A human resources analytics and machine-learning examination of turnover: implications for theory and practice

Dan Avrahami, Dana Pessach, Gonen Singer and Hila Chalutz Ben-Gal

International Journal of Manpower, 2022, vol. 43, issue 6, 1405-1424

Abstract: Purpose - What do antecedents of turnover tell us when examined using human resources (HR) analytics and machine-learning tools, and what are the respective theoretical and practical implications? Although the turnover literature is expansive, empirical evidence on turnover antecedents studied using data science tools remains limited. Design/methodology/approach - To help reinvigorate research in this field, the authors propose a novel examination of turnover antecedents—competencies, commitment, trust and cultural values—using big data tools to develop a granular, case-dependent measure of turnover. Findings - Using archival data from 700,000 employees of a large organization collected over a period of ten years, the authors find that turnover is generally associated with varying levels of these antecedents. However, in more fine-grained analysis, their relation to turnover is contingent upon role, person and cultural background. Originality/value - The authors discuss the implications on turnover and strategic HR research and the potential of Artificial Intelligence and machine-learning methods in the design and implementation of managerial and HR planning initiatives.

Keywords: Turnover; SHRM; HR analytics; Artificial intelligence; Big data; Machine-learning; Data science (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eme:ijmpps:ijm-12-2020-0548

DOI: 10.1108/IJM-12-2020-0548

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