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Forecasting Innovation Pathways (FIP) for new and emerging science and technologies

Author

Listed:
  • Douglas K. R. Robinson

    () (CGS i3 - Centre de Gestion Scientifique i3 - MINES ParisTech - École nationale supérieure des mines de Paris - PSL - PSL Research University - CNRS - Centre National de la Recherche Scientifique)

  • Lu Huang

    (School of Management and Economics - BIT - Beijing Institute of Technology - BIT - Beijing Institute of Technology)

  • Yan Guo

    (Management Science and Engineering, - BIT - Beijing Institute of Technology - BIT - Beijing Institute of Technology)

  • Alan L. Porter

    (School of Public Policy - Georgia Institute of Technology (Georgia Tech))

Abstract

"New" and "Emerging Science" and "Technologies" ("NESTs") have tremendous innovation potential. However this must be weighed against enormous uncertainties caused by many unknowns. The authors of this paper offer a framework to analyze NESTs to help ascertain likely innovation pathways.We have devised a 10-step framework based on extensive Future-oriented Technology Analyses ("FTA") experience, enriched by in-depth case analyses. In the paper, we describe our analytical activities in two case studies. The nanobiosensor experience is contrasted with that of deep brain stimulation in relative quantitative and qualitative emphases.We close the paper by reflecting on this systematic FTA framework for emerging science and technologies, for its intended goal, that is to support decision making.

Suggested Citation

  • Douglas K. R. Robinson & Lu Huang & Yan Guo & Alan L. Porter, 2013. "Forecasting Innovation Pathways (FIP) for new and emerging science and technologies," Post-Print hal-01070417, HAL.
  • Handle: RePEc:hal:journl:hal-01070417
    Note: View the original document on HAL open archive server: https://hal-mines-paristech.archives-ouvertes.fr/hal-01070417
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    References listed on IDEAS

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    1. Jacobsson, Staffan & Johnson, Anna, 2000. "The diffusion of renewable energy technology: an analytical framework and key issues for research," Energy Policy, Elsevier, vol. 28(9), pages 625-640, July.
    2. repec:spr:scient:v:70:y:2007:i:3:d:10.1007_s11192-007-0314-2 is not listed on IDEAS
    3. Simona O. Negro & Marko P. Hekkert, 2008. "Explaining the success of emerging technologies by innovation system functioning: the case of biomass digestion in Germany," Innovation Studies Utrecht (ISU) working paper series 08-08, Utrecht University, Department of Innovation Studies, revised Feb 2008.
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    Cited by:

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    2. repec:spr:scient:v:102:y:2015:i:1:d:10.1007_s11192-014-1392-6 is not listed on IDEAS
    3. Coccia, Mario & Wang, Lili, 2015. "Path-breaking directions of nanotechnology-based chemotherapy and molecular cancer therapy," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 155-169.
    4. Kokshagina, Olga & Gillier, Thomas & Cogez, Patrick & Le Masson, Pascal & Weil, Benoit, 2017. "Using innovation contests to promote the development of generic technologies," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 152-164.
    5. repec:eee:tefoso:v:129:y:2018:i:c:p:285-296 is not listed on IDEAS
    6. Wang, Ming-Yeu & Fang, Shih-Chieh & Chang, Yu-Hsuan, 2015. "Exploring technological opportunities by mining the gaps between science and technology: Microalgal biofuels," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 182-195.
    7. 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.
    8. repec:spr:scient:v:111:y:2017:i:2:d:10.1007_s11192-017-2319-9 is not listed on IDEAS
    9. Arora, Sanjay K. & Foley, Rider W. & Youtie, Jan & Shapira, Philip & Wiek, Arnim, 2014. "Drivers of technology adoption — the case of nanomaterials in building construction," Technological Forecasting and Social Change, Elsevier, vol. 87(C), pages 232-244.
    10. Jun, Seung-Pyo & Park, Do-Hyung, 2016. "Consumer information search behavior and purchasing decisions: Empirical evidence from Korea," Technological Forecasting and Social Change, Elsevier, vol. 107(C), pages 97-111.
    11. repec:eee:tefoso:v:138:y:2019:i:c:p:324-339 is not listed on IDEAS
    12. Kauffman, Robert J. & Liu, Jun & Ma, Dan, 2015. "Innovations in financial IS and technology ecosystems: High-frequency trading in the equity market," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 339-354.
    13. repec:spr:infosf:v:16:y:2014:i:3:d:10.1007_s10796-013-9479-9 is not listed on IDEAS
    14. Cheng, Yu & Huang, Lucheng & Ramlogan, Ronnie & Li, Xin, 2017. "Forecasting of potential impacts of disruptive technology in promising technological areas: Elaborating the SIRS epidemic model in RFID technology," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 170-183.
    15. Breitzman, Anthony & Thomas, Patrick, 2015. "The Emerging Clusters Model: A tool for identifying emerging technologies across multiple patent systems," Research Policy, Elsevier, vol. 44(1), pages 195-205.
    16. 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.

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