The Organisational Effects of Machine-Learning Burnout Prediction Models on Employees' Psychological Well-Being

Author(s)	Dr. Nasser Said Al-Ismaili
Country	Oman
Abstract	This paper examines how the organisational deployment of machine-learning-based burnout prediction models may affect employees' psychological well-being. It addresses a growing gap in digital human resource management and AI ethics: existing work often privileges predictive accuracy and managerial utility, while the lived psychological consequences of being monitored, classified and acted upon through probabilistic systems remain comparatively under-theorised. The study adopts a theoretical-analytical design based on a critical synthesis of peer-reviewed literature on burnout, psychological well-being, HR analytics, algorithmic management and responsible AI. The Job Demands-Resources model and Conservation of Resources theory provide the primary explanatory lenses, while organisational care versus algorithmic surveillance is used as an interpretive contrast rather than an additional variable in the core model. The analysis indicates that the effect is neither inherently beneficial nor inherently harmful.
Keywords	burnout prediction; machine learning; predictive HR analytics; psychological well-being; digital human resource management; algorithmic ethics; organisational care; algorithmic surveillance
Field	Business Administration
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-08-06
DOI	https://doi.org/10.36948/ijfmr.2026.v08i04.85228

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