ORIGINAL RESEARCH article
Front. Psychol.
Sec. Organizational Psychology
Employee–AI collaboration empowers mentor networks to enhance employee creativity: A knowledge-management perspective
Beijing Information Science and Technology University, Beijing, China
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Abstract
This study investigates the impact of mentor network strength on employee creativity, as well as the mediating role of tacit knowledge acquisition and the moderating role of employee-AI collaboration. A three-wave survey was conducted among 523 employees from Chinese enterprises, and the proposed hypotheses were tested using confirmatory factor analysis, hierarchical regression, structural equation modeling, and bootstrapping. The results reveal the following: mentor network strength can promote employee creativity by enhancing cognitive tacit knowledge acquisition and skill-based tacit knowledge acquisition. Specifically, the mediating effect of skill-based tacit knowledge acquisition is more significant than that of cognitive tacit knowledge acquisition. Moreover, employee-AI collaboration positively moderates the relationship between mentor network strength and cognitive and skill-based tacit knowledge acquisition. The positive effect of mentor network strength on employee creativity via cognitive tacit knowledge acquisition and skill-based tacit knowledge acquisition is more significant when there is a higher degree of employee-AI collaboration. From a knowledge management perspective, this study fills a gap by exploring the mechanism by which mentor network strength affects employee creativity and offers a new perspective for interpreting the factors influencing employee creativity. The findings facilitate a deeper understanding of the synergistic effects of human-AI collaboration and interpersonal relationships on employee creativity, providing guidance to help organizations promote intelligent transformation.
Summary
Keywords
Employee–AI collaboration, employeecreativity, Knowledge Management, Mentor Network, Tacit knowledge acquisition
Received
20 November 2025
Accepted
19 January 2026
Copyright
© 2026 Li, Yu and Yin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Jielin Yin
Disclaimer
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