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Forecasting and identifying multi-technology convergence based on patent data: the case of IT and BT industries in 2020

Author

Listed:
  • Jeeeun Kim

    (Seoul National University)

  • Sungjoo Lee

    (Ajou University)

Abstract

Having a new technology opportunity is a significant variable that can lead to dominance in a competitive market. In that context, accurately understanding the state of development of technology convergence and forecasting promising technology convergence can determine the success of a firm. However, previous studies have mainly focused on examining the convergence paths taken in the past or the current state of convergence rather than projecting the future trends of convergence. In addition, few studies have dealt with multi-technology convergence by taking a pairwise-analysis approach. Therefore, this research aimed to propose a forecasting methodology for multi-technology convergence, which is more realistic than pairwise convergence, based on a patent-citation analysis, a dependency-structure matrix, and a neural-network analysis. The suggested methodology enables both researchers and practitioners in the convergence field to plan their technology development by forecasting the technology combination that will occur in the future.

Suggested Citation

  • Jeeeun Kim & Sungjoo Lee, 2017. "Forecasting and identifying multi-technology convergence based on patent data: the case of IT and BT industries in 2020," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 47-65, April.
    • Handle: RePEc:spr:scient:v:111:y:2017:i:1:d:10.1007_s11192-017-2275-4
    DOI: 10.1007/s11192-017-2275-4
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    More about this item

    Keywords

    Technology convergence; Forecasting; Patent-citation analysis; Neural-network analysis; Dependency-structure matrix;
    All these keywords.

    JEL classification:

    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights

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