National Entrepreneurial Activity: ESG, Ecosystem Dynamics, and Technological Context

Examining national Total Early-stage Entrepreneurial Activity (TEA) drivers is crucial amidst the growing influence of Environmental, Social, and Governance (ESG) criteria and rapid technological change. Leveraging established GEM and World Bank data, this research provides fresh insights through a novel synthesis, moving beyond replication. Panel data from 45 countries (2009-2023) were analyzed using a rigorously selected Random Effects regression model, complemented by machine learning techniques (Random Forest, XGBoost), to explore the interplay between ESG performance, technological context, ecosystem factors, and national TEA rates. Significant positive associations with TEA were found for internal ecosystem factors (intentions, employee activity, female-male ratio) and specific ESG dimensions (rule of law, social rights, education spending, gender parity). Conversely, negative links emerged for the lowest income share, renewable electricity output, business sector prominence, and high entrepreneurial status. Machine learning confirmed the entrepreneurial intentions' dominant predictive power. By integrating diverse theories and methods, this study contributes a nuanced perspective. Fostering dynamic entrepreneurship necessitates attention to both internal ecosystem dynamics and foundational ESG elements like good governance and social investments, offering valuable policy insights for the current socio-technological landscape.