Predictive Analysis of Startup Ecosystems: Integration of Technology Acceptance Models with Random Forest Techniques - Daniel Bennet; Sheila Aulia Anjani; Ora Pertiwi Daeli; Dedi Martono; Cicilia Sriliasta Bangun (CORISINTA, 2024)
In the dynamic realm of startup ecosystems, forecasting trends and measuring success pose significant challenges. To tackle this multifaceted issue, a novel research method proposes integrating the Technology Acceptance Model (TAM) with the robust Random Forest algorithm, thereby enhancing predictive accuracy. This innovative approach encompasses various aspects including technical intricacies, financial dynamics, stakeholder interactions, and entrepreneurial challenges. Employing empirical data, such as revenue growth, capital raised, innovation rate, and active users, forms the foundation of this methodology. The model’s efficacy is demonstrated through a process involving training on 80% of the dataset and testing on the remaining 20%, showcasing superior predictive capabilities compared to conventional methods. Comparative analysis with established models like logistic regression further highlights the superiority of the integrated TAM and Random Forest approach, particularly in predicting startup success. These findings offer invaluable insights for entrepreneurs navigating the complexities of the startup landscape, as well as for investors, policymakers, and educators. Understanding and supporting growth dynamics within the startup ecosystem can foster innovation and prosperity. Moreover, in the academic sphere, this research contributes a novel framework for startup prediction, enriching existing knowledge and facilitating informed decision-making. Overall, this research not only provides practical applications for immediate stakeholders but also contributes to advancing the theoretical foundations of startup prognostication, thus serving as a significant milestone in the field.