AI-ENABLED GREEN HRM FOR EMPLOYEE WELL BEING IN HIGH STRESS GREEN SUPPLY CHAIN ROLES.

Abstract [English]

This paper investigates how AI enabled Green Human Resource Management (GHRM) can enhance employee well being in high stress roles within green and circular supply chains, such as reverse logistics, e waste processing and remanufacturing operations. While prior studies predominantly examine environmental and efficiency outcomes of AI adoption in sustainable supply chains, limited attention has been given to its implications for psychological health, stress reduction and work–life balance. Drawing on sustainable HRM, socio technical systems and job demands–resources theories, the study proposes a conceptual framework linking AI supported GHRM practices—predictive analytics for burnout risk, AI driven workload optimization, personalized green training and digital well being monitoring—to key well being outcomes, including perceived stress, engagement and job satisfaction. A mixed methods design is outlined, combining survey data from employees in green supply chain functions with qualitative interviews to capture nuanced experiences of AI mediated HR practices. The paper argues that when ethically designed, AI can strengthen GHRM’s capacity to mitigate psychosocial risks arising from ambitious environmental targets and operational complexity, thereby supporting both human sustainability and organizational performance. The proposed framework and research agenda contribute to emerging debates on responsible AI, sustainable management and the human side of circular economy transitions.