Dual pathways of generative AI use: role ambiguity and self-efficacy in employee-AI collaboration

The growing use of generative AI tools in everyday work improves efficiency but also creates uncertainty about how employees interpret their roles and whether they are willing to collaborate with AI. Drawing on role theory, this study aims to investigate how generative AI (GenAI) use relates to employee-AI collaboration via two distinct pathways: role ambiguity as a hindrance mechanism and role breadth self-efficacy as an enabling mechanism. This study further examines the moderating role of AI literacy in the above relationships.

Survey data were collected from 541 employees working in Chinese high-technology firms, and structural equation modeling was adopted to test the proposed research model.

The empirical results reveal that GenAI use simultaneously increases role ambiguity and role breadth self-efficacy. Role ambiguity negatively predicts employee-AI collaboration, whereas role breadth self-efficacy exerts a positive effect. AI literacy significantly moderates both pathways: it weakens the positive association between GenAI use and role ambiguity, and strengthens the positive link between GenAI use and role breadth self-efficacy. Furthermore, employee-AI collaboration is positively associated with job performance.

These findings extend role theory by verifying that GenAI functions as both a disruptor and an enabler of employees' role perceptions, and AI literacy serves as a vital boundary condition shaping how employees construct and understand their work roles when adopting GenAI. Practically, organizations are suggested to clarify work role boundaries, foster employees' role breadth self-efficacy, and deliver systematic AI literacy training to facilitate high-quality human-AI collaboration.