The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
A review of machine learning applications in human resource management
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.
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Citations
Cites
Literature review as a research methodology: An overview and guidelines
Snyder, H. (2019) · Journal of Business Research · doi:10.1016/j.jbusres.2019.07.039 · Cited by 11,524
Artificial Intelligence in Human Resources Management: Challenges and a Path Forward
Tambe, P., Cappelli, P., & Yakubovich, V. (2019) · California Management Review · doi:10.1177/0008125619867910 · Cited by 1,609
Talent management: A critical review
Lewis, R. E., & Heckman, R. J. (2006) · Human Resource Management Review · doi:10.1016/j.hrmr.2006.03.001 · Cited by 1,341
Determinants of employee engagement and their impact on employee performance
J., A. (2014) · International Journal of Productivity and Performance Management · doi:10.1108/ijppm-01-2013-0008 · Cited by 1,262
Amazon Scraps Secret AI Recruiting Tool that Showed Bias against Women *
Dastin, J. (2022) · Ethics of Data and Analytics · doi:10.1201/9781003278290-44 · Cited by 1,165
An evidence-based review of HR Analytics
Marler, J. H., & Boudreau, J. W. (2017) · The International Journal of Human Resource Management · doi:10.1080/09585192.2016.1244699 · Cited by 913
Machine learning and data mining
Mitchell, T. M. (1999) · Communications of the ACM · doi:10.1145/319382.319388 · Cited by 691
HR and analytics: Why HR is set to fail the big data challenge
Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016) · Human Resource Management Journal · doi:10.1111/1748-8583.12090 · Cited by 633
RAMESES publication standards: meta-narrative reviews
Wong, G., Greenhalgh, T., Westhorp, G., Buckingham, J., & Pawson, R. (2013) · BMC Medicine · doi:10.1186/1741-7015-11-20 · Cited by 610
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Cited by
Ooi, K.-B., Tan, G. W.-H., Al-Emran, M., Al-Sharafi, M. A., Capatina, A., Chakraborty, A., Dwivedi, Y. K., Huang, T.-L., Kar, A. K., Lee, V.-H., et al. (2025) · Journal of Computer Information Systems · doi:10.1080/08874417.2023.2261010 · Cited by 746
Self-citationArtificial Intelligence in Tactical Human Resource Management: A Systematic Literature Review
Votto, A. M., Valecha, R., Najafirad, P., & Rao, H. R. (2021) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2021.100047 · Cited by 322
How can we manage biases in artificial intelligence systems – A systematic literature review
P．S．, D. V. (2023) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2023.100165 · Cited by 239
Chen, Z. (2024) · Human Resource Development Review · doi:10.1177/15344843241273316 · Cited by 41
Technostress of HR professionals: the darker implication of remote work transformations
Nayak, S., Budhwar, P., & Malik, A. (2025) · The International Journal of Human Resource Management · doi:10.1080/09585192.2024.2446508 · Cited by 16
Metadata
- Authors
- Garg, S., Sinha, S., Kar, A. K., & Mani, M.
- Venue
- International Journal of Productivity and Performance Management
- Publisher
- Emerald
- Published
- 2021-02-02
- Volume
- 71
- Issue
- 5
- Pages
- 1590-1610
- ISSN
- 0043-8022 (ISSN-L)
- 1741-0401
- OpenAlex
- W3126911807
- Cited by
- 357
- References
- 56
- FWCI
- 50.13
- Type
- journal article
- Language
- en
- Open access
- closed
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- Garg, S., Sinha, S., Kar, A. K., & Mani, M. (2022). A review of machine learning applications in human resource management. International Journal of Productivity and Performance Management. https://doi.org/10.1108/ijppm-08-2020-0427
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
