Employee turnover in multinational corporations: a supervised machine learning approach

Valerio Veglio (), Rubina Romanello () and Torben Pedersen ()
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Valerio Veglio: University of Pavia
Rubina Romanello: University of Trieste
Torben Pedersen: Copenhagen Business School

Review of Managerial Science, 2025, vol. 19, issue 3, No 1, 687-728

Abstract: Abstract This research explores the potential of supervised machine learning techniques in transforming raw data into strategic knowledge in the context of human resource management. By analyzing a database with over 205 variables and 2,932 observations related to a telco multinational corporation, this study tests the predictive and analytical power of classification decision trees in detecting the determinants of voluntary employee turnover. The results show the determinants of groups of employees who may voluntarily leave the company, highlighting the level of analytical depth of the classification tree. This study contributes to the field of human resource management by highlighting the strategic value of the classification decision tree in identifying the characteristics of groups of employees with a high propensity to voluntarily leave the firm. As practical implication, our study provides an approach that any organization can use to self-assess its own turnover risk and develop tailored retention practices.

Keywords: Machine learning; Classification decision tree; Employee turnover; Employee churn predictive model; Multinational corporation; Employee retention (search for similar items in EconPapers)
JEL-codes: M12 M16 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11846-024-00769-7

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