IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v126y2021i7d10.1007_s11192-021-04011-z.html
   My bibliography  Save this article

Patent data based search framework for IT R&D employees for convergence technology

Author

Listed:
  • Jong Wook Lee

    (Yonsei University)

  • So Young Sohn

    (Yonsei University)

Abstract

As technology convergence is attracting increasing attention, many companies pay attention to human resources planning for the convergence R&D. This study proposes a patent-based R&D employee search framework for the firms planning to develop convergence technologies. We apply this framework to an automobile company searching patent inventors in the Information and Communication Technology (ICT) field, reflecting the future convergence trends in self-driving vehicles. ICT patent inventors were evaluated based on their ICT patents scored in three steps. First, the importance of the ICT areas they cover is considered in terms of International Patent Classification code by taking into account the converge potential with the focal firm’s technologies in the future. Second, the feasibility of convergence with the ICT technologies that the focal firm wants to cover is assessed. Finally, various inherent values of ICT patent are considered. The proposed framework is expected to improve the efficiency of the process of spotting candidates for convergence R&D.

Suggested Citation

  • Jong Wook Lee & So Young Sohn, 2021. "Patent data based search framework for IT R&D employees for convergence technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5687-5705, July.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:7:d:10.1007_s11192-021-04011-z
    DOI: 10.1007/s11192-021-04011-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-021-04011-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-021-04011-z?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.

    References listed on IDEAS

    as
    1. Karvonen, Matti & Kässi, Tuomo, 2013. "Patent citations as a tool for analysing the early stages of convergence," Technological Forecasting and Social Change, Elsevier, vol. 80(6), pages 1094-1107.
    2. Han, Eun Jin & Sohn, So Young, 2016. "Technological convergence in standards for information and communication technologies," Technological Forecasting and Social Change, Elsevier, vol. 106(C), pages 1-10.
    3. Caviggioli, Federico, 2016. "Technology fusion: Identification and analysis of the drivers of technology convergence using patent data," Technovation, Elsevier, vol. 55, pages 22-32.
    4. Jeeeun Kim & Sungjoo Lee, 2017. "Forecasting and identifying multi-technology convergence based on patent data: the case of IT and BT industries in 2020," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 47-65, April.
    5. Jae Young Choi & Seongkyoon Jeong & Kyunam Kim, 2015. "A Study on Diffusion Pattern of Technology Convergence: Patent Analysis for Korea," Sustainability, MDPI, vol. 7(9), pages 1-24, August.
    6. Harhoff, Dietmar & Scherer, Frederic M. & Vopel, Katrin, 2003. "Citations, family size, opposition and the value of patent rights," Research Policy, Elsevier, vol. 32(8), pages 1343-1363, September.
    7. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    8. Kose, Toshihiro & Sakata, Ichiro, 2019. "Identifying technology convergence in the field of robotics research," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 751-766.
    9. Dae Geon Kim & Sang Ok Choi, 2018. "Impact of Construction IT Technology Convergence Innovation on Business Performance," Sustainability, MDPI, vol. 10(11), pages 1-16, October.
    10. Jungpyo Lee & So Young Sohn, 2017. "What makes the first forward citation of a patent occur earlier?," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 279-298, October.
    11. Kim, Tae San & Sohn, So Young, 2020. "Machine-learning-based deep semantic analysis approach for forecasting new technology convergence," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    12. Hyunseok Park & Janghyeok Yoon, 2014. "Assessing coreness and intermediarity of technology sectors using patent co-classification analysis: the case of Korean national R&D," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 853-890, February.
    13. Ying Tang & Xuming Lou & Zifeng Chen & Chengjin Zhang, 2020. "A Study on Dynamic Patterns of Technology Convergence with IPC Co-Occurrence-Based Analysis: The Case of 3D Printing," Sustainability, MDPI, vol. 12(7), pages 1-26, March.
    14. Lee, Won Sang & Han, Eun Jin & Sohn, So Young, 2015. "Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 317-329.
    15. Jean O. Lanjouw & Mark Schankerman, 2004. "Patent Quality and Research Productivity: Measuring Innovation with Multiple Indicators," Economic Journal, Royal Economic Society, vol. 114(495), pages 441-465, April.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Shicheng Tan & Tao Zhang & Shu Zhao & Yanping Zhang, 2023. "Self-supervised scientific document recommendation based on contrastive learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5027-5049, September.

    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. Seo, Wonchul & Afifuddin, Mokh, 2024. "Developing a supervised learning model for anticipating potential technology convergence between technology topics," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    2. 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).
    3. 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.
    4. Ying Tang & Xuming Lou & Zifeng Chen & Chengjin Zhang, 2020. "A Study on Dynamic Patterns of Technology Convergence with IPC Co-Occurrence-Based Analysis: The Case of 3D Printing," Sustainability, MDPI, vol. 12(7), pages 1-26, March.
    5. 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.
    6. Kim, Tae San & Sohn, So Young, 2020. "Machine-learning-based deep semantic analysis approach for forecasting new technology convergence," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    7. Zhao, Shengchao & Zeng, Deming & Li, Jian & Feng, Ke & Wang, Yao, 2023. "Quantity or quality: The roles of technology and science convergence on firm innovation performance," Technovation, Elsevier, vol. 126(C).
    8. Qian Xu & Hua Cheng, 2021. "Research on the Evolution of Textile Technological Convergence in China," Sustainability, MDPI, vol. 13(5), pages 1-13, February.
    9. Zhu, Chen & Motohashi, Kazuyuki, 2022. "Identifying the technology convergence using patent text information: A graph convolutional networks (GCN)-based approach," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    10. Sick, Nathalie & Bröring, Stefanie, 2022. "Exploring the research landscape of convergence from a TIM perspective: A review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    11. Qian Xu & Yabin Yu & Xiao Yu, 2022. "Analysis of the Technological Convergence in Smart Textiles," Sustainability, MDPI, vol. 14(20), pages 1-13, October.
    12. Bo Kyeong Lee & So Young Sohn, 2017. "Exploring the effect of dual use on the value of military technology patents based on the renewal decision," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1203-1227, September.
    13. ZHU Chen & MOTOHASHI Kazuyuki, 2022. "Government R&D spending as a driving force of technology convergence," Discussion papers 22030, Research Institute of Economy, Trade and Industry (RIETI).
    14. Yun, Siyeong & Song, Kisik & Kim, Chulhyun & Lee, Sungjoo, 2021. "From stones to jewellery: Investigating technology opportunities from expired patents," Technovation, Elsevier, vol. 103(C).
    15. Sajad Ashouri & Anne-Laure Mention & Kosmas X. Smyrnios, 2021. "Anticipation and analysis of industry convergence using patent-level indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5727-5758, July.
    16. Won Sang Lee & So Young Sohn, 2017. "Identifying Emerging Trends of Financial Business Method Patents," Sustainability, MDPI, vol. 9(9), pages 1-21, September.
    17. Sasaki, Hajime & Sakata, Ichiro, 2021. "Identifying potential technological spin-offs using hierarchical information in international patent classification," Technovation, Elsevier, vol. 100(C).
    18. Sun, Bing & Yang, Xueting & Zhong, Shen & Tian, Shengnan & Liang, Tian, 2024. "How do technology convergence and expansibility affect information technology diffusion? Evidence from the internet of things technology in China," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    19. Soyea Lee & Junseok Hwang & Eunsang Cho, 2022. "Comparing technology convergence of artificial intelligence on the industrial sectors: two-way approaches on network analysis and clustering analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 407-452, January.
    20. Kangas, H.L. & Ollikka, K. & Ahola, J. & Kim, Y., 2021. "Digitalisation in wind and solar power technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).

    More about this item

    Keywords

    R&D manpower search; Technology convergence; Patent scoring; Doc2vec;
    All these keywords.

    JEL classification:

    • C89 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • M51 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - Firm Employment Decisions; Promotions

    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:spr:scient:v:126:y:2021:i:7:d:10.1007_s11192-021-04011-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.