Considering author sequence in all-author co-citation analysis
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...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/220939 |
| Acceso en línea: | http://hdl.handle.net/10261/220939 |
| Access Level: | acceso abierto |
| Palabra clave: | Author co-citation analysis Co-citation analysis Citation analysis Scientometrics Mapping knowledge domains |
| Sumario: | 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. |
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