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. 321-332). 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 day-

to-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

the online tourism domain (Xiang et al., 2009). Destinations such as visitlondon.com

and http://us. 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 (www.reputationinstitute.com). 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 ever-

increasing 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)

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

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:

Core

Dimensions

id Drivers Literature

[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

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.

[d15] [D] supports ecological initiatives.

Governance [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.

Palmer, 1998; Manning, 1998; Beritelli,

et.al 2007; Gnoth, 1997.

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, 2005.

Performance Lancaster, 1966; Divisekera, 2003;

Liljander & Strandvik, 1997; Oliver, 1993;

Yu, et al., 2007; Yu & Dean, 2001; Bigné

& Andreu, 2004.

Blanco, 2008; Keller, 2008; Nicolau, 2008;

Tearfund, 2002; Tilt, 1997; Dodds & Joppe,

2000.

Products and

Services

Caruana, 1997; Augustyn, 1998; Sönmez,

1998; Sproles, 1999; Vidaver-Cohen, 2007;

Sönmez & Graefe, 1998; D’Amore and

Anuza, 1986; European Commission, 2003.

De Jong etal.,2003; Hjalager1997 and 2002

Jacob et al., 2003; Rindova, 2005; Radu &

Vasile, 2007; Lopez et al., 2003; Rindova,

2005.

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

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.

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 463 106 0 357

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

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

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.

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.

Core

Dimensions

Drivers UGC

total

items

Don't

express

a value

Express

a value

[d1]: [D] offers quality tourism products and services 29 14 15

[d2]: [D] offers a pleasant environment 26 17 9

[d3]: [D] features adequate infrastructure for tourists 13 4 9

[d4]: [D] offers a safe environment 9 6 3

[d5]: [D] offers products and services that are good value for the money 19 12 7

[d6]: [D] continuously improves their tourism products and services 3 0 3

[d7]: [D] presents innovative tourism products and services 16 8 8

[d8]: [D] encourages responsible behaviour between their visitors /

residents

10 1 9

[d9]: [D] offers interesting local culture and traditions 4 2 2

[d10]: [D] has hospitable residents 4 3 1

[d17]: [D] presents accurate information of their tourism products and

services

1 0 1

[d18]: [D] presents an accurate image as a tourism destination 1 1 0

[d19]: [D] uses their resources and infrastructure adequately 6 4 2

[d14]: [D] is responsible in the use of their environment 2 2 0

[d15]: [D] supports ecological initiatives 0 0 0

[d16]: [D] is a sustainable tourism destination 3 2 1

[d20]: [D] outperforms other competitor tourism destinations 1 0 1

[d21]: [D] meets my expectations as a tourism destination 1 0 1

[d22]: [D] offers a satisfying tourism experience 1 0 1

[d11]: [D] tourism industry and organizations cooperates and interacts

between them

2 1 1

[d12]: [D] tourism industry and organizations behave ethically in

confront of their visitors and residents

0 0 0

[d13]: [D] delivers tourism products and services that match their

offering

0 0 0

Total 100% 151 77 74

Governance

2 items =

1.3%

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%

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.

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.

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