Considering author sequence in all-author co-citation analysis

• Published in: Information processing & management 2020-11, Vol.57 (6), p.102300, Article 102300
• Main Authors: Bu, Yi, Wang, Binglu, Chinchilla-Rodríguez, Zaida, Sugimoto, Cassidy R., Huang, Yong, Huang, Win-bin
• Format: Article
• Language: English
• Subjects: Author co-citation analysis; Citation analysis; Citations; Co-citation analysis; Cocitation; Domains; Empirical analysis; Information science; Library and information science; Mapping knowledge domains; Scientometrics; Visualization; Weight; Writers
• ISSN: 0306-4573; 1873-5371
• DOI: 10.1016/j.ipm.2020.102300

•This paper showcases the usage of author sequence in publication bylines in mapping knowledge domains and in co-citation analysis.•This paper combines both quantitative and qualitative methods in evaluating knowledge domain maps.•This paper offers a new framework of evaluating knowledge domain maps.•This paper summarizes previous studies regarding author co-citation analysis in a good manner. Author 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.