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

Authors:
Seongkyoon Jeong;Jae Young Choi
Affiliations:
Department of Research and Development Policy, Korea Institute of Machinery and Materials (KIMM), Daejeon, Korea 305-343;Center for Growth Engine Industries, Korea Institute for Industrial Economics and Trade (KIET) 66, Seoul, Korea
Venue:
Scientometrics
Year:
2012

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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.