How and When AI-Augmented Versus AI-Automated Feedback Sources Influence Employee Feedback Seeking

Two distinct types of AI-involved feedback sources have emerged in practice: AI-automated and AI-augmented feedback sources. AI-automated feedback sources refer to AI systems that independently monitor, evaluate, and provide feedback on employees’ job performance without human managerial involvement. In contrast, AI-augmented feedback sources refer to human managers who integrate AI analysis with their professional expertise to monitor, evaluate, and deliver final feedback on employees’ job performance. Drawing on the stereotype content model, we propose that, depending on feedback source type and feedback valence, employees develop differentiated warmth and competence perceptions of AI-involved feedback sources, which in turn shape their feedback-seeking behavior. Results from a scenario-based experiment and a two-wave field survey show that when delivering negative (vs. positive) feedback, AI-augmented (vs. AI-automated) feedback sources elicit higher warmth perception, thereby promoting employees’ feedback-seeking behavior. Conversely, when delivering positive (vs. negative) feedback, AI-augmented (vs. AI-automated) feedback sources reduce competence perception, leading to lower feedback-seeking behavior. This research advances understanding of the effects of different AI-involved feedback sources and offers guidance on how and when organizations can effectively leverage AI in performance feedback.