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All author cocitation analysis and first author cocitation analysis: A comparative empirical investigation

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  • Eom, Sean

Abstract

The majority of author cocitation analysis (ACA) have relied on the Institute for Scientific Information (ISI) citation databases. ISI convention allows only the retrieval of papers that cite works of which the author is first or sole author. Non-primary authors (authors whose name appear in second or later position) will not be counted when assembling a cocitation frequency matrix. Therefore, this has been a methodological issue in ACA study. This paper empirically examines the impact of the ISI convention on the results of ACA. Previous research has addressed and shed light on some parts of methodological issues, but failed to address issues such as to what extent the use of different approach has resulted in different outcomes in terms of actual intellectual structure of a given academic discipline. Using our data and cociation matrix generation systems, we compare the differences in the process and outcomes of using different cocitation matrices. Our study concludes that all author based ACA is better than first author based ACA to capture all influential researchers in a field. It also identifies more research subspecialties. Finally, all Author based ACA and first author based ACA produce little differences in stress values of MDS outputs.

Suggested Citation

  • Eom, Sean, 2008. "All author cocitation analysis and first author cocitation analysis: A comparative empirical investigation," Journal of Informetrics, Elsevier, vol. 2(1), pages 53-64.
    • Handle: RePEc:eee:infome:v:2:y:2008:i:1:p:53-64
    DOI: 10.1016/j.joi.2007.09.001
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    References listed on IDEAS

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    1. 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.
    2. Yang, Siluo & Wang, Feifei, 2015. "Visualizing information science: Author direct citation analysis in China and around the world," Journal of Informetrics, Elsevier, vol. 9(1), pages 208-225.
    3. Yi Bu & Binglu Wang & Win-bin Huang & Shangkun Che & Yong Huang, 2018. "Using the appearance of citations in full text on author co-citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 275-289, July.
    4. 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.
    5. Raasch, Christina & Lee, Viktor & Spaeth, Sebastian & Herstatt, Cornelius, 2013. "The rise and fall of interdisciplinary research: The case of open source innovation," Research Policy, Elsevier, vol. 42(5), pages 1138-1151.
    6. Xie, Qing & Zhang, Xinyuan & Song, Min, 2021. "A network embedding-based scholar assessment indicator considering four facets: Research topic, author credit allocation, field-normalized journal impact, and published time," Journal of Informetrics, Elsevier, vol. 15(4).
    7. Jeong, Yoo Kyung & Song, Min & Ding, Ying, 2014. "Content-based author co-citation analysis," Journal of Informetrics, Elsevier, vol. 8(1), pages 197-211.
    8. Yi Bu & Tian-yi Liu & Win-bin Huang, 2016. "MACA: a modified author co-citation analysis method combined with general descriptive metadata of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(1), pages 143-166, July.

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