• Asian Journal of Innovation and Policy
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• v.10 no.3
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• pp.330-354
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• 2021

To know the long-term growth patterns and determinants of successful startups, 15-year (2006-2020) panel data of 252 companies that had a growth rate of over 20% every year in the last three years were used. In the first analysis, statistics on the period required to designate a gazelle company or listed on the stock market were examined. In addition, five long-term growth patterns were presented. In the panel analysis, the R&D intensity, operating profit ratio, size, and age of the company were pointed out as determinants of growth. The operating profit margin and R&D intensity have a positive effect on growth. Gibrat's law was not supported, but an inverted U-shape was observed. Jovanovic's law was confirmed. Although many studies tend not to point to profitability as a determinant of long-term growth, this is an important long-term growth factor of a company. The operating profit ratio was used in this study.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.4
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• pp.115-125
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• 2024

This study defined the appraisal items of technology appraisal for investment as innovation characteristics and derived the determining factors for predicting high-growth companies. Through this, we presented a direction for improving the technology appraisal model for investment. High-growth companies were classified into high-growth companies in sales, high-growth companies in operating profit, and high-growth companies in both sales and operating profit. At this time, the concept of a gazelle company was applied and defined as a company with an average growth rate of 20% or more over three years after the appraisal year. As for the analysis results, in terms of technicality (appraisal items), it was significant in predicting high-growth companies in sales and high-growth companies in sales and operating profit. Therefore, it will be possible to increase the discrimination power of predictions by strengthening the technicality (appraisal items). On the other hand, the business feasibility (appraisal items) was significant in predicting high-growth companies in sales and high-growth companies in sales and operating profit, but in a negative direction. This is due to the composition and criteria of the business feasibility (appraisal items), and it was concluded that changes to the composition and criteria for the relevant items are necessary for future model improvement.

• 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.

• Journal of Technology Innovation
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• v.23 no.3
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• pp.373-396
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• 2015

This study analyzed the determinants of high-growth firms using the technology appraisal data of the Korea Technology Finance Corporation. This study is differentiated from previous studies for three reasons. First, it analyzed the determinants of firms that will grow into high-growth firms in the future, not the characteristics of current high-growth firms. Second, it analyzed high-growth firms by dividing them in two aspects: sales and employment. In other words, they were divided into three types: the case in which a firm achieves high growth in both sales increase and creation of jobs, the case in which a firm achieves high growth in creation of jobs but low growth in sales increase, and the case in which a firm achieves high growth in only sales increase but low growth in creation of jobs. Third, this study applied the technology appraisal indicators of Kibo Technology Rating System(KTRS) by the Korea Technology Finance Corporation as the explanatory variable. As a result of analysis, it was found that a firm achieved high growth in both sales and employment if the position in the technology life cycle was appropriate and the technology readiness level was high. However, it turned out that the management system of technical manpower had conflicting effects on high growth of employment and sales. In other words, a firm that had well managed its technical manpower achieved high growth in terms of employment, but rather showed low growth in terms of sales. This result suggests the inference that firms showing high growth in employment may appear mainly in the high-tech industry where management of technical manpower is important. Accordingly, as a result of adding dummy variables that represent whether or not firms are in the high-tech industry, it was found that the result supported the inference, as firms in the high-tech industry were highly likely to achieve high growth in employment.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.77-88
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• 2023

The purpose of this study is to confirm whether the current technology appraisal model for investment, which is designed to identify high-growth SMEs in sales, which is one of the characteristics of gazelle companies, has the possibility of expanding employment effects. For SMEs classified as technology investment adequate firms(TI1-TI6) through technology appraisal for investment between 2016 and 2018 were targeted. At this time, the employment effect was analyzed by dividing the absolute employment effect and the relative employment effect. As a result of the analysis, it was confirmed that the technology appraisal items for investment defined as innovation characteristics did not have significant explanatory power for the absolute employment effect. However, for the relative employment effect, among innovation characteristics, technicality(TC) was found to have significant explanatory power, and this is because the item appraised based on future growth potential. In particular, the relative employment effect is meaningful in terms of the actual employment effect, and the conclusion is drawn that the current technology appraisal model for investment is an appraisal model with the possibility of expansion in terms of employment effect.