Understanding antecedents of customer resistance towards AI-driven drone food delivery service: a hybrid SEM-ANN approach

Purpose This study aims to investigate the factors contributing to customer resistance to artificial intelligence-driven drone food delivery services (AI-DFDS) in the hospitality industry using Aristotle’s model of persuasion, focusing on logos, pathos and ethos dimensions. Design/methodology/approach A hybrid structural equation modeling and artificial neural network (SEM-ANN) approach was used to analyze data collected from 347 respondents. Findings Usage, hedonic, risk, legal and regulatory, value, trust and tradition barriers significantly affect customer resistance, with AI preference moderating the relationship between trust, tradition barriers and customer resistance. In addition, the SEM-ANN model achieved an accuracy rate of 78.4%. Originality/value This research provides critical insights into the reasons for customer resistance to AI-DFDS, presenting a theoretical framework and practical strategies for the hospitality industry to deploy services.