AI LEARNING ANXIETY, JOB INSECURITY, AND INNOVATION: A MEDIATION MODEL IN ASIAN FIRMS
DOI:
https://doi.org/10.14456/aamr.2025.47Keywords:
AI Learning Anxiety, Job Insecurity, Employee Innovation, Threat-Rigidity Theory, Asian FirmsAbstract
The rapid adoption of Artificial Intelligence (AI) presents a critical challenge for organizations, particularly in dynamic Asian economies where institutional pressures for innovation are high. While AI promises efficiency, it also heightens employee anxiety and concerns about job stability. This study investigates the intricate relationship between AI learning anxiety and employees' innovative work behavior, focusing on the mediating role of job insecurity within Asian firms. Drawing on Threat-Rigidity Theory, we hypothesized that AI learning anxiety would increase job insecurity, which, in turn, would dampen innovative work behavior. Using a quantitative approach, data were collected from 403 employees at Chinese internet companies with at least 1 year of experience with AI tools. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for data analysis. The findings reveal that AI learning anxiety significantly heightens job insecurity. Importantly, job insecurity significantly undermined employees' innovative work behavior. Crucially, AI learning anxiety did not directly influence innovation; instead, its negative effect was fully mediated through job insecurity. This demonstrates that employees' emotional responses to AI are transmuted into tangible resource threats, driving defensive rather than innovative behaviors. This study extends Threat-Rigidity Theory to the context of AI. It offers vital insights for management, emphasizing the critical need to address employees' psychological security to foster innovation during AI-driven transformations in Asia.
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Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees' perceptions of our future workplace. Journal of Management & Organization, 24(2), 239-257.
Chen, S., & Zhao, Y. (2025). Explore the AI anxiety attitude of e-commerce employees. International Journal of Human-Computer Interaction, 41(13), 8438-8446.
De Witte, H., Pienaar, J., & de Cuyper, N. (2016). Review of 30 years of longitudinal studies on the association between job insecurity and health and well‐being: Is there causal evidence?. Australian Psychologist, 51(1), 18-31.
Dijkstra, T., & Henseler, J. (2015). Consistent partial least squares path modeling. MIS Quarterly, 39(2), 297-316.
Gustiawan, D., Noermijati, Aisjah, S., & Indrawati, N. (2023). Workplace incivility to predict employee silence: Mediating and moderating roles of job embeddedness and power distance. Cogent Business & Management, 10(1), 2188982.
Hair, J., Ringle, C., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1-2), 1-12.
Hazan, E., Madgavkar, A., Chui, M., Smit, S., Maor, D., Dandona, G., & Huyghues-Despointes, R. (2024). A new future of work: The race to deploy AI and raise skills in Europe and beyond. Retrieved from www.mckinsey.com/mgi/our-research/a-new-future-of-work-the-race-to-deploy-ai-and-raise-skills-in-europe-and-beyond.
He, C., Teng, R., & Song, J. (2024). Linking employees' challenge-hindrance appraisals toward AI to service performance: The influences of job crafting, job insecurity and AI knowledge. International Journal of Contemporary Hospitality Management, 36(3), 975-994.
Henseler, J., Ringle, C., & 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.
Huang, M., & Rust, R. (2018). Artificial intelligence in service. Journal of Service Research, 21, 155-172.
Khanal, S., Zhang, H., & Taeihagh, A. (2025). Development of New Generation of Artificial Intelligence in China: When Beijing’s Global Ambitions Meet Local Realities. Journal of Contemporary China, 34(151), 19-42.
Kim, B., Kim, M., & Lee, J. (2024). The impact of an unstable job on mental health: The critical role of self-efficacy in artificial intelligence use. Current Psychology, 43(18), 16445-16462.
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11, 1-10.
Muthén, L., & Muthén, B. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 9(4), 599-620.
Ngamcharoen, P., Sakdapat, N., & Bhanthumnavin, D. (2024). Development and Evaluation on Cybersecurity Behaviour Measurement Instruments for Undergraduate Students. International Journal of Cyber Criminology, 18(1), 139-156.
Podsakoff, P., MacKenzie, S., Lee, J., & Podsakoff, N. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. The Journal of Applied Psychology, 88(5), 879-903.
Preacher, K., & Hayes, A. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40, 879-891.
Scott, S., & Bruce, R. (1994). Determinants of innovative behavior: A path model of individual innovation in the workplace. Academy of Management Journal, 37(3), 580-607.
Sharif, M., Zhang, L., Asif, M., Alshdaifat, S., & Hanaysha, J. (2025). Artificial intelligence and employee outcomes: Investigating the role of job insecurity and technostress in the hospitality industry. Acta Psychologica, 253, 104733.
Simmering, M., Fuller, C., Richardson, H., Ocal, Y., & Atinc, G. (2014). Marker Variable Choice, Reporting, and Interpretation in the Detection of Common Method Variance: A Review and Demonstration: A Review and Demonstration. Organizational Research Methods, 18(3), 473-511.
Staw, B., Sandelands, L., & Dutton, J. (1981). Threat-rigidity effects in organizational behavior: A multilevel analysis. Administrative Science Quarterly, 26(4), 501-524.
Toufighi, S., Sahebi, I., Govindan, K., Lin, M., Vang, J., & Brambini, A. (2024). Participative leadership, cultural factors, and speaking-up behaviour: An examination of intra-organisational knowledge sharing. Journal of Innovation & Knowledge, 9(3), 100548.
Wang, X., Chen, M., & Chen, N. (2024). How artificial intelligence affects the labour force employment structure from the perspective of industrial structure optimisation. Heliyon, 10(5), e26686.
Wang, Y., & Wang, Y. (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.
Wu, T., Liang, Y., & Wang, Y. (2024). The Buffering Role of Workplace Mindfulness: How Job Insecurity of Human-Artificial Intelligence Collaboration Impacts Employees’ Work–Life-Related Outcomes. Journal of Business and Psychology, 39, 1395-1411.
Xu, G., Qiu, Y., & Qi, J. (2024). Artificial intelligence and labor demand: An empirical analysis of Chinese small and micro enterprises. Heliyon, 10(13), e33893.
Yuangngoen, P., Sakdapat, N., & Ngamcharoen, P. (2025). Analysis on Factors Influencing Medical Health Tourism Behaviour of Generation X. Journal of Mind and Behavior, 46(2), 45-72.
Zhao, X., Lynch Jr., J., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197-206.
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Copyright (c) 2025 Changqiu WEN, Yuanfeng CAI, Chai Ching TAN
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