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Development of a scale measuring destination image

Article in Marketing Intelligence & Planning · June 2010

DOI: 10.1108/02634501011053595

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MIP
28,4 Development of a scale measuring
destination image
Kevin K. Byon
508 Department of Kinesiology,
University of Georgia, Athens, Georgia, USA, and
Received June 2009 James J. Zhang
Revised August 2009,
October 2009,
Department of Tourism, Recreation and Sport Management,
December 2009 College of Health and Human Performance, University of Florida,
Accepted December 2009 Gainesville, Florida, USA

Abstract
Purpose – The purpose of this paper is to develop the scale of destination image (SDI) to assess
destination image affecting the consumption associated with tourism.
Design/methodology/approach – The scale was developed through four steps: review of
literature, formulation of a preliminary scale, confirmatory factor analysis (CFA), and examination
of predictive validity by a structural equation modeling (SEM) analysis. The preliminary scale
consisted of 32 items. Employing a systematic sampling method, a total of 199 research participants
responded to a mail survey.
Findings – In the CFA with maximum likelihood estimation, four factors with 18 pertinent items are
retained. This four-factor model displays good fit to the data, preliminary construct validity, and high
reliability. The SEM analysis reveals that the SDI is found to be positively predictive of tourism
behavioral intentions.
Originality/value – This paper develops an original multi-dimensional 18-item scale measuring
destination image from the perspective of tourists, which can provide academicians and practitioners
with a reliable and valid analytical tool to assess destination image.
Keywords Performance measures, Measurement, testing and instruments, Tourism, Travel
Paper type Research paper

Introduction
Studying the reasons that cause and channel travel has been a focal point of recent
tourism research. In the tourism literature, a wide range of variables have been
identified as influencing factors. These may include, but not limited to, destination
image (Alcaniz et al., 2009; Baloglu and McCleary, 1999; Beerli and Martin, 2004; Chen
and Hsu, 2000; Echtner and Ritchie, 1991; Fakeye and Crompton, 1991), service quality
(Chen and Tsai, 2007; Lee et al., 2005), tourist satisfaction (Yoon and Uysal, 2005), and
perceived risk (Lepp and Gibson, 2003; Sönmez and Graefe, 1998). Of these, destination
image has been repeatedly found to have significant influences on travel-related
behaviors, such as destination choice and future travel intentions (Alcaniz et al., 2009;
Baloglu and McCleary, 1999; Beerli and Martin, 2004; Fakeye and Crompton, 1991; Lee
Marketing Intelligence & Planning et al., 2005). For instance, Alcaniz et al. (2009) found that destination image positively
Vol. 28 No. 4, 2010
pp. 508-532 influenced tourism behavioral intentions toward a resort vacation. Using visitors of the
q Emerald Group Publishing Limited famous Turkey region, Aksu et al. (2009) also found that destination image was
0263-4503
DOI 10.1108/02634501011053595 positively associated with tourists’ re-visit intentions and word-of-mouth behaviors.
Phillips and Jang (2008) investigated how destination image was related to tourist Destination
attitude as a determinant of behavioral intentions, and confirmed that destination image
image substantially explained tourist attitude toward a destination.
Clearly, empirical evidences support the notion that destination image is an
important factor that likely exerts significant impact on the decision-making process of
tourists. Nonetheless, various limitations and weaknesses have been identified in
previous studies; to a great extent, issues were primarily related to measures developed 509
or adopted in these studies. First, a number of researchers (Crompton, 1979; Fakeye and
Crompton, 1991) have indicated that attributes representing destination image should
be context-specific since each destination consists of its unique characteristics. Beerli
and Martin (2004, p. 659) supported the above argument by emphasizing that:
[. . .] the selection of the attributes used in designing a scale will depend largely on the
attractions of each destination, on its positioning, and on the objectives of the assessment of
perceived image.
Nonetheless, they have also indicated the need to develop assessment instruments of
destination image that can be used in broader settings and include fundamental
perspectives of tourists’ cognitive and affective images. Second, measures in previous
studies were usually developed based on the application of exploratory factor analysis
(EFA) as the primary statistical procedure (Aksu et al., 2009; Chalip et al., 2003; Fakeye
and Crompton, 1991; Hosany et al., 2006; Hui and Wan, 2003; Obenour et al., 2005). An
EFA is merited in its capability to identify a simple structure among the variables in a
scale and is most suited for early stage investigations in a conceptual area, in which a
well-developed theoretical framework is not available. When extensive inquires and
investigations have been conducted in a conceptual area and a systematic framework
begins to emerge, a confirmatory factor analysis (CFA) would be more appropriate. This
would be the case for studying destination image when considering the significant
amount of knowledge that has been accumulated from empirical studies. A CFA is a
theory-driven procedure in which the factors of the scale are driven by a well-developed
theoretical framework or previous empirical evidence stemming from exploratory
and/or confirmatory analytical procedures (Bollen, 1989). Third, a majority of previous
studies on destination image involved a college student sample. Although college
students represent a considerable segment of tourists, they are usually limited by their
financial resources, availability and flexibility of travel schedule, incapability to revisit
destinations, and overall consumption level; thus, scales that are developed on this
population exhibit limited the generalizability (Chalip et al., 2003). These limitations and
diverse aspects need to be addressed when developing and improving measures of
destination image. To fill the void, the current study was designed to develop the scale of
destination image (SDI), following the cognitive-affective attitude theory (Bagozzi and
Burnkrant, 1985). The scale development was accomplished through a comprehensive
review of literature, interview with practitioners, a test of content validity, test
administration to a national sample of various sociodemographic backgrounds, testing
construct validity and reliability through conducting a CFA, and a structural equation
modeling (SEM) analysis to examine the scale’s predictive validity. The SDI would
address several limitations associated with previous destination image scales:
MIP .
the SDI would be a sound destination image scale since it was developed based
28,4 on the cognitive-affective attitude theory and rigorous measurement procedures,
including CFA and SEM;
.
the SDI would be multi-dimensional in nature, allowing academicians and
practitioners to know which dimensions of the destination image best and least
explain behavioral intentions (i.e. revisit intention, recommend to others, and
510 intention to attend sport event), so that proper marketing strategies can be
developed; and
.
the SDI could be potentially generalized to other tourism settings as the sample
was drawn nationally.

It was also expected that the developed SDI scale would be frequently adopted by
researchers and tourism marketers to examine tourists’ image of attributes representing a
particular destination. Information derived from SDI would help tourism marketers to
identify which destination image factors would have the most or least relevance on
behavioral intentions, so that effective marketing strategies can be formulated.

Review of literature
Destination image
According to various researchers in tourism studies (Fakeye and Crompton, 1991; Gunn,
1972), there are three types of images that individuals hold of a particular destination:
organic image, induced image, and complex image. These three types of images are
based on individuals’ experience with a particular destination. An organic image arises
from non-tourism information such as geography books, television reports, or magazine
articles. An induced image can arise from tourism-specific information such as a
destination brochure or vacation web site, which is a product of destination marketing
efforts. The major difference between organic image and induced image lies in
individuals’ intention or motivation of travel. In other words, any individual can have an
organic image toward a particular destination even though the individual has no
intention to travel to the destination; whereas, people can purposefully seek travel
information about a destination through its promotional materials and thus hold an
induced image if they have a specific intention to visit the destination (Gunn, 1972).
Complex image can be derived as a result of direct experience of the destination (Fakeye
and Crompton, 1991). Since Gunn’s seminal work on destination image, many
researchers have defined and conceptualized destination image in the context of
tourism. Hunt (1975) defined destination image as perceptions that potential visitors
hold about a destination. When measuring the destination image of Mexico held by US
citizens, Crompton (1979) conceptualized destination image as the sum of cognitive
beliefs and affective impressions that an individual possesses of a particular
destination. Similarly, Baloglu and Bringerg (1997) and Beerli et al. (2002) summarized
that destination image is characterized by subjective perceptions that consist of both
high levels of cognitive aspects (belief) and affective aspects (feeling). Based on these
indications, destination image is an evaluative attitudinal judgment that was comprised
of cognitive and affective elements (Baloglu and MaCleary, 1999). Hence, the
measurement of destination image should reflect both cognitive and affective aspects.
Over the last three decades, many researchers have identified variables that represent
destination image of a particular location (Aksu et al., 2009; Alcaniz et al., 2009; Baloglu
and Bringerg, 1997; Baloglu and McCleary, 1999; Beerli and Martin, 2004; Chalip et al., Destination
2003; Chen and Hsu, 2000; Chi and Qu, 2008; Echtner and Ritchie, 1991; Fakeye and image
Crompton, 1991; Hosany et al., 2006; Hui and Wan, 2003; Lee et al., 2005; Martin and
Bosque, 2008; Obenour et al., 2005; Phillips and Jang, 2008). A majority of these destination
image studies have adopted cognitive image components that were related to beliefs or
perceptions that tourists hold concerning attributes related to a destination (Aksu et al.,
2009; Alcaniz et al., 2009; Chen and Hsu, 2000; Chalip et al., 2003; Fakeye and Crompton, 511
1991; Lee, 2009; Obenour et al., 2005). In an effort to measure destination image toward
Australian and New Zealead’s cities, Chalip et al. (2003) developed the destination image
scale that included 40 items under nine cognitive factors:
(1) developed environment;
(2) natural environment;
(3) value;
(4) sightseeing opportunities;
(5) risk;
(6) novelty;
(7) climate;
(8) convenience; and
(9) family environment.

Fakeye and Crompton (1991) conducted a study on how a specific destination image
(i.e. Lower Rio Grande Valley) was formed in tourists’ minds. The researchers compared
differences in destination image among three groups of non-visitors, first-timers, and
repeaters. Five cognitive destination image factors were examined, including:
(1) social opportunities and attractions;
(2) natural and cultural amenities;
(3) accommodations, transportation, and infrastructure;
(4) food and friendly people; and
(5) bars and evening entertainment.

Obenour et al. (2005) developed a destination image scale that included six cognitive
image dimensions with a total of 28 items:
(1) priority;
(2) attractiveness for overnights;
(3) resources;
(4) facilities;
(5) peripheral attractiveness; and
(6) reputation.

To identify destination image dimensions associated with Singapore, Hui and Wan
(2003) conducted a study involving inbound visitors and identified eight cognitive
image dimensions, including:
MIP (1) leisure and tourist amenities;
28,4 (2) shopping and food paradise;
(3) local residents and nightlife;
(4) political stability;
(5) adventure and weather;
512 (6) culture;
(7) leanliness; and
(8) personal safety and convenience.

Similarly, Aksu et al. (2009) identified five cognitive destination image factors related
to Antalya region of Turkey. The identified factors are listed in the following:
(1) shopping;
(2) health and hygiene;
(3) information;
(4) transportation; and
(5) accommodation.

For above studies, an EFA was the main analytical method to identify destination
image dimensions (Aksu et al., 2009; Alcaniz et al., 2009; Chen and Hsu, 2000;
Chalip et al., 2003; Fakeye and Crompton, 1991; Lee, 2009; Obenour et al., 2005).
In the context of wetlands tourism, Lee (2009) developed a destination image scale as a
part of a large-scale study to examine how destination image, attitude, and tourism
motivation affect future tourism behavior. The scale was comprised of three cognitive
dimensions, including natural scenery, social-cultural aspects, and recreational activities.
However, no psychometric property information with regard to the destination image
scale was reported. Particularly, the destination image construct was treated as a
uni-dimensional concept in the data analysis despite the proposed multi-dimensional
constructs. Adopting Echtner and Ritchie’s (1993) functional-psychological continuum
model, Alcaniz et al. (2009) developed a three-dimensional cognitive destination image
model that included functional, mixed, and psychological factors. In this modeling, a CFA
was employed, that revealed the three-factor model yielded sound psychometric
properties. The modified model, however, was not cross-validated.
Despite increasing popularity of the cognitive destination image model, there has
been a strong argument that tourism destination should not be understood solely by
cognitive image, as a tourist may have an emotional attachment to a certain destination
(Ward and Russell, 1981). Following this conceptualization approach, Russell et al.
(1981) developed a circumplex model of assessing a tourist’s affect associated with a
destination. The model contained two bipolar dimensions, including:
(1) pleasant-unpleasant and arousing-sleepy dimension; and
(2) exciting-gloomy and relaxing-distressing dimension.

Using a multidimensional scaling method, Baloglu and Bringerg (1997) tested Russell
et al.’s (1981) model and confirmed the two bipolar affective aspects, providing further
evidence for the circumplex model’s generalizability in a tourism context
(i.e. Mediterranean countries). In addition, the authors suggested that both cognitive Destination
and affective image be incorporated in the measurement of destination image in order image
to better understand the perception a tourist holds regarding a destination.
Furthermore, Echtner and Ritchie (1991) recognized that destination image had both
functional (e.g. scenery, facilities, activities, and accommodations) and psychological
characteristics (e.g. friendly people, feeling, and atmosphere). The functional aspect
was related to tangibility (i.e. cognitive) and the psychological characteristics included 513
intangible aspects (i.e. affective). These were in line with prior studies related to the
definition and conceptualization of destination image, which suggested that
destination image measurement consist of both cognitive and affective aspects
(Baloglu and Bringerg, 1997; Beerli and Martin, 2004).
Recently, many studies have been conducted following cognitive-affective image
theory (Baloglu and McCleary, 1999; Beerli and Martin, 2004; Hosany et al., 2006;
Lee et al., 2005; Martin and Bosque, 2008; Phillips and Jang, 2008). Baloglu and
McCleary (1999) demonstrated how destination image is formed in the absence of actual
visitation. They identified three cognitive factors (quality of experience, attractions,
and value/entertainment) and two bipolar affective factors (arousing-sleepy and
pleasant-unpleasant; and exciting-gloomy and relaxing-distressing). Following Baloglu
and McCleary’ study, Beerli and Martin (2004) reported a total of five cognitive image
factors that pertained to destination image of a popular vacation site (i.e. Lanzarote in
Spain). The cognitive factors identified were the following:
(1) natural and cultural resources;
(2) general tourist infrastructure;
(3) atmosphere;
(4) social setting and environment; and
(5) sun and beach.

Two affective factors were also identified, including pleasant-unpleasant and

exciting-boring. To examine South Korea’s destination image formed by the 2002 FIFA
World Cup Soccer Games, Lee et al. (2005) developed a five-factor model of destination
image involving spectators from three games of the 2002 FIFA World Cup Soccer Games
and also foreign tourists visiting popular destinations located in South Korea. The model
consisted of four dimensions of cognitive aspects, including:
(1) attraction;
(2) comfort;
(3) value for money; and
(4) exotic atmosphere, and a uni-dimensional measure of affect.

Following a CFA, the five factors of destination image were found to have reasonable
psychometric properties, as evidenced by construct reliability (CR) and factor loadings.
However, two limitations were recognized:
(1) one of the cognitive factors, exotic atmosphere, was measured using a single
item, and as such, the reliability of the factor was unavailable; and
MIP (2) the affect dimension was conceptualized as uni-dimensional despite the
28,4 suggestion that affect is multi-dimensional in nature (Baloglu and Brinberg,
1997; Mehrabian and Russell, 1974; Russell et al., 1981).
Hosany et al. (2006) examined the relationship between destination image and destination
personality. In their study, two cognitive image factors (physical atmosphere and
514 accessibility) and one affective image factor (affective) were validated through construct
validity and criterion validity was established through examining the relationships with
global destination image and intent to recommend to others. Adopting a mixed method
approach, Martin and Bosque (2008) developed a five-factor model of destination image
that included four cognitive factors (infrastructure and socioeconomic environment,
atmosphere, natural environment, and cultural environment) and one affective image
factor (affective). The model demonstrated good psychometric properties as evidenced by
EFA and CFA. Using both EFA and CFA as the factor analytical method, Phillips and
Jang (2008) found a four-factor destination model that included both cognitive and
affective components.
In brief, three important aspects in the review of literature are synthesized:
(1) factors related to destination image are destination-specific (Beerli and Martin,
2004);
(2) when constructing destination image model, it is necessary that both cognitive
and affective aspects be reflected because destination image is a collection of an
individual’s belief and feeling; and
(3) considering the issues associated with currently available scale, a destination
image scale with better valid and reliable evidence is needed.

Predictability of destination image on behavioral intentions

Previous research findings indicated that destination image had both direct and
indirect effect on behavioral intentions (Alcaniz et al., 2009; Baloglu and McCleary,
1999; Bigne et al., 2001; Castro et al., 2007; Chen and Tsai, 2007; Chi and Qu, 2008; Lee,
2009). In these studies, behavioral intentions were usually examined from two different
perspectives, using the terms “intention to (re)visit and willingness to recommend to
others”. Conducting a SEM, Baloglu and McCleary (1999) found that three cognitive
destination image factors (quality of experience, attractions, and value/entertainment)
were positively associated with word-of-mouth (i.e. willingness to recommend to
others). Bigne et al. (2001) investigated interrelationships among destination image,
perceived quality, satisfaction, intention to return, and willingness to recommend to
others in the context of resort visitors. They found that destination image had a direct
effect on intention to return and willingness to recommend to others. Meanwhile,
destination image was also found to have an indirect effect on intention to return and
willingness to recommend to others through quality and satisfaction. Chen and Tsai
(2007) supported Bigne et al.’s (2001) findings by indicating that destination image had
a direct effect on trip quality and behavioral intentions. In addition, destination image
had an indirect effect on behavioral intentions through trip quality, perceived value,
and satisfaction. Recently, Alcaniz et al. (2009) also found a direct effect of cognitive
destination image on tourism behavioral intentions. More specifically, functional
image was only related to revisit intention and psychological image was only related to
intention to recommend, and mixed image was associated with neither of the two
behavioral intentions. Applying a theory of market heterogeneity in their study, Destination
Castro et al. (2007) found that there was strong an indirect relationship between a image
destination image and intention to visit, in which the relationship was moderated by
service quality and tourist satisfaction. Chi and Qu (2008) tested a theoretical model
that examined whether or not destination image had a direct or indirect effect on
behavioral loyalty using a sample of a famous spring tourists. The findings indicated
that destination image was indirectly related to behavioral loyalty through attribute 515
satisfaction and overall satisfaction. Lee (2009) also found the mediating effect of
satisfaction between destination image and future tourism behavior, supporting the
indirect effect of destination image and future tourism behavior.

Method
Participants
According to the image theory proposed by Gunn (1972) and Fakeye and Crompton
(1991), there are three types of image, including organic, induced, and complex that
tourists may hold of a particular destination. The determination of possessing each of the
three images is based on information source, intention, and previous visit experience.
Unlike induced and complex image, organic image can be formed with an absence of
tourism intention and behavior (Gunn, 1972). Our intention in the current study was to
include potential tourists, who already formed an interest in the city, known as a college
town with a very successful intercollegiate athletic program. Therefore, the sampling
frame of the current study was delimited to those who possessed either induced or
complex image toward the study context. Design and selection of research participants
in this study was consistent with this intention. Research participants (N ¼ 199) were
those who requested tourism information from the county’s Visitors and Convention
Bureau about the city during a time period of six months following the National
Collegiate Athletic Association (NCAA) men’s basketball national championship event.
They met the criterion of being potential tourists as they had not been to the city at the
time of inquiring about information; however, by the time of data collection, some of
those on the inquiry list had already visited the city. The inquiry list contained
approximately 6,000 people from all 50 states and Washington, District of Columbia in
the USA, and all of them were at least 18 years old. Of those, 2,000 people were selected
using a systematic random sampling technique; every third person on the list was
chosen to be potential research participants. Only those who lived outside of the county
and without individual affiliation with the university as a student, faculty, or staff were
considered as potential visitors and thus included in the study. According to Wetson and
Gore (2006), a minimum sample size of 200 would be adequate as long as all the
assumptions for a SEM analysis (e.g. normality, missing data, and outliers) are met. In
the current study, there were 199 participants in the research sample, which met Wetson
and Gore’s threshold. After obtaining travel information from the county’s Visitors and
Convention Bureau, over 40 percent of the respondents who requested information about
the city actually visited the city or surrounding communities within a six-month period
following the NCAA men’s basketball championship game.

Development of instrument
Adopting Churchill’s (1979) scale development procedure, the preliminary SDI was
formulated through the following three stages:
MIP (1) an extensive review of literature and developing the preliminary scale;
28,4 (2) conducting a test of content validity through a panel of experts and a pilot
study; and
(3) test administration and examination of measurement properties.

Consistent with similar scales measuring destination image in previous studies that
516 were developed for specific tourist destinations, the SDI in this study consisted of two
conceptual components that included both cognitive image and affective image
perspectives. Under cognitive image, there were five factors: infrastructure, social and
political environment, natural environment, attraction, and value for money (Baloglu
and McCleary, 1999; Beerli and Martin, 2004; Lee et al., 2005). Two factors that measure
affective aspect of destination image were developed based on research findings of
previous studies, including pleasant and arousal (Baloglu and Brinberg, 1997;
Russell et al., 1981). In addition to a comprehensive review of literature, the existent
tourism aspects in natural and community offerings in the city and surrounding
environments were taken into consideration when formulating the items under the
seven factors. Nonetheless, particular efforts were made to ensure that the composed
items were relevant and representative of general features of a wide variety of tourism
destinations. A total of 32 items were written for the factors, with infrastructure having
six items, social and political environment and attraction factors having five items,
respectively, and the remaining factors having four items in each factor. The items were
preceded with the following statements: “The city of xxx is a college town with
excellent intercollegiate athletic programs, achievements (e.g. two national basketball
championships and one national football championships in a recent year), and
reputation. Each of the following items is intended to measure your perceived image of
the city of xxx that may be a potential place for you to visit in the near future. Please
rate the following statements about the city.” Each item was phrased into a Likert
seven-point scale, ranging from 1 ¼ strongly disagree to 7 ¼ strongly agree.
For the purpose of examining predictive validity of the SDI, three items measuring
behavioral intentions were adopted from previous studies (Castro et al., 2007; Chen and
Tsai, 2007). The behavioral intentions items represent three related conceptual areas,
including intention to (re)visit the destination, recommend to others, and intention to
attend sport event. Doing so was based on the following considerations. Within the
tourism literature, re-visit intentions and recommend to others have been the most
frequently adopted constructs used to measure behavioral intentions (Aksu et al., 2009;
Castro et al., 2007; Chen and Tsai, 2007; Chi and Qi, 2008; Hosany et al., 2006).
Additionally, the destination that was being examined in this study was known as a
“college town” with a very successful athletic program. For instance, in the last five
years, the university men’s basketball and football teams won an NCAA record of four
national championship titles. Also, we examined contents of the city webpage, in which
we found that university sport events were listed as one of the main attractions in the
city. Our interview with the community tourism bureau director revealed that a large
percentage of tourists were actually sport event tourists who had specific intentions to
attend sport events that were held on the university campus. Therefore, it was deemed
appropriate to include the variable (i.e. intention to attend sport event) as one of the
sub-items under behavioral intentions factor. The items were preceded with the
following statement: “The following items are for the purpose of measuring your
behavioral intentions towards visiting the city of xxx and attend the university athletic Destination
events. Please rate the following statements using the scale provided.” Each item was image
phrased into a Likert seven-point scale, ranging from 1 ¼ strongly disagree to
7 ¼ strongly agree.
For sample description purpose, various demographic questions were included in
the questionnaire, which included the following: age, gender, family income,
education level, ethnicity, previous travel experience to the community, travel party, 517
travel distance, and residence. Additionally, the respondents were asked about their
attendance, media consumption, and information gathering of the basketball and
football national championship events. Respondents were also assessed of their sport
event attendance and destination visit behaviors after obtaining travel information from
the county’s Visitors and Convention Bureau. All of these questions were phrased in
multiple-choice or filling-a-blank format.
Procedures
Following the development of the SDI scale, it was submitted to a panel of six experts
for a test of content validity. The panel included the director of the county’s Visitors and
Convention Bureau and five university professors: one specializes in sport tourism, one
in business marketing, and three in sport management. Each panel member was
requested to examine the relevance, representativeness, and clarity of each item.
A number of items in the preliminary scale were modified or revised according to the
input of the experts. With this improved version of the scale, a pilot study was
conducted by involving 40 undergraduate students who were enrolled in sport and
physical activity courses. These students represented 15 different academic majors on a
university campus. The students were instructed to examine the relevance, format, and
wording of the items by responding to the SDI scale, as well as other consumption and
background questions in the questionnaire. Acting on the feedback derived from the
pilot study, additional changes and improvements were made to improve the content
validity of the scale. As a result of the content validity test and the pilot study, all of the
32 items in the SDI scale were retained after revisions and modifications were made.
Approval of the study by the Institutional Review Board for the Protection of
Human Participants was obtained. A survey packet was composed that included a
cover letter, informed consent form, the SDI, behavioral intentions items, and
sociodemographic variables. The packet was distributed via postal mail to those who
were systematically selected from the inquiry list provided by the county’s Visitors
and Convention Bureau. A self-addressed and stamped envelope was included in the
mail. As a follow-up procedure, a reminder postcard was sent out to those who did not
return the survey packet in three weeks (Dillman, 2000). A total of 112 questionnaires
were returned after the first mailing. As a result of the reminder postcard, additional
124 questionnaires were returned. Overall, a total of 236 questionnaires were returned
for a response rate of 11.8 percent. This return rate appears to be a low response rate;
however, previous studies that adopted a household mail survey method have
indicated that a typical return rate for a mail survey ranged from 10 to 20 percent
(Oppermann, 2000). Of those 236 questionnaires, 37 questionnaires were discarded due
to more than 10 percent of missing values, in which the authors defined as an
incomplete answer based on previous research evidence (Zhang et al., 1996). The
remaining 199 questionnaires were deemed useable for testing the measurement
properties of the SDI scale.
MIP Data analyses
28,4 Procedures in SPSS 15.0 (SPSS, 2006) were utilized to calculate descriptive statistics
for the sociodemographic variables. AMOS 7.0 (Arbuckle, 2006) was executed to
examine psychometric properties of the SDI scale through conducting a CFA for the
seven latent factors of destination image and behavioral intentions factor, respectively
(Bollen, 1989; Hair et al., 2006). Despite the fact that various scholars suggested using
518 both EFA and CFA when developing a new scale (Hinkin, 1995), there were two reasons
that we employed only CFA for developing the SDI. First, sample size was not large
enough to split into two sets. Second, the initial seven-factor model was an a priori model,
which was developed based on previous research findings. All in all, employing only a
CFA was deemed appropriate.
Following the suggestion of Hair et al. (2006), several model fit indexes were used,
including the chi-square statistic (x 2), the normed chi-square (x 2/df), root mean square
error of approximation (RMSEA), standardized root mean square residual (SRMR),
comparative fit index (CFI), and expected cross-validation index (ECVI). For the
chi-square statistic, nonsignificant difference indicates that there is no difference
between the expected and observed covariance matrices. Bollen (1989) suggested that
cutoff values of less than 3.0 for the normed chi-square are considered reasonable fit. Hu
and Bentler (1999) suggested that RMSEA value of 0.06 also indicates a close fit. Any
values of RMSEA between 0.06 and 0.08 indicate acceptable fit. Values of RMSEA
between 0.08 and 0.10 show mediocre fit (Hu and Bentler, 1999). For the SRMR, any
values less than 0.10 are considered favorable fit (Kline, 2005). A rule of thumb for CFI
is that any values greater than 0.90 indicate an acceptable fit, and a value greater than
0.95 shows a close fit (Hu and Bentler, 1999). Generally, smaller values of ECVI are
considered better fit of the model (Kline, 2005).
To determine convergent validity, standardized indicator loadings and the loadings’
statistical significance were evaluated for each observed variable. Statistically
significant high loading of an item on the respective latent construct indicates good
convergent validity. Generally speaking, an item loading value equal to or greater than
0.71 (i.e. R 2 value $ 0.50) would be considered an acceptable loading for good
convergent validity (Anderson and Gerbing, 1988). To further ensure construct validity,
discriminant validity was evaluated by two tests:
(1) examination of the interfactor correlations; and
(2) comparing squared correlation with average variance explained (AVE) value
for each of the two latent constructs (Fornell and Larcker, 1981).

According to Kline (2005), discriminant validity can be established when interfactor

correlation is below 0.85. A more robust way of measuring discriminant validity was
suggested by Fornell and Larcker (1981), referring that a squared correlation between
two constructs should be lower than the AVE for each construct.
Three tests were employed to measure reliability of the scales:
(1) Cronbach’s coefficient alpha (a).
(2) CR.
(3) AVE values.
The recommended 0.70 cut-off value was adopted to determine internal consistency (a) Destination
and CR (Fornell and Larcker, 1981). A benchmark value of 0.50 was used to evaluate image
AVE (Bagozzi and Yi, 1988). Since AMOS did not provide AVE and CR values, we used
the formulae suggested by Fornell and Larcker (1981). Additionally, a SEM analysis
was conducted to examine the predictive validity of the SDI by testing the relationship
between destination image factor and behavioral intentions factor. The similar fit
index criteria were adopted to examine the structural model as with the measurement 519
model. Path coefficients were used to determine the direct effects of destination image
factor on behavioral intentions factor.

Results
Descriptive statistics
Of the sample, 44.2 percent were male and 55.8 percent were female. Age ranged from
22 to 88 years (M ¼ 50 years, SD ¼ 14.67). A majority of the respondents (73.4 percent)
reported an annual income of over $40,000, with 31.2 percent of the respondents with
a yearly income of over $80,000, representing mid-upper level of income. With regard
to education, a majority of the respondents were well-educated, with 84.4 percent
possessing at least some college experiences, 25.1 percent of the respondents earned
either a master’s or doctoral degree. The sample was predominantly White/
non-Hispanic of nearly 70 percent in the sample, and Black/African American was
second largest (13.6 percent) in the sample. Since this study was conducted at a
national level, travel distance varied (M ¼ 461.50 miles, SD ¼ 530.90 miles), ranging
from 50 miles to over 3,000 miles. In-state residence was somewhat more dominant
than out-of-state residence, showing 60.8 percent lived within the state; whereas,
39.2 percent of the respondents were living outside the state. A majority of the
respondents (85.4 percent) reported that they were not affiliated with the university in
such ways as family member or relative of a current university student, faculty, or
staff. Sociodemographic characteristics of the respondents overall represent diverse
backgrounds of potential tourism consumers (Table I).
Analyses were conducted to examine the normality of data distributions in terms of
skewness and kurtosis. For the skewness and kurtosis cut-off value, absolute value of
3.0 would be considered extreme (Chou and Bentler, 1995). After reviewing the
skewness and kurtosis values, it was found that all of them were well within the
acceptable threshold (Table II).
As far as missing data were concerned, no not missing at random (Rubin, 1987;
Schafer and Graham, 2002) data were found in the current sample, which means that
there were no systematic missing data. Only missing at random (MAR) data were
detected in rare cases, in which situation regression imputation was conducted to deal
with the MAR data.

Measurement model
Data for the SDI scale that contained 32 items under seven factors were submitted to a CFA
using the maximum likelihood estimation method (Arbuckle, 2006). The CFA revealed
that goodness-of-fit of the seven-factor measurement model did not fit the data well (i.e.
x 2 ¼ 1,077.32, p , 0.001, x 2/df ¼ 2.43, RMSEA ¼ 0.085, 90 percent CI ¼ 0.079-0.092,
SRMR ¼ 0.07, CFI ¼ 0.85, and ECVI ¼ 6.62). According to Tabachnick and Fidell (2001),
MIP Frequency
28,4 Variables Category (%) Cumulative percent

Gender Male 88 (44.2) 44.2

Female 111 (55.8) 100.0
Age 18-30 22 (11.0) 7.0
31-40 35 (17.5) 23.6
520 41-50 47 (23.5) 47.7
51-60 48 (24.0) 71.4
61-70 33 (17.0) 88.9
71-90 14 (7.0) 100.0
Household income Less than $20,000 11 (5.5) 5.5
$20,000-39,999 31 (15.6) 21.1
$40,000-59,999 65 (32.7) 53.8
$60,000-79,999 30 (15.1) 68.8
$80,000-99,999 24 (12.1) 80.9
Over $100,000 38 (19.1) 100.0
Education Some high school 2 (1.0) 1.0
High school degree 27 (13.6) 14.6
Some college or technical school 52 (26.1) 40.7
Associate’s degree 20 (10.1) 50.8
Bachelor’s degree 32 (16.1) 66.8
Some graduate work 16 (8.0) 74.9
Master’s degree 33 (16.6) 91.5
Doctorate 17 (8.5) 100.0
Ethnicity American Indian/Alaskan 1 (0.5) 0.5
Asian/Asian-American 12 (6.0) 6.5
Black/African-American 27 (13.6) 20.1
Hawaiian/Pacific Islander 0 (0) 20.1
Hispanic/non-White 5 (2.5) 22.6
White/Hispanic 15 (7.5) 30.2
White/non-Hispanic 139 (69.8) 100.0
Other 0 (0) 100.0
Travel party to Gainesville Alone 18 (9.0) 9.0
With family 122 (61.3) 70.4
Table I. With friends 58 (29.1) 99.5
Frequency distributions Tour group 1 (0.5) 100.0
for the sociodemographic Residence In Florida 119 (59.8) 59.8
variables Out of Florida 80 (40.2) 100.0

model respecification would be needed if the proposed model did not fit the data well.
Additional two evidences supported a model respecification:
(1) poor indicator loadings; and
(2) high interfactor correlations.

In order for the scale to have good convergent validity, item factor loading should be
equal to or greater than 0.71. In the current study, factor loadings of several items
ranged from 0.45 to 0.91. Of 32 items, only 17 items were equal to or greater than 0.71,
indicating a lack of convergent validity. If interfactor correlation is greater than 0.85,
the two factors would show poor discriminant validity (Kline, 2005). Factors of the
measurement model were highly correlated, ranging from 0.89 to 0.94. In particular,
 Destination
Items M SD Skewness Kurtosis
image
INF1 4.80 1.10 20.24 0.65
INF2 4.85 1.19 20.18 0.30
INF3 5.03 1.15 20.50 1.00
INF4 4.54 1.19 20.15 0.36
INF5 4.89 1.05 20.16 0.13 521
INF6 5.12 1.12 20.80 1.05
SPE1 5.14 1.07 0.02 20.73
SPE2 4.73 1.10 20.05 0.27
SPE3 4.55 1.00 0.10 1.28
SPE4 4.92 1.03 0.40 20.62
SPE5 4.89 1.22 20.57 0.39
NAE1 5.31 1.12 20.49 0.49
NAE2 4.71 1.10 20.16 0.07
NAE3 5.16 1.17 20.09 20.48
NAE4 5.18 1.29 20.65 0.08
ATT1 4.84 1.10 20.20 0.31
ATT2 4.84 1.11 20.18 0.03
ATT3 4.72 1.13 0.08 0.41
ATT4 5.14 1.15 20.23 20.19
ATT5 5.70 1.12 20.64 0.01
VAL1 4.87 0.96 20.62 1.34
VAL2 4.60 1.09 20.08 0.43
VAL3 4.86 1.12 20.21 0.31
VAL4 4.70 1.10 20.14 0.05
PLE1 4.67 1.30 20.21 20.05
PLE2 4.78 1.18 20.24 0.42
PLE3 4.49 1.37 20.11 20.46
PLE4 4.51 1.43 20.18 20.44
ARO1 4.07 1.22 20.01 20.10
ARO2 4.30 1.34 20.25 20.27
ARO3 4.45 1.37 20.14 20.13
ARO4 4.39 1.43 0.15 20.19
BI1 4.09 1.64 0.13 20.62
BI2 4.74 1.78 20.43 20.71
BI3 3.46 1.85 0.42 20.94
Table II.
Notes: INF – infrastructure; SPE – social and political environment; NAT – natural environment; Descriptive statistics
ATT – attractions; VAL – value for money; PLE – pleasure; ARO – arousal; BI ¼ behavioral of SDI and behavioral
intentions intentions

natural environment and attractions factors were very highly correlated, so did
pleasure and arousal factors, suggesting that these factors be combined (Kline, 2005).
Social and political environment factor showed also high correlations with natural
environment and attractions factors. In addition, social and political environment did
not have good factor loadings. Conceptually, this factor contains two distinct factors,
such as social environment and political environment. Overall evidences clearly
supported model respecification of the seven-factor model.
The attempt to combining factors and deleting items was based on statistical
criteria and research indications in previous studies. First, natural environment and
attractions factors were combined based on indications in previous study (Baloglu and
MIP McCleary, 1999; Fakeye and Crompton, 1991). Second, a total of 14 items that had
28,4 indicator loading substantially lower than 0.71 were deleted. As a result of the model
respecification, a four-factor model with 18 items was specified that included
infrastructure, attractions, value for money, and enjoyment. Each factor contained at
least three items as suggested by various researchers (Bollen, 1989; Kline, 2005).
Relevant data of the respecified SDI were submitted to a CFA. Overall, goodness of fit of
522 the four-factor model fit the data well. Chi-square statistic was significant (x 2 ¼ 266.51,
p , 0.001). The normed chi-square (x 2/df ¼ 2.07) was lower than the suggested cut-off
value (i.e. , 3.0) and was thus acceptable (Bollen, 1989). The RMSEA value indicated
that the four-factor model had an acceptable fit (RMSEA ¼ 0.073, 90 percent
CI ¼ 0.061-0.086; Hu and Bentler, 1999). SRMR (0.05) was of an acceptable value
(# 0.10; Kline, 2005). ECVI was 1.77, and CFI was 0.93, both of which were considered
acceptable (Kline, 2005). Overall, model fit of the four-factor model improved
significantly, indicating that the four-factor model fit the data well. When compared to
EFA, one advantage of CFA is that it allows comparing various competing models
(Noar, 2003). The CFA in the current study revealed that the four factors were highly
correlated, ranging from r ¼ 0.69 to r ¼ 0.91. These high interfactor correlations
suggest that the four factors be all tied to measure destination image, which can be
hypothesized as a second-order model. Thus, we tested the second-order model and
compared the second-order model to the first-order model utilizing the chi-square
difference test (Kline, 2005). Given both results, the chi-square difference [x 2(2) ¼ 6.53
( p , 0.05)] was statistically significant, indicating the first-order model was more
parsimonious model. Consequently, the first-order model was adopted for further
analyses (Table III).
Nonetheless, not all of the factor loadings were greater than the suggested standard
of 0.71 (Anderson and Gerbing, 1988). Factor loadings for the infrastructure factor
ranged from 0.64 to 0.76, and factor loadings for the attractions factor were acceptable
except for three items that were slightly below the 0.71 threshold. A decision was made
to retain these items due to their theoretical relevance to the constructs. Factor loadings
of the value for money factor ranged from 0.62 to 0.86. All factor loadings for the
enjoyment factor were well above the threshold, ranging 0.79-0.90. Critical ratio values
ranged from 7.36 to 14.74, indicating that all values were statistically significant
(Table IV). Overall, the resolved four-factor of the SDI showed adequate convergent
validity, pending further examination.
Discriminant validity for the SDI was found to be marginally acceptable as some
factors demonstrated relatively high interfactor correlations. Although three interfactor
correlations were slightly above the suggested threshold (i.e. infrastructure and
attractions was r ¼ 0.91, infrastructure and enjoyment was r ¼ 0.85, and attractions

Model x2 df x 2/df RMSEA RMASE CI SRMR CFI ECVI

Seven-factor model (first-order) 1,077.32 443 2.43 0.085 0.079-0.092 0.07 0.85 6.62
Seven-factor model (second-order) 1,117.16 398 2.81 0.096 0.090-0.103 0.07 0.81 6.42
Table III. Four-factor model (first-order) 266.51 129 2.07 0.073 0.061-0.086 0.05 0.93 1.77
Summary of overall Four-factor model (second-order) 273.04 131 2.08 0.074 0.062-0.086 0.05 0.93 1.78
model fit indices
for the SDI Notes: N ¼ 199; CI – confidence interval
Items Indicator loadings Critical ratios Cronbach’s alpha Construct reliability Average variance extracted

Infrastructure (five items)

INF1. City has quality infrastructure (roads, airport, and/or
utilities) 0.64 – 0.82 0.82 0.48
INF2. City has suitable accommodations 0.76 9.00
INF3. City has a good network of tourist information
(tourist centers) 0.73 8.66
INF4. City has a good standard of hygiene and cleanliness 0.67 8.12
INF5. City is safe 0.66 7.94
Attraction (six items)
ATT1. City has good shopping facilities 0.81 – 0.84 0.84 0.47
ATT2. City beautiful natural attractions (parks, forests,
and/or trails) 0.71 10.62
ATT3. City has beautiful scenery 0.68 10.19
ATT4. City has a good climate 0.70 10.60
ATT5. City offers interesting cultural events (festival and/
or concerts) 0.66 9.77
ATT6. City offers interesting historical attractions
(museums and/or art centers) 0.52 7.36
Value for money (three items)
VAL1. City‘s accommodations are reasonably priced 0.70 – 0.78 0.77 0.54
VAL2. City is an inexpensive place to visit 0.62 7.90
VAL3. City offers good value for my travel money 0.86 10.28
Enjoyment (four items)
ENJ1. City is a pleasing travel destination 0.80 – 0.90 0.90 0.69
ENJ2. City is an enjoyable travel destination 0.90 14.74
ENJ3. City is an exciting travel destination 0.83 13.16
ENJ4. City is a novel travel destination 0.79 12.36
Behavioral intentions (three items)
BI1. I am likely to visit the city in the near future 0.67 8.89 0.78 0.61 0.52
BI2. I am likely to recommend the city to those who want
advice on travel 0.79 –
BI3. I have a high likelihood of attending Gator athletic
events in the near future 0.74 –
Notes: N ¼ 199; INF – infrastructure; ATT – attraction; VAL – value for money; ENJ – enjoyment; BI – behavioral intentions
Destination

Indicator loadings,

SDI and behavioral

reliability, average

intentions
critical ratios, Cronbach’s
alpha, construct
image

variance extracted for

523

Table IV.
MIP and value for money was r ¼ 0.86), all other interfactor correlations were within the
28,4 threshold, including r ¼ 0.79 (infrastructure and value for money), r ¼ 0.69 (value for
money and enjoyment), and r ¼ 0.82 (attractions and enjoyment); all of these met
Kline’s (2005) criterion. The Fornell and Larcker’s test found that all squared
correlations in the scale were somewhat greater than the AVE value for respective
construct except for the correlation between value for money and enjoyment. All values
524 of Cronbach’s alpha, CR, and AVE were above the acceptable thresholds (Fornell and
Larcker, 1981; Hair et al., 2006). Based on the overall information of reliability tests, the
factors of destination image were deemed reliable (Table IV). Even though some high
interfactor correlations were found (e.g. infrastructure and attractions) that slightly
exceeded the suggested criterion (Kline, 2005), the decision was made not to combine
the factors (e.g. infrastructure and attractions), mainly due to theoretical considerations
as the factors have been widely conceptualized as distinct factors (Baloglu and
McCleary, 1999). This was particularly reasonable when considering that the overall
discriminant validity and reliability coefficients for the revised SDI were substantially
improved from the initial seven-factor model. Nonetheless, discriminant validity of the
current scale needs to be further validated in future studies.
A CFA was conducted for the behavioral intentions factor to examine the factor
structure among the three items. Findings of the CFA indicated that a one-factor model fit
the data well (x 2 ¼ 2.30, p , 0.001, x 2/df ¼ 2.30, RMSEA ¼ 0.081, SRMR ¼ 0.02,
CFI ¼ 0.99, and ECVI ¼ 0.62). All the indicator loadings were statistically significant
( p , 0.001), which were as follows: 0.67 (intent to re/visit), 0.74 (intent to attend athletic
events), and 0.79 (recommend to other). In terms of reliability, the factor was found to be
reliable via three reliability tests (i.e. Cronbach’s alpha, CR, and AVE) except for only
one measure (i.e. CR was 0.61). Despite the CR value, the other two reliability scores were
deemed reliable, indicating that the model showed reasonable reliability (Table IV).

Structural equation modeling

A SEM analysis was conducted to examine the predictability of the SDI to behavioral
intentions. Following Anderson and Gerbing’s (1988) two-step rule, goodness-of-fit
indexes for the overall structural model was first evaluated prior to estimating
path coefficients for the hypothesized structural model. The overall model fit was good
(x 2 ¼ 22.69, p , 0.05, x 2/df ¼ 1.75, CFI ¼ 0.99, RMSEA ¼ 0.061, 90 percent
CI ¼ 0.009-0.102, and SRMR ¼ 0.041) and all four dimensions of the SDI were
statistically significant ( p , 0.001) and greater than the suggested standard of 0.71
(Anderson and Gerbing, 1988), ranging from 0.73 (value for money) to 0.88 (attraction),
having a satisfied model fit, it was appropriate to proceed with a SEM analysis. The
SEM test found that the SDI predicted a total of 28 percent of the variance in behavioral
intentions (Figure 1). These indicated that the SDI that included four factors
representing destination image could contribute positively to tourists’ decision making,
and they were of predictability to tourism behaviors. According to Cohen’s f 2,
28 percent of combined variance accounted for by the SDI was considered to have
moderate effect size (Cohen, 1988).

Discussion
Destination image has been found to be an important predictor of tourism decision
making (Baloglu and McCleary, 1999; Beerli and Martin, 2004; Chen and Hsu, 2000;
Echtner and Ritchie, 1991; Fakeye and Crompton, 1991). Despite its importance, efforts Destination
to understanding destination image formation have been lacking due to a scarcity of image
measurement instruments possessing sound psychometric properties. Scholars have
relied upon the measurement items of destination image scales developed in general
tourism settings, failing to take into consideration the unique characteristics associated
with particular destinations being examined. Furthermore, these scales have been
developed primarily through involving a student sample and adopting EFA analytical 525
procedures. To fill the void in the literature, the current study was designed to develop
the SDI measuring destination image. Additionally, the uniqueness and merits of this
study also included obtaining a national sample and employing appropriate statistical
analysis procedures, including CFA and SEM to achieve the research purpose.
Based on the review of literature, input from academicians and professionals, and
content validity test, a preliminary scale was developed based on attitudinal theory of
cognition and affection, which contained seven factors (i.e. infrastructure, socio and
political environment, natural environment, attraction, value for money, pleasant, and
arousal) with 32 items (Baloglu and McCleary, 1999; Beerli and Martin, 2004). Owing to
the weak psychometric properties of the seven-factor model, the scale was reduced and
respecified based on statistical criteria and research findings of previous studies, which
led to a four-factor model (i.e. infrastructure, attraction, value for money, and
enjoyment) with 18 items. In this process, two initially separated cognitive dimensions,
natural environment and attraction, were combined into one factor (i.e. attraction) that
was supported by research findings of previous studies (Baloglu and McCleary, 1999;
Fakeye and Crompton, 1991). Baloglu and McCleary (1999) used attraction factor in
their destination image model, which contained natural environment elements. Fakeye
and Crompton (1991) also treated natural and cultural attractions as one dimension (i.e.
natural and cultural amenities). However, this was contrary to Martin and Bosque’s
(2008) findings, which were able to identify natural environment and cultural
environment factors separately through EFA and CFA procedures. This difference
may have resulted from contextual differences. The current study was conducted in the
context of a small community, whereas Martin and Bosque’s study was based on a
famous resort area. As Fakeye and Crompton (1991) suggested, the destination image
factor is context-specific, meaning tourist perceptions may vary according to
destination. This speculation should be examined in future studies to see if the two
factors (natural and cultural environment) are in fact distinct. The social and political
environment factor in the initially developed scale was eliminated due to the following
two considerations:

e8

Recommend to Infrastructure e1
e5
others
0.78 0.88

0.88 Attraction e2
e6 Intent to revisit
0.69 Behavioral 0.53 Destination Figure 1.
intentions image 0.73
Standardized estimates
0.74 Value for money e3
0.81 from the structural model
e7
Attend sport examining relationship
event
Enjoyment e4 between destination image
and behavioral intentions
Notes: c2 = 22.69 ( p < 0.05); c2/df = 1.75; CFI = 0.99; RMSEA = 0.061; SRMR = 0.041
MIP (1) all of the items under this factor had poor loadings; and
28,4 (2) the items were theoretically inconsistent as they reflected two distinct areas of
social and political environments, which might have contributed to the low
factor loadings.

As small-scaled events and the communities hosting these events are more likely to be
526 attractive to domestic travelers, issues related to social and political environments may
not be an important concern, which is particularly true for people living in the
USA. Nonetheless, although the current study failed to retain social and political
environment as an independent factor or two separated factors and thus dropped the
items, the items can be important variables describing the image of a destination,
where there are social and/or political concerns. Further examining the measurement
viability of social and political environment is warranted in future studies (Beerli and
Martin, 2004).
In terms of affective dimensions, pleasure and arousal factors were combined into
one factor (i.e. enjoyment) in the respecified four-factor model. Both factors were initially
developed based on Russell et al.’s (1981) research findings, which have been considered
as a seminal work on affective destination image. Same roots of item generation was
likely the reason that the interfactor correlation was rather high (i.e. r ¼ 0.94),
indicating that discriminant validity of two separate factors was in doubt. This justified
the attempt to combine the two factors into one (i.e. enjoyment). Combining the two
factors was also supported by previous studies (Baloglu and McCleary, 1999; Lee et al.,
2005) treated three variables in the affective domain (i.e. good, pleasant, and nice) as a
unidimension of affect. The same approach was found in numerous studies (Baloglu
and McCleary, 1999; Martin and Bosque, 2008; Phillips and Jang, 2008). However, since
a number of studies still supported Russell et al.’s (1981) assertion that the affective
domain assessment of destination image should be multi-dimensional (Baloglu and
Brinberg, 1997; Sönmez and Sirakaya, 2002), future studies are warranted to further
examine the possibility of multi-dimensionality of affective image. One distinction
between this study and previous studies was that the current study built on the research
findings of previous investigations and employed a CFA to confirm the suggested two
bipolar affective factors as sub-dimensions of SDI (Russell et al., 1981). Nonetheless, due
to the discrepancy between the findings of this study and some previous studies, further
validation efforts are needed in order to enhance the acceptance and ensure the
generalizability of this unidimensional measurement approach.
When using CFA and SEM analyses, the number of items per factor is important for
measurement precision. In terms of optimal number of items per factor, Kline (2005)
suggested that at least three items would be needed if a one-factor model was estimated,
and at least two items would be necessary if two or more factors were estimated.
However, Bollen (1989) argued that two items could cause an estimation problem if
sample size were less than 100. Based on the consensus of previous researchers on
optimal number of indicators per factor, three items per factor would be considered
ideal (Bollen, 1989; Kline, 2005; Marsh et al., 1998). In the current study, the initially
proposed seven-factor model contained at least four indicators per factor. The resulted
SDI scale in the current study was in compliance with this guideline when considering
that the revised four-factor model consists of at least three items per factor. Although a
substantial number of items were reduced, the revised four-factor model could still
maintain the original theoretical meaningfulness on which the seven-factor model was Destination
based (Baloglu and McCleary, 1999; Beerli and Martin, 2004; Lee et al., 2005). image
Many previous studies have revealed that the destination image predicted
consumer’s destination loyalty, including revisit intentions and willingness to
recommend to others (Alcaniz et al., 2009; Bigne et al., 2001; Castro et al., 2007; Chen and
Hsu, 2000; Chen and Tsai, 2007). Following this research evidence, the current study
examined predictability of SDI on behavioral intentions as measured by three items 527
(i.e. re/visit intention, recommend to others, and intent to attend sport event). The result
of the SEM analysis showed that 28 percent of variance in behavioral intentions was
explained by the SDI. Total variance explained was quite consistent with previous
destination image studies where researchers found that a favorable destination image
had a positive effect on visit intentions (Aksu et al., 2009; Alcaniz et al., 2009; Chalip
et al., 2003; Papadimitriou and Gibson, 2008). For instance, Chalip et al. (2003) found
that destination image explained nearly 20 percent of the variance in visiting foreign
countries. Also, the study by Gibson et al. revealed a total of 20.5 percent of the variance
in the intention to travel to China was explained by three destination image factors
(i.e. attraction, money, and convenience). Recently, Aksu et al. (2009) found that
approximately 20 percent of the variance in behavioral intentions was explained by
three destination image factors (i.e. information, transportation, and accommodation).
Alcaniz et al. (2009) supported these findings by recognizing that cognitive destination
image explained 39 and 32 percent of the variance in the intention to recommend and
revisit intentions, respectively.
Based on the above results, marketers should pay special attention to developing
promotional contents that delivers “fun”, “exciting”, “enjoyable”, and “novel” image to
potential tourists. Also, marketers should be encouraged to integrate all four
destination image factors into their promotional resources since the SDI was found to
exert positive influence on behavioral intentions. Continued marketing efforts should
be geared toward improving city’s infrastructures, including accommodation,
delivering new tourism information resources via local tourism bureaus, maintaining
good standard of hygiene and safety. Survey participants generally thought the city’s
price of accommodations were reasonable and their perceived value, which was
product/service quality received and price paid for the quality was generally positive.
Therefore, marketers should continue to provide value for money with potential
tourists. Also, marketers should create promotional contents highlighting attributes
representing the city’s tourism-related attractions such as natural attraction, beautiful
scenery, climate, cultural events, and historical attraction. Furthermore, these
promotional materials should be effectively delivered to potential tourists via various
communication outlets (e.g. brochures, web site, radio, direct mail, and e-mail).
Although research found that consumers tend to be negatively influenced by unwanted
solicitation by companies and corporations (Kotler and Armstrong, 1996), this may not
be the case of this particular group since the survey respondents of the current study
were those who requested information regarding the city. Interesting finding was that
the respondents were interested in attending athletic events. Therefore, when
developing a promotional campaign, the marketers should specifically highlight the
success of the particular athletic teams of the city.
There are several implications associated with the SDI for practitioners in tourism
sectors and particularly, events and organizations related to event tourism.
MIP First, the developed SDI can provide reliable and valid analytical tool to assess destination
28,4 image. The SDI consists of reasonable number of items (i.e. 18 items), which can be easily
administered. With those manageable items, the scale can capture the necessary elements
related to destination image. Marketers may adopt the scale to examine tourism marketing
issues, factors causing changes in destination image and impact of destination image on
tourist’s behaviors so that the marketers can formulate effective marketing strategies that
528 can ultimately help to enhance tourist’s intention to visit.

Limitations and future studies

A number of limitations are recognized in the current study. First, we used a post hoc
analysis design, in which the data were collected following the NCAA men’s basketball
and Bowl Championship Series Football championship games. The athletic successes
might have influenced the survey respondents to form pre-destination image, which
might have impacted behavioral intentions measure. However, the primary focus of this
study was to identify dimensionality associated with measuring destination image; yet,
it was not meant to examine the extent to which the athletic successes impacted
destination image. Without referencing to pre-existing level of destination image, the
predictive effect of the post hoc measures was found to be relatively small, which could
be due to high mean scores and low standard deviations of items as a result of recent
athletic successes. In future studies it would be very constructive to examine the
perceptual differences in destination image between pre-event and post-event, as well
as their impacts on tourism behaviors.
Second, although seven factors were initially proposed as a result of a
comprehensive review of literature, only four factors (i.e. infrastructure, attraction,
value for money, and enjoyment) were sustained in the process of respecification and
conducting the CFA. We relied heavily on a statistical (empirical) standard as we
modified the measurement model. Although it was suggested that the model could be
modified theoretically or empirically, solely relying on empirical criteria may result in
Type I or Type II errors (Kline, 2005). Thus, model modification based more on
theoretical criteria is suggested for future studies. An alternative way to avoid
capitalization on chance in the case of model modification is to test the final model using
an independent sample for cross-validation (MacCallum et al., 1992). However, in the
current study, we tested the respecified four-factor SDI model using the same sample
that was used for examining the original seven-factor model. Hence, caution is needed
in interpretation of the results of the CFA. Future studies are necessary to confirm the
factor structure of the resulting model of SDI.
Third, this study only examined the predictability of SDI based on the behavioral
intentions factor, which was developed as a unidimensional factor. Other theoretically
related variables should be used as criterion variables in future studies. These may
include, but not limited to, destination’s overall image (Alcaniz et al., 2009), satisfaction
(Bigne et al., 2001; Castro et al., 2007; Chen and Tsai, 2007; Chi and Qu, 2008), and actual
behavior (Kaplanidou and Vogt, 2007). In fact, the R 2 value (i.e. 28 percent of the
variance explained) for the SDI explaining behavioral intentions may suggest that a
need exists to consider potential mediating variable between destination image factor
and behavioral intentions factor. Service quality, trip quality, and perceived quality
factors may be considered as potential mediating factors as these variables were found
to be statistically significant mediators on the relationship between destination image Destination
and behavioral intentions (Bigne et al., 2001; Chen and Tsai, 2007; Lee et al., 2005). image
Fourth, the current study did not examine potential antecedents of destination
image. Based on image theory, people that possess organic image, induced image, and
complex image tend to behave differently due to different level of knowledge and
experience (Gunn, 1972; Fakeye and Crompton, 1991). Therefore, it would be interesting
to examine such variables as past behavior, prior knowledge, and familiarity as 529
antecedent variables of the destination image formation. These variables may also
occur as moderating variables on the relationship between destination image and
behavioral intentions.
Lastly, the SDI scale was developed in the context of a small community, known as a
college town. Hence, the developed scale’s application may not be generalizable to other
settings, such as an “urban town”. When applying the SDI to other contexts, it is
necessary to revalidate and even revise the scale. Unique cultural, social, and touristic
attributes related to the study contexts should be included in such applications.

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Corresponding author
Kevin K. Byon can be contacted at: kbyon@uga.edu

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