ISSN: 2222-6990
Open access
Employee mental health is increasingly critical in digital manufacturing environments, where traditional stress management approaches often overlook the dynamic interplay between individual psychological factors and work-related factors. This study integrates the transactional model of stress and coping (tmsc) with self-determination theory (sdt) to propose a dual-path framework that leverages ai-enabled ease of use to enhance employee well-being. A pilot survey involving 50 employees from two electronics factories in shandong, china, employed validated instruments such as the recovery experience questionnaire. Exploratory factor analysis confirmed strong construct validity (kmo > 0.78), and correlation analysis identified significant predictors of stress management. Behavioral coping strategies exhibited the strongest correlation with stress outcomes (r=0.670), followed by perceived organizational support (r=0.638), ai-enabled ease of use (r=0.634), technology self-efficacy (r=0.601), and job autonomy (r=0.591). These findings suggest that when ai tools are perceived as easy to use, they support psychological needs for autonomy and competence, thereby enhancing employees’ capacity to cope with stress. The study highlights the importance of designing modular, user-friendly ai systems that align with cultural values, such as mianzi norms and the operational constraints of resource-limited manufacturing settings.
Aon TELUS Health. (2023). Asia Mental Health Index Report. (pp. 13-14).
Bergdahl, J., Latikka, R., Celuch, M., Savolainen, I., Mantere, E. S., Savela, N., & Oksanen, A. (2023). Self-determination and attitudes toward artificial intelligence: Cross-national and longitudinal perspectives. Telematics and Informatics, 82, 102013. https://doi.org/10.1016/j.tele.2023.102013
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Perspectives in social psychology.
Deci, E. L., & Ryan, R. M. (2000). The "what" and "why" of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
Doe, R. M. (2021). An open, integrated modular format: For flexible and intelligible architecture, engineering and construction design and production. International Journal of Architectural Computing, 19(1), 23-36.
Gross, J. J. (2008). Emotion regulation. Handbook of emotions, 3(3), 497-513.
Hu, J., Gan, Y., Li, Z., Li, X., Xu, T., Qiu, J., Wang, X., & Wei, D. (2024). Examining the moderating role of depressive symptoms on the dynamic interplay between cognitive reappraisal and rumination: Evidence from experience sampling. Behaviour research and therapy, 183, 104645 . https://doi.org/10.1016/j.brat.2024.104645.
Kandoth, S., & Shekhar, S. K. (2022, September). Social influence and intention to use AI: the role of personal innovativeness and perceived trust using the parallel mediation model. In Forum Scientiae Oeconomia (Vol. 10, No. 3, pp. 131-150).
Lazarus, R. S., & Folkman, S. (1984). Stress, Appraisal, and Coping. Springer Publishing Company.
Oladele, O. K. (2023). Data-Driven Work Culture: How AI Enhances Employee Well-Being and Reduces Occupational Stress.
Shandong Electronic Information Industry White Paper. (2023). Annual Report on Electronic Manufacturing Development in Shandong. Jinan: Shandong Electronic Information Association.
World Health Organization. (2022). World mental health report: Transforming mental health for all. World Health Organization. https://www.who.int/publications/i/item/9789240049338
Zhang, X., Chen, X., Dai, L., Long, Y., Wang, Z., & Shindo, K. (2023). The effect of work stress on turnover intention amongst family doctors: a conditional process analysis. The International Journal of Health Planning and Management, 38(5), 1300-1313. https://doi.org/10.1002/hpm.3652
Guo, R., Kelana, B. W. Y., Safar, A. E., & Cheng, L. (2026). Enhancing Employee Well-Being through Ai-Enabled Stress Management: A Pilot Study in the Manufacturing Sector. International Journal of Academic Research in Business and Social Sciences, 16(2), 901-906.
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