Understanding Consumer Purchase Intention Toward Zero-Click GenAI Recommendations: Empirical Evidence from Structural Equation Modeling and Machine Learning

This research examines the relationships among zero-click generative artificial intelligence (GenAI) recommendations, consumer reliance, and purchase intention. Drawing on Diffusion of Innovation Theory, it proposes that perceived ease of use, personalization, relative advantage, observability, and compatibility are associated with reliance on zero-click GenAI recommendations, with advertising skepticism moderating the relationship between reliance and purchase intention. Data from 316 participants were analyzed using partial least squares structural equation modeling, machine-learning techniques, and Bayesian network analysis. The results show that perceived ease of use, personalization, observability, and compatibility are positively associated with reliance, whereas relative advantage is not consistently associated with reliance but emerges as a strong predictor of purchase intention in the machine-learning models. Advertising skepticism weakens the positive relationship between reliance and purchase intention. These findings highlight the role of reliance in zero-click GenAI recommendations and suggest that firms should prioritize personalization, compatibility, and transparent AI-generated recommendations.