卓越
付款
亲社会行为
质量(理念)
心理学
德国的
情感(语言学)
社会心理学
应用心理学
精算学
计算机科学
营销
业务
财务
政治学
历史
认识论
哲学
沟通
考古
法学
作者
David J. Kusterer,Dirk Sliwka
出处
期刊:Management Science
[Institute for Operations Research and the Management Sciences]
日期:2024-08-29
标识
DOI:10.1287/mnsc.2022.02267
摘要
We study biases and the informativeness of subjective performance evaluations in an online experiment, testing the implications of a standard formal framework of rational subjective evaluations. In the experiment, subjects in the role of workers perform a real effort task. Subjects in the role of supervisors observe samples of the workers’ output and assess their performance. We conduct six experimental treatments varying (i) whether workers’ pay depends on the performance evaluation, (ii) whether supervisors are paid for the accuracy of their evaluations, and (iii) the precision of the information available to supervisors. Moreover, we use the exogenous assignment of supervisors to workers to investigate the association between supervisors’ social preferences and their rating quality. In line with the model of optimal evaluations, we find that ratings are more lenient and less informative when they determine bonus payments. Rewards for accuracy reduce leniency and can enhance informativeness. When supervisors have access to more detailed performance information, their ratings vary more with the performance signal and become more informative. Contrary to expectations, we do not find that more prosocial supervisors are systematically more lenient when their ratings affect workers’ payoffs. Instead, they are more diligent in their rating behavior, resulting in more accurate and informative performance evaluations. This paper was accepted by Marie Claire Villeval, behavioral economics and decision analysis. Funding: Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy [Grant EXC 2126/1-390838866]. Supplemental Material: The online supplement and data files are available at https://doi.org/10.1287/mnsc.2022.02267 .
科研通智能强力驱动
Strongly Powered by AbleSci AI