Crafting the future of drinks: a TAM and Jd-R perspective on bartenders’ AI adoption

Abstract

As AI transforms hospitality, its role in creative areas like bartending is underexplored. This study uses the Technology Acceptance Model and Job Demands-Resources theory to examine bartenders’ adoption of AI tools for drink creation. A survey of 384 U.S. bartenders analyzed the impact of ease of use, usefulness, job demands, and resources on AI attitudes, job strain, motivation, and turnover. Results from Partial Least Squares Structural Equation Modeling (PLS-SEM) indicate that both TAM variables significantly shape attitudes toward AI, while job demands increase strain and reduce retention. Although job resources reduce strain, they do not significantly enhance motivation. Additionally, positive AI attitudes and intrinsic motivation reduce turnover intentions, while job strain increases them. Findings extend theory and offer insights for encouraging AI adoption and staff retention.