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

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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.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIP) (NRF-2013R1A2A2A03016904) and also by the BK21 Plus Program (Center for Sustainable and Innovative Industrial Systems, Dept. of Industrial Engineering, Seoul National University) funded by the Ministry of Education, Korea (No. 21A20130012638).

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  1. Department of Industrial Engineering, College of Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea

    Jeeeun Kim

  2. Department of Industrial Engineering, College of Engineering, Ajou University, 206 Worldcup-ro, Yeongtong-gu, Suwon, 16499, Republic of Korea

    Sungjoo Lee

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  1. Jeeeun Kim
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  2. Sungjoo Lee
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Correspondence to Sungjoo Lee.

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Kim, J., Lee, S. Forecasting and identifying multi-technology convergence based on patent data: the case of IT and BT industries in 2020. Scientometrics 111, 47–65 (2017). https://doi.org/10.1007/s11192-017-2275-4

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  • DOI: https://doi.org/10.1007/s11192-017-2275-4

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