IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v18y2024i1s1751157724000063.html
   My bibliography  Save this article

Disruptive development path measurement for emerging technologies based on the patent citation network

Author

Listed:
  • Wang, Xiaoli
  • Liang, Wenting
  • Ye, Xuanting
  • Chen, Lingdi
  • Liu, Yun

Abstract

Studying disruptive innovation development paths for emerging technologies helps trace and grasp key core technologies development, promoting innovation and development in emerging technologies and industries. This paper measures the innovation development path for emerging technology, including: (1) improving the triple citation network and quantifying disruptive measurement by designing a technological disruption model; (2) proposing a contraction method for the citation network from the dataset perspective; (3) proposing a method to extract the main path using technology disruption degree as a criterion for citation networks importance; (4) taking the sintering technology in 3-D printing technology as the empirical object with 12,662 patent families from 1997 to 2019. The empirical results indicate that the disruption degree value is determined by the transitive citation relationship without the co-citation relationship, and the closed-loop structures are effectively removed, thereby reducing the size of the dataset. The proposed disruption quantification method can support effective evaluation of technological innovation levels and decision-making for the research and development (R&D) direction and resource allocation.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:infome:v:18:y:2024:i:1:s1751157724000063
    DOI: 10.1016/j.joi.2024.101493
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157724000063
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.joi.2024.101493?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. Silverberg, Gerald & Verspagen, Bart, 2007. "The size distribution of innovations revisited: An application of extreme value statistics to citation and value measures of patent significance," Journal of Econometrics, Elsevier, vol. 139(2), pages 318-339, August.
    2. Lingfei Wu & Dashun Wang & James A. Evans, 2019. "Large teams develop and small teams disrupt science and technology," Nature, Nature, vol. 566(7744), pages 378-382, February.
    3. Ichiro Watanabe & Soichiro Takagi, 2021. "Technological Trajectory Analysis of Patent Citation Networks: Examining the Technological Evolution of Computer Graphic Processing Systems," The Review of Socionetwork Strategies, Springer, vol. 15(1), pages 1-25, June.
    4. Vladimir Batagelj, 2020. "On fractional approach to analysis of linked networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 621-633, May.
    5. Massucci, Francesco Alessandro & Docampo, Domingo, 2019. "Measuring the academic reputation through citation networks via PageRank," Journal of Informetrics, Elsevier, vol. 13(1), pages 185-201.
    6. Jae Ha Gwak & So Young Sohn, 2018. "A novel approach to explore patent development paths for subfield technologies," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(3), pages 410-419, March.
    7. Jiang, Xiaorui & Zhuge, Hai, 2019. "Forward search path count as an alternative indirect citation impact indicator," Journal of Informetrics, Elsevier, vol. 13(4).
    8. Ruan, Xuanmin & Zhu, Yuanyang & Li, Jiang & Cheng, Ying, 2020. "Predicting the citation counts of individual papers via a BP neural network," Journal of Informetrics, Elsevier, vol. 14(3).
    9. Junmo Kim & Juneseuk Shin, 2018. "Mapping extended technological trajectories: integration of main path, derivative paths, and technology junctures," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1439-1459, September.
    10. 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).
    11. Maresch, Daniela & Gartner, Johannes, 2020. "Make disruptive technological change happen - The case of additive manufacturing," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    12. 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).
    13. John S. Liu & Louis Y.Y. Lu, 2012. "An integrated approach for main path analysis: Development of the Hirsch index as an example," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(3), pages 528-542, March.
    14. Huang, Chen-Hao & Liu, John S. & Ho, Mei Hsiu-Ching & Chou, Tzu-Chuan, 2022. "Towards more convergent main paths: A relevance-based approach," Journal of Informetrics, Elsevier, vol. 16(3).
    15. Cagnin, Cristiano & Havas, Attila & Saritas, Ozcan, 2013. "Future-oriented technology analysis: Its potential to address disruptive transformations," Technological Forecasting and Social Change, Elsevier, vol. 80(3), pages 379-385.
    16. John S. Liu & Louis Y.Y. Lu, 2012. "An integrated approach for main path analysis: Development of the Hirsch index as an example," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(3), pages 528-542, March.
    17. John S. Liu & Louis Y. Y. Lu & Mei Hsiu-Ching Ho, 2019. "A few notes on main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 379-391, April.
    18. Russell J. Funk & Jason Owen-Smith, 2017. "A Dynamic Network Measure of Technological Change," Management Science, INFORMS, vol. 63(3), pages 791-817, March.
    19. Chandra, Praveena & Dong, Andy, 2018. "The relation between knowledge accumulation and technical value in interdisciplinary technologies," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 235-244.
    20. Yu, Dejian & Pan, Tianxing, 2021. "Tracing the main path of interdisciplinary research considering citation preference: A case from blockchain domain," Journal of Informetrics, Elsevier, vol. 15(2).
    21. Pin Li & Guoli Yang & Chuanqi Wang, 2019. "Visual topical analysis of library and information science," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1753-1791, December.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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).
    3. 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).
    4. Kuan, Chung-Huei, 2024. "Integrating prior field knowledge as key documents with main path analysis utilizing key-node path search," Journal of Informetrics, Elsevier, vol. 18(3).
    5. 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.
    6. Abderahman Rejeb & Alireza Abdollahi & Karim Rejeb & Mohamed M. Mostafa, 2023. "Tracing knowledge evolution flows in scholarly restaurant research: a main path analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2183-2209, June.
    7. Flavia Filippin, 2021. "Do main paths reflect technological trajectories? Applying main path analysis to the semiconductor manufacturing industry," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6443-6477, August.
    8. Qu, Guannan & Chen, Jin & Zhang, Ruhao & Wang, Luyao & Yang, Yayu, 2023. "Technological search strategy and breakthrough innovation: An integrated approach based on main-path analysis," Technological Forecasting and Social Change, Elsevier, vol. 196(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. Huang, Chen-Hao & Liu, John S. & Ho, Mei Hsiu-Ching & Chou, Tzu-Chuan, 2022. "Towards more convergent main paths: A relevance-based approach," Journal of Informetrics, Elsevier, vol. 16(3).
    11. Bhatt, Priyanka C. & Lai, Kuei-Kuei & Drave, Vinayak A. & Lu, Tzu-Chuen & Kumar, Vimal, 2023. "Patent analysis based technology innovation assessment with the lens of disruptive innovation theory: A case of blockchain technological trajectories," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    12. 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).
    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).
    14. 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.
    15. Alessandri, Enrico, 2023. "Identifying technological trajectories in the mining sector using patent citation networks," Resources Policy, Elsevier, vol. 80(C).
    16. Zhou, Yong & Yang, Qijin & Lu, Shuo, 2023. "Research on the identification and formation mechanism of the main path of digital technology diffusion: Empirical evidence from China," Technology in Society, Elsevier, vol. 75(C).
    17. 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.
    18. Ichiro Watanabe & Soichiro Takagi, 2022. "NK model-based analysis of technological trajectories: a study on the technological field of computer graphic processing systems," Evolutionary and Institutional Economics Review, Springer, vol. 19(1), pages 119-140, April.
    19. Chung-Huei Kuan, 2023. "Does main path analysis prefer longer paths?," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 841-851, January.
    20. Na Liu & Philip Shapira & Xiaoxu Yue & Jiancheng Guan, 2021. "Mapping technological innovation dynamics in artificial intelligence domains: Evidence from a global patent analysis," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-20, December.

    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:eee:infome:v:18:y:2024:i:1:s1751157724000063. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    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.