A study of the impact of AI-employee collaboration on employee innovation behavior

With the deep integration of digitalization and generative artificial intelligence, artificial intelligence has transformed from a mere technical tool into a core collaborative partner within modern workplaces and human‑resource management. As an interdependent human‑AI working mode characterized by complementary task division, joint task accomplishment, complementary decision‑making, and continuous iterative interaction between employees and AI agents, AI‑employee collaboration is conceptually distinct from simple AI adoption, instrumental AI use, and AI‑assisted technical systems. Drawing on social cognitive theory, the present study aims to investigate how AI‑employee collaboration predicts employee innovation behavior, alongside the mediating role of self‑efficacy and the moderated‑mediation boundary condition of servant leadership.

Three‑wave time‑lagged questionnaire data were collected from 259 employees working in internet‑service, finance, and catering industries across Beijing and Jinan. Hierarchical regression and bootstrap approaches were adopted to test the proposed moderated‑mediation model.

The results show that AI‑employee collaboration exerts a significant positive predictive effect on employee innovation behavior. Self‑efficacy partially mediates the relationship between AI‑employee collaboration and employee innovation behavior. Moreover, servant leadership positively moderates the link between AI‑employee collaboration and self‑efficacy and further strengthens the indirect predictive effect of AI‑employee collaboration on employee innovation behavior through self‑efficacy.

This research enriches existing literature by providing incremental empirical evidence for the internal cognitive mechanism and contextual boundary of AI‑driven employee innovation. Theoretically, it unpacks the cognitive "black box" connecting AI‑employee collaboration and employee innovation and identifies servant leadership as a key situational contingency factor. Practically, these findings deliver concrete, actionable managerial insights for digitally transforming organizations to leverage AI tools and unlock employees' innovative potential.