Applying a Conceptual Framework to Analyze Online Reputation of Tourism Destinations

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1 Inversini, A., Marchiori, E., Dedekind, C., Cantoni, L.(2010) Applying a Cenceptual Framework to Analyze Web Reputation of Tourism Destinations. In U. Gretzel, R. Law, & M. Fuchs (Eds.), Information and Communication Technologies in Tourism 2010 Proceedings of the International Conference in Lugano, Switzerland (pp ). Wien: Springer. Applying a Conceptual Framework to Analyze Online Reputation of Tourism Destinations Alessandro Inversini Elena Marchiori Christian Dedekind Lorenzo Cantoni webatelier.net Faculty of Communication Sciences University of Lugano, Switzerland (alessandro.inversini; elena.marchiori; christian.dedekind.glathar; lorenzo.cantoni)@usi.ch Abstract Destination managers are investing considerable efforts (time and money) in order to market their destination online without considering that unofficial information competitors (e.g. blogs, wiki, media sharing website etc) are gaining more and more popularity among internet users. This research uses online reputation as a metric to make sense out of the huge amount of user generated contents available online applying a conceptual framework to the reputation analysis: Destination Online Reputation (DORM). The model, derived from the popular models used in corporate reputation analysis has been tested within the tourism online domain accessible trough search engine of a popular English destination: London. Results demonstrate the validity of the model in understanding and managing destination online reputation. Keywords: web reputation, destination information competitors, web2.0, destination online reputation. 1 Introduction Tourism has been always recognized as an information intensive domain (Gretzel et al., 2000; Buhalis, 2003). Actually, in few other business areas generation, gathering, processing, application and communication of information are as important for dayto-day operations as for the travel and tourism industry (Poon, 1993). Furthermore, the continuous development of ICT during the last decades has had profound implications for the whole tourism industry (Buhalis, 2000). Tourism can be generally understood as an experience, which needs to be communicated (Inversini and Cantoni, 2009): social media, and in general terms the so called web2.0 are enabling tourists to share information on the internet in the so called read and write web, where the end user has become both information consumer, player (Nicholas, et al., 2007) and provider. Internet has become the primary way used by Destination Management Organizations (DMO) to communicate with prospective tourists (Buhalis, 2003); different strategies can be highlighted within the tourism domain (Choi et al., 2007), and different content providers (Inversini and Buhalis, 2009) are nowadays populating

2 the online tourism domain (Xiang et al., 2009). Destinations such as visitlondon.com and holland.com are reacting to this proliferation of contents created by the users (UGC = user generated contents) and are incorporating UGC as part of their websites (Inversini and Buhalis, 2009). DMO and tourism managers in general, understand that ICT, if managed properly, can generate a tremendous positive value for their organizations (Lee, 2001). On one side, destinations are providing information to prospective travellers in a factual (informative) way (Inversini et.al., forthcoming); on the other side, UGC are going more and more visibility among search engine results (Gretzel, 2006). This research was developed as a first step into a structured analysis of destination online reputation and was based on the Reputation Quotient and the RepTrak models developed by the Reputation Institute ( These models are used in several studies to measure the reputation of firms and other types of organizations e.g. countries (Passow et al., 2005). 2 Related Work Recently Xiang, Wöber and Fesenmaier (2008) and Xiang and Gretzle (2009) described the Online Tourism Domain accessible trough search engines; within this online tourism domain (Xiang et al., 2009), it is actually possible to find official destination and attraction websites (e.g. cultural heritage attraction websites) as well as unofficial sources of information (Xiang and Gretzel, 2009) such as blogs (Thevenot, 2007), online communities, social networks, personal websites etc. Information has become available both from official and unofficial sources (Anderson, 2006). Unofficial websites are competing to reach end users presenting almost the same information as the official websites do (Inversini & Buhalis, 2009). This everincreasing web2.0 phenomenon (O Reilly, 2005), which enables individual users to produce so called User Generated Contents (UGC), is contributing significantly to the massive growth of information on the web. Observing the World Wide Web, it is possible to identify two types of websites: (i) web1.0 websites: web pages of services, business etc. presenting their business, selling a product or integrating business processes (Cantoni and Di Blas, 2002), and (ii) web2.0 websites, which are defined as social websites and primarily contain UGC published by end users (Boulos and Wheelert, 2007). Web2.0 sites (also called social media ), can be generally understood as internet-based applications that encompass media impressions created by consumers, typically informed by relevant experience, and archived or shared online for easier access by other impressionable consumers (Blackshaw, 2006). Social media are important as they help spread within the web the electronic Word of Mouth (Litvin, Goldsmith, & Pan, 2008) which represents a mixture of facts and opinions, impressions and sentiments, founded and unfounded tidbits, experiences, and even rumors (Blackshaw & Nazzaro, 2006). Marketing managers and researchers are exploiting new ways to use social media within the online promotion activities in order to take advantage of this electronic word-of-mouth (Litvin, Goldsmith, & Pan, 2008). Schmallegger & Carson (2008)

3 suggested that the strategy of using blogs as an information channel encompasses communication, promotion, product distribution, management, and research. Other authors propose to view UGC websites as an aggregation of online feedback mechanisms, which use internet bidirectional communication to share opinions about a wide range of topics such as: products, services and events (Dellarocas, 2003), creating a network of digitized word-of-mouth (Henning-Thurau et al., 2004). The aggregation of the entire range of online representations creates the web reputation of organizations (Dellarocas, 2001 and 2005; Bolton et al., 2004). Managing the increasingly diverse range of sites and contents that build the web reputation, requires a cross-disciplinary approach, which incorporates ideas from marketing, social psychology, economics and decision making science (Malaga, 2001). Thus it is possible to argue that the construct online reputation can be formed within the so called Web 2.0, and can be managed by destinations (Inversini, 2009) holistically to attract more tourists. Reputation actually is considered to be a major asset for individuals, firms, organizations and countries. The term has been defined by the Webster s Revised Unabridged Dictionary (1913) as the estimation in which one is held; character in public opinion; the character to attribute to a person, thing or action [ ]. One of the most complete definitions of reputation was presented by Solove (2007): the author explained it as a core component of the identity, defining reputation as the opinion of the public, which is formed upon the behavior and character of an individual, firm or country. According to Fombrun, Gardberg, and Sever (1999), corporate reputation is a collective assessment of a company s ability to provide valued outcomes to a representative group of stakeholders.dowling (2001) complemented this definition by arguing that the sum of all the activities performed by a firm contributes to the creation of its reputation.. This information, which might come from different sources (e.g. press releases, word-of-mouth, advertisement, etc.), is the result of all behaviors, actions or activities performed by a firm. From this information each individual then, creates its own personal perception or reputation. This situation limits the ability of organizations to manage their own reputation, due to the fact that it is not possible to restrict people from making judgments (Solove, 2007). The tourism industry, as any other service industry sells intangible products characterized mainly by being inseparable (production and consumption occurring at the same time), perishable (services cannot be stored and consumed at a later point in time) and heterogeneous (substantial differences in the services due to the human factors as production inputs) (Sirakayaa & Woodsideb, 2005). Dowling (2001) argued that firms in the services or experience industry, and tourism is one of them, should invest more in developing their image and reputation. Furthermore, the author explained that due to the inseparability and heterogeneity nature of the tourism products, customers are keener to select tourism service providers upon their reputation. So that studying tourism related online word of mouths (and more in general social media) and connecting them to the concept of reputation is a starting

4 point to make sense out of the huge amount of contents generated online by the users working on a specific construct (i.e. online reputation). 3 Research Design Destination Online Reputation Model This research presents and describes the application of a conceptual framework, DORM (Destination Online Reputation Model), to analyse the User Generated Contents (UGC) around a tourism destination. Destination online reputation was recently investigated by Inversini, Cantoni and Buhalis (Forthcoming) and Inversini and Cantoni (2009) thanks to content analysis on destination related search engines results. Within this study, researchers have set the following research objective: to test DORM framework, analyzing and measuring how the core dimensions and the reputation drivers are relate to the user generated contents of a tourism destination. DORM considers the specific characteristics of a tourism destination as a unique and complex organizational unit of the tourism industry. Researchers used the Reputation Quotient and the adapted version RepTrak (2006) presented by the Reputation Institute (RI) which are based on 23 drivers that work as predictors of reputation (Vidaver-Cohen, 2007). The drivers are grouped in 7 core dimensions: Organizational Leadership, Product & Services quality, Workplace environment, Performance, Citizenship activities, Innovation initiatives and Governance procedures. Using these two models (RQ and RepTrak) as a base, authors were able to adapt the core dimensions and reputation drivers to the reputation of a tourist destinations considering its peculiar characteristic of the tourism industry. The framework was created and adapted thanks to an extensive literature review and it was validated through semi structured interviews with domain experts (i.e. new media, economics of tourism, brand reputation and practitioners) in order to collect the interviewees perception on how the elements of the proposed model relate and influence the perception of reputation in regards of a tourism destination (Marchiori et al. forthcoming). During the semi structured interviews, domain experts were asked to rank the importance of each of the 7 core dimensions featured by the model and to add any additional element perceived as having an influence upon the overall reputation of a destination and which was not previously considered. Results confirmed the 7 core dimensions and 22 reputation drivers presented in Table 1:

5 Core id Drivers Literature Dimensions Products and [d1] [D] offers quality tourism products and services Services [d2] [D] offers a pleasant environment. [d3] [D] features adequate infrastructure for tourists. [d4] [D] offers a safe environment [d5] [D] offers products and services that are good value for the money Leadership [d17] [D] presents accurate information of their tourism products and services. [d18] [D] presents an accurate image as a tourism destination. [d19] [D] uses their resources and infrastructure adequately. Innovation [d6] [D] continuously improves their tourism products and services [d7] [D] presents innovative tourism products and services [d16] [D] is a sustainable tourism destination. [d20] [D] outperforms other competitor tourism destinations. [d21] [D] meets my expectations as a tourism destination. [d22] [D] offers a satisfying tourism experience. Society [d8] [D] encourages responsible behavior between their visitors / residents. [d9] [D] offers interesting local culture and traditions. [d10] [D] has hospitable residents. Environment [d14] [D] is responsible in the use of their environment. Governance [d15] [D] supports ecological initiatives [d11] [D] tourism industry and organizations cooperates Palmer, 1998; Manning, 1998; Beritelli, and interacts between them et.al 2007; Gnoth, [d12] [D] tourism industry and organizations behave ethically in confront of their visitors and residents. [d13] [D] delivers tourism products and services that match their offering. Caruana, 1997; Augustyn, 1998; Sönmez, 1998; Sproles, 1999; Vidaver-Cohen, 2007; Sönmez & Graefe, 1998; D Amore and Anuza, 1986; European Commission, Jamal & Getz, 1995; Heath & Wall, 1992 Getz, et al., 1998; Gretzel, et al., 2006; Pike, 2008; Ritchie & Crouch, 2003; Heath & Wall, 1992; Presenza, Sheehan, & Ritchie, De Jong etal.,2003; Hjalager1997 and 2002 Jacob et al., 2003; Rindova, 2005; Radu & Vasile, 2007; Lopez et al., 2003; Rindova, Performance Lancaster, 1966; Divisekera, 2003; Liljander & Strandvik, 1997; Oliver, 1993; Yu, et al., 2007; Yu & Dean, 2001; Bigné & Andreu, Tosum, 2002; Crick, 2003; Ryan, 1995 Allen et al., 2005; Carey et al., 1997; Fuchs and Weiermain, 2004; Pizam et.al., 2000; Brunt & Courtney, 1999; Russo & VanDer Blanco, 2008; Keller, 2008; Nicolau, 2008; Tearfund, 2002; Tilt, 1997; Dodds & Joppe, Table 1- DORM core dimensions, drivers and related literature This model was used to analyse DMOonline reputation in order to capture and analyse what actually is said in the online dialogues around a given destination. DORM conceptual framework application This preliminary test of DOMR was conducted thanks to an online case study; the presence of reputation drivers was assessed thanks to a content analysis. London was chosen for this preliminary research.

6 The online case study consisted of three main steps: (i) query selection and search activities, (ii) results classification and (iii) content analysis. Google was used as search engine for the study is the most used search engine, also in the travel sector (Hopkins, 2007; Bertolucci, 2007). 1. Query selection: 10 keywords were selected in order to perform the search on Google. Relevant tourism keywords were selected thanks to two web services given by Yahoo and Google (seggestqueries.googole.com and ff.search.yahoo.com), which suggest related user search for a given term (in this case the input term was London ). Among 15 keywords suggested by the services, only 10 tourism related keywords have been selected for in order to perform the study: (i) london times, (ii) london weather, (iii) london eye, (iv) london underground, (v) london fog, (vi) london England, (vii) london map, (viii) london hotels, (ix) london transport, (x) london zoo. The 10 keywords were used to perform 10 different search activities on google.com (international results only) considering the first three results pages as relevant for the end user (Comescore, 2008). 2. Results classification: unique results (Table 2) obtained from Google, were firstly classified according to Inversini, Cantoni and Buhalis (forthcoming) in: (i) BMOW Brick and mortar organizations websites, including all players that are doing business also in the offline world. Most of these organizations were doing business long before the internet was developed. (ii) MOOWAI Mere online organizations websites and individual websites, including all individual websites mainly blogs and those organizations doing business (almost) exclusively online. These providers couldn t be even conceivable without the info-structure provided by the internet. (iii) not working websites. This classification elaborates the one given by Anderson (2006) and Inversini and Buhalis (2009) because of the extreme complexity of the tourism domain, where the simply difference among official and unofficial sources is not enough. Unique results BMOW NW MOOWAY Google.com UGC 95 Table 2 Unique results classification Among the results obtained considering both organic and sponsored websites (total results: 463), the websites belonging to the MOOWAY (357 results) which contained user generated contents (UGC) were 95 (approximately 20,51%). This first result suggested that social media represented a substantial part of the online tourism domain and play an important role in shaping it (Greztel and Xiang, 2009). 3. Content analysis: The 95 websites hosting user generated contents (UGC) identified were used for a content analysis based on a reputation codebook (Inversini et al., forthcoming) and on the DORM framework. Content analysis moved from

7 previous studies in the field (e.g. Inversini et al., forthcoming; Inversini and Cantoni, 2009; Xiang and Gretzel, 2009). Firstly the coder was asked to classify the 95 UGC websites to the following types (Xiang and Gretzel, 2009) in order to describe the information market around the online tourism domain: Virtual Community (e.g. Lonely Planet, IgoUgo.com, Yahoo Travel); Consumer Review (e.g. Tripadvisor.com); Blogs and blog aggregators (e.g. personal blog, blogspot); Social Networks (e.g. Facebook, Myspace); Media Sharing (Photo/Video sharing e.g. Flickr, YouTube); Other (e.g. Wikipedia, Wikitravel). Secondly, the pages identified as UGC were examined using specific guidelines (Inversini et al., forthcoming) in order to associate the topics contained within the page to the DORM drivers. 4 Results User Generated Contents (UGC) information market around London online tourism domain have been represented in Figure 1. Among the categories selected for the analysis, the majority of websites were classified under the category Other, which counted 34.7% of the total results and it was represented mainly by Wikipedia pages. The rest of the UGC websites were balanced between: Consumer Review (19.7%), Media Sharing (19.7%), Blogs and blog aggregators (17.3%). Few websites were Virtual Community (8.7%) and no mentions for Social Networks and Web1.0 websites. Figure 1 UGC information market around London online tourism domain

8 Once the UGC websites were identified, contents from each single landing page was analyzed and associated to specific drivers. Where more than one driver was presented on the same landing page, coder was asked to classify them using (where needed) more than one driver (e.g. a blog can have a post which talk about Products and Services and a comment about Society, in that case the coder will count two items). From 95 UGCs, the coder was not able to associate 22 search results to any drivers (approximately 12.7% of the total results). A further qualitative analysis showed that the content of these 22 search results was mainly not relevant for the tourism field (i.e. contents about people, journals, advertisements, news, websites guidelines which have London as part of the title name). Keywords which mainly gave applicable websites were: Transport, Map, Hotels in fact they were tourism related keywords. On the contrary, keywords as Fog, Times and Underground were the ones which mainly gave the not-applicable urls in fact they were partially tourism related keywords. Thus from 73 remaining urls, coder found 151 drivers (approximately 2.06 drivers per landing page). Coder was also asked to define the value of the judgments expressed within the following metric: The item does not express any value judgment The item expresses a value judgment: o The item expresses positive value judgments o The item expresses positive value judgments as well as negative judgments o The item expresses more negative value judgments rather than positive ones o The item expresses negative value judgments Table 3 below shows that the online word-of-mouth perceived London with the following reputation dimensions frequencies and argument values: 1) Products and Services dimension counted for 63.6% of the total results with an overall of positive values expressed. Nevertheless a negative mention was d3: [D] features adequate infrastructure for tourists. Comparing this result against the distribution of the drivers on the media, shows that this core dimension is mainly presented on Consumer Review websites, Other and Media Sharing websites. 2) Innovation dimension counted for 12.6%. The vast majority of comments were positive, nevertheless negatives mentions were for d6: [D] continuously improves their tourism products and services; and d7: [D] presents innovative tourism products and services. 3) Society dimension counted for 11.9% with both negative mentions (d8: [D] encourages responsible behaviour between their visitors /residents), as well as positive value judgments. 4) Leadership dimension counted for 5.3% with few positive presences. Nevertheless a negative mention was for the driver d17: [D] presents accurate information of their tourism products and services. 5) Environment dimension counted for 3.3% with few positive mentions as well as items without any judgment expressed.

9 6) Performance dimension counted for 2% with only 3 presences: two were positive and one negative for the driver d22: [D] offers a satisfying tourism experience. 7) Governance dimension counted for 1.3% with one positive presence. The negative mentions counted for 10.3% of the total arguments value results and they were mainly presented on Media Sharing websites (e.g. YouTube.com), Blogs and Consumer Review websites as for example, Tripadvisor.com. No value judgments expressed counted for 51% of the total results and they were mainly in Other media. Out of 77 no-value results 14 were Wikipedia pages which usually presents item description rather than judgments. The not mentioned drivers were part of the reputation dimensions which obtained few mentioned: Environment with the missing driver d15: [D] supports ecological initiatives; and Governance with the missing drivers d12: [D] tourism industry and organizations behave ethically in confront of their visitors and residents; d13: [D] delivers tourism products and services that match their offering.

10 Core Dimensions Drivers UGC Don't Express total express a value items a value Products and Services 96 items = 63.6% Innovation 19 items = 12.6% Society 18 items = 11.9% Leadership 8 items = 5.3% Environment 5 items = 3.3% Performance 3 items = 2% Governance 2 items = 1.3% [d1]: [D] offers quality tourism products and services [d2]: [D] offers a pleasant environment [d3]: [D] features adequate infrastructure for tourists [d4]: [D] offers a safe environment [d5]: [D] offers products and services that are good value for the money [d6]: [D] continuously improves their tourism products and services [d7]: [D] presents innovative tourism products and services [d8]: [D] encourages responsible behaviour between their visitors / residents [d9]: [D] offers interesting local culture and traditions [d10]: [D] has hospitable residents [d17]: [D] presents accurate information of their tourism products and services [d18]: [D] presents an accurate image as a tourism destination [d19]: [D] uses their resources and infrastructure adequately [d14]: [D] is responsible in the use of their environment [d15]: [D] supports ecological initiatives [d16]: [D] is a sustainable tourism destination [d20]: [D] outperforms other competitor tourism destinations [d21]: [D] meets my expectations as a tourism destination [d22]: [D] offers a satisfying tourism experience [d11]: [D] tourism industry and organizations cooperates and interacts between them [d12]: [D] tourism industry and organizations behave ethically in confront of their visitors and residents [d13]: [D] delivers tourism products and services that match their offering Total 100% Table 3- DORM drivers table with presence and argument values results 5 Discussions and Conclusions DORM framework was applied to the analysis of the user generated content around London. Within this particular case, out of the 7 core dimensions analyzed within the UGC information market, only four of them can be considered as predictors of reputation: (i) Products and Services, (ii) Innovation, (iii) Society, and (iv) Leadership dimensions. In addition, the online dialogues for the given keywords about London have been observed mostly in websites which share contents (namely in Other media, Media Sharing, Consumer Reviews and Blogs), than websites which are more related (or present) user profiling characteristics such as virtual communities or social networks.

11 In the presented case study, DORM is able to capture and map the online dialogues (the ones which express values judgments) using only its first 4 dimensions (out of seven). The arguments which express values judgements count approximately 93% of the results. Actually, online reputation investigation with DORM can be carried out only with the first ten drivers (out of 22). Furthermore, within the not applicable user generated contents (the ones not relevant for the tourism domain) no suggestions to complete/increase the core dimensions and driver were found. The lacking of some drivers (and the limited item presence for Environment, Performance, and Governance dimensions), allows to hypothesize some future works: (i) to run the research for other different destinations in order to test DORM and verify if other dimensions are missing; (ii) to use in future research a list of tourism keywords (to query search engines) in order to understand if the limited presence of some drivers are related to the query inquire or to the actual online reputation market around a destination; and (iii) to investigate the official websites in order to have a comparison between the online dialogues and the contents provided by institutional websites or by destination management organization s websites in terms of online reputation. Finally, this kind of study has some limitations. It is (i) time consuming: coder has been extensively trained to analyse and codify each landing page and to catalogue it; (ii) it is related only to one popular destination (London). Nevertheless, destinations managers who are investing time and efforts in online promotion activities, should find in DORM a structured approach to monitor the reputation dimensions of a destination. 6. References Anderson, C. (2006). The Long Tail: Why the Future of Business is Selling Less of More. Hyperion, NY. Bertolucci, J. (2007). Search engine shoot-out. PC World, 25(6), Blackshaw, P. (2006). The consumer-generated surveillance culture. Retrieved October 13, 2008, from Blackshaw, P., & Nazzaro, M. (2006). Consumer-generated media (cgm) 101: Word-ofmouth in the age of the web-fortified consumer. Bolton,G.E., Katok,E., Ockenfels, A. (2004). How Effective Are Electronic Reputation Mechanisms? An Experimental Investigation. Management Science, 50(11), Boulos MN, Wheeler S. (2007) The emerging Web 2.0 social software: an enabling suite of sociable Buhalis, D. (2000). Marketing the competitive destination of the future, Tourism Management. Vol.21(1), pp Buhalis, D. (2003). etourism: Information technology for strategic tourism management. Prentice Hall, Harlow. Cantoni, L. and DiBlas, N. (2002) Teorie e Pratiche della Comunicazione, Apogeo, Milano. Cantoni, L. and Tardini, S. (2006). Internet (Routledge Introductions to Media and Communications). Routledge, London New York. Choi,S., Lehto, XY., Oleary, JT. (2007). What does the consumer want from a DMO website? A study of US and Canadian tourists perspectives. International Journal of Tourism Research. 9, Comescore, (2008), comscore Releases December U.S. Search Engine Rankings, Retrieved March 2008, Dellarocas, C., (2005). Reputation Mechanism Design in Online Trading Environments with Pure Moral Hazard. Information Systems Research,16(2)

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