IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v169y2021ics0040162521002584.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162521002584
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2021.120826?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. Jan W. Rivkin & Nicolaj Siggelkow, 2007. "Patterned Interactions in Complex Systems: Implications for Exploration," Management Science, INFORMS, vol. 53(7), pages 1068-1085, July.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Richarz, Jan & Wegewitz, Stephan & Henn, Sarah & Müller, Dirk, 2023. "Graph-based research field analysis by the use of natural language processing: An overview of German energy research," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    3. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    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. Xuefeng Wang & Pingping Ma & Ying Huang & Junfang Guo & Donghua Zhu & Alan L. Porter & Zhinan Wang, 2017. "Combining SAO semantic analysis and morphology analysis to identify technology opportunities," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 3-24, April.
    4. Mun, Changbae & Yoon, Sejun & Raghavan, Nagarajan & Hwang, Dongwook & Basnet, Subarna & Park, Hyunseok, 2021. "Function score-based technological trend analysis," Technovation, Elsevier, vol. 101(C).
    5. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
    6. Chen, Liang & Xu, Shuo & Zhu, Lijun & Zhang, Jing & Yang, Guancan & Xu, Haiyun, 2022. "A deep learning based method benefiting from characteristics of patents for semantic relation classification," Journal of Informetrics, Elsevier, vol. 16(3).
    7. Richarz, Jan & Wegewitz, Stephan & Henn, Sarah & Müller, Dirk, 2023. "Graph-based research field analysis by the use of natural language processing: An overview of German energy research," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    8. 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.
    9. Ma, Jing & Abrams, Natalie F. & Porter, Alan L. & Zhu, Donghua & Farrell, Dorothy, 2019. "Identifying translational indicators and technology opportunities for nanomedical research using tech mining: The case of gold nanostructures," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 767-775.
    10. Li, Munan & Wang, Wenshu & Zhou, Keyu, 2021. "Exploring the technology emergence related to artificial intelligence: A perspective of coupling analyses," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    11. 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.
    12. Zhang, Yi & Shang, Lining & Huang, Lu & Porter, Alan L. & Zhang, Guangquan & Lu, Jie & Zhu, Donghua, 2016. "A hybrid similarity measure method for patent portfolio analysis," Journal of Informetrics, Elsevier, vol. 10(4), pages 1108-1130.
    13. Kyuwoong Kim & Kyeongmin Park & Sungjoo Lee, 2019. "Investigating technology opportunities: the use of SAOx analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 45-70, January.
    14. Myeongji Oh & Hyejin Jang & Sunhye Kim & Byungun Yoon, 2023. "Main path analysis for technological development using SAO structure and DEMATEL based on keyword causality," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(4), pages 2079-2104, April.
    15. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    16. Fabian Schoden & Marius Dotter & Dörthe Knefelkamp & Tomasz Blachowicz & Eva Schwenzfeier Hellkamp, 2021. "Review of State of the Art Recycling Methods in the Context of Dye Sensitized Solar Cells," Energies, MDPI, vol. 14(13), pages 1-12, June.
    17. Alami, Abdul Hai & Rajab, Bilal & Abed, Jehad & Faraj, Mohammed & Hawili, Abdullah Abu & Alawadhi, Hussain, 2019. "Investigating various copper oxides-based counter electrodes for dye sensitized solar cell applications," Energy, Elsevier, vol. 174(C), pages 526-533.
    18. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    19. Devadiga, Dheeraj & Selvakumar, Muthu & Shetty, Prakasha & Santosh, Mysore Sridhar, 2022. "The integration of flexible dye-sensitized solar cells and storage devices towards wearable self-charging power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:169:y:2021:i:c:s0040162521002584. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.