Understanding the Creation of Destination Images ...

Understanding the Creation of Destination Images through a Festival’s Twitter Conversation Abstract Purpose: The analysis of the potential contribution of festivals in generating a destination image through social media (particularly Twitter) is addressed. Design/methodology/approach: This study follows a multi-method approach by recollecting, analysing and mixing quantitative and qualitative techniques. We focus on the case of Vic (Spain), analysing the destination’s image as projected by different users (administration, private sector, particular users, residents) on Twitter in relation to an international musical festival, El Mercat de Música Viva de Vic (the Vic Live Music Market). Findings: From a theoretical perspective, the study’s results advocate a reconsideration of the role of social media in the processes of creating a destination’s image. It is important to take into account the need to perform a specific analysis for each platform and consider how it operates and which stakeholders prevail by weight, by the clusters they pertain to and by their elicited descriptions. In the particular case of Twitter, the image-formation continuum generated by different actors through different sources is present here on one single platform. Research limitations/implications: This study is limited in terms of being based on only one social media site, and it would be very interesting to complement it by analysing other relevant social media platforms. Practical implications: From a practical point of view, this presents a challenge to destination managers to learn how to work on each specific platform in order to oversee the different destination visions and their resources Originality/value: From our results, we affirm that destination-branding analyses now need a platform-specific approach as well as an in-depth stakeholder analysis, since it is no longer possible to separate producers and consumers in brand image creation. Branding is becoming a more inclusive and collaborative process.

Keywords: Destination Image, Festivals, Social Media, Stakeholders

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1. The Emergence of Social Media in Constructing a Destination Image

Despite the difficulties in the conceptualization of the image of destinations, the literature agrees that it is a complex construct which consists of interrelated evaluations woven into overall impressions and beliefs based on information processing from a variety of sources over time (Baloglu and McCleary, 1999; Choi et al., 2007; Gartner, 1994; Stepchenkova et al., 2007). Literature on this topic first emerged when Gunn’s (1972) pioneer research formulated the ‘dimorphic theory’ demonstrating that a destination image is formed through a process in which two types of actors generate two image typologies: organic and induced. Organic images are formed through tourists’ impressions of a destination without physically visiting the place. They are derived from indirect sources not associated with any marketing activities. Induced images are formed through the result of promotional efforts from other sources projected by a destination’s marketers. Fakeye and Crompton (1991) found that the process of producing a destination’s image may be completed by another stage in which a third image is integrated: the complex image resulting from the tourist’s direct experience, offering feedback and influencing new evaluations of alternative destinations on the next occasion a selection is made. These contributions underlined the assumption that image development is inextricably linked to various forms of information and this idea was developed in Gartner’s (1994) intricate description of image formation. Recognising the implication of image formation on the destination selection process and the possible overlap between organic and induced images, the author identified an image-formation continuum consisting of eight agents (image sub-typologies) generated by different actors through different sources (see Figure 1). Gartner also noted that the credibility of the first four image formation agents ranges from low to medium when compared to the organic image with a higher standing in terms of importance.

** Insert Figure 1 about here

For Tasci and Gartner (2007), destination marketers have the ability to adjust and modify their marketing activities depending on the information reflected by these sources, while autonomous image agents are semi-dynamic and semi-controllable. These authors

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also underlined the fact that organic images are a function of non-commercial information and uncontrolled sources (including word-of-mouth and actual visitation), and that induced images are constituted by the marketing efforts of destination marketers (to include all kinds of promotional resources). From this perspective, differentiating the formation and evolution of images into two separate stages is not advisable since these two processes are arguably interdependent and continuous. If expectations (a result of the induced image) are met and the experience is positive, then modification after the personal travel experience is less likely. Therefore, monitoring modifications in tourists’ images over time enables a richer comprehension of the structural change process of visitors’ images. Gartner believed that all of these sources stimulate and impact two different but hierarchically interrelated image components, or attributes: cognitive, concerning the understanding and evaluation of a known product, and affective, involving motives and feelings that an individual has for selecting a destination. A third potential ‘action’ component is referred to as conative (analogous to behaviour), which drives how a person acts on those thoughts and feelings. Gartner also noted that the type and amount of information sources influence the formation of the cognitive component of images but not the affective counterpart, arguing that an appropriate mixture of image formation agents is critical in a successful destination image-creation strategy. Along this lines, Baloglu and McCleary (1999) revealed that the variety (amount) of information sources, type of information sources, age, and education are variables that influence cognitive evaluations and that these elements and diverse socio-psychological tourism motivations together influence affective evaluations (Sönmez and Sirakaya, 2002). Baloglu and McCleary (1999) also demonstrated that the cognitive component is an antecedent of the affective component and that their combination produces an overall image related to the positive or negative evaluation of the destination. Qu et al. (2011) found that cognitive, affective, and unique dimensions of an image of a tourist destination influence the possibilities of repeat visits and intentions to recommend the destination. At the beginning of the century, several authors emphasised the importance of the Internet in relation to this process. Examples of this can be found in the works of Stepchenkova et al (2007), signalling that websites were quickly becoming the major source of information for destination marketing, travel planning and booking, and online

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induced images were acquiring a prominent role in the destination image construction process (Beerli and Martin, 2004). For Choi et al. (2007) this also has important implications for destination’s marketers: given the ‘flattened nature’ of the online information structure in which the accessibility of information from various sources is equally easy, it is now important for destination marketers to examine and assimilate different image perspectives and adjust positioning strategies for greater effectiveness. In this context, Tasci and Gartner (2007) suggested a holistic concept of image that should include three perspectives: the supply-side (projected) formed by marketing strategy, the independent-side, formed by autonomous and solicited and unsolicited wordof-mouth, and the demand-side (perceived), essentially corresponding to organic image. While supply-side sources were controllable, independent-side sources were semicontrollable and the demand-side sources were uncontrollable. De Jager (2010) adapted this model to provide a case study in which various image formation agents are identified and the image is explored from a supply and demand perspective. During the last decade new Internet spaces and, in particular, social media have acquired a special role in this process. And, from the beginning, Twitter was one of the preferred platforms for Destination Management Organizations (DMOs), among other things, because many of the requests for information are sent as open messages and this encourages other people to take part in unrestricted discussions (Hay, 2010). In a seminal work, Wenger (2008) suggested to DMOs that useful information could be gained by speaking directly to the bloggers and the organisations which used Twitter. For they part, Hay (2010) found that Twitter was misunderstood by many DMOs despite appearing to provide an inexpensive method for providing multi-agency input into local tourism strategies and for offering an enhanced customer experiences, as well as communicating directly with consumers. Yayli et al. (2011) corroborated the notion that Twitter was a potentially rich avenue for DMOs to explore as consumers were increasingly using this platform for trusted sources of information, insights, and opinions. In recent years, several (sometimes conflicting) studies have investigated the role of Twitter in the process of creation of destinations’ images. Hays et al. (2013) found that the majority of DMOs were not currently utilising Twitter to their full effectiveness when it came to the ability to interact and engage with consumers and Twitter was not widely respected as a vital tool in marketing strategies. For their part, Antoniadis et al. (2014)

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supported the conclusion that Twitter does not fail to provide information and possibly to promote countries’ Destination Image and it does not solely retain a role of must-have technological improvement, regardless of its actual usefulness. Adopting a midway position as regards this idea, Guerrero-Solé and Fernández-Cavia (2013) concluded that while some of these destinations are using intensively Twitter and have a considerable influence on it, other destinations have little impact and need to improve their strategies to achieve their objectives. Meanwhile, in his study about Twitter usage patterns in destination marketing and place branding, Sevin (2013) found that it is no longer possible to separate destination marketers and public authorities as the sole producers of brand images and the public as consumers, although the public does not incorporate formal brand elements into their destination accounts; destination branding is now a negotiation process among various stakeholders and audiences that generate a diversity of images (Munar, 2011). Similarly, Ghazali and Cai (2014) proposed a model positing that social media connect suppliers, consumers, and third parties while simultaneously influencing the formation of the three image components through the interaction between them. Moreover, an overall conative destination image is formed by the intersection of provision and the evaluation of cognitive and affective information by and between these actors. While destination marketers believe they can control the information provided to consumers, the interpretation of that information is often uncontrollable due to the dynamic nature of consumers and technology. Consumers now exhibit characteristics of both receivers and senders; their interpretation of suppliers’ messages and their reactions influence and invoke responses and reactions by suppliers and third parties. Depending on the macro social and cultural conditions surrounding technology consumption (Kozinets et al., 2008), the cognitive and affective evaluation of messages on social media sites is now multidirectional, interactive, dynamic, and fluid. It challenges the validity of Gartner’s theory of the formation of the first four destination image agents and the concept of induced images in general. Destination images are increasingly formed by information gathered from consumers’ social media and their daily interaction with other members on social media sites, while the effectiveness of traditional advertising in destination image formation is being questioned. Moreover, despite the indirect role of

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third parties, they have a powerful influence on the affective and cognitive evaluation of destinations, leading to the formation of organic images. The mutual exclusivity of organic, induced, and autonomous images is practically non-existent with the presence of social media sites. Following these assumptions, and although some previous studies assumed that online destination images were basically induced (Beerli and Martin, 2004), Llodrà-Riera et al. (2015) found that the Internet comprises various types of sources in which organic images are predominant and basically uncontrollable. Finally, Garay and Cànoves (2015) showed the prevalence of organic UGC created by tourists and residents in a specialized platform, Tripadvisor.

2. The Image of Festivals in the Destination Image Construction Process and the Emergence of Social Media

In general terms, festivals and special events have been proposed as effective imagebuilding strategies to make destinations creative and unique (Felsenstein and Fleischer, 2003; Li and Vogelsong, 2006; Mehmetoglu and Ellingsen, 2005). This potential has led to festivals being used as an image-enhancement tool, particularly for large cities (Holcomb, 1999; Judd and Fainstein, 1999; Sassen and Roost, 1999; Selby, 2004). But the problem here is the difficulty of measuring this kind of impact taking into account the wide range of ‘attributes’ associated with places often evaluated on multidimensional scales. Moreover, a narrow and one-dimensional interpretation of the term ‘image’ has often been adopted in festival studies, with little consideration for the different dimensions of the image of the festivals’ host cities/destinations. In a globalization context, there is no doubt that festivals are important resources in the adoption of branding strategies and even ‘hard branding’ that seeks to transform fixed cultural capital into a competitive advantage in an environment of increasing competition between cities to differentiate their images from the rest and even some major festivals have arguably even become ‘brands’ in their own right (Evans, 2003). Prentice and Andersen (2003) demonstrated that a festival can effectively reposition a destination’s image or even modify a whole region’s image. Other studies have also underlined the complexity of the impact of festivals on the destination image creation

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process by considering the role of the diverse stakeholders that participate in it (Richards and Wilson (2004). Li and Vogelsong (2006) indicated that measuring the impact of festivals could help the local community to define itself for marketing purposes and they underlined the importance of local support and involvement. In these sense, Jago et al. (2003) argued that the impact of festivals on the destination brand is both direct and indirect. On the one hand, festivals influence the images of the city projected in the media but, on the other, because of the impacts on the community, they influence the image that people form of it. As these authors indicate, the images events projected are not only stories that residents will tell the world, they are also stories they tell themselves. Along these lines, Sharpe (2008) found that some festivals may still be a vehicle that can expand the discussion, debate, and promulgation of ideas typically subjugated in mainstream institutional channels, fostering social change through a new form of ‘pleasure-politics’. These festivals can (re)connect leisure to social change in meaningful and efficacious ways that are especially important for the local community. For Ooi and Pedersen (2010), the different stakeholders should – ideally – collaborate and cooperate to bring about the common good for the community and enhance their own interests. An important implicit argument here is that local residents, as producers and as established audiences, can engage meaningfully in festivals in ways that address both their own needs as well as those of attendees. Empirically, however, evidence to support this theoretical position is scarce (Quinn, 2005). As was the case of the analysis of destination images, social media have erupted as an emergent trend in the literature on festivals in the last few years, and analyses have confirmed that festivals with a defined social media strategy are noticing a revenue increase compared to those without one (Rothschild, 2011). Facebook, Youtube and especially Twitter are well suited to monitor the conversation about the event, along with increasing lead generation but what is even more interesting is that lead generation in the context of event managers is not only directed to consumers seeking single event tickets, but to other key stakeholder groups (Rothschild, 2011). Festival managers in this context are still learning how to manage the conversations that these diverse stakeholders are conducting, including particular groups. While implementing a social media strategy does

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not guarantee success, it provides the best opportunity to meet a new challenge: to harness the marketing intelligence inherent when people communicate. For Lee et al. (2012), social interactions among the audience members within each social media festival page are expected to be more emotionally or affectively engaging, which can also contribute to an increased number of attendees. For these authors, Twitter has an interesting “follow us” function that allows the organizers to stay connected with potential attendees and attract their followers, especially during the event’s celebration, but also splitting efforts evenly before and after it. According to Chierichetti (2013), it is essential for managers to understand that the discourse surrounding a tourist destination and its festival is increasingly based on the conversation that is created on social media. In this environment, it is critical to use the correct language because the user does not want to be seen as a tourist or a traveller but a friend with whom interests and experiences are not necessarily shared off-line. Hudson et al. (2015) found that using social media platforms as Facebook, Youtube and Twitter had a direct effect on emotional attachment to the festival they analysed, and emotional attachment had a direct effect on word-of-mouth, supporting the idea that social media interactions can lead to high levels of emotional engagement and that the emotional responses triggered by marketing communications play a dominant role in explaining behavioural outcomes. With the trend towards commoditization and the increasing price and quality parity, engaging with customers emotionally (through the brand experience) will provide the best opportunity for differentiation and marketers will have to pursue more aggressive social media marketing strategies in order to maintain loyalty amongst fans. Finally, Williams et al. (2015) have suggested that festivals can promote a destination via electronic word-of-mouth (eWOM) on social media, even though the nature of this effect is not yet fully understood. They have indicated that Twitter key users are usually already prominent individuals and that festivals act as both a direct generator as well as an online animator of eWOM. They also found that stakeholders form coherent communication and content clusters when discussing event and destination-related topics on Twitter and indicated that users showed a preference for content from prominent users, who became the brokers of the network due to their high centrality. For the authors, community-based activities, such as cultural events (Getz, 2012), may have differing

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characteristics as they are rooted in a historical context and this may be manifest itself in the patterns of eWOM generated. It is on these premises that we formulate our research objectives. Through a Qualitative Content analysis of the official Twitter account of the Mercat de Música Viva de Vic we aim to analyse the following. From the perspective of the festival, we evaluate whether festivals may be an optimal resource to promote destination images, which destination images are associated with it on the selected social media platform and the role of each stakeholder in this image construction process. From the perspective of the destination image creation process, our objective is to evaluate to what extent the image creation process should be considered a specific analysis for each social media platform. Thus, it is crucial to know who are the predominant stakeholders are and if each mantains its own discourse. From this point of view, it is also necessary to know which kind of image typology (organic, induced, autonomous) or component (affective, cognitive, conative) predominates and, once again, if there are differences among different stakeholders.

3. Methodology, Data Collection and Data Analysis

The case study destination, Vic, is a good example of a Catalan medium-size city; a city of nearly 42.000 inhabitants situated in the centre of Catalonia with an extensive trade and fair tradition. Its commercial capacity has turned it into the neuralgic centre of a wide area in the centre of Catalonia and into one of its current urban hubs. The selected festival, the Mercat de Música Viva de Vic (MMVV) is Vic’s most important projected event and one of the most important music festivals in Catalonia, since it has the potential to attract more than 100,000 attendees and nearly 1,000 music industry professionals annually. It has also become one of the city’s icons. After 27 editions, MMVV has become a reference point in the music industry scene both nationally and internationally, especially in the Mediterranean spectrum. The decision to select the microblogging website Twitter as the social media platform to be analysed was based on the fact that although the MMVV official page in Facebook had nearly 21,500 likes, only 140 individuals were talking about it, while the MMVV official page in Twitter had nearly 5,000 tweets and retweets coming from the

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same MMVV account and its more than 15,000 followers, being the most participative platform in relation to this event’s image (and probably one of the MMVV’s main means of promotion). Twitter data analysis is highly technical and involves quite a few decisions in terms of data treatment. The first one was related to the selection of the own research objectives and methodology, having influence over the Twitter data collection and treatment strategy. Because we wanted to know the weighting of the different stakeholders in the conversation around the MMVV and its content, we decided to adopt a mix quantitative and qualitative methodology. The initial quantitative analysis was descriptive and then we decided to perform a content analysis regarding the qualitative analysis. Content analysis facilitates the systematic coding and analysing of the content and allows for systematically drawing valid conclusions from data “to the context of their use” (Krippendorff, 2012). This kind of mixed-method approaches, combining statistical and hermeneutical analysis in order to assess the diffusion of information on Twitter has been particularly prevalent in recent years in different contexts (Zhang et al., 2011). Our next choice was related to the software we needed to use to achieve our stated objectives. The past few years have seen the emergence of a diversity of ComputerAssisted Qualitative Data Analysis (CAQDAS) software that can be used for different types of digital content analyses. Depending on the research objectives and methodology, different tools can be used to collect data—from web-based analytics services to direct mining the Twitter Application Programming Interface (API), the tool to access the platforms' data layer (Gaffney and Puschmann, 2013). Our decision was to undertake directly mining of the API because we wanted to obtain the highest and richest possible information available about users and conversation surrounding the festival. Ncapture, the Nvivo add-on tool to capture Twitter data, and Nvivo itself were the software we selected, especially as the first permits obtaining the data in a structured database that can be used in Nvivo but also in other kind of spreadsheets, such as Excel. One of the challenges in analysing Twitter data is to choose an appropriate sample to answer a research question but to collect a true random sample is hardly possible because of the limitations in accessing to the Twitter API. The capture tools (as Ncapture) may be used to extract microblogging messages from a Twitter account’s stream (Tweetstream) or from a #hashtag conversation on Twitter. A Tweetstream is a collection

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Comentat [S1]: Mira a ver si esta frase dice lo que debiera. La frase inicial no estaba del todo bien construida (le faltaba alguna cosa). A ver si esta construcción te parece adecuada. Comentat [LGT2R1]: A ver que te parece lo que he cambiado…

of messaging content centred on a particular user account, and it goes back in time. Approximately 3,200 total messages (of one account) may be captured based on the Twitter API. In terms of #hashtag conversations, it is possible to collect discussions around a labelled topic, but the Twitter API for these only goes back about a week. This was decisive in selecting the Tweetstream of the @MercatMusicaVic, the official MMVV organiser account, as we obtained data from 2012, but also the observation that this account was also managing most of the conversation around the festival held by other stakeholders (the @MercatMusicaVic account produced 84% of the #MMVV hashtag quotations in its conversation, followed by a much lower 7% of the group of stakeholders comprised of music industry professionals’ cluster). The @MercatMusicaVic account data was captured in a single capture in January 2016 and constituted 3,226 references (tweets and retweets) posted between September 2012 and January 2016. A significant sample of the conversation directed by this user was made of 5,093 references. In our sample, 1,757 were tweets from the @MercatMusicaVic account and a considerable 1,469 were its retweets of other users’ tweets. The next step was to analyse these collected data. CAQDAS tools (as in our case Nvivo) allow for combining automated (quantitative) with manual (quantitative or/and qualitative) content analysis, identifying patterns and structures of the metrics of the data. In this study we used the three basic types of analysis detected in the literature, analysing the Twitter communication over a certain period of time, examining the development of a given topic (Bruns and Burgess, 2012) and measuring the number of tweets from a particular user or group of users. For this stage, we followed a recognized methodological approach (Thimm et al., 2014) that propose to take two steps in the process of computerassisted content analysis of Twitter Data: a basic content analysis and a speech act analysis. Therefore, after an elementary analysis of the temporal and geographic distribution of the conversation, we performed a basic content analysis or first-level analysis, that normally consists in basic analytical functions on word or phrase-frequency analyses and data visualisations. After reducing the original data (deleting redundant or invalid concepts), we obtained a considerable word-frequency list and we decided to select the first 1,000 words employed in the conversation, which gave us an initial insight as to the main topics of interest therein.

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The next step, the speech act analysis, is related to the categorization of these words in a coding scheme. Although this process can be generated inductively but also deductively, normally is developed in an iterative way, constantly refining words’ categories or families. In our case, due to the huge number of words found in the first step, we chose all of the words with more than 10 quotations (513 in total) that had some meaning for our content analysis. We excluded adjectives or verbs with difficult or invalid classification and coded the valid terms them in relation to some relevant aspects related to the destination and the event itself. The speech act analysis generated interesting information about the types of messages and actions stakeholders wanted to project through communication, mostly cognitive, but also affective and conative. Lastly, to perform the users’ analysis we first obtained a basic description of the users that participated in a greater number of occasions, the most mentioned or the most influential users in the conversation. We then exported the original Ncapture database to an Excel spreadsheet and then we manually identified and classified the users in different stakeholders’ groups, according to the events’ literature (Organisation, Professionals, Media, Associations and NGOS, and Administration). By importing this new database with the stakeholders’ grouping to Nvivo, we were able to determine the weighting of each group of stakeholders in the comments in relation to determinate concepts

Comentat [S3]: He decidido quitar la R de este nombre porque sólo estaba aquí. O lo ponemos en todos o los quitamos de todos, no? Mi opción ha sido quitarla

(quantitative analysis) and in which contexts, sentences or paragraphs each stakeholder

Comentat [LGT4R3]: Pues me parece bien, que les den :p a los del copyright

had spoken about these concepts.

4. Results

4.1 Temporal and Geographical Distribution of the Conversation

The extrapolation of the collected tweets reported very valuable information regarding the temporal and geographical distribution of the tweets tweeted and retweeted by @MercatMusicaVic. The lifecycle of the festival is reflected in the temporal distribution of the conversations. As such, the month in which the festival was held, September, obtained the greatest number of references (tweets and retweets). Conversations grew in number in the months before the festival and significantly decreased the month after, remaining quite low the following semester. On the other hand, the geographical

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distribution reflects the place of origin of the attendees as it also shows that most of the tweets or retweets were sent while the festival was being experienced. Catalonia as a region, with Vic as a central place and the Barcelona metropolitan area as a second core, were the places where the information was generated, although an important number of tweets were tweeted in the rest of Spain and Europe.

4.2 Basic Content Analysis (First-Level): Words’ Frequency ‘@MercatMusicaVic’ was the most used word followed by the hashtag ‘#mmvv’. Both concepts were incorporated in the tweets with a quite an important weighting placed on the total used words. Following, we can see different concepts related to the festival and its identity traits and also about what differentiated it from other festivals as well as with the music industry such as: musica (music), ‘Vic’ (the city of Vic), mercat (market), concert (concert) and nou (new), which hint that the festival serves as a showcase for the presentation of new acts emerging onto the music scene. Terms that related to places, such as the city of ‘Vic’ and plaça (square) referring to the main square of the city, its central point and most iconic MMVV location, or ‘cat’ in reference to Catalonia also appeared amongst the most frequently used words. Place-based elements were particularly present in the most used hashtags, as in the case of various city spaces such as #PlaçaMajor (Main Square) or the Catalan territory and its cities (#Barcelona, #Catalonia) or the markets directly related to the festival, like #mercatfrances (French market), #Benelux, #alemanya (Germany). Similarly, different concepts that refer to a fact or inform of an action or an event developed in a concrete moment stood out, as in the case of avui (today), setembre (September), or verbs like comença (begin). In order to observe these concepts visually, Figure 2 shows the most significant words in the conversation, translated in English (except hashtags) and excluding the ‘@MercatMusicaVic’ user and the hashtag ‘#mmvv’ to all for a better view of topics that were inherent in the conversation.

** Insert Figure 2 about here

4.3 Speech Act Analysis (Second-level): Word Coding

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The family with a predominant weight in the total concepts encoded in nodes and the frequency with which they appear in the speech of our sample is the one that groups everything related to the Festival. @MercatMusicaVic and #mmvv predominate, but a group of concepts that make references to the celebration of the festival itself can also be observed. The creation of a subfamily that groups all of the quotations purely associated with the MMVV professional space also stands out in this family, with diverse concepts highlighted like ‘professionals’, jornades (sessions) and ‘speed meetings’ besides others that allude to professional associations, markets, etc. This reflects the professional dimension of the MMVV and its identity as a music industry meeting point, one of its current main strategic objectives. The following significant family, ‘Music’, references the world of culture and the music industry and its performers and artists in particular. A notable subfamily grouping all of the quotations mentioning musical artists or their works connecting the city of Vic with music (in general) was highlighted here. The next most significant family as measured by the frequency in which its nodes appear is the family which we have named ‘Time’ that includes all references to time in which the present is predominant, a logical grouping considering Twitter’s characteristic immediacy. Next, the ‘Media’ family reflects the important role of stakeholders in this conversation. Then we find a family that has a direct relationship with the host city itself: ‘Vic’. In addition to including the city name, this family presented concepts like plaça (square), sucre (a city zone), catedral (cathedral), ciutat (city) and local. If we also consider the number of times these places have been mentioned, as shown in Figure 3, we can observe the representativeness they acquire through the festival.

** Insert Figure 3 about here The ‘Feelings’ family has to do with anything communicated by users that expresses some type of feeling that projects information pertaining to an affective condition. In this ambit, positive feelings predominate, with expressions such as gràcies (thank you) and felicitats (congratulations) standing out, but also others denoting admiration or positive assessment like ‘big’, bon (good), millor (better/best). In general,

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negative assessments were practically non-existent. The final family, which we have called ‘Territory’, allowed us group concepts like ‘cat’ (denoting to the Catalan region and on many occasions to other social, cultural and political elements of Catalan identity) and other appeals to Catalonia (Catalunya, català, catalans, d’aqui) as well as its capital (‘Barcelona’).

4.3 User Analysis: Who They Are and What They Said

There were 615 total participants in the sample which have been organised into different groups of interest, or stakeholders: Organisation, Administration, Professionals, Media, and Associations, or NGOs. Only one user, @MercatMusicaVic, appears in the first group and directs the conversation with a total of 1,757 tweets. The Professionals group of users is the most numerous, with a total of 265 participants with very diverse profiles, in which cultural agents and managers abound, as well as musicians and all types of artists. The Media group follows with 211 members whom we have divided into different typologies: Publishers, the Press (including magazines), Radio, Television and a category that we have called Internet that includes members from media that primarily communicate online, including recognised bloggers in the culture and music world. Since the Administration group has a total of 31 users, we sorted the group into different sub-groups, highlighting the contributions of the Vic City Council, but also of the Osona District Council, the Barcelona Deputation, the regional Government of Catalonia and its dependent organisms like the Institut Català d’Empreses Culturals (Catalan Institute for Cultural Companies) and its Education and Culture ministries. Another subgroup is made up of the Barcelona, Girona and Mataró City Councils, the Basque Country Government (with which the festival maintains relations in cultural cooperation) and other institutions. The Associations group comprised 11 users of all types, from third sector associations to purely cultural associations. In fact, music associations were key factors both in organizing the festival and in promoting and creating music. Finally, we have obtained two big groups of profiles that basically are defined as festival Attendees: those who have a significant identification with the territory because they reside in Catalonia

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and to a large extent can be considered as residents (with 82 members) and the less numerous rest ‘other attendees’ with 14 total members. As can be expected, a high percentage of the references created by each user group are generated by the Organiser itself, with 1,757 references (tweets and retweets), followed by the group of Professionals, with 652 references, and the online Media, with 298 total references. The rest of the groups have far less references than these groups. This is a conversation that not only offers a particular image of the festival and the city of Vic, but that also has a very important specialisation component with dialogue primarily directed by the Organiser, but also by the volume of users and references by cultural sector Professionals and by online media specialists (Internet). With regards to the main concepts addressed by these different groups or stakeholders starting with the case of the Organiser, the main concepts do not excessively differ from those previously presented in the sample total, given the relative weighting of this account itself within the total. Aspects related to the festival organiser itself and to the information regarding when and where the festival’s events are performed as well as messages of gratitude to the attendees and professionals predominate in the dialogue. In the case of the Professionals, they also highlight some aspects in relation to their own interests, like the professional session fees, meetings or specific artists’ performances. The Media group logically privileged the conversation related to their communicative interests and their audiences especially with regards to the artists, their works and their performances, with a lower interest in the purely professional topics. Finally, the Attendees were particularly interested in the specific live performances, where and when they were celebrated, and in artists and their works, whereas the Associations seemed to be more introspective and were more interested in speaking about their own sectorial topics, with a lower weight in the conversation. As regards the conversation revolving around the destination, we have seen in previous sections that the explicit hashtag references to the city are generally made in relation to the festival itself and to the locations where it takes place, and that city references have an important role in the global conversation. In Figure 4, references to the city (Vic) can be observed that were made to a large extent by the Organiser, followed to a much lesser extent by the Professionals and Media, and with special importance again

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in the case of the Internet Media, but also with the important participation of other types of Media, like the Television and the Radio, in particular.

** Insert Figure 4 about here

Moreover, we were interested in observing the context in which references to the city of Vic are situated for each of the stakeholders by observing the concept relationship for each case. In the most significant three stakeholders, the phrases created around the concept of ‘Vic’ are connected to the festival itself and to the information related to performances in the city and its spaces. In the case of the Professionals, the concept of ‘Vic’ is also related to the artists and participants, and in the case of the Media, the city spaces were more relevant. It is of particular interest to observe several interpretable examples such as an allusion to employment on behalf of the Administration. References to the destination are located within the frame of information related to the event dates, the music, the celebration of this particular festival edition, artists’ photos and/or videos, and some references to the fact that the city turns into a music capital for some days. By observing the attendee’s conversation surrounding this concept, it can be observed that apart from the festival itself, the music and the dates, they mention different public and private city spaces, as in the case of the ‘Plaça Major’ (Main Square), the ‘Temple Romà’ (Roman Temple) and the ‘Jazz Cava’, as well as the inclusion of elements of affective comments (és collonut, that is, ‘it’s great’). Another interesting aspect is the observation relative to the territory and specifically to the name ‘cat’ designating the Catalan territory which is generally employed in relation to online conversation. In the same Figure 4 it can be observed that the Organiser still predominates with these references, but there is also a very important weighting of Administrations and the regional government in particular, the Generalitat, or Government of Catalonia, which is obviously interested in promoting its territory through destinations like the city of Vic and its representative cultural festivals like the MMVV. Finally, another interesting aspect for our analysis was also to observe the behaviour of the stakeholders in relation to concepts that have to do with the expression of some type of feeling in order to demonstrate affective communication. While a concept

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like gràcies (thanks) is to be expected predominately from the Organiser, the affective concept of bo/bona (good) was highlighted by the Attendees (particularly those in the region of Catalonia), a group that did not previously play an important role in other relevant topics in addition to the Organiser, the Professional and the Media groups.

5. Discussion and Conclusions From the perspective of the festival’s influence, the MMVV Twitter account is an optimal resource to promote Vic’s image to a local and international audience. The conversation on Twitter surrounding the MMVV associates the destination with culture and music. Following Evans (2003) we can confirm that the MMVV has arguably become a ‘brand’ in its own right: its association with the city and its public spaces is clear. Moreover, taking into account the weighting of emotional communication in the conversation, the participation in this Twitter conversation has a direct effect on an emotional attachment to the festival, which can be used to maintain loyalty (Hudson et al., 2015). Following Sharpe (2008) we can corroborate that Twitter eWOM can foster social change through the idea of ‘pleasure-politics’ and in our study we have observed that the festival’s Organizer is promoting this. As indicated by Ooi and Pedersen (2010) different stakeholders collaborate in its promotion and performance and residents in particular act effectively in this way. In this study, the festival’s image and the destination’s image that is implicit within it, are projected on Twitter in a collaborative process among various stakeholders and audiences that generate a diversity of images (Sevin, 2013). As indicated by Williams et al. (2015) we have found that stakeholders form coherent communication and content clusters when discussing festival and destination-related topics on Twitter and that users showed a preference for content from prominent users. We would add that each cluster has its own prominent users or “guides”. From the perspective of destination image research, results demonstrate that the image-formation continuum generated by different actors through different sources is present here on one single platform. In this Twitter conversation almost all image agents can be observed, including: overt induced I (institutions, in our case the Administrations through their traditional messages about the festival and the city), covert induced I (in our

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case diverse musical celebrities recommending the festival), covert induced II (specialists such as the Media, speaking about the festival and the city), autonomous (in our case bloggers and the emerging profile of users who generate particular event content), unsolicited organic (attendees, through unsought information), solicited organic (attendees, through word of mouth) and organic (attendees talking about their personal experience and feeding back into the image-formation cycle.) Only one of the agents, overt induced II, traditionally related with travel intermediaries that promote image through requested information, is not present in our conversation, which is logical since our study focused more on individual attendees who organised their own visit, and not tourists requiring an intermediary. Most of the content that was generated in this conversation came from non-commercial information. Following Tasci and Gartner (2007), we can affirm that there is an important weighting of sources that project induced images, especially those controlled by the Organiser and the Administration, but there is also an important weight of uncontrolled sources, including Attendees’ and Professionals’ eWOM. It is also relevant to observe that most of the concepts leading the conversation were related to cognitive (information) and conative (action) attributes, but affective/emotive attributes are still present, especially those communicating positive feelings and/or evaluations. Moreover, we can corroborate the ideas of Choi et al. (2007) regarding the importance for destination marketers to examine and assimilate different image perspectives and adjust positioning strategies for greater effectiveness. The Public Administrations and the MMVV Organiser were conscious of this and adapted their communication to promote the festival and the city among an audience that is now composed of supply-side agents (like the Professionals in our case) and by demand-side agents (like the festival Attendees). In any case, the limits or barriers between supply and demand in the current context are sometimes difficult to define, as Professionals want to also be considered Attendees and some of the latter group communicate as if they were really Professionals. In our results we have seen that social interactions amongst diverse stakeholders participating in a conversation actively engaged participants especially in the case of local attendees, which might contribute to an increased number of future attendees or of repeated visits (Lee et al., 2012).

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From our results, we can affirm that destination branding analysis now needs a platform-specific approach as well as a stakeholder analysis, since it is no longer possible to separate brand image producers and consumers (Munar, 2011). Branding is becoming a more inclusive process. We add that it is possible to see the full path of the process on our selected social medium: from the first questions of some users that are unaware of some festivals and/or city elements, followed by all the information projected through induced images by marketers, the administration and media, and by the unsolicited and solicited organic information projected by other users (in our case, attendees) to the new image that the user can create after reviewing all of this information transformed into conversation. This study is limited in that it focuses on only one social media site, and it would be very interesting to complement it with the analysis of some of the other relevant social media platforms, such as Facebook, Instagram or YouTube. In each case, the prevalence of various stakeholders and the kind of information (more or less visual, more or less unidirectional, or more or less interactive) may provide more diverse results in the form of typologies and components. It is probable that some of the sub-typologies defined by Gartner (1994) can only be found on some platforms, or with different weights in the conversation. In conclusion, marketers and administrations must play a more active role in these spaces since they are quickly replacing other traditional marketing tools. This can be related to Mergel’s (2010) ‘networking strategy’ which highlights the interaction between the public sector and social media users and the diverse effective ways in which they influence consumers. Some examples that we have seen in our results through the Organiser’s and Administration’s tweets were included, such as (a) utilizing consumergenerated content (in our case, retweets) to deliver messages that marketers want to disseminate regarding their destination brand, (b) regularly monitoring consumergenerated content and comments of consumer sentiment, and (c) exploiting consumer comments on consumer-generated content and marketer-generated content to drive product/service/brand development or improvement.

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References Antoniadis K., Vrana V. and Zafiropoulos K. (2014), "PROMOTING EUROPEAN COUNTRIES'DESTINATION IMAGE THROUGH TWITTER", European Jour‑nal of Tourism, Hospitality and Recreation, Vol. 5 No. 1, pp. 85-103. Baloglu S. and McCleary K. W. (1999), "A model of destination image formation", Annals of tourism research, Vol. 26 No. 4, pp. 868-897. Beerli A. and Martin J. D. (2004), "Factors influencing destination image", Annals of tourism research, Vol. 31 No. 3, pp. 657-681. Bruns A. and Burgess J. (2012), "Researching news discussion on Twitter: New methodologies", Journalism Studies, Vol. 13 No. 5-6, pp. 801-814. Chierichetti L. (2013), "Dinámicas interactivas en la promoción turística: los festivales de música en Facebook" in Chierichetti L. (Ed.), Discurso profesional y lingüística de corpus. Perspectivas de investigación, Bérgamo, CELSB, 73-99. Choi S., Lehto X. Y. and Morrison A. M. (2007), "Destination image representation on the web: Content analysis of Macau travel related websites", Tourism management, Vol. 28 No. 1, pp. 118-129. De Jager A. E. (2010), "How dull is Dullstroom? Exploring the tourism destination image of Dullstroom", Tourism Geographies, Vol. 12 No. 3, pp. 349-370. Evans G. (2003), "Hard‑branding the cultural city–from Prado to Prada", International journal of urban and regional research, Vol. 27 No. 2, pp. 417-440. Fakeye P. C. and Crompton J. L. (1991), "Image differences between prospective, firsttime, and repeat visitors to the Lower Rio Grande Valley", Journal of Travel Research, Vol. 30 No. 2, pp. 10-16. Felsenstein D. and Fleischer A. (2003), "Local festivals and tourism promotion: The role of public assistance and visitor expenditure", Journal of Travel Research, Vol. 41 No. 4, pp. 385-392. Gaffney D. and Puschmann C. (2013), "Data collection on Twitter", Twitter and society, Vol. No., pp. 55-67. Garay L. and Cànoves G. (2015), "Barcelona seen through the eyes of TripAdvisor: actors, typologies and components of destination image in social media platforms", Current Issues in Tourism, 10.1080/13683500.2015.1073229. Gartner W. C. (1994), "Image formation process", Journal of Travel & Tourism Marketing, Vol. 2 No. 2-3, pp. 191-216. Ghazali R. M. and Cai L. (2014), "Social media sites in destination image formation", Tourism social media: transformations in identity, community and culture, Vol. 18 No., pp. 73-86. Guerrero-Solé F. and Fernández-Cavia J. (2013), "Activity and influence of destination brands on Twitter: A comparative study of nine Spanish destinations" in (Ed.), Information and communication technologies in tourism 2014, Springer, 227-236. Gunn C. (1972), Vacationscape: Designing Tourist Regions. Austin: Bureau of Business Research. 1988 Tourism Planning, New York, Taylor and Francis. Hay B. (2010) Twitter Twitter–But who is Listening. A review of the current and potential use of Twittering as a tourism marketing tool. CAUTHE 2010 20th International Research Conference: Challenge the Limits. University of Tasmania, Australia. Hays S., Page S. J. and Buhalis D. (2013), "Social media as a destination marketing tool: its use by national tourism organisations", Current Issues in Tourism, Vol. 16 No. 3, pp. 211-239. Holcomb B. (1999), "Marketing Cities for Tourism", The tourist city, Vol. No., pp. 54. 21

Hudson S., Roth M. S., Madden T. J., et al. (2015), "The effects of social media on emotions, brand relationship quality, and word of mouth: An empirical study of music festival attendees", Tourism management, Vol. 47 No., pp. 68-76. Judd D. R. and Fainstein S. S. (1999), The tourist city, Yale, Yale University Press. Kozinets R. V., Hemetsberger A. and Schau H. J. (2008), "The wisdom of consumer crowds collective innovation in the age of networked marketing", Journal of Macromarketing, Vol. 28 No. 4, pp. 339-354. Krippendorff K. (2012), Content analysis: An introduction to its methodology, Sage. Lee W., Xiong L. and Hu C. (2012), "The effect of Facebook users’ arousal and valence on intention to go to the festival: Applying an extension of the technology acceptance model", International Journal of Hospitality Management, Vol. 31 No. 3, pp. 819-827. Li X. R. and Vogelsong H. (2006), "Comparing methods of measuring image change: A case study of a small-scale community festival", Tourism Analysis, Vol. 10 No. 4, pp. 349-360. Llodrà-Riera I., Martínez-Ruiz M. P., Jiménez-Zarco A. I., et al. (2015), "A multidimensional analysis of the information sources construct and its relevance for destination image formation", Tourism management, Vol. 48 No., pp. 319328. Mehmetoglu M. and Ellingsen K. A. (2005), "Do small-scale festivals adopt “market orientation” as a management philosophy?", Event Management, Vol. 9 No. 3, pp. 119-132. Mergel I. (2010), "Government 2.0 revisited: Social media strategies in the public sector", Vol. No. Munar A. M. (2011), "Tourist-created content: rethinking destination branding", International Journal of Culture, Tourism and Hospitality Research, Vol. 5 No. 3, pp. 291-305. Ooi C.-S. and Pedersen J. S. (2010), "City branding and film festivals: Re-evaluating stakeholder's relations", Place Branding and Public Diplomacy, Vol. 6 No. 4, pp. 316-332. Prentice R. and Andersen V. (2003), "Festival as creative destination", Annals of tourism research, Vol. 30 No. 1, pp. 7-30. Qu H., Kim L. H. and Im H. H. (2011), "A model of destination branding: Integrating the concepts of the branding and destination image", Tourism management, Vol. 32 No. 3, pp. 465-476. Quinn B. (2005), "Changing festival places: insights from Galway", Social & Cultural Geography, Vol. 6 No. 2, pp. 237-252. Richards G. and Wilson J. (2004), "The impact of cultural events on city image: Rotterdam, cultural capital of Europe 2001", Urban studies, Vol. 41 No. 10, pp. 1931-1951. Rothschild P. C. (2011), "Social media use in sports and entertainment venues", International Journal of Event and Festival Management, Vol. 2 No. 2, pp. 139150. Sassen S. and Roost F. (1999), "The city: strategic site for the global entertainment industry", The tourist city, Vol. No., pp. 143-154. Selby M. (2004), Understanding urban tourism: Image, culture and experience, London, IB Tauris.

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Sevin E. (2013), "Places going viral: Twitter usage patterns in destination marketing and place branding", Journal of Place Management and Development, Vol. 6 No. 3, pp. 227-239. Sharpe E. K. (2008), "Festivals and social change: Intersections of pleasure and politics at a community music festival", Leisure Sciences, Vol. 30 No. 3, pp. 217-234. Sönmez S. and Sirakaya E. (2002), "A distorted destination image? The case of Turkey", Journal of Travel Research, Vol. 41 No. 2, pp. 185-196. Stepchenkova S., Chen Y. and Morrison A. (2007), "China and Russia: Organic destination images in US media", China Tourism Research, Vol. 3 No. 1, pp. 5572. Tasci A. D. A. and Gartner W. C. (2007), "Destination image and its functional relationships", Journal of Travel Research, Vol. 45 No. 4, pp. 413-425. Thimm C., Einspänner J. and Dang-Anh M. (2014), "Computer-assited content analysis of Twitter data", Weller K., Bruns A. Burgess J., Mahrt M.(eds.), Twitter and Society, New York, Peter Lang, Vol. No., pp. 97-108. Wenger A. (2008), "Analysis of travel bloggers' characteristics and their communication about Austria as a tourism destination", Journal of Vacation Marketing, Vol. 14 No. 2, pp. 169-176. Williams N. L., Inversini A., Buhalis D., et al. (2015), "Community crosstalk: An exploratory analysis of destination and festival eWOM on Twitter", Journal of Marketing Management, Vol. 31 No. 9-10, pp. 1113-1140. Yayli A., Bayram M. and Bayram Ü. (2011) How social media works in destination marketing? A content analysis on Twitter. 10th International Congress Marketing Trends. Paris: France. Zhang M., Jansen B. J. and Chowdhury A. (2011), "Business engagement on Twitter: a path analysis", Electronic Markets, Vol. 21 No. 3, pp. 161-175.

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