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Topic-based technology mapping using patent data analysis: A case study of vehicle tires

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  • Ghaffari, Mohsen
  • Aliahmadi, Alireza
  • Khalkhali, Abolfazl
  • Zakery, Amir
  • Daim, Tugrul U.
  • Yalcin, Haydar

Abstract

The analysis of patent certificates for the purpose of determining the technologies of an industry is a method that has been used by experts and researchers of technology management and technology forecasting for nearly two decades. Meanwhile, using different techniques and software and completing the experiences of past researches have increased the speed, accuracy, and practicality of the relevant reports. In this study, the tire industry has been investigated with regard to its prominent role in the future automobile and transportation industry. All tire-related patent certificates in the last 20 years were extracted from the Derwent Innovation Index database using a search string and IPC codes, and with the help of Latent Dirichlet Allocation (LDA) which is an unsupervised machine learning method, the relevant technology areas were extracted. The analysis of technologies and forecasting future technology areas were conducted regarding the share and growth rate of each technology in two 10-year periods (2000–2009 and 2010–2019) and the study of trends and technical indicators related to the industry and value chain. The analysis of nine technology areas considered by tire industry innovators during the last 20 years, as well as the analysis of trends and effective factors on these technologies indicated that the fields of airless tires and intelligent tires technology areas would be highly welcomed in the future and become the dominant and extensively-used technologies of the tire industry in the future.

Suggested Citation

  • Ghaffari, Mohsen & Aliahmadi, Alireza & Khalkhali, Abolfazl & Zakery, Amir & Daim, Tugrul U. & Yalcin, Haydar, 2023. "Topic-based technology mapping using patent data analysis: A case study of vehicle tires," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    • Handle: RePEc:eee:tefoso:v:193:y:2023:i:c:s0040162523002615
    DOI: 10.1016/j.techfore.2023.122576
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    Cited by:

    1. Seok Jin Youn & Yong-Jae Lee & Ha-Eun Han & Chang-Woo Lee & Donggyun Sohn & Chulung Lee, 2024. "A Data Analytics and Machine Learning Approach to Develop a Technology Roadmap for Next-Generation Logistics Utilizing Underground Systems," Sustainability, MDPI, vol. 16(15), pages 1-32, August.
    2. 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.
    3. Qiang Gao & Man Jiang, 2024. "Exploring technology fusion by combining latent Dirichlet allocation with Doc2vec: a case of digital medicine and machine learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4043-4070, July.

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