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Title

Considering author sequence in all-author co-citation analysis

AuthorsBu, Yi; Wang, Binglu; Chinchilla-Rodríguez, Zaida CSIC ORCID CVN ; Sugimoto, Cassidy R.; Huang, Yong; Huang, Win-bin
KeywordsAuthor co-citation analysis
Co-citation analysis
Citation analysis
Scientometrics
Mapping knowledge domains
Issue Date2020
PublisherElsevier
CitationInformation Processing and Management 57(6): 102300 (2020)
AbstractAuthor co-citation analysis (ACA) is a commonly used method to map knowledge domains and depict scientific intellectual structures. Although all authors’ information has been considered in previous studies, ACA does not distinguish credits of different collaborators within a team. Authors’ sequence in a publication illustrates their contributions and specialty of research, which offers more information as inputs of ACA. This paper considers author sequence in ACA and proposes a sequence-based ACA method. By assigning various weight values to authors with different sequences, this proposed method considers distinct contributions of co-authors influencing the effect of ACA. Extra weight is given to corresponding authors, beyond their sequence, to acknowledge their additional contributions. Results of the empirical study based on the data from the field of Library and Information Science show many details on the visualization maps of the proposed methods, such as the number of sub-fields, the position of sub-fields, the position of authors, and clarity and interpretability of visualization maps. Meanwhile, the current paper proposes a novel framework of evaluating knowledge domain maps with both quantitative and qualitative facets.
URIhttp://hdl.handle.net/10261/220939
DOIhttps://doi.org/10.1016/j.ipm.2020.102300
ISSN0306-4573
Appears in Collections:(CCHS-IPP) Artículos
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