Antecedents and impact of distrust of generative AI in organizations: a socio-psychological perspective

Purpose As organizations increasingly adopt generative artificial intelligence (GAI) to enhance productivity, growing worker distrust may hinder this progress. While there is emerging literature from a technology acceptance perspective, the factors driving distrust from a socio-psychological perspective and the impact of distrust on adoption intentions require deeper exploration. We developed and validated a comprehensive framework to examine GAI distrust within organizations by integrating social cognitive theory and nudge theory. The framework identified pro-social nudges, outcome expectations, security concerns and privacy self-efficacy as key antecedents of distrust and assessed how distrust affects adoption intentions. Design/methodology/approach A survey of 1,005 active workers was conducted in China. The data were analyzed using partial least squares structural equation modeling and the PROCESS macro for SPSS (Model 4). Findings While pro-social nudges and security concerns increased distrust, they did not directly affect adoption intentions. In contrast, positive outcome expectations and privacy self-efficacy reduced distrust and enhanced adoption intentions. The mediating and suppression effects of distrust were also discussed. Originality/value This study offers a novel framework for understanding the antecedents and impact of GAI distrust, providing practical insights for organizations and GAI developers to mitigate distrust within a broader social-psychological context.