기계학습을 활용한 우주스타트업 성공 예측 모형

With the advent of the privately-led “New Space” era, the space industry is diversifying beyond the development of technologies and components that support state-led space missions, expanding into areas such as pharmaceuticals, tourism, and agriculture. Accordingly, the success factors of space startups are likely to differ from those of startups in general industries and may also vary depending on the national context. However, there is little empirical research on this topic. Therefore, the purpose of this study is to propose a theoretical framework that explains the success factors of startups entering the space industry and to empirically validate it using real-world data. To achieve this, we employ the internationalization process theory, which explains the internationalization process of firms, as the theoretical background and use Crunchbase data to examine space startups that emerged after the New Space era. Additionally, we conduct a Feature Importance analysis on Chinese and U.S. space startups to explore cross-national differences in space business success and derive relevant implications.