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The effect of web of science subject categories on clustering: the case of data-driven methods in business and economic sciences

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  • Berndt Jesenko

    (University of Graz)

  • Christian Schlögl

    (University of Graz)

Abstract

The primary goal of this article is to identify the research fronts on the application of data-driven methods in business and economics. For this purpose, the research literature of the business and economic sciences Subject Categories from the Web of Science is mapped using BibExcel and VOSviewer. Since the assignment to subject categories is done at the journal level and since a journal is often assigned to several subject categories in Web of Science, two mappings are performed: one without considering multiple assignments (broad view) and one considering only those (articles from) journals that have been assigned exclusively to the business and economic sciences subject categories and no others (narrow view). A further aim of this article is therefore to identify differences in the two mappings. Surprisingly, engineering sciences play a major role in the broad mapping, in addition to the economic sciences. In the narrow mapping, however, only the following clusters with a clear business-management focus emerge: (i) Data-driven methods in management in general and data-driven supply chain management in particular, (ii) Data-driven operations research analyses with different business administration/management focuses, (iii) Data-driven methods and processes in economics and finance, and (iv) Data-driven methods in Information Systems. One limitation of the narrow mapping is that many relevant documents are not covered since the journals in which they appear are assigned to multiple subject categories in WoS. The paper comes to the conclusion that the multiple assignments of subject categories in Web of Science may lead to massive changes in the results. Adjacent subject areas—in this specific case the application of data-driven methods in engineering and more mathematically oriented contributions in economics (econometrics) are considered in the broad mapping (not excluding subject categories from neighbouring disciplines) and are even over-represented compared to the core areas of business and economics. If a mapping should only consider the core aspects of particular research fields, it is shown in this use case that the exclusion of Web of Science-subject categories that do not belong to the core areas due to multiple assignments (narrow view), may be a valuable alternative. Finally, it depends on the reader to decide which mapping is more beneficial to them.

Suggested Citation

  • Berndt Jesenko & Christian Schlögl, 2021. "The effect of web of science subject categories on clustering: the case of data-driven methods in business and economic sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6785-6801, August.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:8:d:10.1007_s11192-021-04060-4
    DOI: 10.1007/s11192-021-04060-4
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