 Percent defective products shipped -0.02 Number of new product launches [b] 0.13 Number of new patents [b] 0.02 Time to market new products 0.04 Survey of customer satisfaction -0.05 Number of customer complaints 0.04 Market share 0.06 Percent shipments returned due -0.07 to poor quality On-time delivery 0.11 Warranty repair cost 0.06 Customer response time -0.02 Cycle time from order to delivery -0.18 (a.)The boldface data represent the factor loadings that were greater than 0.40. (b.)As noted in the last section of this paper, firms use these measures because they are an important part of their portfolio. Additional Regression Analysis with Independent Variables (Organization Size, Product Life- Cycle Stage, and Market Position) and Four Perspectives of the BSC (H1) Variable Coefficient Estimate Panel A: Financial Perspective [[alpha].sub.0] Intercept [[alpha].sub.0] -2.06 [X.sub.1] Organization Size [[beta].sub.1] 0.30 [X.sub.2] Product Life-Cycle Stage [[beta].sub.2] 0.90 [X.sub.3] Market Position [[beta].sub.3] 0.28 Adjusted [R.sup.2] = 0.124; F [3,50] = 3.501; p = 0.01 Panel B: Internal Business Perspective [[alpha].sub.0] Intercept [[alpha].sub.0] -1.24 [X.sub.1] Organization Size [[beta].sub.1] 0.34 [X.sub.2] Product Life-Cycle Stage [[beta].sub.2] 0.04 [X.sub.3] Market Position [[beta].sub.3] 0.13 Adjusted [R.sup.2] = 0.048; F [3,52] = 1.921; p = 0.138 (n.s.) Panel C: Innovation and Learning Perspective [[alpha].sub.0] Intercept [[alpha].sub.0] -1.16 [X.sub.1] Organization Size [[beta].sub.1] 0.30 [X.sub.2] Product Life-Cycle Stage [[beta].sub.2] 1.79 [X.sub.3] Market Position [[beta].sub.3] -0.02 Adjusted [R.sup.2] = 0.101; F [3,52] = 3.068; p = 0.018 Panel D: Customer Perspective [[alpha].sub.0] Intercept [[alpha].sub.0] -1.37 [X.sub.1] Organization Size [[beta].sub.1] 0.30 [X.sub.2] Product Life Cycle Stage [[beta].sub.2] 0.73 [X.sub.3] Market Position [[beta].sub.3] 0.14 Adjusted [R.sup.2] = -0.054; F [3,52] = 2.056; p = 0.117 (n.s.) Variable t-value p Tolerance VIF Panel A: Financial Perspective [[alpha].sub.0] Intercept -3.10 0.01 NA NA [X.sub.1] Organization Size 1.67 0.09 0.90 1.11 [X.sub.2] Product Life-Cycle Stage 1.36 0.18 0.92 1.09 [X.sub.3] Market Position 1.66 0.10 0.87 1.16 Adjusted [R.sup.2] = 0.124; F [3,50] = 3.501; p = 0.01 Panel B: Internal Business Perspective [[alpha].sub.0] Intercept -1.93 0.06 NA NA [X.sub.1] Organization Size 1.93 0.06 0.87 1.15 [X.sub.2] Product Life-Cycle Stage 0.07 0.72 0.87 1.15 [X.sub.3] Market Position 0.83 0.41 0.88 1.13 Adjusted [R.sup.2] = 0.048; F [3,52] = 1.921; p = 0.138 (n.s.) Panel C: Innovation and Learning Perspective [[alpha].sub.0] Intercept -1.70 0.09 NA NA [X.sub.1] Organization Size 1.60 0.09 0.87 1.15 [X.sub.2] Product Life-Cycle Stage 2.79 0.01 0.87 1.15 [X.sub.3] Market Position -0.13 0.90 0.88 1.13 Adjusted [R.sup.2] = 0.101; F [3,52] = 3.068; p = 0.018 Panel D: Customer Perspective [[alpha].sub.0] Intercept -2.14 0.04 NA NA [X.sub.1] Organization Size 1.70 0.09 0.87 1.15 [X.sub.2] Product Life Cycle Stage 1.21 0.23 0.88 1.15 [X.sub.3] Market Position 0.93 0.35 0.88 1.13 Adjusted [R.sup.2] = -0.054; F [3,52] = 2.056; p = 0.117 (n.s.)
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|Author:||Hoque, Zahirul; James, Wendy|
|Publication:||Journal of Management Accounting Research|
|Article Type:||Statistical Data Included|
|Date:||Jan 1, 2000|
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