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How destination image and evaluative factors affect behavioral intentions?
ARTICLE IN PRESS
Tourism Management 28 (2007) 1115–1122 www.elsevier.com/locate/tourman
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 ‘‘quality–satisfaction–behavioral 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-satisfactionbehavioral 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 regional development. Successful tourism can increase destination’s 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 postvisitation. More specifically, tourist behavior is an aggregate term, which includes pre-visit’s decision-making, onsite experience, experience evaluations and post-visit’s behavioral intentions and behaviors. It has been generally accepted 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 Corresponding author. Tel.: +886 6 2757575x53230; fax: +886 6 2753882. E-mail addresses: [email protected] (C.-F. Chen), [email protected] (D. Tsai).
0261-5177/$ - see front matter r 2006 Published by Elsevier Ltd. doi:10.1016/j.tourman.2006.07.007
intention. The subsequent evaluations include the travel experience or perceived trip quality during the stay, perceived value and overall satisfaction while the future behavioral 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 satisfaction must 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, perceived 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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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 to maximize 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– satisfaction–behavioral 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 individual’s 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 destination choice decision-making process and (2) to condition the after-decision-making behaviors including participation (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 quality based upon the comparison between expectation and actual performance. However, the influence of destination image on after-decision-making behaviors has been neglected 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; TianCole, Crompton, & Willson, 2002). However, the relationships between these antecedents are arguable. Based upon different assumptions, Petrick (2004) classified the relationship quality, perceived value and satisfaction into three models, i.e. the satisfaction model (quality-valuesatisfaction), the value model (quality-satisfactionvalue) and the quality model (the relationship between satisfaction 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 visitor’s 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 visitor’s desires, expectations and needs in relation to the trip. Perceived value: the visitor’s overall appraisal of the net worth of the trip, based on the visitor’s assessment of what is received (benefits), and what is given (costs or sacrifice). Trip quality: the visitor’s assessment of the standard of the service delivery process in association with the trip experience. Destination image: the visitor’s subjective perception of the destination reality.
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3.2. Sample design and data collection H6
Trip quality
H3
Satisfaction
H7
H1 Destination image
H8
H4
H10
H5
H2
Perceived value
H9
Behavioral intention
Fig. 1. The conceptual model of the study.
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 instrument 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 instrument 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 attributes extracted from previous studies (Baloglu & McCleary, 1999; Beerli & Martin, 2004; Etchner & Ritchie, 1993; Walmsley & Young, 1998). Part 2 deals with the measurement 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 ( ¼ 1)’ to ‘strongly agree ( ¼ 5)’. Part 5 presents respondents’ demographic information with seven items, such as gender, age, education level, occupation, monthly income, travel party, and past visitation experience via a categorical scale.
1
The results of scale reliability for the pilot test are destination image (Cronbach a ¼ 0:89), trip quality (Cronbach a ¼ 0:83), perceived value (Cronbach a ¼ 0:91) and behavioral intention (Cronbach a ¼ 0:87).
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 population. 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 occupation for respondents. The great majority of the respondents had a monthly income less than NT$40,000, or approximately $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 destination image and trip quality to examine their dimensionalities and psychometric properties. On that basis, the relationships of destination image, evaluative factors (i.e. trip quality, perceived value and satisfaction), and behavioral 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 less than 0.5 were removed from the scale. The varimax-rotated factor pattern implies that the first factor concerns ‘‘destination brand’’ (5 items, a ¼ 0:819). The second factor relates to ‘‘entertainment’’ (4 items, a ¼ 0:763). The third factor consists of characteristics of the ‘‘nature and culture’’ (3 items, a ¼ 0:659). The fourth factor relates to ‘‘sun and sand’’ (2 items, a ¼ 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. 2
1 $A33 NT$ at the time of study.
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1118 Table 1 Respondent profile Demographic characteristics
Frequency
Percentage (%)
Gender Male Female
169 224
43.0 57.0
Age 18–24 25–34 35–44 45–54 55 and over
148 161 76 5 3
37.6 41.0 19.3 1.2 0.9
Education level Primary High school University Postgraduate
6 92 266 29
1.2 23.4 67.6 7.8
79 21 52 37 81 17 81 25
20.1 5.3 13.2 9.4 20.6 4.3 20.6 6.5
Monthly income (NT$)a p2,000,000 20,001–40,000 40,001–60,000 60,000–80,000 X80,001
108 176 68 16 25
27.4 44.8 17.1 4.2 6.5
Travel party Single Family Friends Tour group
3 158 229 3
0.9 40.1 58.2 0.9
Past experience First-time visit Repeated visit
76 417
19.3 80.7
Occupation Student Housework Civil servant Self-employed Service worker Skilled worker Clerical worker Other
a
33 NT$A1 US$.
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 ¼ 0:848). The second factor relates to ‘‘attractions’’ (4 items, a ¼ 0:748). The third factor concerns ‘‘transport’’ (3 items, a ¼ 0:769). The fourth consists of the attributes of ‘‘amenity’’ (4 items, a ¼ 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-inuse’ 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 intentions. Factors of ‘‘destination brand’’, ‘‘entertainment’’, ‘‘nature and culture’’ and ‘‘sun and sand’’ were served as the measurement variables of destination image. Also, factors of ‘‘hospitality’’, ‘‘attractions’’, ‘‘transport’’ and ‘‘amenity’’ are used as the measurement variables of trip quality. In addition, perceived value, satisfaction and behavioral intentions 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 ¼ 0.930, RMSEA ¼ 0.0716, RMR ¼ 0.0015, NFI ¼ 0.972, NNFI ¼ 0.975, CFI ¼ 0.981, RFI ¼ 0.963, and PNFI ¼ 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 2 Factor analysis of destination image Factor/item
Factor loading
IM1: Destination brand (3.60) Offers personal safety A good quality of life Clean A good name and reputation Hospitable and friendly people
0.783 0.780 0.718 0.647 0.521
IM2: Entertainment (3.51) Good night life A good shopping place Varied gastronomy Exotic
0.760 0.756 0.744 0.574
IM3: Nature and culture (3.92) Great variety of fauna and flora Spectacular landscape Unusual ways of life and customs
0.852 0.658 0.625
IM4: Sun and sand (4.19) Good weather Good beaches
0.810 0.773
Variance explained (%)
Cumulative variance explained (%)
Cronbach a
20.19
20.19
0.82
17.78
37.97
0.76
12.49
50.46
0.66
11.91
62.40
0.61
Table 3 Factor analysis of trip quality Factor/item
Factor loading
TQ1: Hospitality (3.43) Price of accommodation Prices of activities Food and beverage of accommodation Services of accommodation workers Prices of food & beverage Safety of activities
0.761 0.717 0.707 0.698 0.671 0.526
TQ2: Attractions (3.82) Cleanness of beaches Uniqueness of landscape Comfort of built environment Weather
0.830 0.791 0.602 0.546
TQ3: Transport (3.57) Accessibility Internal transport Parking facilities and space
0.767 0.758 0.713
TQ4: Amenity (3.73) Food and beverage provision General infrastructure Travel information Signs and indicators
0.688 0.676 0.622 0.558
Variance explained (%)
Cumulative variance explained (%)
Cronbach a
20.57
20.57
0.85
13.57
34.14
0.75
13.05
47.19
0.77
12.86
60.05
0.76
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 ¼ 0:91, t-value ¼ 14.63, po0:01, and g4 ¼ 0:37, t-value ¼ 2.17, po0:01, respectively) thus supporting H1 and H4. Due to their insignificances on structural coefficients, however, the hypotheses of destination 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 perceived value (b1 ¼ 0:83, t-value ¼ 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 ¼ 0:75, t-value ¼ 9.51, po0:01), supporting H8 while it does not appear to have a significant effect on behavioral
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1120 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 IM2 IM3 IM4
1.000 0.903 0.746 0.711
— 0.070 0.060 0.065
0.46 0.42 0.34 0.33
— 0.75 12.97** 12.38** 10.90**
0.62
Trip quality
TQ1 TQ2 TQ3 TQ4
1.00 0.960 0.929 0.915
— 0.065 0.082 0.068
0.42 0.40 0.39 0.38
— 0.80 14.77** 11.26** 13.51**
0.60
Perceived value
PV1 PV2 PV3
1.00 1.178 1.172
— 0.078 0.078
0.49 0.57 0.57
— 0.84 15.15** 14.98**
0.76
Behavioral intention BI1 BI2
1.00 1.068
— 0.040
0.59 0.63
— 0.92 26.68**
0.93
**po0:01.
β2=0.04 (0.72)
Trip quality
γ3=0.13 (0.78)
γ1=0.91**
-
(14.63) Destination image
Table 5 Goodness of fit indices of model Satisfaction
β3=0.20 (1.03)
β1=0.83** (10.92)
β6=0.54** (7.94) β4=0.75** (9.51)
-
γ4=0.37** (2.16)
γ2=0.07 (0.53)
Perceived value
β5=0.17
Behavioral intention
(1.74) **denotes p<0.01
Criteria
Indicators
w2-test w2 w2/d.f.
p40:05 o5
207.7 3.01 (207.7/69)
Fit indices GFI AGFI PGFI NFI NNFI
40.9 40.9 40.5 40.9 40.9
0.93 0.94 0.61 0.97 0.97
Alternative indices CFI RMSEA RMR
40.95 o0.05 o0.05
0.98 0.02 0.02
Fig. 2. The estimated structural model.
intentions, not supporting H9. Finally, the satisfaction has a significantly positive effect on behavioral intentions (b6 ¼ 0:54, t-value ¼ 7.94, po0:01), supporting H10. To sum up, an evident path ‘‘destination image-trip quality-perceived value-satisfaction-behavioral intentions’’ 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 visitor’s behavioral intentions. Both destination 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 image’s effect
Table 6 Summary of hypothesis testing results Hypothesis
Testing result
H1 H2 H3 H4 H5 H6 H7 H8 H9 H10
Supported Not supported Not supported Supported Supported Not supported Not supported Supported Not supported Supported
Destination image-Trip quality Destination image-Perceived value Destination image-Satisfaction Destination image-Behavioral intentions Trip quality-Perceived value Trip quality-Satisfaction Trip quality-Behavioral intentions Perceived value-Satisfaction Perceived value-Behavioral intentions Satisfaction-Behavioral intentions
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 7 Direct effect, indirect effect and total effect Path
Direct effect
Indirect effect
Total direct
Image-Trip quality Trip quality-Perceived value Perceived value-Satisfaction Satisfaction-Behavioral intentions Image-Behavioral intentions Trip quality-Behavioral intentions Perceived value-Behavioral intentions
0.91 0.83 0.75 0.54 0.37 — —
— — — — 0.31 0.34 0.41
0.91 0.83 0.75 0.54 0.68 0.34 0.41
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 visitor’s behavioral intentions. 5. Conclusions This study investigated the tourist behaviors by constructing a more comprehensive model considering destination image, evaluative factors (i.e. trip quality, perceived value, satisfaction) and behavioral intentions. The structural 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 regarding 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 in previous 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 qualityperceived value-satisfaction-behavioral intentions’’ appears 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 behaviors, 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 customer’s 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, however, 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 satisfaction would occur and furthermore the benefits of positive behaviors could be brought out. The issues allowing better understanding of customer’s 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. References Ashworth, G., & Goodall, B. (1988). Tourist images: Marketing considerations. In B. Goodall, & G. Ashworth (Eds.), Marketing in the tourism industry. The promotion of destination regions (pp. 213–238). London: Croom Helm. Backman, S. J., & Veldkamp, C. (1995). Examination of the relationship between service quality and user loyalty. Journal of Park and Recreation Administration, 13(2), 29–42. Baker, D. A., & Crompton, J. L. (2000). Quality, satisfaction and behavioral intentions. Annals of Tourism Research, 27(3), 785–804. Baloglu, S., & McCleary, K. W. (1999). A model of destination image formation. Annals of Tourism Research, 26(4), 868–897. Beerli, A., & Martin, J. D. (2004). Factors influencing destination image. Annals of Tourism Research, 31(3), 657–681. Bigne, J., Sanchez, M., & Sanchez, J. (2001). Tourism image, evaluation variables and after purchase behavior: Inter-relationships. Tourism Management, 22(6), 607–616. Bojanic, D. C. (1996). Consumer perceptions of price, value and satisfaction in the hotel industry: An exploratory study. Journal of Hospitality & Leisure Marketing, 4(1), 5–22.
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Bolton, R. N., & Drew, J. H. (1991). A multistage model of customers’ assessments of service quality and value. Journal of Consumer Research, 17(March), 375–384. Buhalis, D. D. (2000). Marketing the competitive destination of the future. Tourism Management, 21(1), 97–116. Caruana, A., Money, A. H., & Berthon, P. R. (2000). Service quality and satisfaction: The moderating role of value. European Journal of Marketing, 34(11/12), 1338–1352. Cooper, C., Fletcher, J., Gilbert, D., & Wanhill, S. (1993). Tourism: Principles and practice. UK: Pitman Publishing. Crompton, J. (1979). An assessment of the image of Mexico as a vacation destination and the influence of geographical location upon the image. Journal of Travel Research, 17(4), 18–23. Cronin, J. J., Brady, M. K., & Hult, G. T. M. (2000). Assessing the effects of quality, value and customer satisfaction on consumer behavioral intentions in service environments. Journal of Retailing, 76(2), 193–218. Etchner, C. M., & Ritchie, J. R. B. (1993). The measurement of destination image: An empirical assessment. Journal of Travel Research, 31(3), 3–13. Fakeye, P., & Crompton, J. (1991). Image differences between prospective, first-time, and repeat visitors to the lower Rio Grande valley. Journal of Travel Research, 30(2), 10–16. Hair, J. F. J., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis with readings. Englewood Cliffs, NJ: Prentice-Hall. Hellier, P. K., Geursen, G. M., Carr, R. A., & Rickard, J. A. (2003). Customer repurchase intention: A general structural model. European Journal of Marketing, 37(11/12), 1762–1800. Joreskog, K. G., & Sorbom, D. (1993). LISREL VIII: User’s reference guide. Chicago: Scientific Software International. Kashyap, R., & Bojanic, D. (2000). A structural analysis of value, quality and price perception of business and leisure travellers. Journal of Travel Research, 39(1), 45–51. Lee, C., Lee, Y., & Lee, B. (2005). Korea’s destination image formed by the 2002 world cup. Annals of Tourism Research, 32(4), 839–858. Mansfeld, Y. (1992). From motivation to actual travel. Annals of Tourism Research, 19, 399–419. Murphy, P., Pritchard, M. P., & Smith, B. (2000). The destination product and its impact on traveler perceptions. Tourism Management, 21(1), 43–52. Nunnally, J. C. (1978). Psychometric theory. New York: McGraw-Hill.
Oh, H. (1999). Service quality, customer satisfaction, and customer value: A holistic perspective. Hospitality Management, 18, 67–82. Oh, H. (2000). Diners’ perceptions of quality, value and satisfaction. Cornell Hotel and Restaurant Administration Quarterly, 41(3), 58–66. Peterson, R. (1994). A meta-analysis of Cronbach’s coefficient alpha. Journal of Consumer Research, 21(2), 381–391. Petrick, J. F. (2004). The roles of quality, perceived value and satisfaction in predicting cruise passengers’ behavioral intentions. Journal of Travel Research, 42(4), 397–407. Petrick, J. F., & Backman, S. J. (2002a). An examination of golf travelers’ satisfaction, perceived value, loyalty and intentions to revisit. Tourism Analysis, 6(3/4), 223–237. Petrick, J. F., & Backman, S. J. (2002b). An examination of the construct of perceived value for the prediction of golf travelers’ intentions to repurchase. Journal of Travel Research, 41(1), 38–45. Petrick, J. F., Backman, S. J., & Bixler, R. (1999). An investigation of selected factors’ impact on golfer satisfaction and perceived value. Journal of Park and Recreation Administration, 17(1), 40–59. Petrick, J. F., Morais, D. D., & Norman, W. C. (2001). An examination of the determinants of entertainment vacationers’ intentions to revisit. Journal of Travel Research, 40(1), 41–48. Ryan, C. (2002). From motivation to assessment. In C. Ryan (Ed.), The tourist experience (2nd ed., pp. 58–77). London: Continuum. Sirakaya, E., Petrick, J., & Choi, H. S. (2004). The role of mood on tourism product evaluations. Annals of Tourism Research, 31(3), 517–539. Tam, J. L. M. (2000). The effects of service quality, perceived value and customer satisfaction on behavioral intentions. Journal of Hospitality and Leisure Marketing, 6(4), 31–43. Tian-Cole, S., Crompton, J. L., & Willson, V. L. (2002). An empirical investigation of the relationships between service quality, satisfaction and behavioral intentions among visitors to a wildlife refuge. Journal of Leisure Research, 34(1), 1–24. Walmsley, D. J., & Young, M. (1998). Evaluative images and tourism: The use of personal constructs to describe the structure of destination images. Journal of Travel Research, 36(2), 65–69. Williams, C., & Buswell, J. (2003). Service quality in leisure and tourism. UK: CABI Publishing. Woodruff, R. B. (1997). Customer value: The next source for competitive edge. Journal of the Academy of Marketing Science, 25(2), 139–153. Zeithaml, V. A. (1988). Consumer perceptions of price, quality and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(July), 2–22.
Tourism Management 28 (2007) 1115–1122 www.elsevier.com/locate/tourman
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 ‘‘quality–satisfaction–behavioral 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-satisfactionbehavioral 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 regional development. Successful tourism can increase destination’s 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 postvisitation. More specifically, tourist behavior is an aggregate term, which includes pre-visit’s decision-making, onsite experience, experience evaluations and post-visit’s behavioral intentions and behaviors. It has been generally accepted 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 Corresponding author. Tel.: +886 6 2757575x53230; fax: +886 6 2753882. E-mail addresses: [email protected] (C.-F. Chen), [email protected] (D. Tsai).
0261-5177/$ - see front matter r 2006 Published by Elsevier Ltd. doi:10.1016/j.tourman.2006.07.007
intention. The subsequent evaluations include the travel experience or perceived trip quality during the stay, perceived value and overall satisfaction while the future behavioral 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 satisfaction must 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, perceived 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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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 to maximize 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– satisfaction–behavioral 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 individual’s 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 destination choice decision-making process and (2) to condition the after-decision-making behaviors including participation (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 quality based upon the comparison between expectation and actual performance. However, the influence of destination image on after-decision-making behaviors has been neglected 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; TianCole, Crompton, & Willson, 2002). However, the relationships between these antecedents are arguable. Based upon different assumptions, Petrick (2004) classified the relationship quality, perceived value and satisfaction into three models, i.e. the satisfaction model (quality-valuesatisfaction), the value model (quality-satisfactionvalue) and the quality model (the relationship between satisfaction 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 visitor’s 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 visitor’s desires, expectations and needs in relation to the trip. Perceived value: the visitor’s overall appraisal of the net worth of the trip, based on the visitor’s assessment of what is received (benefits), and what is given (costs or sacrifice). Trip quality: the visitor’s assessment of the standard of the service delivery process in association with the trip experience. Destination image: the visitor’s subjective perception of the destination reality.
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3.2. Sample design and data collection H6
Trip quality
H3
Satisfaction
H7
H1 Destination image
H8
H4
H10
H5
H2
Perceived value
H9
Behavioral intention
Fig. 1. The conceptual model of the study.
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 instrument 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 instrument 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 attributes extracted from previous studies (Baloglu & McCleary, 1999; Beerli & Martin, 2004; Etchner & Ritchie, 1993; Walmsley & Young, 1998). Part 2 deals with the measurement 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 ( ¼ 1)’ to ‘strongly agree ( ¼ 5)’. Part 5 presents respondents’ demographic information with seven items, such as gender, age, education level, occupation, monthly income, travel party, and past visitation experience via a categorical scale.
1
The results of scale reliability for the pilot test are destination image (Cronbach a ¼ 0:89), trip quality (Cronbach a ¼ 0:83), perceived value (Cronbach a ¼ 0:91) and behavioral intention (Cronbach a ¼ 0:87).
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 population. 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 occupation for respondents. The great majority of the respondents had a monthly income less than NT$40,000, or approximately $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 destination image and trip quality to examine their dimensionalities and psychometric properties. On that basis, the relationships of destination image, evaluative factors (i.e. trip quality, perceived value and satisfaction), and behavioral 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 less than 0.5 were removed from the scale. The varimax-rotated factor pattern implies that the first factor concerns ‘‘destination brand’’ (5 items, a ¼ 0:819). The second factor relates to ‘‘entertainment’’ (4 items, a ¼ 0:763). The third factor consists of characteristics of the ‘‘nature and culture’’ (3 items, a ¼ 0:659). The fourth factor relates to ‘‘sun and sand’’ (2 items, a ¼ 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. 2
1 $A33 NT$ at the time of study.
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1118 Table 1 Respondent profile Demographic characteristics
Frequency
Percentage (%)
Gender Male Female
169 224
43.0 57.0
Age 18–24 25–34 35–44 45–54 55 and over
148 161 76 5 3
37.6 41.0 19.3 1.2 0.9
Education level Primary High school University Postgraduate
6 92 266 29
1.2 23.4 67.6 7.8
79 21 52 37 81 17 81 25
20.1 5.3 13.2 9.4 20.6 4.3 20.6 6.5
Monthly income (NT$)a p2,000,000 20,001–40,000 40,001–60,000 60,000–80,000 X80,001
108 176 68 16 25
27.4 44.8 17.1 4.2 6.5
Travel party Single Family Friends Tour group
3 158 229 3
0.9 40.1 58.2 0.9
Past experience First-time visit Repeated visit
76 417
19.3 80.7
Occupation Student Housework Civil servant Self-employed Service worker Skilled worker Clerical worker Other
a
33 NT$A1 US$.
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 ¼ 0:848). The second factor relates to ‘‘attractions’’ (4 items, a ¼ 0:748). The third factor concerns ‘‘transport’’ (3 items, a ¼ 0:769). The fourth consists of the attributes of ‘‘amenity’’ (4 items, a ¼ 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-inuse’ 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 intentions. Factors of ‘‘destination brand’’, ‘‘entertainment’’, ‘‘nature and culture’’ and ‘‘sun and sand’’ were served as the measurement variables of destination image. Also, factors of ‘‘hospitality’’, ‘‘attractions’’, ‘‘transport’’ and ‘‘amenity’’ are used as the measurement variables of trip quality. In addition, perceived value, satisfaction and behavioral intentions 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 ¼ 0.930, RMSEA ¼ 0.0716, RMR ¼ 0.0015, NFI ¼ 0.972, NNFI ¼ 0.975, CFI ¼ 0.981, RFI ¼ 0.963, and PNFI ¼ 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 2 Factor analysis of destination image Factor/item
Factor loading
IM1: Destination brand (3.60) Offers personal safety A good quality of life Clean A good name and reputation Hospitable and friendly people
0.783 0.780 0.718 0.647 0.521
IM2: Entertainment (3.51) Good night life A good shopping place Varied gastronomy Exotic
0.760 0.756 0.744 0.574
IM3: Nature and culture (3.92) Great variety of fauna and flora Spectacular landscape Unusual ways of life and customs
0.852 0.658 0.625
IM4: Sun and sand (4.19) Good weather Good beaches
0.810 0.773
Variance explained (%)
Cumulative variance explained (%)
Cronbach a
20.19
20.19
0.82
17.78
37.97
0.76
12.49
50.46
0.66
11.91
62.40
0.61
Table 3 Factor analysis of trip quality Factor/item
Factor loading
TQ1: Hospitality (3.43) Price of accommodation Prices of activities Food and beverage of accommodation Services of accommodation workers Prices of food & beverage Safety of activities
0.761 0.717 0.707 0.698 0.671 0.526
TQ2: Attractions (3.82) Cleanness of beaches Uniqueness of landscape Comfort of built environment Weather
0.830 0.791 0.602 0.546
TQ3: Transport (3.57) Accessibility Internal transport Parking facilities and space
0.767 0.758 0.713
TQ4: Amenity (3.73) Food and beverage provision General infrastructure Travel information Signs and indicators
0.688 0.676 0.622 0.558
Variance explained (%)
Cumulative variance explained (%)
Cronbach a
20.57
20.57
0.85
13.57
34.14
0.75
13.05
47.19
0.77
12.86
60.05
0.76
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 ¼ 0:91, t-value ¼ 14.63, po0:01, and g4 ¼ 0:37, t-value ¼ 2.17, po0:01, respectively) thus supporting H1 and H4. Due to their insignificances on structural coefficients, however, the hypotheses of destination 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 perceived value (b1 ¼ 0:83, t-value ¼ 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 ¼ 0:75, t-value ¼ 9.51, po0:01), supporting H8 while it does not appear to have a significant effect on behavioral
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1120 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 IM2 IM3 IM4
1.000 0.903 0.746 0.711
— 0.070 0.060 0.065
0.46 0.42 0.34 0.33
— 0.75 12.97** 12.38** 10.90**
0.62
Trip quality
TQ1 TQ2 TQ3 TQ4
1.00 0.960 0.929 0.915
— 0.065 0.082 0.068
0.42 0.40 0.39 0.38
— 0.80 14.77** 11.26** 13.51**
0.60
Perceived value
PV1 PV2 PV3
1.00 1.178 1.172
— 0.078 0.078
0.49 0.57 0.57
— 0.84 15.15** 14.98**
0.76
Behavioral intention BI1 BI2
1.00 1.068
— 0.040
0.59 0.63
— 0.92 26.68**
0.93
**po0:01.
β2=0.04 (0.72)
Trip quality
γ3=0.13 (0.78)
γ1=0.91**
-
(14.63) Destination image
Table 5 Goodness of fit indices of model Satisfaction
β3=0.20 (1.03)
β1=0.83** (10.92)
β6=0.54** (7.94) β4=0.75** (9.51)
-
γ4=0.37** (2.16)
γ2=0.07 (0.53)
Perceived value
β5=0.17
Behavioral intention
(1.74) **denotes p<0.01
Criteria
Indicators
w2-test w2 w2/d.f.
p40:05 o5
207.7 3.01 (207.7/69)
Fit indices GFI AGFI PGFI NFI NNFI
40.9 40.9 40.5 40.9 40.9
0.93 0.94 0.61 0.97 0.97
Alternative indices CFI RMSEA RMR
40.95 o0.05 o0.05
0.98 0.02 0.02
Fig. 2. The estimated structural model.
intentions, not supporting H9. Finally, the satisfaction has a significantly positive effect on behavioral intentions (b6 ¼ 0:54, t-value ¼ 7.94, po0:01), supporting H10. To sum up, an evident path ‘‘destination image-trip quality-perceived value-satisfaction-behavioral intentions’’ 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 visitor’s behavioral intentions. Both destination 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 image’s effect
Table 6 Summary of hypothesis testing results Hypothesis
Testing result
H1 H2 H3 H4 H5 H6 H7 H8 H9 H10
Supported Not supported Not supported Supported Supported Not supported Not supported Supported Not supported Supported
Destination image-Trip quality Destination image-Perceived value Destination image-Satisfaction Destination image-Behavioral intentions Trip quality-Perceived value Trip quality-Satisfaction Trip quality-Behavioral intentions Perceived value-Satisfaction Perceived value-Behavioral intentions Satisfaction-Behavioral intentions
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 7 Direct effect, indirect effect and total effect Path
Direct effect
Indirect effect
Total direct
Image-Trip quality Trip quality-Perceived value Perceived value-Satisfaction Satisfaction-Behavioral intentions Image-Behavioral intentions Trip quality-Behavioral intentions Perceived value-Behavioral intentions
0.91 0.83 0.75 0.54 0.37 — —
— — — — 0.31 0.34 0.41
0.91 0.83 0.75 0.54 0.68 0.34 0.41
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 visitor’s behavioral intentions. 5. Conclusions This study investigated the tourist behaviors by constructing a more comprehensive model considering destination image, evaluative factors (i.e. trip quality, perceived value, satisfaction) and behavioral intentions. The structural 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 regarding 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 in previous 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 qualityperceived value-satisfaction-behavioral intentions’’ appears 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 behaviors, 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 customer’s 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, however, 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 satisfaction would occur and furthermore the benefits of positive behaviors could be brought out. The issues allowing better understanding of customer’s 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. References Ashworth, G., & Goodall, B. (1988). Tourist images: Marketing considerations. In B. Goodall, & G. Ashworth (Eds.), Marketing in the tourism industry. The promotion of destination regions (pp. 213–238). London: Croom Helm. Backman, S. J., & Veldkamp, C. (1995). Examination of the relationship between service quality and user loyalty. Journal of Park and Recreation Administration, 13(2), 29–42. Baker, D. A., & Crompton, J. L. (2000). Quality, satisfaction and behavioral intentions. Annals of Tourism Research, 27(3), 785–804. Baloglu, S., & McCleary, K. W. (1999). A model of destination image formation. Annals of Tourism Research, 26(4), 868–897. Beerli, A., & Martin, J. D. (2004). Factors influencing destination image. Annals of Tourism Research, 31(3), 657–681. Bigne, J., Sanchez, M., & Sanchez, J. (2001). Tourism image, evaluation variables and after purchase behavior: Inter-relationships. Tourism Management, 22(6), 607–616. Bojanic, D. C. (1996). Consumer perceptions of price, value and satisfaction in the hotel industry: An exploratory study. Journal of Hospitality & Leisure Marketing, 4(1), 5–22.
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C.-F. Chen, D. Tsai / Tourism Management 28 (2007) 1115–1122
Bolton, R. N., & Drew, J. H. (1991). A multistage model of customers’ assessments of service quality and value. Journal of Consumer Research, 17(March), 375–384. Buhalis, D. D. (2000). Marketing the competitive destination of the future. Tourism Management, 21(1), 97–116. Caruana, A., Money, A. H., & Berthon, P. R. (2000). Service quality and satisfaction: The moderating role of value. European Journal of Marketing, 34(11/12), 1338–1352. Cooper, C., Fletcher, J., Gilbert, D., & Wanhill, S. (1993). Tourism: Principles and practice. UK: Pitman Publishing. Crompton, J. (1979). An assessment of the image of Mexico as a vacation destination and the influence of geographical location upon the image. Journal of Travel Research, 17(4), 18–23. Cronin, J. J., Brady, M. K., & Hult, G. T. M. (2000). Assessing the effects of quality, value and customer satisfaction on consumer behavioral intentions in service environments. Journal of Retailing, 76(2), 193–218. Etchner, C. M., & Ritchie, J. R. B. (1993). The measurement of destination image: An empirical assessment. Journal of Travel Research, 31(3), 3–13. Fakeye, P., & Crompton, J. (1991). Image differences between prospective, first-time, and repeat visitors to the lower Rio Grande valley. Journal of Travel Research, 30(2), 10–16. Hair, J. F. J., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis with readings. Englewood Cliffs, NJ: Prentice-Hall. Hellier, P. K., Geursen, G. M., Carr, R. A., & Rickard, J. A. (2003). Customer repurchase intention: A general structural model. European Journal of Marketing, 37(11/12), 1762–1800. Joreskog, K. G., & Sorbom, D. (1993). LISREL VIII: User’s reference guide. Chicago: Scientific Software International. Kashyap, R., & Bojanic, D. (2000). A structural analysis of value, quality and price perception of business and leisure travellers. Journal of Travel Research, 39(1), 45–51. Lee, C., Lee, Y., & Lee, B. (2005). Korea’s destination image formed by the 2002 world cup. Annals of Tourism Research, 32(4), 839–858. Mansfeld, Y. (1992). From motivation to actual travel. Annals of Tourism Research, 19, 399–419. Murphy, P., Pritchard, M. P., & Smith, B. (2000). The destination product and its impact on traveler perceptions. Tourism Management, 21(1), 43–52. Nunnally, J. C. (1978). Psychometric theory. New York: McGraw-Hill.
Oh, H. (1999). Service quality, customer satisfaction, and customer value: A holistic perspective. Hospitality Management, 18, 67–82. Oh, H. (2000). Diners’ perceptions of quality, value and satisfaction. Cornell Hotel and Restaurant Administration Quarterly, 41(3), 58–66. Peterson, R. (1994). A meta-analysis of Cronbach’s coefficient alpha. Journal of Consumer Research, 21(2), 381–391. Petrick, J. F. (2004). The roles of quality, perceived value and satisfaction in predicting cruise passengers’ behavioral intentions. Journal of Travel Research, 42(4), 397–407. Petrick, J. F., & Backman, S. J. (2002a). An examination of golf travelers’ satisfaction, perceived value, loyalty and intentions to revisit. Tourism Analysis, 6(3/4), 223–237. Petrick, J. F., & Backman, S. J. (2002b). An examination of the construct of perceived value for the prediction of golf travelers’ intentions to repurchase. Journal of Travel Research, 41(1), 38–45. Petrick, J. F., Backman, S. J., & Bixler, R. (1999). An investigation of selected factors’ impact on golfer satisfaction and perceived value. Journal of Park and Recreation Administration, 17(1), 40–59. Petrick, J. F., Morais, D. D., & Norman, W. C. (2001). An examination of the determinants of entertainment vacationers’ intentions to revisit. Journal of Travel Research, 40(1), 41–48. Ryan, C. (2002). From motivation to assessment. In C. Ryan (Ed.), The tourist experience (2nd ed., pp. 58–77). London: Continuum. Sirakaya, E., Petrick, J., & Choi, H. S. (2004). The role of mood on tourism product evaluations. Annals of Tourism Research, 31(3), 517–539. Tam, J. L. M. (2000). The effects of service quality, perceived value and customer satisfaction on behavioral intentions. Journal of Hospitality and Leisure Marketing, 6(4), 31–43. Tian-Cole, S., Crompton, J. L., & Willson, V. L. (2002). An empirical investigation of the relationships between service quality, satisfaction and behavioral intentions among visitors to a wildlife refuge. Journal of Leisure Research, 34(1), 1–24. Walmsley, D. J., & Young, M. (1998). Evaluative images and tourism: The use of personal constructs to describe the structure of destination images. Journal of Travel Research, 36(2), 65–69. Williams, C., & Buswell, J. (2003). Service quality in leisure and tourism. UK: CABI Publishing. Woodruff, R. B. (1997). Customer value: The next source for competitive edge. Journal of the Academy of Marketing Science, 25(2), 139–153. Zeithaml, V. A. (1988). Consumer perceptions of price, quality and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(July), 2–22.