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The patterns of knowledge spillovers across technology sectors evidenced in patent citation networks

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  • Wonchang Hur

    (Inha University)

Abstract

The innovation literature often argues that major inventions arise through the cumulative synthesis of existing components and principles. An important economic phenomenon associated with this argument is the knowledge spillover. Although increasing attention has been paid to knowledge spillovers as a means to grasp innovation, little is known about its structural characteristics. This study examines the structural patterns of knowledge flow evidenced in patent citations by focusing on two aspects: the reciprocity of citations between technology sectors and the concentration of citing and cited sectors. The results indicate that the knowledge flow tends to be highly reciprocal within pairs of technology sectors instead of having a clear direction and that there are relatively low inflow and outflow concentrations in most sectors, although there are some exceptions. These results suggest that most technological sectors become both a knowledge provider and a knowledge consumer at the same time and they coevolve with reciprocal knowledge exchanges with each other.

Suggested Citation

  • Wonchang Hur, 2017. "The patterns of knowledge spillovers across technology sectors evidenced in patent citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 595-619, May.
    • Handle: RePEc:spr:scient:v:111:y:2017:i:2:d:10.1007_s11192-017-2329-7
    DOI: 10.1007/s11192-017-2329-7
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    Cited by:

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    2. David B. Audretsch & Albert N. Link & Martijn Hasselt, 2019. "Knowledge begets knowledge: university knowledge spillovers and the output of scientific papers from U.S. Small Business Innovation Research (SBIR) projects," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1367-1383, December.
    3. 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).
    4. Jing Ma & Yaohui Pan & Chih-Yi Su, 2022. "Organization-oriented technology opportunities analysis based on predicting patent networks: a case of Alzheimer’s disease," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5497-5517, September.
    5. Hur, Wonchang, 2024. "Entropy, heterogeneity, and their impact on technology progress," Journal of Informetrics, Elsevier, vol. 18(2).
    6. 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.
    7. Hou, Jianhua & Tang, Shiqi & Zhang, Yang & Song, Haoyang, 2023. "Does prior knowledge affect patent technology diffusion? A semantic-based patent citation contribution analysis," Journal of Informetrics, Elsevier, vol. 17(2).

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