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

Malcolm Beynon, David Pickernell and Paul Jones

Technological Forecasting and Social Change, 2025, vol. 210, issue C

Abstract: 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.

Keywords: Cultural goods; Export; Import; Fuzzy c-means; Country level; Clustering; Trade (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:210:y:2025:i:c:s0040162524007121

DOI: 10.1016/j.techfore.2024.123914

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