04_How Destination Image and Evaluative Factors Affect Behavioral Intentions
Transcript of 04_How Destination Image and Evaluative Factors Affect Behavioral Intentions
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7/31/2019 04_How Destination Image and Evaluative Factors Affect Behavioral Intentions
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Tourism Management 28 (2007) 11151122
Research article
How destination image and evaluative factors
affect behavioral intentions?
Ching-Fu Chen, DungChun Tsai
Department of Transportation and Communication Management Science, National Cheng Kung University, 1, Ta-Hsueh Rd. Tainan, 701, Taiwan, ROC
Received 1 November 2005; accepted 17 July 2006
Abstract
Differing from the previous studies, this study proposed a more integrated tourist behavior model by including destination image and
perceived value into the qualitysatisfactionbehavioral intentions paradigm. The structural relationships between all variables with
respect to different stages of tourist behaviors were investigated in the study. The results show that destination image have both direct
and indirect effects on behavioral intentions. In addition, the path destination image-trip quality-perceived value-satisfaction-
behavioral intentions appears evident in this study.
r 2006 Published by Elsevier Ltd.
Keywords: Destination image; Trip quality; Perceived value; Satisfaction; Behavioral intentions
1. Introduction
Tourism has been seen as the driving force for regionaldevelopment. Successful tourism can increase destinations
tourist receipts, income, employment and government
revenues. How to attract the tourists to revisit and/or
recommend the destination to others is crucial for the
success of destination tourism development.
From the perspective of tourist consumption process
(Ryan, 2002; Williams & Buswell, 2003), tourist behavior
can be divided into three stages: pre-, during- and post-
visitation. More specifically, tourist behavior is an aggre-
gate term, which includes pre-visits decision-making, on-
site experience, experience evaluations and post-visits
behavioral intentions and behaviors. It has been generallyaccepted in the literature that destination image has
influence on tourist behaviors (Bigne, Sanchez, & Sanchez,
2001; Fakeye & Crompton, 1991; Lee, Lee, & Lee, 2005).
The tourist behaviors include the choice of a destination to
visit and subsequent evaluations and future behavioral
intention. The subsequent evaluations include the travel
experience or perceived trip quality during the stay,
perceived value and overall satisfaction while the futurebehavioral intentions include the intention to revisit and
the willingness to recommend. There has been a great body
of studies focusing on the interrelationship between
quality, satisfaction and behavioral intentions (Backman
& Veldkamp, 1995; Baker & Crompton, 2000; Cronin,
Brady, & Hult, 2000). However, in recent years perceived
value has been emphasized as the object of attention by
researchers in tourism (Kashyap & Bojanic, 2000; Murphy,
Pritchard, & Smith, 2000; Oh, 1999, 2000; Petrick, 2004;
Petrick & Backman, 2002a, b; Petrick, Backman, & Bixler,
1999; Petrick, Morais, & Norman, 2001; Tam, 2000). Some
studies even argued that the measurement of satisfactionmust be in conjunction with the measure of perceived value
(Oh, 2000; Woodruff, 1997) and perceived value plays the
moderating role between service quality and satisfaction
(Caruana, Money, & Berthon, 2000). Furthermore, per-
ceived value involves the benefits received for the price paid
(Zeithaml, 1988) and is a distinctive concept from quality
and satisfaction. Empirical research also reveal that the
positive impact of perceived value on both future
behavioral intentions and behaviors. Hence, perceived
value, quality and satisfaction all have been shown to be
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0261-5177/$ - see front matterr 2006 Published by Elsevier Ltd.
doi:10.1016/j.tourman.2006.07.007
Corresponding author. Tel.: +886 6 2757575x53230;
fax: +8866 2753882.
E-mail addresses: [email protected] (C.-F. Chen),
[email protected] (D. Tsai).
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good predictors of future behavioral intentions (Baker &
Crompton, 2000; Bojanic, 1996; Cronin et al., 2000;
Petrick, 2004; Tam, 2000).
By understanding the relationships between future
behavioral intentions and its determinants, destination
tourism managers would better know how to build up an
attractive image and improve their marketing efforts tomaximize their use of resources. Hence, the purpose of the
study is twofold. The first is to construct a more integrated
model of tourist consumption process by including
destination image and perceived value into the quality
satisfactionbehavioral intention paradigm. The second is
to examine the relationships between destination image and
evaluative factors (i.e. trip quality, perceived value and
satisfaction) in their prediction of future behavioral
intentions.
2. Conceptual background and hypotheses
Destination image is defined as an individuals mental
representation of knowledge (beliefs), feelings and overall
perception of a particular destination (Crompton, 1979;
Fakeye & Crompton, 1991). Destination image plays two
important roles in behaviors: (1) to influence the destina-
tion choice decision-making process and (2) to condition
the after-decision-making behaviors including participa-
tion (on-site experience), evaluation (satisfaction) and
future behavioral intentions (intention to revisit and
willingness to recommend) (Ashworth & Goodall, 1988;
Bigne et al., 2001; Cooper, Fletcher, Gilbert, & Wanhill,
1993; Lee et al., 2005; Mansfeld, 1992). On-site experience
can be mainly represented as the perceived trip qualitybased upon the comparison between expectation and
actual performance. However, the influence of destination
image on after-decision-making behaviors has been ne-
glected in previous studies except for Bigne et al. (2001)
and Lee et al. (2005). Following the marketing perspective,
Lee et al. (2005) argued that individuals having a favorable
destination image would perceive their on-site experiences
(i.e. trip quality) positively, which in turn would lead to
greater satisfaction levels and behavioral intentions.
The first four hypotheses, therefore, would be:
H1. The more favorable the destination image, the higher
the perceived trip quality.
H2. The more favorable the destination image, the higher
the overall satisfaction.
H3. The more favorable the destination image, the higher
the perceived value.
H4. The more favorable the destination image, the more
positive the behavioral intention.
As aforementioned, service quality has been recognized
as the antecedent of satisfaction and behavioral intentions
in a service setting. In addition, the research by Bigne et al.
(2001) and Lee et al. (2005) also ascertained that higher trip
quality could lead to both higher satisfaction and more
positive behavioral intentions in general.
The fifth and sixth hypotheses, therefore, would be:
H5. The higher the trip quality, the higher the overall
satisfaction.
H6. The higher the trip quality, the more positive the
behavioral intention.
Quality, perceived value and satisfaction have been
recognized as the antecedents of behavioral intentions
(Kashyap & Bojanic, 2000; Petrick, 2004; Tam, 2000; Tian-
Cole, Crompton, & Willson, 2002). However, the relation-
ships between these antecedents are arguable. Based upon
different assumptions, Petrick (2004) classified the relation-
ship quality, perceived value and satisfaction into three
models, i.e. the satisfaction model (quality-value-
satisfaction), the value model (quality-satisfaction-
value) and the quality model (the relationship betweensatisfaction and value is uncertain). The empirical result
shows in favor of the satisfaction model. In other words,
perceived value plays a moderating role between quality
and satisfaction. The evidence is inherent to Caruana et al.
(2000) and Hellier, Geursen, Carr, and Rickard (2003). In
addition, perceived value may be a better predicator of
repurchase intentions than either satisfaction or quality
(Cronin et al., 2000; Oh, 2000).
The last four hypotheses, therefore, would be:
H7. The higher the trip quality, the higher the perceived
value.
H8. The higher the perceived value, the higher the overall
satisfaction.
H9. The higher the perceived value, the more positive the
behavioral intention.
H10. The higher the overall satisfaction, the more positive
the behavioral intention.
The conceptual model of the study is shown as Fig. 1.
Each of the model components is defined as follows:
Behavioral intention: the visitors judgment about the
likeliness to revisit the same destination or the willingness
to recommend the destination to others.
Overall satisfaction: the extent of overall pleasure or
contentment felt by the visitor, resulting from the ability of
the trip experience to fulfill the visitors desires, expecta-
tions and needs in relation to the trip.
Perceived value: the visitors overall appraisal of the net
worth of the trip, based on the visitors assessment of what
is received (benefits), and what is given (costs or sacrifice).
Trip quality: the visitors assessment of the standard of
the service delivery process in association with the trip
experience.
Destination image: the visitors subjective perception of
the destination reality.
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3. Methodology
3.1. Questionnaire design
The questionnaire was designed as the survey instrument
including all constructs of the proposed model to
investigate the hypotheses of interest. The questions in
the questionnaire are based on a review of the literature
and specific destination characteristics. The survey instru-
ment was revised and finalized based on feedback from five
tourism experts and a pilot sample of 25 postgraduate
students studying a tourism management program in
Taiwan. Hence, the content validity of the survey instru-
ment was deemed as adequate.1 The questionnaire consists
of five parts. Part 1 of the questionnaire deals with the
measurement of destination image with 20 attributesextracted from previous studies (Baloglu & McCleary,
1999; Beerli & Martin, 2004; Etchner & Ritchie, 1993;
Walmsley & Young, 1998). Part 2 deals with the measure-
ment of trip quality with 20 items covering the five aspects
of attractions, accessibility, amenity, activities, available
packages, and ancillary services (Buhalis, 2000). Part 3
deals with the measurement of perceived value with three
items including time value, money value and effort value
(Bolton & Drew, 1991). Part 4 deals with the measurement
of single-item overall satisfaction and two-item behavioral
intentions (i.e. likeliness to revisit and willingness to
recommend) following Bigne et al. (2001), Sirakaya,
Petrick, and Choi (2004) and Tian-Cole et al. (2002).
Respondents are asked to indicate their agreement level for
each item, for the first four parts on a five-point Likert-type
scale, from strongly disagree ( 1) to strongly agree
( 5). Part 5 presents respondents demographic informa-
tion with seven items, such as gender, age, education level,
occupation, monthly income, travel party, and past
visitation experience via a categorical scale.
3.2. Sample design and data collection
The empirical study was carried out in Kengtin region,
an important and famous coastal destination in southern
Taiwan, during December 2004. Individuals over the age of
18 years and who were visiting the attractions within the
Kengtin region were considered to be the target popula-tion. Applying the convenient sampling technique, a total
number of 500 questionnaires were delivered and 393
usable samples were obtained, resulting in a response rate
of 78.6%.
The respondent profile is summarized as Table 1. The
great majority of the respondents were aged below 34 but
over 15 (72.2%) with a slight majority of female visitors
(57.0%). In all, 75.4% had a university degree or higher
qualification. Student (20.1%), service worker (20.6%) and
clerk worker (20.6%) were the main divisions of occupa-
tion for respondents. The great majority of the respondents
had a monthly income less than NT$40,000, or approxi-
mately $12002 (72.2%), 98.3% were accompanying family
or friends (98.3%), and 80.7% were revisiting Kengtin.
3.3. Data analysis
The data analysis was conducted in two stages. First,
exploratory factor analyses using principal component
method with varimax rotation were conducted on destina-
tion image and trip quality to examine their dimensional-
ities and psychometric properties. On that basis, the
relationships of destination image, evaluative factors (i.e.
trip quality, perceived value and satisfaction), and beha-
vioral intentions were empirically tested using structural
equation modeling (SEM) technique in the second stage.
4. Empirical results
In this study a multi-attribute approach was employed to
measure destination image and trip quality. As mentioned
above, destination image and trip quality were both
measured using a 20-item scale. Employing the principal
components factor analysis, four factors with an eigenvalue
greater than one explained 62.4% of the variance of
destination image scale. Six items with factor loading lessthan 0.5 were removed from the scale. The varimax-rotated
factor pattern implies that the first factor concerns
destination brand (5 items, a 0:819). The second
factor relates to entertainment (4 items, a 0:763).
The third factor consists of characteristics of the nature
and culture (3 items, a 0:659). The fourth factor relates
to sun and sand (2 items, a 0:607). The arithmetic
means of the four multi-item factors were used to build the
construct destination image for subsequent analysis. The
result of the factor analysis for destination image is shown
in Table 2.
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H6
-
H8
H9
H3
H4 H2
H5
H1
H10
SatisfactionTrip
quality
Destinationimage
Perceived
value
Behavioral
intention
H7
Fig. 1. The conceptual model of the study.
1The results of scale reliability for the pilot test are destination image
(Cronbach a 0:89), trip quality (Cronbach a 0:83), perceived value
(Cronbach a 0:
91) and behavioral intention (Cronbach a 0:
87). 21 $A33 NT$ at the time of study.
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Similarly, four factors with an eigenvalue greater than
one explained 60.5% of the variance of trip quality scale
using the principal components factor analysis. Two items
with loading factors less than 0.5 were removed from the
scale. The varimax-rotated factor pattern implies that the
first factor relates to hospitality (7 items, a 0:848). The
second factor relates to attractions (4 items, a 0:748).
The third factor concerns transport (3 items, a 0:769).
The fourth consists of the attributes of amenity (4 items,
a 0:763). The arithmetic means of the four multi-item
factors were used to build the construct trip quality for
subsequent analysis. The result of the factor analysis for
trip quality is shown in Table 3.
Reliability for each of the factors was obtained using the
calculation of a Cronbach a coefficient. The Cronbach a
coefficients ranged from 0.85 to 0.61 (see Tables 2 and 3).
Six of the eight factors were above the cut-off criterion of
0.7 recommended by Nunnally (1978) while two were just
below this level, namely, nature and culture (0.66) and
sun and sand (0.61). However, Peterson (1994) suggested
that an a value of 0.6 is the criterion-in-use. Therefore, it
suggests that all factors were well above the criterion-in-
use and thus acceptably reliable.Confirmatory factor analysis (CFA) was then conducted
using LISREL VIII (Joreskog & Sorbom, 1993) with
covariance matrix to test the convergent validity of the
constructs used in subsequent analysis. The fit indices
suggested by Joreskog and Sorbom (1993) and Hair,
Anderson, Tatham, and Black (1998) were used to assess
the model adequacy. Convergent validity of CFA results
should be supported by item reliability, construct reliability
and average variance extracted (Hair et al., 1998). As
shown in Table 4, t-values for all the standardized factor
loadings of the items were found to be significant (po0:01).
In addition, construct reliability estimates ranging from
0.75 to 0.92 exceeded the critical value of 0.7 recommended
by Hair et al. (1998), indicating it was satisfactory. The
average variances extracted for all the constructs fell
between 0.60 and 0.93, and were greater than the value
of 0.5 suggested by Hair et al. (1998). Composite scores for
each construct were obtained from the mean scores across
items representing that construct.
The proposed conceptual model in Fig. 1 was tested by
using the five constructs: namely destination image, trip
quality, perceived value, satisfaction and behavioral inten-
tions. Factors of destination brand, entertainment,
nature and culture and sun and sand were served as
the measurement variables of destination image. Also, factorsof hospitality, attractions, transport and amenity
are used as the measurement variables of trip quality. In
addition, perceived value, satisfaction and behavioral inten-
tions were measured by three, one and two items as
mentioned previously, respectively. Employing the covariance
matrix among 14 measurement items as input, the SEM
analysis was conducted to examine the relationships between
each pair of constructs as hypothesized. The results of SEM
analysis were depicted in Fig. 2. The fit indices of the model
are summarized in Table 5. The overall model indicates that
w2 is 207.7 with 69 degrees of freedom (d.f.) (po0.0001).
Technically, the p-value should be greater than 0.05, i.e.,
statistically insignificant. However, in practice the w2-value is
very sensitive to sample size and frequently results in the
rejection of a well-fitting model. Hence, the ratio of w2 over
d.f. has been recommended as a better goodness of fit than w2
(Hair et al., 1998). A common level of the w2/d.f. ratio is
below 5 (though below 3 is better). The w2/d.f. ratio of the
model is 3.01 (i.e., 207.7/69), indicating an acceptable fit.
Furthermore, other indicators of goodness of fit are
GFI 0.930, RMSEA 0.0716, RMR 0.0015,
NFI 0.972, NNFI 0.975, CFI 0.981, RFI 0.963,
and PNFI 0.737. Comparing these with the corresponding
critical values shown in Table 4, it suggests that the
hypothesized model fits the empirical data well.
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Table 1
Respondent profile
Demographic characteristics Frequency Percentage (%)
Gender
Male 169 43.0
Female 224 57.0
Age
1824 148 37.6
2534 161 41.0
3544 76 19.3
4554 5 1.2
55 and over 3 0.9
Education level
Primary 6 1.2
High school 92 23.4
University 266 67.6
Postgraduate 29 7.8
Occupation
Student 79 20.1
Housework 21 5.3Civil servant 52 13.2
Self-employed 37 9.4
Service worker 81 20.6
Skilled worker 17 4.3
Clerical worker 81 20.6
Other 25 6.5
Monthly income (NT$)a
p2,000,000 108 27.4
20,00140,000 176 44.8
40,00160,000 68 17.1
60,00080,000 16 4.2
X80,001 25 6.5
Travel party
Single 3 0.9
Family 158 40.1
Friends 229 58.2
Tour group 3 0.9
Past experience
First-time visit 76 19.3
Repeated visit 417 80.7
a33 NT$A1 US$.
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Within the overall model, the estimates of the structural
coefficients provide the basis for testing the proposed
hypotheses. As shown in Fig. 2, destination image has a
significantly positive effect on trip quality and behavioral
intentions (g1 0:91, t-value 14.63, po0:01, and
g4 0:37, t-value 2.17, po0:01, respectively) thus sup-
porting H1 and H4. Due to their insignificances on
structural coefficients, however, the hypotheses of destina-
tion image has positive effect on perceived value (H2) and
on satisfaction, (H3) is not supported. The trip quality, as
hypothesized, has a significantly positive effect on per-
ceived value (b1 0:83, t-value 10.92, po:01), thus
supporting H5. Nonetheless, it does not have a significant
effect on both satisfaction and behavioral intentions, thus
rejecting H6 and H7, respectively. In addition, the perceived
value has a significantly positive effect on satisfaction
(b4 0:75, t-value 9.51, po0:01), supporting H8 while it
does not appear to have a significant effect on behavioral
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Table 2
Factor analysis of destination image
Factor/item Factor loading Variance explained (%) Cumulative variance explained (%) Cronbach a
IM1: Destination brand (3.60) 20.19 20.19 0.82
Offers personal safety 0.783
A good quality of life 0.780
Clean 0.718
A good name and reputation 0.647
Hospitable and friendly people 0.521
IM2: Entertainment (3.51) 17.78 37.97 0.76
Good night life 0.760
A good shopping place 0.756
Varied gastronomy 0.744
Exotic 0.574
IM3: Nature and culture (3.92) 12.49 50.46 0.66
Great variety of fauna and flora 0.852
Spectacular landscape 0.658
Unusual ways of life and customs 0.625
IM4: Sun and sand (4.19) 11.91 62.40 0.61
Good weather 0.810Good beaches 0.773
Table 3
Factor analysis of trip quality
Factor/item Factor loading Variance explained (%) Cumulative variance explained (%) Cronbach a
TQ1: Hospitality (3.43) 20.57 20.57 0.85
Price of accommodation 0.761
Prices of activities 0.717
Food and beverage of accommodation 0.707
Services of accommodation workers 0.698
Prices of food & beverage 0.671Safety of activities 0.526
TQ2: Attractions (3.82) 13.57 34.14 0.75
Cleanness of beaches 0.830
Uniqueness of landscape 0.791
Comfort of built environment 0.602
Weather 0.546
TQ3: Transport (3.57) 13.05 47.19 0.77
Accessibility 0.767
Internal transport 0.758
Parking facilities and space 0.713
TQ4: Amenity (3.73) 12.86 60.05 0.76
Food and beverage provision 0.688
General infrastructure 0.676Travel information 0.622
Signs and indicators 0.558
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intentions, not supporting H9. Finally, the satisfaction has a
significantly positive effect on behavioral intentions
(b6 0:54, t-value 7.94, po0:01), supporting H10.
To sum up, an evident path destination image-trip
quality-perceived value-satisfaction-behavioral inten-
tions appears in the estimated model. The results of the
hypotheses testing are summarized in Table 6. Note that
trip quality does not directly, but does indirectly, influence
satisfaction through perceived value as a moderating
variable. This finding confirms the arguments of previous
studies (Caruana et al., 2000; Oh, 2000; Woodruff, 1997).
Table 7 reports the direct and indirect effects of all
variables on visitors behavioral intentions. Both destina-
tion image and satisfaction had direct effects on behavioral
intentions while trip quality and perceived value had
indirect effects on behavioral intentions. Total effect of
destination image on behavioral intentions, i.e., sum of
direct and indirect effect through destination images effect
on trip quality, perceived and satisfaction, was found to be
0.68. In a similar way, the total effects of trip quality,
perceived value and satisfaction on behavioral intentions
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Table 4
Convergent validity
Constructs Items Item reliability Construct reliability Average variance extracted
Factor loadings Standard error Standardized factor loading t-value
Destination image IM1 1.000 0.46 0.75 0.62
IM2 0.903 0.070 0.42 12.97**IM3 0.746 0.060 0.34 12.38**
IM4 0.711 0.065 0.33 10.90**
Trip quality TQ1 1.00 0.42 0.80 0.60
TQ2 0.960 0.065 0.40 14.77**
TQ3 0.929 0.082 0.39 11.26**
TQ4 0.915 0.068 0.38 13.51**
Perceived value PV1 1.00 0.49 0.84 0.76
PV2 1.178 0.078 0.57 15.15**
PV3 1.172 0.078 0.57 14.98**
Behavioral intention BI1 1.00 0.59 0.92 0.93
BI2 1.068 0.040 0.63 26.68**
**po0:
01.
-
1=0.83**
(10.92)
-
-(1.03)
4=0.75**
4=0.37**
3=0.13
(0.78)
Destination
image
(14.63)
1=0.91**
Trip
qualitySatisfaction
2=0.04
(0.72)
3=0.20
6=0.54**
(7.94)
Behavioral
intention
(9.51)
5=0.17
(1.74)
Perceived
value
2=0.07
(0.53)(2.16)
**denotes p
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were found to be 0.34, 0.41 and 0.54, respectively. It
indicates that destination image and satisfaction are the
two most important variables to influence visitors
behavioral intentions.
5. Conclusions
This study investigated the tourist behaviors by con-
structing a more comprehensive model considering desti-
nation image, evaluative factors (i.e. trip quality, perceived
value, satisfaction) and behavioral intentions. The struc-
tural relationships between all variables in the study were
tested using data obtained from a visitor questionnaire
survey at Kengtin in southern Taiwan. As Lee et al. (2005)
argued, although broad agreement among scholars regard-
ing the influence of destination image on process, little
empirical research has been done. In addition, the
moderating role of perceived value between quality and
satisfaction has been debatable but frequently neglected inprevious research. This study differs from previous studies
by taking account of destination image and perceived value
in the tourist behavior model.
The structural relationship analysis indicates that
destination image appears to have the most important
effect on behavioral intentions (i.e. intention to revisit and
willingness to recommend). Destination image influences
behavioral intentions in two ways: directly and indirectly.
This finding is consistent with Bigne et al. (2001). In
particular, the path of destination image-trip quality-
perceived value-satisfaction-behavioral intentions ap-
pears evident in this study. Destination image not only
influences the decision-making process but also conditions
after-decision-making behaviors of tourists. In other
words, the influence of destination image is not limited to
the stage of selecting the destination, but also affects the
behavior of tourists in general (Bigne et al., 2001). Hence,
endeavors to build or improve the image of a destination
facilitate loyal visitors revisiting or recommending beha-
viors, thus being critical to the success of destination
tourism development.
Trip quality was found to have an indirect rather than a
direct effect on overall satisfaction as moderated by
perceived value. It implies that unless leading to an
increase in perceived value, trip quality is not guaranteed
to lead to customers overall satisfaction. Subsequently, the
results in positive behavioral intentions would be also
uncertain. Hence, perceived value does play an important
role in affecting the level of satisfaction and future
behavioral intentions of customers. An increase in quality
would generally induce an increase in costs. If a product
with high quality cannot make customers satisfied, how-
ever, the quality in practice is of little use and its induced
cost is wasteful. By better understanding how tourists value
their trip experiences, tourism managers could be able to
device more effective marketing strategies and service
delivery to meet tourists actual needs. Once tourists
perceive their trip experiences valuable, the higher satisfac-
tion would occur and furthermore the benefits of positive
behaviors could be brought out. The issues allowing better
understanding of customers value perception and the role
of perceived value in the relationship between quality and
satisfaction should be addressed and warrant future study.
Acknowledgments
The authors wish to thank Mr. Ting-Yao Wei for his
assistance in data collection and the two referees for their
comments.
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Table 7
Direct effect, indirect effect and total effect
Path Direct effect Indirect effect Total direct
Image-Trip quality 0.91 0.91
Trip quality-Perceived value 0.83 0.83
Perceived value-Satisfaction 0.75 0.75
Satisfaction-Behavioral intentions 0.54 0.54
Image-Behavioral intentions 0.37 0.31 0.68
Trip quality-Behavioral intentions 0.34 0.34
Perceived value-Behavioral intentions 0.41 0.41
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