(Georgia Institute of Technology, 2022-05-02)
Hu, Wenqian
Incentive compensation is often characterized by incomplete contracts. While managerial opportunism has been documented as one of the most pronounced problems with managerial discretion in incomplete contracts, prior work has not investigated the underlying mechanisms driving a loss of productivity. In this study, I experimentally investigate whether replacing human managers’ decision making with algorithm-generated bonus schemes that mimic managers’ decision making improves employee productivity. I find that compensation determined by algorithms generate higher productivity without sacrificing the residual profits. Further, the productivity-inducing effect of algorithms is stronger when the rewards are not contingent on the performance signal. These results are consistent with the idea that it is hard for managers to establish credibility for rewarding employees for their performance in incomplete contracts. Employee productivity can be improved by enhancing their trust in the rewarding mechanism, even when they are not paid a more generous bonus scheme. This study advances our understanding of the behavioral factors influencing employee productivity in incomplete contracts and the potential ways algorithm-based evaluations can be used to improve firm outcomes.