Abstract

This study tested an extended unified theory of acceptance and use of technology (UTAUT) model by integrating artificial intelligence anxiety (AIA) to explain university faculty member’s knowledge-sharing intention (KSI) and knowledge-sharing behavior (KSB) in virtual academic communities. Given the rapid integration of AI in higher education, participation in such communities remains uneven, which underscores the need for an anxiety-informed acceptance lens. A survey of 387 faculty members in Shaanxi, China was analyzed using partial least squares structural equation modeling (PLS-SEM) with five thousand bootstraps and blindfolding. The results indicate that KSI is the strongest predictor of KSB (β = 0.690). The model explains 62% of the variance in KSB and 55% in KSI, demonstrating strong predictive relevance. AIA moderates several relationships. Specifically, it strengthens the effects of social influence (SI) on KSI and KSI on KSB while weakening the effects of effort expectancy (EE) on KSI and facilitating conditions (FC) on KSB. However, it does not significantly moderate the effect of performance expectancy (PE) on KSI. These findings suggest that anxiety amplifies normative pressures while reducing the influence of ease and support. Building AI literacy, leveraging peer role models, and ensuring reliable institutional support may facilitate the translation of intention into behavior while mitigating anxiety-related barriers.