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가상현실 기반 업무공간 융복합 분야 연구 동향 분석: 패스파인더 네트워크와 병렬 최근접 이웃 클러스터링 방법론 활용
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dc.contributor.author하재빈-
dc.contributor.author강주영-
dc.date.issued2022-06-
dc.identifier.issn1229-8476-
dc.identifier.urihttps://aurora.ajou.ac.kr/handle/2018.oak/35621-
dc.description.abstractPurpose Due to the COVID-19 pandemic, many companies are building virtual workplaces based on virtual reality technology. Through this study, we intend to identify the trend of convergence and convergence research between virtual reality technology and work space, and suggest future promising fields based on this. <br>Design/methodology/approach For this purpose, 12,250 bibliographic data of research papers related to Virtual Reality (VR) and Workplace were collected from Scopus from 1982 to 2021. The bibliographic data of the collected papers were analyzed using Text Mining and Pathfinder Network, Parallel Neighbor Clustering, Nearest Neighbor Centrality, and Triangle Betweenness Centrality. Through this, the relationship between keywords by period was identified, and network analysis and visualization work were performed for virtual reality-based workplace research. <br>Findings Through this study, it is expected that the main keyword knowledge structure flow of virtual reality-based workplace convergence research can be identified, and the relationship between keywords can be identified to provide a major measure for designing directions in subsequent studies.-
dc.language.isoKor-
dc.publisher한국정보시스템학회-
dc.title가상현실 기반 업무공간 융복합 분야 연구 동향 분석: 패스파인더 네트워크와 병렬 최근접 이웃 클러스터링 방법론 활용-
dc.title.alternativeInvestigation of Trend in Virtual Reality-based Workplace Convergence Research: Using Pathfinder Network and Parallel Neighbor Clustering Methodology-
dc.typeArticle-
dc.citation.endPage43-
dc.citation.number2-
dc.citation.startPage19-
dc.citation.title정보시스템연구-
dc.citation.volume31-
dc.identifier.bibliographicCitation정보시스템연구, Vol.31 No.2, pp.19-43-
dc.subject.keywordVirtual Reality-
dc.subject.keywordWorkplace-
dc.subject.keywordText Mining-
dc.subject.keywordPathfinder Network(PFnet)-
dc.subject.keywordParallel Nearest Neighbor Clustering(PNNC)-
dc.subject.keywordNearest Neighbor Centrality-
dc.subject.keywordTriangle Betweenness Centrality-
dc.type.otherArticle-
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Kang, Ju Young강주영
Department of Business Intelligence
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