How Employee–AI Collaboration Influences Coworkers’ Helping Behaviour: An Attribution Theory Perspective

Abstract

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.