What went right and what went wrong in my cellar door visit? A worldwide analysis of TripAdvisor's reviews of Wineries & Vineyards

Barbierato, Elena; Bernetti, Iacopo; Capecchi, Irene Article What went right and what went wrong in my cellar door visit? A worldwide analysis of TripAdvisor's reviews of Wineries & Vineyards Wine Economics and Policy Provided in Cooperation with: UniCeSV - Centro Universitario di Ricerca per lo Sviluppo Competitivo del Settore Vitivinicolo, University of Florence Suggested Citation: Barbierato, Elena; Bernetti, Iacopo; Capecchi, Irene (2022) : What went right and what went wrong in my cellar door visit? A worldwide analysis of TripAdvisor's reviews of Wineries & Vineyards, Wine Economics and Policy, ISSN 2212-9774, Firenze University Press, Florence, Vol. 11, Iss. 1, pp. 47-72, https://doi.org/10.36253/wep-10871 This Version is available at: https://hdl.handle.net/10419/284525 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Wine Economics and Policy 11(1): 47-72, 2022 Firenze University Press www.fupress.com/wep ISSN 2212-9774 (online) | ISSN 2213-3968 (print) | DOI: 10.36253/wep-10871 Wine Economics and Policy Citation: Elena Barbierato, Iacopo Bernetti, Irene Capecchi (2022) What went right and what went wrong in my cellar door visit? A worldwide analysis of TripAdvisor’s reviews of Wineries & Vineyards. Wine Economics and Policy 11(1): 47-72. doi: 10.36253/wep-10871 Copyright: © 2022 Elena Barbierato, Iacopo Bernetti, Irene Capecchi. This is an open access, peer-reviewed article published by Firenze University Press (http://www.fupress.com/wep) and distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information fi les. Competing Interests: The Author(s) declare(s) no confl ict of interest. What went right and what went wrong in my cellar door visit? A worldwide analysis of TripAdvisor’s reviews of Wineries & Vineyards Elena Barbierato*, Iacopo Bernetti, Irene Capecchi 1 DAGRI - University of Florence, P.le delle Cascine 18, 50144 Florence, Italy. E-mail: elena.barbierato@unifi .it; iacopo.bernetti@unifi .it; irene.capecchi@unifi .it *Corresponding author. Abstract. Th e purpose of this work is to study the issues of service quality and service failure during visits to cellar doors in the fi ve regions where wine tourism is most developed: Hunter Valley (AU), Mendoza (AR), Napa Valley (the USA), Stellenbosch (ZA), and Tuscany (IT). We propose a methodology based on a combination of sentiment analysis and natural language processing applied to 89,672 TripAdvisor reviews. Th e results indicate that the issues most linked to service quality and service failure are as follows (in the order of importance): the quality of the main wine product, the experience in the tasting room, the organized tours, the empathy of the staff , the reliability of the staff , and the setting of the cellar and landscape. Th ese themes are common to all fi ve wine tourism regions, but each region treats them diff erently. Th e results obtained confi rm and expand the results of previous studies and may prove useful both to professionals (wineries, tour operators, and travel agents) and for the design of a product that meets the needs of wine tourists. Th e main limitation of the study concerns the application of the methodology to the fi ve most developed wine regions in the world; therefore, the results obtained may not be immediately applicable to the wine regions that are starting to develop wine tourism. Keywords: wine tourism, cellar door, service quality, service failure, TripAdvisor, sentiment analysis, natural language processing. 1. INTRODUCTION Wine tourism industry has been shown to play a key role in regional rural development, and thus, tourism is one of the important and developing parts of the wine industry, even on an international scale [1]. Th e feature that most characterizes wine tourism involves visits to cellar doors [2] Visits to cellar doors create a direct relationship between producers and consumers of wine that can last over time [3]. Such visitors oft en research products when they return home, which results in positive word-of-mouth marketing to friends, family, and colleagues [4], [5]. From an economic point of view, direct sales in a cellar bring greater added value because of the minimum distribution costs and the consequent high associated margins [4]. Winery 48 Elena Barbierato, Iacopo Bernetti, Irene Capecchi visits are an important part of wine tourism and contribute to the development of the wine sector [6]. A key element in the success of winery visits is customers’ perceptions of the quality of service. Quality of service is essential to develop the relationship between customers and brands and allows the implementation of relationship marketing strategies [5]. Although a range of tools is available to measure and evaluate the quality of service, only a few of them have been applied to the field of wine tourism. Such tools are predominantly based on questionnaires [5], [7], [8], mainly employing the well-known SERVQUAL model and its variants [9], adapting it to the specificities of the wine sector [5], [10]. However, the use of questionnaires has the limitation of poor generalizability of results. This is because case studies consider a handful of consumers attending one or a few wineries within a single wine tourism region. There is also a lack of studies on the factors that determine service failure in winery visits. Poor service or service failure results in dissatisfaction, which, in turn, manifests in a series of responses that may include complaints and negative word of mouth. In the work of Magnini and Ford [11], service failures were defined as “any service-related mishaps or problems (real or perceived) that transpired during a customer’s experience with a firm.” By better understanding the causes of customer complaints, the number of problematic events can potentially be reduced, and better remedies can be provided. Therefore, attempts to develop a clearer understanding of problem areas benefit both winery owners and customers. Researchers have recently started measuring the service quality and service failure of hotels through the analysis of reviews left by users on travel sites [12], [13]. Travel sites allow users to freely express opinions on the perceived quality of service, and from these reviews, a measure of the quality of service can be obtained through a semantic analysis of the content. These data can be considered complementary to questionnaires, as they have a very different nature. On the one hand, such data is not structured around variables or concerns, as it happens with existing validated questionnaires, so that it cannot be used to provide a summative assessment of quality. On the other hand, it has the potential to discover aspects of quality that are overlooked or given less importance in existing questionnaires. A further gap that emerged from the analysis of the literature is that the research on the quality of service of visits to wine cellars is geographically limited to very few wine regions, located mainly in Australia. International travel sites allow reviews to be accessed from around the world [5, 6,7]. The research questions (RQs) that our work attempts to answer are: RQ1: Is it possible to measure service quality and service failure through a semantic analysis of the reviews made by users on travel sites? RQ2: What are the determinants of service quality that emerge from reviews made by users on travel sites? RQ3: What are the determinants of service failure that emerge from reviews made by users on travel sites? RQ4: What are the factors that determine the quality and failure of service that are common globally? What are the typical determinants of the quality of wine regions? This article is structured as follows. First, the literature review provides an overview of past studies within the theoretical framework of winescape, service quality assessment, and the use of data from social media in the wine tourism industry. Then, the methodology section describes the research context, data collection process, and procedures used to perform content analysis of web reviews. The results section identifies the major themes of service quality and service failure. The discussion section focuses on the answers to the research questions and the comparison of the research findings with the results of other approaches, with reference to SERVQUAL. The last section reports the managerial implications, limitations, and possibilities for future research. 2. LITERATURE REVIEW Wine tourism is a complex product that combines the purchase of a market good, wine, with the enjoyment of intangible assets, such as the landscape and the information provided by the guide and wine producers. The study of wine sales from wineries through the analysis of social media data encompasses four theoretical fields of research: wine tourism, winescapes, theories of quality of service, and content analysis/lexical analysis theory applied to social media. In this literature review, we provide brief references to these three research fields as applied to cellar door visits. 2.1. Wine tourism The number of articles published on wine tourism has been steadily growing since the mid-1990s. Based on prior literature, a framework for wine tourism was explored by Carlsen [14], in which productionand consumption-based research can be placed. A classification of the wine tourism literature was then conducted by Mitchell and Hall [15], who identified eight themes: 49 What went right and what went wrong in my cellar door visit? A worldwide analysis of TripAdvisor’s reviews of Wineries & Vineyards 1) wine tourism development; 2) winery and cellar door; 3) wine tourist behavior; 4) wine events and festivals; 5) marketing and promotion; 6) critical success factors; 7) wine tourism models; and 8) education and more. Between 2005 and 2014, there was an increase in theory building in wine tourism research, referring to theory to provide theory [15]. Theory-based research has focused on the analysis of wine regions’ image [16] and service quality [5]. 2.2. Winescape concept One of the most prolific topics in wine tourism is the analysis of wine regions’ image through the identification of wine tourism attributes [15], [17]. The dimensions of the servicescape (i.e., the atmosphere that enhances the customer experience and influences buyer behavior during the service encounter) were adapted and applied by Peters [18] in a winescape concept by highlighting attributes that are attractive to wine tourists. First, Peters [18] identified three fundamental elements that shape a winescape: “(1) the grapes and their needs, (2) the natural environments that best meet those needs, and (3) the viticulturists and wine makers who determine everything from the varieties of grapes, spacing of the vines, and trellising systems to the final product that enters the bottle.” Johnson and Bruwer’s [19] conceptual definition specifically encapsulated the interplay of natural landscape and setting: heritage, architecture, and artifacts within a winery, winery’s vineyard, cellar door, and wines; complementary products and services; signage and layout; and people at a winery. The winescape scale developed in more recent studies is based on a plurality of theories: servicescape theory, multi-attribute theory [20], and destination choice (push-pull) theory [21]. Thomas et al. [22], from a metaanalysis of 70 supply related winery articles, defined seven key attributes of a winescape: 1. The natural environment and scenery such as the natural landscape, vineyards, and rural setting referred to in the current study as the winescape setting attribute. 2. Built environment such as wineries, cellar doors, and buildings, and the heritage that they convey were identified as the winescape atmospherics attribute. 3. Wine products such as reputable wines, wine variety, and value-for-money wines were referred to as the winescape wine product attribute. 4. Complementary services such as restaurants, accommodation as well as other local produce and craft were identified as the winescape complementary product attribute. 5. Signage and information such as signposting and informational materials were referred to as the winescape signage attribute. 6. Layout and infrastructure connecting the physical attractions such as wine routes and roads were identified as the winescape layout attribute. 7. Service staff who interact with wine tourists were referred to as the winescape service staff attribute. 2.3. Service quality in cellar door visits Service quality (SQ) originates from comparing perceived expectations (E) of a service to perceived performance (P), resulting in the equation SQ = P-E [23]; service failure can be defined as service performance falling short of customer expectations [24]. Most of the research conducted in Australia on service quality at cellar doors is based on an adaptation of the SERVQUAL methodology. The SERVQUAL methodology, proposed by Parasuraman, Zeithaml, and Berry [9], constructs a measure of perceived quality and, therefore, of customer satisfaction through a comparison of customer expectations in approaching a type of product/ service and the perceptions of the product/service after consumption. It is a highly standardized quantitative methodology designed specifically to measure clients’ opinions on the quality of services. This makes it possible to compare the expectations and perceptions of users regarding a specific service. It consists of a series of 22 questions valid for each type of service that make it possible to measure perceived quality and expectations separately for five dimensions considered essential for judging service quality. The dimensions are as follows: 1. Tangible elements (appearance of physical facilities, equipment, and personnel); 2. Reliability (ability to deliver the promised service reliably and accurately); 3. Responsiveness (willingness to help customers and provide service promptly); 4. Reassurance (competence and courtesy of employees and relative ability to inspire trust and confidence); 5. Empathy (caring and personalized assistance given to customers and users). Some authors have adapted and applied SERVQUAL’s methodology to analyze the quality of service during the visits at cellar doors by assessing dimensions using Likert scales. O’Neil and Charters [4], in a study in the Margaret River region (AU), implemented a two-stage methodology, the first stage being a qualitative descriptive analysis 50 Elena Barbierato, Iacopo Bernetti, Irene Capecchi through eight interviews with cellar door operators, and the second stage was based on 150 interviews through a specific questionnaire developed through an adaptation of the SERVQUAL methodology. Similarly, O’Neil et al. [5] applied the SERVQUAL methodology by adapting it to 10 wineries in the Margaret River region and in the Barossa Valley (AU). For both surveys, respondents were asked to rate their perceptions of the dimensions listed on a five-point Likert scale. The scale items were grouped according to whether they were “wine-related” or “staff-related,” and represented many of the original SERVQUAL dimensions. The items that comprised each dimension were based on King et al.’s (1997) [25] service quality model for cellar doors, which emphasized the importance of product and service quality for cellar doors’ success. Griffin and Lopersch [26] applied a modified version of the SERVQUAL model in Canberra District (AU). The authors identified 23 quality attributes across six dimensions: external, internal, service, staff, wine, and convenience attributes. The external and internal attributes related to the physical qualities of wineries, with the former associated with the environment and surroundings of wineries, and the latter relating to the layout and character of tasting rooms. Gill et al. [6], in research in the Margaret River and Swan Valley, instead, adopted a multidimensional model derived from Sweeney and Soutar’s [27] PERVAL and Petrick’s [28] SERV-PERVAL measures. The authors derived five dimensions from the SERV-PERVAL measures: quality (Q), emotional value (EV), price (P), social value (SV), and reputation (R). The questionnaires were structured on a seven-value Likert scale. The only study we found outside Australia was carried out in Greece by Nella and Christou [29], who applied a structural equation model that incorporates three temporal dimensions of the winery experience: before the visit, on-site, and after the visit. 2.4. The use of social media data in wine tourism research According to Lockshin and Corsi [30], social media marketing is an interesting field of research in wine tourism research. There is an increasing amount of social media research on wine. Initially, research focused on the microblogs of wine consumers [31] and then expanded to Twitter [32] and Facebook [33] platforms. In the last three years, social media has been used to study the behavior of wine tourists. Brochado et al. [34] used 4,114 online reviews of 52 wine hotels located in 27 wine regions across 11 countries to identify key themes related to wine hotel experiences. Brochado et al. [35] identified the sustainability dimensions of organized tours from the point of view of tourists by analyzing 878 reviews of 20 tours in Portugal, written on TripAdvisor. Terziyska and Damyanova [36] employed 118 reviews on TripAdvisor to define the attributes of winescapes, as seen from the perspective of travel arrangements for a wine tour company in Piedmont, Italy. Brochado et al. [37] collected 470 wine tourism reviews posted on TripAdvisor in the Douro wine region and used them to identify sensory perceptions during winery visits. Vo Than and Kirova [38] analyzed with netnographic approach 825 original reviews posted on TripAdvisor by tourists who visited Cognac (France). The results showed that the experiences were globally positive and that experiences related to the dimensions of education and entertainment were predominant. Finally, in a recent study [39], social media was used to identify and characterize the behavior of the “Masters of Wine” community on Twitter, as well as to determine the impact of these renowned wine experts through this platform. All Twitter profiles belonging to the Masters of Wine’s award-winning users were identified and analyzed. Additionally, a set of 35,653 tweets posted by the Masters of Wine were retrieved and analyzed using descriptive statistics. 3. METHODOLOGIES 3.1. Study areas Wine, landscape, heritage, and tourism are all keywords that characterize wine tourism both in the Old World of Wine (Europe) and in the New World (the USA, South America, South Africa, and Australia). Currently, vineyards around the world represent not only a fundamental agricultural resource that guarantees rural development but also a great economic resource that allows the enhancement and maintenance of the same cultural wine landscapes and the development of the entire region [40]. According to the rankings on the TripAdvisor platform, based on the number of reviews, the top five wine destinations in the world are: Tuscany (Italy) in first place, followed by Napa Valley (the USA), Hunter Valley (Australia), Stellenbosch (South Africa), and Mendoza (Argentina). While Tuscany has a long tradition, the other wine regions of the New World were established and developed very quickly, but with great success. In Chianti region, the integrated tourist offering is coordinated by eight wine routes. The wine routes, regulated by Italian Law n. 268/1999, bring together wineries, restaurants, hotels, wine bars, and other public and pri- 51 What went right and what went wrong in my cellar door visit? A worldwide analysis of TripAdvisor’s reviews of Wineries & Vineyards vate facilities, clearly using the typical wine of the area as a physical and cultural link between all the subjects involved. This joint management encourages tourists to organize their stay in a way that allows them to experience the territory on an oenological and intellectual level. The museums of wine and vines, which are in almost all Italian regions, help in promoting and communicating wine culture, although they differ in thematic approach, as well as in their size and history [41]. In Napa Valley, the wine sector presents itself as an economy in its own right with many services, wherein the business model focuses on the diversity and originality of the wineries’ design, a new attractive resource to guarantee their reputation and draw the largest number of visitors, both oenophiles and lovers of contemporary art. The suppliers of wine tourism in Napa Valley also recognize the need to continue to evolve as people search for innovation and trends, while maintaining the brand that the region was built upon. This is manifesting itself in the addition of new restaurants, hotels, and wineries. Leaders and suppliers of the Napa wine tourism experience are also looking at creating dedicated bike trails, zip lines, and other activities to add to the physical and figurative landscape of the Napa wine tourism destination experience [42]. In Hunter Valley, food and wine represent one of the top three motivational drives for international tourists to Australia among the aquatic and coastal experiences, nature, and wildlife. In Australia, wine trails are not as developed as in Europe. However, regional and local government agencies have developed tourism routes where wine experiences are part of a broader tourism theme combined with other experiences [41]. The wine industry in Stellenbosch has an active wine tourism market, well-developed facilities, and infrastructure. However, despite the fact that the first South African wine road was established as early as 1971, association networks are currently non-existent or underdeveloped [43]. The vision of Mendoza’s wine tourism development was based on the strength of identity, culture, and landscape. In particular, its architecture has contributed greatly to the prestige and attractiveness of wine tourism. It projects, by recognizing historical dynamics, nature, and society, the quality of life with identity [41]. Despite having different marketing strategies, these wine regions share the same aims: to enhance their heritage, grow their economy, satisfy the needs of existing customers, and attract new ones, in particular through wine routes, organization of events, and the combination of wine and food. Therefore, we have chosen the five main wine tourism destinations according to the TripAdvisor platform as study areas to understand their strengths and weaknesses and, in particular, to draw up useful guidelines at a global level. 3.2. TripAdvisor as an evaluation source TripAdvisor is one of the most popular networks for sharing travel experiences. As of 2018, it had collected over 570 million reviews and opinions on over 1.2 million accommodations. TripAdvisor not only collects reviews on hotels and restaurants but is open to all tourist interest activities, including the specific category of winery visits. One of the most appreciated features of TripAdvisor in marketing research is its reliability. The platform has an efficient, automated quality control and review reliability system that involves many parameters. The system compares incoming reviews for a given activity with the historical patterns already examined for that activity by identifying suspicious anomalies in the patterns (TripAdvisor, 2021, https://www.tripadvisor.com/TripAdvisorInsights/w3690). In addition, several studies have been conducted to analyze the credibility of this website [44]. When writing a review, guests also have the option to rate their overall experience on a scale of 1 to 5 “bubbles,” with 1 being a poor rating and 5 being an excellent rating. Valdivia et al. [45], in recent research, showed that the TripAdvisor rating system presents a problem: users tend to rate the overall experience positively, but there may also be negative comments within a review. Likewise, in an average or negative evaluation, there may be positive evaluations of the relevant aspects of service quality. To analyze and evaluate reviews efficiently, it is necessary to separate positive and negative aspects. Taking this into account, we identified a methodology for identifying and separating positive from negative sentences in reviews. Positive sentences contain the relevant qualitative elements of service quality, whereas negative sentences contain the relevant elements of service failure. Once we obtained two series of subreviews, one with only positive sentences and the other with negative sentences through a text mining analysis, we identified the relevant elements of service quality and service failure. 3.3. Review processing The methodology of this study is articulated in the following steps. First, we identified the most relevant wine-tourism regions at the international level. Then, we verified the reliability of the TripAdvisor platform for the identification of key elements of consumer satisfac- 52 Elena Barbierato, Iacopo Bernetti, Irene Capecchi tion using prior literature. Finally, we built an algorithm for the automatic collection of reviews and lexical and sentiment analyses. 3.3.1. Sentiment analysis Sentiment analysis has been increasingly applied in recent years, especially to the contents of Web 2.0. Sentiment analysis measures the polarity and intensity of the mood of a person’s opinion expressed in a text written in natural language. Sentiment analysis methods can generally be divided into two categories: dictionary-based methods and methods based on supervised classification. The methods of the first category apply sentiment lexicons containing the semantic orientation of words to the sentences in the text. One of the biggest challenges of dictionarybased methods is that the sum of the semantic values of individual words does not necessarily correspond to the polarity of the entire sentence [46]. Therefore, it is necessary to extract further linguistic patterns of the text by conducting morphosyntactic analyses of the text [47]. However, too many specific extraction models limit the application of this method to a specific domain. The most recently applied second category of method uses unsupervised or supervised machine learning algorithms, such as machine learning-based methods and deep learning-based methods. These methods allow the development of more generic models but require data classified according to specific categories. Consequently, the quality of these models is strongly influenced by the reliability of the training and testing sets performed by human classifiers [48]. The literature reveals that there is no superior sentiment analysis method because all tools work differently depending on the specific context in which they are applied or based on the corresponding data source on which they were trained. One of the best sentiment analysis methods for analyzing poorly structured and simple texts, such as reviews in TripAdvisor, is AFINN [49]. AFINN [50] is a dictionary-based method that was initially created in 2009 for tweets downloaded for online sentiment analysis in relation to the United Nations Climate Conference (COP15). It has since been extended to other data domains. The version called AFINN-96 adopted in this work has 2,477 words and uses a score ranging from -5 (very negative) to +5 (very positive). The sentiment scoring procedure is as follows: 1. Each review is broken down into its sentences di={si,1,s1,2,…,si,n} based on punctuation; subsequently, each sentence is broken down into words (w) si,j={wi,j,1),wi,j,2,…,wi,j,l} using a semantic annotation procedure (Kiyavitskaya et al., 2006). 2. The words in each sentence, {wi,j,k}, are searched and compared with the lexicon of polarized words, and each of them is assigned a negative or positive score. Not all words have a sentiment score; therefore, we obtain a subset of polarized words {pwi,j,k}⊆{wi,j,k}. 3. Finally, we sum the weighted context yielding an unbounded polarity score δi,j for each sentence. We chose not to normalize the polarity score on the number of words because satisfaction or dissatisfaction with service quality is proportional to the number of positively or negatively polarized words used to write a review [49]. 3.3.2. Co-occurrence network of high-frequency words To extract useful information from the subreviews to understand the reasons for the quality and failure of the service, we used the co-occurrence graph method of the highest-frequency words. The first step of the analysis was the preprocessing of the sets of subreviews, called corpora. We carried out the following steps: (i) Tokenization of the text that involves division into words of the text itself; (ii) Removal of stopwords defined as words that do not carry significant information for analysis; (iii) Stemming and rooting, which consist of reducing words to the root; (iv) Removal of extra numbers and spaces; (v) Removal of punctuations; (vi) Part-of-speech tagging aimed to assign parts of speech to each word of a given text (such as nouns, verbs, adjectives, and others) based on its definition and context. The co-occurrence network of the higher-frequency words was performed using the KH Coder software. We only used nouns and adjectives, as they are parts of speech with the highest information content [51], [52]. In the analysis, we took 50 words with the highest frequency in the review corpora of each of the five wine regions. The KH Coder provides choices of Jaccard, cosine, or Euclid for measuring the distance between terms; in this research, the distance cosine was chosen. To facilitate the reading of the results, the procedure applied the methods developed by Fruchterman and Reingold [53] and by T. Kamada and S. Kawai [54] to design the wordword network. Community analysis is one of the most recent developments in network theory. A network has a community structure when it is possible to partition it into subnets (also called communities, subgraphs, or clusters) characterized by a density of internal connections (i.e., between 53 What went right and what went wrong in my cellar door visit? A worldwide analysis of TripAdvisor’s reviews of Wineries & Vineyards elements of the community itself) much greater than the density of connections between a community and the other community. Typically, in a network where there is a community structure, there are groups of highly connected nodes, nodes that are isolated, and others that act as a bridge between the different communities. To identify the subgraphs, we used the modularity method for its computational efficiency [55], [56]. 3.3. Reviews processing A flowchart of our procedure is shown in Figure 1. The procedure was divided into the following steps: Step 1 (orange in Figure 1). In the first phase, we harvested the data relating to reviews based on the TripAdvisor URL of the five wine regions. For this purpose, we wrote a procedure in R language based on the “rvest” library (available as supplementary material). The data collected were the title of the review, review, and evaluation of the bubbles. The database obtained was divided into two subsets: positive reviews (PR) with a rating greater than three bubbles and neutral or negative reviews (NNR) with a rating less than or equal to three bubbles. Step 2 (green in Figure 1). The reviews were divided into individual sentences. A sentence is the smallest lexical unit in natural language processing and is defined as a grammatical unit of one or more words that expresses an independent statement. A sentiment analysis procedure was applied to the sentences to assess the polarity (positive or negative) and the relative sentiment score of the perception of service quality or failure. Sentences with positive polarity express service quality, whereas those with negative polarity express service failure. In the subset of S_PR, only sentences with a sentiment score greater than zero were selected, while in S_NNR, only sentences with a sentiment score less than zero were selected. Finally, the sentences belonging to the same review were merged to obtain two sub-reviews, one with only the positive sentences and the other with only the negative sentences. Thus, we obtained two datasets: service quality subreviews (SQRs) and service failure subreviews (SFRs) for the five wine regions. In this study, sentiment analysis was conducted using the “syuzhet” library of R software. Step 3 (cyan in Figure 1). The co-occurrence graph method is one of the most widely used methods for analyzing large databases of unstructured text from social media [57] [58]. The analysis of the co-occurrence network of words allows us to draw a network of relationships between words with a high degree of co-occurrence. This analysis allowed us to extract the most frequently recurring concepts for both service quality and service failure. A co-occurrence network of high-frequency words procedure was applied to the SQRs and SFRs to identify the factors and causes of service quality and service failure during the visits to cellars. The elaborations were carried out using the KH Coder 3 software. 4. RESULTS 4.1 TripAdvisor ranking and sentiment analysis We downloaded reviews and rankings of five wine regions collected in the period from January 2010 to April 2021 for a total of 89,672 reviews of 1,074 wineries. The wine region with the most reviews was Napa Valley with 46,753 reviews related to 387 wineries, followed by Hunter Valley with 13,204 reviews of 118 companies, Stellenbosch with 8,232 reviews of 81 companies, Tuscany with 7,402 reviews of 414 wineries, and Mendoza with 3,581 reviews related to 74 wineries. To understand how users rated their winery experience on TripAdvisor, Figure 1. Flow chart of the proposed methodology. 54 Elena Barbierato, Iacopo Bernetti, Irene Capecchi we analyzed the percentage distribution of the bubbles’ scores (Figure 2). Th e fi gure shows that, in general terms, positive evaluations with fi ve bubbles prevail. In particular, Tuscany had the highest percentage of reviews valued at fi ve bubbles (86%), followed by Mendoza (76%), Hunter Valley (72%), Napa Valley (68%), and Stellenbosch (61%). The four bubbles score was highest in Stellenbosch (25%), followed by Napa Valley (19%), Hunter Valley (17%), Mendoza (15%), and Tuscany (9%). Th e neutral score of the three bubbles was the same for Stellenbosch and Napa Valley (7%) and lower for Mendoza (5%), Hunter Valley (4%), and Tuscany (2%). Finally, two and one bubbles occurred at very low percentages in all fi ve regions. Table 1 reports the results of the sentiment analysis for the subreviews that express service quality (bubbles > 3 and sentiment > 0) and the subreviews related to service failure (bubbles ≤ 3 and sentiment < 0). For bubbles, the region with the highest perceived sentiment for service quality was Tuscany, followed by Mendoza, Napa Valley, Stellenbosch, and Hunter Valley. To validate the diff erences between the means, we performed a pairwise analysis with the van der Waerden test with the correction of the p-values according to the Bonferroni method. Th e advantage of the van der Waerden test is that it obtains a high effi ciency of the standard ANOVA when the assumptions of normality are satisfi ed, but it also provides the robustness of the Kruskal-Wallis test when the assumptions of normality are not satisfi ed. Th e results reported in Table 1 show that the diff erences between the sentiment score of Tuscany compared to the other four wine regions and between the sentiment score of Mendoza compared to the other four regions are statistically signifi cant. Th e diff erences between Stellenbosch, Napa Valley, and Hunter Valley are not signifi cant. For service failure, the region with the highest level of perceived sentiment was Napa Valley, followed by Tuscany, Stellenbosch, Mendoza, and Hunter Valley. Th e pairwise analysis shows that the perceived sentiment in the Napa Valley region was signifi cantly higher than that in Stellenbosch, Hunter Valley, and Mendoza regions, but not signifi cantly higher than that of Tuscany region. Tuscany, in turn, had a statistically signifi cant diff erence compared only to Hunter Valley, while the diff erence with Mendoza and Stellenbosch was not signifi cant. Finally, the perceived sentiment differences between Mendoza, Hunter Valley, and Stellenbosch were not statistically signifi cant. 4.2 Co-occurrence network of service quality The co-occurrence network and cluster analysis, based on databases with positive sentiment according to the AFINN dictionary (AFINN > 0) and positive bubble rankings (bubbles > 3), highlight the elements that characterize the service quality of the wineries in various wine regions, and how they are perceived by the diff erent types of users. Th e analysis was carried out separately for each wine region (Figures A.1-A.5 in the Appendix), with both datasets resulting from the union of all regions (Figure 3). In general terms (Figure 3), the analysis identifi es seven prevalent themes of service quality: “wine,” which characterizes subgraph 1 in Figure 3; “tour,” subgraph 4; “tasting,” subgraph 5; “winery,” subgraph 3; “service,” subgraph 2; “view,” subgraph 6; and “staff ,” subgraph 7. Th e wine theme Th e core product, wine, is central to the perception of service quality both in the global analysis and sepaFigure 2. Distribution of bubbles ranking. Table 1. Statistics, mean, and standard deviation of the sentiment analysis of service quality and service failure. 61 What went right and what went wrong in my cellar door visit? A worldwide analysis of TripAdvisor’s reviews of Wineries & Vineyards item scale. Journal of Retailing, 77(2), 203-220. https://doi.org/10.1016/S0022-4359(01)00041-0. [28] Petrick, J. F. (2002). Development of a multidimensional scale for measuring the perceived value of a service. Journal of Leisure Research, 34(2), 119-134. https://doi.org/10.1080/00222216.2002.11 949965. [29] Nella, A., & Christou, E. (2014). Linking service quality at the cellar door with brand equity building. Journal of Hospitality Marketing and Management, 23(7), 699-721. https://doi.org/10.1080/1936 8623.2014.891959. [30] Lockshin, L., & Corsi, A. M. (2012). Consumer behaviour for wine 2.0: A review since 2003 and future directions. Wine Economics and Policy, 1(1), 2-23. https://doi.org/10.1016/j. wep.2012.11.003. [31] Thach, L. (2009). Wine 2.0 - The next phase of wine marketing? Exploring US winery adoption of wine 2.0 components. Journal of Wine Research, 20(2), 143-157. https://doi. org/10.1080/09571260903169548. [32] Claster, W. B., Caughron, M., & Sallis, P. J. (2010, November). Harvesting consumer opinion and wine knowledge off the social media grape vine utilizing artificial neural networks. In Fourth UKSim European Symposium on Computer Modeling and Simulation 2010 (pp. 206-211). https:// doi.org/10.1109/EMS.2010.109. IEEE. [33] Nicholls, J. (2012). Everyday, everywhere: Alcohol marketing and social media - Current trends. Alcohol and Alcoholism, 47(4), 486-493. https://doi. org/10.1093/alcalc/ags043. [34] Brochado, A., Troilo, M., Rodrigues, H., & Oliveira-Brochado, F. (2019). Dimensions of wine hotel experiences shared online. International Journal of Wine Business Research, 32(1), 59-77. https://doi. org/10.1108/IJWBR-12-2018-0072. [35] Brochado, A., Souto, J., & Brochado, F. (2020). Dimensions of sustainable tour experiences. Journal of Quality Assurance in Hospitality and Tourism, 1-24. https://doi.org/10.1080/152800 8X.2020.1827479. [36] Terziyska, I., & Damyanova, R. (2020). Winescape through the lens of organized travel–a netnography study. International Journal of Wine Business Research, 32(4), 477-492. https://doi.org/10.1108/ IJWBR-09-2019-0050. [37] Brochado, A., Stoleriu, O., & Lupu, C. (2021). Wine tourism: A multisensory experience. Current Issues in Tourism, 24(5), 597-615. https://doi.org/10.1080/ 13683500.2019.1649373. [38] Vo Thanh, T., & Kirova, V. (2018). Wine tourism experience: A netnography study. Journal of Business Research, 83, 30-37. [39] Orduña-Malea, E., Font-Julian, C. I., Ontalba-Ruipérez, J. A., & Compés-López, R. (2021). Masters of Wine on Twitter: Presence, activity, impact and community structure. Wine Economics and Policy, 10(1), 73-88. https://doi.org/10.36253/wep-9055. [40] Lignon-Darmaillac, S. (2014). L’œnotourisme, redécouverte des valeurs patrimoniales des vignobles historiques, développement des vignobles du nouveau-monde. Cultur: Revista de Cultura e Turismo, 8(3), 30-46. [41] Compés Lopez, R., & Szolnoki, G. (2021). Culture and wine tourism. In Sustainable and Innovative Wine Tourism Success Models from All around the World Cajamar Caja Rural (Ed.). Valencia, Spain. [42] Jones, M. F., Singh, N., & Hsiung, Y. (2015). Determining the critical success factors of the wine tourism region of Napa from a supply perspective. International Journal of Tourism Research, 17(3), 261-271. [43] Bruwer, J. (2003). South African wine routes: Some perspectives on the wine tourism industry’s structural dimensions and wine tourism product. Tourism Management, 24(4), 423-435. [44] Ayeh, J. K., Au, N., & Law, R. (2013). ‘Do we believe in TripAdvisor?’ Examining credibility perceptions and online travelers’ attitude toward using user-generated content. Journal of Travel Research, 52(4), 437-452. https://doi. org/10.1177/0047287512475217. [45] Valdivia, A., Hrabova, E., Chaturvedi, I., Luzón, M. V., Troiano, L., Cambria, E., & Herrera, F. (2019). Inconsistencies on TripAdvisor reviews: A unified index between users and Sentiment Analysis Methods. Neurocomputing, 353, 3-16. [46] Wilson, T., Wiebe, J., & Hoffmann, P. (2009). Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis. Computational Linguistics, 35(3), 399-433. https://doi. org/10.1162/coli.08-012-R1-06-90. [47] Clavel, C., & Callejas, Z. (2015). Sentiment analysis: From opinion mining to human-agent interaction. IEEE Transactions on Affective Computing, 7(1), 74-93. https://doi.org/10.1109/TAFFC.2015.2444846. [48] Ribeiro, F. N., Araújo, M., Gonçalves, P., André Gonçalves, M. A., & Benevenuto, F. (2016). Sentibench-a benchmark comparison of state-of-thepractice sentiment analysis methods. EPJ Data Science, 5(1), 1-29. https://doi.org/10.1140/epjds/ s13688-016-0085-1. 62 Elena Barbierato, Iacopo Bernetti, Irene Capecchi [49] Khanna, P., Mishra, S., Kumar, S., & Sinha, A. (2017). SENTIMENT ANALYSIS: An APPROACH TO OPINION MINING FROM Twitter DATA USING R. International. Journal of Advanced Research in Computer Science, 8(8). [50] Nielsen, F. Å. (2011). A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs. arXiv Preprint ArXiv:1103.2903. [51] Hu, Y. H., Chen, Y. L., & Chou, H. L. (2017). Opinion mining from online hotel reviews–a text summarization approach. Information Processing and Management, 53(2), 436-449. https://doi. org/10.1016/j.ipm.2016.12.002. [52] Kobayashi, Y., Ito, R., & Saito, K. (2019). Quantitative analysis of research trends on α-lipoic acid by text mining. Nutrition and Dietary Supplements, 3(1). [53] Fruchterman, T. M. J., & Reingold, E. M. (1991). Graph drawing by force‐directed placement. Software. Software: Practice and Experience, 21(11), 1129-1164. https://doi.org/10.1002/ spe.4380211102. [54] Kamada, T., & Kawai, S. (1989). An algorithm for drawing general undirected graphs. Information Processing Letters, 31(1), 7-15. https://doi. org/10.1016/0020-0190(89)90102-6. [55] Clauset, A., Newman, M. E., & Moore, C. (2004). Finding community structure in very large networks. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics, 70(6 Pt 2), 066111. [56] Newman, M. E. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences of the United States of America, 103(23), 8577-8582. [57] Liu, Y., Li, Y., & Li, W. (2019). Natural language processing approach for appraisal of passenger satisfaction and service quality of public transportation. IET Intelligent Transport Systems, 13(11), 1701-1707. [58] Niezgoda, A., & Nowacki, M. (2020). Experiencing nature: Physical activity, beauty and tension in Tatra national park - Analysis of TripAdvisor reviews. Sustainability, 12(2), 601. [59] Conaway, J. (2002). Napa: the story of an American Eden. Houghton Mifflin Harcourt. [60] Kolyesnikova, N., & Dodd, T. H. (2009). There is no such thing as a free wine tasting: The effect of a tasting fee on obligation to buy. Journal of Travel and Tourism Marketing, 26(8), 806-819. [61] McNamara, N., & Cassidy, F. (2015). Wine tasting: To charge or not to charge? International Journal of Hospitality Management, 49, 8-16. [62] Valdivia, A., Luzón, M. V., & Herrera, F. (2017). Sentiment analysis in TripAdvisor. IEEE Intelligent Systems, 32(4), 72-77. [63] Lin, H., Fan, W., & Chau, P. Y. K. (2014). Determinants of users’ continuance of social networking sites: A self-regulation perspective. Information and Management, 51(5), 595-603. [64] Grégoire, Y., Salle, A., & Tripp, T. M. (2015). Managing social media crises with your customers: The good, the bad, and the ugly. Business Horizons, 58(2), 173-182. [65] Bambauer-Sachse, S., & Mangold, S. (2011). Brand equity dilution through negative online word-ofmouth communication. Journal of Retailing and Consumer Services, 18(1), 38-45. [66] Verhagen, T., Nauta, A., & Feldberg, F. (2013). Negative online word-of-mouth: Behavioral indicator or emotional release? Computers in Human Behavior, 29(4), 1430-1440. [67] Titz, K. (2001). The impact of people, process, and physical evidence on tourism, hospitality, and leisure service quality. Service Quality Management in Hospitality, Tourism and Leisure, 67-83. 63 What went right and what went wrong in my cellar door visit? A worldwide analysis of TripAdvisor’s reviews of Wineries & Vineyards Figure A.1. Co-occurrence of service quality subreview for Hunter Valley region. APPENDIX A 64 Elena Barbierato, Iacopo Bernetti, Irene Capecchi Figure A.2. Co-occurrence of service quality subreview for Mendoza region. 65 What went right and what went wrong in my cellar door visit? A worldwide analysis of TripAdvisor’s reviews of Wineries & Vineyards Figure A.3. Co-occurrence of service quality subreview for Napa Valley region. 66 Elena Barbierato, Iacopo Bernetti, Irene Capecchi Figure A.4. Co-occurrence of service quality subreview for Stellenbosch region. 67 What went right and what went wrong in my cellar door visit? A worldwide analysis of TripAdvisor’s reviews of Wineries & Vineyards Figure A.5 Co-occurrence of service quality subreview for Tuscany region. 68 Elena Barbierato, Iacopo Bernetti, Irene Capecchi Figure A.6. Co-occurrence of service failure subreview for Hunter Valley region. 69 What went right and what went wrong in my cellar door visit? A worldwide analysis of TripAdvisor’s reviews of Wineries & Vineyards Figure A.7. Co-occurrence of service failure subreview for Mendoza region. 70 Elena Barbierato, Iacopo Bernetti, Irene Capecchi Figure A.8. Co-occurrence of service failure subreview for Napa Valley region.