Influence of AI Recommender System on Consumers' Purchase Intention in CBEC Platforms

Artificial intelligence (AI) has become integral across various research fields, including cross-border e-commerce (CBEC), where it addresses challenges such as information overload caused by the rapid growth of online platforms and product diversity. AI recommender systems play a critical role in shaping consumer trust and purchase intention by providing personalized recommendations tailored to individual preferences. However, research on the impact of AI recommender systems within CBEC platforms remains limited. This study examines how intelligent features and recommendation quality influence consumer trust and purchase intention in CBEC by applying the SOR model and focusing on Chinese consumers using platforms like Amazon and Tmall Global. Empirical analysis and the structural equation model (SEM) reveal significant gender differences: male consumers are more influenced by perceived performance, while female consumers place greater emphasis on perceived diversity in recommendations. These insights underscore the importance of tailored AI recommendation strategies that cater to distinct demographic preferences, ultimately boosting consumer trust and encouraging purchase intentions in CBEC contexts.