Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 박현석 | - |
dc.date.accessioned | 2022-02-28T05:30:45Z | - |
dc.date.available | 2022-02-28T05:30:45Z | - |
dc.date.issued | 2020-06 | - |
dc.identifier.citation | JOURNAL OF KNOWLEDGE MANAGEMENT, v. 25, no. 2, page. 454-476 | en_US |
dc.identifier.issn | 1367-3270 | - |
dc.identifier.issn | 1758-7484 | - |
dc.identifier.uri | https://www.emerald.com/insight/content/doi/10.1108/JKM-01-2020-0030/full/html | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/168690 | - |
dc.description.abstract | Purpose The purpose of this paper is to propose a quantitative method for identifying multiple and hierarchical knowledge trajectories within a specific technological domain (TD). Design/methodology/approach The proposed method as a patent-based data-driven approach is basically based on patent classification systems and patent citation information. Specifically, the method first analyzes hierarchical structure under a specific TD based on patent co-classification and hierarchical relationships between patent classifications. Then, main paths for each sub-TD and overall-TD are generated by knowledge persistence-based main path approach. The all generated main paths at different level are integrated into the hierarchical main paths. Findings This paper conducted an empirical analysis by using Genome sequencing technology. The results show that the proposed method automatically identifies three sub-TDs, which are major functionalities in the TD, and generates the hierarchical main paths. The generated main paths show knowledge flows across different sub-TDs and the changing trends in dominant sub-TD over time. Originality/value To the best of the authors' knowledge, the proposed method is the first attempt to automatically generate multiple hierarchical main paths using patent data. The generated main paths objectively show not only knowledge trajectories for each sub-TD but also interactive knowledge flows among sub-TDs. Therefore, the method is definitely helpful to reduce manual work for TD decomposition and useful to understand major trajectories for TD. | en_US |
dc.description.sponsorship | This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (No. 2019S1A5A8036427) and supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (No. 2017R1A2B4012431). | en_US |
dc.language.iso | en | en_US |
dc.publisher | EMERALD GROUP PUBLISHING LTD | en_US |
dc.subject | Technological trajectories | en_US |
dc.subject | Technology decomposition | en_US |
dc.subject | Knowledge persistence | en_US |
dc.subject | Citation network | en_US |
dc.subject | Knowledge network | en_US |
dc.subject | Technological trends | en_US |
dc.title | Hierarchical main path analysis to identify decompositional multi-knowledge trajectories | en_US |
dc.type | Article | en_US |
dc.relation.no | 2 | - |
dc.relation.volume | 25 | - |
dc.identifier.doi | 10.1108/JKM-01-2020-0030 | - |
dc.relation.page | 454-476 | - |
dc.relation.journal | JOURNAL OF KNOWLEDGE MANAGEMENT | - |
dc.contributor.googleauthor | Yoon, Sejun | - |
dc.contributor.googleauthor | Mun, Changbae | - |
dc.contributor.googleauthor | Raghavan, Nagarajan | - |
dc.contributor.googleauthor | Hwang, Dongwook | - |
dc.contributor.googleauthor | Kim, Sohee | - |
dc.contributor.googleauthor | Park, Hyunseok | - |
dc.relation.code | 2020056189 | - |
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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