Unpacking Consumer Views on AI Cooking: A Q-Method Approach

This study examines heterogeneous consumer viewpoints toward AI-enabled cooking in restaurants, focusing on how efficiency, safety, human touch, and cultural authenticity condition acceptance. Existing hospitality research has largely addressed front-of-house robots using Likert-type surveys, leaving limited understanding of consumer interpretations of AI in back-of-house cooking, where craftsmanship and authenticity are central. Using Q methodology, we developed a balanced Q-set of 25 statements drawn from literature on technology acceptance, trust-risk and privacy, service quality and hygiene, anthropomorphism, and culinary authenticity. Forty consumers with recent restaurant experience and awareness of AI cooking systems sorted these statements along a forced quasi-normal distribution. By-person factor analysis (principal components with varimax rotation) yielded four factors. The results identify four distinct viewpoints: (1) Pro-tech Efficiency Optimizers, who value throughput, consistency, and engineered hygiene; (2) Safety & Privacy Guardians, who require strong governance, transparency, and clear liability; (3) Tradition & Human-Touch Custodians, who stress irreplaceable craftsmanship, emotional warmth, and heritage; and (4) Practical Cost Realists, who focus on total cost of ownership, value for money, and safeguards against opportunism. Consensus appears around hygiene, baseline transparency, and personalization, while tensions concern the acceptable level of automation, anthropomorphism, authenticity, and price-value trade-offs. The study extends technology acceptance research to back-of-house AI cooking, proposes segment-tailored design and communication strategies, and supports hybrid human-robot task allocation and informed policy on certification and liability.