International Journal of Advanced Culture Technology

  • 제12권2호
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  • Pages.167-179
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  • 2024
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  • 2288-7202(pISSN)
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  • 2288-7318(eISSN)
국제문화기술진흥원 (The International Promotion Agency of Culture Technology)
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Predictive Model for Evaluating Startup Technology Efficiency: A Data Envelopment Analysis (DEA) Approach Focusing on Companies Selected by TIPS, a Private-led Technology Startup Support Program

  • Jeongho Kim (Dept. of Industrial Systems Engineering, KAIST) ;
  • Hyunmin Park (Dept. of ICT Convergence, Handong Global University) ;
  • JooHee Oh (Dept. of Management and Economics, Handong Global University)
  • 투고 : 2024.03.30
  • 심사 : 2024.05.15
  • 발행 : 2024.06.30
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초록

This study addresses the challenge of objectively evaluating the performance of early-stage startups amidst limited information and uncertainty. Focusing on companies selected by TIPS, a leading private sector-driven startup support policy in Korea, the research develops a new indicator to assess technological efficiency. By analyzing various input and output variables collected from Crunchbase and KIND (Korea Investor's Network for Disclosure System) databases, including technology use metrics, patents, and Crunchbase rankings, the study derives technological efficiency for TIPS-selected startups. A prediction model is then developed utilizing machine learning techniques such as Random Forest and boosting (XGBoost) to classify startups into efficiency percentiles (10th, 30th, and 50th). The results indicate that prediction accuracy improves with higher percentiles based on the technical efficiency index, providing valuable insights for evaluating and predicting startup performance in early markets characterized by information scarcity and uncertainty. Future research directions should focus on assessing growth potential and sustainability using the developed classification and prediction models, aiding investors in making data-driven investment decisions and contributing to the development of the early startup ecosystem.

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참고문헌

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