{"title":"Studying domain structure: a comparative analysis of bibliographic coupling analysis and co-citation analysis considering all authors","authors":"Yanhui Song, Lixin Lei, Lijuan Wu, Shiji Chen","doi":"10.1108/oir-12-2020-0540","DOIUrl":null,"url":null,"abstract":"PurposeThis paper focuses on the differences in domain intellectual structure discovery between author bibliographic coupling analysis (ABCA) and author co-citation analysis (ACA) considering all authors. The purpose of this study is to examine whether and in what ways these two all-author network approaches yield different results.Design/methodology/approachThe sample was collected from the database of Web of Science, including all articles published in Scientometrics and Journal of Informetrics from 2011 to 2020. First, 100 representative authors were selected from each set, and ABCA matrices and ACA matrices were constructed. Second, factor analysis was carried out on the matrices, to detect the intellectual structure of scientometrics and informetrics.FindingsThe intellectual structures identified by ABCA and ACA are similar overall, but the results differ somewhat when it comes to specific structures. The ABCA is more sensitive to some highly collaborative research teams and presents a clearer picture of current intellectual structures and trends while ACA seems to have some advantages in representing the more traditional and proven research topics in the field. The combined use of ABCA and ACA allows for a more comprehensive and specific intellectual structure of research fields.Originality/valueThis paper compares the performance of ABCA and ACA detecting the intellectual structure of the domain from the perspective of all authors, revealing the intellectual structure of scientometrics and informetrics comprehensively.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-12-2020-0540.","PeriodicalId":143302,"journal":{"name":"Online Inf. Rev.","volume":"196 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Online Inf. Rev.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/oir-12-2020-0540","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
PurposeThis paper focuses on the differences in domain intellectual structure discovery between author bibliographic coupling analysis (ABCA) and author co-citation analysis (ACA) considering all authors. The purpose of this study is to examine whether and in what ways these two all-author network approaches yield different results.Design/methodology/approachThe sample was collected from the database of Web of Science, including all articles published in Scientometrics and Journal of Informetrics from 2011 to 2020. First, 100 representative authors were selected from each set, and ABCA matrices and ACA matrices were constructed. Second, factor analysis was carried out on the matrices, to detect the intellectual structure of scientometrics and informetrics.FindingsThe intellectual structures identified by ABCA and ACA are similar overall, but the results differ somewhat when it comes to specific structures. The ABCA is more sensitive to some highly collaborative research teams and presents a clearer picture of current intellectual structures and trends while ACA seems to have some advantages in representing the more traditional and proven research topics in the field. The combined use of ABCA and ACA allows for a more comprehensive and specific intellectual structure of research fields.Originality/valueThis paper compares the performance of ABCA and ACA detecting the intellectual structure of the domain from the perspective of all authors, revealing the intellectual structure of scientometrics and informetrics comprehensively.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-12-2020-0540.
目的研究作者书目耦合分析(ABCA)和作者共被引分析(ACA)在全作者情况下领域智力结构发现的差异。本研究的目的是检验这两种全作者网络方法是否以及以何种方式产生不同的结果。样本从Web of Science数据库中收集,包括2011 - 2020年在《科学计量学》和《信息计量学期刊》上发表的所有文章。首先,从每个集合中选出100名具有代表性的作者,构建ABCA矩阵和ACA矩阵。其次,对矩阵进行因子分析,以检测科学计量学和信息计量学的智力结构。研究结果ABCA和ACA识别出的智力结构总体上是相似的,但在特定结构上结果有所不同。ABCA对一些高度合作的研究团队更敏感,并能更清晰地反映当前的知识结构和趋势,而ACA似乎在代表该领域更传统、更成熟的研究主题方面有一些优势。ABCA和ACA的结合使用允许更全面和具体的研究领域的知识结构。独创性/价值本文从所有作者的角度比较了ABCA和ACA检测领域知识结构的性能,全面揭示了科学计量学和信息计量学的知识结构。同行评议本文的同行评议历史可在:https://publons.com/publon/10.1108/OIR-12-2020-0540。