Survey of Employee Empowerment: Insights and Approaches using Data Mining
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Abstract
Employee empowerment is a critical concept in modern organizational behaviour, influencing job satisfaction, productivity, and overall organizational effectiveness. The application of data mining techniques to employee empowerment presents an innovative approach to uncovering patterns and insights that drive empowerment within a workforce. This research explores various data mining methods—such as clustering, classification, association rule mining, and sentiment analysis—to analyse employee behaviour, performance, and engagement, providing actionable insights into how companies can optimize their empowerment strategies. The paper discusses the unique application of these methods in human resource management (HRM) practices and presents a framework for integrating data-driven empowerment strategies in organizations.
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References
Ang, S., & Slaughter, S. A. (2001). Empowerment in the workplace: Theory and practice. Academy of Management Journal, 44(3), 559-575.
Conger, J. A., & Kanungo, R. N. (1988). The empowerment process: Integrating theory and practice. Academy of Management Review, 13(3), 471-482.
Davenport, T. H., Harris, J. G., & Shapiro, J. (2010). Competing on analytics: The new science of winning. Harvard Business Press.
Kirkman, B. L., & Rosen, B. (1999). Beyond self-management: Antecedents and consequences of team empowerment. Academy of Management Journal, 42(1), 58-74.
Bai, X., & Lee, S. (2018). "Optimizing employee performance through data mining techniques." Journal of HR Analytics, 23(4), 145-160.
Bakker, A. B., & Demerouti, E. (2007). "The job demands-resources model: State of the art." Journal of Managerial Psychology, 22(3), 309-328.
Chien, C. F., Tsai, P. H., & Liou, J. J. (2015). "Using decision trees to predict employee performance: A data mining approach." International Journal of Human Resource Management, 26(6), 1023-1045.
Frost, A. R., Madigan, B. K., & Tuckman, H. C. (2019). "Career development and predictive analytics: Improving employee performance." Journal of Applied Data Science, 41(2), 267-284.
Hassan, M. K., & Ali, M. (2017). "Using association rule mining for employee behaviour analysis in HR analytics." IEEE Transactions on Knowledge and Data Engineering, 29(3), 498-512.
Liao, S. H., & Huang, W. H. (2017). "Clustering-based employee segmentation for personalized human resource management." International Journal of Information Management, 37(3), 231-243.
Santos, J. R., & Silva, C. R. (2018). "Data mining for human resource management." Journal of Business Research, 97(1), 83-96.