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Knowledge flow out of the domain of information science: a bibliometric and citation analysis study

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  • Ming-yueh Tsay

    (National Chengchi University)

Abstract

Through the bibliometric approach and citation analysis, this study analyzes the disciplines and subjects of literature citing important information science journals during the period from 1998 to 2010. The four information science journals under study include Journal of the American Society for Information Science and Technology, Information Processing and Management, the Journal of Information Science, and the Journal of Documentation. The Ulrich’s Periodical Directory, Library of Congress Subject Headings retrieved from WorldCat and the LISA database were used to identify the main classes, subclasses, and subjects of citing journals. We also indentify and analyze the highly citing journals, the main classes and subclasses of citing journals for the four journals under study as well as highly cited subjects in journals related to library and information science. Overall, the knowledge flow out of the domain of information science mainly includes information science itself, and also science and technology at a lower percentage. Moreover, there are minor outputs for various other subjects. The comparison of knowledge flow into and out of the domain of information science reveals the main knowledge flow is into information science itself. This comparison also reveals significant knowledge flow from computer science to information science.

Suggested Citation

  • Ming-yueh Tsay, 2015. "Knowledge flow out of the domain of information science: a bibliometric and citation analysis study," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 487-502, January.
    • Handle: RePEc:spr:scient:v:102:y:2015:i:1:d:10.1007_s11192-014-1339-y
    DOI: 10.1007/s11192-014-1339-y
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    References listed on IDEAS

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    1. Ming-yueh Tsay, 2011. "A bibliometric analysis and comparison on three information science journals: JASIST, IPM, JOD, 1998–2008," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(2), pages 591-606, November.
    2. Bluma C. Peritz & Judit Bar-Ilan, 2002. "The sources used by bibliometrics-scientometrics as reflected in references," Scientometrics, Springer;Akadémiai Kiadó, vol. 54(2), pages 269-284, June.
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    Cited by:

    1. Yue, Zenghui & Xu, Haiyun & Yuan, Guoting & Pang, Hongshen, 2019. "Modeling study of knowledge diffusion in scientific collaboration networks based on differential dynamics: A case study in graphene field," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 375-391.
    2. Lyu, Haihua & Bu, Yi & Zhao, Zhenyue & Zhang, Jiarong & Li, Jiang, 2022. "Citation bias in measuring knowledge flow: Evidence from the web of science at the discipline level," Journal of Informetrics, Elsevier, vol. 16(4).
    3. Meijun Liu & Xiao Hu & Jiang Li, 2018. "Knowledge flow in China’s humanities and social sciences," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(2), pages 607-626, March.
    4. Guijie Zhang & Luning Liu & Fangfang Wei, 2019. "Key nodes mining in the inventor–author knowledge diffusion network," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 721-735, March.
    5. Ruimin Ma & Erjia Yan, 2016. "Uncovering inter-specialty knowledge communication using author citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 839-854, November.

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