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Caveats for the use of Web of Science Core Collection in old literature retrieval and historical bibliometric analysis

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  • Liu, Weishu

Abstract

By using publications from Web of Science Core Collection (WoSCC), Fosso Wamba and his colleagues published an interesting and comprehensive paper in Technological Forecasting and Social Change to explore the structure and dynamics of artificial intelligence (AI) scholarship. Data demonstrated in Fosso Wamba's study implied that the year 1991 seemed to be a “watershed” of AI research. This research note tried to uncover the 1991 phenomenon from the perspective of database limitation by probing the limitations of search in abstract/author keywords/keywords plus fields of WoSCC empirically. The low availability rates of abstract/author keywords/keywords plus information in WoSCC found in this study can explain the “watershed” phenomenon of AI scholarship in 1991 to a large extent. The findings in this study are also applicable to other WoSCC-based studies focusing on fields from natural science, social sciences, to arts and humanities. Besides, some other caveats for the use of WoSCC in old literature retrieval and historical bibliometric analysis were also mentioned in the discussion section. This research note complements Fosso Wamba and his colleagues’ study and also helps avoid improper interpretation in the use of WoSCC in old literature retrieval and historical bibliometric analysis.

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  • 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).
    • Handle: RePEc:eee:tefoso:v:172:y:2021:i:c:s0040162521004558
    DOI: 10.1016/j.techfore.2021.121023
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    References listed on IDEAS

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    1. Weishu Liu, 2019. "The data source of this study is Web of Science Core Collection? Not enough," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1815-1824, December.
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    6. Fosso Wamba, Samuel & Bawack, Ransome Epie & Guthrie, Cameron & Queiroz, Maciel M. & Carillo, Kevin Daniel André, 2021. "Are we preparing for a good AI society? A bibliometric review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
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    8. Li Tang & Guangyuan Hu & Weishu Liu, 2017. "Funding acknowledgment analysis: Queries and caveats," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(3), pages 790-794, March.
    9. Junwen Zhu & Weishu Liu, 2020. "A tale of two databases: the use of Web of Science and Scopus in academic papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 321-335, April.
    10. Adèle Paul-Hus & Nadine Desrochers & Rodrigo Costas, 2016. "Characterization, description, and considerations for the use of funding acknowledgement data in Web of Science," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(1), pages 167-182, July.
    11. Liu, Weishu & Hu, Guangyuan & Tang, Li, 2018. "Missing author address information in Web of Science—An explorative study," Journal of Informetrics, Elsevier, vol. 12(3), pages 985-997.
    12. Petr Heneberg, 2013. "Lifting the fog of scientometric research artifacts: On the scientometric analysis of environmental tobacco smoke research," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(2), pages 334-344, February.
    13. Weishu Liu & Li Tang & Guangyuan Hu, 2020. "Funding information in Web of Science: an updated overview," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1509-1524, March.
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    6. Frimpong J. Alex & Gangfeng Tan & Philip K. Agyeman & Prince O. Ansah & Isaac O. Olayode & Jamshid V. Fayzullayevich & Shuang Liang, 2022. "Bibliometric Network Analysis of Trends in Cyclone Separator Research: Research Gaps and Future Direction," Sustainability, MDPI, vol. 14(22), pages 1-22, November.
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