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Identification of technology development trends based on subject–action–object analysis: The case of dye-sensitized solar cells

Author

Listed:
  • Liliana Mitkova

    (IRG - Institut de Recherche en Gestion - UPEM - Université Paris-Est Marne-la-Vallée - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12)

  • Wang Xuefeng
  • Pengjun Qui
  • Donghua Zhu
  • Ming Lei
  • Alan L. Porter

    (School of Public Policy - Georgia Institute of Technology [Atlanta])

Abstract

Identification of technology development trends is essential for supporting decision-makers in forecasting and identifying related innovation activities as well the industrial growth. In difference of the traditional technology development trends based on SAO, which usually predicting the trend by finding key problems and technologies used to resolve them, the method proposed in this paper allows to identify what industry goal can be achieved by the interaction between the problems and technologies, the detailed paths to achieve it and the key countries which can encourage the trend by engaging technological innovation scenarios. This method builds a technology roadmapping (TRM) with seven layers based on Subject-Action-Object (SAO) analysis, which is composed by material, technology, influencing factor, component, product, goal and application area. It is a combination of vertical and horizontal TRM and has the advantages of both of them. Comparing with the existing technology roadmapping, this method indicates not only the development paths of the key technologies but also those of them that are most opportune to resolve the specific problem and achieve the particular industry goal. Furthermore, with the intention to identify the key countries supporting the technological development, this method sets three indicators acting as the discriminating standards. To demonstrate the effectiveness of the method, this paper presents a case study of literature related to dye-sensitized solar cells. The result reveals the industry goal and trends in the field of DSSCs, in addition, it can identify the detail approach of the trends and the key countries to support it.

Suggested Citation

  • 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.
    • Handle: RePEc:hal:journl:hal-01202391
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    Citations

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    Cited by:

    1. 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).
    2. An, Jaehyeong & Kim, Kyuwoong & Mortara, Letizia & Lee, Sungjoo, 2018. "Deriving technology intelligence from patents: Preposition-based semantic analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 217-236.
    3. 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.
    4. 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).
    5. 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).
    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. Seunghyun Oh & Jaewoong Choi & Namuk Ko & Janghyeok Yoon, 2020. "Predicting product development directions for new product planning using patent classification-based link prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1833-1876, December.
    8. 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.
    9. Jiwon Yu & Young Jae Han & Hyewon Yang & Sugil Lee & Gildong Kim & Chulung Lee, 2022. "Promising Technology Analysis and Patent Roadmap Development in the Hydrogen Supply Chain," Sustainability, MDPI, vol. 14(21), pages 1-20, October.
    10. Mun, Changbae & Yoon, Sejun & Raghavan, Nagarajan & Hwang, Dongwook & Basnet, Subarna & Park, Hyunseok, 2021. "Function score-based technological trend analysis," Technovation, Elsevier, vol. 101(C).
    11. Erzurumlu, S. Sinan & Pachamanova, Dessislava, 2020. "Topic modeling and technology forecasting for assessing the commercial viability of healthcare innovations," Technological Forecasting and Social Change, Elsevier, vol. 156(C).
    12. 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.
    13. Joung, Junegak & Kim, Kwangsoo, 2017. "Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 281-292.
    14. Li, Xin & Xie, Qianqian & Jiang, Jiaojiao & Zhou, Yuan & Huang, Lucheng, 2019. "Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 687-705.
    15. Chakraborty, Swagata & Nijssen, Edwin J. & Valkenburg, Rianne, 2022. "A systematic review of industry-level applications of technology roadmapping: Evaluation and design propositions for roadmapping practitioners," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    16. Pavel Bakhtin & Ozcan Saritas & Alexander Chulok & Ilya Kuzminov & Anton Timofeev, 2017. "Trend monitoring for linking science and strategy," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 2059-2075, June.
    17. Yang, Zaoli & Zhang, Weijian & Yuan, Fei & Islam, Nazrul, 2021. "Measuring topic network centrality for identifying technology and technological development in online communities," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    18. 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.
    19. Li, Xin & Xie, Qianqian & Daim, Tugrul & Huang, Lucheng, 2019. "Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 432-449.
    20. 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.
    21. Sungchul Kim & Dongsik Jang & Sunghae Jun & Sangsung Park, 2015. "A Novel Forecasting Methodology for Sustainable Management of Defense Technology," Sustainability, MDPI, vol. 7(12), pages 1-17, December.

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