Abstract Purpose This paper aims to address which resources provided by an entrepreneurial ecosystem (EE) are necessary for deep technology entrepreneurship. Design/methodology/approach The authors used a novel approach known as necessary condition analysis (NCA) to data on EEs and deep-tech startups from 132 countries, collected in a global innovation index and Crunchbase data sets. The NCA makes it possible to identify whether an EEs resource is a necessary condition that enables entrepreneurship. Findings Necessary conditions are related to political and business environment; education, research and development; general infrastructure; credit; trade; diversification and market size; and knowledge absorption capacity. Research limitations/implications The results show that business and political environments are the most necessary conditions to drive deep-tech entrepreneurship. Practical implications Policymakers could prioritize conditions that maximize entrepreneurial output levels rather than focusing on less necessary elements. Social implications Some resources require less performance than others. So, policymakers should consider allocating policy efforts to strengthen resources that maximize output levels. Originality/value Studies on deep-tech entrepreneurship are scarce. This study provides a bottleneck analysis that can guide the formulation of policies to support deep-tech entrepreneurship, as it allows to identify priority areas for resource allocation.

ABSTRACT This research seeks to enrich the existing theories by analyzing what are the barriers affecting the export process of small and medium-sized non-tech companies in emerging markets, while simultaneously examining the relationship between internal and external barriers in this context. Non-tech companies are characterized by little knowledge and limited resources. How could this kind of company reach the global market? What barriers hinder the exporting process the most? A confirmatory factor analysis was carried out, and the validated model based on 71 responses obtained was adjusted in a selected sector. The results suggest that the governmental and sociocultural barriers are the most significant from the perspective of external barriers, while market barriers and the manager’s personal dimensions have greater significance from the perspective of internal barriers. Our paper offers a theoretical contribution by developing a measurement model (a) testing the existing barriers simultaneously with the managerial/entrepreneurial barriers and with (b) the focus on non-tech firms. For practitioners, the identification of barriers can be useful to foster specific actions in terms of public policies, in addition to internal actions focused on the development of the managers’ capacities.