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

Title
The taxonomy of research collaboration in science and technology: evidence from mechanical research through probabilistic clustering analysis
Author
최재영
Keywords
Research collaboration; Research and development strategy; Clustering; Gaussian mixture; INTERNATIONAL COLLABORATION; CO-AUTHORSHIPS; MODEL; DETERMINANTS; PROJECTS; QUALITY; IMPACTS
Issue Date
2012-06
Publisher
Springer Science + Business Media
Citation
SCIENTOMETRICS, JUN 2012, 91(3), p719-p735, 17p.
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.
URI
https://link.springer.com/article/10.1007/s11192-012-0686-9http://repository.hanyang.ac.kr/handle/20.500.11754/69846
ISSN
0138-9130
DOI
10.1007/s11192-012-0686-9
Appears in Collections:
GRADUATE SCHOOL OF TECHNOLOGY & INNOVATION MANAGEMENT[S](기술경영전문대학원) > TECHNOLOGY MANAGEMENT(기술경영학과) > Articles
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