Enhancing Employee Well-Being through Ai-Enabled Stress Management: A Pilot Study in the Manufacturing Sector

HRMARS - 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.