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Analysis of international competitive situation of key core technology in strategic emerging industries: New generation of information technology industry as an example

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  • Fengyang Wang
  • Zongyuan Huang

Abstract

In the context of the current technological revolution and unprecedented major changes, countries are facing the situation of accelerating the development of key core technologies, which is caused by the transformation from the dispute over trade to the dispute over ecology and scientific and technological strength. Competitive situation analysis is an important link of key core technology innovation. The construction of a universal model of international competitive situation analysis of key core technology can provide scientific support for decision makers of science and technology innovation to solve technical difficulties. This study takes the new generation of information technology industry as an example, identifies key core technologies of the industry and evaluates the competitive situation of the major world countries. Studies indicate that in the field of new generation information technology, the US and Japan is in the leading position globally. In addition, China has active innovation activities in all fields, but overall there remains a considerable gap with the world-leading level, and its R&D quality needs to be further improved.

Suggested Citation

  • Fengyang Wang & Zongyuan Huang, 2023. "Analysis of international competitive situation of key core technology in strategic emerging industries: New generation of information technology industry as an example," PLOS ONE, Public Library of Science, vol. 18(6), pages 1-29, June.
  • Handle: RePEc:plo:pone00:0287034
    DOI: 10.1371/journal.pone.0287034
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    References listed on IDEAS

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    1. Freeman, Chris, 2002. "Continental, national and sub-national innovation systems--complementarity and economic growth," Research Policy, Elsevier, vol. 31(2), pages 191-211, February.
    2. Xuandi Gong & Jinluan Ren & Xinyan Wang & Li Zeng, 2022. "Technical Trends and Competitive Situation in Respect of Metahuman—From Product Modules and Technical Topics to Patent Data," Sustainability, MDPI, vol. 15(1), pages 1-23, December.
    3. Tylecote, Andrew, 2019. "Biotechnology as a new techno-economic paradigm that will help drive the world economy and mitigate climate change," Research Policy, Elsevier, vol. 48(4), pages 858-868.
    4. Choi, Mincheol & Lee, Chang-Yang, 2021. "Technological diversification and R&D productivity: The moderating effects of knowledge spillovers and core-technology competence," Technovation, Elsevier, vol. 104(C).
    5. Albert, M. B. & Avery, D. & Narin, F. & McAllister, P., 1991. "Direct validation of citation counts as indicators of industrially important patents," Research Policy, Elsevier, vol. 20(3), pages 251-259, June.
    6. Santoalha, Artur & Consoli, Davide & Castellacci, Fulvio, 2021. "Digital skills, relatedness and green diversification: A study of European regions," Research Policy, Elsevier, vol. 50(9).
    7. Noh, Heeyong & Song, Young-Keun & Lee, Sungjoo, 2016. "Identifying emerging core technologies for the future: Case study of patents published by leading telecommunication organizations," Telecommunications Policy, Elsevier, vol. 40(10), pages 956-970.
    8. Harhoff, Dietmar & Scherer, Frederic M. & Vopel, Katrin, 2003. "Citations, family size, opposition and the value of patent rights," Research Policy, Elsevier, vol. 32(8), pages 1343-1363, September.
    9. Ranfeng Qiu & John Cantwell, 2018. "The international geography of general purpose technologies (GPTs) and internationalisation of corporate technological innovation," Industry and Innovation, Taylor & Francis Journals, vol. 25(1), pages 1-24, January.
    10. Na Liu & Philip Shapira & Xiaoxu Yue & Jiancheng Guan, 2021. "Mapping technological innovation dynamics in artificial intelligence domains: Evidence from a global patent analysis," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-20, December.
    11. Breitzman, Anthony & Thomas, Patrick, 2015. "The Emerging Clusters Model: A tool for identifying emerging technologies across multiple patent systems," Research Policy, Elsevier, vol. 44(1), pages 195-205.
    12. Jorgenson, Dale W. & Vu, Khuong M., 2016. "The ICT revolution, world economic growth, and policy issues," Telecommunications Policy, Elsevier, vol. 40(5), pages 383-397.
    13. Francois P. Kabore & Walter G. Park, 2019. "Can patent family size and composition signal patent value?," Applied Economics, Taylor & Francis Journals, vol. 51(60), pages 6476-6496, December.
    14. Manajit Chakraborty & Maksym Byshkin & Fabio Crestani, 2020. "Patent citation network analysis: A perspective from descriptive statistics and ERGMs," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-28, December.
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