Construction of an Adaptive Communication Model for Cross-border E-commerce AI Customer Service under Cultural Context Differences: A Case Study of the Russian Market

Abstract

Against the backdrop of the rapid expansion of Sino-Russian cross-border e-commerce, AI customer service systems face significant challenges in adapting to high-context cultural environments. Based on Hall’s high-/low-context theory, this study analyzes typical user complaint cases from the Russian market and identifies a structural mismatch between the low-context communication patterns of existing AI customer service systems and the high-context expectations of Russian users. This mismatch manifests in three key dimensions: rigid language, poor contextual interpretation, and an inability to build relationships. To address these issues, this paper proposes an adaptive communication model comprising three integrated layers: a context perception layer, a strategy generation layer, and an interaction execution layer. Driven by a culturally sensitive communication strategy library, the model is designed to shift AI customer service from standardized responses to contextualized communication. By integrating mechanisms such as emotion recognition, indirect intent inference, and relationship-building dialogue, the model aims to enhance user satisfaction and trust in high-context markets. This study offers a structured framework for incorporating cultural theory into the design of cross-border AI customer service systems and suggests directions for future research, including expansion to other high-context regions and multimodal interaction.