Strategy, Choice of Performance Measures, and Performance

We examine the relationship between quality-based manufacturing strategy and the use of different types of performance measures, as well as their separate and joint effects on performance. A key part of our investigation is the distinction between financial and both objective and subjective nonfinancial measures. Our results support the view that performance measurement diversity benefits performance as we find that, regardless of strategy, firms with more extensive performance measurement systems—especially those that include objective and subjective nonfinancial measures—have higher performance. But our findings also partly support the view that the strategy-measurement ‘‘fit’’ affects performance. We find that firms that emphasize quality in manufacturing use more of both objective and subjective nonfinancial measures. However, there is only a positive effect on performance from pairing a qualitybased manufacturing strategy with extensive use of subjective measures, but not with objective nonfinancial measures. INTRODUCTION Performance measures play a key role in translating an organization’s strategy into desired behaviors and results (Campbell et al. 2004; Chenhall and Langfield-Smith 1998; Kaplan and Norton 2001; Lillis 2002). They also help to communicate expectations, monitor progress, provide feedback, and motivate employees through performancebased rewards (Banker et al. 2000; Chenhall 2003; Ittner and Larcker 1998b; Ittner et al. 1997; Ittner, Larcker, and Randall 2003). Traditionally, firms have primarily used financial measures for these purposes (Balkcom et al. 1997; Kaplan and Norton 1992). But with the ‘‘new’’ competitive realities of increased customization, flexibility, and responsiveness, and associated advances in manufacturing practices, both academics and practitioners have argued that traditional financial performance measures are no longer adequate for these functions (Dixon et al. 1990; Fisher 1992; Ittner and Larcker 1998a; Neely 1999). Indeed, many We acknowledge the helpful suggestions by Tom Groot, Jim Hesford, Ranjani Krishnan, Fred Lindahl, Helene Loning, Michal Matejka, Ken Merchant, Frank Moers, Mark Peecher, Mike Shields, Sally Widener, workshop participants at the University of Illinois, the 2002 AAA Management Accounting Meeting in Austin, the 2002 World Congress of Accounting Educators in Hong Kong, and the 2003 AAA Annual Meeting in Honolulu. An earlier version of this paper won the best paper award at the 9th World Congress of Accounting Educators in Hong Kong (2002). 186 Van der Stede, Chow, and Lin Behavioral Research in Accounting, 2006 accounting researchers have identified the continued reliance on traditional management accounting systems as a major reason why many new manufacturing initiatives perform poorly (Banker et al. 1993; Ittner and Larcker 1995). In light of this development in theory and practice, the current study seeks to advance understanding of the role that performance measurement plays in executing strategy and enhancing organizational performance. It proposes and empirically tests three hypotheses about the performance effects of performance measurement diversity; the relation between quality-based manufacturing strategy and firms’ use of different types of performance measures; and the joint effects of strategy and performance measurement on organizational performance. The distinction between objective and subjective performance measures is a pivotal part of our investigation. Prior empirical research has typically only differentiated between financial and nonfinancial performance measures. We go beyond this dichotomy to further distinguish between nonfinancial measures that are quantitative and objectively derived (e.g., defect rates), and those that are qualitative and subjectively determined (e.g., an assessment of the degree of cooperation or knowledge sharing across departmental borders). Making this finer distinction between types of nonfinancial performance measures contributes to recent work in accounting that has begun to focus on the use of subjectivity in performance measurement, evaluation, and incentives (e.g., Bushman et al. 1996; Gibbs et al. 2004; Ittner, Larcker, and Meyer 2003; MacLeod and Parent 1999; Moers 2005; Murphy and Oyer 2004). Using survey data from 128 manufacturing firms, we find that firms with more extensive performance measurement systems, especially ones that include objective and subjective nonfinancial measures, have higher performance. This result holds regardless of the firm’s manufacturing strategy. As such, our finding supports the view that performance measurement diversity, per se, is beneficial. But we also find evidence that firms adjust their use of performance measures to strategy. Firms that emphasize quality in manufacturing tend to use more of both objective and subjective nonfinancial measures, but without reducing the number of financial measures. Interestingly, however, combining quality-based strategies with extensive use of objective nonfinancial measures is not associated with higher performance. This set of results is consistent with Ittner and Larcker (1995) who found that quality programs are associated with greater use of nontraditional (i.e., nonfinancial) measures and reward systems, but combining nontraditional measures with extensive quality programs does not improve performance. However, by differentiating between objective and subjective nonfinancial measures—thereby going beyond Ittner and Larcker (1995) and much of the extant accounting literature—we find that performance is higher when the performance measures used in conjunction with a quality-based manufacturing strategy are of the subjective type. Finally, we find that among firms with similar quality-based strategies, those with less extensive performance measurement systems have lower performance, whereas those with more extensive performance measurement systems do not. In the case of subjective performance measures, firms that use them more extensively than firms with similar qualitybased strategies actually have significantly higher performance. Thus, a ‘‘mismatch’’ between performance measurement and strategy is associated with lower performance only when firms use fewer measures than firms with similar quality-based strategies, but not when they use more. The paper proceeds as follows. The next section builds on the extant literature to formulate three hypotheses. The third section discusses the method, sample, and measures. Strategy, Choice of Performance Measures, and Performance 187 Behavioral Research in Accounting, 2006 The fourth section presents the results. The fifth section provides a summary, discusses the study’s limitations, and suggests possible directions for future research. HYPOTHESES Although there is widespread agreement on the need to expand performance measurement, two different views exist on the nature of the desirable change (Ittner, Larcker, and Randall 2003; Ruddle and Feeny 2000). In this section, we engage the relevant literatures to develop three hypotheses. Collectively, the hypotheses provide the basis for comparing the two prevailing schools of thought on how performance measurement should be improved; that of performance measurement diversity regardless of strategy versus that of performance measurement alignment with strategy (Ittner, Larcker, and Randall 2003). The Performance Measurement Diversity View A number of authors have argued that broadening the set of performance measures, per se, enhances organizational performance (e.g., Edvinsson and Malone 1997; Lingle and Schiemann 1996). The premise is that managers have an incentive to concentrate on those activities for which their performance is measured, often at the expense of other relevant but non-measured activities (Hopwood 1974), and greater measurement diversity can reduce such dysfunctional effects (Lillis 2002). Support for this view is available from economicsbased agency studies. Datar et al. (2001), Feltham and Xie (1994), Hemmer (1996), Holmstrom (1979), and Lambert (2001), for example, have demonstrated that in the absence of measurement costs, introducing incentives based on nonfinancial measures can improve contracting by incorporating information on managerial actions that are not fully captured by financial measures. Analytical studies have further identified potential benefits from using performance measures that are subjectively derived. For example, Baiman and Rajan (1995) and Baker et al. (1994) have shown that subjective measures can help to mitigate distortions in managerial effort by ‘‘backing out’’ dysfunctional behavior induced by incomplete objective performance measures, as well as reduce noise in the overall performance evaluation. However, the literature also has noted potential drawbacks from measurement diversity. It increases system complexity, thus taxing managers’ cognitive abilities (Ghosh and Lusch 2000; Lipe and Salterio 2000, 2002). It also increases the burden of determining relative weights for different measures (Ittner and Larcker 1998a; Moers 2005). Finally, multiple measures are also potentially conflicting (e.g., manufacturing efficiency and customer responsiveness), leading to incongruence of goals, at least in the short run (Baker 1992; Holmstrom and Milgrom 1991), and organizational friction (Lillis 2002). Despite these potential drawbacks, there is considerable empirical support for increased measurement diversity. For example, in a study of time-series data in 18 hotels, Banker et al. (2000) found that when nonfinancial measures are included in the compensation contract, managers more closely aligned their efforts to those measures, resulting in increased performance. Hoque and James (2000) and Scott and Tiessen (1999) also have found positive relations between firm performance and increased use of different types of performance measures (e.g., financial and nonfinancial). These results are consistent with nonfinancial performance measures containing additional information not reflected in financial measures. Although prior studies like these have advanced our understanding of performance measurement design, they share the limitation of not distinguishing among measures that are determined objectively and ones that are based on subjective judgment. A key difference between objective and subjective measures is that the latter are often less accurate and 188 Van der Stede, Chow, and Lin Behavioral Research in Accounting, 2006 reliable, and are more open to raters’ biases (Campbell 1990; Fulk et al. 1985; Hawkins and Hastie 1990; Heneman 1986). Prendergast and Topel (1993), for example, have suggested that allowing subjective judgment in performance evaluation can reduce employee motivation. This result is due to the latitude for evaluators to ignore performance measures that are included in the performance plan, and to use measures that differ from those originally planned. Moreover, when evaluations are subjective, employees may divert job effort toward influencing their supervisors’ evaluations (Milgrom 1988; Prendergast 1993; Prendergast and Topel 1996). Whether limitations like these outweigh the benefits of subjective measures is an important question. To date, only a few studies have empirically examined the use and effects of subjectivity in performance measurement. Of particular relevance to the current study, Ittner, Larcker, and Meyer (2003) analyzed how subjective performance measures were used in a leading international financial services provider. They found that leaving room for subjectivity allowed supervisors to ignore many performance measures, and short-term financial measures became the de facto determinants of bonus awards. Employee dissatisfaction with the system was so high that the firm reverted to basing bonuses strictly on revenue. Moers (2005) also found that the use of subjective performance measures induces performance evaluation bias. In a study of managerial compensation plans in a privately owned Dutch maritime firm, he found that the use of multiple objective and subjective performance measures was related to more compressed and more lenient performance ratings. These findings by Ittner, Larcker, and Meyer (2003) and Moers (2005) are useful for understanding the uses and limitations of subjective performance measures; however, their generalizability is limited by each study having focused on only one company. In sum, prior studies have argued for performance measurement to extend beyond just financial measures, and importantly for this study, a case has been made for differentiating between objective and subjective nonfinancial measures. Although arguments have been proffered about the downsides of performance measurement diversity, in general, and about the inclusion of subjective performance measures, in particular, the expectation is that: H1: Firm performance will be positively associated with performance measurement diversity, particularly when the performance measurement system is extended to include (more) objective and subjective nonfinancial performance measures. The Performance Measurement Alignment View In contrast to proponents of performance measurement diversity, contingency theory maintains that the optimal design of performance measurement systems depends on the organization’s strategy (and other organizational characteristics), and that performance will be higher only if both are aligned (for reviews, see Chenhall 2003; Fisher 1995; LangfieldSmith 1997). In this study, we specifically consider the performance measurement implications of quality-based manufacturing strategy. The primary reason for this focus is that quality-based initiatives have challenged the relevance of traditional financial measures (Abernethy and Lillis 1995; Dixon et al. 1990), and ‘‘one way in which this challenge has been met is by performance measurement system expansion’’ (Lillis 2002, 498). For example, nonfinancial measures can help ensure that quality improvement results, which often take considerable time to materialize, are not subjugated to financial results (Daniel and Reitsperger 1991; Ittner and Larcker 1995). Moreover, some of the key dimensions of quality-focused strategies, such as those focused Strategy, Choice of Performance Measures, and Performance 189 Behavioral Research in Accounting, 2006 on knowledge sharing and cooperativeness (Lillis 2002), are difficult to quantify and, thus, may need to be assessed subjectively. Prior studies in this area, however, have not distinguished between objective and subjective nonfinancial performance measures. Moreover, although some prior studies have found associations between the choice of performance measures and the type of strategy pursued (e.g., Abernethy and Lillis 1995; Daniel and Reitsperger 1991; Ittner and Larcker 1995; Perera et al. 1997), they have rarely investigated the performance effects of such choices. Or, as Ittner and Larcker (1998a, 221) and Chenhall (2003, 142) have emphasized, the scant empirical evidence that has been reported on the strategy-measure-performance relationship—only some of which relates to manufacturing strategy—is equivocal at best. In sum, considering the limited evidence in conjunction with the failure of prior work to distinguish between objective and subjective performance measures, there is room for further investigation. Next, we review related prior studies as the basis for developing two hypotheses. Abernethy and Lillis (1995) found that reliance on traditional (cost efficiency-based) measures was positively associated with performance for firms pursuing a nonflexible manufacturing strategy, and negatively associated with performance for firms pursuing a flexible manufacturing strategy. However, their study did not examine the performance of firms that combine nontraditional performance measures with a flexible manufacturing strategy. Furthermore, discriminatory power was low because most firms in the study extensively used virtually all of the performance measures. Ittner and Larcker (1995) focused on firms using advanced quality programs (TQM). Their sample consisted of 249 firms from the automobile and computer industries in four countries. Using data from a consulting firm, they found ‘‘no support for the proposition that, holding other determinants of performance constant, the highest performance levels should be achieved by organizations making the greatest use of both TQM practices and nontraditional information and reward systems’’ (Ittner and Larcker 1995, 2). On the contrary, among firms with extensive use of advanced quality programs, those with a strong reliance on nontraditional performance measures had lower performance than those with less extensive use of such measures. In another study based on the same sample, Ittner and Larcker (1997) examined the relation between quality-focused strategies and strategic control practices, including the importance placed on quality performance in determining managerial compensation. The performance effects (on ROA, ROS, sales growth, and selfreported performance) of a match between the two were generally insignificant. While the findings of both their 1995 and 1997 studies are informative, Ittner and Larcker (1995) caution that both industries in their sample may be so competitive that there was insufficient variation in the variables for identifying significant performance effects. A further limitation is that the studies only had access to measures of the overall importance placed on nonfinancial and quality performance. There was no information about the kinds and numbers of measures used of each type, nor was it possible to distinguish between objective and subjective nonfinancial performance measures. Said et al. (2003) also examined the fit between operational and competitive circumstances and firms’ choice of performance measures, as well as the performance effects of including nonfinancial measures in compensation contracts. A notable advance over prior studies is the use of a large sample (1,441 firm-years for firms using nonfinancial performance measures in managerial bonus plans, matched against the same number of firm-years for firms using exclusively financial measures). The results indicated that firms with a greater quality focus made greater use of nonfinancial measures. Furthermore, consistent 190 Van der Stede, Chow, and Lin Behavioral Research in Accounting, 2006 with the contingency-based approach to performance measurement, firms that relied on nonfinancial measures either more or less than a benchmark model had lower performance. Notably, these performance effects diverged from those of Ittner, Larcker, and Randall (2003) where negative deviations from the benchmark had no detectible effect and positive deviations were associated with higher performance. However, Said et al. (2003) only used crude indicators (either a dummy variable or the total weight) to reflect a firm’s use or nonuse of nonfinancial measures and, hence, like the other studies of quality initiatives, they did not distinguish among different types of nonfinancial performance measures. Taken as a whole, the contingency theory-based studies have made a case for the need to align performance measures with quality strategy. Yet they have produced only limited, and at times mixed, findings on the relation between quality initiatives and performance measure use and their joint effect on performance. Our next two hypotheses provide focus to further empirical investigation and extend prior work by incorporating the distinction between objective and subjective nonfinancial measures. H2: Firms that place more emphasis on quality in the manufacturing strategy will extend their performance measurement system to include (more) objective and subjective nonfinancial performance measures. H3: Firms that place more emphasis on quality in the manufacturing strategy will have higher performance only when they match their performance measurement system to their strategy, that is, when they extend their performance measurement system to include (more) objective and subjective nonfinancial performance measures. Together, these two hypotheses posit that firms pursuing a quality-based manufacturing strategy will tend to use more extensive performance measurement systems that include objective and subjective nonfinancial performance measures, and when they do—thus aligning their choice of performance measures with their manufacturing strategy—they will exhibit superior performance.