Microenterprises’ internal capability landscape and how it relates to the firms’ global performance (GP) is sparsely studied and understood. Discrete relationships between a capability and GP may have some empirical evidence, but how microenterprises’ capabilities quantitatively relate to each other and together to GP still had no answer. Our model investigates the impact of dynamic managerial capabilities (DMCs) and entrepreneurial orientation (EO) on operational capabilities (OCs), and of those on GP, moderated by competitive intensity (CI). The data were acquired in a survey by questionnaire to 402 Portuguese microenterprises and treated using covariance-based structural equation modeling. We confirm that DMCs and EO have a positive, statistically significant, and substantive impact on OCs, explaining over half its variance, where any relation to GP is fully mediated by OCs. Furthermore, we found that OCs hold a positive, statistically significant, and substantive impact on GP, explaining nearly a quarter of its variance. CI as a moderator, with a marginal effects analysis, shows limited significance in a short range of values and never any substantive significance. Our results highlight that, for a healthy microenterprise business ecosystem, a great deal of attention and capacitation must be given to microenterprises’ managers, specifically their DMCs, EO, and, eventually, OCs.

The conceptualization of administrative distance exists for over 20 years. Despite its ubiquity, we found an unsatisfactory theoretical and practical depth when it comes to its operationalization, and studies that narrow onto its development are scarce. We have set, therefore, to improve both the theoretical scope and measurement of administrative distance. We achieved this using an inductive approach, which allowed us to infer from observed results, such observation suggesting the addition of the variables in the Doing Business Report, as they capture a previously omitted and relevant aspect of administrative distance: bureaucratic efficiency. We use a reference model, featuring a panel random-effects regression, as a benchmark for the study of our proposal. Our results showed an improved model with a significantly higher explanatory capacity while observing that the new measure is both significant and independent from the existing administrative distance measure, being complementary. This work opens several avenues for future research, having meaningful consequences for the development of better institutional distance models.