The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
A Machine Learning Approach to Identify Features to Determine “Cost to the Company” of Management Graduates
|
|
Author(s): Manohar Kapse (Jaipuria Institute of Management, Indore, India), Vinod Sharma (Symbiosis Centre for Management and Human Resource, Symbiosis International University, Pune, India), Yogesh Mahajan (Symbiosis Centre for Management and Human Resource, Symbiosis International University, Pune, India)and Jeanne Poulose (School of Business and Management, Christ University, Delhi NCR, India)
Copyright: 2027
Pages: 21
Source title:
Encyclopedia of Modern Artificial Intelligence
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Founding Editor-in-Chief, Information Resources Management Journal (IRMJ), USA)
DOI: 10.4018/407571
Purchase
|
Abstract
This study presents a novel machine learning approach aimed at identifying features critical for determining the 'Cost to the Company' (CTC) of management graduates. With the increasing complexity of job markets and the diversity of roles undertaken by management professionals, accurately assessing the CTC becomes paramount for both employers and employees. Traditional methods often rely on simplistic metrics or subjective assessments, leading to potential inaccuracies and biases. In contrast, this approach leverages advanced machine learning techniques to analyze a comprehensive dataset encompassing various factors such as academic background, skills, internships, industry exposure, and extracurricular activities. Through feature selection algorithms and predictive modeling, the authors aim to elucidate the most influential factors contributing to CTC determination. By identifying these critical features, the methodology not only enhances the precision of CTC estimation but also provides valuable insights for career planning and talent management strategies within the management domain.
Related Content
|
Frederic Andres.
© 2027.
14 pages.
|
|
Kalsoom Safdar, Khairul Najmy Abdul Rani, Mohd Aminudin Jamlos, Siti Julia Rosli, Muhammad Usman Younus, Zanab Safdar.
© 2027.
27 pages.
|
|
Bani Adam, Binastya Anggara Sekti, Muhammad Adi Zacky Zahran.
© 2027.
24 pages.
|
|
Swetha Margaret T. A., Renuka Devi D..
© 2027.
31 pages.
|
|
Maurice Saluschke, Michael Schulz.
© 2027.
30 pages.
|
|
Mirjam Sepesy Maučec, Gregor Donaj.
© 2027.
16 pages.
|
|
Jorge A. Ruiz-Vanoye, Ocotlan Diaz-Parra, Ricardo A. Barrera-Cámara, Alejandro Fuentes-Penna, Francisco R. Trejo-Macotela, Jaime Aguilar-Ortiz, Eric Simancas-Acevedo.
© 2027.
21 pages.
|
|
|