Dissertation
Wine and dine with AI: a mixed methods study of wine service satisfaction drivers and alignment with AI-supported technologies
Doctor of Education (Ed.D.), Drexel University
Sep 2026
DOI:
https://doi.org/10.17918/00011591
Abstract
As artificial intelligence (AI) becomes increasingly integrated into hospitality and wine service, its ability to support customer satisfaction in high-contact settings remains unclear. The purpose of this multiphase mixed methods study was to identify the drivers of customer satisfaction in fine-dining wine service, examine whether AI-supported wine recommendation technologies are perceived as capable of aligning with those drivers, explore differences across customer groups, and incorporate industry professionals' perspectives on the potential role of AI in wine service. Survey data from 154 participants were analyzed to examine satisfaction drivers, perceived AI capabilities, demographic differences, and preferences for AI-supported service. Open-ended survey responses and interviews with five wine service professionals provided qualitative perspectives that were integrated with quantitative findings through triangulation. Customers placed the greatest importance on wine quality and value, tailored recommendations, and accessible information. Perceived AI capability ratings exceeded corresponding satisfaction ratings across all 11 measures, yet 67.6% of participants disagreed that they would prefer an AI-based tool over a human or sommelier. Qualitative customer findings emphasized personalization, knowledgeable guidance, and meaningful interaction. The five industry professionals interviewed identified there were some opportunities for AI assistance, but emphasized that responsiveness, expertise, and human connection were central to effective wine service. Triangulation revealed that customers were more consistent in what they valued than in what they believed AI could provide; perceived AI capability did not necessarily translate into preference for AI; and acceptance of AI-supported wine service depended in part on preserving human interaction and the fine-dining experience. The findings suggest that AI-supported wine recommendation technologies may be most appropriately implemented as optional decision-support tools that translate customer preferences into personalized guidance, offer multiple pathways to a satisfying wine decision, and complement human expertise without disrupting the dining experience.
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Details
- Title
- Wine and dine with AI
- Creators
- Serena R. Zelezny
- Contributors
- Kristen Betts (Advisor)
- Awarding Institution
- Drexel University
- Degree Awarded
- Doctor of Education (Ed.D.)
- Publisher
- Drexel University
- Number of pages
- xii, 137 pages
- Resource Type
- Dissertation
- Language
- English
- Academic Unit
- College of Arts and Sciences; School of Education; Drexel University
- Other Identifier
- 991022233844804721