Sheetal Abhishek, Madan Shilpa, Lee Rui Ling, Savani Krishna
Faculty of Business, The Hong Kong Polytechnic University, Li Ka Shing Tower, Hung Hom, Kowloon, Hong Kong.
Lee Kong Chian School of Business, Singapore Management University, 50 Stamford Road, Singapore 178899.
PNAS Nexus. 2025 Aug 26;4(8):pgaf229. doi: 10.1093/pnasnexus/pgaf229. eCollection 2025 Aug.
Scientists studying culture typically focus on a small number of theoretical constructs, such as individualism-collectivism, when seeking to explain cultural differences in psychological tendencies and behaviors. However, existing theories of culture could have missed out on important constructs that are useful for explaining cross-cultural differences. We used an abductive approach combining prediction and explanation to uncover important cultural values. In the prediction phase, based on 594 attitudes, values, and beliefs included in the World Values Survey, a neural network could classify respondents' nationalities with 90% accuracy in out-of-sample data. In the explanation phase, a feature importance analysis identified the values that contributed the most to predicting individuals' countries of origin. The top 60 variables resulting from this analysis were used to create the (ML-CVI), a tool to help future researchers uncover explanations for cross-cultural differences. Four follow-up studies demonstrated ML-CVI's theoretical and practical relevance. Specifically, Americans were less likely than Mexicans to comply with COVID-19 lockdowns, and this difference was explained by Americans' stronger Christian nationalism. Moreover, Indians were more likely than Americans to engage in proenvironmental behavior, and this difference was driven by Indians' stronger perseverance. Thus, the ML-CVI broadens the range of explanatory factors available to researchers by helping them identify explanations for cultural differences that they would not have been able to identify based on traditional theories of cultural values. Overall, this research highlights that machine learning-based abductive reasoning can help expand the range of explanatory frameworks in social science research.
研究文化的科学家在试图解释心理倾向和行为的文化差异时，通常会关注少数理论结构，如个人主义-集体主义。然而，现有的文化理论可能遗漏了一些对解释跨文化差异有用的重要结构。我们采用了一种结合预测和解释的溯因方法来揭示重要的文化价值观。在预测阶段，基于世界价值观调查中包含的594种态度、价值观和信仰，一个神经网络可以在样本外数据中以90%的准确率对受访者的国籍进行分类。在解释阶段，特征重要性分析确定了对预测个人原籍国贡献最大的价值观。由此分析得出的前60个变量被用于创建机器学习文化价值观索引（ML-CVI），这是一种帮助未来研究人员揭示跨文化差异解释的工具。四项后续研究证明了ML-CVI的理论和实践相关性。具体而言，美国人比墨西哥人更不可能遵守新冠疫情封锁措施，这种差异可以用美国人更强的基督教民族主义来解释。此外，印度人比美国人更有可能参与环保行为，这种差异是由印度人更强的毅力驱动的。因此，ML-CVI通过帮助研究人员识别基于传统文化价值观理论无法识别的文化差异解释，拓宽了研究人员可用的解释因素范围。总体而言，这项研究强调基于机器学习的溯因推理有助于扩展社会科学研究中的解释框架范围。
