Enhancing Leadership Management Through Fusion of Employee Information: A Unified Framework

Abstract

In the dynamic environments of today’s organizations, efficient leadership management becomes an essential requirement for driving organizational achievement and developing a culture of excellence. The core of this endeavor lies in the ability to use improved data analytics to facilitate interpreting and optimizing employee performance. In line with this, this research presents a fusion framework that integrates multiple pieces of information and extracts useful insights about employee performance which can leader’s decision-making process. In particular, we integrate hypothesis testing for handling outliers and anomalies in fused information, then we introduce Random Forest (RF) to perform forecasting and analysis of the fused information about employee performance through examining the complicated interactions between employee-related features such as work-life balance, job happiness, and education level. Using a case study of IBM employees, the proposed fusion approach explores multifaceted features persuading employee abrasion and workforce dynamics. Extensive experimentations in the real world demonstrate the effectiveness of the proposed fusion framework is at supporting evidence-based tactics for organizational performance and staff retention as well as refining leadership decision-making.