• KDI Journal of Economic Policy
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• v.35 no.1
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• pp.63-101
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• 2013

'Venture firms' in Korea are the firms who are certified as 'venture', whose certification types are defined by a law ('Special Law for the Support of Venture Firms'), and therefore encompass not only the venture capital-financed companies, which are usually regarded as ventures in USA and European countries, but also other types of firms such as R&D-intensive firms and the firms with financial guarantee or loans through technology evaluation ('technology finance or loan firms'). This paper examines the differences in the Korean venture firms' growth between the venture certification types. For the empirical analysis, this paper uses the lists of venture-certified firms from 1998 to 2010 which are then linked with their financial data in Korea Enterprises Database (KED). According to the results of empirical analyses, the companies in the 'venture capital-financed firms' type show greater growth rate in sales and the number of regular employees 3 and 5 years after first venture certification than the firms in type of 'technology finance/loan firms'. Moreover, the newly certified companies in 'R&D-intensive firms' type are also showing faster growth than the 'technology finance/loan firms' since 2003 where the venture industry has undergone a consolidation phase after the blast of so-called 'IT venture bubble' in 2001~2002. These results imply that the so-called 'venture firms' in Korea are composed with heterogeneous firm groups with different characteristics and that the companies selected through market mechanism ('venture capital-financed firms') outperforms the companies selected on the basis of policy interests ('technology finance/loan firms') in terms of the growth in sales and employment. On the basis of these findings, this paper suggests that the current venture-support policy should consider the different policy demands of firms across the type of venture certification more actively and that should refocus the objective of policies on facilitating venture capital market rather than emphasizing the nominal increase in the number of venture-certified firms.

• Journal of the Korea Management Engineers Society
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• v.23 no.4
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• pp.105-123
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• 2018

The purpose of this study is to verify whether the technology grade, which is the result of technology appraisal by Technology Credit Bureau, can be extended and applied to the venture certification system. We confirmed that there was a significant difference in the average financial performance for three years after the certification and appraisal of the two groups after matching the venture certification enterprise group and the technology appraisal enterprise group in 2015 through the propensity score matching method. As a result, there was no significant difference in the financial performance of venture certified firms and technology appraisal firms, so we confirmed that the technology grade can be expanded and applied to the venture certification system. As a result of estimating the technology grade conforming to the venture certification system, it was concluded that technology outstanding firm(T1-T4) is a technology grade suitable for the venture certification system.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.1-20
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• 2021

As the Great Reset is discussed at the World Economic Forum due to the COVID-19 pandemic, artificial intelligence, the driving force of the 4th industrial revolution, is also in the spotlight. However, corporate research in the field of artificial intelligence is still scarce. Since 2000, related research has focused on how to create value by applying artificial intelligence to existing companies, and research on how startups seize opportunities and enter among existing businesses to create new value can hardly be found. Therefore, this study analyzed the cases of startups using the comprehensive framework of the multi-level perspective with the research question of how artificial intelligence based startups, a sub-industry of software, have different innovation patterns from the existing software industry. The target firms are gazelle firms that have been certified as venture firms in South Korea, as start-ups within 7 years of age, specializing in machine learning modeling purposively sampled in the medical, finance, marketing/advertising, e-commerce, and manufacturing fields. As a result of the analysis, existing software companies have achieved process innovation from an enterprise-wide integration perspective, in contrast machine learning technology based startups identified unit processes that were difficult to automate or create value by dismantling existing processes, and automate and optimize those processes based on data. The contribution of this study is to analyse the birth of artificial intelligence-based startups and their innovation patterns while validating the framework of an integrated multi-level perspective. In addition, since innovation is driven based on data, the ability to respond to data-related regulations is emphasized even for start-ups, and the government needs to eliminate the uncertainty in related systems to create a predictable and flexible business environment.