AI-Powered Recommendation Systems and Customer Engagement Among International Students in Malaysia: The Mediating Role of Perceived Trust
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Abstract
AI has become an integral part of life, businesses, and marketing. This study examines how AI-powered recommendation systems influence customer engagement among international students in Malaysia, with a specific focus on the mediating role of perceived trust. Drawing on the technology acceptance model (TAM) and customer engagement theory, the research investigates the effects of three key recommendation system characteristics which are personalization, accuracy, and cultural relevance on customer engagement. It also examines the effect of perceived trust on customer engagement as well as the mediating role of perceived trust between AI-powered recommendation characteristics and customer engagement. Using a quantitative approach, data were collected from 297 international students, who are users of online retailers, through an online survey and analysed using Smart PLS 4. The results reveal that both personalization and accuracy positively enhance customer engagement, while perceived trust emerges as the strongest direct predictor. Mediation analysis demonstrates that trust partially mediates the personalization-engagement relationship and accuracy-engagement link, but does not significantly mediate cultural relevance's effects. These findings contribute to the literature on AI in consumer behaviour by highlighting trust's central role in technology adoption among transient populations. The study offers practical insights for online retailers seeking to optimize AI recommendations for international students, while identifying cultural relevance as an area requiring further investigation.
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