Abstract
Purpose
The 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/approach
A 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.
Findings
Customer 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/implications
The 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 implications
Restaurant 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/value
The 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.
Keywords
Citation
Barrera-Barrera, R. (2023), "Identifying the attributes of consumer experience in Michelin-starred restaurants: a text-mining analysis of online customer reviews", British Food Journal, Vol. 125 No. 13, pp. 579-598. https://doi.org/10.1108/BFJ-05-2023-0408
Publisher
:Emerald Publishing Limited
Copyright © 2023, Ramón Barrera-Barrera
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Customer experience has its origin in the experience economy paradigm of Pine and Gilmore (1998), according to which, consumers seek to achieve a unique and memorable experience when they buy a product or service. According to these authors, the creation of positive, unique and memorable experiences leads to competitive advantages for a company that can differentiate its offer from the offers of other competitors. This paradigm has special relevance in the services sector and particularly in the hospitality and restaurant industry. Conceptually, customer experience is similar to the concept that Parasuraman et al. (1988) proposed of perceived service quality. Consumers make subjective evaluations of their current experience compared to the expected experience (Jeong and Jang, 2011). As a consequence, an emotional response is evoked in the consumer: either positive, neutral, or negative (Björk and Kauppinen-Räisänen, 2016).
Experiences are subjective and lived out with different intensities, in so far as they are based on the personal perceptions of each individual (Björk and Kauppinen-Räisänen, 2016). Therefore, the individual also assumes an active role in creating the experience (Gan et al., 2017). Experiences are also linked to individual memories, which are more likely to be remembered when they are shared with other people or occur at a place or time of certain relevance, which is particularly true for gastronomic experiences (Björk and Kauppinen-Räisänen, 2016). For example, we tend to remember a birthday celebration, the first romantic dinner with our partner, a Christmas celebration with family, or a dinner at a fancy restaurant.
Researchers have highlighted the multidimensional nature of experiences (Björk and Kauppinen-Räisänen, 2014; Yrjölä et al., 2019). The restaurant experience of the customer is particularly related to knowledge, observation and perceptions of restaurant attributes during their dining experience (Jeong and Jang, 2011; Mathayomchan and Taecharungroj, 2020). Therefore, one of the priority research objectives has been to determine the factors that comprise the restaurant experience. For the restaurant industry, knowing the attributes evaluated by consumers is important, in order to offer a positive gastronomic experience that achieves customer satisfaction, positive word of mouth and loyalty (Mathayomchan and Taecharungroj, 2020). At the same time, satisfaction with gastronomic experiences influences traveller satisfaction with the tourist destination (Jiménez-Beltrán et al., 2016). Gastronomy that attracts the attention of tourists becomes an important reason for visiting a tourist destination (Castillo-Manzano et al., 2021; Björk and Kauppinen-Räisänen, 2016) and making a significant contribution to its economic development (Pezenka and Weismayer, 2020). In particular, the gastronomic experience in a Michelin-starred restaurant may be the main reason for visiting a destination (Björk and Kauppinen-Räisänen, 2014).
The study of dining and restaurant experiences is well documented in the literature. Although there is no strict consensus, four main attributes are identified: food quality, customer service, decor and ambience and value for money (e.g. Jeong and Jang, 2011; Gan et al., 2017; Liu and Tse, 2018; Yrjölä et al., 2019; Mathayomchan and Taecharungroj, 2020; Aktas-Polat and Polat, 2021; Luo and Xu, 2021; Kim and Hwang, 2022, etc.). These attributes are also present in luxury-restaurant experiences, although consumers tend to have higher expectations with regard to performance at a luxury restaurant (Rita et al., 2023). Thus, it has been suggested in previous studies that the characteristics of a luxury-restaurant experience are excellent food with distinctive presentations, high levels of service quality, a sophisticated and exclusive ambiance and high prices (Lee and Hwang, 2011; Chen and Peng, 2015; Yang and Mattila, 2016).
The conventional methodological approach used by researchers to analyse restaurant experiences has been the design of questionnaires based on the previous literature and administered to samples of restaurant consumers (e.g. Lee and Hwang, 2011; Jeong and Jang, 2011; Chen and Peng, 2015; Yrjölä et al., 2019). However, the use of questionnaires and surveys has some notable drawbacks: higher costs and time for data collection, reduced number of responses and failure to reflect the restaurant experience in full (Vu et al., 2019). In addition, according to Gan et al. (2017), the main drawback of this methodology is that the respondents are unlikely to describe their experiences as freely as they might in online restaurant reviews. Instead, their experience is conditioned by the questions and items that might appear in the questionnaire, and significantly biased results can be expected. Another methodological approach is based on the use of samples of customer-rated attributes evaluated on review websites (e.g. Zhang et al., 2010; Yan et al., 2015; Kim et al., 2016). However, the main limitation of these studies is that there may be other attributes of the restaurant experience, beyond those that appear on the website used for data collection.
More recently, some studies have analysed restaurant experience with text-mining techniques applied to massive volumes of online customer reviews. The main advantage of that approach is the identification of attributes, unlike those that are traditionally used to reflect the restaurant experience. However, some authors have previously identified the attributes from the existing literature using those qualitative analyses, with no reference to customer opinions (e.g. Gan et al., 2017; Mathayomchan and Taecharungroj, 2020). In line with that approach, Rita et al. (2023) conducted a sentiment analysis of online reviews from restaurants with Michelin stars, but the attributes proposed were based on the theoretical findings of previous research.
Studies that have sought to determine the attributes of the restaurant experience a posteriori, analysing customer reviews with appropriate qualitative techniques are scarce (e.g. Pantelidis, 2010; Vu et al., 2019; Pezenka and Weismayer, 2020; Luo and Xu, 2021; Nilashi et al., 2021; Aktas-Polat and Polat, 2021), and more difficult to find, particularly in the context of Michelin-starred restaurants. To address that research gap, the main objective of this paper is to identify the principal attributes of customer experience in Michelin-starred restaurants. To that end, a sample of 70,233 online reviews of 224 Spanish restaurants in TripAdvisor was analysed with the latent Dirichlet allocation (LDA) algorithm. A lexicon-based sentiment analysis was then performed to estimate the customer evaluation of restaurant topics. Finally, the effects of the attributes on restaurant ratings were calculated using a logistic regression analysis. To the best knowledge of the author, this empirical study is the first to address customer experience at Michelin-starred Spanish restaurants with such a large sample of online reviews.
The structure of the paper is as follows. The literature will be reviewed in Section 2. The methodology and empirical work will be described in Section 3 and the results will then be presented in Section 4. Finally, the conclusions of the study and their implications will be discussed in Section 5.
2. Literature review
2.1 Consumer experience in Michelin-starred restaurants
The Michelin Guide is highly regarded as the most important gastronomic reference for critics, the general public and for restaurants themselves (Chiang and Guo, 2021). The first edition was published in France in 1900 by the brothers André and Edouard Michelin, founders of the Michelin tire company, and contained information to make travel easier for motorists: maps, instructions for changing a wheel, where to refuel and overnight accommodation offering meals. Interest in knowing the recommended restaurants grew over time, until the Michelin Guide had become a global reference in the field of gastronomy. Currently, over 30,000 establishments are rated in the Michelin Guide in over 30 countries across three continents. The countries with the highest number of restaurants awarded Michelin stars are the following: France (627), Japan (432), Italy (378), Germany (326), Spain (228) and the United States (206) (Michelin Guide, 2022).
The restaurants are awarded one, two and three stars following their evaluation in accordance with five universal criteria: “the quality of the ingredients, the harmony of flavours, the mastery of techniques, the personality of the chef as expressed through their cuisine and […], consistency both across the entire menu and over time” (Michelin Guide, 2022). This evaluation is carried out by anonymous, independent and professional inspectors of different nationalities, who visit restaurants around the world without ever revealing their identity. One Michelin star denotes good food that is worth stopping for, two stars signify excellent food that is worth making an extra effort to find, and three stars indicate exceptional food that is well worth the trip (Henderson, 2017). Restaurants awarded Michelin stars gain fame, recognition and prestige, significantly increasing the number of visitors who want to taste and to relish their cuisine (Chiang and Guo, 2021).
Michelin-starred restaurants have attracted the attention of researchers. Among the issues investigated, the following can be highlighted: the creative process in haute-cuisine restaurants (Vargas-Sanchez and López-Guzmán, 2022), the relationship between gastronomy tourism and Michelin-starred restaurants (Castillo-Manzano et al., 2021; Batat, 2021; Madeira et al., 2022), the restaurant websites (Daries et al., 2018; Montargot et al., 2022), brand communications on social media (Fissi et al., 2022), the dimensions of sustainable food experiences offered in the luxury gastronomic industry (Batat, 2020), the effect of service excellence and guest delight on guest affective commitment (Panchapakesan et al., 2022), and why consumers are willing to pay a price premium for a luxury gastronomic experience (Kiatkawsin and Han, 2019). Studies that have been directly focused on the factors evaluated during the dining experience in a Michelin-starred restaurant are scare. One exception is the research conducted by Rita et al. (2023). These authors considered that the four classic attributes of the restaurant experience identified in the literature review (food, service, ambience and price) were also suitable for measuring consumer experiences at Michelin-starred restaurants.
2.2 Online restaurant reviews
Electronic word-of-mouth or eWOM refers to informal communications between consumers on products, services and the companies that sell them through the Internet (Litvin et al., 2008). Among the most important types of eWOM are the customer opinions posted on online review sites (Hennig-Thurau et al., 2004). Positive online reviews and high ratings are associated with satisfied consumer experiences, increasing the likelihood of a purchase, whereas negative online reviews and low ratings are considered a type of customer complaint, which have the opposite effect (Vermeulen and Seegers, 2009). The influence of online reviews on firm performance has also been widely demonstrated. Customer ratings, for example, have a statistically positive relationship with sales of beers (Clemons et al., 2006), books (Chevalier and Mayzlin, 2006) and hotel bookings (Ye et al., 2009), etc.
Online reviews are especially important extra sources of information in the hospitality and restaurant industry, as the quality of each tourism product or service, due to its intangible nature, is difficult to assess before purchase and consumption (Litvin et al., 2008). The ratings and comments that are posted on websites such as TripAdvisor.com, Yelp.com and Booking.com are consulted by travellers to reduce uncertainty and perceived purchase-related risks (Anderson, 2012). Those online reviews are also an excellent instrument for studying customer satisfaction with any one particular hotel or restaurant service (Kim et al., 2015), because they are seen as more objective and wide-ranging information sources that are free of sample bias, as opposed to conventional questionnaires (Schuckert et al., 2015).
In particular, online restaurant reviews usually have three components: (1) an overall customer rating; (2) customer ratings of different attributes including food quality, customer service, decor and value for money; and (3) positive and negative comments expressing customer opinions toward restaurant experiences. Researchers have usually collected these online reviews using web-scraping techniques. Although the purposes are diverse, two different research methodologies can be differentiated, depending on the nature of the data analysed. Quantitative methodologies are used in empirical studies based on samples of customer ratings, whose main objective is to analyse the effect of different attributes of the restaurant experience on various outcomes, such as restaurant popularity, revisit intention, restaurant performance, etc. On the other hand, qualitative methodologies are used in research studies based on the collection of customers' opinions. For example, topic modelling has been used to identify the attributes that form the restaurant experience, while sentiment analysis is useful to quantify the customer satisfaction with these attributes. Topic modelling and sentiment analysis are complementary and frequently used with traditional quantitative methodologies (e.g. linear regression analysis, analysis of variance, logistic regression, etc.). Table 1 shows a review of empirical studies related to online restaurant reviews, detailing purposes, datasets, methodologies and major findings.
3. Research methodology
3.1 Data collection
The restaurant data collected were as follows: restaurant name, number of Michelin stars, restaurant rating and online reviews written in Spanish. That language was chosen to guarantee proper text-mining analysis without mixing opinions from different languages and, at the same time, to achieve a high number of online reviews. The web-scraping process was applied to TripAdvisor.com in October 2022. The result was a sample of 70,233 online reviews on 224 restaurants (Table 2), representing 98.24% of all Spanish restaurants mentioned in the Michelin Guide (2022).
3.2 Pre-processing of online reviews
Online review pre-processing took place in two phases. First, the online reviews were divided into sentences following the recommendations of previous studies (e.g. Gan et al., 2017; Mathayomchan and Taecharungroj, 2020). The identification of topics with the LDA algorithm from online reviews can be problematic, as different attributes of the restaurant experience can be evaluated in the same review. Hence, it was assumed that sentences had a closer relation to a specific topic than a complete comment. Secondly, the sentence cleaning process was carried out, which generally implies the conversion of all words to lower case, the elimination of numeric characters, punctuation marks and those words with a high frequency of appearance, but of no significance to the essential semantic meaning and therefore to the objectives of the research, known as stopwords (Miner et al., 2012).
3.3 Topic modelling – latent Dirichlet allocation (LDA) algorithm
The latent Dirichlet allocation (LDA) algorithm was used to identify topics from a collection of documents (Guo et al., 2017), in a similar way to clustering methods for numeric data (Silge and Robinson, 2017). The underlying assumption of this model is that each document is a mixture of topics, and each topic follows a multinomial probability distribution over words (Blei et al., 2003). The LDA model was estimated using the collapsed Gibbs sampling procedure, following the indications of Kwartler (2017). The optimal number of topics was previously determined using the “CaoJuan2009” indicator (Cao et al., 2009), calculated for a range of topics established by the researcher. In addition, the LDA algorithm also estimated a matrix with the probabilities of each document referring to the different topics.
3.4 Sentiment analysis
Sentiment analysis, an area of Natural Language Processing (NLP), has as its objective the identification of emotions and their quantification (e.g. positive, neutral or negative), or the identification of a certain type of emotional state (e.g., happy, sad, or angry) in relation to a particular subject in a text document (Alaei et al., 2019; Liu et al., 2022). Three different techniques can be distinguished for sentiment analysis: (1) machine learning methods; (2) lexicon-based methods; and (3) hybrid methods (Alaei et al., 2019; Liu et al., 2022). Machine learning methods use algorithms where a model is trained with a data set of previously determined sentiments. After training the model, the algorithm predicts the sentiment of the other text documents. Lexicon-based methods use dictionaries of words classified into a set of predefined types of emotions with a sentiment score. In hybrid approaches, machine learning and lexicon-based approaches are used together to perform a sentiment analysis.
The main advantages of lexicon-based sentiment analysis are universality and easy practical implementation (Liu et al., 2022). For these reasons, this approach was used in this paper to measure the polarity of the sentences of the online reviews, specifically, the AFINN-11 sentiment lexicon (Nielsen, 2011) (Spanish version). The sentiment score of the sentences was calculated by summing the ratings of the words that were cross-referenced with the AFINN-11 lexicon. And, following Gan et al. (2017), the sentiment score of each restaurant topic reflected the sum of the sentiment scores of the sentences, proportionally weighted by the number of sentences of each restaurant, as follows:
i = 1 … Nsentences of topic j in restaurant k
j = 1 … Ntopics.
k = 1 … Nrestaurants.
3.5 Logistic regression analysis
Finally, a multinomial logistic regression analysis using SPSS V.26 was performed to assess the influence of attributes on restaurant ratings. This linear regression model is recommended when the dependent variable is categorical with more than two values (e.g. ordinal or nominal scales), while the explanatory variables can be continuous or categorical (Peng et al., 2002). Mathematically, the logistic regression model is expressed as:
4. Results
4.1 Topics of restaurant experience
Once the tokenization and cleaning process of the 70,233 online reviews was complete, a document-term matrix (DTM) was created with the entire corpus of sentences. Taking this matrix as an input, the CaoJuan2009 indicator was calculated for a range between 2 and 15 topics. The minimum value of this metric was reached in the solution of eight topics. In addition, as later shown in the estimation of the LDA model, this solution showed easily interpretable topics in semantic terms and with clearly different meanings, in line with the suggestion of Sievert and Shirley (2014).
The graphical visualisation of the model estimated with the LDA algorithm is shown in Figure 1. The topics are represented with circles on the left, while the words that are most related to each topic appear on the right. The topics are numbered according to their importance, which is determined by the size of the circles. The blue bars and the red bars indicate the total number of occurrences of that word in the entire set of online comments, and in each topic, respectively. In addition, the relevance metric was set at 0.6, to order the terms by their importance for each topic, following the indications of Sievert and Shirley (2014). The document-topic probabilities matrix was used to assign the sentences to the topics with a major probability. Approximately, 75% of the sentences were assigned with a probability greater than 0.7, which suggests that the algorithm classified the sentences among the topics with a relatively high power of discrimination. Finally, the researcher labelled and described the topics, using the most closely related words and the most likely content of the sentences in each one (Table 3).
4.2 Influence of attributes on restaurant ratings
The results of the logistic regression analysis showed a positive and statistically significant relationship between the restaurant ratings and the sentiment scores of the attributes of the restaurant experience (χ2: 80.353; p < 0.001) (Table 4). Specifically, 4-star restaurants significantly increased their probability of achieving 4.5-star status by improving customer attention (Wald's χ2: 11.794; p < 0.01), food quality (Wald's χ2: 6.222; p < 0.05) and tasting menus (Wald's χ2: 4.673; p < 0.05). Similarly, sentiment score improvements with regard to customer attention (Wald's χ2: 11.964; p < 0.01), wine pairing (Wald's χ2: 6.409; p < 0.05), food quality (Wald's χ2: 4.166; p < 0.05) and service failures (Wald's χ2: 4.104; p < 0.05) implied significantly higher probabilities of a restaurant rating improving from 4.5 stars to 5 stars. No significant effects were observed for the rest of the attributes or the number of Michelin stars on restaurant ratings. The influence of attributes on the probability of a restaurant increasing its rating from 3.5 stars to 4 stars could not be estimated by SPSS since there were only three 3.5-star restaurants within the sample. However, the sentiment score for service failures was significantly more negative in 3.5-star restaurants (mean: −0.275) than in 4-star restaurants (mean: −0.057) (Student's t-test: −3.795; p < 0.01). There were no significant differences between the restaurants with these ratings for the other attributes. The model presented a good fit with an R2 Nagelkerke of 0.459, indicating that 45.9% of the variation in the dependent variable was explained by the independent variables. Figure 2 shows the effect of each attribute on the estimated probability that a restaurant will be able to increase its rating from a previous level, eliminating the influence of all other attributes.
5. Conclusions and discussion of results
5.1 Conclusions
There is a broad consensus among researchers that four attributes related to restaurant experiences are worth highlighting: customer attention, food quality, decor and ambience and value for money. Past research has also used these attributes to define the luxury-restaurant experience, with the difference that consumer expectations toward their performance are higher (e.g. Yang and Matila, 2016). However, it has been shown in this study that the experience in Michelin-starred restaurants goes beyond the evaluation of those four attributes. These findings are possible due to the analysis of online customer reviews with text-mining methodologies, with which a much more complete overview of the restaurant experience can be obtained.
Frequently, authors have stated that food quality is the main factor of a restaurant experience (Zhang et al., 2010; Yan et al., 2015; Kim et al., 2015; Gan et al., 2017; Pezenka and Weismayer, 2020; Mathayomchan and Taecharungroj, 2020; Kim and Hwang, 2022). In this paper, the significant effect of food quality has also been demonstrated on restaurant ratings. However, in the specific context of Michelin-starred restaurants, the differences between customer ratings are mainly explained by customer attention. A finding that leads to the conclusion that the effect of the attributes can vary depending on the type of restaurant (luxury or non-luxury restaurant), as previously suggested by Mathayomchan and Taecharungroj (2020). Customer attention in dining experiences at Michelin-starred restaurants is also very relevant according to the study by Panchapakesan et al. (2022), who highlighted the role of service excellence in achieving consumer delight at luxury restaurants.
Researchers generally assume a linear relationship between attributes and customer satisfaction or restaurant performance (e.g. Zhang et al., 2010; Yan et al., 2015; Kim et al., 2016). However, it has been suggested in this study that the effects of the attributes vary depending on the score of the restaurants. In other words, low-rating restaurants need to pay attention to certain factors and high-rating restaurants to other ones, in order to improve customer satisfaction.
5.2 Theoretical implications
Researchers have lent considerable attention to restaurant experience. Unfortunately, previous research on consumer experience in the context of Michelin-starred restaurants is limited. The contribution of this paper to the extant restaurant-experience literature is that the attributes evaluated by consumers during their dining experience in those restaurants have been identified. The findings confirm the presence of the four traditional factors of the restaurant experience: customer attention, food quality, decor and ambience and value for money. However, the Michelin-starred restaurant experience is more complex than has previously been suggested in the literature. Along with these attributes, the dishes that make up the tasting menus, including the products and ingredients used in their preparation, as well as the variety and quality of the wines offered to accompany the dishes are also described and evaluated by consumers. Possibly, its relevance is linked more to luxury restaurants than to other types of restaurants (e.g. fast food restaurants). The context of the dining experience was also found in the online reviews, showing the active role of the individual in creating the experience. Consumers narrated their previous expectations of dining at a Michelin-starred restaurant, the reason for the restaurant visit (e.g. a celebration of an anniversary or birthday, a business lunch, etc.), the companions (e.g. friends, partner or family), the global assessment of the experience and future behavioural intentions. Gan et al. (2017) pointed out that context reflects the personal and unique setting of the consumer experience in the restaurant. Finally, a dissatisfier attribute related to common restaurant service failures was identified. Theoretically, these findings share similarities to those of both Herzberg et al. (1959) and Crompton (2003), which suggested that consumer satisfaction is explained by motivator/satisfier and hygiene/dissatisfier attributes.
5.3 Methodological implications
From a methodological point of view, the use of online reviews as a source of data collection offers a more complete and realistic view of the restaurant experience, as it is based on opinions freely expressed by consumers. Consequently, the text-mining analysis of these online reviews offers the advantage of identifying attributes beyond those that traditionally make up the restaurant experience. On the contrary, researchers who make use of predetermined questionnaires for their empirical studies condition the identifying attributes of the restaurant in so far as they must appear in the questionnaires (Gan et al., 2017). The results have also shown that research methodologies based on text mining analysis (e.g. topic modelling and sentiment analysis) are powerful tools for understanding consumer behaviour.
On the other hand, researchers often use linear regression analysis to test the effect of restaurant experience attributes on different dependent variables (e.g. Yan et al., 2015; Pezenka and Weismayer, 2020). However, the logistic regression analysis is more appropriate given the categorical nature of the dependent variable (Mathayomchan and Taecharungroj, 2020; Wang et al., 2023). In addition, this statistical analysis is also useful to determine whether the effects of the independent variables differ depending on the values of the dependent variable.
5.4 Practical implications
The findings of this research also have important implications for Michelin-starred restaurants. If customer satisfaction is to be improved, customer attention followed by food quality must be priorities. A positive evaluation of the professionalism, education, kindness and friendliness of the chef and the rest of the restaurant team will significantly increase the ratings of a restaurant. Likewise, restaurant managers should strive to improve creativity, the combination of flavours and textures, visual presentation and product quality used for the preparation of the dishes. Beyond the improvement of customer attention and food quality, restaurants increase their probability of achieving culinary excellence, symbolised in 5-star status, if they offer an error-free service and a highly appreciated wine list among diners.
In addition, restaurants with lower ratings should have as a priority objective the correction of service failures to increase customer satisfaction. Problems with reservations, poor telephone service, removal of plates or glasses from the table without consent, long waiting times between courses, errors with the bill, excessively long payment times, unfriendly attitudes and poor presentation of dishes are frequently mentioned in the online reviews related with this issue. The identification and correction of these service failures makes it possible to improve the experience offered to the consumer (Oviedo-García et al., 2019). In addition, it is important to detect these errors during the provision of the service, so as to offer timely solutions before negative online reviews are generated (Kim and Hwang, 2022).
Finally, restaurant decoration was the attribute that consumers evaluated best of all. However, its influence on satisfaction was not significant. The results indicated that the attribute was positively evaluated both in restaurants with low and high ratings and its improvement never implied greater consumer satisfaction. Results that were in line with the research of Kim and Hwang (2022), who also found no significant effect of that attribute on consumer satisfaction.
Michelin-starred restaurants are also considered important tourist attractions (Castillo-Manzano et al., 2021). Two basic lines are proposed from the perspective of destination tourism management. Firstly, the creation of culinary schools could not only contribute to achieving gastronomic excellence, but also to the development of tourist destinations. And, secondly, Michelin-starred restaurants and each destination management organisation (DMO) must jointly develop marketing strategies to promote tourist destinations with a reputation for excellence in gastronomy. The implementation of these actions will definitely be of benefit to both the tourism industry and gastronomy in general.
5.5 Limitations and future lines of research
In any research paper, the results obtained depend largely on the data sample collected. Specifically, the quality of samples based on online reviews can be negatively affected by grammatical mistakes, misspellings and jargon. On the other hand, domain-specific dictionaries can improve the results of sentiment analysis (Liu et al., 2022). However, to the best of the author's knowledge, restaurant-specific sentiment dictionaries are as yet unavailable. It was also assumed in the study that the online reviews were based on truthful opinions. However, there is ample evidence that online manipulations occur to favour a company's own products or services and to harm competitors (Mellinas et al., 2016). Finally, it is necessary to point out that the results of this research are circumscribed within the context of Spanish restaurants with Michelin stars. Future research should be developed with restaurants from other countries using opinions written in other languages that may either refute or uphold the conclusions of this study.
Figures
Empirical studies related to online restaurant reviews
| Authors | Data | Main purpose | Method | Major findings |
|---|---|---|---|---|
| Zhang et al. (2010) | 1,242 online reviews from Dianping.com | To analyse the influence of online reviews on the popularity of restaurants | Regression analysis | Restaurant popularity was influenced by customer ratings of the quality of food, environment, service and the volume of online reviews |
| Pantelidis (2010) | 2,471 online reviews from www.london-eating.co.uk | To identify the most important factors that constitute the restaurant experience | Content analysis | The main factors that customers evaluated in their restaurant experience were as follows: food, service, ambience, price, menu and decor (in that order) |
| Yan et al. (2015) | 10,136 online reviews from Koubei website | To analyse the influence of online reviews on restaurant revisit intentions | Regression analysis | Restaurant customer revisit intentions were influenced by customer ratings of food quality, price and value, service quality and ambience |
| Kim et al. (2016) | Online reviews of more than 70 restaurants in 16 states in the USA from TripAdvisor, Urbanspoon and Foursquare | To analyse the influence of online reviews on the financial performance of restaurants | Hierarchical multiple regressions | Customer overall rating, guests served per working hour, customer rating of food quality and number of online reviews had a significant impact on restaurant performance |
| Gan et al. (2017) | 268,442 online reviews of 7,508 restaurants from Yelp | To analyse the influence of online reviews on overall customer rating | Sentiment analysis Multilevel models | Sentiment toward food, service and context were the main attributes affecting overall customer rating, followed by sentiment toward price and ambience |
| Vu et al. (2019) | 40,948 online reviews of 2,265 restaurants from TripAdvisor | To analyse the differences in online reviews between Western and Asian restaurant customers | Sentiment analysis of customer comments Z and chi-square tests | The Western group had significantly higher preferences for service, price and ambience than the Asian group |
| Pezenka and Weismayer (2020) | 100,831 online reviews of restaurants located in the city of Washington D.C. from TripAdvisor | To identify the most important factors linked to the restaurant experience To analyse differences between local and visitor customers To analyse the influence of factors on overall customer rating | Sentiment analysis of customer comments Mann–Whitney U test Linear regression | Restaurant experience attributes: drinks, facilities, food, business, menu, reservations, cleanliness, desserts, location, quietness, payment, ambience, value and staff Greater differences between locals and visitors for drinks and facilities Food was the attribute with the greatest influence on the overall customer rating |
| Mathayomchan and Taecharungroj (2020) | 935,386 online reviews of 5,010 restaurants from Google | To analyse the influence of online reviews on overall customer ratings | Sentiment analysis of customer comments Logistic regression | Sentiment toward food is the most important attribute affecting overall customer ratings, followed by sentiment toward service, ambience and value |
| Luo and Xu (2021) | 112,412 online reviews of American restaurants from Yelp | To identify the most important factors of the restaurant experience | Sentiment analysis of customer comments Various text-mining algorithms | Four major attributes were identified: service, food, place and experience |
| Nilashi et al. (2021) | 6,342 online reviews of vegetarian restaurants in Bangkok from TripAdvisor | To develop a method for customer restaurant segmentation | LDA algorithm for text mining, SOM for data clustering and CART for preference prediction | LDA identified four dimensions: food, value, service and atmosphere Using these factors, four customers' segments were presented |
| Aktas-Polat and Polat (2021) | 2,585 reviews on TripAdvisor for 46 five-star hotel restaurants in Istanbul | To discover the factors affecting customer delight, satisfaction and dissatisfaction in fine dining experiences | The latent Dirichlet allocation algorithm | The most important factors for customer delight, satisfaction and dissatisfaction were staff, view and food quality, respectively |
| Rita et al. (2023) | 8,871 reviews on TripAdvisor of 87 restaurants in Europe | To investigate how sentiments toward food, service, ambience and price change after a restaurant is awarded a Michelin Star | Sentiment analysis of customer comments | Sentiments toward service, food and ambience decreased, while price sentiment showed a significant increase, after restaurants were awarded a Michelin Star |
Source(s): The author's own work
Sample of online restaurant reviews
| Online reviews | % | |
|---|---|---|
| Number of stars | ||
| One star (181 restaurants) | 51,115 | 72.8 |
| Two stars (32 restaurants) | 10,451 | 14.9 |
| Three stars (11 restaurants) | 8,667 | 12.3 |
| Total | 70,233 | 100 |
| Restaurant rating | ||
| 5 (25 restaurants) | 6,378 | 9.1 |
| 4.5 (172 restaurants) | 58,521 | 83.3 |
| 4 (24 restaurants) | 4,755 | 6.8 |
| 3.5 (3 restaurants) | 579 | 0.8 |
| Total | 70,233 | 100 |
Source(s): The author's own work
Identified attributes of the restaurant experience
| No | Latent topic and description | Relevant terms | Sample of most related customer reviews |
|---|---|---|---|
| 1 | Tasting menu Customer description and evaluation of the dishes included in the tasting menus | Dessert, rice, dish, flavour, foie, chocolate, egg, ice cream, cream, cheese, cod, sauce, tuna, hake, tartar, fish, meat, salad, truffle, tomato, mushrooms, lamb, apple, crispy, soup, shrimp, suckling pig, cooked, octopus and lobster | “We started with some carrots with a bowl of cheese and truffles (excellent), then potato chips with marinated mussels (stupendous), then lemon gelatin and curried cream of turron (surprising), followed by beef fillet tataki with olive oil caviar and hot paprika, after which an aioli sauce on potato and cuttle-fish that was very well prepared, then a chick-pea stew with a poached egg that was all quite splendid, followed by tuna in breadcrumb sauce, after which roast piglet cooked to perfection, continuing with a sort of melon sorbet and mint with pineapple and ice-cream granita (very refreshing), and we finished with a sweet chocolate mousse and groundnut ice-cream” |
| 2 | Food quality Customer evaluation of the visual presentation, creativity, flavours, textures, cooking techniques and quality of the products | Flavours, dish, product, cuisine, presentation, textures, quality, flavour, menu, material, raw, food, elaboration, excellent, elaborated, experience, surprising, ingredients, mix, palate, traditional, tasting, presentations, traditional, explosion, senses, combination, region, elaborations and technique | “It is really the best preparation of products from the Mediterranean area, treated with the greatest possible care, simplicity, and originality by professional Japanese cooks, known by all for the care that they take over the basic ingredients, respecting freshness and taste” |
| 3 | Value for money It reflects the overall assessment of the restaurant experience related to the price, customer satisfaction and future behavioural intentions | Price, star, quality, Michelin, restaurant, food, relationship, worth, menu, deserving, level, expensive, experience, dish, excellent, best, cheap, tasting, cuisine, high, product, category, treatment, recommendable, account, eat and attention | “A Michelin star restaurant where you can eat traditional food with well-prepared products (extensive and varied menu, with a section on traditional food with a touch of innovation), with servings that are so well balanced and a reasonable price, three difficult factors to find in star-rated restaurants; I am a little tired of restaurants with stars serving dishes with long names, for which you pay outlandish amounts and you are often left to go hungry” |
| 4 | Service failures Related to common service restaurant failures such as reservations mistakes, poor telephone service, removal of plates or glasses from the table without consent, long waiting times between courses, errors with the bill, excessively long payment times, unfriendly attitudes and poor presentation of dishes | Table, dish, waiter, reservation, time, water, bread, detail, bill, menu, wine, minutes, problem, wrong, waiters, cup, order, diners, serve, waitress, eat, restaurant, bottle, coffee, people, wait, person, take, arrive and bad | “Awful, after visiting them year after year because of their good cuisine, I have to say that the customer care was no good, we had a reservation for 10 people and they made a mistake with the date, and we were expected to return another day, I am sad to say that their customer care is intolerable” |
| 5 | Customer attention Customer evaluation of the professionalism, education, kindness, and friendliness of the chef and the rest of the restaurant team | Chef, team, staff, kitchen, treatment, room, thanks, attention, friendly, professional, experience, waiters, friendliness, food, greet, attentive, professionalism, Diego, nearby, dish, restaurant, Jordi, attentive, table, professionals, Carlos, detail, boss, excellent and Pepe | “The care, professional approach and friendliness catch your attention in the first place, as it is not everywhere where that at once warm and exquisite atmosphere is evoked and it is all an achievement of the team that make up the restaurant, highlighting the management of the maître, whose name I am sorry I cannot recall right now, and lastly to highlight the guiding light of the restaurant, that is to say the cook who came into the dining room and walked around, table by table, interested in knowing our opinions on the evening meal” |
| 6 | Wine pairing Customer description and evaluation of the quality and variety of the wines offered by the restaurant to pair the dishes | Menu, wine, tasting, dish, pairing, menu, dessert, ordered, chose, long, short, Euros, price, drank, starters, red, taste, white, bottle, choice, sommelier, appetisers, person, glasses, cellar, cava and selection | “The wine pairing was to begin with a Rueda, white wine (Fenomenal 11- Happy grapes), quite good, followed by another white wine D.O Empordà (Cigonyes Blanc 11- Castell de Perelada), excellent, after which a red wine D.O Costers del Segre (Cérvoles Negre 06), also excellent and to end a Cava D.O Empordá (Stars Brut Nature 09 from Castell de Perelada), an excellent champagne” |
| 7 | Context Related to previous expectations, dining purpose, companions, overall customer satisfaction and behavioural intentions | Restaurant, star, Michelin, experience, eat, expectations, friends, celebrate, visit, day, best, dine, couple, week, anniversary, Madrid, enjoy, birthday, go, visit, best, city, hotel, desire, high, Barcelona, months, return and days | “I had wanted to visit the Aponiente restaurant for some time, and I feared it might be a disappointment, having such high expectations, having visited some of the best restaurants of Spain (Can Roca, Akelarre, Diverxo, Quique Dacosta, etc …), but without a doubt the experience exceeded my expectations” |
| 8 | Decor and ambience Customer description and evaluation of the location, views of the restaurant, decoration of the different rooms (reception, dining room, rest room, toilets, etc.), dining room accessories (tables, chairs, cutlery, crockery, tablecloths, etc.) and ambience (temperature, sound and lighting) | Premises, decor, ambience, table, views, pleasant, restaurant, cosy, dining room, food, terrace, space, surroundings, quiet, staff, kitchen, treatment, price, elegant, attention, room, interior, decorated, beautiful, located, modern, entrance, bar, garden and building | “A very classic atmosphere and decoration (in the best sense of the word), with a fine carpet and beautiful ornamental plaster casts on the walls (damaged in some parts by the chairs scraping against the walls), with very comfortable velvet seats and, of course, fine silver-plated cutlery, which was changed with each dish” |
Source(s): The author's own work
Results of the logistic regression analysis
| Dependent | Predictor | B | Std. Error | Wald's χ2 | Sig |
|---|---|---|---|---|---|
| 4-star to 4.5-star rating | Intercept | −5.116 | 1.754 | 8.505 | 0.004** |
| Tasting menu | 5.070 | 2.345 | 4.673 | 0.031* | |
| Customer attention | 9.508 | 2.769 | 11.794 | 0.001** | |
| Value for money | 3.221 | 2.549 | 1.597 | 0.206 | |
| Food quality | 7.561 | 3.031 | 6.222 | 0.013* | |
| Wine pairing | −0.795 | 2.676 | 0.088 | 0.766 | |
| Context | 3.615 | 2.735 | 1.747 | 0.186 | |
| Service failures | 1.497 | 3.513 | 0.182 | 0.670 | |
| Decor and ambiance | 0.327 | 1.389 | 0.055 | 0.814 | |
| 4.5-star to 5-star rating | Intercept | −6.247 | 1.979 | 9.967 | 0.002** |
| Tasting Menu | −3.754 | 3.151 | 1.419 | 0.234 | |
| Customer attention | 5.856 | 1.693 | 11.964 | 0.001** | |
| Value | −1.470 | 3.314 | 0.197 | 0.657 | |
| Food quality | 5.322 | 2.607 | 4.166 | 0.041* | |
| Wine pairing | 8.150 | 3.219 | 6.409 | 0.011* | |
| Context | −5.233 | 3.589 | 2.126 | 0.145 | |
| Service failures | 8.678 | 4.284 | 4.104 | 0.043* | |
| Decor and ambiance | −0.319 | 1.723 | 0.034 | 0.853 |
Note(s): *p < 0.05; **p < 0.01; ***p < 0.001
Source(s): The author's own work
References
Agresti, A. (2019), An Introduction to Categorical Data Analysis, John Wiley & Sons, Hoboken, NJ.
Aktas-Polat, S. and Polat, S. (2021), “Discovery of factors affecting tourists' fine dining experiences at five-star hotel restaurants in Istanbul”, British Food Journal, Vol. 124 No. 1, pp. 221-238.
Alaei, A.R., Becken, S. and Stantic, B. (2019), “Sentiment analysis in tourism: capitalizing on big data”, Journal of Travel Research, Vol. 58 No. 2, pp. 175-191.
Anderson, C. (2012), “The impact of social media on lodging performance”, Cornell Hospitality Report, Vol. 12 No. 15, pp. 4-11.
Batat, W. (2020), “Pillars of sustainable food experiences in the luxury gastronomy sector: a qualitative exploration of Michelin-starred chefs' motivations”, Journal of Retailing and Consumer Services, Vol. 57 July, 102255.
Batat, W. (2021), “The role of luxury gastronomy in culinary tourism: an ethnographic study of Michelin-Starred restaurants in France”, International Journal of Tourism Research, Vol. 23 No. 2, pp. 150-163.
Björk, P. and Kauppinen-Räisänen, H. (2014), “Culinary-gastronomic tourism – a search for local food experiences”, Nutrition and Food Science, Vol. 44 No. 4, pp. 294-309.
Björk, P. and Kauppinen-Räisänen, H. (2016), “Exploring the multi-dimensionality of travellers' culinary-gastronomic experiences”, Current Issues in Tourism, Vol. 19 No. 12, pp. 1260-1280.
Blei, D.M., Ng, A.Y. and Jordan, M.I. (2003), “Latent dirichlet allocation”, Journal of Machine Learning Research, Vol. 3, pp. 993-1022.
Cao, J., Xia, T., Li, J., Zhang, Y. and Tang, S. (2009), “A density-based method for adaptive LDA model selection”, Neurocomputing, Vol. 72, pp. 1775-1781.
Castillo-Manzano, J.I., Castro-Nuño, M., López-Valpuesta, L. and Zarzoso, Á. (2021), “Quality versus quantity: an assessment of the impact of Michelin-starred restaurants on tourism in Spain”, Tourism Economics, Vol. 27 No. 5, pp. 1166-1174.
Chen, A. and Peng, N. (2015), “The effects of luxury restaurant environments on diners' emotions and loyalty. Incorporating diner expectations into an extended Mehrabian-Russell model”, International Journal of Contemporary Hospitality Management, Vol. 7 No. 2, pp. 236-260.
Chevalier, J.A. and Mayzlin, D. (2006), “The effect of word of mouth on sales: online book reviews”, Journal of Marketing Research, Vol. 43 No. 3, pp. 345-354.
Chiang, C.F. and Guo, H.W. (2021), “Consumer perceptions of the michelin Guide and attitudes toward michelin-starred restaurants”, International Journal of Hospitality Management, Vol. 93, pp. 1-9.
Clemons, E.K., Gao, G. and Hitt, L.M. (2006), “When online reviews meet hyperdifferentiation: a study of the craft beer industry”, Journal of Management Information Systems, Vol. 23 No. 2, pp. 149-171.
Crompton, J.L. (2003), “Adapting Herzberg: a conceptualization of the effects of hygiene and motivator attributes on perceptions of event quality”, Journal of Travel Research, Vol. 41 No. 3, pp. 305-310.
Daries, N., Cristobal-Fransi, E., Ferrer-Rosell, B. and Marine-Roig, E. (2018), “Maturity and development of high-quality restaurant websites: a comparison of Michelin-starred restaurants in France, Italy and Spain”, International Journal of Hospitality Management, Vol. 73 February, pp. 125-137.
Fissi, S., Gori, E., Marchi, V. and Romolini, A. (2022), “Social media, brand communication and customer engagement in Michelin-starred restaurants during a time of crisis”, British Food Journal, Vol. 125 No. 13, pp. 16-33.
Gan, Q., Ferns, B.H., Yu, Y. and Jin, L. (2017), “A text mining and multidimensional sentiment analysis of online restaurant reviews”, Journal of Quality Assurance in Hospitality and Tourism, Vol. 18 No. 4, pp. 465-492.
Guo, Y., Barnes, S.J. and Jia, Q. (2017), “Mining meaning from online ratings and reviews: tourist satisfaction analysis using Latent Dirichlet allocation”, Tourism Management, Vol. 59, pp. 467-483.
Henderson, J.C. (2017), “Street food, hawkers and the michelin Guide in Singapore”, British Food Journal, Vol. 119 No. 4, pp. 790-802.
Hennig-Thurau, T., Gwinner, K.P., Walsh, G. and Gremler, D.D. (2004), “Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the Internet?”, Journal of Interactive Marketing, Vol. 18 No. 1, pp. 38-52.
Herzberg, F., Mausner, B. and Snyderman, B. (1959), The Motivation to Work, John Wiley, New York.
Jeong, E. and Jang, S.S. (2011), “Restaurant experiences triggering positive electronic word-of-mouth (eWOM) motivations”, International Journal of Hospitality Management, Vol. 30 No. 2, pp. 356-366.
Jiménez-Beltrán, J., López-Guzmán, T. and Santa-Cruz, F.G. (2016), “Gastronomy and tourism: profile and motivation of international tourism in the city of Córdoba, Spain”, Journal of Culinary Science and Technology, Vol. 14 No. 4, pp. 347-362.
Kiatkawsin, K. and Han, H. (2019), “What drives customers' willingness to pay price premiums for luxury gastronomic experiences at michelin-starred restaurants?”, International Journal of Hospitality Management, Vol. 82, May, pp. 209-219.
Kim, J. and Hwang, J. (2022), “Who is an evangelist? Food tourists' positive and negative eWOM behavior”, International Journal of Contemporary Hospitality Management, Vol. 34 No. 2, pp. 555-557.
Kim, W.G., Lim, H. and Brymer, R.A. (2015), “The effectiveness of managing social media on hotel performance”, International Journal of Hospitality Management, Vol. 44, pp. 165-171.
Kim, W.G., Li, J.J. and Brymer, R.A. (2016), “The impact of social media reviews on restaurant performance: the moderating role of excellence certificate”, International Journal of Hospitality Management, Vol. 55, pp. 41-51.
Kwartler, T. (2017), Text Mining in Practice with R, John Wiley & Sons, Hoboken, NJ.
Lee, J.H. and Hwang, J. (2011), “Luxury marketing: the influences of psychological and demographic characteristics on attitudes toward luxury restaurants”, International Journal of Hospitality Management, Vol. 30 No. 3, pp. 658-669.
Litvin, S.W., Goldsmith, R.E. and Pan, B. (2008), “Electronic word-of-mouth in hospitality and tourism management”, Tourism Management, Vol. 29 No. 3, pp. 458-468.
Liu, P. and Tse, E.C.Y. (2018), “Exploring factors on customers' restaurant choice: an analysis of restaurant attributes”, British Food Journal, Vol. 120 No. 10, pp. 2289-2303.
Liu, J., Yu, Y., Mehraliyev, F., Hu, S. and Chen, J. (2022), “What affects the online ratings of restaurant consumers: a research perspective on text-mining big data analysis”, International Journal of Contemporary Hospitality Management, Vol. 34 No. 10, pp. 3607-3633.
Luo, Y. and Xu, X. (2021), “Comparative study of deep learning models for analyzing online restaurant reviews in the era of the COVID-19 pandemic”, International Journal of Hospitality Management, Vol. 94, pp. 1-8.
Madeira, A., Palrão, T., Mendes, A.S. and Ottenbacher, M.C. (2022), “The culinary creative process of Michelin Star chefs”, Tourism Recreation Research, Vol. 47 No. 3, pp. 258-276.
Mathayomchan, B. and Taecharungroj, V. (2020), “How was your meal? Examining customer experience using Google maps reviews”, International Journal of Hospitality Management, Vol. 90, pp. 1-13.
Mellinas, J.P., Martínez María-Dolores, S.M. and Bernal García, J.J. (2016), “Effects of the Booking.com scoring system”, Tourism Management, Vol. 57, pp. 80-83.
Michelin Guide (2022), “Michelin Guide – the official website”, available at: https://guide.michelin.com/es/es (accessed 10 May 2023).
Miner, G., Delen, D., Elder, J., Fast, A., Hill, T. and Nisbet, B. (2012), Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications, Academic Press, Waltham, MA.
Montargot, N., Kallmuenzer, A. and Kraus, S. (2022), “Haute cuisine three-star restaurants' representation on websites and dining guides: a lexicometric analysis”, International Journal of Contemporary Hospitality Management, Vol. 34 No. 9, pp. 3376-3393.
Nielsen, F.A. (2011), “A new anew: evaluation of a word list for sentiment analysis in microblogs”, Proceedings of the ESWC2011 Workshop on ‘Making Sense of Microposts’: Big Things Come in Small Packages, Keraklion, Crete.
Nilashi, M., Ahmadi, H., Arji, G., Alsalem, K.O., Samad, S., Ghabban, F., Alzahrani, A.O., Ahani, A. and Alarood, A.A. (2021), “Big social data and customer decision making in vegetarian restaurants: a combined machine learning method”, Journal of Retailing and Consumer Services, Vol. 62, pp. 1-18.
Oviedo-García, M.A., Vega-Vázquez, M., Castellanos-Verdugo, M. and Orgaz-Agüera, F. (2019), “Tourism in protected areas and the impact of servicescape on tourist satisfaction, key in sustainability”, Journal of Destination Marketing and Management, Vol. 12, pp. 74-83.
Panchapakesan, P., Amin, M. and Herjanto, H. (2022), “How luxury restaurants will enhance the concept of guest delight”, Journal of Hospitality and Tourism Insights, Vol. 5 No. 2, pp. 311-330.
Pantelidis, I.S. (2010), “Electronic meal experience: a content analysis of online restaurant comments”, Cornell Hospitality Quarterly, Vol. 51 No. 4, pp. 483-491.
Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1988), “SERVQUAL: a multiple-item scale for measuring service quality”, Journal of Retailing, Vol. 64 No. 1, pp. 12-40.
Peng, C.-Y.J., Lee, K.L. and Ingersoll, G.M. (2002), “An introduction to logistic regression analysis and reporting”, The Journal of Educational Research, Vol. 96 No. 1, pp. 3-14.
Pezenka, I. and Weismayer, C. (2020), “Which factors influence locals' and visitors' overall restaurant evaluations?”, International Journal of Contemporary Hospitality Management, Vol. 32 No. 9, pp. 2793-2812.
Pine, B.J. and Gilmore, J.H. (1998), “Welcome to the experience economy”, Harvard Business Review, Vol. 76, pp. 97-105.
Rita, P., Vong, C., Pinheiro, F. and Mimoso, J. (2023), “A sentiment analysis of Michelin-starred restaurants”, European Journal of Management and Business Economics, Vol. 32 No. 3, pp. 276-295.
Schuckert, M., Liu, X. and Law, R. (2015), “A segmentation of online reviews by language groups: how English and non-English speakers rate hotels differently”, International Journal of Hospitality Management, Vol. 48, pp. 143-149.
Sievert, C. and Shirley, K.E. (2014), “LDAvis: a method for visualizing and interpreting topics”, In Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, pp. 63-70.
Silge, J. and Robinson, D. (2017), Text Mining with R: A Tidy Approach, O’Reilly, Sebastopol, CA.
Vargas-Sanchez, A. and López-Guzmán, T. (2022), “Creative process and culinary experiences in michelin-starred restaurants”, Journal of Culinary Science and Technology, Vol. 20 No. 2, pp. 97-116.
Vermeulen, I.E. and Seegers, D. (2009), “Tried and tested: the impact of online hotel reviews on consumer consideration”, Tourism Management, Vol. 30 No. 1, pp. 123-127.
Vu, H.Q., Li, G., Law, R. and Zhang, Y. (2019), “Exploring tourist dining preferences based on restaurant reviews”, Journal of Travel Research, Vol. 58 No. 1, pp. 149-167.
Wang, X., Zheng, J., Tang, L. and Luo, Y. (2023), “Recommend or not? The influence of emotions on passengers' intention of airline recommendation during COVID-19”, Tourism Management, Vol. 95, pp. 1-14.
Yan, X., Wang, J. and Chau, M. (2015), “Customer revisit intention to restaurants: evidence from online reviews”, Information Systems Frontiers, Vol. 17 No. 3, pp. 645-657.
Yang, W. and Mattila, A. (2016), “Why do we buy luxury experiences?”, International Journal of Contemporary Hospitality Management, Vol. 28 No. 9, pp. 1848-1867.
Ye, Q., Law, R. and Gu, B. (2009), “The impact of online user reviews on hotel room sales”, International Journal of Hospitality Management, Vol. 28 No. 1, pp. 180-182.
Yrjölä, M., Rintamäki, T., Saarijärvi, H., Joensuu, J. and Kulkarni, G. (2019), “A customer value perspective to service experiences in restaurants”, Journal of Retailing and Consumer Services, Vol. 51, pp. 91-101.
Zhang, Z., Ye, Q., Law, R. and Li, Y. (2010), “The impact of e-word-of-mouth on the online popularity of restaurants: a comparison of consumer reviews and editor reviews”, International Journal of Hospitality Management, Vol. 29 No. 4, pp. 694-700.