Menezes Barreto Júnior, Joel and Hong León, Duliet and Perez Fernández, Jaime and Congo Mavambo, António Ramos and Domingas Chivango, Natalia Águeda joel.menezes@unic.co.ao, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED (2026) Algorithmic human resource management and employee well-being: currentevidence from a systematic literature review. Journal of Management and Human Resources, 4 (2). ISSN 3091-1850
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Abstract
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.
| Item Type: | Article |
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| Uncontrolled Keywords: | artificial intelligence, algorithmic human resource management, employee well-being, human resource analytics, algorithmic fairness |
| Subjects: | Subjects > Social Sciences |
| Divisions: | Universidad Internacional do Cuanza > Research > Articles and books |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 16 Sep 2026 08:55 |
| Last Modified: | 16 Sep 2026 08:55 |
| URI: | https://repositorio.funiber.org/id/eprint/29886 |
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