The role of location in FinTech formation

ABSTRACT

Given the rapid emergence of FinTechs, the objective of this paper is to determine location-specific factors associated with FinTech establishment intensity using Porter’s diamond framework. The analysis is based on a country-level dataset covering the period of 2007–2017 and 107 countries. The results reveal that greater FinTech establishment intensity characterizes smaller countries, countries with stronger information and communications technology (ICT) services clusters, and countries that have experienced a crisis during the recent decade. Greater FinTech establishment intensity is also observed in countries with greater tertiary education enrolment rates, stronger university-industry cooperation, greater fixed line availability, and overall ICT readiness. The macroeconomic situation and indicators of financial development prove to be important determinants of FinTech formation. Given the importance of several dimensions of location’s diamond in FinTech formation, FinTech entrepreneurs could benefit from a careful analysis of the diamond of locations that they are considering as potential places of doing business. Countries hoping to become more attractive FinTech establishment sites, in turn, should focus on the elimination of weaknesses in the location’s diamond in close co-operation with FinTechs.

KEYWORDS:

• FinTechs
• start-up establishment
• location
• Porter’s diamond model

Acknowledgments

The authors appreciate the insightful feedback obtained from Prof. Alistair R. Anderson, the two anonymous referees, the participants of research seminars at TalTech School of Business and Governance, participants of IFZ FinTech Colloquium 2018, and participants of “Workshop on Fintech Adoption and Economic Behavior: Where Do We Stand?”, organized by LaRGE Research Centre (University of Strasbourg) April 1–2 2019.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplemental material

Supplemental data for this article can be accessedhere

Notes

1. Tsvetkova and Partridge (2019) do find that the applicability of KTSE differs across US high-tech subsectors.

2. Our dataset does not include countries with no established FinTechs. The number of countries covered decreases in estimations due to missing data on some of the used variables for some countries.

3. The dependent variable may remain somewhat biased due to some FinTechs having no data about the year of their establishment or country of residence or being out of the radar of Crunchbase. The latter is more likely in the case of non-English speaking and smaller countries.

4. Binomial regression models were preferred to Poisson models due to overdispersion of the count of FinTechs (mean 80.2 and standard deviation 347.6) and confirmation from the log-likelihood ratio test of overdispersion.

5. Ahi and Laidroo (2019) show that some of the used regulatory indicators may remain insignificant even for traditional financial intermediaries’ activities.

Additional information

Funding

This work was supported by the Tallinn University of Technology under grants B57 ‘Efficiency in Financial Sector in Light of Changing Regulatory Environment’ and BHV1 ‘Digital Development in Finance’. The funding providers had no role in the research process from study design to submission.