Analysis of Employee Competencies and Identification of High-Performance Employees Based on Individual Competency Using Self-Organizing Maps

This study examines employee competencies and identifies high-performance employees by analyzing individual competency metrics. Using a self-organizing map (SOM) with ۲۰۰۰ nodes, network training and evaluation were conducted through the Viscovery Profiler software. The assessment incorporated both an evaluation center and a ۳۶۰-degree performance evaluation, ensuring a comprehensive analysis of employee competencies. Hierarchical clustering (SOM Ward Clusters) determined segmentation, with clusters evaluated for key performance indicators such as personality fit, teamwork, creativity, decision-making, and leadership. Statistical tests, including the Kappa correlation coefficient and Pearson correlation, assessed the alignment between the two evaluation methods. While the Kappa test revealed no direct relationship between clusters from both methods, the Pearson correlation coefficient (۰.۴۸۱) indicated a significant positive relationship between competency scores and performance evaluation. These findings highlight the reliability of assessment center evaluations in predicting real-world performance, aiding organizations in talent management and employee development.

کلیدواژه ها:

Employee Competencies ، High-Performance Employees ، Self-Organizing Map ، Individual Competency