Abstract
The current chapter presents a critical evaluation of the modes of knowledge and technology transfer from academia, based on the evaluation of data which spans over the last three decades and a case study of technology transfer in the fields of artificial intelligence, data science, and smart robotics. A need emerges for reevaluating and revisiting university policies with regards to its third mission. Such policies should be set to guide the activities of the Technology Transfer Offices of universities, to balance between technology commercialization, which is more linear in nature, and technology transfer with industry, which is more holistic, interactive, and entrepreneurial in nature. Greater emphasis on technology transfer and more intimate cooperation with industry may result in an increase in research funding as well as in improved level and significance of research. More than that, such policies are more likely to be met by support of the academic faculty, and they should be an inherent part of the development of entrepreneurial activities in universities, which include education that is suitable to the needs of the industry and society, as well as more significant and effective research. It is recommended that the achievements of the universities regarding the third mission should be quantified and ranked on the national and international levels. They should be based on multiple indices to reflect various modes of achieving such goals, in view of the range of mechanisms of university–industry interactions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
In 2018, The Samuel Neaman Institute was commissioned by the National Council for Research and Development (MOLMOP) at the Ministry of Science and Technology to perform a comprehensive mapping of activities in the Israeli academy, industry and government sectors in artificial intelligence, data science and smart robotics and to explore the possibilities for promoting and developing these fields in Israel. For this purpose, the research group conducted interviews with nearly 90 specialists from around the Israeli ecosystem and surveyed employees from 160 companies. The research team published several different research reports, all can be found on the Neaman Institute website: https://www.neaman.org.il/Artificial-Intelligence-Data-Science-and-Smart-Robotics
- 2.
- 3.
- 4.
References
Allen, T., & O’Shea, R. (2014). The roadblock to commercialization. In The Financial Times. https://www.ft.com/content/a1ea7c72-18cc-11e4-933e-00144feabdc0
Benson, G. (2018, February 22). How industry collaboration with academia advances the field of AI—AI News. AI News. https://artificialintelligence-news.com/2018/02/22/industry-collaboration-academia-advances-field-ai/
Bentur, A., Zonnenshein, A., Nave, R., Barzani, E., Zatkovesky, I., & Dayan, T. (2019). University-industry relations: Evidence based insights. Neaman Institute, Technion. https://www.neaman.org.il/Files/University-IndustryRelations-EvidenceBasedInsights_20190603185633.795.pdf
Bush, V. (1995). Science: The Endless Frontier, A Report to The President, Office of Scientific and Research Development. 1945. Reprint, North Stratford: Ayer Co.
Cohen, W. M., Nelson, R. R., & Walsh, J. P. (2002). Links and impacts: The influence of public research on industrial R&D. Management Science, 48(1), 1–23.
Deloitte. (2019). Strategies in academic and health system technology transfer to industry. Report to the Israeli National Council for Research and Development, Deloitte.
Eesley, C. E., & Miller, W. F. (2018). Impact: Stanford University’s economic impact via innovation and entrepreneurship. Trends® in Entrepreneurship, 14(2), 130–278.
Engell, J. (2016, May). Exit interview: Lita Nelsen on MIT tech transfer, Startups & Culture. Xconomy. https://xconomy.com/boston/2016/05/31/exit-interview-lita-nelsen-on-mit-tech-transfer-startups-culture/
Etzioni, O. (2019). AI Academy under Siege. Inside Higher Ed. https://www.insidehighered.com/views/2019/11/20/how-stop-brain-drain-artificial-intelligence-experts-out-academia-opinion
Etzkowitz, H., & Leydesdorff, L. (2000). The dynamics of innovation: From National Systems and “mode 2” to a triple Helix of university–industry–government relations. Research Policy, 29(2), 109–123.
Finne, H., Arundel, A., Balling, G., Brisson, P., & Erselius, J. (2009). Metrics for knowledge transfer from public research organizations in Europe: Report from the European Commission’s expert group on knowledge transfer metrics.
Finne, H., Day, A., Piccaluga, A., Spithoven, A., Walter, P., & Wellen, D. (2011). A Composite Indicator for Knowledge Transfer. Report from the European Commission’s Expert Group on Knowledge Transfer Indicators.
Gan-El, O. (2018, February 22). There is a war on workers in the AI field. I’ve already seen everything. Globes. https://www.globes.co.il/news/article.aspx?did=1001224848
Getz, D., Katz Shacham, O., Klein, R., Rozenberg, S., & Barzani, E. (2019). Artificial Intelligence Data Science and Smart Robotics. Streamlining the transfer of knowledge from academia and research institutes in the healthcare system to industry. Neaman Institute, Technion (HEB). https://www.neaman.org.il/EN/Artificial-Intelligence-Data-Science-and-Smart-Robotics-First-report-Streamlining-the-transfer-of-knowledge-from-academia-and-research-institutes-in-the-healthcare-system-to-industry
Getz, D., Katz Shacham, O., Klein, R., Tzezana, R., Rosenberg, S., Shoham, A., Barzani, E., Leck, E., & Tziperfal, S. (2018a). Artificial Intelligence, Data Science, and Smart Robotics- First report summary. Neaman Institute, Technion. https://www.neaman.org.il/Files/Summary-ENG-Artificial-Intelligence-Data-Science-and-Smart-Robotics_20190103155717.804.pdf
Getz, D., Katz Shacham, O., Klein, R., Tzezana, R., Rosenberg, S., Shoham, A., Tziperfal, S., Barzani, E., & Leck, E. (2018b). Artificial Intelligence, Data Science, and Smart Robotics Companies-Survey. July 2019. https://www.neaman.org.il/en/Artificial-Intelligence-Data-Science-and-Smart-Robotics-Companies-Survey
Gibb, A. (2012). Exploring the synergistic potential in entrepreneurial university development: Towards the building of a strategic framework. Annals of Innovation & Entrepreneurship, 3(1), 16742.
Graham, R. (2014). Creating university-based entrepreneurial ecosystems: Evidence from emerging world leaders. Massachusetts Institute of Technology.
Grimpe, C., & Hussinger, K. (2016). Formal and informal knowledge and technology transfer from academia to industry: Complementarity effects and innovation performance. Industry and Innovation, 20(8), 683–700.
Hattori, K. (2018). Technion and Intel inaugurate joint center for artificial intelligence. American Technion Sosiety. https://ats.org/news/technion-and-intel-inaugurate-joint-center-for-artificial-intelligence/#v
Holi, M. T., Wickramasinghe, R., & Van Leeuwen, M. (2008). Metrics for the evaluation of knowledge transfer activities at universities (p. 5). Library House.
Huggett, B. (2014). Reinventing tech transfer. Nature Biotechnology, 32(12), 1184.
Kenney, M., & Patton, D. (2009). Reconsidering the Bayh-Dole Act and the current university invention ownership model. Research Policy, 38(9), 1407–1422.
Khamis, A. (2015). Machine intelligence and its role in strengthening Industry-Academia Collaboration. April. https://doi.org/10.13140/RG.2.1.5007.8241
Lavie, P. (2019). Personal communication, Technion. Israel Institute of Technology.
Levy, R. (2017, January 11). The industry that steals the doctorates from the academy. Themarker. https://www.themarker.com/technation/1.263652
Lin, M. D. (2020). Accelerating the translation of artificial intelligence from ideas to routine clinical workflow. Academic Radiology, 27(1), 121–122. https://doi.org/10.1016/j.acra.2019.08.019
Link, A. N., & Siegel, D. S. (2005). Generating science-based growth: An econometric analysis of the impact of organizational incentives on university–industry technology transfer. European Journal of Finance, 11(3), 169–181.
Love, B. J. (2014). Do university patents pay off-evidence from a survey of university inventors in computer science and electrical engineering. Yale Journal of Law & Technology, 16(2), 285.
Merrill, S. A., & Mazza, A. M. (Eds.). (2011). Managing university intellectual property in the public interest. National Academies Press.
Metz, C. (2019, September 6). When the A.I. professor leaves, students suffer, study says. The New. https://www.nytimes.com/2019/09/06/technology/when-the-ai-professor-leaves-students-suffer-study-says.html
Microsoft Research. (2018). NeurIPS conference analytics. https://www.microsoft.com/en-us/research/?post_type=msr-blog-post&p=480543&preview=tru
OECD. (2019). Artificial intelligence in society. OECD Publishing. https://doi.org/10.1787/eedfee77-en
Ouchi, W. G. (2011). An ecosystem for entrepreneurs at UCLA, An invitation for campus-wide input. UCLA. https://ucla.app.box.com/s/f0sagdnxi76ui1oqmratjqmhand45dcg
Patel, S., Rexford, J., Zorn, B., & Morrisett, G. (2019). Evolving academia / Industry relations in computing research (Issue June). https://arxiv.org/pdf/2001.06684.pdf
Peng, T. (2019). Are commercial labs stealing academia’s AI thunder. Medium. https://medium.com/syncedreview/are-commercial-labs-stealing-academias-ai-thunder-dd51cf4bd8d6
Rauw, B., Smith, L., Honig, S., & Medovoi, O. (2014). UCLA ecosystem for entrepreneurs, Part III: A strategic blueprint for entrepreneurship at UCLA submitted for campus discussion. UCLA. https://ucla.app.box.com/v/Entrep-Ecosystem-III
Reslinski, M. A., & Wu, B. S. (2016). The value of royalty. Nature Biotechnology, 34(7), 685.
Sampat, B. N. (2006). Patenting and US academic research in the 20th century: The world before and after Bayh-Dole. Research Policy, 35(6), 772–789.
Sample, I. (2017, November 1). “We can’t compete”: Why universities are losing their best AI scientists. The Guardian. https://www.theguardian.com/science/2017/nov/01/cant-compete-universities-losing-best-ai-scientist
Schaeffer, V., Öcalan-Özel, S., & Pénin, J. (2018). The complementarities between formal and informal channels of university–industry knowledge transfer: A longitudinal approach. The Journal of Technology Transfer, 45, 1–25.
Schwab, K. (2017). The fourth industrial revolution. Penguin Random House.
Thursby, J. G., & Thursby, M. C. (2007). University licensing. Oxford Review of Economic Policy, 23(4). Proceedings from The ISPIM Innovation Conference—Innovation, The Name of The Game (June 2018), Stockholm: Sweden.
Tijssen, R. J., Yegros-Yegros, A., & Winnink, J. J. (2016). University–industry R&D linkage metrics: Validity and applicability in world university rankings. Scientometrics, 109(2), 677–696.
U.S. Department of Commerce. (2013, October). The innovative and entrepreneurial university: Higher education, Innovation & Entrepreneurship in focus. US Department of Commerce, Economic Development Administration.
UCLA (2011, September). UCLA ecosystem for entrepreneurs, Part II: Transition to a new technology transfer process. UCLA. https://ucla.app.box.com/v/Ecosystem-Report-II
Valdivia, W. D. (2013). University start-ups: Critical for improving technology transfer. Center for Technology Innovation at Brookings. Brookings Institution.
Valentin, F., & Jensen, R. L. (2007). Effects on academia-industry collaboration of extending university property rights. The Journal of Technology Transfer, 32(3), 251–276.
Vaquero-García, A., del Río, M. D. L. C., & Álvarez-García, J. (2016). Best university practices and tools in entrepreneurship. In M. Peris-ortiz, J. A. Gomez, F. Velez-Torres, & C. Rueda-Armengot (Eds.), Education tools for entrepreneurship (pp. 183–198). Springer.
Yoon, H., & Lee, J. J. (2013). Entrepreneurship education and research commercialization of engineering-oriented universities: An assessment and monitoring of recent development in Korea. International Journal of Engineering Education, 29(5), 1068–1079.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Bentur, A., Getz, D., Shacham, O.K. (2021). System Analysis of Technology Transfer Policies and Models in Higher Education. In: Sinuany-Stern, Z. (eds) Handbook of Operations Research and Management Science in Higher Education. International Series in Operations Research & Management Science, vol 309. Springer, Cham. https://doi.org/10.1007/978-3-030-74051-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-74051-1_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-74049-8
Online ISBN: 978-3-030-74051-1
eBook Packages: Business and ManagementBusiness and Management (R0)