Earticle

초록

영어

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.

목차

Abstract
1. INTRODUCTION
2. RELATED STUDIES
2.1 Initial startup success factors
2.2 Data Envelopment Analysis (DEA)
3. EMPIRICAL ANALYSIS
3.1 Data
3.2 Regression analysis
4. MACHINE LEARNING MODEL
5. CONCLUSION
REFERENCES

키워드

저자

• Jeongho Kim [ Dept. of Industrial Systems Engineering, KAIST, Korea ]
• Hyunmin Park [ Dept. of ICT Convergence, Handong Global University, Korea ]
• JooHee Oh [ Professor, Dept. of Management and Economics, Handong Global University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Culture Technology(IJACT)

• 간기

  계간

• pISSN

  2288-7202

• eISSN

  2288-7318

• 수록기간

  2013~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 600 DDC 700