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Comparing all-author and first-author co-citation analyses of information science

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  • Zhao, Dangzhi
  • Strotmann, Andreas

Abstract

Although it is generally understood that different citation counting methods can produce quite different author rankings, and although “optimal” author co-citation counting methods have been identified theoretically, studies that compare author co-citation counting methods in author co-citation analysis (ACA) studies are still rare. The present study applies strict all-author-based ACA to the Information Science (IS) field, in that all authors of all cited references in a classic IS dataset are counted, and in that even the diagonal values of the co-citation matrix are computed in their theoretically optimal form. Using Scopus instead of SSCI as the data source, we find that results from a theoretically optimal all-author ACA appear to be excellent in practice, too, although in a field like IS where co-authorship levels are relatively low, its advantages over classic first-author ACA appear considerably smaller than in the more highly collaborative ones targeted before. Nevertheless, we do find some differences between the two approaches, in that first-author ACA appears to favor theorists who presumably tend to work alone, while all-author ACA appears to paint a somewhat more recent picture of the field, and to pick out some collaborative author clusters.

Suggested Citation

  • Zhao, Dangzhi & Strotmann, Andreas, 2008. "Comparing all-author and first-author co-citation analyses of information science," Journal of Informetrics, Elsevier, vol. 2(3), pages 229-239.
  • Handle: RePEc:eee:infome:v:2:y:2008:i:3:p:229-239
    DOI: 10.1016/j.joi.2008.05.004
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    References listed on IDEAS

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    16. Kim, Ha Jin & Jeong, Yoo Kyung & Song, Min, 2016. "Content- and proximity-based author co-citation analysis using citation sentences," Journal of Informetrics, Elsevier, vol. 10(4), pages 954-966.
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    21. Bu, Yi & Ni, Shaokang & Huang, Win-bin, 2017. "Combining multiple scholarly relationships with author cocitation analysis: A preliminary exploration on improving knowledge domain mappings," Journal of Informetrics, Elsevier, vol. 11(3), pages 810-822.
    22. Wang, Feifei & Jia, Chenran & Wang, Xiaohan & Liu, Junwan & Xu, Shuo & Liu, Yang & Yang, Chenyuyan, 2019. "Exploring all-author tripartite citation networks: A case study of gene editing," Journal of Informetrics, Elsevier, vol. 13(3), pages 856-873.

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