Abstract
This study investigates the role of motivational, occupational, and demographic factors in employee retention, in the context of increasing workforce mobility and intensifying competition for talent. The research aims to identify and quantify the main risk and protective factors influencing employees’ decision to leave the organization, using modern predictive analysis methods. The study employs the IBM HR Analytics Employee Attrition & Performance dataset, a benchmark dataset frequently used in HR Analytics literature, comprising 1,470 observations and 35 variables relevant to organizational behavior. The methodology combines binary logistic regression and the Random Forest algorithm to evaluate relationships between motivational, occupational, and demographic variables and the probability of voluntary departure. Data were preprocessed and balanced to correct class imbalance, and bivariate relationships were analyzed using the Spearman coefficient. Results indicate that working overtime is associated with a 3.41 times higher probability of departure, while high levels of job involvement and job satisfaction are negatively associated with the probability of voluntary turnover. Additionally, the analysis indicates a non-linear relationship between work-life balance and employee retention, suggesting the presence of an optimal level of organizational flexibility. The conclusions highlight the need for differentiated, data-driven organizational strategies to reduce staff turnover and strengthen employee retention in contemporary organizations.
Cuvinte cheie
employee retention
motivational factors
HR analytics
employee attrition
predictive modeling.
Istoric articol
Publicat
31.08.2026
Informații autori
Citare recomandată
Ana-Maria Săcuiu, Angela-Eliza Micu, Bianca Elena Miron (2026). The Impact of Motivational Factors on Employee Retention in the Digital Era: A Predictive Analysis Based on IBM HR Analytics Data. Constantin Brâncuși University of Târgu Jiu Economics Series, 2(1), 776–785. https://doi.org/10.65631/jes.4.2026.71
Referințe bibliografice
Allen, T. D., Golden, T. D., & Shockley, K. M. (2015). How effective is telecommuting? Assessing the status of our scientific findings. Psychological Science in the Public Interest, 16(2), 40–68. https://doi.org/10.1177/1529100615593273 Aman-Ullah, A., Mehmood, W., Amin, S., & Abbas, Y. A. (2022). Human capital and employee retention: A systematic literature review. Journal of Economic and Administrative Sciences, 38(3), 205–228. https://doi.org/10.1108/JEAS-05-2021-0096 Armstrong, M., & Taylor, S. (2023). Armstrong’s handbook of human resource management practice (16th ed.). London: Kogan Page. Bakker, A. B., & Demerouti, E. (2017). Job demands–resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22(3), 273–285. https://doi.org/10.1037/ocp0000056 Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324 Chung, D., Yun, J., & Kim, J. (2023). Predictive model of employee attrition based on stacking ensemble learning. Expert Systems with Applications, 215, 119364. https://doi.org/10.1016/j.eswa.2022.119364 Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010 Gagné, M., & Deci, E. L. (2005). Self-determination theory and work motivation. Journal of Organizational Behavior, 26(4), 331–362. https://doi.org/10.1002/job.322 Griffeth, R. W., Hom, P. W., & Gaertner, S. (2000). A meta-analysis of antecedents and correlates of employee turnover: Update, moderator tests, and research implications for the next millennium. Journal of Management, 26(4), 463–508. https://doi.org/10.1177/014920630002600001 Hausknecht, J. P., Rodda, J., & Howard, M. J. (2009). Targeted employee retention: Performance-based and job-related differences in reported reasons for staying. Human Resource Management, 48(2), 269–288. https://doi.org/10.1002/hrm.20279 Herzberg, F. (1959). The motivation to work (2nd ed.). New York: Wiley. Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (3rd ed.). Hoboken: Wiley. https://doi.org/10.1002/9781118548387 Jaharuddin, N. S., & Zainol, L. N. (2019). The impact of work-life balance on job engagement and turnover intention. The South East Asian Journal of Management, 13(1). https://doi.org/10.21002/seam.v13i1.10912 James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning: With applications in R (2nd ed.). New York: Springer. https://doi.org/10.1007/978-1-0716-1418-1 Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. New York: Springer. https://doi.org/10.1007/978-1-4614-6849-3 Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR Analytics. The International Journal of Human Resource Management, 28(1), 3–26. https://doi.org/10.1080/09585192.2016.1244699 Robbins, S. P., & Judge, T. A. (2019). Organizational behavior (18th ed.). New York: Pearson. Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67. https://doi.org/10.1006/ceps.1999.1020 Săcuiu, A.-M., Micu, A.-E., & Miron, B. E. (2026). Employee retention in contemporary organizations: A predictive analysis of motivational, occupational, and demographic factors. In Proceedings of the International Scientific Symposium "Horizons of Economic Research in a Global Context" (pp. 176–179). "Academica Brâncuși" Publisher. ISSN 3153–2179. Spearman, C. (1904). The proof and measurement of association between two things. The American Journal of Psychology, 15(1), 72–101. https://doi.org/10.2307/1412159 Stock, R. M. (2015). Is boreout a threat to frontline employees’ innovative work behavior? Journal of Product Innovation Management, 32(4), 574–592. https://doi.org/10.1111/jpim.12239 Vukonjanski, J., Nikolić, M., Hadžić, O., Terek, E., & Nedeljković, M. (2018). Job satisfaction, organizational commitment and job involvement: The mediating role of job involvement. Frontiers in Psychology, 9, 132. https://doi.org/10.3389/fpsyg.2018.00132