Prediction of the Employee Turnover Intention Using Decision Trees

Ana Živković, Dario Šebalj, Jelena Franjković

2024

Abstract

This study examines the effectiveness of Decision Tree methodology in predicting employee turnover intention, an area in which this method has received limited research. In this paper, primary research was conducted and four Decision Tree algorithms were applied to a sample of 511 respondents. The study incorporates several predictor variables into the model, including job satisfaction, perceived organizational commitment, perceived organizational justice, perceived organizational support, and perceived alternative job opportunities, to assess their influence on turnover intention. The assessment measure of the model was Recall. The results indicate that the Decision Tree model using the RandomTree algorithm is relatively successful in predicting turnover intentions (almost 60% accuracy rate), with job satisfaction, especially opportunities for personal growth and affective organizational commitment being significant predictors. Other influencing factors include satisfaction with salary and the job itself, as well as interpersonal relationships. This study underscores the potential of the Decision Tree method in human resource management and provides a basis for future research on the role of predictive analytics in understanding employee turnover dynamics.

Paper Citation

in Harvard Style

Živković A., Šebalj D. and Franjković J. (2024). Prediction of the Employee Turnover Intention Using Decision Trees. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 325-336. DOI: 10.5220/0012538400003690

in Bibtex Style

@conference{iceis24,
author={Ana Živković and Dario Šebalj and Jelena Franjković},
title={Prediction of the Employee Turnover Intention Using Decision Trees},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2024},
pages={325-336},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012538400003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - Prediction of the Employee Turnover Intention Using Decision Trees
SN - 978-989-758-692-7
AU - Živković A.
AU - Šebalj D.
AU - Franjković J.
PY - 2024
SP - 325
EP - 336
DO - 10.5220/0012538400003690
PB - SciTePress