Digital entrepreneurial ecosystems: an empirical contribution using SMAA

Framing of the research. The concept of digital entrepreneurial ecosystems stands at the crossroads between the concepts of the digital ecosystem and the entrepreneurial ecosystem. We start with a summary of the data concerning the digital entrepreneurial pillars emerging in literature to provide robust and reliable measurement of digital entrepreneurial ecosystems. Purpose of the paper. The aim of the paper is to measure and compare digital entrepreneurial ecosystems in European countries to ensure a productive context for new venture creation. Methodology. We apply Stochastic Multicriteria Acceptability Analysis (SMAA) as a precise, robust, and reliable measurement approach to the Digital Economy and Society Index (DESI) data. Results. The main contribution of this work is the provision of a probabilistic ranking that is more robust and reliable than the conventional single ranking derived from composite indices constructed with a single weight vector. Research limitations. We applied SMAA allowing for a limited variation of the weights assigned in the computation of DESI. Allowing for a wider range of variation may provide further relevant insights. Furthermore, the database used for the operationalization of digital entrepreneurial ecosystem pillars may be enriched by adding further variables, thus enhancing the robustness of the analysis. Managerial implications. Our work provides relevant managerial implications for policymakers and businesses. The analysis identifies strengths and weaknesses of the different countries thus offering useful guidelines for policy makers aiming to support territorial development and for businesses to identify market opportunities. Originality of the paper. The originality of the paper lies in the application of SMAA methodology to digital entrepreneurial ecosystem literature, thus providing an empirical contribution to such a novel topic. We start from data used to compute the DESI index which, like most of the existing indices, is computed relying on fixed weights, thus being affected by a degree of subjectivity. The application of SMAA methodology allows us to consider how a variation in the assigned weights can affect the final ranking.