Identifying the attributes of consumer experience in Michelin-starred restaurants: a text-mining analysis of online customer reviews

Abstract

PurposeThe main goal of this paper is to identify the attributes of consumer experience in Michelin-starred restaurants and to estimate their effects on restaurant ratings.Design/methodology/approachA sample of 70,233 online reviews of 224 Spanish Michelin-starred restaurants were analysed with the latent Dirichlet allocation algorithm. A sentiment analysis and a logistic regression analysis were also employed to estimate the effect of attributes on restaurant ratings.FindingsCustomer attention, food quality, decor and ambience and value for money are frequently used to define restaurant experience. However, it is shown in this study that the experience in a Michelin-starred restaurant goes beyond the evaluation of those four attributes. Furthermore, the effect of the factors that were identified on customer satisfaction differed depending on the restaurant ratings.Research limitations/implicationsThe findings are linked to the context of Spanish Michelin-starred restaurants. It is also assumed in this study that online reviews are based on truthful opinions.Practical implicationsRestaurant managers should primarily focus on customer attention and food quality to achieve customer satisfaction. In addition, those restaurants with an error-free service and a highly appreciated wine list among diners are more likely to achieve the culinary excellence that deserves a 5-star rating on TripAdvisor.Originality/valueThe attributes of the restaurant experience are frequently identified in literature reviews. Research based on text-mining analyses of customer reviews to discover a posteriori the factors that define a restaurant experience is scarce, and particularly difficult to find in the context of Michelin-starred restaurants.