How Employee-AI Collaboration Influences Coworkers' Helping Behaviour: An Attribution Theory Perspective

As artificial intelligence (AI) is increasingly integrated into the workplace, employee-AI collaboration is evolving from a personal productivity tool to a social cue that coworkers can observe and interpret. Existing research has largely emphasised the performance and well-being effects of employee-AI collaboration; however, few studies have revealed, from the observer's perspective, its potential negative spillover mechanisms on coworkers' helping behaviour. Based on attribution theory, this study constructs a theoretical model of 'employee-AI collaboration-coworker attributions-coworker helping behaviour', distinguishing two mechanisms-laziness attribution and responsibility-avoidance attribution-and examines the boundary role of human-AI task interdependence. Study 1, based on 375 two-wave coworker survey responses, tested the hypotheses using hierarchical regression and bootstrapping methods. Study 2 employed a 2 × 2 scenario experiment to further test the effects of employee-AI collaboration and human-AI task interdependence on coworker attributions and willingness to help. The results indicate that higher perceived employee-AI collaboration is associated with lower coworker helping behaviour; laziness attribution and responsibility-avoidance attribution play a mediating role between perceived employee-AI collaboration and coworker helping behaviour. The higher the human-AI task interdependence, the more likely coworkers are to interpret employee-AI collaboration as laziness or responsibility-avoidance, thereby reinforcing the aforementioned negative effects.