A decade of research on machine learning techniques for predicting employee turnover: A systematic literature review

Mariam Al Akasheh, Esraa Faisal Malik, Omar Hujran, Nazar Zaki

Research output: Contribution to journalArticlepeer-review

Abstract

This study presents a comprehensive and systematic review of the machine learning (ML) techniques used to predict employee turnover in the past decade. A total of 52 relevant peer-reviewed studies published between 2012 and April 2023 were selected. The results indicate that over 20 ML techniques have been used to predict employee turnover in various institutions. In addition, this comprehensive review demonstrates that most machine learning approaches used to predict employee turnover were based on supervised learning, with 96% of the articles (50 out of 52) in this category, random forest technique. Furthermore, the review reveals that the most critical factors for predicting employee turnover have been salary and overtime. This study makes a valuable contribution to the field by offering a systematic analysis of the ML algorithms used for predicting employee turnover, in addition to providing an overview of the most significant works in this field produced in the past decade. This study offers important reference regarding the essential ML approaches used in employee turnover prediction and provides future directions for researchers and industries.

Original languageEnglish
Article number121794
JournalExpert Systems with Applications
Volume238
DOIs
Publication statusPublished - Mar 15 2024

Keywords

  • Employee Turnover
  • Human Resources
  • Machine Learning
  • Systematic Literature Review

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence

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