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River deep, mountain high: of long run knowledge trajectories within and between innovation clusters1

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  • Önder Nomaler
  • Bart Verspagen

Abstract

We bring together the topics of geographical clusters and technological trajectories, and shift the focus of the analysis of regional innovation to main technological trends rather than firms. We define a number of inventive clusters in the US space and show that long chains of citations mostly take place between these clusters. This is reminiscent of the idea of global pipelines of knowledge transfer that is found in the geographical literature. The deep citations are used to identify technological trajectories, which are the main directions along which incremental technological progress accumulates into larger changes. While the origin and destination of these trajectories are concentrated in space, the intermediate nodes travel long distances and cover many locations across the globe. We conclude by calling for more theoretical and empirical attention to the ‘deep rivers’ that connect the ‘high mountains’ of local knowledge production.

Suggested Citation

  • Önder Nomaler & Bart Verspagen, 2016. "River deep, mountain high: of long run knowledge trajectories within and between innovation clusters1," Journal of Economic Geography, Oxford University Press, vol. 16(6), pages 1259-1278.
    • Handle: RePEc:oup:jecgeo:v:16:y:2016:i:6:p:1259-1278.
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    File URL: http://hdl.handle.net/10.1093/jeg/lbw035
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    Citations

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    Cited by:

    1. Muhamed Kudic & Mariia Shkolnykova, 2020. "From biotech to bioeconomy: New empirical evidence on the technological transition to plant-based bioeconomy based on patent data," Bremen Papers on Economics & Innovation 2002, University of Bremen, Faculty of Business Studies and Economics.
    2. Alessandri, Enrico, 2023. "Identifying technological trajectories in the mining sector using patent citation networks," Resources Policy, Elsevier, vol. 80(C).
    3. Kim, Jieun & Lee, Changyong, 2017. "Novelty-focused weak signal detection in futuristic data: Assessing the rarity and paradigm unrelatedness of signals," Technological Forecasting and Social Change, Elsevier, vol. 120(C), pages 59-76.
    4. Fang Han & Sejun Yoon & Nagarajan Raghavan & Hyunseok Park, 2022. "Investigating Company’s Technical Development Directions Based on Internal Knowledge Inheritance and Inventor Capabilities: The Case of Samsung Electronics," Sustainability, MDPI, vol. 14(5), pages 1-19, March.

    More about this item

    Keywords

    Patent citations; regional concentration of inventive activities; technological trajectories;
    All these keywords.

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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