Algorithmic human resource management and employee well-being: currentevidence from a systematic literature review

Joel Menezes Barreto Júnior; Duliet Hong León; Jaime Perez Fernández; António Ramos Congo Mavambo; Natalia Águeda Domingas Chivango

Resumen:

This study examines the relationship between artificial intelligence (AI), algorithmic human resource management, and employee well-being through a systematic review of the scientific literature published between 2020 and 2026. The review addresses the growing need to understand how algorithm-supported decision-making influences employee experience, psychological well-being, organizational trust, and perceptions of fairness in contemporary workplaces. The study followed the PRISMA 2020 guidelines and included peer-reviewed articles retrieved from Scopus, Web of Science, ScienceDirect, and SpringerLink. Following the identification, screening, and eligibility stages, 82 studies were selected for qualitative thematic analysis and descriptive synthesis. The findings indicate that artificial intelligence enhances recruitment and selection, performance evaluation, and human resource analytics while simultaneously introducing challenges related to digital surveillance, algorithmic opacity, technological anxiety, and reduced employee autonomy. Overall, the evidence suggests that algorithmic human resource management can promote employee well-being when implemented within transparent governance frameworks characterized by human oversight, algorithmic fairness, ethical data governance, and active employee participation. The review contributes by synthesizing current evidence, identifying major research trends and knowledge gaps, and highlighting future research priorities, particularly for Latin American organizational contexts, where empirical evidence remains limited.