IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v128y2023i4d10.1007_s11192-023-04652-2.html
   My bibliography  Save this article

Main path analysis for technological development using SAO structure and DEMATEL based on keyword causality

Author

Listed:
  • Myeongji Oh

    (Dongguk University)

  • Hyejin Jang

    (Dongguk University)

  • Sunhye Kim

    (Dongguk University)

  • Byungun Yoon

    (Dongguk University)

Abstract

Main path analysis (MPA) is a method for efficiently analyzing technological trends, which change rapidly in competitive environments. In general, MPA is based on citation networks, and it is used to derive the most key path in a complex network. However, the existing studies using MPA do not use important textual information of patents, except for citation data. In this paper, we suggest a new MPA based on patent documents to identify the main path of technological evolution. For this purpose, first, we used the subject-action-object structure to derive core keywords based on causal relationships in patent claims. Second, the DEcision-MAking Trial and Evaluation Laboratory (DEMATEL) technique was applied to draw link weights between patents where causal relationships of keywords were reflected. Finally, a main path in a patent network was identified using the global main path and key-route main path analysis methods. In this paper, we collected and analyzed patent data related to self-driving car technologies, and we verified the technical changes in the main path obtained based on the proposed approach. We found that the generic technologies of the self-driving operation had the strongest influence on the other self-driving car technologies in the sensing-planning-acting steps.

Suggested Citation

  • Myeongji Oh & Hyejin Jang & Sunhye Kim & Byungun Yoon, 2023. "Main path analysis for technological development using SAO structure and DEMATEL based on keyword causality," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(4), pages 2079-2104, April.
    • Handle: RePEc:spr:scient:v:128:y:2023:i:4:d:10.1007_s11192-023-04652-2
    DOI: 10.1007/s11192-023-04652-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-023-04652-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-023-04652-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hwang, Seonho & Shin, Juneseuk, 2019. "Extending technological trajectories to latest technological changes by overcoming time lags," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 142-153.
    2. Liao, Shu-Chun & Chou, Tzu-Chuan & Huang, Chen-Hao, 2022. "Revisiting the development trajectory of the digital divide: A main path analysis approach," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    3. Hiran H. Lathabai & Thara Prabhakaran & Manoj Changat, 2017. "Contextual productivity assessment of authors and journals: a network scientometric approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 711-737, February.
    4. Jiang, Xiaorui & Zhuge, Hai, 2019. "Forward search path count as an alternative indirect citation impact indicator," Journal of Informetrics, Elsevier, vol. 13(4).
    5. Yu, Dejian & Sheng, Libo, 2021. "Influence difference main path analysis: Evidence from DNA and blockchain domain citation networks," Journal of Informetrics, Elsevier, vol. 15(4).
    6. Yang, Chao & Huang, Cui & Su, Jun, 2018. "An improved SAO network-based method for technology trend analysis: A case study of graphene," Journal of Informetrics, Elsevier, vol. 12(1), pages 271-286.
    7. Sang-Bing Tsai & Jie Zhou & Yang Gao & Jiangtao Wang & Guodong Li & Yuxiang Zheng & Peng Ren & Wei Xu, 2017. "Combining FMEA with DEMATEL models to solve production process problems," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-15, August.
    8. Joung, Junegak & Kim, Kwangsoo, 2017. "Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 281-292.
    9. Bruno Miranda Henrique & Vinicius Amorim Sobreiro & Herbert Kimura, 2018. "Building direct citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 817-832, May.
    10. Xuefeng Wang & Pingping Ma & Ying Huang & Junfang Guo & Donghua Zhu & Alan L. Porter & Zhinan Wang, 2017. "Combining SAO semantic analysis and morphology analysis to identify technology opportunities," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 3-24, April.
    11. Graham Dixon & P. Sol Hart & Christopher Clarke & Nicole H. O’Donnell & Jay Hmielowski, 2020. "What drives support for self-driving car technology in the United States?," Journal of Risk Research, Taylor & Francis Journals, vol. 23(3), pages 275-287, March.
    12. Guo, Junfang & Wang, Xuefeng & Li, Qianrui & Zhu, Donghua, 2016. "Subject–action–object-based morphology analysis for determining the direction of technological change," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 27-40.
    13. Chen, Liang & Xu, Shuo & Zhu, Lijun & Zhang, Jing & Xu, Haiyun & Yang, Guancan, 2022. "A semantic main path analysis method to identify multiple developmental trajectories," Journal of Informetrics, Elsevier, vol. 16(2).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jang, Hyejin & Lee, Suyeong & Yoon, Byungun, 2023. "Data-driven techno-socio co-evolution analysis based on a topic model and a hidden Markov model," Technovation, Elsevier, vol. 126(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yu, Dejian & Yan, Zhaoping, 2023. "Main path analysis considering citation structure and content: Case studies in different domains," Journal of Informetrics, Elsevier, vol. 17(1).
    2. Xiaorui Jiang & Junjun Liu, 2023. "Extracting the evolutionary backbone of scientific domains: The semantic main path network analysis approach based on citation context analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(5), pages 546-569, May.
    3. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    4. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
    5. Chen, Liang & Xu, Shuo & Zhu, Lijun & Zhang, Jing & Yang, Guancan & Xu, Haiyun, 2022. "A deep learning based method benefiting from characteristics of patents for semantic relation classification," Journal of Informetrics, Elsevier, vol. 16(3).
    6. Zhou, Xiao & Huang, Lu & Porter, Alan & Vicente-Gomila, Jose M., 2019. "Tracing the system transformations and innovation pathways of an emerging technology: Solid lipid nanoparticles," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 785-794.
    7. Richarz, Jan & Wegewitz, Stephan & Henn, Sarah & Müller, Dirk, 2023. "Graph-based research field analysis by the use of natural language processing: An overview of German energy research," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    8. Vicente-Gomila, J.M. & Artacho-Ramírez, M.A. & Ting, Ma & Porter, A.L., 2021. "Combining tech mining and semantic TRIZ for technology assessment: Dye-sensitized solar cell as a case," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    9. Dejian Yu & Zhaoping Yan, 2022. "Combining machine learning and main path analysis to identify research front: from the perspective of science-technology linkage," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 4251-4274, July.
    10. Liang Chen & Shuo Xu & Lijun Zhu & Jing Zhang & Xiaoping Lei & Guancan Yang, 2020. "A deep learning based method for extracting semantic information from patent documents," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 289-312, October.
    11. Wang, Xiaoli & Liang, Wenting & Ye, Xuanting & Chen, Lingdi & Liu, Yun, 2024. "Disruptive development path measurement for emerging technologies based on the patent citation network," Journal of Informetrics, Elsevier, vol. 18(1).
    12. Mun, Changbae & Yoon, Sejun & Raghavan, Nagarajan & Hwang, Dongwook & Basnet, Subarna & Park, Hyunseok, 2021. "Function score-based technological trend analysis," Technovation, Elsevier, vol. 101(C).
    13. Jiang, Man & Yang, Siluo & Gao, Qiang, 2024. "Multidimensional indicators to identify emerging technologies: Perspective of technological knowledge flow," Journal of Informetrics, Elsevier, vol. 18(1).
    14. Zhu, Lin & Cunningham, Scott W., 2022. "Unveiling the knowledge structure of technological forecasting and social change (1969–2020) through an NMF-based hierarchical topic model," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    15. Jeong, Yujin & Park, Inchae & Yoon, Byungun, 2019. "Identifying emerging Research and Business Development (R&BD) areas based on topic modeling and visualization with intellectual property right data," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 655-672.
    16. Zhang, Yi & Huang, Ying & Porter, Alan L. & Zhang, Guangquan & Lu, Jie, 2019. "Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 795-807.
    17. Xuefeng Wang & Huichao Ren & Yun Chen & Yuqin Liu & Yali Qiao & Ying Huang, 2019. "Measuring patent similarity with SAO semantic analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 1-23, October.
    18. Dejing Kong & Jianzhong Yang & Lingfeng Li, 2020. "Early identification of technological convergence in numerical control machine tool: a deep learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1983-2009, December.
    19. Kyuwoong Kim & Kyeongmin Park & Sungjoo Lee, 2019. "Investigating technology opportunities: the use of SAOx analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 45-70, January.
    20. Shuo Xu & Liyuan Hao & Xin An & Hongshen Pang & Ting Li, 2020. "Review on emerging research topics with key-route main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 607-624, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:128:y:2023:i:4:d:10.1007_s11192-023-04652-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.