The Impact of AI-Powered Recommendation Systems on Consumer Retention in Online Marketplaces: The Mediating Roles of Perceived Relevance and Customer Satisfaction

Abstract

As AI recommendation systems gain traction, the way customers navigate online marketplaces has fundamentally changed. The effect of the four factors, Recommendation Accuracy, Recommendation Diversity, Real-Time Personalization, and Context-Aware Suggestions on the Consumer Retention Rate is discussed in this study. It also examines the role of Perceived Relevance and Customer Satisfaction in the drivers of consumers' continued use of online marketplace platforms, when it comes to the role of AI-powered recommendation features. A quantitative research method was used and a cross sectional survey design was employed. The sample size of the respondents was 200 individuals who experienced the use of online marketplaces featuring personalized recommendation feature in the past. The seven constructs in the conceptual framework were measured using a structured questionnaire, which was based on a Likert scale ranging from 1 to 5. The collected data were analysed with SPSS and PLS-SEM with SmartPLS version 3.2.8. Indicator loadings, Cronbach's alpha, composite reliability, average variance extracted, and discriminant validity were all used to evaluate the measurement model. Path coefficients, bootstrapping, coefficient of determination, effect size, and predictive relevance and collinearity statistics were used to evaluate the structural model. The results show that Recommendation Accuracy and Recommendation Diversity have a positive impact on Perceived Relevance and Consumer Retention Rate. Other metrics that have a positive impact on Customer Satisfaction and Consumer Retention Rate are Real-Time Personalization and Context-Aware Suggestions. Moreover, Perceived Relevance and Customer Satisfaction are both positive factors affecting consumer retention. The findings also indicate that the three relationships between AI-powered recommendation features and retention can be explained by Perceived Relevance and Customer Satisfaction. Of all the factors that were studied, Recommendation Accuracy had a particular impact on Perceived Relevance, and Real-Time Personalization had a significant impact on Customer Satisfaction. The study finds that online marketplaces can enhance consumer retention by offering accurate, diverse, responsive and appropriate recommendations to consumers' current contexts. The results provide valuable theoretical and practical guidance for designing an effective and responsible AI-based recommendation strategy for online platforms.