Applying a Conceptual Framework to Analyze Online Reputation of
Tourism Destinations
a
a
a
Alessandro Inversini , Elena Marchiori , Christian Dedekind , and
Lorenzo Cantoni
a
a
webatelier.net Faculty of Communication Sciences University of Lugano, Switzerland
(name.surname)@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.
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 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
3.1 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
Products
and
Services
Leadership
Innovation
id
[d
1]
[d
2]
[d
3]
[d
4]
[d
5]
[d
17
]
[d
18
]
[d
19
]
[d
6]
[d
7]
Performanc
e
Society
[d
16
]
[d
20
]
[d
21
]
[d
22
]
[d
8]
[d
9]
Environmen
t
Governance
[d
10
]
[d
14
]
[d
15
]
[d
11
]
[d
12
]
[d
13
]
Drivers
Literature
[D] offers quality tourism 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.
[D] offers a pleasant environment.
[D] features adequate infrastructure for tourists.
[D] offers a safe environment
[D] offers products and services that are good
value for the money
[D] presents accurate information of their
tourism products and services.
[D] presents an accurate image as a tourism
destination.
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.
[D] uses their resources and infrastructure
adequately.
[D] continuously improves their tourism
products and services
[D] presents innovative tourism products and
services
[D] is a sustainable tourism destination. [D]
outperforms other competitor tourism
destinations.
De Jong etal.,2003; Hjalager1997 and
2002 Jacob et al., 2003; Rindova, 2005;
Radu & Vasile, 2007; Lopez et al., 2003;
Rindova, 2005.
Lancaster, 1966; Divisekera, 2003;
Liljander & Strandvik, 1997; Oliver,
1993; Yu, et al., 2007; Yu & Dean, 2001;
Bigné & Andreu, 2004.
[D] meets my expectations as a tourism
destination.
[D] offers a satisfying tourism experience.
[D] encourages responsible behavior between
their visitors / residents.
[D] offers interesting local culture and
traditions.
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
[D] has hospitable residents.
[D] is responsible in the use of their
environment.
Blanco, 2008; Keller, 2008; Nicolau,
2008; Tearfund, 2002; Tilt, 1997; Dodds
& Joppe, 2000.
[D] supports ecological initiatives.
[D] tourism industry and organizations
cooperates and interacts between them
Palmer, 1998; Manning, 1998; Beritelli,
et.al 2007; Gnoth, 1997.
[D] tourism industry and organizations behave
Table
dimensions,
drivers and related literature
ethically1.
in DORM
confront ofcore
their visitors
and
residents.
[D] delivers tourism products and services that
match their offering.
This model was used to analyse DMOonline reputation in order to capture and analyse what
Unique
BMO
N
MOOW
actually is said in the online dialogues around a given destination.
results
W
W
AY
463
106
0
357
Google.c
om
UGC
95
Core
Drivers
U
Do
Expr
3.2 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.
Table 2. Unique results classification
Core
Dimensions
Products
and
Services
id
Leadership
[d
17
]
[d
1]
[d
2]
[d
3]
[d
4]
[d
5]
Drivers
Literature
[D] offers quality tourism products and services
Caruana, 1997; Augustyn, 1998;
Sönmez, 1998; results
Sproles, 1999;
Among
the
[D] offers a pleasant environment.
Vidaver-Cohen, 2007; Sönmez & Graefe,
obtained
1998; D’Amoreconsidering
and Anuza, 1986;
[D]
features
adequate
infrastructure
for
tourists.
European Commission,
2003.
both organic and sponsored websites (total results: 463), the websites belonging
to the MOOWAY
(357 results) which contained user[D]
generated
contents
(UGC) were 95 (approximately 20,51%). This
offers a safe
environment
first result suggested that social media
represented
a substantial
part of the online tourism domain
[D] offers
products and services
that are good
valueitfor(Greztel
the moneyand Xiang, 2009).
and play an important role in shaping
[D] presents accurate information of their
tourism products and services.
Jamal & Getz, 1995; Heath & Wall, 1992
Getz, et al., 1998; Gretzel, et al., 2006;
3. Content analysis: The 95 websites hosting user generated contents (UGC)
identified
Pike, 2008;
Ritchie &were
Crouch,used
2003;
[d
presents an accurate image as a tourism
Heath
& Wall, 1992;and
Presenza,
Sheehan,
for a content analysis based 18on a[D]
reputation
codebook
(Inversini
et
al.,
forthcoming)
on
the
destination.
]
Ritchie,
2005.
DORM framework. Content analysis
moved from previous studies in the &field
(e.g.
Inversini et al.,
[d
[D] uses their resources and infrastructure
forthcoming; Inversini and Cantoni,
2009; Xiang and Gretzel, 2009). Firstly the coder was asked to
19
adequately.
]
classify the 95 UGC websites [dto the following types (Xiang and Gretzel, 2009) in order to describe
Innovation
[D] continuously improves their tourism
De Jong etal.,2003; Hjalager1997 and
the information market around6]the products
online and
tourism
servicesdomain:
2002 Jacob et al., 2003; Rindova, 2005;
[d
7]
Performanc
e
[d
16
]
[d
20
]
[d
[D] presents innovative tourism products and
services
[D] is a sustainable tourism destination. [D]
outperforms other competitor tourism
destinations.
[D] meets my expectations as a tourism
Radu & Vasile, 2007; Lopez et al., 2003;
Rindova, 2005.
Lancaster, 1966; Divisekera, 2003;
Liljander & Strandvik, 1997; Oliver,
1993; Yu, et al., 2007; Yu & Dean, 2001;
Bigné & Andreu, 2004.
?
?
?
?
?
?
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.
Fig. 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
o
o
o
The item expresses positive value judgments
The item expresses positive value judgments as well as negative judgments
The item expresses more negative value judgments rather than positive ones
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.
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.
Table 3. DORM drivers table with presence and argument values results
Core
Dimensions
Products
and
Services
Leadership
Innovation
id
[d
1]
[d
2]
[d
3]
[d
4]
[d
5]
[d
17
]
[d
18
]
[d
19
]
[d
6]
[d
7]
Performanc
e
Society
[d
16
]
[d
20
]
[d
21
]
[d
22
]
[d
8]
[d
9]
Environmen
t
[d
10
]
[d
14
]
[d
15
]
[d
Drivers
Literature
[D] offers quality tourism 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.
[D] offers a pleasant environment.
[D] features adequate infrastructure for tourists.
[D] offers a safe environment
[D] offers products and services that are good
value for the money
[D] presents accurate information of their
tourism products and services.
[D] presents an accurate image as a tourism
destination.
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.
[D] uses their resources and infrastructure
adequately.
[D] continuously improves their tourism
products and services
[D] presents innovative tourism products and
services
[D] is a sustainable tourism destination. [D]
outperforms other competitor tourism
destinations.
De Jong etal.,2003; Hjalager1997 and
2002 Jacob et al., 2003; Rindova, 2005;
Radu & Vasile, 2007; Lopez et al., 2003;
Rindova, 2005.
Lancaster, 1966; Divisekera, 2003;
Liljander & Strandvik, 1997; Oliver,
1993; Yu, et al., 2007; Yu & Dean, 2001;
Bigné & Andreu, 2004.
[D] meets my expectations as a tourism
destination.
[D] offers a satisfying tourism experience.
[D] encourages responsible behavior between
their visitors / residents.
[D] offers interesting local culture and
traditions.
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
[D] has hospitable residents.
[D] is responsible in the use of their
environment.
[D] supports ecological initiatives.
Blanco, 2008; Keller, 2008; Nicolau,
2008; Tearfund, 2002; Tilt, 1997; Dodds
& Joppe, 2000.
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