IDEAS home Printed from https://ideas.repec.org/p/eti/dpaper/23024.html
   My bibliography  Save this paper

Identifying Technology Opportunity Using a Dual-attention Model and a Technology-market Concordance Matrix

Author

Listed:
  • MOTOHASHI Kazuyuki

Abstract

To understand the role of new technologies in innovation, it is crucial to develop a methodology that links technology and market information. Conventionally, the relationship between technology and the market has been analyzed using a technology-industry concordance matrix, but the granularity of market information is confined by industrial classification systems. In this study, we propose a new methodology for extracting keyword-level market information related to firms’ technology. Specifically, we developed a dual-attention model to identify technical keywords from firms’ websites. We then vectorized the market information (extracted keywords) and technology information (patents) using word embedding to construct technology-market concordance matrices. Matrices were generated based on a group of high-growth companies that suggest new technologies and market opportunities in the automotive, electronics, and pharmaceutical industries.

Suggested Citation

  • MOTOHASHI Kazuyuki, 2023. "Identifying Technology Opportunity Using a Dual-attention Model and a Technology-market Concordance Matrix," Discussion papers 23024, Research Institute of Economy, Trade and Industry (RIETI).
    • Handle: RePEc:eti:dpaper:23024
    as

    Download full text from publisher

    File URL: https://www.rieti.go.jp/jp/publications/dp/23e024.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Park, Mingyu & Geum, Youngjung, 2022. "Two-stage technology opportunity discovery for firm-level decision making: GCN-based link-prediction approach," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    2. Neuhäusler, Peter & Frietsch, Rainer & Kroll, Henning, 2019. "Probabilistic concordance schemes for the re-assignment of patents to economic sectors and scientific publications to technology fields," Discussion Papers "Innovation Systems and Policy Analysis" 60, Fraunhofer Institute for Systems and Innovation Research (ISI).
    3. Jeong, Yujin & Park, Inchae & Yoon, Byungun, 2019. "Identifying emerging Research and Business Development (R&BD) areas based on topic modeling and visualization with intellectual property right data," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 655-672.
    4. IKEUCHI Kenta & MOTOHASHI Kazuyuki & TAMURA Ryuichi & TSUKADA Naotoshi, 2017. "Measuring Science Intensity of Industry using Linked Dataset of Science, Technology and Industry," Discussion papers 17056, Research Institute of Economy, Trade and Industry (RIETI).
    5. Ola Olsson, 2005. "Technological Opportunity and Growth," Journal of Economic Growth, Springer, vol. 10(1), pages 31-53, January.
    6. Samuel Kortum & Jonathan Putnam, 1997. "Assigning Patents to Industries: Tests of the Yale Technology Concordance," Economic Systems Research, Taylor & Francis Journals, vol. 9(2), pages 161-176.
    7. repec:iab:iabfme:201707(en is not listed on IDEAS
    8. Lee, MyoungHoon & Kim, Suhyeon & Kim, Hangyeol & Lee, Junghye, 2022. "Technology Opportunity Discovery using Deep Learning-based Text Mining and a Knowledge Graph," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    9. Eilers, Kathi & Frischkorn, Jonas & Eppinger, Elisabeth & Walter, Lothar & Moehrle, Martin G., 2019. "Patent-based semantic measurement of one-way and two-way technology convergence: The case of ultraviolet light emitting diodes (UV-LEDs)," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 341-353.
    10. Jan Kinne & Janna Axenbeck, 2020. "Web mining for innovation ecosystem mapping: a framework and a large-scale pilot study," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2011-2041, December.
    11. Cantwell, John & Piscitello, Lucia, 2000. "Accumulating Technological Competence: Its Changing Impact on Corporate Diversification and Internationalization," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 9(1), pages 21-51, March.
    12. Giovanni Dosi, 2000. "Sources, Procedures, and Microeconomic Effects of Innovation," Chapters, in: Innovation, Organization and Economic Dynamics, chapter 2, pages 63-114, Edward Elgar Publishing.
    13. Dorner, Matthias & Harhoff, Dietmar, 2018. "A novel technology-industry concordance table based on linked inventor-establishment data," Research Policy, Elsevier, vol. 47(4), pages 768-781.
    14. Klevorick, Alvin K. & Levin, Richard C. & Nelson, Richard R. & Winter, Sidney G., 1995. "On the sources and significance of interindustry differences in technological opportunities," Research Policy, Elsevier, vol. 24(2), pages 185-205, March.
    15. Lee, Changyong & Jeon, Daeseong & Ahn, Joon Mo & Kwon, Ohjin, 2020. "Navigating a product landscape for technology opportunity analysis: A word2vec approach using an integrated patent-product database," Technovation, Elsevier, vol. 96.
    16. Daniel K. N. Johnson, 2002. "The OECD Technology Concordance (OTC): Patents by Industry of Manufacture and Sector of Use," OECD Science, Technology and Industry Working Papers 2002/5, OECD Publishing.
    17. Choi, Kwang Hun & Kwon, Gyu Hyun, 2023. "Strategies for sensing innovation opportunities in smart grids: In the perspective of interactive relationships between science, technology, and business," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    18. Kim, Hyunwoo & Hong, Suckwon & Kwon, Ohjin & Lee, Changyong, 2017. "Concentric diversification based on technological capabilities: Link analysis of products and technologies," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 246-257.
    19. Lee, Mingook & Lee, Sungjoo, 2017. "Identifying new business opportunities from competitor intelligence: An integrated use of patent and trademark databases," Technological Forecasting and Social Change, Elsevier, vol. 119(C), pages 170-183.
    20. Utterback, James M & Abernathy, William J, 1975. "A dynamic model of process and product innovation," Omega, Elsevier, vol. 3(6), pages 639-656, December.
    21. Sanjay K. Arora & Jan Youtie & Philip Shapira & Lidan Gao & TingTing Ma, 2013. "Entry strategies in an emerging technology: a pilot web-based study of graphene firms," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(3), pages 1189-1207, June.
    22. Audretsch, David B & Vivarelli, Marco, 1996. "Firms Size and R&D Spillovers: Evidence from Italy," Small Business Economics, Springer, vol. 8(3), pages 249-258, June.
    23. Sandner, Philipp G. & Block, Joern, 2011. "The market value of R&D, patents, and trademarks," Research Policy, Elsevier, vol. 40(7), pages 969-985, September.
    24. Yoon, Byungun & Park, Inchae & Coh, Byoung-youl, 2014. "Exploring technological opportunities by linking technology and products: Application of morphology analysis and text mining," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 287-303.
    25. Yoon, Janghyeok & Park, Hyunseok & Seo, Wonchul & Lee, Jae-Min & Coh, Byoung-youl & Kim, Jonghwa, 2015. "Technology opportunity discovery (TOD) from existing technologies and products: A function-based TOD framework," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 153-167.
    26. Kwon, Heeyeul & Kim, Jieun & Park, Yongtae, 2017. "Applying LSA text mining technique in envisioning social impacts of emerging technologies: The case of drone technology," Technovation, Elsevier, vol. 60, pages 15-28.
    27. Kwon, Heeyeul & Park, Yongtae & Geum, Youngjung, 2018. "Toward data-driven idea generation: Application of Wikipedia to morphological analysis," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 56-80.
    28. Abdullah Gök & Alec Waterworth & Philip Shapira, 2015. "Use of web mining in studying innovation," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 653-671, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wu, Yingwen & Ji, Yangjian, 2023. "Identifying firm-specific technology opportunities from the perspective of competitors by using association rule mining," Journal of Informetrics, Elsevier, vol. 17(2).
    2. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
    3. Seol, Youngjin & Lee, Seunghyun & Kim, Cheolhan & Yoon, Janghyeok & Choi, Jaewoong, 2023. "Towards firm-specific technology opportunities: A rule-based machine learning approach to technology portfolio analysis," Journal of Informetrics, Elsevier, vol. 17(4).
    4. Lee, MyoungHoon & Kim, Suhyeon & Kim, Hangyeol & Lee, Junghye, 2022. "Technology Opportunity Discovery using Deep Learning-based Text Mining and a Knowledge Graph," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    5. Lee, Changyong & Jeon, Daeseong & Ahn, Joon Mo & Kwon, Ohjin, 2020. "Navigating a product landscape for technology opportunity analysis: A word2vec approach using an integrated patent-product database," Technovation, Elsevier, vol. 96.
    6. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    7. Jungpyo Lee & So Young Sohn, 2021. "Recommendation system for technology convergence opportunities based on self-supervised representation learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 1-25, January.
    8. Park, Youngjin & Yoon, Janghyeok, 2017. "Application technology opportunity discovery from technology portfolios: Use of patent classification and collaborative filtering," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 170-183.
    9. Yun, Siyeong & Song, Kisik & Kim, Chulhyun & Lee, Sungjoo, 2021. "From stones to jewellery: Investigating technology opportunities from expired patents," Technovation, Elsevier, vol. 103(C).
    10. Lijie Feng & Yuxiang Niu & Zhenfeng Liu & Jinfeng Wang & Ke Zhang, 2019. "Discovering Technology Opportunity by Keyword-Based Patent Analysis: A Hybrid Approach of Morphology Analysis and USIT," Sustainability, MDPI, vol. 12(1), pages 1-35, December.
    11. Song, Kisik & Kim, Karp Soo & Lee, Sungjoo, 2017. "Discovering new technology opportunities based on patents: Text-mining and F-term analysis," Technovation, Elsevier, vol. 60, pages 1-14.
    12. Choi, Kwang Hun & Kwon, Gyu Hyun, 2023. "Strategies for sensing innovation opportunities in smart grids: In the perspective of interactive relationships between science, technology, and business," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    13. Cantner, Uwe & Pyka, Andreas, 1998. "Technological evolution -- an analysis within the knowledge-based approach," Structural Change and Economic Dynamics, Elsevier, vol. 9(1), pages 85-107, March.
    14. Yoon, Byungun & Park, Inchae & Coh, Byoung-youl, 2014. "Exploring technological opportunities by linking technology and products: Application of morphology analysis and text mining," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 287-303.
    15. Christoph Stich & Emmanouil Tranos & Max Nathan, 2023. "Modeling clusters from the ground up: A web data approach," Environment and Planning B, , vol. 50(1), pages 244-267, January.
    16. Ardito, Lorenzo & Ernst, Holger & Messeni Petruzzelli, Antonio, 2020. "The interplay between technology characteristics, R&D internationalisation, and new product introduction: Empirical evidence from the energy conservation sector," Technovation, Elsevier, vol. 96.
    17. Marie-Claude BELIS-BERGOUIGNAN, 2009. "An evolutionist analysis of sectoral dynamics (In French)," Cahiers du GREThA (2007-2019) 2009-18, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).
    18. Han, Xiaotong & Zhu, Donghua & Lei, Ming & Daim, Tugrul, 2021. "R&D trend analysis based on patent mining: An integrated use of patent applications and invalidation data," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    19. Henry Lahr & Andrea Mina, 2014. "Liquidity, Technological Opportunities, and the Stage Distribution of Venture Capital Investments," Financial Management, Financial Management Association International, vol. 43(2), pages 291-325, June.
    20. Rammer, Christian & Es-Sadki, Nordine, 2023. "Using big data for generating firm-level innovation indicators - a literature review," Technological Forecasting and Social Change, Elsevier, vol. 197(C).

    More about this item

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eti:dpaper:23024. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: TANIMOTO, Toko (email available below). General contact details of provider: https://edirc.repec.org/data/rietijp.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.