Development of a Success Prediction Model for Crowdfunding Based on Machine Learning Reflecting ESG Information

This study aims to develop a success prediction model for crowdfunding by integrating ESG (Environmental, Social, and Governance) factors using machine learning techniques. Crowdfunding, a modern financing method conducted through online platforms, has become a popular avenue for raising funds, particularly for creative projects, startups, and social enterprises. Incorporating ESG factors into crowdfunding success prediction models is crucial, as these factors represent environmental responsibility, social accountability, and ethical governance, which are increasingly important to investors and sponsors. To achieve the research objectives, this study employed advanced machine learning algorithms, including XGBoost, LightGBM, AdaBoost, CatBoost, and NGBoost, to analyze data from a prominent reward-based crowdfunding platform in Korea, 'Wadiz.' Text mining techniques were used to quantify unstructured ESG-related data, and the predictive performance of models including ESG factors was compared with models excluding them. The results demonstrated that prediction accuracy significantly improved when ESG factors were incorporated. Among the ESG dimensions, environmental information contributed the highest prediction performance, followed by social activity information and governance information. The findings of this study contribute to academia by expanding the understanding of non-financial factors in crowdfunding success prediction and integrating ESG elements into machine learning models. This research also provides practical implications by offering insights for platform operators, project creators, and investors to improve decision-making processes through the strategic use of ESG factors. The proposed model enhances crowdfunding platforms' reliability and success rates, supports project makers in designing impactful campaigns, and aids investors in making sustainable and data-driven investment decisions. This study advances theoretical understanding and provides actionable strategies for enhancing the effectiveness of crowdfunding in practice.