생성형 AI 기반 외식서비스에 대한 인식이 수용태도와 이용의도에 미치는 영향 - 자기효능감과 AI FOMO의 확장된 UTAUT 모형 적용 -

Abstract

This study aims to predict the adoption behavior of generative AI-based dining service by developing and validating an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model. The model incorporates self-efficacy, AI FOMO(Fear of Missing Out), and attitude as additional constructs. A survey was conducted targeting food tech consumers with prior experience using generative AI, and the collected data were analyzed using structural equation modeling to test the proposed hypotheses. The key findings are as follows: First, self-efficacy positively influenced performance expectancy and effort expectancy, while effort expectancy positively affected performance expectancy. Second, performance expectancy, effort expectancy, facilitating conditions, and social influence had significant positive effects on AI acceptance attitudes. Third, social influence positively impacted AI FOMO, which in turn positively influenced AI acceptance attitudes. Fourth, AI acceptance attitude strongly influenced the intention to use AI. Additionally, the analysis of indirect effects in the research model revealed that attitude plays a pivotal mediating role within the UTAUT framework, and the roles of AI FOMO and self-efficacy as new extended variables were identified.