The taxonomy of research collaboration in science and technology: evidence from mechanical research through probabilistic clustering analysis
- Authors
- Jeong, Seongkyoon; Choi, Jae Young
- Issue Date
- Jun-2012
- Publisher
- SPRINGER
- Keywords
- Clustering; Gaussian mixture; Research and development strategy; Research collaboration
- Citation
- SCIENTOMETRICS, v.91, no.3, pp.719 - 735
- Indexed
- SCIE
SSCI
SCOPUS
- Journal Title
- SCIENTOMETRICS
- Volume
- 91
- Number
- 3
- Start Page
- 719
- End Page
- 735
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/165273
- DOI
- 10.1007/s11192-012-0686-9
- ISSN
- 0138-9130
- 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 RD 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.
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