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Identification of technological knowledge intermediaries

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  • Hyojeong Lim

    (Seoul National University)

  • Yongtae Park

    (Seoul National University)

Abstract

Intermediaries in a technological knowledge network have recently been highlighted as crucial innovation drivers that accelerate technological knowledge flows. Although the patent network analysis has been frequently used to monitor technological knowledge structures, it has examined only sources or recipients of the technological knowledge by mainly estimating technological knowledge inflows or outflows of a network node. This study, therefore, aims to identify technological knowledge intermediaries when a technology-level knowledge network is composed of several industries. First, types of technological knowledge flows are deductively classified into four types by highlighting industry affiliations of source technologies and recipient technologies. Second, a directed technological knowledge network is generated at the technology class level, using patent co-classification analysis. Third, for each class, mediating scores are measured according to the four types. The empirical analysis illustrates the Korea’s technological knowledge network between 2000 and 2008. As a result, the four types of mediating scores are compared between industries, and industry-wise technological knowledge intermediaries are identified. The proposed approach is practical to explore converging processes in technology development where technology classes act as technological knowledge intermediaries among diverse industries.

Suggested Citation

  • Hyojeong Lim & Yongtae Park, 2010. "Identification of technological knowledge intermediaries," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(3), pages 543-561, September.
    • Handle: RePEc:spr:scient:v:84:y:2010:i:3:d:10.1007_s11192-009-0133-8
    DOI: 10.1007/s11192-009-0133-8
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    2. 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.
    3. 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.
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    5. 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.
    6. Qiao Zheng & Lu-Cheng Huang & Fei-Fei Wu & Wu Dan & Zhang Hui, 2017. "Analyzing Technological Knowledge Diffusion Among Technological Fields Using Patent Data: The Example of Microfluidics," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 14(01), pages 1-17, February.
    7. José-Antonio Belso-Martínez & Manuel Expósito-Langa, 2015. "Persistence and extinction of brokerage roles in clusters: the role of status, former experiences and extra-cluster relationships," Papers in Evolutionary Economic Geography (PEEG) 1501, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Jan 2015.
    8. 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.
    9. Aaldering, Lukas Jan & Song, Chie Hoon, 2021. "Of leaders and laggards - Towards digitalization of the process industries," Technovation, Elsevier, vol. 105(C).
    10. Li, Shuying & Zhang, Xian & Xu, Haiyun & Fang, Shu & Garces, Edwin & Daim, Tugrul, 2020. "Measuring strategic technological strength :Patent Portfolio Model," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
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