Employee-AI Collaboration and Bootlegging: A Moderated Dual-path Model

Han Mingyan; Zhao Jingyou; Li Zhi

doi:doi:10.16538/j.cnki.fem.20240901.104

Summary

Employee-AI collaboration holds significant potential for driving employee innovation. Moving beyond traditional focus on employees’ formal innovation, this paper examines the impact of employee-AI collaboration on employee bootlegging and reveals the underlying mechanisms from the perspective of conservation of resources theory. Using a three-wave data from 291 employees working with AI, the results indicate that: (1) Employee-AI collaboration increases information search support and ideas exploration support. (2) Information search support and ideas exploration support mediate the influence of employee-AI collaboration on employee bootlegging. Employee-AI collaboration enhances employee bootlegging by supporting employees in searching information and exploring ideas, which alleviates their lacking of innovative resources and thus motivates them to engage in bootlegging. (3) AI fluency strengthens the impact of employee-AI collaboration on information search support and ideas exploration support, as well as the indirect influence of employee-AI collaboration on employee bootlegging through the above support. This demonstrates that the impact of employee-AI collaboration is biased; employees who are skilled and confident in using AI have a greater advantage in resource acquisition and are more inclined to engage in bootlegging. The findings enrich the understanding on the impact and mechanisms of employee-AI collaboration, and also provide theoretical insights for organizations to build effective employee-AI interaction systems and guide employees towards reasonable bootlegging.