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Health warning: might contain multiple personalities—the problem of homonyms in Thomson Reuters Essential Science Indicators

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  • Anne-Wil Harzing

    (Middlesex University)

Abstract

Author name ambiguity is a crucial problem in any type of bibliometric analysis. It arises when several authors share the same name, but also when one author expresses their name in different ways. This article focuses on the former, also called the “namesake” problem. In particular, we assess the extent to which this compromises the Thomson Reuters Essential Science Indicators ranking of the top 1 % most cited authors worldwide. We show that three demographic characteristics that should be unrelated to research productivity—name origin, uniqueness of one’s family name, and the number of initials used in publishing—in fact have a very strong influence on it. In contrast to what could be expected from Web of Science publication data, researchers with Asian names—and in particular Chinese and Korean names—appear to be far more productive than researchers with Western names. Furthermore, for any country, academics with common names and fewer initials also appear to be more productive than their more uniquely named counterparts. However, this appearance of high productivity is caused purely by the fact that these “academic superstars” are in fact composites of many individual academics with the same name. We thus argue that it is high time that Thomson Reuters starts taking name disambiguation in general and non-Anglophone names in particular more seriously.

Suggested Citation

  • Anne-Wil Harzing, 2015. "Health warning: might contain multiple personalities—the problem of homonyms in Thomson Reuters Essential Science Indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 2259-2270, December.
    • Handle: RePEc:spr:scient:v:105:y:2015:i:3:d:10.1007_s11192-015-1699-y
    DOI: 10.1007/s11192-015-1699-y
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    References listed on IDEAS

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    Cited by:

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    6. Jinseok Kim & Jenna Kim & Jason Owen‐Smith, 2021. "Ethnicity‐based name partitioning for author name disambiguation using supervised machine learning," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(8), pages 979-994, August.
    7. Liu, Weishu, 2021. "Caveats for the use of Web of Science Core Collection in old literature retrieval and historical bibliometric analysis," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
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