Predicting Cross-border E-commerce Purchase Behavior in Organic Products: A Machine Learning Approach Integrating Cultural Dimensions and Digital Footprints - Ma, Xiaowen; Jiang, Xiaoxiao (International Journal of Computer and Information System (IJCIS), 2024)
This study proposes an integrated machine learning approach for predicting cross-border e-commerce purchase behavior in organic products, incorporating cultural dimensions and digital footprints. Through the analysis of 1.2 million transactions across 15 major cross-border e-commerce platforms spanning 2021-2023, the research develops a novel predictive framework combining adaptive neural networks with cultural dimension analysis. The methodology employs a multi-stage data processing pipeline achieving 88.5% accuracy in cross-cultural prediction scenarios. Implementation of the proposed framework demonstrates significant improvements in market performance metrics, including a 23.5% increase in customer retention rates and 18.7% enhancement in conversion rates. The study introduces a sophisticated digital footprint analysis methodology, successfully processing 8.5 million interaction data points with a mean accuracy of 0.892 across different cultural contexts. Results indicate strong correlations (r=0.82) between cultural factors and purchasing patterns, validating the framework's effectiveness in cross-cultural prediction scenarios. The research contributes to both theoretical understanding of cross-border e-commerce dynamics and practical applications in international trade operations, while establishing new methodological approaches for integrating cultural dimensions with machine learning in e-commerce contexts.