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A Study on Dynamic Patterns of Technology Convergence with IPC Co-Occurrence-Based Analysis: The Case of 3D Printing

Author

Listed:
  • Ying Tang

    (School of Economics and Management, Xi’an University of Posts and Telecommunications, Xi’an 710061, China)

  • Xuming Lou

    (School of Economics and Management, Xi’an University of Posts and Telecommunications, Xi’an 710061, China)

  • Zifeng Chen

    (School of Economics and Management, Xi’an University of Posts and Telecommunications, Xi’an 710061, China)

  • Chengjin Zhang

    (School of Modern Post, Xi’an University of Posts and Telecommunications, Xi’an 710061, China)

Abstract

Technology convergence has become a typical characteristic of innovation, which affects the evolution of industrial structures and the core competitiveness of organizations. However, the existing research has mainly focused on the development of core areas of convergence, ignoring the potential breakthroughs that emerging peripheral convergence may bring. Therefore, this research put forward a comprehensive methodology based on IPC (International Patent Classification) co-occurrence analysis to study the dynamic patterns of technology convergence from the perspectives of reinforcing convergence and novel convergence. For the former, convergence trends in each period were explored by using association rules, and the convergence degree was measured based on the number of patents containing different IPC codes. Then, the corresponding core technical fields were identified by using information entropy. For the latter, a community detection algorithm based on IPC co-occurrence network was adopted to investigate the convergence trend by period, and important technology fields were identified by the centrality indicators. The methodology proposed in this study is beneficial for firms to seize technological opportunities in technology convergence.

Suggested Citation

  • Ying Tang & Xuming Lou & Zifeng Chen & Chengjin Zhang, 2020. "A Study on Dynamic Patterns of Technology Convergence with IPC Co-Occurrence-Based Analysis: The Case of 3D Printing," Sustainability, MDPI, vol. 12(7), pages 1-26, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:2655-:d:337896
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    7. Sasaki, Hajime & Sakata, Ichiro, 2021. "Identifying potential technological spin-offs using hierarchical information in international patent classification," Technovation, Elsevier, vol. 100(C).
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    9. Choi, Jaewoong & Yoon, Janghyeok, 2022. "Measuring knowledge exploration distance at the patent level: Application of network embedding and citation analysis," Journal of Informetrics, Elsevier, vol. 16(2).
    10. 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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