National boundaries and country averages are commonly used as delimiters and proxies for culture. By doing so, not enough attention is paid to cultural heterogeneity within and overlays between countries. Deploying a Kohonen self-organizing map (SOM) as an unsupervised machine learning technique on 106,382 individual-level survey data from 66 countries, this article identifies distinct worldwide cultural prototypes, isolates dominantly occurring prototypes within countries, and uses them to calculate cultural core values. It also provides new measures for within-country cultural heterogeneity, between-country cultural differences, and cultural isolation. The results not only show the usefulness of machine learning algorithms in inductive international business research, but also have managerial relevance for international marketing and management.

国界和国家平均值通常用作文化的分隔符和代理。这样做并没有对国家内部和国家之间的文化异质性给予足够的重视。本文在来自 66 个国家的 106,382 个个人层面调查数据上部署 Kohonen 自组织地图 (SOM) 作为无监督机器学习技术，识别不同的全球文化原型，隔离国家内主要发生的原型，并使用它们来计算文化核心价值. 它还为国内文化异质性、国家间文化差异和文化隔离提供了新的衡量标准。结果不仅显示了机器学习算法在归纳国际商业研究中的有用性，而且对国际营销和管理具有管理相关性。