Beyond the hype: understanding barriers to AI adoption through lens of protection motivation theory

Purpose: This study explores how perceived self-threat, skepticism and distrust influence employees’ intentions to adopt artificial intelligence (AI), both directly and indirectly, through anticipated adverse outcomes. Design/methodology/approach: Using protection motivation theory (PMT) as a theoretical framework, data were gathered from 597 employees. This study employed covariance-based structural equation modeling (CB-SEM) to evaluate the proposed hypotheses. Findings: The results indicate that perceived self-threat, skepticism and distrust significantly and negatively impact employees’ intention to use AI. Specifically, elevated levels of these psychological factors heighten concerns about privacy and job security, which in turn diminish the likelihood of AI adoption. Originality/value: This study provides new insights into how perceived self-threat, skepticism and distrust affect AI adoption intentions through anticipated adverse outcomes. It enriches the literature by highlighting the psychological barriers to AI adoption and underscores the need for targeted managerial strategies to address these challenges.