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Vol. 6 No. 1 (2025)
2025 Edition

Articles

AI-Driven Recognition of Cross-Cultural Consumer Behavioral Differences and Commercial Applications in Global E-Commerce

Published 2025-11-07

  • Meera Kulkarni
    • ,
  • Daniel Chen

Meera Kulkarni
Department of Business Information Systems, McCombs School of Business, USA

Daniel Chen
Department of Data Science & Intelligent Systems, Cockrell School of Engineering, USA

Abstract

Cross-cultural differences significantly influence consumer purchasing behaviors in global e-commerce ecosystems. Traditional analytical frameworks struggle to capture the nuanced behavioral patterns emerging from diverse cultural contexts, resulting in suboptimal personalization strategies and reduced conversion rates. This research presents an artificial intelligence-driven framework for recognizing and quantifying cross-cultural consumer behavioral differences across multiple dimensions including decision-making processes, risk perception, information processing preferences, and transaction patterns. The proposed methodology integrates graph neural networks with multimodal feature extraction techniques to analyze behavioral data from 847,392 consumers across 23 countries. The framework achieved 89.7% accuracy in predicting cultural behavioral clusters and demonstrated substantial improvements in commercial outcomes. Implementation across three e-commerce platforms yielded average conversion rate increases of 34.2%, customer engagement improvements of 41.8%, and marketing ROI enhancements of 28.6%. The research contributes novel insights into AI-powered cross-cultural consumer analytics while providing actionable frameworks for global e-commerce optimization.

Keywords

Cross-Cultural Consumer Behavior, Artificial Intelligence, Graph Neural Networks, E-Commerce Personalization

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