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The startup ecosystem, characterized by its dynamism, presents significant challenges in predicting its future trajectory. Traditional analytical methods often fall short in comprehensively addressing the myriad factors that shape this ecosystem. This research aims to enhance the predictability of trends within the startup landscape by integrating the Technology Acceptance Model (TAM) with the advanced Random Forest algorithm. While existing literature has extensively explored the challenges startups face and the nuances of stakeholder interactions, the integration of TAM's constructs with key empirical attributes, specifically Investment Dynamics, Startup Metrics, Stakeholder Interactions, Entrepreneurial Challenges, and Technological Infrastructure, is a pioneering approach. Drawing from a comprehensive dataset that spans a diverse array of startups, this study operationalizes TAM's constructs in conjunction with the specified attributes. The subsequent application of the Random Forest algorithm offers a novel predictive methodology. Initial results highlight the superior predictive capabilities of this integrated model compared to traditional approaches. The findings provide insights into the intricate relationship between technological perceptions, as framed by TAM, and the tangible realities of the startup domain. The fusion of TAM with state-of-the-art machine learning signifies a groundbreaking direction in startup ecosystem research. This innovative approach offers stakeholders an enhanced analytical tool, ensuring more informed decision-making and a deeper grasp of the multifaceted nature of startup ecosystems.
Kanivia et al. (Mon,) studied this question.