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Combining tech mining and semantic TRIZ for technology assessment: Dye-sensitized solar cell as a case

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  • Vicente-Gomila, J.M.
  • Artacho-Ramírez, M.A.
  • Ting, Ma
  • Porter, A.L.

Abstract

In a competitive business environment, an early understanding of the dynamics of technological change is crucial to help policymakers and managers make better-informed decisions. Bibliometric analyses help in studying trends and technological evolution. Tech mining (text analyses of science and technology information resources) enhances Bibliometric analyses. However, more often than not, such analyses focus on a specific technological area, and mainly result in incremental advance forecasts. An analysis of the interconnected dynamics of technology change warrants new approaches for identifying technology emergence, technological substitution, and the influences of vital socioeconomic forces. This paper introduces a unique combination that applies a tech mining and semantic TRIZ as a case study to Dye-Sensitized Solar Cell (DSSC) technology. This methodological combination brings broader insights to the emergence of DSSC in conjunction with related technologies that affect its progress, enriching the associated technological progression's empirical characterization.

Suggested Citation

  • Vicente-Gomila, J.M. & Artacho-Ramírez, M.A. & Ting, Ma & Porter, A.L., 2021. "Combining tech mining and semantic TRIZ for technology assessment: Dye-sensitized solar cell as a case," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    • Handle: RePEc:eee:tefoso:v:169:y:2021:i:c:s0040162521002584
    DOI: 10.1016/j.techfore.2021.120826
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    1. Dong, Andy & Sarkar, Somwrita, 2015. "Forecasting technological progress potential based on the complexity of product knowledge," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 599-610.
    2. Yue, Gentian & Wang, Lei & Zhang, Xin'an & Wu, Jihuai & Jiang, Qiwei & Zhang, Weifeng & Huang, Miaoliang & Lin, Jianming, 2014. "Fabrication of high performance multi-walled carbon nanotubes/polypyrrole counter electrode for dye-sensitized solar cells," Energy, Elsevier, vol. 67(C), pages 460-467.
    3. Mozaffari, Samaneh & Nateghi, Mohammad Reza & Zarandi, Mahmood Borhani, 2017. "An overview of the Challenges in the commercialization of dye sensitized solar cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 675-686.
    4. 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.
    5. Porter, Alan L. & Garner, Jon & Carley, Stephen F. & Newman, Nils C., 2019. "Emergence scoring to identify frontier R&D topics and key players," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 628-643.
    6. Ying Huang & Jannik Schuehle & Alan L. Porter & Jan Youtie, 2015. "A systematic method to create search strategies for emerging technologies based on the Web of Science: illustrated for ‘Big Data’," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 2005-2022, December.
    7. Zhang, Yi & Porter, Alan L. & Hu, Zhengyin & Guo, Ying & Newman, Nils C., 2014. "“Term clumping” for technical intelligence: A case study on dye-sensitized solar cells," Technological Forecasting and Social Change, Elsevier, vol. 85(C), pages 26-39.
    8. Holguín-Veras, José & Amaya Leal, Johanna & Seruya, Barbara B., 2017. "Urban freight policymaking: The role of qualitative and quantitative research," Transport Policy, Elsevier, vol. 56(C), pages 75-85.
    9. Yi Zhang & Xiao Zhou & Alan L. Porter & Jose M. Vicente Gomila, 2014. "How to combine term clumping and technology roadmapping for newly emerging science & technology competitive intelligence: “problem & solution” pattern based semantic TRIZ tool and case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1375-1389, November.
    10. Jun, H.K. & Careem, M.A. & Arof, A.K., 2013. "Quantum dot-sensitized solar cells—perspective and recent developments: A review of Cd chalcogenide quantum dots as sensitizers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 148-167.
    11. Jan W. Rivkin & Nicolaj Siggelkow, 2007. "Patterned Interactions in Complex Systems: Implications for Exploration," Management Science, INFORMS, vol. 53(7), pages 1068-1085, July.
    12. Jose M. Vicente-Gomila, 2014. "The contribution of syntactic–semantic approach to the search for complementary literatures for scientific or technical discovery," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 659-673, September.
    13. Liliana Mitkova & Wang Xuefeng & Pengjun Qui & Donghua Zhu & Ming Lei & Alan L. Porter, 2015. "Identification of technology development trends based on subject–action–object analysis: The case of dye-sensitized solar cells," Post-Print hal-01202391, HAL.
    14. Gong, Jiawei & Sumathy, K. & Qiao, Qiquan & Zhou, Zhengping, 2017. "Review on dye-sensitized solar cells (DSSCs): Advanced techniques and research trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 234-246.
    15. Sengupta, D. & Das, P. & Mondal, B. & Mukherjee, K., 2016. "Effects of doping, morphology and film-thickness of photo-anode materials for dye sensitized solar cell application – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 356-376.
    16. Popoola, Idris K. & Gondal, Mohammed A. & Qahtan, Talal F., 2018. "Recent progress in flexible perovskite solar cells: Materials, mechanical tolerance and stability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3127-3151.
    17. Guo, Junfang & Wang, Xuefeng & Li, Qianrui & Zhu, Donghua, 2016. "Subject–action–object-based morphology analysis for determining the direction of technological change," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 27-40.
    18. Giordano, Vincenzo & Fulli, Gianluca, 2012. "A business case for Smart Grid technologies: A systemic perspective," Energy Policy, Elsevier, vol. 40(C), pages 252-259.
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    Cited by:

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    2. Puccetti, Giovanni & Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2023. "Technology identification from patent texts: A novel named entity recognition method," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    3. Peng Shao & Runhua Tan & Qingjin Peng & Wendan Yang & Fang Liu, 2023. "An Integrated Method to Acquire Technological Evolution Potential to Stimulate Innovative Product Design," Mathematics, MDPI, vol. 11(3), pages 1-24, January.

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