• Title/Summary/Keyword: Gazelle-firms

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Regional Characteristics of Job Creation by Gazelle-firms (지역별 가젤형 기업의 고용창출 특성 분석)

  • Sa, Hoseok
    • Journal of the Economic Geographical Society of Korea
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    • v.22 no.3
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    • pp.304-320
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    • 2019
  • Interest in gazelle-firms, which contribute to sustained job creation, is increasing. This study aims to analyze the regional characteristics of employment by gazelle-firms in terms of job quality as well as job quantity. Furthermore, the characteristics by industry for each type are compared. The major results are as follows. First, the regional characteristics of job creation are varied in terms of the quantitative and qualitative aspects. Second, it is found that the characteristics by industry for each type are different. Therefore, each type should have discriminative strategy. These results provide policy implications that gazelle-firms' policies should be tailed to regional characteristics in order to foster gazelle-firms more effectively.

Innovation Patterns of Machine Learning and a Birth of Niche: Focusing on Startup Cases in the Republic of Korea (머신러닝 혁신 특성과 니치의 탄생: 한국 스타트업 사례를 중심으로)

  • Kang, Songhee;Jin, Sungmin;Pack, Pill Ho
    • 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.