Learning from Inconsistent Performance Feedback

Cassandra R. Chambers (), Marlon Alves () and Pedro Aceves ()
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Cassandra R. Chambers: Carey Business School, Johns Hopkins University, Baltimore, Maryland 21202
Marlon Alves: SKEMA Business School, Université Côte d’Azur (GREDEG), 92150 Suresnes, France
Pedro Aceves: Carey Business School, Johns Hopkins University, Baltimore, Maryland 21202

Organization Science, 2025, vol. 36, issue 4, 1509-1530

Abstract: 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.

Keywords: performance feedback; organizational learning; ambiguity; microfoundations (search for similar items in EconPapers)
Date: 2025
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