Startup Survival Forecasting: A Multivariate AI Approach Based on Empirical Knowledge

Abstract

Predicting the survival of startups is a complex challenge due to the multifaceted nature of entrepreneurial ecosystems and the dynamic interplay of internal and external factors. Despite advances in empirical research, existing models often lack integration with robust conceptual frameworks. This study addresses these gaps by developing a multivariate AI-driven model for predicting startup survival, leveraging Lipschitz extensions, neural networks, and linear regression. Using a dataset of 20 startups, selected across diverse industries and evaluated on attributes such as team dynamics, market conditions, and financial metrics, the model demonstrated high accuracy and clustering capabilities. Key findings highlight the pivotal role of team dynamics and product differentiation in determining survival probabilities. By integrating conceptual insights with empirical data, the study bridges gaps in existing literature and offers a practical decision-making tool for entrepreneurs, investors, and policymakers. These findings underscore the importance of fostering collaborative, innovative ecosystems to enhance entrepreneurial success and societal well-being.

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