چكيده به لاتين
Competency is a cognitive (such as knowledge and skills), emotional (like attitudes and values), behavioral and motivational feature which enables a person to succeed in a particular job or position. On the other hand, the success and failure of an organization is broadly dependent on the quality of its leaders. The basic difference between a successful and unsuccessful organization is defined in terms of leadership. Therefore, the groups and organizations have considered leadership to make themselves more effective. The potential commonality between neuroscience and organizational leadership is a direct result of developments that have taken place in neuroscience over the past decade. Finally, in comparison with traditional social and behavioral approaches, neuroscience approaches to organizational management and leadership can determine and predict psychological and behavioral states through the study of brain activity. Neuroleadership is an emerging field in organizational research whose focus is on the application of neuroscience achievements in areas such as leadership development and management, research on managers, change in management, counseling, education and training of individuals. In this research, it has been tried to predict the level of competency of IT managers in terms of Neuroleadership and using their brain signals. To do so, firstly, valid models in the Neuroleadership area were investigated and then based on the well-known SCARF model the level of competency of 20 IT managers was assessed and compared by using the NLI-SCARF standard questionnaire with 360-degree feedback method. Then, by implementing the EEG, the brain signals were recorded at rest (eye-closed) from this group of managers. Then the brain signals of managers were analyzed and categorized based on the power spectrum density of frequency by using four machine learning algorithms of classification including linear discriminant analysis, nive bayes, support vector machine and k-nearest neighbor. The results showed that the support vector machine algorithm with a resolution rate of 69.44% was more capable of classifying the brain signals of managers than other algorithms. In other words, the level of competence of IT managers based on their brain signals is predictable in terms of Neuroleadership, with an accuracy of 69.44%.
