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1 2024

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Prediction of voluntary employee turnover using machine learning

Nowak Marcin

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

2024

z. 201

263--273

Purpose: The aim of the article was to develop a method for predicting the occurrence of voluntary employee turnover intentions. Design/methodology/approach: The objectives are achieved through the employment of machine learning algorithms, specifically decision tree algorithms, support vector machines, k-nearest neighbors, and naive Bayes classifiers. The article includes a literature review on voluntary employee turnover and the fundamentals of machine learning. It then presents the developed method for predicting employee turnover, which is evaluated under real-world conditions. Findings: The research demonstrates that the proposed machine learning methods can effectively predict voluntary employee turnover intentions. The analysis and results indicate that these predictive models can identify early signs of turnover with significant accuracy, providing valuable insights into employee retention dynamics. Research limitations/implications: (The study's limitations include the potential for overfitting in machine learning models and the need for large, high-quality datasets to train the models. Future research should focus on testing the proposed methods in various organizational settings and exploring additional variables that may influence employee turnover intentions. Practical implications: The practical outcome of this research is the creation of a tool for more effective human resource management, particularly in the context of talent management. Organizations can use this tool to identify employees at risk of leaving and implement targeted retention strategies, ultimately reducing turnover rates and associated costs. Social implications: By reducing voluntary employee turnover, organizations can foster more stable and supportive work environments, contributing to overall employee well-being and job satisfaction. This can enhance public perception of corporate social responsibility and positively influence industry standards. Originality/value: This paper introduces a novel application of machine learning techniques to predict voluntary employee turnover intentions. The findings are valuable to human resource professionals, organizational managers, and scholars in the fields of management and quality sciences, offering a data-driven approach to improving employee retention strategies.