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THE EFFECTS OF USING BALANCED SCORECARD MEASURES IN EXECUTIVE COMPENSATION ON ORGANIZATIONAL PERFORMANCE.

INTRODUCTION

It has been argued that balanced scorecard (BSC) measures tailored to fit the organizational context can improve performance (Sila, 2007; Fisher, 1998). The proper fit, which involves reconciling various organizational characteristics and their effects (Venkatraman, 1989; Venkatraman & Camillus, 1984), has been considered as a prerequisite of organizational success (HassabElnaby, Said, & Wier, 2005). Kaplan and Norton (1996) proposed that BSC measures should be linked to executive compensation. In general, Roberts, Albright, and Hibbets (2004) found performance and incentives to be highly correlated, and Jensen and Murphy (1990) suggested that managers' and shareholders' interests could be aligned through appropriate incentives.

However, heavy reliance on financial measures in executive compensation could lead to short-term focus and behaviors (Bartlett, Johnson, & Reckers, 2014; Ittner, Larcker, & Rajan, 1997; Bushman, Indjejikian, & Smith, 1996). Nonfinancial leading indicators embedded in the BSC could re-focus attention to long-term performance (Bartlett, Johnson, & Reckers, 2014).

Little empirical evidence exists on performance impacts of BSC-based compensation plans (Ittner, Larcker, & Meyer, 2003). Although HassabElnaby, Said, and Wier (2005) studied the context and performance consequences of nonfinancial measures in compensation contracts, they did not explicitly consider the BSC. Crabtree, and DeBusk (2008) reported that organizations adopting the BSC performed significantly better than non-adopters in the same industry. Larcker (1983) found increased capital investment in organizations with incentive plans and a positive market reaction to the disclosure of such plans, while McConnell and Muscarella (1985) reported a positive association between measurement systems and share price. Yet, Gaver and Gaver (1993) found no evidence of increased capital spending after the adoption of incentive programs. Such inconsistent findings warrant further study of performance effects of BSC use in executive compensation.

This study extends prior research by investigating the performance consequences of BSC-based measures in executive compensation, including the fit between BSC use and organizational characteristics. It poses the following research questions: (a) Does BSC use in executive compensation affect organizational performance? (b) Is organizational performance affected by the fit between BSC use in executive compensation and organizational characteristics? As such, it can help link performance measurement and incentive compensation literatures. First, related literature is reviewed and the study's hypotheses developed. Next, research method is outlined, followed by the findings of the study. Finally, conclusions, limitations, and future research directions are discussed.

LITERATURE AND HYPOTHESES

Balanced Scorecard Use in Executive Compensation and Organizational Performance

Several studies have found associations between the use of financial and nonfinancial performance measures (although not necessarily termed BSC measures), and organizational performance. For example, Hoque and James (2000) found that BSC use was significantly correlated with organizational performance, and Crabtree and DeBusk (2008) demonstrated that the use of BSC improved shareholder returns. Similarly, Davis, and Albright (2004) found that bank branches using the BSC performed better than those that did not, and Ittner, Larcker, and Meyer (2003) reported that organizations using a broad set of financial and nonfinancial measures earned higher stock returns. In addition, Banker, Lee, and Potter (1996) discovered that customer satisfaction measures were significantly associated with future financial performance.

Furthermore, several studies have also argued that properly designed BSCs can link strategic goals to performance targets and help organizations evaluate managerial performance against targets (Bartlett, Johnson, & Reckers, 2014). For example, considering BSC measures in executive incentives, Budde (2007) asserted that linking BSC measures to incentive contracts can align the interests of owners and employees, and Banker, Lee, and Potter (1996) provided evidence of behavioral and performance effects of remuneration linked to nonfinancial measures. More generally, Ittner, Larcker, and Rajan (1997) concluded that any financial or nonfinancial measures that convey information about desired managerial actions should be included in incentive contracts to motivate managers to improve performance.

It has been further argued that organizational performance includes the dimensions of effectiveness and efficiency (Berry, Broadbent, & Otley, 2005; Lebas & Euske, 2002). For organizations that aim to maximize shareholder value, return on equity, dividends, and stock price can serve as indicators of long-term effectiveness (HassabElnaby, Said, & Wier, 2005; Said, HassabElnaby, & Wier, 2003; Wallace, 1997; Bushman, Indjejikian, & Smith, 1996) and revenue per employee, accounts receivable turnover, net assets turnover, and current ratio as indicators of operational efficiency (Berry, Broadbent, & Otley, 2005).

Although Pollanen et al. (2017) found both efficiency and effectiveness measures to be related to organizational performance, no consensus presently exists regarding the categorization of specific indicators as efficiency and effectiveness indicators. Nonetheless, Murphy (1985) suggested that executive compensation would best be based on both market-based and operational performance measures. Therefore, this study postulates a main relationship between BSC use and overall organizational performance (but also explores effectiveness and efficiency dimensions in additional analyses), as follows:

H1: The use of the balanced scorecard (BSC) measures in executive compensation is associated positively with organizational performance.

Balanced Scorecard Fit with Organizational Characteristics and Performance

Organizational performance can be affected by various organizational and environmental factors. For example, Said, HassabElnaby, and Wier (2003) argued that the adoption and use of performance measures is a choice, with their potential net benefit depending on contextual factors. Similarly, Ittner and Larcker (1998) suggested that the optimal choice of performance measures is a function of many factors. Jermias and Gani (2005) found that the fit between strategy and contextual variables had a positive relationship with performance, while HassabElnaby, Said, and Wier (2005) discovered that the relationship between nonfinancial measures and organizational performance is contingent on the fit of these measures and organizational characteristics. Based on prior studies, Pollanen and Xi (2015) considered six characteristics, namely, strategy, structure, ownership, industry, quality, and culture.

Strategy is seen as an important consideration in the design of performance measures and incentives. For example, Chenhall (2005) argued that the influence of strategic performance measurement systems on organizational performance is indirect through the mediating alignment of manufacturing with strategy and organizational learning, while Langfield-Smith (1997) proposed that control systems customized to fit strategy can enhance competitive advantage. Organizations with prospector strategy have been found to emphasize nonfinancial measures and those with defender strategy financial measures (Ittner, Larcker, & Rajan, 1997; Said, HassabElnaby, & Wier, 2003). Gani and Jermias (2012) discovered that prospectors used more performance-based compensation systems than the defenders and that a misfit between strategy and control systems (e.g., BSC) was associated with lower performance.

Different strategies may require different organizational structures. For example, prospectors could gain greater advantage from decentralized structure than defenders (Jermias & Gani, 2005). Greater authority and accountability are typically granted to managers of decentralized units than to managers of units in centralized organizations (Chenhall, 2003). However, if operations are diverse, for example, in organizations with multiple segments and foreign subsidiaries, control systems complexity increases (Ashbaugh-Skaife, Collins, & Kinney, 2007). Because of their greater and more complex information needs, formal control systems are thus more appropriate for decentralized than centralized organizations (Chenhall, 2003).

Decentralized organizations are typically large publicly traded companies, whereas centralized organizations can be small public or private companies or family enterprises. Institutional owners of large organizations with large investments have an incentive to monitor management and power to influence management to improve ineffective controls (Ashbaugh-Skaife, Collins, & Kinney, 2007). Ashbaugh-Skaife, Collins, and Kinney (2007) found that large publicly traded organizations reporting to the Securities and Exchange Commission (SEC) were more likely to provide early warnings of significant control issues. Thus, managers of such organizations may be more motivated to use BSC measures, as they could provide early warnings of impending major problems.

Performance measures should also be tailored to reflect the needs and characteristics of different industries (Ely, 1991). For example, Speckbacher, Bischof, and Pfeiffer (2003) found that BSC use was lower in consumer goods and retail industries than in others, and Ittner, Larcker, and Rajan (1997) demonstrated that organizations in highly regulated utilities and telecommunication industries emphasized nonfinancial measures more than others. On the other hand, Chenhall (2003) argued that standardized and administrative controls, including financial measures, are needed for organizations in technology-intensive industries. Government regulations, professional standards, and competitive pressures could also require organizations to revise their control systems (DiMaggio & Powell, 1983).

Organizations that emphasize quality management have also been known to benefit from nonfinancial measures. Nonfinancial measures can communicate the importance of quality focus to employees (Ittner & Larcker, 1997). Ittner, Larcker, and Rajan (1997) found that organizations with quality focus emphasized nonfinancial measures in executive compensation more than others and argued that nonfinancial quality measures can provide additional information about managerial performance. Said, HassabElnaby, and Wier (2003) agreed that the use of nonfinancial measures in executive compensation was greater in quality-focused organizations than in others, as they can reflect managerial efforts and lead to innovation and improved future performance. Similarly, HassabElnaby, Said, and Wier (2005) found that quality focus was related to the retention of nonfinancial measures in compensation contracts.

BSC measures, however, can influence, and be influenced by, cultural factors (Chhokar, Brodbeck, & House, 2008). For example, Chow et al. (2002) found that local Taiwanese organizations exhibited Taiwanese culture but US organizations in Taiwan mirrored US culture. Carr and Tomkins (1998) concluded that culture is capable of influencing management styles and analytical approaches, and Schuler and Rogovsky (1998) found culture to be an important factor affecting compensation practices of multinational organizations. Bourguignon, Malleret, and Norreklit (2004) pointed out that the BSC is consistent with the North American culture in which it was developed, and that its top-down approach does not fit well in the French context.

Pollanen and Xi (2015) found that all six organizational characteristics can influence BSC use in executive compensation but did not consider its fit or effects on organizational performance. This study investigates both the fit between BSC use in executive compensation and organizational characteristics and the extent to which such fit contributes to organizational performance. The fit approach is considered particularly appropriate for studying relationships between evolving systems and performance (HassabElnaby, Said, & Wier, 2005; Jermias & Gani, 2005), which is the case in this study. This study posits performance to be better in organizations with greater fit, as follows:

H2: The fit between the use of the balanced scorecard (BSC) measures in executive compensation and organizational characteristics is associated positively with organizational performance.

RESEARCH METHOD

Sample and Data Collection

This study used the Compustat database to collect data on the utilized sample which consisted of 330 companies, chosen from the 4-digit Standard Industry Classification (SIC) categories. Occasional missing values were obtained from the Osiris database and from company websites. Thus, there are no remaining missing values, and the sample size for all results reported in this study is 330. The study covered BSC adoption and use from 1992 to 2009, inclusive, the period during which many organizations adopted BSCs.

Measurement of Variables

The variables were measured by using pre-validated measures, if they were available and feasible. Based on the SEC filings and related public documents, it was determined whether or not the organizations reported using BSC measures in their executive compensation (1 = users; 0 = others). Using principal component analysis (PCA), composite performance measures were constructed based on a total of 23 performance indicators and also for the subcategories of 11 effectiveness and 12 efficiency indicators, as shown in Appendix 1 (HassabElnaby, Said, & Wier, 2005; Said, HassabElnaby, & Wier, 2003; Wallace, 1997; Bushman, Indjejikian, & Smith, 1996).

Following HassabElnaby, Said, and Wier (2005), the fitted factor method was used to measure the fit between BSC use and organizational characteristics, which captures the fit by the predicted values in the following model: BSC use = f (strategy, industry, quality, structure, culture, ownership). That is, BSC use was first regressed on organizational characteristics and its predicted values then used in testing the fit hypothesis, H2. Composite measures were also used for strategy, structure, and ownership, each of which have multiple indicators. The measures of organizational characteristics (Pollanen & Xi, 2015) are shown in Appendix 2.

The composite measures used for performance, strategy, structure, and ownership reflect the relative optimally weighted contributions of all individual indicators of each variable, derived through the PCA. As the individual indicators of each variable are expressed in different measurement units, each indicator was treated as a separate component in the respective analyses. For each variable, the component with the largest eigenvalue was automatically retained in the PCA and used as the "composite measure", because it accounts for the largest proportion of the total variance.

RESULTS AND DISCUSSION

Balanced Scorecard Use and Organizational Performance

Ordinary least-squares regressions were used to assess the effects of BSC use on organizational performance (H1) using the following model, including seven control variables:

Performance = [alpha] + [Z.sub.1]*M&A + [Z.sub.2] *New_CEO + [Z.sub.3]* Financial_health + [Z.sub.4] *Product_dev_cycle + [Z.sub.5] *Product_life_cycle + [Z.sub.6] *Industry_ROA + [Z.sub.7] *Industry_volatility + [beta]*BSC_use + error (1)

Where:

Performance = Composite measure derived from 23 optimally weighted performance indicators (Appendix 1) through principal components analysis (PCA)

M&A = Merger/acquisition during the current or previous year coded 1; 0 otherwise (Zephyr/Osiris)

New_CEO = New CEO hired during the current or previous year coded 1; 0 otherwise (Osiris/Lexis-Nexis)

Financial_health = Altman Z-score of bankruptcy prediction (Compustat)

Product_dev_cycle = Length of product development cycle, coded 1 for long; 0 otherwise (US National Academy of Engineering industry classification)

Industry_ROA = Industry Return on Assets (ROA) ratio (Compustat)

Industry_volatility = Standard deviation of industry ROA ratio (Compustat)

BSC_use = Reported use of BCS measures in executive compensation coded 1; 0 otherwise (SEC filings)

The regression model was first run without control variables and then again with the following control variables: mergers and acquisitions (M & A) (Wallace, 1997); new CEO (Ittner, Larcker, & Rajan, 1997; Wallace, 1997); financial health (Altman Z-score); and length of product development and life cycles (Said, HassabElnaby, & Wier, 2003; HassabElnaby, Said, & Wier, 2005); and industry Return on Assets (ROA) ratio and industry volatility (standard deviation of industry ROA) (Ittner, Larcker, & Rajan, 1997). For the composite performance measure based on all 23 performance indicators, the first component with the highest eigenvalue (4.79) and the highest proportion of the total variance explained (20.8%) was retained and used.

The results of the regression analyses are presented in Table 1. Performance is significantly and positively associated with BSC use, without and with control variables (p<0.01 and p<0.05, respectively).

The explanatory power of the model increased considerably when the control variables were included ([R.sup.2]=0.0328 and [R.sup.2]=0.1088, without and with control variables, respectively). Two control variables, industry volatility and financial health, are significant (p<0.01 and p<0.05, respectively). These findings indicate that the use of BSC measures in executive compensation makes a moderate contribution to organizational performance, particularly when this relationship is controlled for several potential confounding factors.

In order to explore more nuanced performance dimensions, the PCA was also used to create separate composite measures of the 11 effectiveness indicators and the 12 efficiency indicators. For both effectiveness and efficiency, the first component with the highest eigenvalue (2.88 for effectiveness; 2.53 efficiency) and the highest proportion of the variance explained (26% for effectiveness; 21% for efficiency) were retained.

The results of the regression analyses are presented in Table 2. Without control variables, both effectiveness and efficiency are significantly and positively associated with BSC use (p<0.01 for effectiveness; p<0.10 for efficiency).

With control variables, the coefficients for BSC use are also significant (p<0.10 for effectiveness and p<0.05 for efficiency). The control variables increase the explanatory power of both models ([R.sup.2]=0.0945 for effectiveness; [R.sup.2]=0.0234 for efficiency). For effectiveness, financial health and industry volatility are significant (p<0.01 for both); for efficiency, no control variables are significant.

These findings indicate that BSC use in executive compensation contributes more to longer-term effectiveness than shorter-term operational efficiency. This result could be explained by the fact that executives are typically concerned with longer-term strategic outcomes (effectiveness); whereas, lower-level managers, who may not be covered by executive compensation plans, are responsible for operational performance (efficiency).

Balanced Scorecard Fit with Organizational Characteristics and Performance

The following model, including the control variables, was used to test the effects of the fit between BSC use and organizational characteristics on organizational performance (Eh):

Performance = [alpha] + [Z.sup.1] *M&A + [Z.sup.2] *New_CEO + [Z.sup.3]* Financial_health + [Z.sup.4] *Product_dev_cycle + [Z.sup.5] *Product_life_cycle + [Z.sup.6] *Industry_ROA + [Z.sup.7] *Industry_volatility + [beta]*BSC_fit + error (2)

Where:

Performance = Composite measure derived from 23 optimally weighted performance indicators (Appendix 1) through principal components analysis (PCA)

M&A = Merger/acquisition during the current or previous year coded 1; 0 otherwise (Zephyr/Osiris)

New_CEO = New CEO hired during the current or previous year coded 1; 0 otherwise (Osiris/Lexis-Nexis)

Financial_health = Altman Z-score of bankruptcy prediction (Compustat)

Product_dev_cycle = Length of product development cycle, coded 1 for long; 0 otherwise (US National Academy of Engineering industry classification)

Industry_ROA = Industry Return on Assets (ROA) ratio (Compustat)

Industry_volatility = Standard deviation of industry ROA ratio (Compustat)

BSC_fit = Fitted (predicted) values of BSC use in executive compensation depending on organizational characteristics, obtained from regressing BSC use on organizational characteristics, i.e., BSC use = f (strategy, industry, quality, structure, culture, ownership).

As for testing [H.sup.1], the composite measure of performance was used, and the regression model was again first run without control variables and then with the control variables. The results in Table 3 are similar to those for BSC use alone. Performance is significantly and positively associated with BSC fit, without and with control variables (p<0.01 and p<0.10, respectively; [R.sup.2]=0.0261 and [R.sup.2]=0.1070, respectively). Two control variables, financial health and industry volatility, are again significant (p<0.01 for both).

The results for both effectiveness and efficiency, presented in Table 4, are also significant (p<0.01 for effectiveness; p<0.10 for efficiency). The fit model produced greater explanatory power than the corresponding BSC model ([R.sup.2]=0.0568 for effectiveness; [R.sup.2]=0.0302 for efficiency). In the analyses with the control variables, the coefficients for BSC fit are also significant for both effectiveness and efficiency (p<0.01 for effectiveness; p<0.10 for efficiency). The explanatory power of the fit models increased substantially ([R.sup.2]=0.1319 for effectiveness; [R.sup.2]=0.1224 for efficiency) over the corresponding BSC models. The results for effectiveness, however, could be affected by several control variables, most notably by industry volatility and product life cycle (p<0.01 for both).

These results indicate that the fit between BSC use and organizational characteristics has the greatest impact on long-term performance (effectiveness) when its effects are controlled for several potential confounding factors. They also imply that the fit can be a complex construct and that it may be useful to consider a greater number of organizational characteristics.

LIMITATIONS

In spite of significant contributions of this study, several limitations should also be noted. It is possible that some organizations that used BSCs did not explicitly disclose their use, or they used other similar measurement schemes without identifying them as BSCs. The organizations chose whether or not to use BCSs, disclose their use, and link BSC measures to executive compensation. Some may have done so for legitimacy reasons in order to address actual or perceived governance problems rather than to improve performance. BSC use was assumed to improve performance, but a reverse relationship could also be plausible, that is, well performing organizations could be more likely to use BSC-based executive compensation schemes.

CONCLUSIONS

This study reveals significant associations between BSC use in executive compensation and organizational performance, supporting the findings of Crabtree and DeBusk (2008), Hoque and James (2000), and Davis and Albright (2004). The results also provide evidence that organizational performance is a function of the fit between BSC use and organizational characteristics, as proposed by Gani and Jermias (2012), Jermias and Gani (2005), and Said, HassabElnaby, and Wier (2003). Furthermore, the results support effectiveness and efficiency as rather distinct performance dimensions, but BSC use and BSC fit are more closely related to the effectiveness dimension that reflects longer-term performance outcomes.

This study contributes to literature by providing new evidence with benefits to researchers and practitioners. It extends prior research through a comprehensive investigation of the use of BSC-based measures (financial and nonfinancial) in executive compensation, the fit between BSC use and organizational characteristics, and their performance consequences. In addition, it uses several data collection and analytical methods, including multiple measures for key variables and several control variables. The findings demonstrate that organizations can improve performance by adopting BSC measures that fit with their context and by using them as a basis for executive incentives.

FUTURE RESEARCH DIRECTIONS

In future research, a combination of survey and public data can potentially provide a richer understanding of the effects of BSC use in executive compensation on performance. In addition, reverse causal relationships between performance and BSC use could also be investigated, as well as, measures of BSC use, BSC fit, and performance continued to be refined. For example, instead of using the fitted factor scores to measure the fit, future studies could use different analytical techniques and consider a greater number of organizational characteristics.

A cknowledgements:

Some preliminary results of the research project on which this study is based were presented at the American Accounting Association Mid-Atlantic Meeting, Baltimore, MD, 2011; the Canadian Academic Accounting Association Annual Conference, Toronto, 2011; and the American Accounting Association Annual Meeting, Denver, CO, 2011. The authors thank the anonymous reviewers for their constructive comments that have helped shape this study.

REFERENCES

Ashbaugh-Skaife, H., Collins, D. W., & Kinney, W. R. (Jr) (2007). The discovery and reporting of internal control deficiencies prior to SOX-mandated audits. Journal of Accounting and Economics, 44 (1/2), 166-192.

Banker, R., Lee, S., & Potter, G. (1996). A field study of the impact of a performance-based incentive plan. Journal of Accounting and Economics, 21 (2), 195-226.

Bartlett, G., Johnson, E., & Reckers, P. (2014). Accountability and role effects in balanced scorecard performance evaluations when strategy timeline is specified. European Accounting Review, 23 (1), 143-165.

Berry, A. J., Broadbent, J., & Otley, D. (2005). Management control: theories, issues and performance, (2nd Ed.). New York, NY: Palgrave Macmillan.

Bourguignon, A., Malleret, V., & Norreklit, H. (2004). The American balanced scorecard versus the French tableau de bord: the ideological dimension. Management Accounting Research, 15 (2), 107-134.

Budde, J. (2007). Performance measure congruity and the balanced scorecard. Journal of Accounting Research, 45 (3), 515-539.

Bushman, R., Indjejikian, R., & Smith, A. (1996). CEO compensation: the role of individual performance evaluation. Journal of Accounting and Economics, 21 (2), 161-193.

Carr, C., & Tomkins, C. (1998). Context, culture and the role of the finance function in strategic decisions: a comparative analysis of Britain, Germany, the USA and Japan. Management Accounting Research, 9 (2), 213-239.

Chenhall, R. H. (2003). Management control systems design within its organizational context: findings from contingency-based research and directions for the future. Accounting, Organizations and Society, 28 (2/3), 127-168.

Chenhall, R. H. (2005). Integrative strategic performance measurement systems, strategic alignment of manufacturing, learning and strategic outcomes: an exploratory study. Accounting, Organizations and Society, 30 (5), 395-422.

Chhokar, J. S., Brodbeck, F. C., & House, R. J. (2008). Culture and leadership across the world: the GLOBE book of in-depth studies of 25 societies. Mahwah, NJ: Lawrence Erlbaum Associates Publishers.

Chow, C. W., Harrison, G. L., McKinnon, J. L., & Wu, A. (2002). The organizational culture of public accounting firms: evidence from Taiwanese local and US affiliated firms. Accounting, Organizations and Society, 27 (4/5), 347-360.

Crabtree, A. D., & DeBusk, G. K. (2008). The effects of adopting the balanced scorecard on shareholder returns. Advances in Accounting, 24 (1), 8-15.

Davis, S., & Albright, T. (2004). An investigation of the effect of balanced scorecard implementation on financial performance. Management Accounting Research, 15 (2), 135-153.

DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: institutional isomorphism and collective rationality in organizational fields. American Sociology Review, 48 (2), 147-160.

Ely, K. (1991). Interindustry differences in the relation between compensation and organizational performance. Journal of Accounting Research, 29 (1), 37-58.

Fisher, J. G. (1998). Contingency theory, management control systems and organizational outcomes: past results and future directions. Behavioral Research in Accounting, 10 (Supplement), 47-64.

Gani, L., & Jermias, J. (2012). Effects of strategy: management control system misfits on firm performance. Accounting Perspectives, 11 (3), 165-196.

Gaver, J. J., & Gaver, M. (1993). The association between performance plan adoption and corporate capital investment: a note. Journal of Management Accounting Research, (Fall), 145-158.

HassabElnaby, H. R., Said, A. A., & Wier, B. (2005). The retention of nonfinancial performance measures in compensation contracts. Journal of Management Accounting Research, 17, 23-42.

Hoque, Z., & James, W. (2000). Linking balanced scorecard measures to size and market factors: impact on organizational performance. Journal of Management Accounting Research, 12, 1-17.

Ittner, C. D., & Larcker D. F. (1997). Quality strategy, strategic control systems, and organizational performance. Accounting, Organizations and Society, 22 (3/4), 293-314.

Ittner, C. D., & Larcker D. F. (1998). Innovations in performance measurement: trends and research implications. Journal of Management Accounting Research, 10, 205-238.

Ittner, C. D., Larcker D. F., & Meyer, M. W. (2003). Subjectivity and the weighting of performance measures: evidence from a balanced scorecard. The Accounting Review, 78 (3), 725-758.

Ittner, C. D., Larcker D. F., & Rajan, M. (1997). The choice of performance measures in annual bonus contracts. The Accounting Review, 72 (2), 231-255.

Jensen, M., & Murphy, K. (1990). CEO incentives: it's not how much you pay, but how. Harvard Business Review, (May-June), 138-149.

Jermias, J., & Gani, L. (2005). Ownership structure, contingent fit, and business unit performance: a research model and empirical evidence. The International Journal of Accounting, 40 (1), 65-85.

Kaplan, R. S., & Norton, D. P. (1996). Using the balanced scorecard as a strategic management system. Harvard Business Review, (January-February), 75-85.

Langfield-Smith, K. (1997). Management control systems and strategy: a critical review. Accounting, Organizations and Society, 22 (2), 207-232.

Larcker, D. F. (1983). The association between performance plan adoption and corporate capital investment. Journal of Accounting and Economics, 5, 3-30.

Lebas, M., & Euske, K. (2002). A conceptual and operational delineation of performance. In A. Neely (Ed.), Business performance measurement: theory and practice, (pp. 65-79). Cambridge, UK: Cambridge University Press.

McConnell, J. J., & Muscarella, C. J. (1985). Corporate capital expenditure decisions and the market value of the firm. Journal of Financial Economics, 14 (3), pp. 399-422.

Murphy, K. (1985). Corporate performance and managerial remuneration: an empirical analysis. Journal of Accounting and Economics, 7 (1/3), 11-42.

Pollanen, R., Abdel-Maksoud, A., Elbanna, S., & Manama, H. (2017). Relationships between strategic performance measures, strategic decision making, and organizational performance: empirical evidence from Canadian public organizations. Public Management Review, 19 (5), 725-746.

Pollanen, R. M., & Xi, K. K. (2015). Organizational characteristics and use of balanced scorecard measures in executive compensation. International Journal of Business and Public Administration, 12 (1), 68-81.

Roberts, M. L., Albright, T. L., & Hibbets, A. R. (2004). Debiasing balanced scorecard evaluations. Behavioral Research in Accounting, 16, 75-88.

Said, A. A., HassabElnaby, H. R., & Wier, B. (2003). An empirical investigation of the performance consequences of nonfinancial measures. Journal of Management Accounting Research, 15, 193-223.

Schuler, R. S., & Rogovsky, N. (1998). Understanding compensation practice variations across organizations: the impact of national culture. Journal of International Business Studies, 29(1), 159-177.

Sila, I. (2007). Examining the effects of contextual factors on TQM and performance through the lens of organizational theories: an empirical study. Journal of Operations Management, 25 (1), 83-109.

Speckbacher, G., Bischof, J., & Pfeiffer, T. (2003). A descriptive analysis on the implementation of balanced scorecards in German-speaking Countries. Management Accounting Research, 14 (4), 361-387.

Venkatraman, N. (1989). The concept of fit in strategy research: toward verbal and statistical correspondence. The Academy of Management Review, 14 (3), 423-444.

Venkatraman, N., & Camillus, J. C. (1984). Exploring the concept of fit in strategic management. The Academy of Management Review, 9 (3), 513-525.

Wallace, J. S. (1997). Adopting residual income-based compensation plans: do you get what you pay for? Journal of Accounting and Economics, 24 (3), 275-300.

About the Authors:

Raili Pollanen is an Associate Professor, Accounting, at the Sprott School of Business, Carleton University, Ottawa, Canada. Her research interests are in the areas of performance measurement, management, control, and accountability in both public and private sectors.

Kenneth Kangwu Xi is a Financial Advisor at Justice Canada, Ottawa, Canada.
Appendix 1
Performance Measures

Performance dimensions  Performance indicators

Effectiveness           Return on assets (ROA) (%)
                        Return on equity (ROE) (%)
                        Return on investment (ROI) (%)
                        Gross profit margin (%)
                        Earnings before interest and taxes margin (%)
                        Net profit margin (%)
                        Dividend payout (%)
                        Stock return (%)
                        Price/Earnings ratio
                        Dividend yield (%)
                        Price/Cashflow ratio
Efficiency              Sales/Employee ($1,000s)
                        Labor cost/Employee ($1,000s)
                        Net income/Employee ($1,000s)
                        Sales growth (%)
                        Earnings-per-share growth (%)
                        Inventory turnover (times)
                        Accounts receivable turnover (times)
                        Total asset turnover (times)
                        Operating cycle (days)
                        Current assets/Current liabilities (Quick ratio)
                        Interest coverage ratio
                        Dividend/Earnings ratio

Appendix 2
Measures of Organizational Characteristics

Characteristic  Performance measures

Strategy        a) R & D expenditures/Sales ratio (Ittner, Larcker, &
                   Rajan, 1997; Said, HassabElnaby, & Wier, 2003)
                b) Market value/Book value ratio (Ittner, Larcker, &
                   Rajan, 1997; Said, HassabElnaby, & Wier, 2003)
                c) Labour costs/Sales ratio
Ownership       a) Institutional ownership percentage (Ashbaugh-Skaife,
                   Collins, & Kinney, 2007)
                b) Independence (control) indicator (Osiris database)
Industry        Industry codes SIC 01 to SIC 89 (Ittner, Larcker, &
                Rajan, 1997; Said, HassabElnaby, & Wier, 2003)
Culture         Cultural cluster categories (Chhokar, Brodbeck, & House,
                2008)
Quality         Quality awards (yes/no) (Ittner, Larcker, & Rajan, 1997;
                Said, HassabElnaby, & Wier, 2003)
Structure       a) Business segments (number) (Ashbaugh-Skaife, Collins,
                   & Kinney, 2007)
                b) Foreign subsidiaries (yes/no) (Ashbaugh-Skaife,
                   Collins, & Kinney, 2007)


Raili M. Pollanen

Carleton University

Kenneth Kangwu Xi

Justice Canada
Table 1
Regression Results (H1)--Regressing Performance on BSC Use

A) Without control
variables                  Predicted sign  Coeff.  t-stat  p-value

BSC use                          +          0.655    3.33  0.001 (***)
Intercept                                  -0.328   -2.36  0.019 (**)

F-test = 0.0010 (***); VIF = 1.03; [R.sup.2]= 0.0328; Adj. [R.sup.2] =
0.0298.
(***) 1% significance level; (**) 5% significance level; (*) 10%
significance level.

B) With control variables  Predicted sign  Coeff.  t-stat  p-value

BSC use                          +          0.507    2.55  0.011 (**)
Mergers and acquisitions         +          0.363    1.82  0.069 (*)
New chief executive              -         -0.421   -1.74  0.083 (*)
Financial health                 +          0.053    2.57  0.011 (**)
Product development cycle        -         -0.512   -1.55  0.123
Product life cycle               +          0.460    1.40  0.163
Industry ROA                     +         -0.000   -0.02  0.987
Industry volatility              -         -0.115   -3.41  0.001 (***)
Intercept                                  -0.095   -0.42  0.675

F-test = 0.0000 (***); VIF =1.12; [R.sup.2] = 0.1088; Adj. [R.sup.2] =
0.0866.
(***) 1% significance level; (**) 5% significance level; (*) 10%
significance level.

Table 2
Exploratory Regression Results--Regressing Effectiveness and Efficiency
on BSC Use

A) Without control variables                             Predicted sign

Effectiveness                 BSC use                          +
                              Intercept
Efficiency                    BSC use                          +
                              Intercept

A) Without control variables  Coeff.  t-stat  p-value

Effectiveness                  0.523    2.82  0.005 (***)
                              -0.263   -2.00  0.046 (**)
Efficiency                     0.199    1.86  0.064 (*)
                              -0.994   -1.31  0.189

Effectiveness: F-test = 0.0051 (***); VIF = 1.02; [R.sup.2] = 0.0239;
Adj. [R.sup.2] = 0.0209.
Efficiency: F-test = 0.0638 (*); VIF = 1.01; [R.sup.2] = 0.0104; Adj.
[R.sup.2] = 0.0074.
(***) 1% significance level; (**) 5% significance level; (*) 10%
significance level.

B) With control variables                                Predicted sign

Effectiveness                 BSC use                          +
                              Mergers and acquisitions         +
                              New chief executive              -
                              Financial health                 +
                              Product development cycle        -
                              Product life cycle               +
                              Industry ROA                     +
                              Industry volatility              -
                              Intercept
Efficiency                    BSC use                          +
                              Mergers and acquisitions         +
                              New chief executive              -
                              Financial health                 -
                              Product development cycle        -
                              Product life cycle               +
                              Industry ROA                     +
                              Industry volatility              -
                              Intercept

B) With control variables     Coeff.  t-stat  p-value

Effectiveness                  0.356    1.90  0.059 (*)
                               0.366    1.94  0.054 (*)
                              -0.227   -1.00  0.320
                               0.052    2.69  0.008 (***)
                              -0.607   -1.95  0.053 (*)
                               0.294    0.95  0.345
                              -0.037   -1.53  0.127
                              -0.104   -3.27  0.001 (***)
                               0.168    0.78  0.435
Efficiency                     0.236    2.11  0.036 (**)
                              -0.056   -0.50  0.617
                               0.094    0.69  0.493
                              -0.018   -1.62  0.107
                               0.015    0.08  0.935
                               0.053    0.29  0.774
                               0.011    0.79  0.429
                              -0.004   -0.19  0.848
                              -0.139   -1.09  0.275

Effectiveness: F-test = 0.0001 (***); VIF = 1.10; [R.sup.2] = 0.0945;
Adj. [R.sup.2] = 0.0727.
Efficiency: F-test = 0.4665 (*); VIF = 1.02; [R.sup.2] = 0.0234; Adj.
[R.sup.2] = -0.0009.
(***) 1% significance level; (**) 5% significance level; (*) 10%
significance level.

Table 3
Regression Results (H2)--Regressing Organizational Performance on BSC
Fit

A) Without control
variables                  Predicted sign  Coeff.  t-stat  p-value

BSC fit                          +          1.046    2.81  0.005 (***)
Intercept                                  -0.567   -2.47  0.014 (**)

F-test = 0.0052 (***); VIF = 1.03; [R.sup.2] = 0.0261; Adj. [R.sup.2] =
0.0228.
(***) 1 % significance level; (**) 5% significance level; (*) 10%
significance level.

B) With control variables  Predicted sign  Coeff.  t-stat  p-value

BSC fit                          +          0.667    1.78  0.076 (*)
Mergers and acquisitions         +          0.455    2.09  0.038 (**)
New chief executive              -         -0.340   -1.30  0.194
Financial health                 +          0.083    2.68  0.008 (***)
Product development cycle        -         -0.534   -1.48  0.140
Product life cycle               +          0.390    1.08  0.280
Industry ROA                     +         -0.005   -0.16  0.870
Industry volatility              -         -0.129   -3.30  0.001 (***)
Intercept                                  -0.226   -0.75  0.451

F-test = 0.0001 (***); VIF = 1.12; [R.sup.2] = 0.1070; Adj. [R.sup.2] =
0.0822.
(***) 1 % significance level; (**) 5% significance level; (*) 10%
significance level.

Table 4

Exploratory Regression Results--Regressing Effectiveness and Efficiency
on BSC Fit

A) Without control variables                             Predicted sign

Effectiveness                 BSC fit                          +
                              Intercept
Efficiency                    BSC fit                          +
                              Intercept

A) Without control variables  Coeff.  t-stat     p-value

Effectiveness                  1.078    4.21     0.000 (***)
                              -0.551   -3.51     0.001 (***)
Efficiency                     0.429    1.81     0.071 (*)
                              -0.261   -1.79     0.075 (*)

Effectiveness: F-test = 0.0000 (***); VIF = 1.06; [R.sup.2] = 0.0568;
Adj. [R.sup.2] = 0.0536.
Efficiency: F-test = 0.0025 (*); VIF =1.03; [R.sup.2] = 0.0302; Adj.
[R.sup.2] = 0.0270.
(***) 1 % significance level; (**) 5% significance level; (*) 10%
significance level.

With control variables                                   Predicted sign

Effectiveness                 BSC fit                          +
                              Mergers and acquisitions         +
                              New chief executive              -
                              Financial health                 +
                              Product development cycle        -
                              Product life cycle               +
                              Industry ROA                     +
                              Industry volatility              -
                              Intercept
Efficiency                    BSC fit                          +
                              Mergers and acquisitions         +
                              New chief executive              -
                              Financial health                 -
                              Product development cycle        -
                              Product life cycle               +
                              Industry ROA                     +
                              Industry volatility              -
                              Intercept

With control variables        Coeff.  t-stat     p-value

Effectiveness                  0.816    3.25     0.001 (***)
                               0.055    0.37     0.708
                              -0.409   -2.34     0.020 (**)
                               0.046    2.22     0.028 (**)
                              -0.493   -2.04     0.043 (**)
                               0.698    2.89     0.004 (***)
                              -0.014   -0.70     0.484
                              -0.073   -2.78     0.006 (***)
                              -0.303   -1.51     0.132
Efficiency                     0.547    1.79     0.074 (*)
                               0.158    0.91     0.365
                              -0.064   -0.31     0.758
                              -0.033   -1.32     0.188
                              -0.035   -0.12     0.903
                               0.020    0.07     0.943
                               0.014    0.61     0.542
                               0.003    0.10     0.918
                              -0.225   -0.94     0.350

Effectiveness: F-test = 0.0000 (***); VIF = 1.16; [R.sup.2] = 0.1319;
Adj. [R.sup.2] = 0.1078.
Efficiency: F-test = 0.0000 (*); VIF = 1.14; [R.sup.2] = 0.1224; Adj.
[R.sup.2] = 0.0982.
(***) 1 % significance level; (**) 5% significance level; (*) 10%
significance level.
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Author:Pollanen, Raili M.; Xi, Kenneth Kangwu
Publication:International Journal of Business, Accounting and Finance (IJBAF)
Article Type:Report
Date:Mar 22, 2018
Words:5907
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