Does cultural distance matter? A decision tree machine-learning exploration on international travellers’ destination choices

Abstract

While many studies have explored the influence of cultural distance on international travelers' destination choices, the findings have been inconsistent and fragmented. We systematically analyze the relationship between cultural distance and destination choices by applying machine learning techniques to the comprehensive dataset provided by the United Nations World Tourism Organization (UNWTO). In a tree of models split in accordance with Hofstede's cultural dimensions, we find positive, negative, concave, and convex cultural distance–destination choice relationships across countries. Countries with the same pattern are grouped by the similarity of their distinctive cultural traits.