Saving face: Leveraging artificial intelligence‐based negative feedback to enhance employee job performance - Pei, Jialiang; Wang, Hongli; Peng, Qiuping; Liu, Shanshi (Human Resource Management, 2024)
AbstractNegative 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.