The growing adoption of artificial intelligence (AI)-driven recommendation systems has significantly transformed content consumption patterns on digital streaming platforms such as Netflix, Amazon, and Spotify. Despite their widespread use, concerns regarding consumer trust, data privacy, and algorithmic transparency remain critical. This study aims to examine the factors influencing consumer trust in AI-generated recommendations and its impact on user behavior in the context of Surat city. A quantitative research design was employed, and primary data were collected from 110 respondents using a structured questionnaire. The study applies descriptive statistics, reliability analysis, correlation, and regression techniques to analyze the relationships between key variables such as perceived accuracy, recommendation quality, privacy concerns, and consumer trust. The findings reveal that perceived accuracy and recommendation quality have a significant positive effect on trust (β = 0.312, p < 0.001), while privacy concerns also play a significant role (β = 0.272, p < 0.01). Additionally, consumer trust is found to positively influence user behavior (r = 0.313), indicating that users rely on AI recommendations for content selection and discovery despite moderate trust levels. The model explains 20.1% of the variance in consumer trust (R² = 0.201), suggesting the presence of additional influencing factors. The study contributes to the existing literature by integrating technological and psychological determinants of trust in AI systems within an emerging urban context. It also highlights the presence of a “trust–usage gap,” where users continue to engage with AI systems despite concerns regarding privacy and transparency. The findings offer valuable implications for both researchers and practitioners in designing more transparent, reliable, and user-centric AI-driven recommendation systems.
Patel et al. (Fri,) studied this question.