While artificial intelligence (AI) is rapidly evolving, its adoption in the hospitality sector – especially in restaurants – remains uncertain. Restaurant managers, as key decision-makers, are under-represented in current research. This study aims to identify distinct managerial preference groups and explore the drivers and barriers of AI adoption.
Using Q-methodology, a hybrid qualitative–quantitative technique, the study segments 33 restaurant managers based on their attitudes toward AI. Factor extraction combined centroid factor analysis and principal component analysis with varimax rotation to ensure interpretability.
Five distinct managerial groups emerged, ranging from AI advocates to sceptics. The study reveals that AI adoption is shaped by complex considerations, such as guest expectations, operational efficiency and brand identity. Adoption is not binary but context-dependent.
Understanding managerial typologies helps tailor AI implementation strategies. Balancing efficiency with human-centric service is key for successful integration. Insights can support hospitality businesses, policymakers and technology developers in aligning AI tools with managerial concerns.
Theoretically, this study contributes to hospitality and technology-acceptance literature by offering a typology of managerial attitudes using Q-methodology. It challenges binary interpretations of AI resistance and highlights the role of emotional, operational, and ethical concerns in shaping digital transformation. These findings support more targeted and effective AI integration strategies across the sector.
