Absolute, average-based, and rank-based aspirations

Abstract

Research Summary: A key strategic challenge is balancing exploration and exploitation. When individuals' exploration activity is guided by problemistic search, should managers encourage high aspirations? Past work has shown that optimal aspiration (the aspiration level that leads to the highest level of performance in the long run) is lower in more turbulent environments. This past work assumes that aspirations are specified as absolute performance, but search is often triggered by performance shortfalls relative to others. Using a simple and analytically tractable model, we show that in such cases, the optimal aspiration may instead increase with turbulence (with rank-based aspirations) or stay constant (with average-based aspirations). Our analyses have interesting implications for target setting and for understanding how aspiration specification impacts exploration in organizations. Managerial Summary: Performance targets (aspirations) drive improvement efforts, and thus it is important to understand optimal target levels. Although optimal targets are well known to depend on the uncertainty of the environment, our paper shows that this relationship depends on whether the targets are set in absolute, average-based, or rank-based terms. In more uncertain environments, performance targets specified in absolute terms should be lower, while those specified in rank-based terms (such as aiming to outperform a certain number of competitors) should be higher. The optimal performance target specified relative to average outcomes is always the same: do better than average. We show that these contrasting results are due to the spillover effects of one's own improvements on others. Our paper highlights the implications for how targets should be set in different contexts.