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. © 2023 Elsevier B.V.
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.
ABSTRACT
User experience (UX) research has been critically impacted by the recent COVID-19 pandemic and the sanitary restrictions put in place. Observational or perceptual studies can be adapted remotely with participants using their own computer and internet access. However, studies based on the unconscious and automatic physiological states of participants use neurophysiological measurements that requires highly specific hardware. Electrodermal activity (EDA) or electrocardiogram (ECG) based studies are complex to transpose to a remote environment since researchers have no physical contact with the participants. To address this concern, our research team previously developed a remote instrument that can collect the EDA and the ECG activity at the participants' location through a moderated self-installation of sensors. We developed a protocol for remote physiological data collection that we pilot tested with 2 UX studies. After each study, we administered an open-ended questionnaire regarding the full experience of remote data-collection from both the moderator's and the participant's side. We collected 92 responses total which provided us with a rich dataset that we analyzed through a thematic analysis lens in order to uncover the success factors of remote psychophysiological data collection. Operational support, moderator-participant collaboration, individual characteristics, and technological capabilities clearly emerged as drivers for success. This project aimed to develop a rigorous and contextually relevant protocol for remote physiological data collection in UX evaluations, train our research team on the developed protocol, and provide guidance regarding remote physiological data collection activities.
ABSTRACT
As a consequence of lockdowns due to the coronavirus disease (COVID-19) and the resulting restricted social mobility, several billion people worldwide have recently had to replace physical face-to-face communication with computer-mediated interaction. Notably, the adoption rates of videoconferencing increased significantly in 2020, predominantly because videoconferencing resembles face-to-face interaction. Tools such as Zoom, Microsoft Teams, and Cisco Webex are used by hundreds of millions of people today. Videoconferencing may bring benefits (e.g., saving of travel costs, preservation of environment). However, prolonged and inappropriate use of videoconferencing may also have an enormous stress potential. A new phenomenon and term emerged, Zoom fatigue, a synonym for videoconference fatigue. This paper develops a definition for Zoom fatigue and presents a conceptual framework that explores the major root causes of videoconferencing fatigue and stress. The development of the framework draws upon media naturalness theory and its underlying theorizing is based on research published across various scientific fields, including the disciplines of both behavioral science and neuroscience. Based on this theoretical foundation, hypotheses are outlined. Moreover, implications for research and practice are discussed.