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

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  • 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
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    3. 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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