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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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    as
    1. Janghyeok Yoon & Sungchul Choi & Kwangsoo Kim, 2011. "Invention property-function network analysis of patents: a case of silicon-based thin film solar cells," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(3), pages 687-703, March.
    2. Jackie Krafft & Francesco Quatraro & Pier Paolo Saviotti, 2011. "The knowledge-base evolution in biotechnology: a social network analysis," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 20(5), pages 445-475.
    3. Bronwyn H. Hall & Adam B. Jaffe & Manuel Trajtenberg, 2001. "The NBER Patent Citation Data File: Lessons, Insights and Methodological Tools," NBER Working Papers 8498, National Bureau of Economic Research, Inc.
    4. Jaffe, Adam B, 1986. "Technological Opportunity and Spillovers of R&D: Evidence from Firms' Patents, Profits, and Market Value," American Economic Review, American Economic Association, vol. 76(5), pages 984-1001, December.
    5. Zifeng Chen & Jiancheng Guan, 2011. "Mapping of biotechnology patents of China from 1995–2008," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 73-89, July.
    6. Jiancheng Guan & Ying He, 2007. "Patent-bibliometric analysis on the Chinese science — technology linkages," Scientometrics, Springer;Akadémiai Kiadó, vol. 72(3), pages 403-425, September.
    7. Gilsing, Victor & Nooteboom, Bart & Vanhaverbeke, Wim & Duysters, Geert & van den Oord, Ad, 2008. "Network embeddedness and the exploration of novel technologies: Technological distance, betweenness centrality and density," Research Policy, Elsevier, vol. 37(10), pages 1717-1731, December.
    8. 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.
    9. Xianwen Wang & Xi Zhang & Shenmeng Xu, 2011. "Patent co-citation networks of Fortune 500 companies," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(3), pages 761-770, September.
    10. Patel, Parimal & Pavitt, Keith, 1994. "The continuing, widespread (and neglected) importance of improvements in mechanical technologies," Research Policy, Elsevier, vol. 23(5), pages 533-545, September.
    11. Bart Verspagen, 1997. "Measuring Intersectoral Technology Spillovers: Estimates from the European and US Patent Office Databases," Economic Systems Research, Taylor & Francis Journals, vol. 9(1), pages 47-65.
    12. Choi, Jin Young & Lee, Jong Ha & Sohn, So Young, 2009. "Impact analysis for national R&D funding in science and technology using quantification method II," Research Policy, Elsevier, vol. 38(10), pages 1534-1544, December.
    13. Galende, Jesus & de la Fuente, Juan Manuel, 2003. "Internal factors determining a firm's innovative behaviour," Research Policy, Elsevier, vol. 32(5), pages 715-736, May.
    14. Jennifer H. Chen & Show-Ling Jang & Sonya H. Wen, 2010. "Measuring technological diversification: identifying the effects of patent scale and patent scope," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(1), pages 265-275, July.
    15. Aragonés-Beltrán, P. & Chaparro-González, F. & Pastor-Ferrando, J.P. & Rodríguez-Pozo, F., 2010. "An ANP-based approach for the selection of photovoltaic solar power plant investment projects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(1), pages 249-264, January.
    16. Ta-Shun Cho & Hsin-Yu Shih, 2011. "Patent citation network analysis of core and emerging technologies in Taiwan: 1997–2008," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(3), pages 795-811, December.
    17. Nelson, Andrew J., 2009. "Measuring knowledge spillovers: What patents, licenses and publications reveal about innovation diffusion," Research Policy, Elsevier, vol. 38(6), pages 994-1005, July.
    18. 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.
    19. Loet Leydesdorff, 2008. "Patent classifications as indicators of intellectual organization," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(10), pages 1582-1597, August.
    20. Hyojeong Lim & Yongtae Park, 2010. "Identification of technological knowledge intermediaries," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(3), pages 543-561, September.
    21. Park, Jongyong & Lee, Hakyeon & Park, Yongtae, 2009. "Disembodied knowledge flows among industrial clusters: A patent analysis of the Korean manufacturing sector," Technology in Society, Elsevier, vol. 31(1), pages 73-84.
    22. Han, Yoo-Jin & Park, Yongtae, 2006. "Patent network analysis of inter-industrial knowledge flows: The case of Korea between traditional and emerging industries," World Patent Information, Elsevier, vol. 28(3), pages 235-247, September.
    23. Chen Liu & Wei Shan & Jing Yu, 2011. "Shaping the interdisciplinary knowledge network of China: a network analysis based on citation data from 1981 to 2010," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 89-106, October.
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    8. 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).
    9. Sung-Seok Ko & Namuk Ko & Doyeon Kim & Hyunseok Park & Janghyeok Yoon, 2014. "Analyzing technology impact networks for R&D planning using patents: combined application of network approaches," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 917-936, October.
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    11. Zeyu Xing & Li Wang & Debin Fang, 2023. "Unraveling the dynamics and identifying the “superstars” of R&D alliances in IUR collaboration: a two-mode network analysis in China," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-19, December.
    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. 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.
    15. Taiye Luo & Zhengang Zhang, 2021. "Multi-network embeddedness and innovation performance of R&D employees," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 8091-8107, September.
    16. Eunsook Jeon & Kyungkook Kim & Hyunjeong Park & Keuntae Cho, 2023. "Global Collaboration in Technology Sectors during the COVID-19 Pandemic: A Patent Review," Sustainability, MDPI, vol. 15(15), pages 1-19, August.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. Caviggioli, Federico, 2016. "Technology fusion: Identification and analysis of the drivers of technology convergence using patent data," Technovation, Elsevier, vol. 55, pages 22-32.
    22. 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.
    23. 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).
    24. 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).

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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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