In this article, we address this shortcoming by exploring the concept of AI-based sustainable service—an offering that embeds artificial intelligence in ways that meet the needs of current consumers by contributing to socio-economic equalities and conserving the natural environment.

This study aimed to identify whether or not four service situations varying according to positive and negative combinations of physical evidence and functional service influence store image and purchase behavioral intentions of customers at bulgogi restaurants. The video-scenario technique was used for the study. Data were analyzed with the SPSS (Window 19.0) package using frequency analysis, one-way ANOVA, 2 by 2 factorial ANOVA, exploratory factor analysis, and multiple regression analysis to confirm the hypotheses. The combined effect of functional service and physical evidence influenced store image and purchase intention. In terms of seperate effect of physical evidence and functional service, the effect of employee service on store image was more powerful than that of physical evidence, even though the effect differed depending on the situation. Purchase intention was only influenced by functional service quality from employees under the four different scenarios. Thus, when opening a Korean restaurant, proper management of tangible evidence suitable to service, and the prices expected from local customers should be determined. Additionally, extremely high or low levels of physical evidence management should be avoided.

This study reviews the SERVQUAL model theoretically and statistically in relation to the nonlinearity of the perceived service quality. Perceived service quality measurement based on the SERVQUAL model assumes that consumers evaluate service quality by comparing their perceptions with their expectations. However, previous studies adopt linear factor analysis to discuss the SERVQUAL model. The present study assumes that consumers evaluate the service quality with a standard to admit the difference between their expectations and perceptions, and that their perceived service quality follows a nonlinear response. A nonlinear SERVQUAL model based on a nonlinear factor analysis model is proposed to understand the characteristics. The proposed model employs a threshold specification that represents the space in which consumers admit their discrepancy. The study extends a nonlinear factor analysis model to a nonparametric model in order to examine the functional aspects that from the perceived service quality. A nonparametric SERVQUAL model is adopted without any assumptions of the functional form. The empirical studies on the retail sector shows that the nonlinear model performs better than the linear model, and that the nonparametric model estimates the nonlinear function for perceived service quality. The results from the proposed model in our study provide insights in a consumer’s perceived service quality recognized as nonlinear and asymmetric. We conclude that the functional form for perceived service quality should be considered when specifying the measurement model for SERVQUAL. In addition, we discuss future work for a nonlinear measurement model and a nonparametric factor analysis.