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Using technological entropy to identify technology life cycle

Author

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  • Lin, Deming
  • Liu, Wenbin
  • Guo, Yinxin
  • Meyer, Martin

Abstract

Identification of technology life cycles(TLC) provides a crucial basis for managing national policy, regional planning, and enterprise investment. Thus, it is a significant challenge to determine the stages of TLC. To this end, an entropy-based indicator is proposed, as well as a quantitative method based on the S-curve of entropy is established to identify the stages of TLC. Furthermore, the effectiveness of the method is verified by the analogy of three typical cases (thin-film-transistor liquid-crystal displays, cathode ray tubes, and nano-biosensors). It is clear that the entropy calculation produces a sum of overall distributions for patent applications against the researchers in the field to be studied, which can be used to find out the stage changes of TLC, while the other analysis considers trends of many patent active measures such as patent applications and citations collectively, to figure out the changes. Thus, the former constructs an index that has clear meanings and then uses its characterization to identify the changes logically, while the latter can only try to identify them empirically often with no trivial difficulties as these trends are often inconsistent. Finally, three-dimensional (3D) printing is investigated as an empirical case study, which reveals that 3D printing is still in its growth stage.

Suggested Citation

  • Lin, Deming & Liu, Wenbin & Guo, Yinxin & Meyer, Martin, 2021. "Using technological entropy to identify technology life cycle," Journal of Informetrics, Elsevier, vol. 15(2).
    • Handle: RePEc:eee:infome:v:15:y:2021:i:2:s1751157721000080
    DOI: 10.1016/j.joi.2021.101137
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    Cited by:

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    3. 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).
    4. Terrazas-Santamaria Diana & Mendoza-Palacios Saul & Berasaluce-Iza Julen, 2023. "An Alternative Approach to Frequency of Patent Technology Codes: The Case of Renewable Energy Generation," Economics - The Open-Access, Open-Assessment Journal, De Gruyter, vol. 17(1), pages 1-14, January.
    5. 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.
    6. 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.
    7. 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).

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