Learning from Inconsistent Performance Feedback

微观基础 计算机科学 质量(理念) 工作(物理) 知识管理 绩效改进 决策质量 非正面反馈 心理学 认知心理学 管理 经济 机械工程 哲学 团队效能 物理 认识论 量子力学 电压 工程类 宏观经济学
作者
Cassandra R. Chambers,Marlon Fernandes Rodrigues Alves,Pedro Aceves
出处
期刊:Organization Science [Institute for Operations Research and the Management Sciences]
标识
DOI:10.1287/orsc.2022.16833
摘要

Organizations and the decision makers within them are increasingly subject to inconsistent performance feedback—feedback that contains elements that are incompatible with each other—which can lead to multiple interpretations of performance feedback. When this occurs, decision makers often recode inconsistent performance feedback as successful and continue with their current strategies, which allows them to avoid any self-threat from negative elements of performance feedback but implies that they do not learn from inconsistent performance feedback because they do not change. In contrast, we explore whether decision makers can learn from inconsistent performance feedback. Leveraging over 10 years of complete behavioral records in an online community and a laboratory experiment, we study how decision makers respond to inconsistent performance feedback stemming from multiple evaluators who do not agree on performance quality. Consistent with prior work, we find that decision makers change their strategies less after inconsistent performance feedback. Departing from prior work, we show a corresponding increase in clarification efforts aimed at better understanding which performance strategies work well. Importantly, clarification efforts mediate improved future performance. Our results suggest that inconsistent performance feedback can trigger deeper learning and enhanced performance, contributing to performance feedback theory and research on the microfoundations of organizational learning. Funding: The authors thank the University of São Paulo, Johns Hopkins University, and SKEMA Business School for institutional and financial support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.16833 .
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