| Abstract: | Many firms that use multiple lead measures in their performance measurement systems do not validate the causal model linking these measures to future financial outcomes, and the cause‐and‐effect relationships in the model are often left to subjective estimates that may be prone to errors. Using an experiment, this study examines how the accuracy of assumptions about the relative importance of lead measures in a causal model affects managerial performance and knowledge, when managers are given the opportunity to learn over multiple periods. The results show that having inaccurate relative weights on lead measures improves performance, reduces performance variability, and enhances knowledge, relative to not having any weights. Furthermore, performance is similar under accurate versus inaccurate relative weights, whereas knowledge is better under inaccurate than accurate relative weights, providing no support for the biasing effects of inaccurate relative weights. The findings suggest that, at least under certain circumstances, managers benefit even if they are given inaccurate relative weights on lead measures, and they are able to correct those inaccuracies to reach a comparable level of performance and knowledge as if they had been given accurate relative weights. |
