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Artificial intelligence as an enabler for achieving human resource resiliency: past literature, present debate and future research directions
- Source :
- International Journal of Industrial Engineering and Operations Management, Vol 6, Iss 4, Pp 326-347 (2024)
- Publication Year :
- 2024
- Publisher :
- Emerald Publishing, 2024.
-
Abstract
- Purpose – Artificial intelligence (AI) can enhance human resource resiliency (HRR) by providing the insights and resources needed to adapt to unexpected changes and disruptions. Therefore, the present research attempts to develop a framework for future researchers to gain insights into the actions of AI to enable HRR. Design/methodology/approach – The present study used a systematic literature review, bibliometric analysis, and network analysis followed by content analysis. In doing so, we reviewed the literature to explore the present state of research in AI and HRR. A total of 98 articles were included, extracted from the Scopus database in the selected field of research. Findings – The authors found that AI or AI-associated techniques help deliver various HRR-oriented outcomes, such as enhancing employee competency, performance management and risk management; enhancing leadership competencies and employee well-being measures; and developing effective compensation and reward management. Research limitations/implications – The present research has certain implications, such as increasing the HR team's proficiency, addressing the problem of job loss and how to fix it, improving working conditions and improving decision-making in HR. Originality/value – The present research explores the role of AI in HRR following the COVID-19 pandemic, which has not been explored extensively.
Details
- Language :
- English
- ISSN :
- 26906104 and 26906090
- Volume :
- 6
- Issue :
- 4
- Database :
- Directory of Open Access Journals
- Journal :
- International Journal of Industrial Engineering and Operations Management
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.ff91735dd6e543cda4d32693df221218
- Document Type :
- article
- Full Text :
- https://doi.org/10.1108/IJIEOM-05-2023-0047/full/pdf