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

Seongkyoon Jeong and Jae Young Choi ()
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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

Scientometrics, 2012, vol. 91, issue 3, No 5, 719-735

Abstract: 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.

Keywords: Research collaboration; Research and development strategy; Clustering; Gaussian mixture; 62H30 (search for similar items in EconPapers)
JEL-codes: C38 (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11192-012-0686-9

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