• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.177-191
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• 2023

This study analyzes the types of linkages between major projects used by firms that attracted venture capital among firms that received government support in the field of SME startups. It identifies the types of linkages between support programs related to attracting venture investment and verifies the usefulness of integrated and cooperative support. The main findings of this study are as follows. First, Startup Success Packages, Startup Foundation Funds*, Youth Entrepreneurship Centers, and Training are the main programs used by startups and venture firms, and support-implementing agencies use these programs to provide support for each stage of growth. Second, the majority of startups and venture firms receiving policy support for job creation and manpower enhancement projects. Third, export-type growth companies receive continuous support from MSS, MOTIE, MSIT, and KIPO. Fourth, job creation programs drive the employment performance and creation of companies. Fifth, local government support projects tend to rely heavily on central government support programs. Sixth, growth companies in the startup and venture sector have a clear link to credit guarantee scheme by KIBO. These findings provide empirical evidence on the necessity and feasibility of integrated and collaborative support, and are expected to contribute to the direction of better support policies.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.124-132
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• 2023

In order to support start-ups effectively with limited resources, it is necessary to provide support tailored to start-up companies. Businesses that are operating in local incubation centers may have different demands for the support depending on the region characteristics. This study was worked on companies that have entered into the incubation center 'Youth Cube', which is operated by Ansan City. We investigated the perception of importance in the field of support to identify support policy priorities, and the satisfaction level of each field of support currently supported. Then We conducted an IPA analysis to derive the direction of support. The Ansan area has regional characteristics that it is located in the metropolitan area with well-equipped start-up infrastructure and many innovative institutions, As a result of the importance survey, technology commercialization was recognized as the most important, and education/mentoring was recognized as the least important. Satisfaction with the space and facilities that can be used free of charge was the highest, and the satisfaction was the lowest for entrepreneurship education/mentoring, as was the degree of importance. In addition, according to each quadrant, we derived support that should be maintained, support that should be intensively improved, support that should be given a low policy priority, and support directions that should be kept in mind to avoid excessive support.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.3
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• pp.13-29
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• 2021

The purpose of this study was to analyze the spatial distribution and location factors of startups by generation (young and middle-aged) in Seoul. To this end, a research model was established that included factors of industry, population, and startup institutions by generation in 424 administrative districts using the Seoul Business Enterprise Survey(2018), which includes data on the age group of entrepreneurs. As an analysis method, descriptive statistics were conducted to confirm the frequency, average and standard deviation of startups by generation and major variables in the administrative districts of Seoul, and spatial distribution and characteristics of startups by generation were analyzed through global and local spatial autocorrelation analysis. In particular, the spatial distribution of startups in Seoul was confirmed in-depth by categorizing and analyzing startups by major industries. Afterwards, an appropriate spatial regression analysis model was selected through the Lagrange test, and based on this, the location factors affecting startups by generation were analyzed. The main results derived from the research results are as follows. First, there was a significant difference in the spatial distribution of young and middle-aged startups. The young people started to startups in the belt-shaped area that connects Seocho·Gangnam-Yongsan-Mapo-Gangseo, while middle-aged people were relatively active in the southeastern region represented by Seocho, Gangnam, Songpa, and Gangdong. Second, startups by generation in Seoul showed various spatial distributions according to the type of business. In the knowledge high-tech industries(ICT, professional services) in common, Seocho, Gangnam, Mapo, Guro, and Geumcheon were the centers, and the manufacturing industry was focused on existing clusters. On the other hand, in the case of the life service industry, young people were active in startups near universities and cultural centers, while middle-aged people were concentrated on new towns. Third, there was a difference in factors that influenced the startup location of each generation in Seoul. For young people, high-tech industries, universities, cultural capital, and densely populated areas were significant factors for startup, and for middle-aged people, professional service areas, low average age, and the level of concentration of start-up support institutions had a significant influence on startup. Also, these location factors had different influences for each industry. The implications suggested through the study are as follows. First, it is necessary to support systematic startups considering the characteristics of each region, industry, and generation in Seoul. As there are significant differences in startup regions and industries by generation, it is necessary to strengthen a customized startup support system that takes into account these regional and industrial characteristics. Second, in terms of research methods, a follow-up study is needed that comprehensively considers culture and finance at the large districts(Gu) level through data accumulation.