How does human-AI collaboration task complexity affect employee work engagement? The roles of humble leadership and AI self-efficacy

Introduction: With the rapid advancement of artificial intelligence (AI) technology, human-AI collaboration has become increasingly prevalent in workplaces, profoundly impacting employees' psychology and behavior. Based on the Job Demands-Resources (JD-R) theory, this study examines the effects of human-AI collaboration task complexity (HAI-C task complexity) on employees' work engagement, with human-AI collaboration tech-learning anxiety (HAI-C tech-learning anxiety) as a mediator, and explores the moderating roles of humble leadership and AI self-efficacy. Methods: This study employed a three-wave longitudinal survey design to collect matched data from 497 employees. Hierarchical regression analysis, along with bootstrapping methods, was employed for empirical testing. Results: The findings indicate that HAI-C task complexity negatively affects employees' work engagement by amplifying their HAI-C tech-learning anxiety. AI self-efficacy can mitigate this negative indirect impact of HAI-C task complexity on work engagement. Humble leadership indirectly alleviates this negative indirect effect by enhancing employees' AI self-efficacy. Discussion: The findings reveal the inhibitory effect of HAI-C task complexity on employees' work engagement. From the two dimensions of job resources and personal resources, it explores corresponding mitigation mechanisms, as well as the contextual and psychological intervention mechanisms involved in how individuals evaluate job demands. This provides novel theoretical perspectives and practical implications for understanding the practical value of human-AI collaboration in organizational contexts and for enhancing employees' work engagement within human-AI collaboration frameworks.