[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v181y2022ics0040162522002852.html
   My bibliography  Save this article

Technology life cycle analysis: From the dynamic perspective of patent citation networks

Author

Listed:
  • Huang, Ying
  • Li, Ruinan
  • Zou, Fang
  • Jiang, Lidan
  • Porter, Alan L.
  • Zhang, Lin
Abstract
Technology development is a blend of inheritance and innovation that follows certain rules along a technology trajectory. This trajectory charts a technology's evolution and is among the best-supporting data for decision making. Technology life cycle (TLC) analysis, as one of the foundational topics in the field of technology management, is of vital importance for describing the evolutionary path of technology. However, most current methods simply rely on static patent indicators, which neglect the dynamic aspects of a technology's development. To overcome this limitation, we propose a framework of diverse characteristics and novel procedures for identifying the entire span of a technology's life cycle from a series of patent citation networks. After retrieving patent data with a well-defined search strategy, a sequence of patent citation networks is constructed year by year. Network attribute indicators are then calculated for each of the networks and used to inform an evolution model, which reveals the TLC. To illustrate the strengths and potential of this approach, we have taken additive manufacturing technology as an example for analysis. Through the framework, we are able to demonstrate the technology at various stages of its maturity and how it has changed over time, along with some areas of competitive advantage and promising future opportunities.

Suggested Citation

  • Huang, Ying & Li, Ruinan & Zou, Fang & Jiang, Lidan & Porter, Alan L. & Zhang, Lin, 2022. "Technology life cycle analysis: From the dynamic perspective of patent citation networks," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    • Handle: RePEc:eee:tefoso:v:181:y:2022:i:c:s0040162522002852
    DOI: 10.1016/j.techfore.2022.121760
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162522002852
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2022.121760?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. Grimaldi, Michele & Cricelli, Livio & Di Giovanni, Martina & Rogo, Francesco, 2015. "The patent portfolio value analysis: A new framework to leverage patent information for strategic technology planning," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 286-302.
    2. Roberto Fontana & Alessandro Nuvolari & Bart Verspagen, 2009. "Mapping technological trajectories as patent citation networks. An application to data communication standards," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 18(4), pages 311-336.
    3. Arthur, W Brian, 1989. "Competing Technologies, Increasing Returns, and Lock-In by Historical Events," Economic Journal, Royal Economic Society, vol. 99(394), pages 116-131, March.
    4. Chen, Ssu-Han & Huang, Mu-Hsuan & Chen, Dar-Zen, 2012. "Identifying and visualizing technology evolution: A case study of smart grid technology," Technological Forecasting and Social Change, Elsevier, vol. 79(6), pages 1099-1110.
    5. Hanlin You & Mengjun Li & Keith W. Hipel & Jiang Jiang & Bingfeng Ge & Hante Duan, 2017. "Development trend forecasting for coherent light generator technology based on patent citation network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 297-315, April.
    6. Lin, Deming & Liu, Wenbin & Guo, Yinxin & Meyer, Martin, 2021. "Using technological entropy to identify technology life cycle," Journal of Informetrics, Elsevier, vol. 15(2).
    7. 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.
    8. Ying Huang & Donghua Zhu & Yue Qian & Yi Zhang & Alan L. Porter & Yuqin Liu & Ying Guo, 2017. "A hybrid method to trace technology evolution pathways: a case study of 3D printing," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 185-204, April.
    9. Ying Huang & Jannik Schuehle & Alan L. Porter & Jan Youtie, 2015. "A systematic method to create search strategies for emerging technologies based on the Web of Science: illustrated for ‘Big Data’," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 2005-2022, December.
    10. Gao, Lidan & Porter, Alan L. & Wang, Jing & Fang, Shu & Zhang, Xian & Ma, Tingting & Wang, Wenping & Huang, Lu, 2013. "Technology life cycle analysis method based on patent documents," Technological Forecasting and Social Change, Elsevier, vol. 80(3), pages 398-407.
    11. Huailan Liu & Zhiwang Chen & Jie Tang & Yuan Zhou & Sheng Liu, 2020. "Mapping the technology evolution path: a novel model for dynamic topic detection and tracking," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2043-2090, December.
    12. Martin Kalthaus, 2020. "Knowledge recombination along the technology life cycle," Journal of Evolutionary Economics, Springer, vol. 30(3), pages 643-704, July.
    13. Dosi, Giovanni, 1993. "Technological paradigms and technological trajectories : A suggested interpretation of the determinants and directions of technical change," Research Policy, Elsevier, vol. 22(2), pages 102-103, April.
    14. von Wartburg, Iwan & Teichert, Thorsten & Rost, Katja, 2005. "Inventive progress measured by multi-stage patent citation analysis," Research Policy, Elsevier, vol. 34(10), pages 1591-1607, December.
    15. Lee, Changyong & Cho, Yangrae & Seol, Hyeonju & Park, Yongtae, 2012. "A stochastic patent citation analysis approach to assessing future technological impacts," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 16-29.
    16. Loet Leydesdorff & Floortje Alkemade & Gaston Heimeriks & Rinke Hoekstra, 2015. "Patents as instruments for exploring innovation dynamics: geographic and technological perspectives on “photovoltaic cells”," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 629-651, January.
    17. 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.
    18. Ta-Shun Cho & Hsin-Yu Shih, 2011. "Patent citation network analysis of core and emerging technologies in Taiwan: 1997–2008," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(3), pages 795-811, December.
    19. Chang, Shu-Hao & Fan, Chin-Yuan, 2016. "Identification of the technology life cycle of telematics: A patent-based analytical perspective," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 1-10.
    20. 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.
    21. Gergely Palla & Albert-László Barabási & Tamás Vicsek, 2007. "Quantifying social group evolution," Nature, Nature, vol. 446(7136), pages 664-667, April.
    22. Euiseok Kim & Yongrae Cho & Wonjoon Kim, 2014. "Dynamic patterns of technological convergence in printed electronics technologies: patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 975-998, February.
    23. Birgitte Andersen, 1999. "The hunt for S-shaped growth paths in technological innovation: a patent study," Journal of Evolutionary Economics, Springer, vol. 9(4), pages 487-526.
    24. Lee, Changyong & Kim, Juram & Kwon, Ohjin & Woo, Han-Gyun, 2016. "Stochastic technology life cycle analysis using multiple patent indicators," Technological Forecasting and Social Change, Elsevier, vol. 106(C), pages 53-64.
    25. Haupt, Reinhard & Kloyer, Martin & Lange, Marcus, 2007. "Patent indicators for the technology life cycle development," Research Policy, Elsevier, vol. 36(3), pages 387-398, April.
    26. Péter Érdi & Kinga Makovi & Zoltán Somogyvári & Katherine Strandburg & Jan Tobochnik & Péter Volf & László Zalányi, 2013. "Prediction of emerging technologies based on analysis of the US patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(1), pages 225-242, April.
    27. Dedehayir, Ozgur & Steinert, Martin, 2016. "The hype cycle model: A review and future directions," Technological Forecasting and Social Change, Elsevier, vol. 108(C), pages 28-41.
    28. Huenteler, Joern & Schmidt, Tobias S. & Ossenbrink, Jan & Hoffmann, Volker H., 2016. "Technology life-cycles in the energy sector — Technological characteristics and the role of deployment for innovation," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 102-121.
    29. Mina, A. & Ramlogan, R. & Tampubolon, G. & Metcalfe, J.S., 2007. "Mapping evolutionary trajectories: Applications to the growth and transformation of medical knowledge," Research Policy, Elsevier, vol. 36(5), pages 789-806, June.
    30. Munan Li, 2015. "A novel three-dimension perspective to explore technology evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1679-1697, December.
    31. Huang, Ying & Porter, Alan L. & Zhang, Yi & Lian, Xiangpeng & Guo, Ying, 2019. "An assessment of technology forecasting: Revisiting earlier analyses on dye-sensitized solar cells (DSSCs)," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 831-843.
    32. E. A. Leicht & G. Clarkson & K. Shedden & M. E.J. Newman, 2007. "Large-scale structure of time evolving citation networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 59(1), pages 75-83, September.
    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. Sajjad Shokouhyar & Mehrdad Maghsoudi & Shahrzad Khanizadeh & Saeid Jorfi, 2024. "Analyzing supply chain technology trends through network analysis and clustering techniques: a patent-based study," Annals of Operations Research, Springer, vol. 341(1), pages 313-348, October.
    2. 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).
    3. Na Zhang & Chao Sun & Min Xu & Xuemei Wang & Jia Deng, 2023. "Catching Up of Latecomer Economies in ICT for Sustainable Development: An Analysis Based on Technology Life Cycle Using Patent Data," Sustainability, MDPI, vol. 15(11), pages 1-29, June.
    4. Xi, Xi & Ren, Feifei & Yu, Lean & Yang, Jing, 2023. "Detecting the technology's evolutionary pathway using HiDS-trait-driven tech mining strategy," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    5. Podrecca, Matteo & Culot, Giovanna & Tavassoli, Sam & Orzes, Guido, 2024. "Artificial intelligence for climate change: a patent analysis in the manufacturing sector," Papers in Innovation Studies 2024/12, Lund University, CIRCLE - Centre for Innovation Research.

    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. 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. 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.
    3. Ying Huang & Donghua Zhu & Yue Qian & Yi Zhang & Alan L. Porter & Yuqin Liu & Ying Guo, 2017. "A hybrid method to trace technology evolution pathways: a case study of 3D printing," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 185-204, April.
    4. Muhamed Kudic & Mariia Shkolnykova, 2020. "From biotech to bioeconomy: New empirical evidence on the technological transition to plant-based bioeconomy based on patent data," Bremen Papers on Economics & Innovation 2002, University of Bremen, Faculty of Business Studies and Economics.
    5. Alessandri, Enrico, 2023. "Identifying technological trajectories in the mining sector using patent citation networks," Resources Policy, Elsevier, vol. 80(C).
    6. Xi, Xi & Ren, Feifei & Yu, Lean & Yang, Jing, 2023. "Detecting the technology's evolutionary pathway using HiDS-trait-driven tech mining strategy," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    7. 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.
    8. Nicoló Barbieri & François Perruchas & Davide Consoli, 2020. "Specialization, Diversification, and Environmental Technology Life Cycle," Economic Geography, Taylor & Francis Journals, vol. 96(2), pages 161-186, March.
    9. Shih-Chang Hung & John S. Liu & Louis Y. Y. Lu & Yu-Chiang Tseng, 2014. "Technological change in lithium iron phosphate battery: the key-route main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(1), pages 97-120, July.
    10. 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.
    11. Kuan, Chung-Huei & Huang, Mu-Hsuan & Chen, Dar-Zen, 2018. "Missing links: Timing characteristics and their implications for capturing contemporaneous technological developments," Journal of Informetrics, Elsevier, vol. 12(1), pages 259-270.
    12. Ciarli, Tommaso & Ràfols, Ismael, 2019. "The relation between research priorities and societal demands: The case of rice," Research Policy, Elsevier, vol. 48(4), pages 949-967.
    13. Euiseok Kim & Yongrae Cho & Wonjoon Kim, 2014. "Dynamic patterns of technological convergence in printed electronics technologies: patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 975-998, February.
    14. Sung-Seok Ko & Namuk Ko & Doyeon Kim & Hyunseok Park & Janghyeok Yoon, 2014. "Analyzing technology impact networks for R&D planning using patents: combined application of network approaches," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 917-936, October.
    15. 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.
    16. Fei Yuan & Kumiko Miyazaki, 2017. "Trajectory Identification as Proxies for Discerning the Dynamic Nature of Technological Change — The Case of Electric Vehicles Industry," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 14(01), pages 1-20, February.
    17. Wang, Lili & Jiang, Shan & Zhang, Shiyun, 2020. "Mapping technological trajectories and exploring knowledge sources: A case study of 3D printing technologies," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    18. Martin Ho & Henry CW Price & Tim S Evans & Eoin O'Sullivan, 2023. "Order in Innovation," Papers 2302.13076, arXiv.org.
    19. Li, Munan & Porter, Alan L. & Suominen, Arho & Burmaoglu, Serhat & Carley, Stephen, 2021. "An exploratory perspective to measure the emergence degree for a specific technology based on the philosophy of swarm intelligence," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    20. Wang, Zhinan & Porter, Alan L. & Wang, Xuefeng & Carley, Stephen, 2019. "An approach to identify emergent topics of technological convergence: A case study for 3D printing," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 723-732.

    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:tefoso:v:181:y:2022:i:c:s0040162522002852. 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.sciencedirect.com/science/journal/00401625 .

    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.