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Artificial intelligence as an enabler for achieving human resource resiliency: past literature, present debate and future research directions

Authors :
Gayatri Panda
Manoj Kumar Dash
Ashutosh Samadhiya
Anil Kumar
Eyob Mulat-weldemeskel
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