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Correlation Monitoring Method and model of Science-Technology-Industry in the AI Field: A Case of the Neural Network

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  • Xiaoli Wang
  • Yun Liu
  • Lingdi Chen
  • Yifan Zhang

Abstract

This article aims to analyze the correlation status and development trend among science, technology and industry in the Artificial Intelligence (AI) subfield. First, it constructs the classification system and retrieval strategy for the AI subfield, and performs data cleaning and data finishing. Second, it designs the criteria and judgment method of the Industry Development Stages (IDS) in the AI subfield with the Delphi survey method. Third, it designs the monitoring method and model of the Science-Technology-Industry (STI) correlation with the methods of scientometrics and clustering, which involves three dimensions: science and IDS, technology and IDS, science & technology clustering and IDS; especially the monitoring with the global and national perspectives. Finally, it conducts empirical application in the field of neural network. The results illustrate that the correlation monitoring method can be conducive to grasp accurately the development opportunity and trend, the advantage subfield, and the development status of Science and Technology (S&T) for the dominant countries. The United States leads the global development in the empirical field. The proposed correlation monitoring method in this article can also be applied in other fields.

Suggested Citation

  • Xiaoli Wang & Yun Liu & Lingdi Chen & Yifan Zhang, 2022. "Correlation Monitoring Method and model of Science-Technology-Industry in the AI Field: A Case of the Neural Network," SAGE Open, , vol. 12(4), pages 21582440221, December.
    • Handle: RePEc:sae:sagope:v:12:y:2022:i:4:p:21582440221141299
    DOI: 10.1177/21582440221141299
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    1. Dorner, Matthias & Harhoff, Dietmar, 2018. "A novel technology-industry concordance table based on linked inventor-establishment data," Research Policy, Elsevier, vol. 47(4), pages 768-781.
    2. Pao-Long Chang & Chao-Chan Wu & Hoang-Jyh Leu, 2010. "Using patent analyses to monitor the technological trends in an emerging field of technology: a case of carbon nanotube field emission display," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(1), pages 5-19, January.
    3. Byeongki Jeong & Janghyeok Yoon, 2017. "Competitive Intelligence Analysis of Augmented Reality Technology Using Patent Information," Sustainability, MDPI, vol. 9(4), pages 1-22, March.
    4. Esther Nanzayi Ngayua & Jianjia He & Kwabena Agyei-Boahene, 2021. "Applying advanced technologies to improve clinical trials: a systematic mapping study," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1217-1238, February.
    5. Benedetto Lepori & Sybille Hinze & Wolfgang Glänzel, 2016. "Special issue: papers from the 20th International Conference on Science and Technology Indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 2161-2164, December.
    6. White, Gareth R.T. & Samuel, Anthony, 2019. "Programmatic Advertising: Forewarning and avoiding hype-cycle failure," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 157-168.
    7. Nguyen, Ai Linh & Liu, Wenyuan & Khor, Khiam Aik & Nanetti, Andrea & Cheong, Siew Ann, 2020. "The golden eras of graphene science and technology: Bibliographic evidences from journal and patent publications," Journal of Informetrics, Elsevier, vol. 14(4).
    8. Fang Han & Christopher L. Magee, 2018. "Testing the science/technology relationship by analysis of patent citations of scientific papers after decomposition of both science and technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 767-796, August.
    9. Ling Tan & Ji Guo & Selvarajah Mohanarajah & Kun Zhou, 2021. "Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2389-2417, July.
    10. Markus A. Kirchberger & Larissa Pohl, 2016. "Technology commercialization: a literature review of success factors and antecedents across different contexts," The Journal of Technology Transfer, Springer, vol. 41(5), pages 1077-1112, October.
    11. Juan Vicente García Manjón & Rafael Mompó & Judith Redoli, 2016. "Accelerating Innovation in Small and Medium-Sized Enterprises in the ICT Services Sector," SAGE Open, , vol. 6(3), pages 21582440166, September.
    12. Gort, Michael & Klepper, Steven, 1982. "Time Paths in the Diffusion of Product Innovations," Economic Journal, Royal Economic Society, vol. 92(367), pages 630-653, September.
    13. 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.
    14. Kong YuSheng & Masud Ibrahim, 2020. "Innovation Capabilities, Innovation Types, and Firm Performance: Evidence From the Banking Sector of Ghana," SAGE Open, , vol. 10(2), pages 21582440209, May.
    15. Xiaoyu Liu & Alan L. Porter, 2020. "A 3-dimensional analysis for evaluating technology emergence indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 27-55, July.
    16. Naveed Naeem Abbas & Tanveer Ahmed & Syed Habib Ullah Shah & Muhammad Omar & Han Woo Park, 2019. "Investigating the applications of artificial intelligence in cyber security," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 1189-1211, November.
    17. Huy-Cuong Vo-Thai & Trinh-Hoang Hong-Hue & My-Linh Tran, 2021. "Technological- and Non-Technological Innovation During the Growth Phase of Industry Life Cycle: An Evidence From Vietnamese Manufacturing Enterprises," SAGE Open, , vol. 11(3), pages 21582440211, July.
    18. Moro, Alberto & Boelman, Elisa & Joanny, Geraldine & Garcia, Juan Lopez, 2018. "A bibliometric-based technique to identify emerging photovoltaic technologies in a comparative assessment with expert review," Renewable Energy, Elsevier, vol. 123(C), pages 407-416.
    19. Hu, Xiaojun & Rousseau, Ronald, 2018. "A new approach to explore the knowledge transition path in the evolution of science & technology: From the biology of restriction enzymes to their application in biotechnology," Journal of Informetrics, Elsevier, vol. 12(3), pages 842-857.
    20. Weijuan Li & Pengcheng Zhang, 2021. "Developing the transformation of scientific and technological achievements in colleges and universities to boost the development of low-carbon economy [Social stability risk assessment of land expr," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 16(2), pages 305-316.
    21. Huang, Mu-Hsuan & Yang, Hsiao-Wen & Chen, Dar-Zen, 2015. "Increasing science and technology linkage in fuel cells: A cross citation analysis of papers and patents," Journal of Informetrics, Elsevier, vol. 9(2), pages 237-249.
    22. Takano, Yasutomo & Kajikawa, Yuya, 2019. "Extracting commercialization opportunities of the Internet of Things: Measuring text similarity between papers and patents," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 45-68.
    23. Adrian Furnham, 2021. "Publish or perish: rejection, scientometrics and academic success," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 843-847, January.
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