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http://dx.doi.org/10.3743/KOSIM.2022.39.1.309

Introducing Keyword Bibliographic Coupling Analysis (KBCA) for Identifying the Intellectual Structure  

Lee, Jae Yun (명지대학교 인문대학 문헌정보학과)
Chung, EunKyung (이화여자대학교 사회과학대학 문헌정보학과)
Publication Information
Journal of the Korean Society for information Management / v.39, no.1, 2022 , pp. 309-330 More about this Journal
Abstract
Intellectual structure analysis, which quantitatively identifies the structure, characteristics, and sub-domains of fields, has rapidly increased in recent years. Analysis techniques traditionally used to conduct intellectual structure analysis research include bibliographic coupling analysis, co-citation analysis, co-occurrence analysis, and author bibliographic coupling analysis. This study proposes a novel intellectual structure analysis method, Keyword Bibliographic Coupling Analysis (KBCA). The Keyword Bibliographic Coupling Analysis (KBCA) is a variation of the author bibliographic coupling analysis, which targets keywords instead of authors. It calculates the number of references shared by two keywords to the degree of coupling between the two keywords. A set of 1,366 articles in the field of 'Open Data' searched in the Web of Science were collected using the proposed KBCA technique. A total of 63 keywords that appeared more than 7 times, extracted from 1,366 article sets, were selected as core keywords in the open data field. The intellectual structure presented by the KBCA technique with 63 key keywords identified the main areas of open government and open science and 10 sub-areas. On the other hand, the intellectual structure network of co-occurrence word analysis was found to be insufficient in the overall structure and detailed domain structure. This result can be considered because the KBCA sufficiently measures the relationship between keywords using the degree of bibliographic coupling.
Keywords
intellectual structure; Keyword Bibliographic Coupling Analysis; keyword; bibliographic coupling; open data;
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Times Cited By KSCI : 15  (Citation Analysis)
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