Disconnect Between AI Satisfaction and Technostress: An Empirical Study

The rapid integration of Artificial Intelligence (AI) into the workplace has birthed a productivity paradox, where operational efficiency often coexists with heightened employee technostress. While existing literature suggests that user satisfaction acts as a psychological buffer against this stress, this study empirically challenges that assumption. Grounded in the Technology Acceptance Model (TAM) and Technostress Theory, this research analyzes the interplay between AI Experience (AX), AI Satisfaction (AS), Technostress (TS), and the moderating role of AI Self-Efficacy (AIS). Data was collected from 269 knowledge workers in high-adoption sectors and analyzed using PLS-SEM. The results reveal a critical Satisfaction Paradox, while AI Experience significantly drives Satisfaction and directly reduces Technostress, AI Satisfaction itself fails to mitigate stress or mediate the experience-stress relationship. Furthermore, while AI Self-Efficacy does not moderate satisfaction, exploratory analysis reveals it acts as a crucial boundary condition that strengthens the negative effect of AI Experience on Technostress. These findings suggest that organizations cannot rely on user satisfaction as a proxy for well-being; digital resilience is built through positive cognitive appraisal and technical confidence, not merely affective appreciation.