A Predictive Framework for Sustainable Human Resource Management Using tNPS-Driven Machine Learning Models

R Kanesaraj Ramasamy (), Mohana Muniandy and Parameswaran Subramanian
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R Kanesaraj Ramasamy: Faculty of Computing Informatics, Multimedia University, Cyberjaya 63100, Malaysia
Mohana Muniandy: Faculty of Computing Informatics, Multimedia University, Cyberjaya 63100, Malaysia
Parameswaran Subramanian: School of Business and Management, Christ University, 30, Valor Ct, Lavasa 412112, Maharashtra, India

Sustainability, 2025, vol. 17, issue 13, 1-28

Abstract: This study proposes a predictive framework that integrates machine learning techniques with Transactional Net Promoter Score (tNPS) data to enhance sustainable Human Resource management. A synthetically generated dataset, simulating real-world employee feedback across divisions and departments, was used to classify employee performance and engagement levels. Six machine learning models such as XGBoost, TabNet, Random Forest, Support Vector Machines, K-Nearest Neighbors, and Neural Architecture Search were applied to predict high-performing and at-risk employees. XGBoost achieved the highest accuracy and robustness across key performance metrics, including precision, recall, and F1-score. The findings demonstrate the potential of combining real-time sentiment data with predictive analytics to support proactive HR strategies. By enabling early intervention, data-driven workforce planning, and continuous performance monitoring, the proposed framework contributes to long-term employee satisfaction, talent retention, and organizational resilience, aligning with sustainable development goals in human capital management.

Keywords: sustainable human resource management; Transactional Net Promoter Score (tNPS); predictive analytics; workforce optimization; machine learning in HR; employee performance prediction; organizational sustainability; human-centric AI systems (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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