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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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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).
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Caviggioli, Federico, 2016. "Technology fusion: Identification and analysis of the drivers of technology convergence using patent data," Technovation, Elsevier, vol. 55, pages 22-32.
    14. 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.
    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.
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    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.

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    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

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