International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 15, No. 6, 8 Dec. 2025
Cover page and Table of Contents: PDF (size: 751KB)
Enhancing Employee Engagement through Machine Learning: Insights from K-Means Clustering Analysis
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Author(s)
Hemanth Kumar Tummalapalli
1,*
G. Kamal
1
Y. V. Naga Kumari
1
J. N. V. R. Swarup Kumar
2
Y. Chitra Rekha
3
Index Terms
Employee Engagement, Machine Learning, K-Means Clustering, Survey Analysis, Organizational Strategy, Attrition Prediction
Abstract
This study provides insight into how machine learning methods, in particular k-means clustering algorithm could contribute to greater degree of employee engagement in the businesses. Using Work-Life Balance, Environment Satisfaction and Job Satisfaction found in employee survey data as an illustrative lens of the engagement phenomenon, patterns are identified that differ from traditional perspectives with implications for organizational actions. The study categorizes workers in clusters and identifies the significant gaps of satisfaction among them, using k-means clustering. Logistic regression analysis is used for the prediction of attrition risk, which also helps in determining factors responsible behind employee retention. The findings reveal the importance of understanding such facilitators to generate targeted interventions and strategies that foster a positive work environment and improve organisational performance. This approach ensures less attrition risks, and better job satisfaction leading to greater overall organisation productivity / wellbeing.
Cite This Paper
Hemanth Kumar Tummalapalli, G. Kamal, Y. V. Naga Kumari, J. N. V. R. Swarup Kumar, Y. Chitra Rekha, "Enhancing Employee Engagement through Machine Learning: Insights from K-Means Clustering Analysis", International Journal of Education and Management Engineering (IJEME), Vol.15, No.6, pp. 15-26, 2025. DOI:10.5815/ijeme.2025.06.02
Reference
[1]Bakker, A. B., Albrecht, S. (2018). Work engagement: current trends. Career Development International, 23(1), 4-11.
[2]Harter, J. K., Schmidt, F. L., Hayes, T. L. (2002). Business-unit-level relationship between employee satisfaction, employee engagement, and business outcomes: a meta-analysis. Journal of Applied Psychology, 87(2), 268.
[3]Saks, A. M. (2006). Antecedents and consequences of employee engagement. Journal of Managerial Psychology, 21(7), 600-619.
[4]Macey, W. H., Schneider, B. (2008). The meaning of employee engagement. Industrial and Organizational Psychology, 1(1), 3-30.
[5]Hakanen, J. J., Bakker, A. B., Schaufeli, W. B. (2006). Burnout and work engagement among teachers. Journal of School Psychology, 43(6), 495-513.
[6]Davenport, T. H., Harris, J. G. (2007). Competing on analytics: the new science of Winning. Harvard Business Review Press, Language, 15(217), 24.
[7]Huang, Z. (1998). Extensions to the k-means algorithm for clustering large datasets with categorical values. Data Mining and Knowledge Discovery, 2(3), 283-304.
[8]Palani, K., Stynes, P., Pathak, P. (2021, April). Clustering Techniques to Identify Low-engagement Student Levels. In CSEDU (2) (pp. 248-257).
[9]Raza, D. M., Hasan, F. (2021). Employee Engagement and Turnover utilizing Logistic Regression. 2021 IEEE 8th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Dehradun, India, 2021, pp. 1-6. https://doi.org/10.1109/UPCON52273.2021.9667566
[10]Baghdadi, J. D., Wong, M., Comulada, W. S., & Uslan, D. Z. (2018). Lack of insurance as a barrier to care in sepsis: A retrospective cohort study. Journal of Critical Care, 46, 134-138.
[11]Shafie, M. R., Khosravi, H., Farhadpour, S., Das, S., Ahmed, I. (2024). A cluster based human resources analytics for predicting employee turnover using optimized Artificial Neural Networks and data augmentation. Decision Analytics Journal, 11, 100461. https://doi.org/10.1016/j.dajour.2024.100461
[12]Gupta, K. P. (2023). Exploring student engagement in virtual classrooms: a person-centred approach using cluster analysis. International Journal of Educational Management, 37(1), 117-134. https://doi.org/10.1108/IJEM-08-2022-0309
[13]Zada, I., Ali, S., Khan, I., Hadjouni, M., Elmannai, H., Zeeshan, M., Jameel, A. (2022). Performance Evaluation of Simple K-Mean and Parallel K-Mean Clustering Algorithms: Big Data Business Process Management Concept. Mobile Information Systems, 2022(1), 1277765. https://doi.org/10.1155/2022/1277765
[14]Kanavos, A., Karamitsos, I., Mohasseb, A. (2023). Exploring Clustering Techniques for Analyzing User Engagement Patterns in Twitter Data. Computers, 12(6), 124. https://doi.org/10.3390/computers12060124
[15]Pratibha, G., Hegde, N. P. (2022). HR Analytics: Early Prediction of Employee Attrition using KPCA and Adaptive K-means based Logistic Regression. 2022 Second International Conference on Interdisciplinary Cyber Physical Systems (ICPS), Chennai, India, 2022, pp. 11-16. https://doi.org/10.1109/ICPS55917.2022.00010
[16]Sarkar, S., Ejaz, N., Maiti, J. et al. (2022). An integrated approach using growing self-organizing map-based genetic K-means clustering and tolerance rough set in occupational risk analysis. Neural Comput & Applic, 34, 9661–9687. https://doi.org/10.1007/s00521-022-06956-5
[17]Ybañez, A., Ancheta, R., Evangelista, S. S., Aro, J. L., Maturan, F., Atibing, N. M., Ocampo, L. (2023). How can we use machine learning for characterizing organizational identification—a study using clustering with picture fuzzy datasets. International Journal of Information Management Data Insights, 3(1), 100157. https://doi.org/10.1016/j.jjimei.2023.100157
[18]Kahn, W. A. (1990). Psychological conditions of personal engagement and disengagement at work. Academy of management journal, 33(4), 692-724.
[19]Agility PR. (2024). Employee engagement trends to watch: New research recaps the state of engagement last year and identifies 4 key drivers for 2024. Retrieved from https://www.agilitypr.com
[20]Edenred Benefits. (2024). The Complete Guide to Improving Employee Engagement in 2024. Retrieved from Edenred Benefits
[21]CIPD. (2021). Employee engagement: an evidence review. Retrieved from CIPD
[22]Eniola, O. E. (2022). Employee Engagement Outlooks in the Era of COVID-19: Implications for Human Resource Management. International Journal of Human Resource Studies, 12(1), 7191-7191.
[23]Forrester. (2023). Employee Experience In 2024: A Turbulent (But Not Bleak) Year. Retrieved from Forrester
[24]Stein, D., Hobson, N., Jachimowicz, J. M., Whillans, A. (2021). How companies can improve employee engagement right now. Harvard Business Review, 1-10.
[25]Schaufeli, W. B., Bakker, A. B. (2004). Job demands, job resources, and their relationship with burnout and engagement: A multi-sample study. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior, 25(3), 293-315.
[26]Perrin, T. (2008). Closing the Engagement Gap. A Road Map for Driving Superior Business Performance, Global Workforce Study.
[27]Bakker, A. B., Demerouti, E., Verbeke, W. (2004). Using the job demands-resources model to predict burnout and performance. Human Resource Management: Published in Cooperation with the School of Business Administration, The University of Michigan and in alliance with the Society of Human Resources Management, 43(1), 83-104.
[28]Albrecht, S. L., Green, C. R., Marty, A. (2021). Meaningful Work, Job Resources, and Employee Engagement. Sustainability, 13(7), 4045. https://doi.org/10.3390/su13074045
[29]Bishop, C. M. (2006). Pattern recognition and machine learning. Springer google scholar, 2, 1122-1128.
[30]Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT Press.
[31]Sutton, R. S., Barto, A. G. (2018). Reinforcement learning: An introduction. MIT Press.
[32]Brynjolfsson, E., Mcafee, A. N. D. R. E. W. (2017). Artificial intelligence, for real. Harvard Business Review, 1, 1-31.
[33]Domingos, P. (2015). The master algorithm: How the quest for the ultimate learning machine will remake our world. Basic Books.
[34]O’neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.
[35]Christianto, K., Fendyanto, F., Bernanda, D. Y., Andry, J. F., Lee, F. S. (2023, November). Employee’s satisfaction index analysis and prediction using k-means clustering, decision tree, and association rules algorithm. In AIP Conference Proceedings (Vol. 2693, No. 1). AIP Publishing.
[36]Niemczynowicz, A., Kycia, R. A. (2023). The analysis of engagement at the workplace of Generation Z—machine learning in management. Managing Generation Z, 83.
[37]Gede, D. U., Huluka, A. T. (2024). Effects of employee engagement on organizational performance: case of public universities in Ethiopia. Futur Bus J, 10, 32. https://doi.org/10.1186/s43093-024-00315-7
[38]Fan, C. Y., Fan, P. S., Chan, T. Y., Chang, S. H. (2012). Using hybrid data mining and machine learning clustering analysis to predict the turnover rate for technology professionals. Expert Systems with Applications, 39(10), 8844-8851.
[39]Garg, S., Sinha, S., Kar, A. K., Mani, M. (2022). A review of machine learning applications in human resource management. International Journal of Productivity and Performance Management, 71(5), 1590-1610.
[40]Zhang, L., Li, H. (2023). The promotion of the concept of sustainable development to the reform of enterprise human resource management. International Journal of Knowledge-Based Development, 13(2-4), 344-362.
[41]Binanto, I., Tumanggor, A. (2024). Comparison of the K-Means method with and without Principal Component Analysis (PCA) in predicting employee resignation. In E3S Web of Conferences (Vol. 475, p. 02009). EDP Sciences.
[42]Setianto, S. K., Jatikusumo, D. (2020). Employee Turnover Analysis Using Comparison of Decision Tree and Naive Bayes Prediction Algorithms on K-Means Clustering Algorithms at PT. AT. Jurnal Mantik, 4(3), 1573-1581.