Citation Export
DC Field | Value | Language |
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dc.contributor.author | Nam, Moonju | - |
dc.contributor.author | Ko, Jindeuk | - |
dc.contributor.author | Lee, Jooyeoun | - |
dc.date.issued | 2022-01-01 | - |
dc.identifier.uri | https://aurora.ajou.ac.kr/handle/2018.oak/36786 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85127612811&origin=inward | - |
dc.description.abstract | This study measures the R & D efficiency of 33 OECD countries using data envelopment analysis (DEA), and analyzes the effect of regulation on such R & D efficiency through quantile regression (QR). Gross expenditure on R & D and the total number of researchers are selected as the input variables in the DEA, and the total number of papers and triadic patent families corresponding to representative outputs in the field of science and technology are selected as the output variables. Three reguation indexes are used for QR analysis. Results show that regulations have a positive effect on the R & D efficiency of countries in the bottom 10%, and a negative effect on that of countries in the top 10%. Such findings suggest the need to change the government's regulatory intensity relative to each country's R & D efficiency level to increase national-level R & D efficiency. | - |
dc.language.iso | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.subject.mesh | Efficiency levels | - |
dc.subject.mesh | Input variables | - |
dc.subject.mesh | National level | - |
dc.subject.mesh | OECD countries | - |
dc.subject.mesh | Output variables | - |
dc.subject.mesh | Quantile regression | - |
dc.subject.mesh | R & D efficiency | - |
dc.subject.mesh | Regulation | - |
dc.subject.mesh | Science and Technology | - |
dc.subject.mesh | Triadic patents | - |
dc.title | Analysis of the Relationship Between Regulation and R & D Efficiency Using Quantile Regression | - |
dc.type | Conference | - |
dc.citation.conferenceDate | 2022.1.17. ~ 2022.1.20. | - |
dc.citation.conferenceName | 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022 | - |
dc.citation.edition | Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022 | - |
dc.citation.endPage | 63 | - |
dc.citation.startPage | 60 | - |
dc.citation.title | Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022 | - |
dc.identifier.bibliographicCitation | Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022, pp.60-63 | - |
dc.identifier.doi | 10.1109/bigcomp54360.2022.00022 | - |
dc.identifier.scopusid | 2-s2.0-85127612811 | - |
dc.identifier.url | http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9736461 | - |
dc.subject.keyword | data envelopment analysis | - |
dc.subject.keyword | quantile regression | - |
dc.subject.keyword | R & D efficiency | - |
dc.subject.keyword | regulation | - |
dc.type.other | Conference Paper | - |
dc.description.isoa | false | - |
dc.subject.subarea | Artificial Intelligence | - |
dc.subject.subarea | Computer Science Applications | - |
dc.subject.subarea | Computer Vision and Pattern Recognition | - |
dc.subject.subarea | Information Systems and Management | - |
dc.subject.subarea | Health Informatics | - |
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