Causality in Performance Measurement Model

Abstract

Causal relations among performance measures are key features of performance measurement models (PMM), such as balanced scorecard that has been popularized by Kaplan and Norton [1992, 1996, 2000]. Norreklit [2000], however, cautions that notion of causality in Kaplan and Norton's balanced scorecard is confused among concepts of (a) empirically refutable causes and effects, (b) internal logic of accounting, and (c) (equi)finality of alternative means and desired ends. Furthermore, Norreklit [2002] suggests that balanced scorecard's popularity might be due to persuasiveness of balanced scorecard-inspired rhetoric rather than its demonstrable cause and effect nature. Because causal relations are important to validity and perhaps viability of PMM, this study investigates causality within a PMM that is similar to the balanced scorecard. This study addresses two research questions about causality in context of one organization's enduring and reportedly successful PMM. The first question is: Are causal relations perceived by designers and users of PMM? The second question is: Are valid causal relations apparent in PMM archival data? This study discusses three reasons for importance of separating cause-and-effect relations from logical and finality relations within PMM. First, a reliable, predictive financial model requires measurement of leading and lagging cause-and-effect relations among performance outcomes at different links in value chain. Second, consistency between empirically established cause-and-effect and finality relations can improve organizational communication and learning. Third, cause-and-effect relations that are understood, communicated, and made part of an incentive system can improve effectiveness of motivation and incentives. The study first uses qualitative analyses of interview data to demonstrate evidence of perceptions of cause-and-effect, logical, and finality relations by employees at several levels in firm. The study then uses econometric analyses of performance data to refute claims of causal relations among performance measures. The findings support primacy of logical and finality relations championed by top management as drivers of design and use of PMM. This raises question of whether a PMM without reliable cause-and-effect relations can serve purposes of financial prediction, learning, communication, and motivation. If not, perhaps logical and finality relations are sufficient to make a PMM successful in practice. Alternatively, more attention to measurement and PMM construction might be warranted.