One size fits all? Using machine learning to study heterogeneity and dominance in the determinants of early-stage entrepreneurship

Abstract

Despite the vast number of studies exploring the determinants of entrepreneurship, few have been able to distinguish the relative importance of these factors. Traditional regression-based approaches, upon which such studies are based, are unable to fully capture heterogeneous and complex non-linear patterns in the determinants of entrepreneurship. To address these limitations, we adopt a novel approach, using machine learning to study heterogeneity and dominance in the social-cognitive determinants of early-stage entrepreneurship. We apply decision tree algorithms to a large-scale dataset from the Global Entrepreneurship Monitor. Our results reveal that the dominant determinants, irrespective of entrepreneurial pathway, are individual entrepreneurial self-efficacy and networks, with factors such as cultural perceptions being relatively unimportant, despite substantial attention in the literature. The results also show considerable heterogeneity in the factors contributing to entrepreneurship, highlighting the need for academics and policy makers to consider the likelihood that there is no single set of motivating factors.