The growing implementation of AI technologies in the workplace has created new opportunities and challenges for organizations and employees. This study investigates how AI Anxiety (AIA), Digital Nudging (DN), and Perceived Organizational Support (POS) influence Learning (L) and Work Performance (WP), using data from 301 employees working in content and digital-related roles at LINE MAN Wongnai, a Thai tech company actively adopting AI tools. The study is grounded in the “Job Demands-Resources (JD-R) theory” and employs “Partial Least Squares Structural Equation Modeling (PLS-SEM)” to analyze structural relationships.
The results reveal that moderate levels of AI Anxiety, when accompanied by strong POS, can positively motivate Learning, highlighting the moderating role of POS in this non-linear relationship. Both POS and DN positively influence employee learning, while DN also directly contributes to Work Performance. However, the effect of DN is not universally effective and varies depending on the level of perceived support. The model explains 51.8% of the variance in Learning and 38.8% in Work Performance (R²). These findings underscore the importance of emotional support and thoughtful digital interventions in helping employees adapt during AI-driven transformation.

Alqhaiwi, Z. O. (2024). A bright side to unethical pro-organizational behavior: Improving work performance by satisfying psychological needs. Journal of Business Research, 184(0), 114879.
Bai, X., Guo, R., & Gu, X. (2024). Effect of teachers’ TPACK on their behavioral intention to use technology: Chain mediating effect of technology self-efficacy and attitude toward use. Education and Information Technologies, 29(1), 1013-1032.
Bakker, A. B., & Demerouti, E. (2007). The job demands‐resources model: State of the art. Journal of Managerial Psychology, 22(3), 309-328.
Bakker, A. B., & Demerouti, E. (2017). Job demands–resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22(3), 273–285.
Bakker, A. B., Demerouti, E., & Sanz-Vergel, A. I. (2014). Burnout and work engagement: The JD–R approach. Annual Review of Organizational Psychology and Organizational Behavior, 1(0), 389-411.
Benitez, J., Ruiz, L., & Popovic, A. (2022). Impact of mobile technology-enabled HR gamification on employee performance: An empirical investigation. Information & Management, 59(4), 103647.
Bezuijen, X. M., van Dam, K., van den Berg, P. T., & Thierry, H. (2010). How leaders stimulate employee learning: A leader–member exchange approach. Journal of Occupational and Organizational Psychology, 83(3), 673-693.
Brehm, J. W. (1966). A theory of psychological reactance (Vol. 1). New York: Academic Press.
Chen, D., Liu, W., & Liu, X. (2024). What drives college students to use AI for L2 learning? Modeling the roles of self-efficacy, anxiety, and attitude based on an extended technology acceptance model. Acta Psychologica, 249(0), 104442.
Chu, X., Yu, J., Litifu, A., Zhao, W., Wei, X., Wang, P., & Wei, J. (2024). Organizational support and task performance: a multiple mediation model. Frontiers in Psychology, 14(0), 1258177.
Crawford, E. R., Lepine, J. A., & Rich, B. L. (2010). Linking job demands and resources to employee engagement and burnout: A theoretical extension and meta-analytic test. J Appl Psychol, 95(5), 834-848.
Dan Florin, S., & Romascanu, M. (2024). The influence of AI Anxiety and Neuroticism in Attitudes toward Artificial Intelligence. European Journal of Sustainable Development, 13(0), 191-202.
Dawes, J. (2008). Do data characteristics change according to the number of scale points used? An experiment using 5-point, 7-point and 10-point scales. International Journal of Market Research, 50(1), 61-104.
Demerouti, E. (2018). Multiple levels in job demands-resources theory: Implications for employee well-being and performance. In E. Diener, S. Oishi & L. Tay (Eds.), Handbook of well-being (pp. 1–13). Salt Lake City, UT: Noba Scholar.
Demerouti, E. (2025). Job demands-resources and conservation of resources theories: How do they help to explain employee well-being and future job design? Journal of Business Research, 192(0), 115296.
Demerouti, E., & Bakker, A. B. (2023). Job demands-resources theory in times of crises: New propositions. Organizational Psychology Review, 13(3), 209-236.
Demerouti, E., Nachreiner, F., & Schaufeli, W. (2001). The Job Demands–Resources Model of Burnout. Journal of Applied Psychology, 86(3), 499-512. doi: 10.1037/0021-9010.86.3.499
Eisenberger, R., Huntington, R., Hutchison, S., & Sowa, D. (1986). Perceived organizational support. Journal of Applied psychology, 71(3), 500–507.
El-Kassar, A.-N., Dagher, G. K., Lythreatis, S., & Azakir, M. (2022). Antecedents and consequences of knowledge hiding: The roles of HR practices, organizational support for creativity, creativity, innovative work behavior, and task performance. Journal of Business Research, 140(0), 1-10.
Erebak, S., & Turgut, T. (2021). Anxiety about the speed of technological development: Effects on job insecurity, time estimation, and automation level preference. The Journal of High Technology Management Research, 32(2), 100419.
Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 101-107.
Gupta, P., Kim, Y. J., Glikson, E., & Woolley, A. (2024). Using Digital Nudges to Enhance Collective Intelligence in Online Collaboration: Insights from Unexpected Outcomes. MIS Quarterly, 48(1), 393-408.
Hair, J., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017). Advanced issues in partial least squares structural equation modeling. Thousand Oaks, CA: Sage Publications, Inc.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24.
Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012). The use of partial least squares structural equation modeling in strategic management research: a review of past practices and recommendations for future applications. Long Range Planning, 45(5-6), 320-340.
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(0), 115-135.
Hettler, F. M., Schumacher, J.-P., Anton, E., Eybey, B., & Teuteberg, F. (2024). Understanding the user perception of digital nudging in platform interface design. Electronic Commerce Research(0), 1-38.
Houdek, P. (2024). Nudging in organizations: How to avoid behavioral interventions being just a façade. Journal of Business Research, 182(0), 114781.
Hu, L. t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
Iyer, P., & Bright, L. F. (2024). Navigating a paradigm shift: Technology and user acceptance of big data and artificial intelligence among advertising and marketing practitioners. Journal of Business Research, 180(0), 114699.
Kim, B.-J., & Kim, M.-J. (2024). The influence of work overload on cybersecurity behavior: A moderated mediation model of psychological contract breach, burnout, and self-efficacy in AI learning such as ChatGPT. Technology in Society, 77(0), 102543.
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration (ijec), 11(4), 1-10.
Kurtessis, J. N., Eisenberger, R., Ford, M. T., Buffardi, L. C., Stewart, K. A., & Adis, C. S. (2017). Perceived organizational support: A meta-analytic evaluation of organizational support theory. Journal of Management, 43(6), 1854-1884.
Lam, S. S., Chen, X.-P., & Schaubroeck, J. (2002). Participative decision making and employee performance in different cultures: The moderating effects of allocentrism/idiocentrism and efficacy. Academy of Management Journal, 45(5), 905-914.
Lee, D. Y., & Jo, Y. (2023). The job demands-resource model and performance: the mediating role of employee engagement. Frontiers in Psychology, 14(0), 1194018.
Li, J., & Huang, J.-S. (2020). Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory. Technology in Society, 63(0), 101410.
Li, Y., Song, Y., Sun, Y., & Zeng, M. (2024). When do employees learn from artificial intelligence? The moderating effects of perceived enjoyment and task-related complexity. Technology in Society, 77(0), 102518.
Li, Y., Zhao, L., Cao, C., & Yang, D. (2025). The double-edged sword effect of algorithmic transparency: An empirical study of gig workers’ work disengagement under algorithmic management. Information & Management, 62(2), 104100.
Lyddy, C., Good, D., Yuan, S., Michel, E., Ahlvik, C., & Reb, J. (2025). Reperceiving personal resources within JD-R: Mindfulness influences burnout and work engagement by shaping perceptions of job demands and job resources. Journal of Business Research, 193(0), 115307.
Ma, L., Yu, P., Zhang, X., Wang, G., & Hao, F. (2024). How AI use in organizations contributes to employee competitive advantage: The moderating role of perceived organization support. Technological Forecasting and Social Change, 209(0), 123801.
Malodia, S., Kaur, P., Ractham, P., Sakashita, M., & Dhir, A. (2022). Why do people avoid and postpone the use of voice assistants for transactional purposes? A perspective from decision avoidance theory. Journal of Business Research, 146(0), 605-618.
Mele, C., Spena, T. R., Kaartemo, V., & Marzullo, M. L. (2021). Smart nudging: How cognitive technologies enable choice architectures for value co-creation. Journal of Business Research, 129(0), 949-960.
Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434.
Probst, T. M., Jiang, L., & Benson, W. (2018). Job insecurity and anticipated job loss: A primer and exploration of possible interventions. In C. R. Leana & J. Meuris (Eds.), The Oxford Handbook of Job Loss and Job Search (pp. 31-53). New York: Oxford University Press.
Prodanova, J., & Kocarev, L. (2021). Is job performance conditioned by work-from-home demands and resources? Technology in Society, 66(0), 101672.
Ren, S., & Liu, D. (2022). Effects of digital nudging on the adoption of APPs for value co-creation among online consumers. Information Technology and Management, 25(0), 1-12.
Rhoades, L., & Eisenberger, R. (2002). Perceived organizational support: a review of the literature. Journal of Applied Psychology, 87(4), 698.
Ritala, P., Aaltonen, P., Ruokonen, M., & Nemeh, A. (2024). Developing industrial AI capabilities: An organisational learning perspective. Technovation, 138(0), 103120.
Rodríguez, M. E., Guerrero-Roldán, A. E., Baneres, D., & Karadeniz, A. (2022). An intelligent nudging system to guide online learners. International Review of Research in Open and Distributed Learning, 23(1), 41-62.
Rubel, M. R. B., Kee, D. M. H., Daghriri, Y. Q., & Rimi, N. N. (2023). Does perceived organizational support matter? The effect of high-commitment performance management on supervisors’ performance. Frontiers in Psychology, 13(0), 837481.
Sarstedt, M., Ringle, C. M., Smith, D., Reams, R., & Hair Jr, J. F. (2014). Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers. Journal of Family Business Strategy, 5(1), 105-115.
Schaufeli, W. B. (2016). Job insecurity research is still alive and kicking twenty years later: A commentary. Australian Psychologist, 51(1), 32-35.
Schiavo, G., Businaro, S., & Zancanaro, M. (2024). Comprehension, apprehension, and acceptance: Understanding the influence of literacy and anxiety on acceptance of artificial Intelligence. Technology in Society, 77(0), 102537.
Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS Quarterly(0), 553-572.
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven, CT: Yale University Press.
Tummers, L. G., & Bakker, A. B. (2021). Leadership and Job Demands-Resources Theory: A Systematic Review. Frontiers in Psychology, 12(0), 722080.
Vedadi, A., Warkentin, M., Straub, D. W., & Shropshire, J. (2024). Fostering information security compliance as organizational citizenship behavior. Information & Management, 61(5), 103968.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly(0), 425-478.
Wang, Q., Liu, X., & Huang, K.-W. (2024). Investigating employees’ occupational risks and benefits resulting from artificial intelligence: An empirical analysis. Information & Management, 61(8), 104036.
Wang, Y.-Y., & Wang, Y.-S. (2022). Development and validation of an artificial intelligence anxiety scale: an initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619-634.
Wong, A. H. K., Wu, C. X., Whitla, P., & Snell, R. S. (2022). How LMX and marketing capabilities guide and motivate customer-facing employees’ learning. Journal of Business Research, 138(0), 161-169.
Yang, H., & Sundar, S. S. (2025). AI anxiety: Explication and exploration of effect on state anxiety when interacting with AI doctors. Computers in Human Behavior: Artificial Humans, 3(0), 100128.
Zhang, X., Shen, K. N., & Xu, B. (2024). Double-edged sword of knowledge inertia: Overcoming healthcare professionals’ resistance in innovation adoption. Technovation, 133(0), 103011.
Zhao, S., & Liu, Y. (2024). Job demands-resources on digital gig platforms and counterproductive work behavior. Frontiers in Psychology, 15(0), 1378247.
Zhou, Q. W., Chen, K. Y., & Cheng, S. (2024). Bringing employee learning to AI stress research: A moderated mediation model. Technological Forecasting and Social Change, 209(0), 14.