Unpacking the dual psychological paths of employee-AI collaboration on creativity: The role of proactive behavior - Baorong Guo; Zaifeng Wu; Quan Wen; Yifeng Peng (PLoS ONE, 2026)
The deep integration of artificial intelligence (AI) into organizational settings has significantly transformed employees’ work patterns, underscoring the need to investigate the mechanisms through which employee–AI collaboration influences creativity. Grounded in social cognitive theory, this study examines how employee–AI collaboration affects creativity via internal (self-efficacy) and external (performance pressure) mechanisms, and explores the moderating role of proactive behavior. Using convenience sampling, 733 valid responses were collected from six technology-driven enterprises. The theoretical model was tested through confirmatory factor analysis and regression analyses using AMOS and the Process macro for SPSS. Our results reveal that employee-AI collaboration is positively associated with creativity through two statistically supported indirect pathways: parallel indirect effects via self-efficacy (internal pathway) and performance pressure (external pathway), and a sequential indirect association in which higher self-efficacy is related to higher performance pressure, which in turn is positively related to creativity. Moreover, proactive behavior appears to moderate these relationships, such that the association between employee-AI collaboration and self-efficacy is more pronounced at higher levels of proactive behavior, whereas the association between employee-AI collaboration and performance pressure is less pronounced at higher levels of proactive behavior, yielding a pattern consistent with moderated indirect effects on creativity through both routes. These findings provide theoretical insights into the complex psychological processes underlying creativity in AI-enabled work environments and offer practical implications for optimizing AI applications and developing targeted interventions to foster employee creativity.