Abstract
Based on the assumption that the success of an organization is largely determined by the knowledge and skills of its employees, human resource (HR) departments invest considerable resources in the employee recruitment process with the aim of selecting the best, most suitable employees. Due to the high cost of the recruitment process along with its high rate of uncertainty, HR recruiters utilize a variety of methods and instruments to improve the efficiency and effectiveness of this process. Thus far, however, neurological methods, in which neurobiological signals from an examined person are analyzed, have not been utilized for this purpose. This study is the first to propose a neuro-based decision support system to classify cognitive functions into levels, whose target is to enrich the information and indications regarding the candidate along the employee recruitment processes. We first measured relevant functional and cognitive abilities of 142 adult participants using traditional computer-based assessment, which included a battery of four tests regarding executive functions and intelligence score, consistent with actual recruitment processes. Second, using electroencephalogram (EEG) technology, which is one of the dominant measurement tools in NeuroIS research, we collected the participants' brain signals by administering a resting state EEG (rsEEG) on each participant. Finally, using advanced machine and deep learning algorithms, we leveraged the collected rsEEG to classify participants' levels of executive functions and intelligence score. Our empirical analyses show encouraging results of up to 72.6% accuracy for the executive functions and up to 71.2% accuracy for the intelligence score. Therefore, this study lays the groundwork for a novel, generic (non-stimuli based) system that supports the current employee recruitment processes, that is based on psychological theories of assessing executive functions. The proposed decision support system could contribute to the development of additional medium of assessing employees remotely which is especially relevant in the current Covid-19 pandemic. While our method aims at classification rather than at explanation, our intriguing findings have the potential to push forward NeuroIS research and practice.
Original language | American English |
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Article number | 113930 |
Journal | Decision Support Systems |
Volume | 168 |
DOIs | |
State | Published - 1 May 2023 |
Keywords
- Deep learning
- Employee recruitment
- Machine learning
- NeuroIS
- Resting state EEG
All Science Journal Classification (ASJC) codes
- Information Systems and Management
- Information Systems
- Developmental and Educational Psychology
- Arts and Humanities (miscellaneous)
- Management Information Systems