Human–AI Interaction in AI-Based Multilingual Restaurant Ordering Systems: Evidence from Jakarta

The rapid advancement of artificial intelligence (AI) has transformed service delivery in the hospitality industry, particularly through digital ordering systems in restaurant environments. One emerging application is AI-based multilingual ordering systems, which aim to reduce communication barriers and improve service efficiency in multicultural urban settings. This study examines human–AI interaction in AI-based multilingual restaurant ordering systems within Jakarta’s hospitality sector. Specifically, it investigates the relationships among perceived AI interaction quality, perceived ease of use, trust in AI systems, perceived service efficiency, customer satisfaction, and continued use intention. A quantitative research design was employed using survey data collected from 230 restaurant customers in Jakarta who had experience with digital ordering systems. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that perceived AI interaction quality and perceived ease of use significantly enhance customer trust in AI-based ordering systems. Trust was found to positively influence perceived service efficiency and customer satisfaction, while customer satisfaction emerged as the strongest predictor of continued use intention. This study contributes to the literature on human–AI interaction and smart hospitality by providing empirical evidence on AI-based multilingual service systems in an emerging market context. The findings offer practical insights for hospitality providers seeking to implement AI-enabled service technologies and improve customer experience through multilingual digital service systems.