Analysis of the Impact of Artificial Intelligence on Middle-Aged Workers' Employment Willingness: Based on the Context of Delayed Retirement

Ming Fu

doi:10.62177/apemr.v3i2.1103

Authors

Ming Fu Guangzhou Institute of Science and Technology

DOI:

https://doi.org/10.62177/apemr.v3i2.1103

Keywords:

Artificial Intelligence, Delayed Retirement, Middle-Aged Workers, Employment Intention, Regional Differences, Multivariate Regression, Vocational Training

Abstract

The rapid development of AI and China’s delayed retirement policy have significantly challenged middle-aged workers’ employment willingness. This study utilized a cross-sectional survey of 889 pre-retirement individuals in Beijing, Guangzhou, and Lanzhou, using multivariate regression analysis to examine key influencing factors. Results indicate that employment willingness is significantly higher among males and highly educated individuals, while widespread AI adoption in eastern and northern regions increases pressure on low-educated groups. Notably, household economic pressure correlates negatively with work intentions. The study concludes that AI's impact varies across demographics, necessitating targeted vocational training and social support to help middle-aged workers adapt to the modern job market.

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References

Bankins, S., Ocampo, A. C. G., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2024). A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. Journal of Organizational Behavior, 45(2), 159–182.

Chang, C.-H., Xu, H., & Xie, B. (2023). Aging workforce in the context of technological advancements: Toward a socio-ecological model. Work, Aging and Retirement, 9(4), 323–328.

Dai, Y., Zhao, Z., Sui, J., & Xu, J. (2025). The impact of delayed retirement on labor employment, fertility rate and economic growth in China. International Review of Economics & Finance, 100, 104103.

Ferdous, S. (2023, June 14). Are older workers ready for an AI takeover at work? Oxford Institute of Population Ageing, University of Oxford.

Haan, P., & Tolan, S. (2019). Labor supply and fiscal effects of partial retirement: The role of entry age and the timing of pension benefits. The Journal of the Economics of Ageing, 14, 100187.

Hinder, F., Vaquet, V., & Hammer, B. (2024). One or two things we know about concept drift—a survey on monitoring in evolving environments. Part A: Detecting concept drift. Frontiers in Artificial Intelligence, 7, 1330257.

Ranasinghe, T., Grosse, E. H., Glock, C. H., & Jaber, M. Y. (2024). Never too late to learn: Unlocking the potential of aging workforce in manufacturing and service industries. International Journal of Production Economics, 270, 109193.

Truxillo, D. M., Brady, G., Fraccaroli, F., Zaniboni, S., & Yaldiz, L. M. (2026). Work is changing: Implications for an aging, age-diverse workforce. Human Resource Management Review, 36(2), 101131.

Úbeda-García, M., Marco-Lajara, B., Zaragoza-Sáez, P. C., & Poveda-Pareja, E. (2025). Artificial intelligence, knowledge and human resource management: A systematic literature review of theoretical tensions and strategic implications. Journal of Innovation & Knowledge, 10(6), 100809.

Valtonen, A., Piri, R., Kaasinen, E., & Raisamo, R. (2024). Mitigating employee resistance and achieving well-being in digital transformation with personalized assistance. Information Systems Frontiers. Advance online publication.

Wang, Z., Pan, S.-Y., & Dong, W. (2024). Public management approaches to an aging workforce: Organizational strategies for adaptability and efficiency. Frontiers in Psychology, 15, 1439271.

Xie, H., Fang, Y., & Zhang, Y. (2023). Providing digital technology training as a way to retain older workers: The importance of perceived usefulness and growth need. Work, Aging and Retirement, 9(4), 376–392.

Zhao, G., Zhou, D., & Fu, Y. (2024). Study on the impact of delayed retirement on the sustainability of the basic pension insurance fund for urban employees in China. Sustainability, 16(10), 3969.