From Big Data to Cultural Intelligence: An AI-Powered Framework and Machine Learning Validation for Global Marketing

This research addresses the ‘cultural blind spot’ in Big Data and AI, where algorithms treat global user-generated content monolithically, fostering biased marketing models. It proposes a dynamic ‘contextual value amplification’ framework, integrating Impression Management and Construal Level Theories. The study argues that service context—luxury versus budget—systematically reconfigures how cultural values are expressed in online customer reviews. A dual-method approach was applied to 284,746 negative hotel reviews. First, a high-dimensional fixed-effects model provided evidence for ‘cultural complaint signatures’ and revealed a novel mechanism: the luxury context amplifies individualists’ focus on relational Service but dampens their focus on transactional Value. Second, an XGBoost model offered computational validation. Including these theoretically derived features improved the model’s ability to classify a reviewer’s cultural orientation by over 220%. The study proposes a dynamic, context-contingent theory of cross-cultural expression, offers a methodological template fusing econometrics and machine learning to mitigate bias, and advances a conceptual framework for ‘Cultural Intelligence’.