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The aim of the paper is to develop novel scientific metrics approach to the European Smart Specialization Strategy. The European Union (EU) has introduced Smart Specialization Strategy (S3) to increase the innovation and competitive potential of its member states by identifying promising economic areas for investment and specialization. While the evaluation of Smart Specialization Strategy requires measurable criteria for the comparison of rate and level of development of countries and regions, policy makers lack efficient and viable tools for mapping promising sectors for smart specialization. To cope with this issue, we used a text mining approach to analyze the business description of startups from Nordic and Baltic countries in order to identify sectors in which entrepreneurs from these regions see new business opportunities. In particular, a topic modeling, Latent Dirichlet Allocation approach is employed to classify business descriptions and to identify sectors, in which start-up entrepreneurs identify possibilities of smart specialization. The results of the analysis show country-specific differences in national startup profiles as well as variations among entrepreneurs coming from developed and less developed EU regions in terms of detecting business opportunities. Finally, we present policy implications for the European Smart Specialization Strategy

The aim of the paper is to develop novel scientific metrics approach to the European Smart Specialization Strategy. The European Union (EU) has introduced Smart Specialization Strategy (S3) to increase the innovation and competitive potential of its member states by identifying promising economic areas for investment and specialization. While the evaluation of Smart Specialization Strategy requires measurable criteria for the comparison of rate and level of development of countries and regions, policy makers lack efficient and viable tools for mapping promising sectors for smart specialization. To cope with this issue, we used a text mining approach to analyze the business description of startups from Nordic and Baltic countries in order to identify sectors in which entrepreneurs from these regions see new business opportunities. In particular, a topic modeling, Latent Dirichlet Allocation approach is employed to classify business descriptions and to identify sectors, in which start-up entrepreneurs identify possibilities of smart specialization. The results of the analysis show country-specific differences in national startup profiles as well as variations among entrepreneurs coming from developed and less developed EU regions in terms of detecting business opportunities. Finally, we present policy implications for the European Smart Specialization Strategy

The aim of the paper is to develop novel scientific metrics approach to the European Smart Specialization Strategy. The European Union (EU) has introduced Smart Specialization Strategy (S3) to increase the innovation and competitive potential of its member states by identifying promising economic areas for investment and specialization. While the evaluation of Smart Specialization Strategy requires measurable criteria for the comparison of rate and level of development of countries and regions, policy makers lack efficient and viable tools for mapping promising sectors for smart specialization. To cope with this issue, we used a text mining approach to analyze the business description of startups from Nordic and Baltic countries in order to identify sectors in which entrepreneurs from these regions see new business opportunities. In particular, a topic modeling, Latent Dirichlet Allocation approach is employed to classify business descriptions and to identify sectors, in which start-up entrepreneurs identify possibilities of smart specialization. The results of the analysis show country-specific differences in national startup profiles as well as variations among entrepreneurs coming from developed and less developed EU regions in terms of detecting business opportunities. Finally, we present policy implications for the European Smart Specialization Strategy. DOI: http://dx.doi.org/10.5755/j01.eis.0.12.21869

AbstractThe European Union (EU) Member States in central and eastern Europe (CEE) witnessed a surge in outward foreign direct investment (OFDI) between 2000 and the start of the global financial crisis. This article investigates whether the European integration process altered the relative importance of host country location factors. In particular, we investigate to what extent knowledge‐seeking is a relevant investment motive, which has been documented as a key determinant for OFDI from other emerging economies. We apply a discrete choice approach to model foreign location choice of firms from CEE countries (CEECs) within the EU 27 (1996–2010). We find that the EU integration process is related with increasing importance of market access and less emphasis on labour cost advantages. We find heterogeneity in the valuation of foreign knowledge‐related assets. The location probability within the EU15 is positively associated with knowledge‐seeking. It also plays a role for technology‐intensive industries and larger firms.