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Government-sponsored university-industry collaboration and the production of nanotechnology patents in US universities

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  • Branco Ponomariov

Abstract

This paper investigates the effectiveness of university research centers as a Science and Technology policy mechanism. Using the case of nanotechnology, it assesses if establishing dedicated research centers at universities influences their patterns of knowledge production, and specifically the production of commercially relevant nano-knowledge. Based on bibliometric data on patents and publications for a panel of the top patenting US universities (n = 82), the study finds that nano-centers positively impact nano-patent production at universities. Policy and theoretical implications are discussed. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Branco Ponomariov, 2013. "Government-sponsored university-industry collaboration and the production of nanotechnology patents in US universities," The Journal of Technology Transfer, Springer, vol. 38(6), pages 749-767, December.
  • Handle: RePEc:kap:jtecht:v:38:y:2013:i:6:p:749-767
    DOI: 10.1007/s10961-013-9301-3
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    1. Shu-Hao Chang, 2018. "A pilot study on the connection between scientific fields and patent classification systems," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 951-970, March.
    2. Elizabeth A. Corley & Barry Bozeman & Xuefan Zhang & Chin-Chang Tsai, 2019. "The expanded scientific and technical human capital model: the addition of a cultural dimension," The Journal of Technology Transfer, Springer, vol. 44(3), pages 681-699, June.
    3. Andrea Filippetti & Maria Savona, 2017. "University–industry linkages and academic engagements: individual behaviours and firms’ barriers. Introduction to the special section," The Journal of Technology Transfer, Springer, vol. 42(4), pages 719-729, August.
    4. Wang, Wenjing & Lu, Shan, 2021. "University-industry innovation community dynamics and knowledge transfer: Evidence from China," Technovation, Elsevier, vol. 106(C).
    5. Rosa Caiazza & Aileen Richardson & David Audretsch, 2015. "Knowledge effects on competitiveness: from firms to regional advantage," The Journal of Technology Transfer, Springer, vol. 40(6), pages 899-909, December.
    6. Rojas, Mariana Giovanna Andrade & Solis, Edgar Rogelio Ramirez & Zhu, John JianJun, 2018. "Innovation and network multiplexity: R&D and the concurrent effects of two collaboration networks in an emerging economy," Research Policy, Elsevier, vol. 47(6), pages 1111-1124.
    7. David P. Leech & John T. Scott, 2017. "Nanotechnology documentary standards," The Journal of Technology Transfer, Springer, vol. 42(1), pages 78-97, February.
    8. Annita Nugent & Ho Fai Chan & Uwe Dulleck, 2022. "Government funding of university-industry collaboration: exploring the impact of targeted funding on university patent activity," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 29-73, January.
    9. Gang Du & Xi Liang & Xiaoling Ouyang & Chunming Wang, 0. "Risk prediction of hypertension complications based on the intelligent algorithm optimized Bayesian network," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-22.
    10. Ankrah, Samuel & AL-Tabbaa, Omar, 2015. "Universities–industry collaboration: A systematic review," Scandinavian Journal of Management, Elsevier, vol. 31(3), pages 387-408.
    11. Chang, Shu-Hao, 2017. "The technology networks and development trends of university-industry collaborative patents," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 107-113.
    12. Glenda Kruss & Mariette Visser, 2017. "Putting university–industry interaction into perspective: a differentiated view from inside South African universities," The Journal of Technology Transfer, Springer, vol. 42(4), pages 884-908, August.
    13. Amalya L. Oliver, 2022. "Holistic ecosystems for enhancing innovative collaborations in university–industry consortia," The Journal of Technology Transfer, Springer, vol. 47(5), pages 1612-1628, October.
    14. Gang Du & Xi Liang & Xiaoling Ouyang & Chunming Wang, 2021. "Risk prediction of hypertension complications based on the intelligent algorithm optimized Bayesian network," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 966-987, November.

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    More about this item

    Keywords

    Nanotechnology; University patenting; Science and technology policy; University research centers; O31; O32; O33; I23;
    All these keywords.

    JEL classification:

    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions

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