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Developmental Trajectories in Electrical Steel Technology Using Patent Information

Author

Listed:
  • Donghyun You

    (Department of Information System, Hanyang University, Seoul 04763, Korea)

  • Hyunseok Park

    (Department of Information System, Hanyang University, Seoul 04763, Korea)

Abstract

Recently there has been growing demand for low-electricity consuming transformers and electric vehicles due to global trend of reducing use of fossil fuels and the role of electrical steel became important. Tracing and analyzing research trend and development of electrical steel will give insight for development of R&D direction and strategies. We used patent citation network and GBFP (Genetic Backward-Forward Path analysis) to identify technological trajectories of electrical steel domain and patent contents with other papers to qualitatively analyze research trend of the domain. As a result, we found that some sub-domains of electrical steel domain had close technological relationship to each other in their developmental paths and suggested further R&D direction in the electrical steel technology.

Suggested Citation

  • Donghyun You & Hyunseok Park, 2018. "Developmental Trajectories in Electrical Steel Technology Using Patent Information," Sustainability, MDPI, vol. 10(8), pages 1-15, August.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:8:p:2728-:d:161644
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    References listed on IDEAS

    as
    1. Christopher L. Benson & Christopher L. Magee, 2015. "Technology structural implications from the extension of a patent search method," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 1965-1985, March.
    2. Christopher L Benson & Christopher L Magee, 2015. "Quantitative Determination of Technological Improvement from Patent Data," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-23, April.
    3. Sungchul Choi & Hyunseok Park, 2016. "Investigation of Strategic Changes Using Patent Co-Inventor Network Analysis: The Case of Samsung Electronics," Sustainability, MDPI, vol. 8(12), pages 1-13, December.
    4. Bart Verspagen, 2007. "Mapping Technological Trajectories As Patent Citation Networks: A Study On The History Of Fuel Cell Research," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 10(01), pages 93-115.
    5. Magee, C.L. & Basnet, S. & Funk, J.L. & Benson, C.L., 2016. "Quantitative empirical trends in technical performance," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 237-246.
    6. Janghyeok Yoon & Kwangsoo Kim, 2011. "Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 213-228, July.
    7. Martinelli, Arianna, 2012. "An emerging paradigm or just another trajectory? Understanding the nature of technological changes using engineering heuristics in the telecommunications switching industry," Research Policy, Elsevier, vol. 41(2), pages 414-429.
    8. Arianna Martinelli & Önder Nomaler, 2014. "Measuring knowledge persistence: a genetic approach to patent citation networks," Journal of Evolutionary Economics, Springer, vol. 24(3), pages 623-652, July.
    9. Christopher L. Benson & Christopher L. Magee, 2013. "A hybrid keyword and patent class methodology for selecting relevant sets of patents for a technological field," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 69-82, July.
    10. Fleming, Lee & Sorenson, Olav, 2001. "Technology as a complex adaptive system: evidence from patent data," Research Policy, Elsevier, vol. 30(7), pages 1019-1039, August.
    11. Christopher L. Benson & Christopher L. Magee, 2013. "Erratum to: A hybrid keyword and patent class methodology for selecting relevant sets of patents for a technological field," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 83-83, July.
    12. Lee Fleming, 2001. "Recombinant Uncertainty in Technological Search," Management Science, INFORMS, vol. 47(1), pages 117-132, January.
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    Cited by:

    1. Singh, Anuraag & Triulzi, Giorgio & Magee, Christopher L., 2021. "Technological improvement rate predictions for all technologies: Use of patent data and an extended domain description," Research Policy, Elsevier, vol. 50(9).
    2. Lai, Kuei-Kuei & Bhatt, Priyanka C. & Kumar, Vimal & Chen, Hsueh-Chen & Chang, Yu-Hsin & Su, Fang-Pei, 2021. "Identifying the impact of patent family on the patent trajectory: A case of thin film solar cells technological trajectories," Journal of Informetrics, Elsevier, vol. 15(2).
    3. Anuraag Singh & Giorgio Triulzi & Christopher L. Magee, 2020. "Technological improvement rate estimates for all technologies: Use of patent data and an extended domain description," Papers 2004.13919, arXiv.org.
    4. Feng, Sida & Magee, Christopher L., 2020. "Technological development of key domains in electric vehicles: Improvement rates, technology trajectories and key assignees," Applied Energy, Elsevier, vol. 260(C).
    5. Mun, Changbae & Yoon, Sejun & Raghavan, Nagarajan & Hwang, Dongwook & Basnet, Subarna & Park, Hyunseok, 2021. "Function score-based technological trend analysis," Technovation, Elsevier, vol. 101(C).
    6. 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.
    7. Zhenfu Li & Yixuan Wang & Zhao Deng, 2022. "Research on Evolution Characteristics and Factors of Nordic Green Patent Citation Network," Sustainability, MDPI, vol. 14(13), pages 1-21, June.

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