ItemArticle

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

Title
A Predictive Framework for Sustainable Human Resource Management Using tNPS-Driven Machine Learning Models
Creator
Ramasamy, R Kanesaraj; Muniandy, Mohana; Subramanian, Parameswaran
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.
Source
Sustainability (Switzerland);Volume;17;Issue;13;Article No.;5882;
Date
01-01-2025
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
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
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
Rights
All Open Access; Gold Open Access
Relation
ISSN: 20711050;
Format
online
Language
English
Type
Article

Citation

Ramasamy, R Kanesaraj; Muniandy, Mohana; Subramanian, Parameswaran, “A Predictive Framework for Sustainable Human Resource Management Using tNPS-Driven Machine Learning Models,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23543.

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