The taxonomy of research collaboration in science and technology: evidence from mechanical research through probabilistic clustering analysis
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jeong, Seongkyoon | - |
dc.contributor.author | Choi, Jae Young | - |
dc.date.accessioned | 2022-07-16T14:48:01Z | - |
dc.date.available | 2022-07-16T14:48:01Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2012-06 | - |
dc.identifier.issn | 0138-9130 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/165273 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.title | The taxonomy of research collaboration in science and technology: evidence from mechanical research through probabilistic clustering analysis | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Choi, Jae Young | - |
dc.identifier.doi | 10.1007/s11192-012-0686-9 | - |
dc.identifier.scopusid | 2-s2.0-84862803010 | - |
dc.identifier.wosid | 000303533600005 | - |
dc.identifier.bibliographicCitation | SCIENTOMETRICS, v.91, no.3, pp.719 - 735 | - |
dc.relation.isPartOf | SCIENTOMETRICS | - |
dc.citation.title | SCIENTOMETRICS | - |
dc.citation.volume | 91 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 719 | - |
dc.citation.endPage | 735 | - |
dc.type.rims | ART | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | ssci | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Information Science & Library Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Information Science & Library Science | - |
dc.subject.keywordAuthor | Clustering | - |
dc.subject.keywordAuthor | Gaussian mixture | - |
dc.subject.keywordAuthor | Research and development strategy | - |
dc.subject.keywordAuthor | Research collaboration | - |
dc.identifier.url | https://link.springer.com/article/10.1007/s11192-012-0686-9 | - |
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