Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Ramasamy, R Kanesaraj; Muniandy, Mohana; Subramanian, Parameswaran Title A name given to the resource A Predictive Framework for Sustainable Human Resource Management Using tNPS-Driven Machine Learning Models Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Sustainability (Switzerland);Volume;17;Issue;13;Article No.;5882; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3390/su17135882" target="_blank" rel="noreferrer noopener">https://doi.org/10.3390/su17135882</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010319900?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010319900?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource 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 Publisher An entity responsible for making the resource available Multidisciplinary Digital Publishing Institute (MDPI) Relation A related resource ISSN: 20711050; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online