• Journal of Technology Innovation
• /
• v.24 no.3
• /
• pp.29-52
• /
• 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.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.13 no.4
• /
• pp.13-22
• /
• 2018

In this study, we examined the accuracy of the assessment items for selecting start-ups used in the project to support start-ups and verified their validity in determining whether they are appropriate assessment items based on selection criteria. The results of 973 start-ups that applied for the project fostering startup leading universities were collected and logistic regression was performed using SPSS 18.0. The study results are summarized as follows. First, the differences in characteristics of start-ups were identified in terms of selection. Second, the impact of selection by assessment items was gender in 2015, capability of the founder, business establishment in 2016, performance and potential in the global market, and business startup in 2017. Third, the overall selection accuracy analysis for the last three years confirmed that the accuracy of the selection is lower each year and that the accuracy of the selection is lower than the accuracy of the non-selection. This means that the current assessment items for selecting start-ups are inaccurate for selection, and that changes in the items due to changes in the start-up environment each year have led to lower accuracy of selection. It is meaningful that this study raised the importance of assessment items and the need for improvement of assessment items for the screening functions of good start-ups to enhance efficiency of the policies for startup support.

• 한국벤처창업학회:학술대회논문집
• /
• 2018.04a
• /
• pp.123-125
• /
• 2018

본 연구는 우리나라 대학의 창업교육센터(Entrepreneurship Education Center)에 대한 조사를 수행하여 선행 조사DB와 비교 분석하였다. 창업교육센터 실태조사를 바탕으로 대학별 정량적 정성적 지표를 측정하여 상위그룹과 하위그룹의 구분하고 두 집단의 차이점과 성공운영 요인을 분석하였다. 창업교육센터의 일반 현황과 특성, 교직원(교수자수, 교원창업자수), 교과목(과목, 수강인 원수), 학내외 프로그램(창업동아리, 창업경진대회, 창업캠프), 재정운영(예산, 전담인력) 등의 계량 지표는 대학정보공시(대학 알리미)와 중앙일보 대학평가 DB를 기초하여 조사하였다. 그리고 운영상의 문제점, 센터의 성과측정 항목 등 정성지표는 창업교육센터장을 대상으로 설문 조사하였다. 또한, 국내 창업교육센터의 표본특성을 살펴본 후 상위그룹과 하위그룹의 차이를 규명하여 상위그룹이 보유하고 있는 인적 물적 역량과 프로그램의 분석하여 창업교육센터의 활성화 방안을 제시하였다. 본 연구는 교육부의 "산학협력 선도대학(LINC) 육성사업 (2012년~2106년)"에서 창업교육을 총괄 지원하기 위해 2012년 대학에 설치된 전담조직인 61개 창업교육센터의 성과를 분석하고 대학평가에서 상위 순위에 있는 대학 그룹들의 차이점을 도출하는데 의미가 있다. 또한 2017년에 새롭게 지정된 LINC+ 사업 ('17년~) 창업교육 분야의 정책적 시사점을 제시한다. 본 연구의 목적을 달성하기 위해 문헌연구와 창업교육센터 운영에 대한 구조적 설문지를 개발하여 실태를 분석하였다. 이 결과는 창업교육센터의 효율적 운영을 위해 대학교직원 연구자 공무원 학생 및 창업교육센터를 포함한 창업지원 기관의 이해 관계자에게 향후의 발전을 위한 자료로써 의미가 있을 것이다.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.18 no.4
• /
• pp.207-222
• /
• 2023

In order to improve employment rates, most universities operate programs to strengthen students' employment and entrepreneurship, regardless of whether they are selected as the Leading Industry-Innovative University (LINC) or not. In particular, in the case of non-metropolitan universities are risking their lives to improve employment rates. In order to overcome the limitations of university establishment type and university location, which absolutely affect the employment rate, we are operating a startup education & startup support program in order to strengthen employment and entrepreneurship, and capstone design & field training as industry-academia-linked education programs are always available. Although there are studies on effectiveness verification centered on LINC (Leaders in Industry-University Cooperation) in previous studies, but a longitudinal study was conducted on all factors of university factors, startup education & startup support, and capstone design & field training as industry-university-linked education programs as factors affecting the employment rate based on public disclosure indicators. No cases of longitudinal studies were reported. This study targets 116 universities that satisfy the conditions based on university disclosure indicators from 2018 to 2020 that were recently released on university factors, startup education & startup support, and capstone design & field training as industry-academia-linked education programs as factors affecting the employment rate. We analyzed the differences between the LINC (Leaders in Industry-University Cooperation) 51 participating universities and 64 non-participating universities. In addition, considering that there is no historical information on the overlapping participation of participating students due to the limitations of public indicators, the Exposure Effect theory states that long-term exposure to employment and entrepreneurship competency enhancement programs will affect the employment rate through competency enhancement. Based on this, the effectiveness of the 2nd LINC+ (socially customized Leaders in Industry-University Cooperation) was verified from 2017 to 2021 through a longitudinal causal relationship analysis. As a result of the study, it was found that the startup education & startup support and capstone design & field training as industry-academia-linked education programs of the 2nd LINC+ (socially customized Leaders in Industry-University Cooperation) did not affect the employment rate. As a result of the longitudinal causal relationship analysis, it was reconfirmed that universities in metropolitan areas still have higher employment rates than universities in non-metropolitan areas due to existing university factors, and that private universities have higher employment rates than national universities. Among employment and entrepreneurship competency strengthening programs, the number of people who complete entrepreneurship courses, the number of people who complete capstone design, the amount of capstone design payment, and the number of dedicated faculty members partially affect the employment rate by year, while field training has no effect at all by year. It was confirmed that long-term exposure to the entrepreneurship capacity building program did not affect the employment rate. Therefore, it was reconfirmed that in order to improve the employment rate of universities, the limitations of non-metropolitan areas and national and public universities must be overcome. To overcome this, as a program to strengthen employment and entrepreneurship capabilities, it is important to strengthen entrepreneurship through participation in entrepreneurship lectures and actively introduce and be confident in the capstone design program that strengthens the concept of PBL (Problem Based Learning), and the field training program improves the employment rate. In order for actually field training affect of the employment rate, it is necessary to proceed with a substantial program through reorganization of the overall academic system and organization.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.19 no.2
• /
• pp.215-235
• /
• 2024

As artificial intelligence (AI) technology is recognized as a key technology that will determine future national competitiveness, competition for AI technology and industry promotion policies in major countries is intensifying. This study aims to present implications for domestic policy making by analyzing the policies of major countries on the start-up of AI companies, which are the basis of the AI industry ecosystem. The top four countries and the EU for the number of new investment attraction companies in the 2023 AI Index announced by the HAI Research Institute at Stanford University in the United States were selected, The United States enacted the National AI Initiative Act (NAIIA) in 2021. Through this law, The US Government is promoting continued leadership in the United States in AI R&D, developing reliable AI systems in the public and private sectors, building an AI system ecosystem across society, and strengthening DB management and access to AI policies conducted by all federal agencies. In the 14th Five-Year (2021-2025) Plan and 2035 Long-term Goals held in 2021, China has specified AI as the first of the seven strategic high-tech technologies, and is developing policies aimed at becoming the No. 1 AI global powerhouse by 2030. The UK is investing in innovative R&D companies through the 'Future Fund Breakthrough' in 2021, and is expanding related investments by preparing national strategies to leap forward as AI leaders, such as the implementation plan of the national AI strategy in 2022. Israel is supporting technology investment in start-up companies centered on the Innovation Agency, and the Innovation Agency is leading mid- to long-term investments of 2 to 15 years and regulatory reforms for new technologies. The EU is strengthening its digital innovation hub network and creating the InvestEU (European Strategic Investment Fund) and AI investment fund to support the use of AI by SMEs. This study aims to contribute to analyzing the policies of major foreign countries in making AI company start-up policies and providing a basis for Korea's strategy search. The limitations of the study are the limitations of the countries to be analyzed and the failure to attempt comparative analysis of the policy environments of the countries under the same conditions.