Interpretable Gradient Boosted Modeling of Employee Attrition: A SHAP-Based Framework for HR Analytics

Article Sidebar

PDF
DOI: https://doi.org/10.28924/2291-8639-23-2025-236
Published: Oct 8, 2025
Cite as:
Warawut Narkbunnum, Kanjana Hinthaw, Interpretable Gradient Boosted Modeling of Employee Attrition: A SHAP-Based Framework for HR Analytics, Int. J. Anal. Appl., 23 (2025), 236.

Main Article Content

Warawut Narkbunnum, Kanjana Hinthaw

Abstract

This study examines employee attrition using interpretable machine learning techniques, with a focus on enhancing strategic decision-making in human resource management. Three models—Logistic Regression, Random Forest, and Gradient Boosted Trees (GBT)—were evaluated, with GBT selected for its compatibility with SHAP (SHapley Additive exPlanations). SHAP was used to decompose the influence of variables such as Monthly Income, Over Time, and Job Satisfaction. The findings validate key HR theories, including Herzberg’s Two-Factor Theory and the Job Embeddedness framework, offering both predictive performance and theoretical alignment. The proposed model functions as a decision-support tool, providing actionable insights for HR professionals while contributing to the advancement of Management Technology through explainable AI.

Article Details

References

  1. D.G. Allen, J.I. Hancock, J.M. Vardaman, D.N. Mckee, Analytical Mindsets in Turnover Research, J. Organ. Behav. 35 (2013), S61-S86. https://doi.org/10.1002/job.1912.
  2. A. Margherita, Human Resources Analytics: A Systematization of Research Topics and Directions for Future Research, Hum. Resour. Manag. Rev. 32 (2022), 100795. https://doi.org/10.1016/j.hrmr.2020.100795.
  3. V. Peltokorpi, D.G. Allen, Job Embeddedness and Voluntary Turnover in the Face of Job Insecurity, J. Organ. Behav. 45 (2023), 416-433. https://doi.org/10.1002/job.2728.
  4. G. Marín Díaz, J.J. Galán Hernández, J.L. Galdón Salvador, Analyzing Employee Attrition Using Explainable Ai for Strategic Hr Decision-Making, Mathematics 11 (2023), 4677. https://doi.org/10.3390/math11224677.
  5. S. Gupta, G. Bhardwaj, M. Arora, R. Rani, P. Bansal, and R. Kumar, Employee Attrition Prediction in Industries using Machine Learning Algorithms, in: 2023 10th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 945–950, 2023.
  6. P.W. Hom, T.W. Lee, J.D. Shaw, J.P. Hausknecht, One Hundred Years of Employee Turnover Theory and Research., J. Appl. Psychol. 102 (2017), 530-545. https://doi.org/10.1037/apl0000103.
  7. M.A. Akasheh, E.F. Malik, O. Hujran, N. Zaki, A Decade of Research on Machine Learning Techniques for Predicting Employee Turnover: A Systematic Literature Review, Expert Syst. Appl. 238 (2024), 121794. https://doi.org/10.1016/j.eswa.2023.121794.
  8. A. Benabou, F. Touhami, M. Abdelouahed Sabri, Predicting Employee Turnover Using Machine Learning Techniques, Acta Inform. Pragensia 14 (2025), 112-127. https://doi.org/10.18267/j.aip.255.
  9. S. Bhutada, K.R. Lakshmi, G. Rajaramesh, Employee Attrition Prediction Based on Gradient Boosting Approach, Asian J. Res. Comput. Sci. 17 (2024), 58-65. https://doi.org/10.9734/ajrcos/2024/v17i12529.
  10. Z. Taner, O. Areta Hızıroğlu, K. Hızıroğlu, Leveraging Machine Learning Methods for Predicting Employee Turnover Within the Framework of Human Resources Analytics, J. Intell. Syst.: Theory Appl. 7 (2024), 145-158. https://doi.org/10.38016/jista.1440879.
  11. C. Molnar, Interpretable machine learning: A Guide for Making Black Box Models Explainable, Leanpub, 2022.
  12. C. Rudin, Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead, Nat. Mach. Intell. 1 (2019), 206-215. https://doi.org/10.1038/s42256-019-0048-x.
  13. M. Raghavan, S. Barocas, J. Kleinberg, K. Levy, Mitigating Bias in Algorithmic Hiring, in: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, ACM, New York, 2020, pp. 469-481. https://doi.org/10.1145/3351095.3372828.
  14. S.M. Lundberg, S.I. Lee, A Unified Approach to Interpreting Model Predictions, in: Advances in Neural Information Processing Systems 30 (NIPS 2017), 2017.
  15. M. Subramony, N. Krause, J. Norton, G.N. Burns, The Relationship Between Human Resource Investments and Organizational Performance: A Firm-Level Examination of Equilibrium Theory., J. Appl. Psychol. 93 (2008), 778-788. https://doi.org/10.1037/0021-9010.93.4.778.
  16. W.H. Mobley, Intermediate Linkages in the Relationship Between Job Satisfaction and Employee Turnover., J. Appl. Psychol. 62 (1977), 237-240. https://doi.org/10.1037/0021-9010.62.2.237.
  17. A.L. Rubenstein, M.B. Eberly, T.W. Lee, T.R. Mitchell, Surveying the Forest: A Meta‐analysis, Moderator Investigation, and Future‐oriented Discussion of the Antecedents of Voluntary Employee Turnover, Pers. Psychol. 71 (2017), 23-65. https://doi.org/10.1111/peps.12226.
  18. J.R. Hinrichs, L.A. Mischkind, Empirical and Theoretical Limitations of the Two-Factor Hypothesis of Job Satisfaction., J. Appl. Psychol. 51 (1967), 191-200. https://doi.org/10.1037/h0024470.
  19. M.D. Benítez-Márquez, E.M. Sánchez-Teba, G. Bermúdez-González, E.S. Núñez-Rydman, Generation Z Within the Workforce and in the Workplace: A Bibliometric Analysis, Front. Psychol. 12 (2022), 736820. https://doi.org/10.3389/fpsyg.2021.736820.
  20. Y. Baruch, D.M. Rousseau, Integrating Psychological Contracts and Ecosystems in Career Studies and Management, Acad. Manag. Ann. 13 (2019), 84-111. https://doi.org/10.5465/annals.2016.0103.
  21. J. Zhang, H. Chen, Application of Decision Tree Algorithm in Human Resource Management, in: 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS), IEEE, 2023, pp. 1-6. https://doi.org/10.1109/ICICACS57338.2023.10099554.
  22. S.M. Lundberg, G. Erion, H. Chen, A. DeGrave, J.M. Prutkin, et al., From Local Explanations to Global Understanding with Explainable AI for Trees, Nat. Mach. Intell. 2 (2020), 56-67. https://doi.org/10.1038/s42256-019-0138-9.
  23. D. Chung, J. Yun, J. Lee, Y. Jeon, Predictive Model of Employee Attrition Based on Stacking Ensemble Learning, Expert Syst. Appl. 215 (2023), 119364. https://doi.org/10.1016/j.eswa.2022.119364.
  24. B. Konda, Predictive Analysis for Employee Turnover Prevention Using Data-Driven Approach, Int. J. Sci. Eng. Appl. 13 (2024), 112–116. https://doi.org/10.7753/ijsea1308.1024.
  25. W. Wu, S. Fukui, Using Human Resources Data to Predict Turnover of Community Mental Health Employees: Prediction and Interpretation of Machine Learning Methods, Int. J. Ment. Health Nurs. 33 (2024), 2180-2192. https://doi.org/10.1111/inm.13387.
  26. T. Arromrit, K. Srisakaew, N. Roswhan, W. Mahikul, A Supervised Machine Learning Method for Predicting the Employees Turnover Decisions, in: 2023 IEEE 8th International Conference On Software Engineering and Computer Systems (ICSECS), IEEE, 2023, pp. 122-127. https://doi.org/10.1109/ICSECS58457.2023.10256357.
  27. A. Raza, K. Munir, M. Almutairi, F. Younas, M.M.S. Fareed, Predicting Employee Attrition Using Machine Learning Approaches, Appl. Sci. 12 (2022), 6424. https://doi.org/10.3390/app12136424.
  28. K. Sekaran, S. S, Interpreting the Factors of Employee Attrition Using Explainable AI, in: 2022 International Conference on Decision Aid Sciences and Applications (DASA), IEEE, 2022, pp. 932-936. https://doi.org/10.1109/DASA54658.2022.9765067.
  29. İ.T. Baydili, B. Tasci, Predicting Employee Attrition: Xai-Powered Models for Managerial Decision-Making, Systems 13 (2025), 583. https://doi.org/10.3390/systems13070583.
  30. M.R. Sareddy, S. Khan, Ai-driven Human Resource Management: Enhancing Transparency and Security with Machine Learning, J. Artif. Intell. Capsul. Netw. 6 (2025), 512-528. https://doi.org/10.36548/jaicn.2024.4.009.
  31. Z. Sun, Determining Human Resource Management Key Indicators and Their Impact on Organizational Performance Using Deep Reinforcement Learning, Sci. Rep. 15 (2025), 5690. https://doi.org/10.1038/s41598-025-86910-2.
  32. R. Paudel, Sanaz Tehrani, Alex Sherm, Balancing Act: Integrating Qualitative and Quantitative Data Driven for Recruitment and Selection Process, J. Info Sains: Inform. Sains 14 (2024), 162-177. https://doi.org/10.54209/infosains.v14i02.4545.
  33. F. Conte, A. Siano, Data-driven Human Resource and Data-Driven Talent Management in Internal and Recruitment Communication Strategies: An Empirical Survey on Italian Firms and Insights for European Context, Corp. Commun.: Int. J. 28 (2023), 618-637. https://doi.org/10.1108/ccij-02-2022-0012.
  34. L. Lin, K. Wang, Enhancing Talent Retention in Work 4.0 Era: An Improved Fuzzy Analytical Hierarchy Process and Fuzzy Decision-Making Trial and Evaluation Laboratory Methodology, Int. J. Fuzzy Syst. 27 (2024), 1453-1470. https://doi.org/10.1007/s40815-024-01844-7.
  35. R. Salehzadeh, M. Ziaeian, Decision Making in Human Resource Management: A Systematic Review of the Applications of Analytic Hierarchy Process, Front. Psychol. 15 (2024), 1400772. https://doi.org/10.3389/fpsyg.2024.1400772.
  36. G. Marín Díaz, J.J. Galán Hernández, J.L. Galdón Salvador, Analyzing Employee Attrition Using Explainable Ai for Strategic Hr Decision-Making, Mathematics 11 (2023), 4677. https://doi.org/10.3390/math11224677.
  37. A. Mahmoodi, L. Hashemi, M.M. Tahan, M. Jasemi, R.C. Millar, Design a Technology Acceptance Model by Applying System Dynamics: An Analysis Based on Key Dimensions of Employee Behavior, J. Model. Manag. 18 (2022), 1454-1475. https://doi.org/10.1108/jm2-12-2021-0306.
  38. F. Almeida, A. Junça Silva, S.L. Lopes, I. Braz, Understanding Recruiters’ Acceptance of Artificial Intelligence: Insights from the Technology Acceptance Model, Appl. Sci. 15 (2025), 746. https://doi.org/10.3390/app15020746.