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The taxonomy of research collaboration in science and technology: evidence from mechanical research through probabilistic clustering analysis

Author

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  • Seongkyoon Jeong

    (Korea Institute of Machinery and Materials (KIMM))

  • Jae Young Choi

    (Korea Institute for Industrial Economics and Trade (KIET) 66)

Abstract

This paper suggests an empirical framework to classify research collaboration activities with developed indicators that carry on a previous theoretical framework (Wagner [Science and Technology Policy for Development, Dialogues at the Interface, 2006]; Wagner et al. [Linking effectively: Learning lessons from successful collaboration in science and technology. DB-345-OSTP, 2002]) by employing the Gaussian mixture model, an advanced probabilistic clustering analysis. By further exploring the method upon a profound evidence-based reflection of actual phenomena, this paper also proposes an exploratory analysis to manage and evaluate research projects upon their differentiated classification in a preceding perspective of research collaboration and R&D management. In addition, the results show that international collaboration tends to be associated with more evenly committed collaboration, and that collaboration featuring a higher degree of funding or dispersed commitments generally results in larger outcomes than research clustered on the opposite side of the framework.

Suggested Citation

  • Seongkyoon Jeong & Jae Young Choi, 2012. "The taxonomy of research collaboration in science and technology: evidence from mechanical research through probabilistic clustering analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 719-735, June.
    • Handle: RePEc:spr:scient:v:91:y:2012:i:3:d:10.1007_s11192-012-0686-9
    DOI: 10.1007/s11192-012-0686-9
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    References listed on IDEAS

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    Cited by:

    1. Ping Zhou & Huibao Tian, 2014. "Funded collaboration research in mathematics in China," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(3), pages 695-715, June.
    2. Wildgaard, Lorna, 2016. "A critical cluster analysis of 44 indicators of author-level performance," Journal of Informetrics, Elsevier, vol. 10(4), pages 1055-1078.

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    More about this item

    Keywords

    Research collaboration; Research and development strategy; Clustering; Gaussian mixture;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

    Statistics

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