PREDICTING CONSUMER RESPONSES TO AI-POWERED RECOMMENDATIONS: THE ROLE OF TECHNOLOGY READINESS, TRUST AND PERCEIVED EXPERTISE

The increasing integration of artificial intelligence (AI) into digital commerce has transformed recommendation systems from passive information tools into influential components of consumer decision-making. However, consumers do not respond to AI-powered recommendations uniformly. Their responses may depend on their readiness to adopt emerging technologies, their level of trust in AI systems, and their perceptions of the expertise underlying algorithmic recommendations. This paper proposes a conceptual framework for predicting consumer responses to AI-powered recommendations by integrating technology readiness, trust, and perceived expertise. Drawing on recent research on AI recommendation systems and consumer behaviour, the paper conceptualises technology readiness as an antecedent of consumers’ willingness to engage with AI, while trust and perceived expertise are considered key psychological mechanisms shaping recommendation acceptance. The proposed framework suggests that consumers with higher levels of technological optimism and innovativeness are more likely to develop favourable perceptions of AI recommendations, whereas discomfort and insecurity may reduce acceptance. Trust is expected to mediate the relationship between AI recommendation characteristics and consumer response, while perceived expertise may strengthen or weaken the effectiveness of AI-generated recommendations depending on product type and decision context. The framework contributes to the emerging literature by integrating technological, psychological, and source-related factors into a unified predictive model of consumer response.