International Expansion Strategy in Fast-Growing Businesses: Using Bayesian Networks to Identify Influencing Factors
- Leandro Aparecido da Silva
- João Florêncio da Costa Júnior
- Afrânio Galdino de Araújo
Abstract
The article aims to identify the key factors influencing strategic decisions regarding international expansion amongst rapidly growing digital businesses in Amsterdam, Berlin, London, New York, and Paris; whilst uncovering prominent trends that can provide guidance to investors and managers engaged in the internationalization process. The data used for this study was gathered from business networking events held between 2016 and 2020, with a specific focus on the short-term strategic growth options pursued by these companies. To analyse the data, the authors employed Bayesian Network modelling, comprising two components: 'International Expansion' as the primary factor (parent node) and 'Factors of Interest' as the secondary factor (child node). The findings indicate that companies based in Amsterdam, New York, and Paris are primarily influenced by 'Exits' and 'Acquisitions' when making decisions about international expansion. In contrast, businesses in Berlin, London, New York, and Paris lean towards alternative venture capital options such as 'Debt,' 'Venture Debt,' and 'Private Equity.' The results suggest that companies in London and Berlin possess mature business models, significantly impacting their ability to attract investments and expand internationally. Moreover, this research not only demonstrates the applicability of Bayesian Networks in analysing strategic choices but also provides valuable insights into how these choices influence a company's internationalization strategy, offering practical guidance to investors and managers. Finally, the study highlights the existence of distinct intentions for international expansion amongst CEOs and founders in the regions under investigation, indicating the need for further research to comprehend these unique idiosyncrasies.
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- DOI:10.5539/ijbm.v19n1p1
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