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Using patents prospectively to identify emerging, high-impact technological clusters

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  • Connie K N Chang
  • Anthony Breitzman

Abstract

This article describes and illustrates the usefulness of a sophisticated methodological tool based on patents, citations, co-citations, and clustering of patents for identifying emerging, high-impact technology clusters and trends. This tool can provide a greater understanding of how emerging, technological clusters form from previous research conducted; the organizations involved; inventors' locations; the core technologies being built upon and technologies currently being pursued; and an early indication of commercial applications that may result. This tool should be of value to program managers interested in tracing the results of funded research projects to economic outcomes and societal impacts; policy-makers interested in fostering regional innovation; and evaluators who are curious how patents can be used as a prospective tool for innovative activity. This article covers in broad strokes the development and validation of the tool, its limitations, key results and highlights from analyzing the top 100 emerging technological clusters, and concluding thoughts. Copyright , Beech Tree Publishing.

Suggested Citation

  • Connie K N Chang & Anthony Breitzman, 2009. "Using patents prospectively to identify emerging, high-impact technological clusters," Research Evaluation, Oxford University Press, vol. 18(5), pages 357-364, December.
    • Handle: RePEc:oup:rseval:v:18:y:2009:i:5:p:357-364
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    File URL: http://hdl.handle.net/10.3152/095820209X481066
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    Cited by:

    1. Huang, Lu & Chen, Xiang & Ni, Xingxing & Liu, Jiarun & Cao, Xiaoli & Wang, Changtian, 2021. "Tracking the dynamics of co-word networks for emerging topic identification," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    2. Ponomariov, Branco & Toivanen, Hannes, 2014. "Knowledge flows and bases in emerging economy innovation systems: Brazilian research 2005–2009," Research Policy, Elsevier, vol. 43(3), pages 588-596.
    3. Anthony Breitzman & Patrick Thomas, 2015. "Inventor team size as a predictor of the future citation impact of patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 631-647, May.
    4. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    5. Ahmad Barirani & Bruno Agard & Catherine Beaudry, 2013. "Discovering and assessing fields of expertise in nanomedicine: a patent co-citation network perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 1111-1136, March.
    6. 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.
    7. Yuan Zhou & Fang Dong & Yufei Liu & Zhaofu Li & JunFei Du & Li Zhang, 2020. "Forecasting emerging technologies using data augmentation and deep learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 1-29, April.
    8. Kwon, Seokbeom & Liu, Xiaoyu & Porter, Alan L. & Youtie, Jan, 2019. "Research addressing emerging technological ideas has greater scientific impact," Research Policy, Elsevier, vol. 48(9), pages 1-1.

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