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Identification of Vacant and Emerging Technologies in Smart Mobility Through the GTM-Based Patent Map Development

Author

Listed:
  • Jiwon Yu

    (Department of Industrial Management Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul 02841, Korea)

  • Jong-Gyu Hwang

    (Center of New Transportation Innovation Research, Korea Railroad Research Institute, 176 Cheoldobangmulgwan-ro, Uiwang 16105, Gyeonggi, Korea)

  • Jumi Hwang

    (Department of Industrial Management Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul 02841, Korea)

  • Sung Chan Jun

    (Department of Industrial Management Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul 02841, Korea)

  • Sumin Kang

    (Department of Aerospace Engineering, University of Michigan, 500 S. State Street, Ann Arbor, MI 48109, USA)

  • Chulung Lee

    (School of Industrial Management Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul 02841, Korea)

  • Hyundong Kim

    (Division of Interdisciplinary Studies for Creativity, Daejin University, 1007 Hoguk-ro, Pocheon-si 11159, Gyeonggi-do, Korea)

Abstract

With the development of the online platforms and the Internet of Things (IoT), various transportation services have been provided, and the lifestyle of the general public has changed significantly. However, the speed of development of technologies and services for the mobility handicapped has been relatively slow. Accordingly, in this paper, the smart mobility patent data for the mobility handicapped is subdivided through clustering to derive the mobility handicapped-related vacant technologies, and the prospect of the vacant technology is verified. For each cluster, a technology level map is generated in consideration of the technology growth level and the scope of authority of the vacant technology derived through the generative topographic map (GTM) patent map, and the level of the vacant technology is checked in terms of quantity and quality. Both indicators perform time series analyses on superior technology to predict technology trends and determine the technology’s promisingness. Unlike the precedent studies that focused only on quantitative analysis methods, this paper identified the usefulness of the technology through clustering and various verification processes and materialized it as a vacant technology that is applicable to actual R&D. Accordingly, through this empirical paper, it is possible to understand the current level of vacant technology in smart mobility for the mobility handicapped and establish an R&D strategy to prevent monopoly in technology in the future market and maintain competitiveness. It can also be utilized for new technology development in consideration of convergence with currently developed technology.

Suggested Citation

  • Jiwon Yu & Jong-Gyu Hwang & Jumi Hwang & Sung Chan Jun & Sumin Kang & Chulung Lee & Hyundong Kim, 2020. "Identification of Vacant and Emerging Technologies in Smart Mobility Through the GTM-Based Patent Map Development," Sustainability, MDPI, vol. 12(22), pages 1-22, November.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:22:p:9310-:d:442386
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    References listed on IDEAS

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    3. Kai Guo & Tiantian Zhang & Yan Liang & Jiyao Zhao & Xiangmin Zhang, 2023. "Research on the promotion path of green technology innovation of an enterprise from the perspective of technology convergence: configuration analysis using new energy vehicles as an example," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(6), pages 4989-5008, June.
    4. Wooseok Jang & Yongtae Park & Hyeonju Seol, 2021. "Identifying emerging technologies using expert opinions on the future: A topic modeling and fuzzy clustering approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6505-6532, August.
    5. Eunsuk Chun & Sungchan Jun & Chulung Lee, 2021. "Identification of Promising Smart Farm Technologies and Development of Technology Roadmap Using Patent Map Analysis," Sustainability, MDPI, vol. 13(19), pages 1-22, September.

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