Adoption of measures to mitigate the impact of COVID-19: In search of a Hofstedian explanation for patterns among individual countries and country clusters

Highlights

• Looking at COVID-19 safety measures from the perspective of the Hofstede model, we find that Power Distance Index (PDI) and the Individualism dimension (IDV) have a considerable explanatory power across countries around the globe.

• We apply Principal Components Analysis (PCA) and a cluster analysis to see whether the behavioural patterns across countries can be grouped, and the subsequent groupings partly coincide with Wursten’s mental image clustering scheme.

• The techniques applied provide a basis for further data-driven exercises to adjust Wursten’s scheme.

• Our findings provide insights into how decisions and guidance to citizens in the realm of pandemic situations or other (sanitary) crisis) ought to be conveyed in function of the cultural cluster to which a country pertains.

Abstract

We consider multiple safety measures in relation to the COVID-19 virus and look at their adoption levels for a variety of 15 individual countries, based on data from Yougov.co.uk. Subsequently, we establish correlation coefficients between measure-specific uptake levels and Hofstede dimension scores for all countries considered. We notably find that Power Distance Index (PDI) and Individualism (IDV) have a considerable explanatory power.

In addition, we carried out a Principal Components Analysis (PCA) and a cluster analysis to see whether the behavioural patterns across countries can be grouped, and which Hofstede dimensions correlate strongest with the two main components that follow from the PCA. The PCA provides further confirmation of PDI and IDV being the most important explanatory factors for the uptake of measures across countries. The cluster analysis, in turn, reveals four broad groups, which only partly coincide with the way that the mental image clustering scheme by Wursten (2019) allots countries into its respective clusters. Hence, this provides a basis to suggest that data-driven exercises like the ones from our paper can serve to adjust Wursten’s intuitive scheme.