Associative business structures (business associations) and non-associative business structures (those without any formal or juridical associative agreement) provide platforms to share knowledge, gain insights and collaborate. One additional benefit to participate in these structures is the opportunity for business development. In some cases, and especially for SMEs, they provide important market opportunities and access to relevant stakeholders. This literature review is aimed at identifying the concepts about associative and non-associative business structures in the context of the digital age and digital transformation. The most often referenced concepts (business networks/business relationships, the diversity of actors in a network/business ecosystems, innovative networks, and stakeholder networks/business associations) and not yet prominent concepts (digital associations/digital networks, digital ecosystems/digital environment and digital entrepreneurship, e-markets/e-business environment and e-clusters/e-hubs/digital innovation hubs) provide input and ideas of how associative business structures could be supportive and relevant for SME business development in the digital era. As a navigation aid and for future research, these concepts were summarized in a business ecosystem framework (BEF) for the digital age.

In the context of the development of information technologies, the concerns about assessing the effects of digital transformation have increased. Although it is intuitively accepted that digital transformation has a favourable impact on macroeconomic variables (based on the interdependencies between micro- and macroeconomic performance), there is little scientific research providing evidence of this. Building on this identified research problem, this study aims to bridge the gap between theory and practice. After assessing the extent to which the world’s economies have responded to the need for digital transformation, an econometric analysis was conducted to quantify the impact of digital transformation on economic and social outcomes. To ensure the representativeness of the results, the econometric analysis was conducted on a sample of 46 countries selected according to the size of their gross national income per capita. The NRI (Network Readiness Index) and the sub-indices associated with the economic environment (future technologies, business, and economy) were used as independent variables. Gross domestic product (GDP) was used as a dependent variable. The results indicate that NRI has a positive and significant impact on GDP per capita. Analysis at the sub-indices level partially confirms this result and highlights that their contributions to the growth of macroeconomic performance may be different. The study results have practical utility as they provide clues on the structural efficiency of the benchmarks underpinning the digital transformation. To increase the positive impact on macroeconomic outcomes, policy-makers can propose and implement policies to facilitate access to those technologies that prove to be more effective.

Research background: Digital ecosystems in Europe are heterogenous organizations involving different economies, industries, and contexts. Among them, Digital Innovation Hubs (DIHs) are considered a policy-driven organization fostered by the European Commission to push companies’ digital transition through a wide portfolio of supporting services. Purpose of the article: There are DIHs existing in all European economies, but literature needs more precise indications about their status and nature. The purpose is to study a distribution of DIHs and differences in portfolios of DIHs’ services across European economies. Therefore, the paper wants to deliver more precise data on effects on national and European policies. This is required to define their final role and scope in the complex dynamics of the digital transition, depending on regional context and heterogeneity of industries. Methods: Data on 38 economies was collected from the S3 platform (on both existing and in preparation DIHs) and further verified by native speaking researchers using manual web scrapping of websites of DIHs identified from S3. To find potential similarities of digital ecosystems in different economies as emanated by the existence of DIHs, clusterization (Ward’s method and Euclidean distances) was applied according to the services offered. Economies were clustered according to the number of DIHs and the spread of DIHs intensity in different cities. The results were further analyzed according to the scope of the provided services. Findings & value added: The applied clustering classified European economies in four different sets, according to the types of services offered by the DIHs. These sets are expression of the different digitalization statuses and strategies of the selected economies and, as such, the services a company can benefit from in a specific economy. Potential development-related reasons behind the data-driven clustering are then conjectured and reported, to guide companies and policy makers in their digitalization strategies.