Promoting the internationalization of European SMEs using Big Data

Abstract

Globalization of the economy and severe competition encourage businesses to pursue internationalization opportunities, which contribute considerably to the economic development of nations, industries, and productivity. Small and medium-sized firms (SMEs), which play an important role in economic growth and transformation, face international competition and are obliged to compete in global marketplaces. SMEs may encounter several obstacles on their path to internationalization, including informational barriers. These are characterized as difficulties in discovering, choosing, and reaching overseas markets induced by information inefficiencies. Such restrictions may even deter SMEs from engaging in international activities if they have not yet begun the process of internationalizing their business. This dissertation provides a decision-making tool that helps European SMEs to identify the most economically advantageous European areas for the internationalization of their business operations, minimizing information ambiguity and enhancing market analysis. The most important elements are included in a common gravity model of commerce, which is based on more than 80 variables taken from seven distinct open access databases, using big data and machine learning techniques.