AI and Consumer Perception of Expertise: A Conceptual Framework for Studying Algorithmic Trust in Wine Recommendations

Artificial intelligence (AI) is transforming how consumers in credence-based markets search for, interpret, and trust product information. In the wine sector, where authenticity and quality depend on symbolic and experiential cues, AI-driven recommendation systems increasingly act as new intermediaries. This paper develops a conceptual framework explaining how consumers perceive algorithmic expertise and form trust in AI-generated wine recommendations. Integrating theories of information asymmetry, signalling, source credibility, and trust in automation, the framework identifies AI transparency and source framing as key drivers of perceived expertise and trustworthiness. These perceptions, moderated by literacy, cultural orientation, and risk, influence purchase intention and reliance on AI advice. The study highlights AI as both a signalling and screening institution that can reduce but also redistribute information asymmetries in agri-food markets. The paper concludes with methodological and policy directions for ensuring transparent and consumer-centred AI adoption in the food and wine industries.