Entrepreneurial activity in the international trade in cultural goods: A fuzzy clustering analysis

This study offers a novel country-level longitudinal investigation of conditions, including, income, urbanity, education, R&D, and entrepreneurial activity, driving international trade, for imports and exports. The configurational (clustering) approach places emphasis on country and year groupings, offering ‘targeted’ understanding on country level variations of international trade in cultural goods. The study explores context sensitive conditions affecting international trade in cultural goods, including environment for entrepreneurship, and entrepreneurial processes. Emphasis is given to configurational considerations of clusters of country-year observations based on conditions. Inferences inferred will be country groups-based perspectives. Using UIS and GEM datasets, fuzzy c-means clustering is employed for economic development-related conditions measuring, income, urbanity, education, R&D, and entrepreneurial activity, to establish clusters of country-year observations, based on differences in the condition values describing them. These clusters are defined to give qualitative understanding of their individuality. Validation of clusters is undertaken with consideration of differences on levels of international trade of cultural goods, in terms of forms of imports and exports. To complement the validation, cluster profiling is undertaken, with consideration of population age and poverty levels. The study contributes increased understanding concerning drivers (conditions) of trade in cultural goods, and impact of entrepreneurship in both imports and exports. • The study offers a novel country level analysis of international trade in cultural goods. • Contributes to discussion on trade drivers in cultural goods and impact of entrepreneurship in both imports and exports. • A country-level analysis of namely: income, urbanity, education, research and development, and entrepreneurial activity. • Uses longitudinal data and a fuzzy c-means clustering approach. • Employed a configurational approach focuses on country and year groupings, providing nuanced understanding.