Embracing or resisting AI? Mapping restaurant managers' views on AI-based front-of-house solutions

What is it about?

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 underrepresented 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 (CFA) and principal component analysis (PCA), 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. The study extends hospitality research by applying Q-methodology to reveal nuanced managerial perspectives. It offers a typology of AI adoption readiness, outlining specific enablers and constraints. These findings support more targeted and effective AI integration strategies across the sector.

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Photo by Clay Banks on Unsplash

Photo by Clay Banks on Unsplash