Does the Human-Likeness of Artificial Intelligence in Performance Management Systems Matter?
Shanzi Xue, Yuan Pan · Academy of Management Proceedings · 2025
Artificial Intelligence (AI) has emerged as a transformative tool in performance management, enhancing both individual and organizational performance. However, we still know little about the influence of human-like characteristics in AI-driven performance management systems (PMS) on employee outcomes. Drawing on social exchange theory, this study examines the role of human-like traits in AI-based PMS, including anthropomorphism, empathy, and interaction quality, in shaping employee commitment and organizational citizenship behavior (OCB). Using a sequential mixed-methods approach, we first conducted two experiments to test the proposed relationships and examine the mediating effects of interpersonal and informational justice, followed by an additional field survey and finally, by conducting interviews to deepen our understanding and help explain unexpected results. Findings of the experiments indicate that empathy and interaction quality in AI-based PMS significantly enhances employee commitment and OCB. Interpersonal and informational justice have different mediation effects on these relationships. The findings from the ongoing field survey and interview studies will be used to help explain the results. Based on the findings, we provide recommendations for the design of AI-driven performance management systems.