• Title/Summary/Keyword: Industry-University Cooperation(IUC)

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The Effects of Government Financial Support on the Performance of Industry-University Cooperation: Focus on LINC Program (정부 재정지원이 산학협력 성과에 미치는 영향 분석: 산학협력 선도대학 육성사업을 중심으로)

  • Moon, Hyungjin;Lee, Heesang
    • Journal of Technology Innovation
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    • v.24 no.3
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    • pp.29-52
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    • 2016
  • As Industry-University Cooperation (IUC) has been emphasized for securing national competitiveness in a knowledge-based economy, the South Korean government has promoted related policies, including a financial support program for facilitating IUC activities. This study examines the effects of Leaders in INdustry-university Cooperation (LINC) program, which is one of the government financial support programs for IUC, on the performance of IUC by propensity score matching. The results show that LINC program positively influences the number of technology transfers and the number of faculty that participated in start-ups. The results of this study are expected to provide implications for improving IUC policies and the financial support program.

Investigating the Characteristics of Academia-Industrial Cooperation-based Patents for their Long-term Use (지속적 활용이 가능한 산학협력 특허 특성 분석)

  • Park, Sang-Young;Choi, Youngjae;Lee, Sungjoo
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.22 no.3
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    • pp.568-578
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    • 2021
  • Patents that are research results from industry-university cooperation (IUC) are a source of innovation, and play an important role in economic growth, such as technology transfer and commercialization. For this reason, there are many efforts to revitalize IUC, but in general, company patents are achievements that can be commercialized, rather than research achievements, so not all patents are used for business, even after their creation as the outcome of IUC. Therefore, this research supports the design of measures in which IUC can ultimately be linked to successful utilization of patents by identifying the purposes of IUC, even after it has been successfully promoted, and patents have been filed as a result. To this end, first, the patents registered for industry-academia cooperation in the United States are collected, and second, a predictive model is designed, with unexpired and expired patents predicted using machine learning techniques. The final identified patents are intended to derive available factors in terms of marketability and technicality. This study is expected to help predict the utilization of unexpired and expired patents, and is expected to contribute to setting goals for research results from technical cooperation between corporate and university officials planning early IUC.