Accounting for nonlinear utility functions in composite measures of productivity and performance

Abstract Composite measures of productivity and performance combine different indicators of work activities into a single index. The term nonlinearities refers to the phenomenon that the relationship between the scores on an indicator of productivity and the value of that level of the indicator to the organization is not always linear. For example, more is not always better. This research evaluated whether accounting for such nonlinear relationships in productivity measures adds new information to the composite measures. Data from five organizations showed that the correlations between a composite measure which includes nonlinear relationships and one which does not were very high, but treatment effect sizes and decisions resulting from the two measurement systems were substantially different. It was concluded that inclusion of nonlinearities added important information to composite measures of productivity and that accounting for nonlinearities should result in more valid composites. Applications of this issue for other topics such as performance appraisal and promotions were discussed.