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

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.

Citation

Avrahami, D., Pessach, D., Singer, G. and Chalutz Ben-Gal, H. (2022), "A human resources analytics and machine-learning examination of turnover: implications for theory and practice", International Journal of Manpower, Vol. 43 No. 6, pp. 1405-1424. https://doi.org/10.1108/IJM-12-2020-0548