A MACHINE LEARNING APPROACH TO PREDICTING MANAGERIAL SKILLS AND LEADERSHIP PERFORMANCE
By
Anju Khandelwal1 and Avanish Kumar2
1Balaji Institute of Management and Human Resource Development, Sri Balaji University, Pune, Maharashtra, India- 411033
2Department of Math Sciences and Computer Application, Bundelkhand University, Jhansi, Uttar Pradesh, India-284128
Email: dranju20khandelwal@gmail.com, dravanishkumar@gmail.com
(Received: August 14, 2024; In format: September 23, 2024; Revised: December 27, 2024; Accepted: April 29, 2025)
DOI: https://doi.org/10.58250/jnanabha.2025.55104
Abstract
The evaluation of managerial skills and leadership potential of employees is a significant challenge for Human Resource (HR) departments. Traditional methods for selecting and assessing managerial-level employees, which often rely on subjective judgment and may not fully align with organizational needs, present numerous difficulties for recruiters. While knowledge and skills are important parameters, this research explores the application of machine learning techniques to provide a more objective evaluation of managerial skills, with the goal of enhancing leadership performance. Traditional methods of skill assessment, such as performance reviews and psychometric tests, are often limited by subjectivity and scalability issues. This study demonstrates the potential of machine learning to offer a more objective, data-driven approach to skill evaluation. Machine learning algorithms were used to develop models predicting key managerial skills, including leadership, communication, and strategic thinking. The results indicate that machine learning models can effectively predict managerial skill parameters with high accuracy, outperforming traditional evaluation methods. The study also emphasizes the importance of feature selection, revealing that specific performance indicators and behavioral traits are strong predictors of managerial effectiveness. Incorporating machine learning into the skill evaluation process can provide more reliable and actionable insights, aiding in the development of tailored training programs and facilitating betterinformed managerial promotion decisions. This research contributes to the expanding field of data-driven human resource management by offering a practical approach for organizations seeking to strengthen their leadership capabilities. Future research should focus on redefining these models and exploring their application across diverse cultural with organizational contexts.
2020 Mathematical Sciences Classification: 90-XX, 90C23, 90C31.
Keywords and Phrases: Thermal radiation, Maxwell fluid, Cattaneo-Christov dual diffusion, Exponentially stretching sheet, Magnetohydrodynamics.