IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v119y2017icp170-183.html
   My bibliography  Save this article

Identifying new business opportunities from competitor intelligence: An integrated use of patent and trademark databases

Author

Listed:
  • Lee, Mingook
  • Lee, Sungjoo

Abstract

This study aims to analyze the position of technology-centered companies in complex market dynamics and discover new business opportunities from competitor intelligence. For this, we consider both technology and market characteristics in providing competitor intelligence by utilizing patent data as a representative proxy for a firm's technology, and trademark data as an information source for the firm's target goods and services. To analyze the two types of data, a collaborative filtering approach together with portfolio analyses and association mining techniques were adopted. Theoretically, this is one of the earliest attempts to combine patent data and trademark data to investigate corporate strategies. In practice, the research results are expected to be used as a decision criterion to diagnose the economic value that companies can obtain by entering the market, as well as the technological value to be passed onto their customers. Thus, the proposed approach can be useful to support effective technology and business strategies in a firm.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:tefoso:v:119:y:2017:i:c:p:170-183
    DOI: 10.1016/j.techfore.2017.03.026
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162517303839
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2017.03.026?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Teng, Fei & Sun, Yuling & Chen, Fang & Qin, Aning & Zhang, Qi, 2021. "Technology opportunity discovery of proton exchange membrane fuel cells based on generative topographic mapping," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    3. Rockett Katharine, 2023. "Is Data the New Gold? Considering Intellectual Property Protection and Regulation of Data," Economics - The Open-Access, Open-Assessment Journal, De Gruyter, vol. 17(1), pages 1-15, January.
    4. Anisur R. Faroque & Farhad Uddin Ahmed & Mahabubur Rahman & Mohammad Osman Gani & Sina Mortazavi, 2023. "Exploring the individual and joint effects of founders' and managers' experiential knowledge on international opportunity identification," Asian Business & Management, Palgrave Macmillan, vol. 22(4), pages 1274-1300, September.
    5. Motohashi, Kazuyuki & Zhu, Chen, 2023. "Identifying technology opportunity using dual-attention model and technology-market concordance matrix," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    6. Yougen Cao & Shengce Ren & Mei Du, 2022. "Strategic trademark management: a systematic literature review and prospects for future research," Journal of Brand Management, Palgrave Macmillan, vol. 29(5), pages 435-453, September.
    7. Yun, Siyeong & Song, Kisik & Kim, Chulhyun & Lee, Sungjoo, 2021. "From stones to jewellery: Investigating technology opportunities from expired patents," Technovation, Elsevier, vol. 103(C).
    8. Andersson, David E. & Ekman, Anton & Huila, Anton & Tell, Fredrik, 2023. "Industrial design rights and the market value of firms," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    9. Ioannis Anagnostopoulos & Anas Rizeq, 2021. "Conventional and neural network target‐matching methods dynamics: The information technology mergers and acquisitions market in the USA," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(2), pages 97-118, April.
    10. Kyungtae Kim & Sungjoo Lee, 2018. "How Can Big Data Complement Expert Analysis? A Value Chain Case Study," Sustainability, MDPI, vol. 10(3), pages 1-21, March.
    11. 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.
    12. Aaldering, Lukas Jan & Leker, Jens & Song, Chie Hoon, 2019. "Uncovering the dynamics of market convergence through M&A," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 95-114.
    13. Younghoon Lee, 2022. "Identifying Competitive Attributes Based on an Ensemble of Explainable Artificial Intelligence," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(4), pages 407-419, August.
    14. 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).
    15. Changyong Lee & Gyumin Lee, 2019. "Technology opportunity analysis based on recombinant search: patent landscape analysis for idea generation," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 603-632, November.
    16. 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).

    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:eee:tefoso:v:119:y:2017:i:c:p:170-183. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    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.