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Assessing coreness and intermediarity of technology sectors using patent co-classification analysis: the case of Korean national R&D

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  • Hyunseok Park

    (Pohang University of Science and Technology)

  • Janghyeok Yoon

    (Konkuk University)

Abstract

Rapid technological advancements and increasing research and development (R&D) costs are making it necessary for national R&D plans to identify the coreness and intermediarity of technologies in selecting projects and allocating budgets. Studies on the coreness or intermediarity of technology sectors have used patent citations, but there are limitations to dealing with patent data. The limitations arise from the most current patents and patents that do not require citations, e.g. Korean patents. Further, few or no studies have simultaneously considered both coreness and intermediarity. Therefore, we propose a patent co-classification based method to measure coreness and intermediarity of technology sectors by incorporating the analytic network process and the social network analysis. Using IPC co-classifications of patents as technological knowledge flows, this method constructs a network of directed knowledge flows among technology sectors and measures the long-term importance and the intermediating potential of each technology sector, despite the limitations of patent-based analyses. Considering both coreness and intermediarity, this method can provide more detailed and essential knowledge for decision making in planning national R&D. We demonstrated this method using Korean national R&D patents from 2008 to 2011. We expect that this method will help in planning national R&D in a rapidly evolving technological environment.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:98:y:2014:i:2:d:10.1007_s11192-013-1109-2
    DOI: 10.1007/s11192-013-1109-2
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    9. Yong-Jae Lee & Young Jae Han & Sang-Soo Kim & Chulung Lee, 2022. "Patent Data Analytics for Technology Forecasting of the Railway Main Transformer," Sustainability, MDPI, vol. 15(1), pages 1-25, December.
    10. Seunghyun Oh & Jaewoong Choi & Namuk Ko & Janghyeok Yoon, 2020. "Predicting product development directions for new product planning using patent classification-based link prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1833-1876, December.
    11. Chie Hoon Song, 2021. "Exploring and Predicting the Knowledge Development in the Field of Energy Storage: Evidence from the Emerging Startup Landscape," Energies, MDPI, vol. 14(18), pages 1-20, September.
    12. 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).
    13. Guan, Jiancheng & Liu, Na, 2015. "Invention profiles and uneven growth in the field of emerging nano-energy," Energy Policy, Elsevier, vol. 76(C), pages 146-157.
    14. 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.
    15. Caviggioli, Federico, 2016. "Technology fusion: Identification and analysis of the drivers of technology convergence using patent data," Technovation, Elsevier, vol. 55, pages 22-32.
    16. Inkyung Cho & Jungkyu Park & Eunnyeong Heo, 2018. "Measuring Knowledge Diffusion in Water Resources Research and Development: The Case of Korea," Sustainability, MDPI, vol. 10(8), pages 1-15, August.
    17. Liu, Weiwei & Song, Yifan & Bi, Kexin, 2021. "Exploring the patent collaboration network of China's wind energy industry: A study based on patent data from CNIPA," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    18. Guiyang Zhang & Chaoying Tang, 2018. "How R&D partner diversity influences innovation performance: an empirical study in the nano-biopharmaceutical field," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1487-1512, September.
    19. 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.
    20. Moehrle, Martin G. & Frischkorn, Jonas, 2021. "Bridge strongly or focus – An analysis of bridging patents in four application fields of carbon fiber reinforcements," Journal of Informetrics, Elsevier, vol. 15(2).
    21. Zhang, Ningning & You, Dingyi & Tang, Le & Wen, Ke, 2023. "Knowledge path dependence, external connection, and radical inventions: Evidence from Chinese Academy of Sciences," Research Policy, Elsevier, vol. 52(4).
    22. Noh, Heeyong & Lee, Sungjoo, 2020. "What constitutes a promising technology in the era of open innovation? An investigation of patent potential from multiple perspectives," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    23. Song, Bomi & Suh, Yongyoon, 2019. "Identifying convergence fields and technologies for industrial safety: LDA-based network analysis," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 115-126.
    24. 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.

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    More about this item

    Keywords

    Patent analysis; Analytic network process (ANP); Social network analysis (SNA); National R&D investment;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

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