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The Role of Neuroscience in Talent Management (Case Study: Iranian Oil Industry)
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Alireza Koushkie1   , Ali Farjami2 , Kambiz Banihashemi3 , Ali Khadem4 |
1- Associate Professor, Department of Public Administration, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran , koushkie@atu.ac.ir
2- PhD Student in Public Administration - Organizational Behavior, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.
3- Medical Instructor, Department of Sport Injuries and Corrective Exercises, Faculty of Physical education and sport science, Allameh Tabataba’i University, Tehran, Iran.
4- Assistant Professor, Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran |
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Abstract: (957 Views) |
The present study aimed to investigate the feasibility of employing neuroscience methods, specifically electroencephalography (EEG) and machine learning, to identify individuals with high potential for transformational leadership. The research included 61 managers and supervisors from the oil industry employed in Tehran. Data were collected using the Multifactor Leadership Questionnaire (MLQ) and EEG recordings. Following the acquisition of neural signals and necessary preprocessing steps, power-related features across different frequency bands were computed. Machine learning techniques, particularly the Support Vector Machine (SVM) algorithm, were then applied to detect patterns associated with high transformational leadership potential. The results demonstrated that individuals with high transformational leadership capabilities could be distinguished from others with an accuracy of 96.36%. These findings suggest that neuroscience methods combined with machine learning can serve as powerful tools for identifying and recruiting transformational leadership talents. Given the critical role of transformational leadership in organizational success, integrating these approaches may enhance organizational performance and efficiency. It is recommended that such tools be implemented experimentally alongside traditional assessment methods within talent management systems to optimize leadership identification and recruitment processes.
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| Keywords: Talent management, organizational neuroscience, transformational leadership, (EEG/qEEG), machine learning. |
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Full-Text [PDF 743 kb]
(257 Downloads)
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Type of Study: Research |
Subject:
Management
Received: 2025/02/22 | Accepted: 2025/05/19 | Published: 2026/04/30
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