Adapting e-commerce supply chains through AI and user reviews: a contingency perspective from Vietnam and Taiwan

Purpose This study aims to improve platform-based supply chain performance by examining how artificial intelligence (AI)–enabled analytics can translate user-generated reviews into context-sensitive operational decisions. Focusing on Shopee's Vietnam and Taiwan markets, the study investigates how AI-driven decision support systems (DSS) enhance logistics responsiveness under heterogeneous market conditions. Design/methodology/approach The study applies sentiment analysis and topic modeling to approximately 37,000 user reviews, combined with simulation modeling to evaluate alternative logistics responses. A contingency-based DSS framework is developed to operationalize contextual fit between market conditions and AI-supported supply chain strategies. Findings Results show that logistics failures and service expectations differ significantly across markets, requiring adaptive rather than standardized AI deployment. Context-aware AI integration improves delivery performance, reduces mismatch between user expectations and logistics execution, and supports more resilient supply chain configurations. Originality/value This study advances supply chain literature by conceptualizing algorithmic adaptability as a contingency factor. It provides a reproducible analytical pipeline linking user reviews, AI analytics, and decision support, offering practical guidance for platform operators managing cross-market logistics complexity.