Impact of AI-Driven Personalized Product Recommendation Systems on Online Purchase Intention: Indian E-Commerce Shoppers' Perceived Relevance and Trust

Artificial Intelligence, Personalized Recommendation Systems, Consumer Trust, Perceived Relevance, Online Purchase Intention, Indian E-Commerce

The rapid development of artificial intelligence (AI) in e-commerce has altered how customers look for, evaluate, and purchase goods online. AI-powered tailored recommendation systems are essential in influencing shopping decisions. This study examines how Indian e-commerce customers' intents to make online purchases are impacted by these AI-driven recommendation systems, with a particular emphasis on the roles of perceived relevance and consumer trust. We used a structured questionnaire to gather data from 160 Indian online shoppers using a quantitative research design. The investigation used SPSS and Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate both the measurement and structural models. The findings demonstrate that customer trust is significantly increased by the degree of recommendation customisation and the transparency of AI recommendations. In the meantime, perceived relevance is mostly impacted by suggestion accuracy. Online purchase intentions are positively impacted by both perceived relevance and customer trust, with perceived relevance being a stronger predictor. Mediation study demonstrates that trust and perceived relevance are major psychological elements via which the features of AI recommendation systems affect purchase intentions. The results show that without clear and tailored recommendations, algorithmic accuracy alone is insufficient to influence consumer behaviour. By offering data from the Indian e-commerce scene, a new digital sector that has not yet received much attention, this study contributes to the expanding corpus of research on AI in marketing. The results provide useful information for e-commerce platforms, highlighting the significance of developing AI recommendation systems that prioritize relevance, transparency, and customisation in order to foster customer confidence and boost purchase intentions.