Explainable AI Prevention Pathways for Employee Turnover in Digital Transformation Enterprises: Model Construction and Strategy Optimisation Based on Ensemble Learning + SHAP

In the context of the digital economy, the experience-based decision-making and process fragmentation limitations of traditional personnel management systems are difficult to adapt to the needs of agility and precision. The research focuses on the inherent logic and practical path of system iteration, and proposes a collaborative transformation framework through three-dimensional analysis of technology, process, and organization and verification of state-owned enterprise cases. The technical dimension integrates big data and AI to achieve intelligent decision-making, the process dimension reconstructs full life cycle management, improves efficiency and experience, and the organizational dimension promotes the transformation of HR into a strategic partner.

References

Reddy, A. M., Yarlagadda, S., & Akkinen, H. (2021). An extensive analytical approach on human resources using random forest algorithm. arXiv preprint arXiv:2105.07855.
Vijayan, N. E. (2025). Mitigating Attrition: Data-Driven Approach Using Machine Learning and Data Engineering. arXiv preprint arXiv:2502.17865.
Căvescu, A. M., & Popescu, N. (2025). Predictive Analytics in Human Resources Management: Evaluating AIHR’s Role in Talent Retention. AppliedMath, 5(3), 99.
Dutraj, R., & Sengupta, P. R. (2025). Aligning Human Resources to Businesses through Human Resource Analytics. Asian Journal of Management, 16(2), 73-81.
Chaudhary M., Gaur L., Chakrabarti A., Singh G., Jones P., Kraus S. (2025)An integrated model to evaluate the transparency inpredicting employee churn using explainable artificial intelligence.Journal of Innovation and Knowledge.

Versions

2026-01-18 (2)
2025-12-30 (1)

Keywords

Digital economy;
personnel management system;
digital transformation;
human resource management;
AI technology

How to Cite

Liu, Z., Qi, H., & Chen, Y. (2026). Explainable AI Prevention Pathways for Employee Turnover in Digital Transformation Enterprises: Model Construction and Strategy Optimisation Based on Ensemble Learning + SHAP. International Journal of Multidisciplinary Research, 1(3), 66–70. https://doi.org/10.65231/ijmr.v1i3.84 (Original work published December 30, 2025)

This work is licensed under a Creative Commons Attribution 4.0 International License.

[1] Reddy, A. M., Yarlagadda, S., & Akkinen, H. (2021). An extensive analytical approach on human resources using random forest algorithm. arXiv preprint arXiv:2105.07855.

[2] Vijayan, N. E. (2025). Mitigating Attrition: Data-Driven Approach Using Machine Learning and Data Engineering. arXiv preprint arXiv:2502.17865.

[3] Căvescu, A. M., & Popescu, N. (2025). Predictive Analytics in Human Resources Management: Evaluating AIHR’s Role in Talent Retention. AppliedMath, 5(3), 99.

[4] Dutraj, R., & Sengupta, P. R. (2025). Aligning Human Resources to Businesses through Human Resource Analytics. Asian Journal of Management, 16(2), 73-81.

[5] Chaudhary M., Gaur L., Chakrabarti A., Singh G., Jones P., Kraus S. (2025)An integrated model to evaluate the transparency inpredicting employee churn using explainable artificial intelligence.Journal of Innovation and Knowledge.