AI ANXIETY, TECHNOLOGICAL EFFICACY, AND GEN Z CAREER COMMITMENT IN HOSPITALITY MANAGEMENT
DOI:
https://doi.org/10.55197/qjssh.v7si5.1738Keywords:
artificial intelligence anxiety, technological efficacy, Gen Z career commitment, service automationAbstract
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.
References
[1] Bajrami, D.D., Terzić, A., Petrović, M.D., Radovanović, M., Tretiakova, T.N. (2021): In hospitality, stress is on the house: Turnover intentions, job insecurity, and time pressure in COVID-19 setting. – International Journal of Contemporary Hospitality Management 33(7): 2404-2426.
[2] Bandura, A. (1977): Self-efficacy: Toward a unifying theory of behavioral change. – Psychological Review 84(2): 191-215.
[3] Che Ahmat, N.H., Radzi, S.M., Zahari, M.S.M. (2023): Psychological strain and early-career turnover intentions among hospitality interns. – International Journal of Contemporary Hospitality Management 35(4): 1320-1339.
[4] Cohen, J. (1988): Statistical power analysis for the behavioral sciences (2nd ed.). – Lawrence Erlbaum Associates 567p.
[5] Fadzil, M., Jamaluddin, A. (2025): Gen Z workforce dynamics and educational alignment in the service sector. – Asia Pacific Journal of Tourism Research 30(2): 112-128.
[6] Faul, F., Erdfelder, E., Buchner, A., Lang, A.G. (2009): Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. – Behavior Research Methods 41(4): 1149-1160.
[7] Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M. (2021): A primer on partial least squares structural equation modeling (PLS-SEM). – SAGE Publications 384p.
[8] Henseler, J., Ringle, C.M., Sarstedt, M. (2015): A new criterion for assessing discriminant validity in variance-based structural equation modeling. – Journal of the Academy of Marketing Science 43(1): 115-135.
[9] Hobfoll, S.E. (1989): Conservation of resources: A new attempt at conceptualizing stress. – American Psychologist 44(3): 513-524.
[10] Kock, N. (2015): Common method bias in PLS-SEM: A full collinearity assessment approach. – International Journal of e-Collaboration 11(4): 1-10.
[11] Lent, R.W., Brown, S.D., Hackett, G. (1994): Toward a unifying social cognitive theory of career and academic interest, choice, and performance. – Journal of Vocational Behavior 45(1): 79-122.
[12] Li, J.J., Bonn, M.A., Ye, B.H. (2023): Hotel employee AI anxiety and its impact on job performance and intention to stay: The moderating role of technological support. – International Journal of Hospitality Management 108: 12p.
[13] Lv, X.Q., Li, C.S., Zhang, C.H. (2022): Artificial intelligence displacement anxiety and employee outcomes in the hotel sector: A conservation of resources perspective. – Tourism Management 92: 12p.
[14] Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y., Podsakoff, N.P. (2003): Common method biases in behavioral research: A critical review of the literature and recommended remedies. – Journal of Applied Psychology 88(5): 879-903.
[15] Prentice, C., Lopes, S.D., Wang, X. (2023): The impact of artificial intelligence and employee tech-efficacy on service quality and career outcomes. – Journal of Hospitality and Tourism Management 54: 112-121.
[16] Shmueli, G., Sarstedt, M., Hair, J.F., Cheah, J.H., Ting, H., Vaithilingam, S., Ringle, C.M. (2019): Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. – European Journal of Marketing 53(11): 2322-2347.
[17] Wang, P.Q., Yan, L., Santoso, C. (2026): Generational engagement with AI in hospitality: Human–AI interaction perspectives across the service process. – Current Issues in Tourism 29(7): 1281-1294.
[18] Wu, T.J., Zhang, R.X. (2024): Exploring the impacts of intention towards human-robot collaboration on frontline hotel employees’ positive behavior: An integrative model. – International Journal of Hospitality Management 123: 12p.
[19] Wu, T.J., Zhang, R.X., Zhang, Z. (2025): Navigating the human-artificial intelligence collaboration landscape: Impact on quality of work life and work engagement. – Journal of Hospitality and Tourism Management 62: 276-283.
[20] Zhou, L., Wang, Y., Chen, X. (2023): Career expectations and industry retention of Generation Z hospitality graduates. – Journal of Hospitality and Tourism Education 35(3): 180-195.
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Copyright (c) 2026 MOHD ZOOL FADLI IBRAHIM, RAJA ISKANDAR PUTERA RAJA MUSTAPHA, JOESRI BIN MOHAMAD SABER, KHAIRIL ANUAR BAHARI, NOORSA RIZA JOHARI, RIANA NURMALASARI
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