Co-Served Dining by Humans and Automations: The Effects of Experience Quality in Intelligent Restaurants

Abstract

Automation has been widely applied and has greatly affected quality management in the catering industry. Intelligent restaurants refer to those in which smart devices and artificial intelligence (AI) technologies (such as robots and self-service technologies) are embedded in the restaurant environment. However, the existing research on intelligent restaurants has mostly focused on the technological development of equipment. Hence, this interdisciplinary study, integrating insights from hospitality management and human–computer interaction, examines how human-provided and automated-provided services interactively influence customers’ dining experience quality in intelligent restaurants, and how they affect customers’ perceived value and their social media sharing generation. This study develops a measurement scale of dining experience quality in intelligent restaurants that contains human-provided experience and automated-provided experience through in-depth interviews with 15 customers (Study1), and a model was proposed and verified using partial least-squares structural equation modelling (PLS-SEM) analysis on a sample of 493 customers dining in intelligent restaurants (Study 2), which shows that the quality of dining experience has a positive effect on customer perceived value, overall satisfaction in intelligent restaurants, and social media sharing generation. Specifically, automated-provided services offer functional value, while human employees mainly provide perceived emotional value. Perceived functional value has a greater impact on overall satisfaction with intelligent restaurants. The originality of this research is that it integrates services provided by humans and services provided by automated devices and clarifies the different roles of functional and emotional value in shaping customers’ perceived value. These findings provide a new research perspective for intelligent restaurants and insight into the optimization of service quality and automation systems in intelligent restaurants, thereby promoting sustainable business practices in the industry.