Artificial Intelligence-Induced Psychological Pressure and Turnover Intention in the Hotel sector: A Serial Mediation Approach

The artificial intelligence (AI) paradox occurs when a technology intended to assist people becomes a threat to their positions and generates turnover intention. Drawing on the Job Demands–Resources (JD–R) model, this study investigates how AI use affects employees’ turnover intention in the hotel industry. It also examines the serial mediating effects of technostress, job burnout, and job insecurity. Using a purposive sampling technique, data were collected from 310 frontline hotel workers in Indonesia. Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 3.0 was employed to analyze the data. The results indicate that AI has a significant and positive effect on technostress, job burnout, job insecurity, and turnover intention. Specifically, artificial intelligence significantly increases employees’ technostress and also exerts direct positive effects on job burnout, job insecurity, and turnover intention. Moreover, technostress significantly increases both job burnout and job insecurity. However, the direct effect of technostress on turnover intention is not supported. Turnover intention is instead significantly and positively predicted by job burnout and job insecurity. The mediation analysis reveals a serial indirect effect, such that technostress influences turnover intention indirectly through job burnout and job insecurity. This study extends the JD–R framework to AI adoption by demonstrating how technology-driven job demands shape a sequential psychological process leading to turnover intention. It introduces a serial mediation model that has not been examined collectively in prior research.