The contingency impact of culture on health security capacities for pandemic preparedness: A moderated Bayesian inference analysis

Abstract

Managing pandemics is an enduring societal problem because major health emergencies have historically led to substantial changes and developments. While extant research has examined cultural and institutional factors that have influenced how governments have responded to the COVID-19 pandemic, there has been far less exploration of the factors associated with differences in the provision of preventative collective services, such as building health security capacities. This article examines the contingency impact of national culture on the association between a country's economic development and its pandemic preparedness. Methodically, the study uses a moderated Bayesian inference analysis, which is a machine learning technique that has been called for in international business research. Unlike traditional frequentist linear regression analysis, which aims to identify a single set of best-fit coefficients for a specified set of variables, Bayesian regression analysis generates posterior distributions of coefficients based on priors for an average of multiple potential models using the Markov Chain Monte Carlo technique. The use of moderated Bayesian inference analysis provides a novel approach to analyzing complex data in international business research. The study's findings can support governments in their resource allocation and policy development to address shortcomings in their preparedness for infectious disease outbreaks.