A Predictive Framework for Sustainable Human Resource Management Using tNPS-Driven Machine Learning Models
subject: employee performance prediction; human-centric AI systems; machine learning in HR; organizational sustainability; predictive analytics; sustainable human resource management; Transactional Net Promoter Score (tNPS); workforce optimization
description: 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. 2025 by the authors.
creator: Ramasamy, R Kanesaraj; Muniandy, Mohana; Subramanian, Parameswaran
source: Sustainability (Switzerland);Volume;17;Issue;13;Article No.;5882;
publisher: Multidisciplinary Digital Publishing Institute (MDPI)
date: 01-01-2025
rights: All Open Access; Gold Open Access
relation: ISSN: 20711050;
format: online
language: English
type: Article
coverage: Ramasamy R.K., Faculty of Computing Informatics, Multimedia University, Cyberjaya, 63100, Malaysia; Muniandy M., Faculty of Computing Informatics, Multimedia University, Cyberjaya, 63100, Malaysia; Subramanian P., School of Business and Management, Christ University, 30, Valor Ct, Maharashtra, Lavasa, 412112, India