Predicting Employee Turnover in High-Tech Enterprises Using Machine Learning: Based on the Psychological Contract Perspective

DOI

10.2991/978-94-6463-488-4_38How to use a DOI?

Keywords

Employee turnover; Psychological contract; Machine learning; Prediction model

Abstract

High-tech enterprises are boosting technological innovation and economic growth in countries worldwide. Compared with general enterprises, high-tech enterprises are characterized by technology-intensive and high employee turnover rates, relying more on human capital, especially researchers with core technical expertise. However, high turnover rates and unexpected departures of key employees place a huge financial burden on enterprises, along with the risk of technology leakage. Therefore, this study establishes a theoretical model of voluntary employee turnover based on psychological contract theory and previous theoretical studies. We also categorize employee turnover characteristics into four dimensions: Individual conditions, Material incentives, Development opportunities, and Environmental support. Given that previous related studies lacked the combination of theory and data-driven methods, this study applies the IBM HR dataset and selects features for each dimension through the PCA method, for which machine learning models are constructed, including logistic regression, random forests, SVMs, decision trees, and XGBoost, and their performances are evaluated. In addition, the importance of different dimensions is analyzed, and it is found that material incentives have the greatest impact on employee turnover.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yiting Zhang
AU  - Ziling Cai
AU  - Hongyang Fei
PY  - 2024
DA  - 2024/08/29
TI  - Predicting Employee Turnover in High-Tech Enterprises Using Machine Learning: Based on the Psychological Contract Perspective
BT  - Proceedings of the 2024 2nd International Conference on Digital Economy and Management Science (CDEMS 2024)
PB  - Atlantis Press
SP  - 341
EP  - 352
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-488-4_38
DO  - 10.2991/978-94-6463-488-4_38
ID  - Zhang2024
ER  -