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

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

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)1405-1424
Number of pages20
JournalInternational Journal of Manpower
Issue number6
StatePublished - 22 Aug 2022

Bibliographical note

Funding Information:
This paper was partially supported by the Koret Foundation Grant for Smart Cities and Digital Living 2030.

Publisher Copyright:
© 2022, Emerald Publishing Limited.


  • Artificial intelligence
  • Big data
  • Data science
  • HR analytics
  • Machine-learning
  • SHRM
  • Turnover


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