AI Transparency and Employee Innovation: The Mediating Role of Psychological Safety and the Moderating Effect of AI Self-Efficacy

Authors

  • Qian Li Kuala Lumpur University of Science & Technology image/svg+xml Author
  • Peilin Li Kuala Lumpur University of Science & Technology image/svg+xml Author
  • Chan Sai Keong Kuala Lumpur University of Science & Technology image/svg+xml Author

DOI:

https://doi.org/10.65455/dxhkxd06

Keywords:

AI Transparency, Psychological Safety, Employee Innovation, AI Self-Efficacy

Abstract

As AI technologies become more integrated into everyday organizational workflows, it is essential to understand how employees interpret and interact with these systems to support innovative outcomes.This study investigates how transparency in AI systems influences employee innovation, considering the role of psychological safety and individual confidence in using AI. Data were gathered from 447 knowledge workers in small and medium-sized enterprises in China who regularly interact with AI tools. Analysis using structural equation modeling shows that clearer AI processes enhance employees’ sense of psychological safety, which in turn supports innovative actions. Moreover, employees with greater confidence in using AI benefit more from transparency, amplifying its positive impact on innovation. The results underscore that promoting innovation requires not only transparent system design but also initiatives that strengthen employees’ AI skills and create psychologically supportive environments. This research contributes to understanding the human side of AI adoption by linking system transparency to practical organizational outcomes and offering guidance for effective AI–human integration in workplaces.

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Published

2026-05-19

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

How to Cite

AI Transparency and Employee Innovation: The Mediating Role of Psychological Safety and the Moderating Effect of AI Self-Efficacy. (2026). Applied Artificial Intelligence Research, 2(2). https://doi.org/10.65455/dxhkxd06