A review of machine learning applications in human resource management
International Journal of Productivity and Performance Management
ISSN: 1741-0401
Article publication date: 2 February 2021
Issue publication date: 6 May 2022
Abstract
Purpose
This paper reviews 105 Scopus-indexed articles to identify the degree, scope and purposes of machine learning (ML) adoption in the core functions of human resource management (HRM).
Design/methodology/approach
A semi-systematic approach has been used in this review. It allows for a more detailed analysis of the literature which emerges from multiple disciplines and uses different methods and theoretical frameworks. Since ML research comes from multiple disciplines and consists of several methods, a semi-systematic approach to literature review was considered appropriate.
Findings
The review suggests that HRM has embraced ML, albeit it is at a nascent stage and is receiving attention largely from technology-oriented researchers. ML applications are strongest in the areas of recruitment and performance management and the use of decision trees and text-mining algorithms for classification dominate all functions of HRM. For complex processes, ML applications are still at an early stage; requiring HR experts and ML specialists to work together.
Originality/value
Given the current focus of organizations on digitalization, this review contributes significantly to the understanding of the current state of ML integration in HRM. Along with increasing efficiency and effectiveness of HRM functions, ML applications improve employees' experience and facilitate performance in the organizations.
Keywords
Acknowledgements
Authors would like to acknowledge the Department of Management Studies, Indian Institute of Technology Delhi for their support for this study.
Citation
Garg, S., Sinha, S., Kar, A.K. and Mani, M. (2022), "A review of machine learning applications in human resource management", International Journal of Productivity and Performance Management, Vol. 71 No. 5, pp. 1590-1610. https://doi.org/10.1108/IJPPM-08-2020-0427
Publisher
:Emerald Publishing Limited
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