AI Anxiety to Fatigue and Its Consequences: The Moderating Role of AI Usage Motivation

This study explores how AI generates psychological stress, leading to negative outcomes. A fatigue-based model was constructed, linking three AI fatigue antecedents AI job replacement anxiety, AI learning anxiety, and AI outcome uncertainty to AI fatigue and its consequences, including frustration stemming from technological stress and decreased organizational cohesion. Within the stress-response-outcome framework and the theory of technological stress, the study found that each type of anxiety significantly contributes to AI fatigue, and this fatigue is influenced by users' AI usage motives. Using survey data from 212 South Korean AI users and structural equation modeling, the results indicate that AI job replacement anxiety has the greatest impact on fatigue, with work-motive users being more susceptible. Beyond identifying these statistical relationships, the analysis also highlights how AI fatigue functions as a central psychological mechanism linking multidimensional AI-related anxieties to both individual emotional reactions and collective organizational dynamics in AI-rich workplaces. The findings suggest that companies can design targeted skills enhancement programs, increase AI transparency, and create motivation-sensitive support systems to promote healthier human-computer interaction and sustain long-term adoption of AI technologies.