Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 박현석 | - |
dc.date.accessioned | 2019-05-29T01:15:49Z | - |
dc.date.available | 2019-05-29T01:15:49Z | - |
dc.date.issued | 2019-01 | - |
dc.identifier.citation | IEEE ACCESS, v. 7, Page. 8135-8150 | en_US |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://ieeexplore.ieee.org/document/8605517 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/106135 | - |
dc.description.abstract | The aim of this paper is to develop and test the metrics to quantitatively identify technological discontinuities in a knowledge network. We first analyzed the various conceptual frameworks for defining such discontinuities and arrived at four metrics. We tested the four metrics: Metric 1 and 2 are the normalized versions of previously existing metrics and Metric 3 and 4 are newly developed from the innovation theories, by using a patent set representative of the magnetic information storage domain The three representative patents associated with a well-known breakthrough technology in the domain, the giant magneto-resistance spin valve sensor, were selected based on qualitative studies, and the metrics were tested by how well each identifies the selected patents as top-ranked patents. The empirical results show that, first, global citation structure-based metrics clearly provide better performance in the identification of technological discontinuities than local citation count-based metrics which have not been shown as clearly before, second, non-continuous nodes on the major knowledge networks are not at all related to technological discontinuities, and, third, the two global metrics (Metric2: z-score of Persistence and Metric 4: z-score of Persistence times # of converging main paths) successfully identified the three selected patents as top-ranked patents out of over 30 000 patents. | en_US |
dc.description.sponsorship | This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning under Grant 2017R1A2B4012431 and in part by the SUTD-MIT International Design Center. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | en_US |
dc.subject | Knowledge discontinuity | en_US |
dc.subject | quantitative simulation | en_US |
dc.subject | knowledge networks | en_US |
dc.subject | patent citation networks | en_US |
dc.title | Quantitative Identification of Technological Discontinuities | en_US |
dc.type | Article | en_US |
dc.relation.volume | 7 | - |
dc.identifier.doi | 10.1109/ACCESS.2018.2890372 | - |
dc.relation.page | 8135-8150 | - |
dc.relation.journal | IEEE ACCESS | - |
dc.contributor.googleauthor | Park, Hyunseok | - |
dc.contributor.googleauthor | Magee, Christopher L. | - |
dc.relation.code | 2019036307 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF INFORMATION SYSTEMS | - |
dc.identifier.pid | hp | - |
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