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Technology roadmapping for competitive technical intelligence

Author

Listed:
  • Yi Zhang

    (BIT - Beijing Institute of Technology)

  • Douglas K. R. Robinson

    (LISIS - Laboratoire Interdisciplinaire Sciences, Innovations, Sociétés - INRA - Institut National de la Recherche Agronomique - UPEM - Université Paris-Est Marne-la-Vallée - ESIEE Paris - CNRS - Centre National de la Recherche Scientifique)

  • Alan L. Porter

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

  • Donghua Zhu

    (LSCE - Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] - UVSQ - Université de Versailles Saint-Quentin-en-Yvelines - INSU - CNRS - Institut national des sciences de l'Univers - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique - DRF (CEA) - Direction de Recherche Fondamentale (CEA) - CEA - Commissariat à l'énergie atomique et aux énergies alternatives)

  • Guangquan Zhang
  • Jie Lu

    (Department of Chemistry - MIT - Massachusetts Institute of Technology)

Abstract

Understanding the evolution and emergence of technology domains remains a challenge, particularly so for potentially breakthrough technologies. Though it is well recognized that emergence of new fields is complex and uncertain, to make decisions amidst such uncertainty, one needs to mobilize various sources of intelligence to identify known–knowns and known–unknowns to be able to choose appropriate strategies and policies. This competitive technical intelligence cannot rely on simple trend analyses because breakthrough technologies have little past to inform such trends, and positing the directions of evolution is challenging. Neither do qualitative tools, embracing the complexities, provide all the solutions, since transparent and repeatable techniques need to be employed to create best practices and evaluate the intelligence that comes from such exercises. In this paper, we present a hybrid roadmapping technique that draws on a number of approaches and integrates them into a multi-level approach (individual activities, industry evolutions and broader global changes) that can be applied to breakthrough technologies. We describe this approach in deeper detail through a case study on dye-sensitized solar cells. Our contribution to this special issue is to showcase the technique as part of a family of approaches that are emerging around the world to inform strategy and policy.

Suggested Citation

  • Yi Zhang & Douglas K. R. Robinson & Alan L. Porter & Donghua Zhu & Guangquan Zhang & Jie Lu, 2015. "Technology roadmapping for competitive technical intelligence," Post-Print hal-01276909, HAL.
    • Handle: RePEc:hal:journl:hal-01276909
    DOI: 10.1016/j.techfore.2015.11.029
    Note: View the original document on HAL open archive server: https://hal.science/hal-01276909
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    Cited by:

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    2. Kim, Junhan & Geum, Youngjung, 2021. "How to develop data-driven technology roadmaps:The integration of topic modeling and link prediction," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    3. 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.
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    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. Dirk Meissner & Maxim Kotsemir, 2016. "Conceptualizing the innovation process towards the ‘active innovation paradigm’—trends and outlook," Journal of Innovation and Entrepreneurship, Springer, vol. 5(1), pages 1-18, December.
    7. Azimi, Sasan & Rahmani, Rouhollah & Fateh-rad, Mahdi, 2019. "Investment cost optimization for industrial project portfolios using technology mining," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 243-253.
    8. 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.
    9. Grinin, Leonid E. & Grinin, Anton L. & Korotayev, Andrey, 2017. "Forthcoming Kondratieff wave, Cybernetic Revolution, and global ageing," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 52-68.
    10. Nayak, Bishwajit & Bhattacharyya, Som Sekhar & Krishnamoorthy, Bala, 2021. "Explicating the role of emerging technologies and firm capabilities towards attainment of competitive advantage in health insurance service firms," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
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    12. Yuskevich, Ilya & Hein, Andreas Makoto & Amokrane-Ferka, Kahina & Doufene, Abdelkrim & Jankovic, Marija, 2021. "A metamodel of an informational structure for model-based technology roadmapping," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    13. Landoni, Matteo & ogilvie, dt, 2019. "Convergence of innovation policies in the European aerospace industry (1960–2000)," Technological Forecasting and Social Change, Elsevier, vol. 147(C), pages 174-184.
    14. 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.
    15. de Alcantara, Douglas Pedro & Martens, Mauro Luiz, 2019. "Technology Roadmapping (TRM): a systematic review of the literature focusing on models," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 127-138.
    16. Leurent, Martin & Da Costa, Pascal & Jasserand, Frédéric & Rämä, Miika & Persson, Urban, 2018. "Cost and climate savings through nuclear district heating in a French urban area," Energy Policy, Elsevier, vol. 115(C), pages 616-630.
    17. Ogden, Joan & Jaffe, Amy Myers & Scheitrum, Daniel & McDonald, Zane & Miller, Marshall, 2018. "Natural gas as a bridge to hydrogen transportation fuel: Insights from the literature," Energy Policy, Elsevier, vol. 115(C), pages 317-329.
    18. Xu, Shuo & Hao, Liyuan & Yang, Guancan & Lu, Kun & An, Xin, 2021. "A topic models based framework for detecting and forecasting emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 162(C).

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