A COMPREHENSIVE ANALYSIS OF MACHINE LEARNING-BASED RETENTION OF STAFF MEMBERS
DOI:
https://doi.org/10.58225/sw.2026.1-92-100Keywords:
Deep learning, Random Forest, Stacking-Based Transfer Learning, Human Resource ManagementAbstract
In the hypercompetitive business environment, retaining employees remains a top challenge for companies trying to maintain stability, productivity and lower costs associated with turnover. An attempt to ascertain different factors that influence retention such as corporate culture, employee empowerment, organizational learning, talent development strategies, entrepreneurship education adoption of cultural intelligence and application technology like artificial intelligence and machine learning. This study synthesizes literature review along with empirical evidences. Many predictive modeling approaches including Random Forests, deep learning and Stacking-Based Transfer Learning, and Logistic Regression have a high degree of potential to predict turnover, find critical reasons for attrition, and lead proactive retention strategies. The mediating and moderating roles of job engagement, socialization tactics, organization size, and protean career orientations in predicting retention outcomes are examined across diverse organizational contexts (e.g., banks, startups, global work context). The findings emphasize the importance of integrating data-driven analytics and human-oriented strategies, offering HR practitioners beneficial advice on developing focused interventions to enhance employee satisfaction and sustainable stability in modern, multilingual work settings.
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Copyright (c) 2026 Mohit Yadav, Khushwant Singh, Vugar Hacimahmud Abdullayev, Fernando Moreira, Dheer Dhwaj Barak, Raj Kumar Sharma, Sunil Kumar, Deepak Kumar (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
