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Wake-up of sleeping beauty patent families: The global non-equilibrium diffusion of technological knowledge

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  • Song, Haoyang
  • Hou, Jianhua
  • Yang, Xiucai
  • Liu, Ruoyu

Abstract

Transformative technologies are the key to leading future technology innovation and economic development. However, with the exchange of technological knowledge among countries and the explosive growth of patented technology, the uneven flow and application of technology across geographic regions has become increasingly apparent, making it more and more difficult to track and absorb potentially transformative technology (PTT). As a proxy, the wake-up trajectories of the sleeping beauty patent family (SBPF) reflect the unbalanced distribution and application of PTT but still lack in-depth discussion. Therefore, this study adopted the parameter-free criteria to identify SBPFs and revealed the globalized diffusion patterns of PTT behind SBPFs' wake-up time and pace trajectories by taking “Polymerase Chain Reaction” technology as an example. The findings show that SBPFs’ wake-up has five-time and four-spatial trajectories and presents a small-scale diffusion in local areas. Meanwhile, PTT shows five diffusion patterns, achieving an unbalanced and centralized diffusion worldwide. These provide theoretical support for predicting the global development of PTT, and practical guidance for choosing technical direction, grasping market opportunities, and optimizing the national innovation environment.

Suggested Citation

  • Song, Haoyang & Hou, Jianhua & Yang, Xiucai & Liu, Ruoyu, 2024. "Wake-up of sleeping beauty patent families: The global non-equilibrium diffusion of technological knowledge," Technology in Society, Elsevier, vol. 79(C).
    • Handle: RePEc:eee:teinso:v:79:y:2024:i:c:s0160791x24002549
    DOI: 10.1016/j.techsoc.2024.102706
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    1. Fernández, Ana María & Ferrándiz, Esther & Medina, Jennifer, 2022. "The diffusion of energy technologies. Evidence from renewable, fossil, and nuclear energy patents," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    2. Kyriakos Drivas & Claire Economidou & Konstantinos N. Konstantakis & Panayotis G. Michaelides, 2022. "Technological Leaders, Laggards and Spillovers: A Network GVAR Analysis," Open Economies Review, Springer, vol. 33(2), pages 231-269, April.
    3. Yang, Jinqing & Bu, Yi & Lu, Wei & Huang, Yong & Hu, Jiming & Huang, Shengzhi & Zhang, Li, 2022. "Identifying keyword sleeping beauties: A perspective on the knowledge diffusion process," Journal of Informetrics, Elsevier, vol. 16(1).
    4. Lai, Kuei-Kuei & Bhatt, Priyanka C. & Kumar, Vimal & Chen, Hsueh-Chen & Chang, Yu-Hsin & Su, Fang-Pei, 2021. "Identifying the impact of patent family on the patent trajectory: A case of thin film solar cells technological trajectories," Journal of Informetrics, Elsevier, vol. 15(2).
    5. Daim, Tugrul & Lai, Kuei Kuei & Yalcin, Haydar & Alsoubie, Fayez & Kumar, Vimal, 2020. "Forecasting technological positioning through technology knowledge redundancy: Patent citation analysis of IoT, cybersecurity, and Blockchain," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    6. Johannes Pol & Jean-Paul Rameshkoumar, 2018. "The co-evolution of knowledge and collaboration networks: the role of the technology life-cycle," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(1), pages 307-323, January.
    7. J. van Der Pol & J-P. Rameshkoumar & D. Virapin & B. Zozime, 2015. "The co-evolution of knowledge and collaboration networks: the role of the technology life-cycle," Post-Print hal-02269511, HAL.
    8. Deyu Li & Gaston Heimeriks & Floor Alkemade, 2020. "The emergence of renewable energy technologies at country level: relatedness, international knowledge spillovers and domestic energy markets," Industry and Innovation, Taylor & Francis Journals, vol. 27(9), pages 991-1013, October.
    9. J. van der Pol & J-P. Rameshkoumar & D. Virapin & B. Zozime, 2015. "The co-evolution of knowledge and collaboration networks: the role of the technology life-cycle," Post-Print hal-02269511, HAL.
    10. Jianhua Hou & Xiucai Yang & Haoyang Song & Haiyue Yao, 2023. "Will patent family be dormant? Research on the identification and characteristics of sleeping beauty’s patent family," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(10), pages 5361-5387, October.
    11. Wang, Lili & Jiang, Shan & Zhang, Shiyun, 2020. "Mapping technological trajectories and exploring knowledge sources: A case study of 3D printing technologies," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    12. Fernández, Ana María & Ferrándiz, Esther & Medina, Jennifer, 2022. "The diffusion of energy technologies. Evidence from renewable, fossil, and nuclear energy patents," MPRA Paper 123361, University Library of Munich, Germany.
    13. Lili Wang & Zexia Li, 2021. "Knowledge flows from public science to industrial technologies," The Journal of Technology Transfer, Springer, vol. 46(4), pages 1232-1255, August.
    14. Tsouri, Maria & Hansen, Teis & Hanson, Jens & Steen, Markus, 2022. "Knowledge recombination for emerging technological innovations: The case of green shipping," Technovation, Elsevier, vol. 114(C).
    15. Hiroko Nakamura & Shinji Suzuki & Yuya Kajikawa & Masataka Osawa, 2015. "The effect of patent family information in patent citation network analysis: a comparative case study in the drivetrain domain," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(2), pages 437-452, August.
    16. Jianhua Hou & Xiucai Yang, 2019. "Patent sleeping beauties: evolutionary trajectories and identification methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 187-215, July.
    17. Sun, Bing & Yang, Xueting & Zhong, Shen & Tian, Shengnan & Liang, Tian, 2024. "How do technology convergence and expansibility affect information technology diffusion? Evidence from the internet of things technology in China," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    18. Ying Tang & Xuming Lou & Zifeng Chen & Chengjin Zhang, 2020. "A Study on Dynamic Patterns of Technology Convergence with IPC Co-Occurrence-Based Analysis: The Case of 3D Printing," Sustainability, MDPI, vol. 12(7), pages 1-26, March.
    19. Jian Du & Yishan Wu, 2018. "A parameter-free index for identifying under-cited sleeping beauties in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 959-971, August.
    20. Lidan Jiang & Jingyan Chen & Yuhan Bao & Fang Zou, 2022. "Exploring the patterns of international technology diffusion in AI from the perspective of patent citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5307-5323, September.
    21. Elena M. Tur & Evangelos Bourelos & Maureen McKelvey, 2022. "The case of sleeping beauties in nanotechnology: a study of potential breakthrough inventions in emerging technologies," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 69(3), pages 683-708, December.
    22. 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.
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