Afraid of AI, Addicted to AI: Unraveling the Fear–Dependency Paradox
Faruk Dursun · SAGE Open · 2026
Classical technology acceptance theory treats fear as a barrier to adoption. Yet emerging evidence suggests that in professional AI contexts, fear may paradoxically drive rather than deter usage. This study investigates this counterintuitive dynamic by examining the relationship between AI anxiety and AI dependency among 421 working adults in Turkey, using structural equation modeling (SEM) and multi-group analysis (MGA). We introduce the “control paradox”—a novel theoretical construct proposing that individuals who fear losing cognitive autonomy to AI increasingly surrender that autonomy through defensive, compulsive overuse. Four principal findings emerge. First, AI anxiety comprises four empirically validated dimensions—learning anxiety, job displacement fear, sociotechnical blindness, and AI configuration anxiety—with distinct effects on dependency. Second, job displacement fear is the strongest predictor of dependency (β = 0.474, p < .001), revealing a professional threat-response mechanism not captured by existing TAM/UTAUT frameworks. Third, sociotechnical awareness functions as a protective factor (β = −0.225, p < .01), demonstrating that critical AI literacy—rather than technical skill alone—disrupts the fear–dependency cycle. Fourth, MGA reveals that the job displacement fear–dependency relationship is gender-invariant (males: β = 0.360, p < .001; females: β = 0.329, p = .006; Fisher’s Z = 0.285, p = .776), establishing the control paradox as a structural feature of professional AI adoption that transcends demographic variation. These findings extend technology acceptance theory, reframe AI dependency as professionally motivated rather than deficit-driven, and yield concrete recommendations for AI literacy education, workforce policy, and ethical technology design.