There are no files associated with this item.
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.title | TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE | - |
dc.citation.volume | 180 | - |
dc.contributor.author | Lee, MyoungHoon | - |
dc.contributor.author | Kim, Suhyeon | - |
dc.contributor.author | Kim, Hangyeol | - |
dc.contributor.author | Lee, Junghye | - |
dc.date.accessioned | 2023-12-21T13:50:27Z | - |
dc.date.available | 2023-12-21T13:50:27Z | - |
dc.date.created | 2022-12-14 | - |
dc.date.issued | 2022-07 | - |
dc.description.abstract | To capture emerging technologies in the fast-changing technology market, use of information concerning new technology-based firms (NTBFs) is strongly encouraged, in addition to the information about the technology itself. Especially, NTBFs rapidly respond to technological change, and their investment information is a significant criterion of technology valuation. Therefore, this study proposes a new technology opportunity discovery (TOD) framework that exploits text mining by deep learning and a knowledge graph (KG) by using three data sources: technology, NTBF, and investor data. First, a technology-classification model was developed using technical text data acquired using Doc2vec and logistic regression, and then this model assigned highly-relevant technology fields to NTBFs using NTBFs' investor relation text data. Next, a KG that considers technology, NTBF, and NTBF's investor was constructed to represent their relations for TOD by using the results of previous steps. Lastly, considering inter-connectivities of such factors, a TOD index that measures the potential of technologies was proposed. The accuracy and validity of the methods were demonstrated empirically, and an evaluation of emerging technologies identified by the analysis was provided. Our framework will be of great significance as a useful alternative to provide new insights for emerging technologies in the industry and market. | - |
dc.identifier.bibliographicCitation | TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, v.180 | - |
dc.identifier.doi | 10.1016/j.techfore.2022.121718 | - |
dc.identifier.issn | 0040-1625 | - |
dc.identifier.scopusid | 2-s2.0-85129244815 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/60410 | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S004016252200244X | - |
dc.identifier.wosid | 000800612300002 | - |
dc.language | 영어 | - |
dc.publisher | Elsevier BV | - |
dc.title | Technology Opportunity Discovery using Deep Learning-based Text Mining and a Knowledge Graph | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Business; Regional & Urban Planning | - |
dc.relation.journalResearchArea | Business & Economics; Public Administration | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | ssci | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Technology opportunity discovery | - |
dc.subject.keywordAuthor | Text mining | - |
dc.subject.keywordAuthor | Doc2vec | - |
dc.subject.keywordAuthor | Knowledge graph | - |
dc.subject.keywordAuthor | Logistics regression | - |
dc.subject.keywordAuthor | Index | - |
dc.subject.keywordPlus | EMERGING TECHNOLOGIES | - |
dc.subject.keywordPlus | SPILLOVERS | - |
dc.subject.keywordPlus | PRODUCTS | - |
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Tel : 052-217-1404 / Email : scholarworks@unist.ac.kr
Copyright (c) 2023 by UNIST LIBRARY. All rights reserved.
ScholarWorks@UNIST was established as an OAK Project for the National Library of Korea.