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Research on potential disruptive technology identification based on technology network.

Mingli Ding1, Wangke Yu1,2, Ran Li1

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Identifying disruptive technology is challenging. This study uses life cycle theory and network dynamics to pinpoint future tech trends, finding data acquisition and AI in unmanned aerial vehicles (UAVs) are key disruptive areas.

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Area of Science:

  • Technology Management
  • Innovation Studies
  • Artificial Intelligence

Background:

  • Disruptive technologies significantly reshape industries and economies.
  • Identifying emerging disruptive technologies remains a complex challenge.
  • Existing methods lack technical granularity and effectiveness in topic identification.

Purpose of the Study:

  • To enhance the technical relevance of disruptive technology identification.
  • To improve the granularity and effectiveness of identifying disruptive technology topics.
  • To propose a novel model for identifying potential disruptive technologies.

Main Methods:

  • Applying life cycle theory to divide technology time stages.
  • Constructing and analyzing dynamic technology networks.
  • Utilizing Latent Dirichlet Allocation (LDA) topic modeling for content clarification.

Main Results:

  • The model effectively identifies potential disruptive technologies.
  • Data acquisition, main equipment, and ground platform intelligence are key disruptive areas in large civil UAVs.
  • The approach demonstrates feasibility and effectiveness using a case study.

Conclusions:

  • The proposed model offers a robust framework for identifying disruptive technologies.
  • Understanding specific disruptive elements like data acquisition and AI is crucial for future technological advancement.
  • This research provides valuable insights for technology forecasting and strategic planning.