Abstract: In the digital-intelligence era, employee-AI collaboration has become an important work pattern. Against this backdrop, it has significance to explore how employees collaborate with AI, why different collaboration patterns emerge, what the impacts of distinct collaboration patterns are, and how to intervene in them. To address these questions, this study conducts a series of explorations focusing on the typologies, antecedents, and consequences of employee-AI collaboration. Firstly, based on the dual dimensions of dominance and interaction, the employee-AI collaboration is divided into four typologies: Augmentation, Symbiosis, Assistance, and Substitution, while developing corresponding measurement scales. Secondly, grounded in sociotechnical systems theory, this study identifies influencing factors across four dimensions (employee, AI, task, and organization) and investigates their synergistic effects through a configurational approach. Finally, based on the cognitive-affective processing system theory, this study introduces cognitive-affective dual mechanisms and explores the moderating role of four management interventions to reveal how different types of employee-AI collaboration shape employee performance and well-being. This study theoretically expands the frameworks for employee-AI collaboration research, and practically it provides critical insights for achieving high efficiency of employee-AI collaboration in organizations.

Key words: Human-AI collaboration, AI, job performance, job well-being