A Review of Machine Learning Application in the Talent Acquisition Process

Abstract

Purpose - This paper reviews 45 Scopus-indexed articles, a significant body of research, to identify the degree, scope and purposes of machine learning (ML) adoption in the core functions of talent acquisition (TA).

Design/methodology/approach—This review has employed a semi-systematic approach, a unique, comprehensive literature analysis method involving a structured search and review process. This approach, emerging from multiple disciplines and using different methods and theoretical frameworks, was deemed appropriate due to ML research's diverse origins and methods, ensuring a thorough review.

Findings: The review suggests that talent acquisition has embraced ML and is attracting attention from technology-oriented practitioners. ML applications, particularly those using random forest and decision tree algorithms for classification, are most robust in recruitment and talent acquisition. However, the early stage of ML applications for complex tasks underscores the need for collaboration between HR experts and ML specialists, highlighting the field's interdisciplinary nature. (Fang et al., 2016).

Originality/value: This review significantly enhances the understanding of ML integration in talent acquisition in the current digitalisation era. More importantly, it highlights the potential of ML applications to significantly improve the efficiency and effectiveness of talent acquisition functions, thereby enhancing employee performance and contributing to organisational success. This potential should inspire hope and excitement about the future of talent acquisition.