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Keywords = Transactional Net Promoter Score (tNPS)

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28 pages, 631 KiB  
Article
A Predictive Framework for Sustainable Human Resource Management Using tNPS-Driven Machine Learning Models
by R Kanesaraj Ramasamy, Mohana Muniandy and Parameswaran Subramanian
Sustainability 2025, 17(13), 5882; https://doi.org/10.3390/su17135882 - 26 Jun 2025
Viewed by 419
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 [...] Read more.
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. Full article
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