A Multiple-Task Measurement Framework for Assessing Maximum-Typical Performance

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Abstract
This study presents a novel measurement framework for assessing and predicting maximum and typical performance. The proposed measurement approach addresses the need for organizations to assess maximum and typical performance changes over time in complex job settings requiring coordination of multiple tasks with changing priorities. We present results of an experiment in which participants engaged in a complex task with multiple task elements and instructions to either maximize a different task element in each of four performance blocks (variable-priority condition) or treat all task elements with equal priority (stable-priority condition). We estimated growth curves corresponding to each task element and calculated the area under each growth curve as a summary performance index. Growth curves corresponding to the maximized, high-priority task element in the variable-priority condition reflected maximum performance, whereas those corresponding to the deemphasized, lower priority elements reflected typical performance. We compared the shape of the maximum and typical growth curves in the variable-priority condition to their corresponding performance trajectories in the stable-priority condition. In addition, we tested the moderating influence of individual differences in action-state orientation on the obtained maximum and typical performance estimates. Results indicated support for the proposed measurement framework in terms of its usefulness for inducing sustained levels of maximum performance and for identifying and correcting sources of the maximum-typical performance discrepancy.