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Topical connections between the institutions within an organisation (institutional co-authorships, direct citation links and co-citations)

Author

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  • Lutz Bornmann

    (Administrative Headquarters of the Max Planck Society)

  • Loet Leydesdorff

    (University of Amsterdam)

Abstract

In recent years, numerous studies have been published which have used bibliometric data to look at collaborations in research. This study presents a proposal with which the topical connections of the institutions of an organization can be investigated through analysis of co-authorships, direct citation links, and co-citations. Based on various bibliometric data sets for an organization whose institutions are used as an example, this study illustrates the possibility of comparing the self-perception of institutions of this organization (co-authorships, direct citation links) with a view to (possible) mutual collaboration with the external perception (co-citations). This comparison is made firstly for the whole organization with the aid of network graphs; secondly, the comparison is presented in a table for a specific institution and its (possible) collaborations in the organization. Particularly the tabular breakdown of the links between the institutions can provide concrete indications of possible further collaboration between the institutions which have not yet manifested themselves in co-authorships.

Suggested Citation

  • Lutz Bornmann & Loet Leydesdorff, 2015. "Topical connections between the institutions within an organisation (institutional co-authorships, direct citation links and co-citations)," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 455-463, January.
    • Handle: RePEc:spr:scient:v:102:y:2015:i:1:d:10.1007_s11192-014-1425-1
    DOI: 10.1007/s11192-014-1425-1
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    References listed on IDEAS

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    Cited by:

    1. Bornmann, Lutz & Stefaner, Moritz & de Moya Anegón, Felix & Mutz, Rüdiger, 2016. "Excellence networks in science: A Web-based application based on Bayesian multilevel logistic regression (BMLR) for the identification of institutions collaborating successfully," Journal of Informetrics, Elsevier, vol. 10(1), pages 312-327.
    2. Rui Song & Hao Xu & Li Cai, 2019. "Academic Collaboration in Entrepreneurship Research from 2009 to 2018: A Multilevel Collaboration Network Analysis," Sustainability, MDPI, vol. 11(19), pages 1-17, September.
    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. Chen Yang & Tingting Liu & Xiaohong Chen & Yiyang Bian & Yuewen Liu, 2020. "HNRWalker: recommending academic collaborators with dynamic transition probabilities in heterogeneous networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 429-449, April.
    5. Blanca de-Miguel-Molina & Vicente Chirivella-González & Beatriz García-Ortega, 2016. "Corporate philanthropy and community involvement. Analysing companies from France, Germany, the Netherlands and Spain," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(6), pages 2741-2766, November.
    6. 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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