Destination Personality: Effects on Satisfaction, Identification, Word-of

<strong>Destination</strong> <strong>Personality</strong>: <strong>Effects</strong> on Satisfaction, Identification, Word-of-
Mouth and Revisit Intentions
Introduction
Tourism is one of the largest and fastest growing industries worldwide. As new tourism
destinations appear and established ones develop or decline, destination marketers face the
daunting task of reaching out to consumers in an increasingly competitive global arena
(Echtner & Ritchie, 2003; Murphy et al., 2007). To achieve this, tourism managers nowadays
need to apply modern branding techniques and effective positioning strategies (Calantone et
al., 1989; Hankinson, 2009; Hanna & Rowley, 2011; Hosany et al., 2006; Skinner, 2008).
The branding literature suggests people tend to assign human personality traits to and
identify with inanimate objects such as brands (e.g., Aaker & Fournier, 1995). Brand
personality and customer–brand identification play a decisive role in brand choice, purchase
likelihood, and ultimately brand success (Aaker, 1997; Demirbag Kaplan et al., 2010). Also,
there is consensus in the marketing literature that retaining customers is good business,
positive word-of-mouth is the most effective means of promotion, and customer satisfaction is
the prime driver for consumer behaviour (Brown et al., 2005; Mittal & Kamakura, 2001).
Notwithstanding the differences between tourism and other products, the foregoing puts
brand personality, identification, revisit behaviour, word-of-mouth, and satisfaction high on
the agenda of tourism practitioners and scholars alike. Yet, the extant literature lacks an
integrative framework that addresses these issues. In response to calls for more encompassing
models in the study of consumer behaviour in tourism (e.g., Chi & Qu, 2008; Echtner &
Ritchie, 2003), this study is—to the best of our knowledge—first to investigate the
interrelationships among destination personality, tourist satisfaction, tourist–destination
identification, positive word-of-mouth, and revisit intentions (see Figure 1).
Conceptual background and hypotheses
Anthropomorphism theory maintains that people tend to humanise non-human things (Boyer,
1996). In line with the anthropic principle, consumers tend to attribute personalities to brands.
Brand personality concerns the set of human characteristics associated with a brand (Aaker,
1997). It can help to differentiate brands and augment their meaning to the consumer (Aaker
& Fournier, 1995). Recent research suggests this brand-as-person perspective can be extended
to tourism destinations; people endow places with personality qualities too (Demirbag Kaplan
et al., 2010; Hosany et al., 2006; Murphy et al., 2007). <strong>Destination</strong> personality refers to the set
of human characteristics that tourists associate with a destination (Ekinci & Hosany, 2006).
Positive trait inferences are likely to result in favourable tourist attitudes toward the
destination (cf. Aaker, 1997; Fournier, 1998). Thus, the more a destination is associated with
favourable human characteristics, the more satisfied a tourist will be.
H1: There is a positive relationship between destination personality and tourist
satisfaction.
Identity theory posits that the self is a structure of multiple identities that reflect roles in
differentiated networks of interaction (Arnett et al., 2003). The self-concept is composed of a
personal identity, which involves idiosyncratic characteristics (e.g., abilities and interests), as
well as a social identity, which consists of salient group classifications that enable people to
locate themselves and others within the social environment (Turner et al., 1994). Social
identification refers to a sense of connectedness with a group together with the emotional
significance of that belongingness (Mael & Ashforth, 1992). Individuals tend to express
1

themselves by identifying with different social groupings, ranging from genetic features such
as gender and ethnicity to brands and organisations (e.g., Fournier, 1998; Turner et al., 1994).
Interestingly, a place may become a part of the self-concept in that it can evoke strong
cognitive and psychological attachments (Simpson & Siguaw, 2008). Tourist identification
with a destination refers to the extent to which a tourist perceives a sense of connectedness to
a destination and defines him- or herself in terms of it (cf. Mael & Ashforth, 1992). Thus,
destinations with a strong personality are likely to evoke tourist–destination identification by
virtue of effectively reflecting tourists’ desired self-concepts.
H2: There is a positive relationship between destination personality and tourist–
destination identification.
Promoting is defined as providing positive information about a visited destination in
social situations (cf. Arnett et al., 2003). Brand personality can serve as a basis for
differentiation that often leads to favourable customer attitudes and behaviours toward the
brand (e.g., Aaker, 1997; Brakus et al., 2009). Under this premise, tourists are likely to single
out, speak highly of, and revisit destinations that possess a distinctive and favourable
character (Chi & Qu, 2008; Ekinci & Hosany, 2006).
H3: There is a positive relationship between destination personality and promoting.
H4: There is a positive relationship between destination personality and revisit intentions.
Satisfaction may result in a re-evaluation of the salience of identities in that positive
feelings and emotions are important for the formation, maintenance, and development of
identities (Arnett et al., 2003). By virtue of their positive affective state, satisfied tourists are
likely to feel a deep rapport with the visited destination (Hou et al., 2005; Ryan, 1995) and
create or reinforce their identity in relation to it.
H5: There is a positive relationship between tourist satisfaction and tourist–destination
identification.
Positive word-of-mouth originates from customer satisfaction, especially in cases when
experience surpasses expectations (Brown, 2005). It is therefore plausible that satisfied
tourists will speak favourably of a visited destination to their circle of acquaintances upon
return (Weaver et al., 2007). Further, they are likely to revisit the destination (Chi and Qu,
2008). This can be ascribed to risk minimising behaviour: given the considerable monetary
sacrifice incurred in tourist activities, people might be reluctant to take the risk of an unknown
destination and opt for visiting one they are already satisfied with (Alegre & Juaneda, 2006).
H6: There is a positive relationship between tourist satisfaction and promoting.
H7: There is a positive relationship between tourist satisfaction and revisit intentions.
Individuals are likely to enact behaviours that they believe are consistent with a salient
identity in that identities require self-expression (Arnett et al., 2003). Thus, tourists that foster
strong relational bonds and identify with a destination are likely to exhibit higher tendencies
to return there and ‘talk up’ this destination in conversations with friends and acquaintances.
H8: There is a positive relationship between tourist–destination identification and
promoting.
H9: There is a positive relationship between tourist–destination identification and revisit
intentions.
Method
We approached a total of 746 postgraduate students on the campus of a large UK University
and asked them whether they were willing to take part in a survey on their most recent
overseas vacation. We asked the respondents that showed interest to complete a self-
2

administered questionnaire. A total of 529 postgraduates across a range of subjects agreed to
participate (70.9%). We excluded 39 questionnaires from the analysis due to considerable
missing data resulting in a total of 490 eligible responses (92.6% of those students that agreed
to participate). The descriptive statistics reveal that women (48.8%) and men (51.2%) almost
equally comprised the study sample. The great majority were below 30 years of age (85.9%)
and single (77.3%). Most respondents were Asian (38.4%) followed by UK (37.6%) and EU
nationals (13.9%). The respondents further reported that they had on average visited eight
foreign countries and the majority (63.8%) visited the focal destination for the first time.
Although student samples are not typically representative of the wider population
(Peterson, 2001), considerable empirical work suggests students can be as suitable
participants as nonstudents in consumer behaviour studies. Prior research utilising student
samples has yielded reliable findings (e.g., Barone et al., 2000; Sharma et al., 2010) and there
is evidence to support a high correspondence between non-student and student samples (e.g.,
Palmatier et al., 2009; Völckner & Sattler, 2007). Also, students constitute a potentially
lucrative segment for the tourism industry (e.g., Field, 1999; Gallarza & Saura, 2006; Litvin,
2003). Hence, they are a relevant, appropriate, and important segment to investigate for the
purposes of this study.
We undertook an extensive review of the pertinent literature and a series of personal
interviews to develop a draft questionnaire. Three academic researchers with a background in
consumer behaviour and tourism evaluated the content validity of the measures selected; they
judged the extent to which each item represented the construct in question (Zaichkowsky,
1985). We tested the revised questionnaire with five postgraduate students to ensure effective
semantic design and instrument format and indentified no problems with respect to any of the
measures or response formats.
We measured satisfaction with six items (i.e., [<strong>Destination</strong> Name (DN)] was a great
destination to visit; during my visit to [DN], I accomplished the purpose of my vacation; all
things considered (e.g., time, effort, money), I am satisfied with my visit to [DN]; I have
pleasant memories from my visit to [DN]; my visit to [DN] met my expectations; and on the
whole, my choice to visit [DN] has been a wise one) adapted from Gallarza and Saura (2006)
and Lee et al. (2007). We measured identification with three items (i.e., [DN] fits well to me;
I strongly identify with [DN]; and I feel attached to [DN]) adapted from Homburg et al.
(2009). We measured promoting with three items (i.e., I bring up [DN] in a positive way in
conversations I have with friends and acquaintances; I “talk up” [DN] to people I know; and
in social situations, I often speak favourably about [DN]) adapted from Arnett et al. (2003).
We anchored the response formats for these constructs by (1) “strongly disagree” and (7)
“strongly agree.” We used a single-item (i.e., how likely is it that you would revisit [DN] in
the near future) drawn from Yoon and Uysal (2005) to measure revisit intentions. Its response
format ranged from (-3) “very unlikely” to (+3) “very likely.” We operationalised destination
personality based on Aaker (1997) and Hosany et al. (2006) and used a rating scale ranging
from (1) “not at all descriptive” to (7) “extremely descriptive.”
In line with Podsakoff et al.’s (2003) recommendations for minimising common method
bias (CMB) during the research design phase, we ensured respondents that there were no right
or wrong answers; used anonymous self-completed questionnaires; employed different types
of response scale formats; did not group together most construct items by variable; and
counterbalanced the order of some criterion and predictor variables. We also conducted an ex
post statistical analysis (discussed subsequently) to assess the possibility of CMB.
Analysis and results
We initially assessed and purified the scales through a combination of item-to-total
correlations and exploratory factor analysis. The results of the factor analysis revealed that the
3

items for satisfaction, identification, promoting, and revisit intentions loaded strongly on their
corresponding factor (> .6). With respect to the destination personality measures, the analysis
revealed five factors with eigenvalues greater than 1. However, the scree test showed a
significant dip between the sixth and seventh factor. This indicates that a six factor solution
explains the factor structure better. After elimination of items with low factor loadings and
significant cross-loadings, a clean factor structure emerged explaining 70.1% of the total
variance in destination personality (see Table 1). We then derived meaning from the emergent
factors in the context of tourism destinations and labelled them excitement, sophistication,
activeness, dependability, philoxenia (a Greek word specific to the tourism industry; it means
the opposite of xenophobia), and ruggedness.
In order to establish convergent and discriminant validity, we subjected the retained set of
items to confirmatory factor analysis and specifically ran two measurement models due to
sample size restrictions. The model results show a good fit to the data (see Table 2). Though
the chi-square value are significant (χ 2 (60) = 141.52, p < .01), the other fit indices reveal a
good fitting model (NFI = .99, NNFI = .99, CFI = .99, RMSEA = .053). We also estimated a
second-order model for destination personality (Brakus et al., 2009). The results exhibit an
acceptable model fit too (χ 2 (293) = 1029.12, p < .001, NFI = .95, NNFI = .96, CFI = .96,
RMSEA = .072). All factor loadings of the items on their posited indicators exceed .58 and
the t-values greater than 8.39 (p < .01), thus indicating convergent validity.
We assessed discriminant validity through model comparisons with φ freed versus fixed
at one for all construct pairs (Gerbing & Anderson, 1988). All ∆χ 2 are significant at the .05
level. Further the variance shared between the latent constructs is lower than the average
variance extracted (AVE) for each individual construct, suggesting that the measures are not
collinear (Fornell and Larcker, 1981). Table 3 presents the correlations, descriptive statistics,
internal consistency estimates, and AVE for the study constructs. We empirically tested for
CMB in our data through Harman’s test: we estimated a single latent factor reflected by all
the study’s manifest items (Podsakoff et al., 2003). The model statistics show a poor fit to the
data (χ 2 (703) = 62330.50, p < .001; NFI= 0.83; NNFI= 0.84; CFI= 0.84; RMSEA= 0.42),
suggesting that CMB is not a major issue.
We then ran a structural model to test our hypothesised relationships. Given the sample
size restrictions, we used composite measures of the six destination personality dimensions
(see Brakus et al., 2009). The single-item construct revisit intentions was assumed to have a
reliability of .90 for estimation purposes (Anderson & Gerbing, 1988). The model fits the data
well (χ 2 (144) = 335.23, p < .001, NFI = .99, NNFI = .99, CFI = .99, RMSEA = .052). Table 4
shows the standardised parameter estimates, t-values, and significance levels for the
hypothesised paths. The results suggest that, except for H7, the model links are all significant
(p < .01) and in accordance with their respective hypotheses. Overall, the model explains a
great amount (more than half) of the observed variance in the endogenous constructs of
satisfaction (60%), identification (57%), promoting (57%), and revisit intentions (52%).
Discussion
The findings reveal that although tourists appear to ascribe distinct personality characteristics
to tourism destinations, Aaker’s (1997) penta-factorial brand personality structure is not fully
applicable in this case (see also Demirbag Kaplan et al., 2010; Ekinci & Hosany, 2006; Ekinci
et al., 2007; Murphy et al., 2007b). In our case, destination personality can be portrayed in
terms of the established characteristics excitement, sophistication, and ruggedness and the
novel ones activeness, dependability, and philoxenia. The emergence of these tourismspecific
personality dimensions is not surprising considering the idiosyncrasies of the tourism
product offering, which mainly delivers emotional experiences rather than tangible features.
Interestingly, philoxenia has resemblance with the conviviality dimension identified by
4

Hosany et al. (2006). Further, we identified a shift of individual adjectives between
personality dimensions relative to Aaker’s (1997) scale. This was also the case in some prior
research (e.g., Hosany et al., 2006; Murphy et al., 2007b). The phenomenon can be attributed
to brand-adjective interaction; individual words may assume varying meanings depending on
the type of product they are assigned to describe (Ekinci & Hosany, 2006). It appears that
destination marketers need to consider tourism-specific traits when developing differentiated
destination personalities. Clearly, more work is needed to refine and validate a personality
framework that is applicable across a wider range of tourism destinations.
Further, the results suggest destination personality is not only related to tourist
satisfaction and promoting, which is in line with previous research (see Ekinci & Hosany,
2006) but also a prime determinant of revisit intentions. That destination personality
encourages tourist–destination identification is an important, fresh finding in the field.
Marketers should bear this in mind when trying to craft destination personality and attract
congruent market segments.
An intriguing result from this study is also what did not work: the direct impact of
satisfaction on revisit intentions was not corroborated. While most work in tourism aligns to
the general marketing literature and converges to the conclusion that satisfaction results in
return intentions (e.g., Chi & Qu, 2008; Um et al., 2006), some studies suggest this link does
not always hold (e.g., Bigné et al., 2001). The explanation for this may lie in the additional
ramifications of the tourism context: people infrequently purchase vacations and novelty
seeking is a key component driving tourist behaviour (Pearce & Kang, 2009). Nevertheless,
under no circumstances should destination managers underestimate the importance of tourist
satisfaction given (1) its role as chief predictor of tourist–destination identification and
promoting and (2) its indirect effects on revisit intentions via identification.
Another finding of interest concerns the beneficial outcomes of identification. Tourists
with strong psychological attachments to a destination not only intend to revisit, but also
serve as goodwill ambassadors. This is of vital importance for destination managers because
positive word-of-mouth incurs no expenses and is far more credible and effective than
marketer-sourced promotions (Simpson & Siguaw, 2008). Tourism scholars should take note
that identification fulfils a self-definitional need and provides tourists with emotional returns
(cf. Homburg et al., 2009) and can thus enhance understanding of tourist behaviour.
Future studies could incorporate into the present model additional components of
consumer behaviour in tourism, such as motivations, normative influence, and self-construal.
Also, an important direction for further research is to explore the role of tourist experience in
shaping destination personality. It is hoped that this study will inspire further research on the
interface between tourists and tourism destinations. Research along these lines is sorely
needed.
5

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8

Appendices
Figure 1: Research model
H 1 (+)
<strong>Destination</strong> H3 (+)
<strong>Personality</strong>
H 2 (+)
H 3 (+)
H 4 (+)
Tourist
Satisfaction
H 5 (+)
Tourist-
<strong>Destination</strong>
Identification
9
H 7 (+)
H 9 (+)
H 6 (+)
H 8 (+)
Promoting
Revisit
Intentions

Table 1: Exploratory factor analysis for destination personality a
<strong>Destination</strong>
personality traits
Factor 1
Excitement
Factor 2
Sophistication
Factor 3
Activeness
10
Factor 4
Dependability
Factor 5
Philoxenia
Factor 6
Ruggedness
Charming .712 .179 .112 .197 .282 -.016
Original .702 .131 .210 .335 .029 .102
Imaginative .701 .203 .162 .152 .200 .173
Exciting .694 .203 .356 .056 .174 .195
Unique .608 .295 .275 .117 .141 .150
Spirited .603 .182 .296 .090 .157 .213
Upper-class .081 .852 .055 .120 .022 .068
Glamorous .243 .837 .115 -.004 .092 .052
Elegant .318 .779 -.009 .168 .059 .076
Sophisticated .180 .700 .179 .248 .114 .079
Trendy .075 .670 .420 .005 .150 .083
Energetic .332 .135 .782 .188 .256 .159
Active .276 .124 .776 .207 .191 .147
Dynamic .331 .227 .659 .172 .086 .237
Lively .413 .180 .601 .100 .369 .144
Reliable .286 .163 .151 .734 .105 -.059
Responsible .012 .213 .327 .696 .102 .260
Stable .023 .319 .254 .670 .029 .141
Sincere .426 .031 -.005 .640 .234 .173
Honest .298 -.125 -.073 .637 .326 .193
Funny .100 .109 .253 .093 .803 .040
Warm .291 .086 .099 .220 .722 .072
Cheerful .421 .181 .263 .211 .661 .018
Tough .119 .088 .104 .135 -.011 .864
Rugged .172 -.022 .104 .113 .149 .758
Bold .164 .299 .306 .139 -.021 .679
Eigenvalues 10.43 2.34 1.74 1.59 1.19 .94
% of Variance 40.11 9.00 6.71 6.13 4.57 3.60
KMO = .92, Bartlett’s test: χ 2 (325) = 7899.82, p < .01
a Varimax rotation converging in 7 iterations

Table 2: Measurement models
Measurement model 1 Measurement model 2
First-order construct measurement summary:
Confirmatory factor analysis
Factors and items
Standardised
loadings
t-values
11
<strong>Destination</strong> personality measurement summary:
Second-order confirmatory factor analysis
Factors and items
Standardised
loadings
t-values
Satisfaction First-Order Factors
Sat1 .83 18.50 Excitement
Sat2 .89 20.44 Exc1 a .64 —
Sat3 .87 19.86 Exc2 .72 12.48
Sat4 .90 20.98 Exc3 .79 13.56
Sat5 .88 20.26 Exc4 .87 14.57
Sat6 .85 19.21 Exc5 .74 14.44
Exc6 .82 13.88
Identification Sophistication
Id1 .81 17.46 Soph1 a .68 —
Id2 .89 19.91 Soph2 .68 10.72
Id3 .79 16.81 Soph3 .79 11.99
Soph4 .86 12.73
Promoting Soph5 .81 12.28
Pr1 .78 16.39 Activeness
Pr1 .90 20.12 Act1 a .75 —
Pr1 .83 17.73 Act2 .87 17.86
Act3 .95 19.42
Revisit intentions Act4 .80 16.03
Rev1 .98 24.26 Dependability
Dep1 a .67 —
Dep2 .78 12.67
Dep3 .73 11.95
Dep4 .68 11.38
Dep5 .61 10.33
Philoxenia
Ph1 a .66 —
Ph2 .72 11.96
Ph3 .91 13.43
Ruggedness
Rug1 a .64 —
Rug2 .78 10.91
Rug3 .77 10.89
Fit indices: χ 2 (60) = 141.52, p < .001, NFI = .99,
NNFI = .99, CFI = .99, RMSEA = .053
a
Item fixed to set the scale.
Second-Order Factor
<strong>Destination</strong> personality
Excitement .92 14.27
Sophistication .60 9.48
Activeness .83 13.61
Dependability .75 10.77
Philoxenia .79 11.01
Ruggedness .58 8.39
Fit indices: χ 2 (293) = 1029.12, p < .001, NFI = .95,
NNFI = .96, CFI = .96, RMSEA = .072

Table 3: Correlations and measurement statistics
Measures X1 X2 X3 X4 X5
<strong>Destination</strong> personality X1 1.00
Satisfaction X2 .67* 1.00
Identification X3 .60* .63* 1.00
Promoting X4 .58* .59* .61* 1.00
Revisit intentions X5 .52* .53* .51* .43* 1.00
x 4.78 5.51 4.74 4.67 5.13
SD .93 1.46 1.47 1.49 1.80
α .85 .94 .86 .86 -
ρ .80 .90 .79 .79 -
AVE .50 .76 .69 .70 -
* p < .01.
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Table 4: Structural equation model results
Hypotheses Standardised loadings t-values
<strong>Destination</strong> personality → Satisfaction .78 10.44*
<strong>Destination</strong> personality → Identification .41 5.18*
<strong>Destination</strong> personality → Promoting .27 3.34*
<strong>Destination</strong> personality → Revisit intentions .31 3.15*
Satisfaction → Identification .39 5.38*
Satisfaction → Promoting .20 2.65*
Satisfaction → Revisit intentions .17 1.89
Identification → Promoting .36 4.99*
Identification → Revisit intentions .31 3.58*
Fit Indices: χ 2 (144) = 335.23, p < .001, NFI = .99, NNFI = .99, CFI = .99, RMSEA = .052
*p < .01
13