Abstract:

To address employee' AI anxiety arising from increased system complexity and heightened information uncertainty in the application of AI to corporate safety management and decision-making, a mechanism model of "AI anxiety-human-AI trust-human-AI collaborative decision quality" was developed based on UMT. It introduced system transparency as a boundary condition to explain how employees appraised and coped with AI-related threats in contexts such as risk warnings and algorithmic black boxes. A questionnaire survey was conducted with a sample of 523 employees from AI-adopting enterprises. Confirmatory factor analysis(CFA) and structural equation modeling were employed to test the measurement model, path relationships, and moderating effects, while controlling for variables such as gender, age, education, job type, and AI usage frequency. The results show that AI anxiety reduces the quality of human-AI collaborative decision-making. Human-AI trust partially mediates the relationship between AI anxiety and collaborative decision-making quality. System transparency positively moderates the effect of human-AI trust on collaborative decision-making quality, such that higher transparency facilitates the translation of trust into higher-quality collaboration.

Key words: artificial intelligence (AI) anxiety, safety management, human-AI trust, system transparency, human-AI collaborative decision quality, uncertainty management theory(UMT)