Citation

Ramasamy, R. Kanesaraj and Muniandy, Mohana and Subramanian, Parameswaran (2025) A Predictive Framework for Sustainable Human Resource Management Using tNPS-Driven Machine Learning Models. Sustainability, 17 (13). p. 5882. ISSN 2071-1050

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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.

Item Type:	Article
Uncontrolled 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
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	28 Jul 2025 07:24
Last Modified:	30 Jul 2025 19:17
URII:	http://shdl.mmu.edu.my/id/eprint/14298

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