ABSTRACT
This paper combines available NLP technologies for Serbian languages and traditional data science methods in order to analyze collected dataset on the news headlines related to the COVID-19 pandemics. As an addition to NLP technologies for the Serbian language, a specialized database was created in an attempt to enhance the research within the field. Within the paper, the database was exploratory analyzed, and perspectives of the work with the data were thoroughly explored. © 2023 IEEE.
ABSTRACT
Since its inception six years ago, the Innovation and Entrepreneurship Theory and Practice minitrack of the Hawaiian International Conference on System Science (HICSS) has focused on the intersection of knowledge management and system science with innovation and entrepreneurship. Whereas the context of traditional knowledge management research is established organizations, this minitrack features the works of researchers who apply system science methods to knowledge management in the context of innovation and entrepreneurship, which is distinguished by uncertainty and resource constraints. The minitrack, part of the HICSS Knowledge Innovation and Entrepreneurial Systems track, continues to attract quality manuscripts relevant to the researcher, instructor, and practitioner, despite this being the second year that the coronavirus pandemic has forced the conference format to be virtual. This year's eight minitrack papers investigate environments for innovation including accelerators, incubators, and makerspaces, the digitalization of innovation, and sustainability. © 2022 IEEE Computer Society. All rights reserved.
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.