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Technology Commercialization through Sustainable Knowledge Sharing from University-Industry Collaborations, with a Focus on Patent Propensity

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  • Junghee Han

    (Graduate School of Smart City Science Management, Hongik University, Room 306-2, Sejoung-roJochiwon, (30016) Sejong, Korea; hjh0037@ hongik.ac.kr; Tel.: +82-010-5430-5049† The current study was supported by the Hongik University New Faculty Research Support Fund.
    The current study was supported by the Hongik University New Faculty Research Support Fund.)

Abstract

The process of technology commercialization involves numerous hurdles, from the original idea to its translation into the market. Patenting brings to light the research activities created through university-industry collaborations. This paper explores patent propensity, as related to commercializing innovative activities resulting from collaborations between universities and regional small and medium enterprises (SMEs). To achieve our aims, we apply a robust regression analysis to test five research hypotheses using firm-level data on 263 firms located in the Gwangju region of Korea. Our empirical results show that certain industry characteristics are negatively related to the propensity to patent. In addition, and contrary to expectations, it is found that the InnoBiz firms that the government designated as innovative SMEs are not performing any differently from general firms. It is only the CEO’s academic credentials that are found to be positively related to propensity to patent. From the findings, we can conclude that patenting propensity is likely related to CEOs’ managerial strategies rather than the particular characteristics of the technology in question. We also suggest new technology commercialization related with universities’ policies and support to promote regional SMEs including conglomerated firms as well as universities.

Suggested Citation

  • Junghee Han, 2017. "Technology Commercialization through Sustainable Knowledge Sharing from University-Industry Collaborations, with a Focus on Patent Propensity," Sustainability, MDPI, vol. 9(10), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:10:p:1808-:d:114286
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    2. Leogrande, Angelo & Drago, Carlo & Mallardi, Giulio & Costantiello, Alberto & Magaletti, Nicola, 2024. "Patenting Propensity in Italy: A Machine Learning Approach to Regional Clustering," SocArXiv nftv3, Center for Open Science.
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    5. Sabrina L. Woltmann & Lars Alkærsig, 2018. "Tracing university–industry knowledge transfer through a text mining approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 449-472, October.
    6. Chi-Yo Huang & Min-Jen Yang & Jeen-Fong Li & Hueiling Chen, 2021. "A DANP-Based NDEA-MOP Approach to Evaluating the Patent Commercialization Performance of Industry–Academic Collaborations," Mathematics, MDPI, vol. 9(18), pages 1-26, September.
    7. Xiaodan Kong & Qi Xu & Tao Zhu, 2019. "Dynamic Evolution of Knowledge Sharing Behavior among Enterprises in the Cluster Innovation Network Based on Evolutionary Game Theory," Sustainability, MDPI, vol. 12(1), pages 1-23, December.
    8. Teng, Sin Yong & Touš, Michal & Leong, Wei Dong & How, Bing Shen & Lam, Hon Loong & Máša, Vítězslav, 2021. "Recent advances on industrial data-driven energy savings: Digital twins and infrastructures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).

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