AI ANXIETY, TECHNOLOGICAL EFFICACY, AND GEN Z CAREER COMMITMENT IN HOSPITALITY MANAGEMENT

The rapid deployment of artificial intelligence (AI) and frontline service automation across the hospitality, tourism, and culinary sectors has fundamentally altered operational workflows while coinciding with an acute talent retention crisis among Generation Z (Gen Z) graduates. Grounded in Social Cognitive Career Theory (SCCT) and Conservation of Resources (COR) Theory, this study examines how AI anxiety and technological self-efficacy shape Gen Z undergraduates' long-term career commitment. A quantitative cross-sectional research design was executed, gathering primary survey data from N = 350 senior undergraduates enrolled in hospitality, tourism, and culinary management programs, of whom 89.1% possessed direct industrial automation exposure and 10.9% completed simulated lab training. The empirical model was evaluated using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4. Structural results revealed that the framework accounted for 43.2% of the variance in Gen Z career commitment (R2 = 0.432). Specifically, AI anxiety exhibited a statistically significant negative relationship with career commitment β= -0.312, p < 0.001, f2 = 0.142), supporting COR theory by illustrating how job displacement fears deplete psychological resources and induce career detachment. Conversely, technological self-efficacy demonstrated a dominant, positive direct impact on career commitment (β = 0.485, p < 0.001, f2 = 0.343), reinforcing SCCT by demonstrating that digital confidence acts as a vital cognitive enabler. Comparative effect size analysis (f2) confirmed that building technological self-efficacy exerts a substantially stronger predictive influence on talent retention than the psychological barrier posed by AI anxiety. Practical implications advocate that hotel operators restructure frontline operations toward hybrid human and AI collaboration, while higher education institutions must modernize curricula to foster domain-specific technological efficacy. Ultimately, securing long-term graduate retention requires equipping emerging talent with robust digital confidence rather than solely attempting to eliminate automation fears.