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Triple Helix innovation in China’s dye-sensitized solar cell industry: hybrid methods with semantic TRIZ and technology roadmapping

Author

Listed:
  • Yi Zhang

    (Beijing Institute of Technology)

  • Xiao Zhou

    (Beijing Institute of Technology)

  • Alan L. Porter

    (Georgia Institute of Technology
    Search Technology, Inc.)

  • Jose M. Vicente Gomila

    (Universitat Politecnica de Valencia)

  • An Yan

    (Beijing Institute of Technology)

Abstract

In recent years, the Triple Helix model has identified feasible approaches to measuring relations among universities, industries, and governments. Results have been extended to different databases, regions, and perspectives. This paper explores how bibliometrics and text mining can inform Triple Helix analyses. It engages Competitive Technical Intelligence concepts and methods for studies of Newly Emerging Science & Technology (NEST) in support of technology management and policy. A semantic TRIZ approach is used to assess NEST innovation patterns by associating topics (using noun phrases to address subjects and objects) and actions (via verbs). We then classify these innovation patterns by the dominant categories of origination: Academy, Industry, or Government. We then use TRIZ tags and benchmarks to locate NEST progress using Technology Roadmapping. Triple Helix inferences can then be related to the visualized patterns. We demonstrate these analyses via a case study for dye-sensitized solar cells.

Suggested Citation

  • Yi Zhang & Xiao Zhou & Alan L. Porter & Jose M. Vicente Gomila & An Yan, 2014. "Triple Helix innovation in China’s dye-sensitized solar cell industry: hybrid methods with semantic TRIZ and technology roadmapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(1), pages 55-75, April.
    • Handle: RePEc:spr:scient:v:99:y:2014:i:1:d:10.1007_s11192-013-1090-9
    DOI: 10.1007/s11192-013-1090-9
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    References listed on IDEAS

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    Cited by:

    1. Yang, Chao & Huang, Cui & Su, Jun, 2018. "An improved SAO network-based method for technology trend analysis: A case study of graphene," Journal of Informetrics, Elsevier, vol. 12(1), pages 271-286.
    2. Zhang, Yi & Huang, Ying & Porter, Alan L. & Zhang, Guangquan & Lu, Jie, 2019. "Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 795-807.
    3. Sabrina L. Woltmann & Lars Alkærsig, 2018. "Tracing university–industry knowledge transfer through a text mining approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 449-472, October.
    4. Hong Wu & Huifang Yi & Chang Li, 2021. "An integrated approach for detecting and quantifying the topic evolutions of patent technology: a case study on graphene field," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6301-6321, August.
    5. Ma, Tingting & Zhou, Xiao & Liu, Jia & Lou, Zhenkai & Hua, Zhaoting & Wang, Ruitao, 2021. "Combining topic modeling and SAO semantic analysis to identify technological opportunities of emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    6. Chao Yang & Donghua Zhu & Xuefeng Wang & Yi Zhang & Guangquan Zhang & Jie Lu, 2017. "Requirement-oriented core technological components’ identification based on SAO analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1229-1248, September.
    7. Tao Zhuang & Shuliang Zhao & Mingliang Zheng & Jianxun Chu, 2021. "Triple helix relationship research on China's regional university–industry–government collaborative innovation: Based on provincial patent data," Growth and Change, Wiley Blackwell, vol. 52(3), pages 1361-1386, September.
    8. Zhang, Yi & Robinson, Douglas K.R. & Porter, Alan L. & Zhu, Donghua & Zhang, Guangquan & Lu, Jie, 2016. "Technology roadmapping for competitive technical intelligence," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 175-186.
    9. Arenal, Alberto & Armuña, Cristina & Feijoo, Claudio & Ramos, Sergio & Xu, Zimu & Moreno, Ana, 2020. "Innovation ecosystems theory revisited: The case of artificial intelligence in China," Telecommunications Policy, Elsevier, vol. 44(6).
    10. Zhou, Xiao & Huang, Lu & Porter, Alan & Vicente-Gomila, Jose M., 2019. "Tracing the system transformations and innovation pathways of an emerging technology: Solid lipid nanoparticles," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 785-794.
    11. Zhang, Yi & Zhang, Guangquan & Chen, Hongshu & Porter, Alan L. & Zhu, Donghua & Lu, Jie, 2016. "Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 179-191.
    12. Munan Li, 2015. "A novel three-dimension perspective to explore technology evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1679-1697, December.
    13. Huang, Ying & Porter, Alan L. & Zhang, Yi & Lian, Xiangpeng & Guo, Ying, 2019. "An assessment of technology forecasting: Revisiting earlier analyses on dye-sensitized solar cells (DSSCs)," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 831-843.
    14. Leonid Gokhberg & Dirk Meissner & Ilya Kuzminov, 2023. "What semantic analysis can tell us about long term trends in the global STI policy agenda," The Journal of Technology Transfer, Springer, vol. 48(6), pages 2249-2277, December.
    15. Stephen F. Carley & Nils C. Newman & Alan L. Porter & Jon G. Garner, 2018. "An indicator of technical emergence," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 35-49, April.

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