AI dependency and employee burnout: a nonlinear self-determination perspective

Purpose Drawing on self-determination theory (SDT), this study aims to examine how employees’ artificial intelligence (AI) dependency is associated with burnout in hospitality. It tests the curvilinear nature of this association, the roles of competence and autonomy as need-related pathways and the moderating role of perceived organizational support (POS). Design/methodology/approach Data were collected from 375 AI-using hospitality employees in China through a three-wave time-lagged survey. The measurement model was validated using confirmatory factor analysis. Hypotheses were tested using hierarchical regression with quadratic and interaction terms, and conditional indirect effects were examined using a derivative-based approach with delta-method confidence intervals. Findings AI dependency was associated with burnout in a U-shaped pattern with burnout decreasing from low to moderate dependency and increasing at higher dependency. Consistent with SDT AI dependency was associated with competence and autonomy in inverted U-shaped patterns. Competence and autonomy helped account for this curvilinear association with moderate dependency linked to higher competence and autonomy and lower burnout and high dependency linked to the opposite pattern. POS attenuated the negative curvature in these relationships fundamentally altering how AI dependency relates to employees psychological needs at higher AI dependency levels. Research limitations/implications This study extends hospitality technology and employee well-being research by conceptualizing AI dependency as a nonlinear antecedent of burnout and by integrating SDT with POS as a boundary condition. It clarifies when AI-enabled work shifts from supporting psychological needs to undermining them, and offers a mechanism-based explanation of how organizations can preserve employee well-being as AI becomes embedded in hospitality workflows. Originality/value This study advances hospitality AI and employee well-being research by shifting attention from AI use and adoption to employees’ perceived AI dependency. It develops a nonlinear SDT-based framework that explains how AI dependency is linked to burnout through competence and autonomy and identifies POS as a contextual boundary condition in AI-enabled hospitality work.