Purpose:This is a study of the effect of AI-powered personalized content recommendation systems on consumer trust and satisfaction on digital platforms, focusing on over-the-top (OTT) streaming services. It seeks to investigate the influences of various factors like the relevance, accuracy, transparency and data privacy of the algorithmic recommendations on trust towards algorithmic recommendations and the overall satisfaction.

Method:The research is conceptually and analytically based on systematic review and synthesis of available empirical and theoretical literature on AI-driven personalization, recommendation algorithms and consumer trust. Netflix is taken as a representative case because of its extensive use of advanced recommendation systems. Key constructs related to personalization, trust and satisfaction are identified and conceptually analysed to build an integrative understanding of the inter-relationship between them.

Findings:The findings show that personalized content recommendations have a positive effect on consumer satisfaction by improving content discovery and decreasing information load. However, consumer trust is greatly influenced by perceptions of algorithmic transparency, fairness and privacy protection. While highly accurate and relevant recommendations lead to increased satisfaction, concerns over data collection practices, algorithmic bias and lack of explainability can create distrust. Trust is found to be an important mediating variable that determines whether personalization efforts result in sustained user satisfaction and loyalty.

Implications:The study has important implications for platform designers, managers and policymakers. Digital platforms should balance the efficiency of personalisation with ethical considerations, including promoting transparency and providing more control for users, while also implementing privacy-aware AI practices. Such measures can enhance consumer trust and satisfaction and facilitate the creation of responsible and sustainable AI-based recommendation systems...