Saving face: Leveraging artificial intelligence‐based negative feedback to enhance employee job performance
Jialiang Pei, Hongli Wang, Qiuping Peng, Shanshi Liu
South China University of Technology South China Agricultural University
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摘要与影响
Negative performance feedback is vital for stimulating employees to enhance their performance despite resulting in stress and adverse work outcomes. Fortunately, artificial intelligence (AI)‐enabled automated agents have gradually assumed certain functions led by human leaders, such as providing feedback. Drawing from regulatory focus theory, we propose that AI‐based feedback systems can serve as a “remediation” tool, effectively mitigating employees' apprehensions about receiving negative feedback. In two studies, we found that for employees who fear losing face, AI‐based negative feedback motivates promotion‐focused cognition—motivation to learn—representing a learning mechanism to promote job performance and impedes their prevention‐focused cognition—interpersonal rumination—reducing the depletion needed for job performance. These findings present novel perspectives on using AI in performance feedback.
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经济 / 管理AI and HR Technologies
Job Satisfaction and Organizational Behavior · Human Resource Development and Performance Evaluation
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