NAVIGATING ALGORITHMIC AVERSION: CONSUMER TRUST AND ADOPTION OF AI-GENERATED RECOMMENDATIONS IN HIGH-INVOLVEMENT CATEGORIES

This article examines the phenomenon of algorithmic aversion and the factors influencing consumer trust and adoption of AI-generated recommendations, particularly in high-involvement categories such as finance, healthcare, and education. As artificial intelligence becomes increasingly embedded in decision-making processes, consumers are often faced with the challenge of relying on algorithmic systems for complex and high-stakes decisions. Using a narrative literature review, this study synthesizes insights from marketing, information systems, and consumer psychology to explore the dual nature of consumer responses, characterized by both algorithmic aversion and appreciation. The findings identify key themes, including the role of trust, perceived competence, transparency, and explainability in shaping acceptance of AI recommendations. The study also highlights the mechanisms underlying algorithmic aversion, such as perceived lack of empathy, sensitivity to errors, and threats to autonomy. In addition, several moderating factors are identified, including involvement level, consumer expertise, trust in technology, and cultural context. A conceptual framework is proposed to illustrate how these factors interact to influence adoption, reliance, or rejection of AI-generated recommendations. The article contributes by integrating trust theory with algorithmic decision-making and offers practical implications for designing human-centered AI systems.