Reimagining Workplace Relationships: The Impact of Employee-Generative AI Collaboration
Ruoxi Zhang, Jia‐Min Li, Tung‐Ju Wu · Academy of Management Proceedings · 2025
The rise of generative artificial intelligence (generative AI) is fundamentally transforming how work gets done in organizations. Although existing literature has documented the performance benefits of employee-generative AI collaboration, it has generally overlooked its effects on interpersonal dynamics within the workplace. Drawing on social exchange theory, we propose that employee-generative AI collaboration may reshape the perceived value and nature of workplace relationships. Through a three-wave longitudinal survey, our findings indicate that employee-generative AI collaboration is negatively associated with the perceived instrumentality of work relationships, which subsequently reduces helping behavior among colleagues while increasing family involvement. Additionally, the perceived credibility of generative AI acts as a critical boundary condition, moderating both the direct relationship between employee-generative AI collaboration and perceived instrumentality of work relationships, as well as the strength of the mediated pathways. This research makes significant theoretical contributions by extending social exchange theory into the realm of employee-generative AI collaboration and provides practical insights on how to effectively manage the integration of generative AI.