Data-based Startup Profile Analysis in the European Smart Specialization Strategy: A Text Mining Approach

Levan Bzhalava,Jari Kaivo-Oja,Sohaib S Hassan

doi:10.5755/j01.eis.0.12.21869

Abstract

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

Highlights

Thurik, 1999; Zacharakis et al, 1999)
As Wennekers and Thurik (1999) suggest, entrepreneurs play an indispensable role in economic growth because their activities create variety of ideas and initiatives
“variety, competition, selection and imitation (...) expand and transform the productive potential of a regional or national economy” (Wennekers and Thurik, 1999: 50). This implies that entrepreneurs facilitate the operation of market selection mechanisms and promote innovation activities as well as stimulate industry evolution

Summary

Introduction

1999; Zacharakis et al, 1999). it is considered a source of innovative and competitive power of an economy (Wennekers and Thurik, 1999; Zacharakis et al, 1999; Praag and Versloot, 2007). 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.

Objectives

Results

Conclusion