Impact of mega sport events on destination image and country image.
Kim, Jeeyoon ; Kang, Joon Ho ; Kim, Yu-Kyoum 等
Introduction
Countries and local communities host mega sport events such as the
Olympics or the FIFA World Cup with expectations to bring benefits such
as economic uplift, new job creation, image improvement, promotion of
domestic products in the global marketplace, attraction of foreign
investment, increase in tourism, acceleration of urban development,
infrastructure improvement, accumulation of event management expertise,
enhancement of the national team's performance, stimulation of
sports participation, social unification, enhancement in national pride,
and introduction of sustainable development (e.g., Cashman, 2002;
Kaplanidou, 2012).
Among these benefits, improvement of the host's image is
central because it is the foundation of various other outcomes including
increased tourist visitation and a stronger brand of the host country
and its products, which drive the bulk of economic gains (Kim &
Morrison, 2005). According to an economic impact analysis on the 2012
London Olympics, increased tourist visits and related revenues were
expected to comprise from half to three-quarters of the economic legacy
(Agha, Fairley, & Gibson, 2012). The economic impact of the 2018
Winter Olympic Games in Pyeongchang is estimated to be $59 billion of
which $39.8 billion is driven by increased long-term tourist visitation
and $10.5 billion by promotion and improvement of the host country or
local community's image (Park & Ju, 2011).
Two related streams of research examine the host country's
image improvement: One focuses on destination image (i.e., the host
country or local community's image as a tourism site) and the
second focuses on country image (i.e., overall image of the country).
From the tourism perspective, many studies exist regarding the impact of
mega sport events on destination image that present results of improved
destination image after hosting sport events (Pike, 2002). Host
countries are commonly "optimistic" about their respective
image improvement (Baade & Matheson, 2004), which is the primary
justification for funding the sport event (Chalip, Green, & Hill,
2003). However, this "optimism" needs to be tempered with
realism as several studies also demonstrate mixed or no impact on the
host's destination image (Li & Kaplanidou, 2013; Preuss &
Alfs, 2011). On the other hand, from the international marketing
perspective, host countries commonly utilize mega sport events believing
they are effective marketing outlets for enhancing the country's
image and thus the brand of the host country's product (Dinnie,
2004; Kotler & Gertner, 2002). However, there is a void of research
in the sport context leaving the claimed benefits unverified. Further
research addressing potentially unjustified optimism on the benefits of
destination and country image improvement is imperative, but challenging
due to various methodological issues. One of the critical issues in
image change studies is the predominant use of cross-sectional design,
which raises concerns about internal validity. A pre-post design
improves the quality of evidence regarding the impact of a mega-sport
event.
Although studied in parallel, destination and country images are
similar and interrelated constructs based on shared characteristics
(e.g., overlapping research topics, correlated marketing goals, and
shared theoretical base; Mossberg & Kleppe, 2005). A
cross-examination of the two images is necessary to advance research in
destination and country image (for clearer understanding through
comparison, cross-application of knowledge, and availing integrative
marketing; Elliot, Papadopoulos, & Kim, 2011). However, no
comprehensive research exists on the two images with respect to mega
sport events; as a result, an empirical study laying out the foundation
for interdisciplinary research and integrated image marketing is in
order. In this sense, it is necessary to conduct an empirical research
to understand the relationship between two images and their connection
to relevant behavioral intentions (i.e., visit and purchase intentions)
(Nadeau, Heslop, O'Reilly, & Luk, 2008).
Therefore, this research aims to: (1) provide theoretical
discussions regarding the impacts of hosting a mega sport event on
destination and country images; (2) empirically examine the impact
through pre-post study design; and (3) examine destination and country
image's link to the consumer's behavioral intentions.
Theoretical Framework
The concepts of destination and country images are based on the
"associative network memory model" (Anderson, 1983) where the
memories and knowledge of a product (i.e., image) is constructed by the
association of nodes connected through links. The more salient the nodes
and the tighter the links are, the more likely the information is
processed by the "sloppy" cognitive processors (i.e.,
consumers), which leads to stronger brand image and to competitiveness
in the market. Brand elements such as mega events, celebrities, iconic
structures, geography, history, art, music, political and social issues,
product quality, and people (i.e., nodes) and their tight associations
to the place (i.e., links) determine the strength of the brand and are
also the key factors in image formation (Smith, 2005). In the context of
mega sport events' impact on the host's image, mega sport
events function as the salient brand element where the event image
transfers to and strengthens the host country's image. Event image
is "the cumulative interpretation of meanings or associations
attributed to events by consumers" (Gwinner, 1997, p. 147), which
is linked to the physical, emotional, social, organizational, and
environmental facets of the event (Kaplanidou, 2010). When the
predominant event image transfers to destination or country images,
people's attitude towards the event transfers as well (i.e.,
attitude theory; Fishbein, 1967). Transfer of the positive image of and
positive attitude towards the sport event to destination and country
images is the bedrock of economic gain from hosting mega sport events,
as it is linked to increased visit intention (i.e., intention to visit
the destination for tourism purposes) and purchase intention (i.e.,
intention to purchase the products made by the country) by consumers.
Hypothesis Development
Destination Image
In tourism research, destination image is defined as "the sum
of beliefs, ideas and impressions that a person has of a
destination" (Crompton, 1979, p. 18). Mega sport events such as the
Olympics or the FIFA World Cup are powerful brand elements where the
event image effectively transfers to destination image (Kotler &
Gertner, 2002). With the expectation of massive media attention, host
countries and local communities seek to build stronger destination
brands by building associations with the event image, which drive
tourist visitation and other related profits. These benefits are vital
for the host to rationalize the bid of the sport event, to secure public
funds, and to legitimize the expenditure of public subsidy (Chalip et
al., 2003). Various studies support the positive influence of mega sport
events on destination image and on visit intention (e.g., Kaplanidou
& Gibson, 2010; Kaplanidou & Vogt, 2007). It is "commonly
assumed that these impacts [of hosting mega sport events] are primarily
positive in nature" (Ritchie, 1984, p. 4), which explains the
increased popularity and intense competition in bidding to host mega
sport events (Lee, Taylor, Lee, & Lee, 2005).
Despite the prevailing assumption that hosting a sport event will
enhance destination image, this optimistic expectation is put into
question as various studies report inconsistent findings (i.e.,
positive, negative, mixed, or no effects) on the impact of hosting a
mega sport event (e.g., Gallarza, Saura, & Garcia, 2002; Kim,
Gursoy, & Lee, 2006). For example, Chalip, Green, and Hill (2003)
conducted an experiment to measure the impact of a [televised] motor
race on destination image. The results presented both positive and
negative effects of sports event media on destination image with
marginal effect. Similarly, a study on the US public's perception
of Chinese brands before and after the 2008 Olympics found no evidence
of change on the host country's (China) image; however, the study
found significant change in perception of subgroups with high interest
and more media exposure (Li & Kaplanidou, 2013). Thus, the value of
sports media exposure should not be exaggerated without rigorous
evidence. The explanation of insignificant, mixed, or negative outcomes
on destination image improvement is summarized as follows (Getz &
Fairley, 2003; Xing & Chalip, 2006): (1) negative externalities
(e.g., traffic congestion, environmental damage) and mismanagement of
the event, (2) the lack of adequate media management when negative
publicity about the sport event arises, (3) the lack of interest and
support from the media and event organizers in destination promotion,
and (4) the discrepancy between the sport event image and the
established image of the destination.
Inconsistent findings on the impact of sport events on destination
image raise questions about measurement issues. Accurate measurement of
the impact of hosting sport events is imperative as "less than
reliable economic benefits calculations cast a dark shadow on the return
on investment claims made by both sport event organizers and government
backers" (Lee & Taylor, 2005, p. 596). Various studies (e.g.,
Baade & Matheson, 2004; Gertner, 2011; Richie, 1984) question the
validity of impact assessment due to the following four measurement
issues: (1) large scope of population (oftentimes across countries and
continents), (2) difficulty in targeting a relevant sample of the
population, (3) need to continually measure image change over time, and
(4) difficulty of controlling other possible non-event related
influences. Researchers advocate for a non-cross-sectional design with
rigorous sampling to overcome the aforementioned limitations. For
example, Karadakis and Kaplanidou (2012) attempted to accurately measure
the legacy of the 2010 Winter Olympics through a longitudinal study.
Therefore, additional research with various waves of data collection
will help advance this line of inquiry. Hypothesis 1 is offered and
tested through a pre-post study design.
H1: Destination image (i.e., Urban, Nature, Culture, Value, Safety,
Climate, Convenience images) of the host country will positively change
after the Olympics.
Country Image
In international marketing, "country image" is approached
in three hierarchical scopes: (1) country image (i.e., overall image of
a country; Martin & Eroglou, 1993), (2) product-country image (i.e.,
image of product and country, where the product and country image is
considered separated but closely related; Roth & Romeo, 1992), and
(3) country-of-origin image (i.e., product's image related to
particular made-in country image; Bilkey & Nes, 1982). In this
research, country image will be adopted for its strength in capturing
the affective aspects of the country as well as the cognitive beliefs
(Martin & Eroglou, 1993). The consideration of emotions and feelings
of the country is critical (1) in understanding image which is comprised
of both affective and cognitive components, and (2) as affective
components "can lead to much stronger reactions [of consumers] than
pure cognitions" (Roth & Diamantopoulos, 2009, p. 728).
Country image is defined as "the sum of beliefs and
impressions people hold about a place" (Kotler & Gertner, 2002,
p. 251). Country image is important for the country's product, as
it functions as a "halo" (i.e., transferred attribute used as
background information; Thorndike, 1920) or "a construct itself
that summarizes the consumer's belief about product
attributes" when consumers evaluate the products (Bilkey & Nes,
1982). Mega sport events can positively impact the host's country
image through image transfer. Kotler and Gertner (2002) suggested that
hosting prestigious sport events promote and revamp country image.
Dinnie (2004) recognized "sport as a determinant of country image
perception" (p. 108), and Kavaratzis (2005) identified "mega
event" as a mature form of place marketing. By transferring the
image of mega sport events, the host country's image can be
improved, which contributes to a stronger brand and to core strength for
competitive advantage in the international business market. However,
there is a void of empirical research evaluating the impact of mega
sport event on the host's country image. Therefore, it is necessary
to conduct an empirical research on a sport event's impact on
country image to understand how to improve marketability of the country
and its products. Hypothesis 2 is derived from the above discussion and
evidence.
H2: Country image (i.e., People, Political, Social, Economic, and
Cultural images) of the host country will positively change after the
Olympics.
Destination and Country Images on Behavioral Intentions
Destination and country images both concern the image of a place.
Both concepts share the same theoretical construct of image and are
built upon the associative network memory model (Anderson, 1983) and
attitude theory (Fishbein, 1967). Although the two images have
conceptual similarities and are inherently interrelated, the two images
have been studied exclusively in tourism and international marketing.
Mossberg and Kleppe's conceptual research (2005) was one of
the few initiative studies to examine both destination and country
images. They found that destination and country image studies overlapped
in targeting the same object (i.e., the image of a place) with the same
goal to gain competitive advantage through stronger brand image in the
international tourism or business market. The differences laid in that
country image put more emphasis on the domestic product's image
than the overall image, while the scope of destination image varied from
state, local community, and country. However, both images were
acknowledged as "a generic pool of associations of a place which is
not linked to any particular context" (p. 497, p. 499). This
indicates possibilities of inter- or cross-disciplinary application of
the findings as the image concept is employed to both context of either
tourism or international marketing.
Examining destination and country images' cross-effects on
behavioral intentions is a necessary initiative step in order to justify
more comprehensive research, as the eventual goal of the two streams of
research is to induce touristic visitation by consumers and purchase of
the country's product through improved images (Dinnie, 2004;
Stepchenkova & Mills, 2010). Nadeau and colleagues (2008)
incorporated the constructs of country image (i.e., overall country and
people images) into destination image. In their empirical test of the
integrative model of destination and country images and their effect on
travel intention, the overall image of the country and its people
presented a direct effect on destination image and indirect influence on
visit intention. As part of a model exploring the relations of country
image, product belief, and destination belief (i.e., image) and their
influence on product and destination receptivities (encompassing
intention), Elliot and colleagues (2013) found significant influence of
country image on both product and destination receptivity, and of
destination belief only on destination receptivity. The results of the
two studies identified the cross-effect between destination and country
images, indicating the necessities of theoretical convergence based on
attitude theory. These studies highlighted the close interactions among
destination and country image constructs, and the academic and marketing
benefits of a comprehensive research on the two images. However, further
research investigating the relation between destination and country
images and identifying which image is the key construct that influences
the consumer's behavioral intentions is needed. Such research lays
out the essential foundation for comprehensive research on destination
and country images. Based on the discussion and the evidence presented
above, hypotheses 3 and 4 are proposed.
H3: Destination image of the host country will influence the
behavioral intentions (i.e., visit intention and purchase intention of
country products).
H4: Country image of the host country will influence the behavioral
intentions (i.e., visit intention and purchase intention of country
products).
Methodology
We conducted a pre-post study on the 2008 Beijing Olympic Games.
Considering constraints such as geographical distance and cost,
population familiar with and geographically close to the host country
(i.e., short-haul market) was selected as a more feasible and immediate
target market. Short-haul travel holds a major portion of the overall
tourism earnings, because the likelihood of actual travel is higher
("distant destinations have great difficulty attracting more than a
1% or 2% share of departures"; McKercher, Chan, & Lam, 2008, p.
208). In this sense, we prioritized the short-haul market with the
expectation of further research to be conducted on a less familiar
population in the future. Thus, we selected South Koreans as our
population, which is one of the top tourism markets for China partly due
to geographical proximity and active business ties between the two
countries.
First, we investigated potential South Korean tourists' change
in perception of destination and country images of China before and
after the Olympics. Furthermore, we compared the image changes among
different groups based on their sport involvement, media consumption
level, and visit experience to the host country. In the second part, we
conducted a SEM analysis with destination image, country image, visit
intention, and purchase intention to comprehensively depict the
relations among the constructs of interest.
Participant and Sampling
The theoretical population of this study was the general public of
Korea who watched the Olympics only through media channels (i.e., TV,
internet). Target participants were people living in Seoul ages 18 to 67
because this age group is (1) the largest media consumer in general and
of the Olympics (Choi, 2010), and (2) the biggest spender on tourism and
product purchase (Kim & Yoo, 2002). Compared to the census (gender:
51.4% male, average age: 39.3, average monthly income: $4,061.68),
demographics of our sample adequately matched the general population in
both the pre- (gender: 54% male, average age: 37.14, average monthly
income: $4,363.63) and post-games surveys (gender: 56% male, average
age: 39.58, average monthly income: $4,423.29).
The pre-games survey took place 130-160 days prior to the Games.
This time frame was chosen because the Olympics promotional campaigns by
the national Olympic committee and by the three main TV host
broadcasters of Korea started 100 days prior to the Games. We used quota
sampling based on gender and age (4 levels, 10-years interval). Using
self-administered questionnaires, four assistants trained with
experience in survey-based research collected data through the intercept
method at malls and main streets located downtown. We obtained 628
questionnaires after eliminating 24 incomplete questionnaires.
For the post-games survey, we randomly selected 480 participants
from the 628 participants of the pre-games survey and contacted them
through email (contact information was collected in the pre-games
survey) 333 days after the Games, which was immediately after the peak
of the Olympics-related media exposure. Response rate was 39.79% with
191 participants responding. We retained 172 questionnaires in the
analysis after the elimination of 12 incomplete questionnaires and seven
ineligible responses for the analysis (based on the screening questions,
we excluded participants who visited the host country between the first
and second waves of survey and participants influenced by unique
personal experience). Non-response bias and attrition bias of pre- and
post-games participants were not of critical concern due to an adequate
match between census and sample demographics (Armstrong & Overton,
1977; Dooley & Linder, 2003).
Instruments
We adopted and modified Chalip and Green's destination image
scale (1996) with 40 items on 9 sub-dimensions (i.e., Developed
Environment, Natural Environment, Value, Sightseeing Opportunities,
Safety, Novelty, Climate, Convenience, and Family Environment). Based on
the literature review (e.g., Hwang, Hwang, Jung, & Choi, 2007; Park,
2009) and expert panel discussion, Chalip and Green's scale was
selected for the following reasons. First, the selected scale is the
most comprehensive to capture Korean's image dimensions of China
and thus may avail more useful implications for visit intention. Second,
the selected scale incorporates the images dimensions that are key
considerations for Korean's tourism decision making (e.g., cost,
hygiene, leisure opportunities, and cultural heritage). And third, the
selected scale demonstrated good psychometric properties including
content validity, convergent validity, and discriminant validity in the
previous uses (e.g., Chalip et al., 2003). For the country image scale,
we developed a scale with 31 items on 5 dimensions (i.e., People,
Political, Social, Economic, and Cultural) based on various studies on
country image measurement (e.g., Martin & Eroglu, 1993; Roth &
Diamantopoulos, 2009). All items for destination and country images were
7-point Likert-type scales anchored by 1 = strongly disagree and 7 =
strongly agree. The items were initially written in English and
translated into Korean. Then, native speakers of both languages
translated each item back into English to check reliability. An expert
panel discussion with two professors and seven doctoral students in
sport management took place to check face validity. In addition, prior
to the main test administration, we pilot tested the scale with a sample
of college students (n = 47) for the following reasons (Groves, 2004):
(a) to examine the psychometric properties of the preliminary pool of
items; (b) to assess the variability in outcome measures that assisted
in determining the sample size for the main study; and (c) to identify
problematic areas related to instrument design, item construction, and
sampling method. Such preliminary analyses are necessary in order to
purify and refine the survey prior to conducting the principal study.
Each item's total correlation for destination and country image
scales ranged from .559 to .955. We eliminated the survey items based on
expert evaluation on translation validity and parsimony as well as the
results from the pilot study; destination image scale to 20 items on 7
dimensions (i.e., Urban, Nature, Culture, Value, Safety, Climate, and
Convenience) and country image scale to 15 items on 5 dimensions.
Descriptions of dimensions and items are provided in Table 2.
Additionally, we included questions on visit intentions to the host
country (2 items), purchase intention of the host country's
products (2 items), sport involvement (4 items; developed based on
Zaichkowsky, 1985, and Shank & Beasley, 1998), previous visit
experience to the host country before the Olympics (1 item), and
demographics (5 items; age, gender, occupation, income level, and email
contact information). In the post-games survey, we collected information
on the Olympic related media consumption during the event (5 items; main
consumption source, consumption time per week through TV, consumption
time per week through internet, three most memorable incidents of the
Olympics, and three reasons of being interested in Olympic related
media), visit experience to the host country since the first wave of
survey (for screening purpose), and the three most memorable incidents
related to the host country between the first and second wave surveys
(open-ended question; for screening purpose).
In recognition of the possibility of biasing effects from measuring
multiple constructs in a single survey administration, we drafted items
in ways that reduced ambiguity of their meanings during the instrument
development stage (e.g., substituting vague words such as many and
sometimes with specific words). In addition, we conducted Harman's
one-factor test (Harman, 1976). No single factor emerged, nor did one
general factor account for the majority of variance. Taken together,
common methods variance was likely not a serious threat in this study.
The main study consists of two parts: first, through mean structure
analysis and descriptive analysis, we examined the changes in
destination and country images after the Olympics. We used participants
who completed the survey both before and after the events (n = 172).
Second, using responses from pre-game survey (n = 628), we conducted a
SEM examining the influence of destination and country images on tourist
and consumer behaviors.
Results
Study 1
Measurement Assessment. To assess the psychometric properties of
the scales, we conducted a confirmatory factor analysis (CFA) using
Mplus7. The data met the linearity assumption and severe
multicollinearity or singularity was not present. However, the
normalized Mardia's coefficient (1985) of skewness was 37.42 (p
< .001) and the kurtosis was 35.86 (p < .001), indicating a lack
of multivariate normality. Thus, we adopted the Satorra-Bentler (1994)
correction method. The measurement model fit the data adequately (S-B
[chi square]/df = 711.35/494 = 1.43, CFI = .92, SRMR = .07, RMSEA = .05,
WRMR = 1.04).
All factor loadings were significant in the predicted direction (p
< .001; ranging from .56 to .99). As a measure of internal
consistency, we used Raykov's structural equation modeling (SEM)
method (Raykov, 2001) rather than the more widely used Cronbach's
coefficient alpha to assess scale reliability. Raykov's method is
considered to yield a less biased estimate than Cronbach's
coefficient alpha in all types of measurement models except for the
essentially T-equivalent model (Graham, 2006). All reliability
coefficients except for People were larger than .70, ranging from .64 to
.92. All of the average variance extracted (AVE) values except for
People, Economic, and Urban image were greater than .50 (ranging from
.37 to .86; refer to study 2 for explanation on AVE values). Thus, the
measures demonstrated fair convergent validity and reliability (Hair et
al., 1992). The factors are presented in detail in Table 2.
We examined the discriminant validity for each construct by
performing multiple [chi square] difference tests of unity between all
pairs of constructs. The unconstrained model (correlation estimated
freely) was significantly better than the constrained model in all
comparisons (the smallest adjusted [DELTA]S-B [chi square] was 65.04, p
< .001). In addition, the AVE values for all constructs were larger
than the corresponding squared inter-construct correlations, providing
additional support for discriminant validity (Fornell & Larcker,
1981). In aggregate, the results indicate that the measures possessed
adequate psychometric properties.
Results on Destination and Country Images Change. To examine the
changes in destination and country images before and after the Games, we
conducted a series of multiple-group SEM analyses with a mean structure
(Table 1). Based on widely accepted guidelines of model fit indices
(Weston & Gore, 2006; Yu, 2002), the model fit the data well (S-B
[chi square]/df = 1383.37/1034 = 1.34, CFI = .95, SRMR = .06, RMSEA =
.04, WRMR = 0.99).
Overall, participants positively perceived Nature and Culture
images of the host country while they negatively perceived Safety and
Convenience images. However, Convenience image significantly improved
([[DELTA].sub.[chi]], change in latent mean, = .22, p < .05) after
the event. The overall participants' ratings on all country image
dimensions were below neutral point except Cultural image. Additional
analyses took place by grouping the participants based on sport
involvement. Convenience image showed significant positive change
([[DELTA].sub.[chi]] = .44, p < .05) in the high sport involvement
group (n = 72), while Climate image change was significant but negative
([[DELTA].sub.[chi]] = -.23, p < .05) in the low sport involvement
group (n = 80). We conducted a comparison of the high and low media
consumption groups. The high group (n = 68) averaged 753.34 minutes of
Olympic related media consumption per week through TV and internet and
the low group (n = 68) averaged 131.38 minutes. Convenience image had a
significant positive change in both high ([[DELTA].sub.[chi]]= .34, p
< .05) and low ([[DELTA].sub.[chi]]= .34, p < .05) media
consumption groups. In terms of visit experience to the host country,
significant negative change occurred in Culture image
([[DELTA].sub.[chi]]= -.39, p < .05) among people with visit
experience (n = 85). Among people with no visit experience to the host
country (n = 87), there was significant improvement in Convenience image
([[DELTA].sub.[chi]]= .42, p < .05). Hypothesis 1 was partially
supported as Convenience image significantly changed after the event in
the collective group and as the significance of image change varied
among subgroups.
Regarding the country image changes, hypothesis 2 was partially
supported, but only by image changes in subgroups but not in the
collective group. Cultural image in the high media consumption group
([[DELTA].sub.[chi]] = -.26, p < .05), Social image in the low media
consumption group ([[DELTA].sub.[chi]]= .19, p < .05), and Cultural
image among people with visit experience to the host country
([[DELTA].sub.[chi]]= -.56, p < .05) exhibited significant changes
before and after the Games. All changes in Cultural image were negative
while Social image changes were positive. However, neither overall
participants nor the two sport involvement groups reported significant
change in any country image dimensions. Additionally, there was no
significant change in visit intention ([[mu].sub.pre] = 4.52,
[[mu].sub.post] = 4.43, [[DELTA].sub.[chi]] = -.09, p > .05) or
purchase intention ([[mu].sub.pre] =3.17, [[mu].sub.post] = 3.07,
[[DELTA].sub.[chi]] = -.10, p > .05) before and after the Games.
Study 2
Measurement Assessment. The data met the linearity assumption and
severe multicollinearity or singularity was not present. However, the
normalized Mardia's coefficient of skewness was 58.80 (p < .001)
and the kurtosis was 70.17 (p < .001), indicating lack of
multivariate normality. Thus, we adopted the Satorra-Bentler (1994)
correction method. The measurement model fit the data adequately (S-B
2/df = 1005.59/611 = 1.43, CFI = .97, SRMR = .04, RMSEA = .03, WRMR =
1.28).
All factor loadings were significant in the predicted direction (p
< .001; ranging from .51 to .96; Table 2). All reliability
coefficients were larger than .70 (ranging from .71 to .93) and all of
average variance extracted (AVE) values except for People, Economic, and
Urban images were greater than .50 (ranging from .45 to .87). The
measures demonstrated fair convergent validity and reliability. The
unconstrained model (correlation estimated freely) was significantly
better than the constrained model in all comparisons (smallest adjusted
S-B [chi square] was 219.36, p < .001). In addition, the AVE values
for all constructs except Economic image were larger than the
corresponding squared inter-construct correlations (Table 3), providing
support for discriminant validity.
We decided not to drop the three constructs with AVE values lower
than .50 (i.e., People, Economic, and Urban). Our justification is both
empirical and theoretical. First, following widely recommended
statistical guidelines, we use the confidence interval of AVE values
rather than using point estimate. The confidence interval of these
constructs with point AVE estimate lower than .50 includes .50,
suggesting we can't conclude that AVE values of those constructs
are lower than .50. Second, factor loadings of the constructs with AVE
values lower than .50 are all greater than .60, which is a commonly used
cut-off value (Hair et al., 1992). And third, the three constructs
demonstrate good discriminant validity. Lastly, based on previous
literature and expert review, the three constructs with AVE values lower
than .50 have good content validity and still prove necessary in
achieving the research purpose of the current study. Overall, the
results suggest adequate psychometric properties for the measures.
Structural Equation Modeling. We conducted a SEM analysis to
examine the impact of destination and country images on tourist and
consumer behaviors. Like any pre-post study, the sample of post-study is
inherently the subset of the pre-study. Therefore, it is necessary to
use both the subset and overall group only if significant change in the
subset is expected regarding the main characteristics of interest. There
is no theory or empirical evidence to suggest that stable
characteristics such as the relationship between images and behavioral
intentions changes within relatively short periods of time. It is
important to understand the distinction between the change in images and
behavioral intentions and the change in the relationship between images
and behavioral intentions. Ratings on images and behavioral intentions
can fluctuate within a short timeframe. However, the relationship
between them is rather enduring over time. In fact, the additional path
analysis shows this relationship strength and pattern was consistent
between the pre-and post-samples.
For the reasons above, we only used the overall group (i.e., the
pre-games survey respondents) to examine the relationship between images
and behavioral intentions. The model was statistically equivalent to the
measurement model and the results indicate good fit of the model to the
data (S-B [chi square]/df = 1005.59/611 = 1.43, CFI = .97, SRMR = .04,
RMSEA = .03, WRMR = 1.28). The model's estimates appear in Table 4
and Figure 1. Regarding the influence of destination images on
behavioral intentions, Urban (standardized [gamma] = .33, S.E. = .08, p
< .01) and Value images (standardized [gamma] = .10, S.E. = .05, p =
.04) had significant influence on Visit Intention while Urban
(standardized [gamma] = .38, S.E. = .08, p < .01), Safety
(standardized [gamma] = .21, S.E. = .06, p < .01), and Convenience
images (standardized [gamma] = .16, S.E. = .07, p = .02) had significant
influence on Purchase Intention. For country image, no path from country
image dimensions to Visit Intention and Purchase Intention was
significant. Overall, hypothesis 3 was supported while hypothesis 4 was
not.
Discussion
The contribution of this paper is (1) adding empirical evidence for
the impact of hosting mega sport events on destination and country
images through a pre-post study, (2) calling the common expectations of
positive image changes into question by presenting mixed results, (3)
providing theoretical understanding of image change in relation to sport
involvement, media exposure, and previous visit experience, (4)
verifying the close relation among destination and country image and
visit and purchase intentions, and (5) establishing destination image as
the key construct affecting the constructs of interest.
This research's mixed findings align with inconsistent
findings from previous research (e.g., Baade & Matheson, 2004;
Gertner, 2011; Getz & Fairley, 2003; Kim et al., 2006), which argues
against the general belief of the positive impact of mega sport events
and challenges the justification of public funding for the event. The
occurrence of negative and insignificant impact of mega sport events
alerts the romantic optimism of huge positive impacts, urging for an
approach to the issue with healthy skepticism. One of the plausible
explanations for the mixed and partially insignificant findings in this
study may lie in our focus on the short-haul market. Based on schema
theory (Barlett, 1932; Lynch & Schuler, 1994), when presented with
new information, people recall the established schema and evaluate its
congruence with the new information. While congruent information is
easily accepted, incongruent information is put under scrutiny likely to
be distorted or "filtered out" to avoid conflict with the
established schema (Xing & Chalip, 2006). Consumers of short-haul
markets tend to have strongly established schema of the host country due
to familiarity, which may have negated the influence of mega sport event
on the host's images (Chalip, et al., 2003).
[FIGURE 1 OMITTED]
In regards to destination and country images change, the event had
a positive impact on convenience image. The high sport involvement
group, high and low media consumption groups, and no visit experience
group also exhibited improvement in convenience image. This improvement
insinuates the successful inclusion of iconic structures as the new
image component of the host country--the modern and convenient-to-use
infrastructure such as the national stadium, the national aquatic
center, and the new international airport. Applying the concept of image
transfer and associative network memory model, the new sport facilities
became the salient "nodes" linked with the host country's
image. Additional analysis reported all 172 participants identified the
Olympics as the host country's key image component, and 14.0% of
them mentioned the "new sport facilities" as their
first-recollected image component of the 2008 Olympics. Our results
suggest that the Chinese organizers effectively conveyed the convenience
features to the Korean audience and that the event image successfully
transferred to and associated with the host's images. Regarding the
convenience image change process, there are two plausible explanations
that are (1) through cognitive processing of the convenience features
for people highly involved in the information processing and (2) through
the "halo effect" (Thorndike, 1920) for people with low
involvement for information processing. Further research on the image
change process is recommended.
Interestingly, the event had a negative influence on culture and
cultural image among people with previous visit experience to the host
country. Applying schema theory (Barlett, 1932; Lynch & Schuler,
1994) and expectation disconfirmation (Trail & James, 2013), this
potentially reveals a case where the established schema is adjusted as
incongruence with the new information is substantial, and where strong
negative expectation disconfirmation leads to highly negative
perceptions. In this study, people with visit experience had high
expectations (i.e., highest pre-games evaluation) and may have had
strong positive cultural images established from previous visits (i.e.,
image of abundant cultural heritage and historic attractions; Gibson,
Qi, & Zhang, 2008). But, the new modern image as the Olympic host
country (Zhang & Zhao, 2009) may have conflicted with the image of
rich culture and traditions falling short of their expectations in
cultural aspects, leading to negative image change. Our supposition
brings attention to "(in)congruence" and
"expectation" in destination and country images research, but
further verification is needed as other potential influences of external
factors may have influenced the change of cultural images.
The change in social image was positive in the low media
consumption group. With a higher response rate of watching the Olympics
(1) as it happens to be available on the news media (12.6% vs. 3.2%),
and (2) to follow up on the popular material of conversation (10.4% vs.
1.8%), the low consumption group was revealed as more exposed to easily
accessible and widely but succinctly covered newscasts (compared to
sport telecast or Olympic-focused programs of the high group) based on
the data collected on media consumption in this study. During the 2008
Olympics, newscasts in Asia (Preuss & Alfs, 2011) and in Korea (Lee
& Kang, 2010) predominantly projected refined, well-ordered and
organized social image of the host country (e.g., well-organized Games,
well-trained volunteers), possibly leading to the positive social image
change only among the low group where there was room for improvement.
This claim conjectures the influence of media source on image
perception. Further content analysis with a focus on different media
sources is necessary to verify the conjecture, as our study is
descriptive and does not explain causal relationships in the Olympics,
media source and image changes.
Destination image demonstrated significant influence on visit and
purchase intentions while country image had no significance (Figure 1).
Urban and value images were influential factors that drove visit
intention, while urban, safety, and convenience images had significant
effects on purchase intention. Another interesting finding was that
country image had no effect on either of the intentions while
destination image significantly influenced both. The significant
connection between purchase intention (normally linked to country image)
and destination image was a compelling finding, identifying destination
image as the key construct in the relationship among all the other
constructs of interest. However, in order to adopt destination image as
the key construct, it is imperative to develop further studies
investigating this potential link.
For the next step in examining the relationship between destination
and country images, two potential interpretations are suggested as a
starting point based on previous research. First is to understand
destination image as a pathway linking country image and intentions.
This claim aligns with Nadeau and colleagues' research (2008) where
country image was found to be "directly relevant to destination
beliefs and indirectly to intentions through evaluation of the
destination" (p. 101). If this is the case, the importance of
destination image raises as a factor with direct influence on behavioral
intentions, and as the mediator that links country image and intentions.
The second interpretation is to view destination image a sub-dimension
of country image. Conceptually, country image concerns the overall image
of country, and touristic image (i.e., destination image) can be
included as part of the scope (Anholt, 2004) because the country is
inclusive of all the destinations. In this case, destination image could
be treated as the decisive dimension in the overall country image
construct affective on visit and purchase intentions, as touristic image
had a more direct and significant influence on behavioral intentions.
Further research on the mechanism of destination and country images is
necessary to advance the theoretical understanding on the topic.
Marketing Implications
The close connection among the constructs of interest highlighted
the demand of an integrated marketing strategy for destination and
country images. An integrated strategy strengthens the effectiveness by
simultaneously coping the closely related constructs and enhances the
efficiency through collective efforts of the fields of tourism and
international marketing. Destination image is identified as the key
construct in the relationship with direct effects on both behavioral
intentions. Additionally, in this case, destination image is an
advantageous starting point in the host country image marketing as it is
more highly perceived than country image (Table 4). Therefore, focus
should lie on establishing a strong positive destination image linked to
the event when planning an integrated image marketing strategy.
The complex impact of a mega sport event on destination and country
images calls for elaborate marketing strategies on each dimension. In
this case, the host country successfully conveyed the new facility as
the brand element, establishing convenience image. As such,
communication plans customized for each dimension should be established.
Possible conflict among dimensions or between specific dimensions and
the event image needs consideration as well. The possible conflict of
the Olympics' modern image with the host country's image of
abundant cultural heritage is a good example of such conflict. To avoid
similar conflicts, marketers should cogitate on the expected outcomes of
hosting mega sport events dimension by dimension from the planning
stage. When conflicts are anticipated, strategically prioritizing the
key dimension is beneficial. Prioritizing the dimensions identified to
affect the consumer's behavioral intentions is also a logical
criterion. Divergent outcomes among subgroups suggest the importance of
consumer segmentation as well, as consumers had distinctive image
perceptions and image change patterns based on sport involvement, media
consumption level, and previous visit experience.
Limitations and Suggestions
There are several limitations that need to be noted in this paper.
First, the case of Koreans' image perception of China has limited
applicability in representing the international perspective, as it is
more focused on the short-haul market and as the relationship between
the two countries has unique facets. Thus, further research examining
the impact of mega sport events on the host's images in different
contexts (i.e., different countries with various geographical distance,
familiarity, cultural communality) and cross-cultural studies are
suggested. Particularly, research on a less familiar population (i.e.,
long-haul market) can provide valuable implications for the destination
image and possibly report a greater image change through the Olympics.
However, it is important to understand that both less and more familiar
populations are equally valuable. Thus, it would be ideal to collect
data from multiple countries, which is achievable only through multiple
studies in the future.
Second, adding to a simple SEM analysis, further research providing
empirical support of the close-relations among the two images and
behavioral intentions and investigating the mechanism of two image
constructs is in need. Specifically, understanding the role of
destination image in relation to country image is critical in developing
collaborative image marketing strategies. Additionally, research
simultaneously exploring the image formation process of destination and
country images would be beneficial.
Third, this research is limited to two waves of data collection
with five to six months passing between the two waves. There are
limitations as (1) the sport event image may have already impacted the
host image by the first wave of survey, and (2) the impact may decay
after the Games. For better assessment of the impact of mega sport event
on the host's image, a longitudinal study covering from the bid
initiation to the aftermath phase of the Games with multiple waves of
data collection is needed. Fourth, to understand and predict the
consumer's behavior, measures of consumer behavioral intentions
were used instead of the actual behavior. This might have inflated the
consumer's response to the image change, suggesting research with
measure on actual behavior.
Lastly, due to the impracticability of complete control over
external influences, non-Olympic related factors may have contaminated
the results of this research. Despite endeavors to minimize the
influence through various screening questions, limitations in this
aspect still exists. Experimental research is expected to cope with such
limitations, but challenges of less vivid description in the real world
will be faced. We suggest conducting research on a less prestigious
sport event where only a part of the population is aware of the event,
and then comparing the two groups of people aware of and not aware of
the event. This approach can counter both issues of the control of
external factors and the realistic description of the practical field.
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Table 1
Mean Structural Analysis on Destination and Country Image Changes
Destination Image
Urban Nature Culture Value
General Pre-games 4.74 5.50 5.68 4.34
(n=172) Post-games 4.80 5.53 5.60 4.29
Mean diff. 0.06 0.03 -0.08 -0.05
p-value 0.54 0.78 0.44 0.64
Sport High Pre-games 4.82 5.57 5.79 4.54
Involve. (n=72) Post-games 4.94 5.52 5.69 4.32
Mean diff. 0.12 -0.05 -0.10 -0.22
p-value 0.45 0.79 0.52 0.16
Low Pre-games 4.75 5.41 5.56 4.20
(n=80) Post-games 4.74 5.50 5.40 4.25
Mean diff. -0.01 0.09 -0.16 0.05
p-value 0.90 0.53 0.31 0.92
Media High Pre-games 4.55 5.49 5.70 4.45
Consump. (n=68) Post-games 4.88 5.54 5.60 4.30
Mean diff. 0.33 0.05 -0.10 -0.15
p-value 0.06 0.80 0.57 0.37
Low Pre-games 4.77 5.44 5.66 4.24
(n=68) Post-games 4.78 5.57 5.67 4.36
Mean diff. 0.01 0.13 -0.01 0.12
p-value 0.70 0.48 0.97 0.48
Previous Yes Pre-games 4.82 5.63 5.82 4.48
Visit to (n=85) Post-games 4.69 5.40 5.43 4.35
the host Mean diff. -0.13 -0.23 -0.39 -0.13
country p-value 0.41 0.14 * 0.01 0.32
No Pre-games 4.66 5.36 5.53 4.19
(n=87) Post-games 4.86 5.59 5.67 4.36
Mean diff. 0.20 0.23 0.14 0.17
p-value 0.27 0.16 0.55 0.26
Destination Image
Safety Climate
General Pre-games 2.56 3.60
(n=172) Post-games 2.62 3.56
Mean diff. 0.06 -0.04
p-value 0.53 0.99
Sport High Pre-games 2.62 3.76
Involve. (n=72) Post-games 2.85 3.71
Mean diff. 0.23 -0.05
p-value 0.09 0.83
Low Pre-games 2.43 3.56
(n=80) Post-games 2.42 3.33
Mean diff. -0.01 -0.23
p-value 0.80 * 0.03
Media High Pre-games 2.61 3.66
Consump. (n=68) Post-games 2.65 3.65
Mean diff. 0.04 -0.01
p-value 0.97 0.13
Low Pre-games 2.53 3.54
(n=68) Post-games 2.62 3.67
Mean diff. 0.09 0.13
p-value 0.14 0.56
Previous Yes Pre-games 2.51 3.55
Visit to (n=85) Post-games 2.61 3.49
the host Mean diff. 0.10 -0.06
country p-value 0.76 0.69
No Pre-games 2.61 3.65
(n=87) Post-games 2.79 3.70
Mean diff. 0.18 0.05
p-value 0.09 0.72
Country Image
Conve. People Political Social
General Pre-games 2.97 3.30 2.78 3.04
(n=172) Post-games 3.19 3.30 2.89 3.06
Mean diff. 0.22 0.00 0.11 0.02
p-value * 0.02 0.96 0.36 0.87
Sport High Pre-games 3.01 3.38 2.96 3.17
Involve. (n=72) Post-games 3.45 3.43 3.06 3.42
Mean diff. 0.44 0.05 0.10 0.25
p-value * 0.00 0.66 0.84 0.15
Low Pre-games 2.92 3.23 2.66 2.91
(n=80) Post-games 2.98 3.18 2.79 2.80
Mean diff. 0.06 -0.05 0.13 -0.11
p-value 0.51 0.74 0.37 0.33
Media High Pre-games 2.95 3.24 2.65 3.13
Consump. (n=68) Post-games 3.29 3.22 2.96 3.04
Mean diff. 0.34 -0.02 0.31 -0.09
p-value * 0.02 0.96 0.06 0.64
Low Pre-games 2.90 3.38 2.99 2.94
(n=68) Post-games 3.24 3.34 2.81 3.13
Mean diff. 0.34 -0.04 -0.18 0.19
p-value * 0.02 0.95 0.34 *0.03
Previous Yes Pre-games 3.00 3.30 2.73 3.08
Visit to (n=85) Post-games 3.13 3.17 2.82 2.97
the host Mean diff. 0.13 -0.13 0.09 -0.11
country p-value 0.28 0.18 0.54 0.48
No Pre-games 2.94 3.31 2.82 2.99
(n=87) Post-games 3.36 3.40 2.91 3.22
Mean diff. 0.42 0.09 0.09 0.23
p-value * 0.00 0.57 0.81 0.06
Country Image
Econ. Cultural
General Pre-games 2.77 5.24
(n=172) Post-games 2.80 5.10
Mean diff. 0.03 -0.14
p-value 0.97 0.21
Sport High Pre-games 2.95 5.30
Involve. (n=72) Post-games 3.04 5.05
Mean diff. 0.09 -0.25
p-value 0.39 0.18
Low Pre-games 2.61 5.23
(n=80) Post-games 2.61 5.17
Mean diff. 0.00 -0.06
p-value 0.97 0.19
Media High Pre-games 2.78 5.37
Consump. (n=68) Post-games 2.91 5.11
Mean diff. 0.13 -0.26
p-value 0.48 * 0.04
Low Pre-games 2.75 5.11
(n=68) Post-games 2.80 5.07
Mean diff. 0.05 -0.04
p-value 0.65 0.88
Previous Yes Pre-games 2.79 5.46
Visit to (n=85) Post-games 2.69 4.90
the host Mean diff. -0.10 -0.56
country p-value 0.37 * 0.00
No Pre-games 2.76 5.02
(n=87) Post-games 2.87 5.15
Mean diff. 0.11 0.13
p-value 0.34 0.27
* p < .05
Table 2
Measurement Properties of Scale Items
Dimensions Scale Items Est. S.E.
Destination Image
Urban 1. China has urbanized cities .73 .03
Image as a 2. China has a developed .63 .03
modernized business industry
place 3. China has modern streets .73 .03
and buildings
Nature 1. China has many opportunities .88 .02
to enjoy nature
Image as a place 2. China has a beautiful natural .90 .01
with natural scenery
scenic beauty 3. China has many natural .80 .02
spectacles
Culture 1. China has a rich cultural .88 .02
heritage
Image as a place 2. China has a unique culture .87 .02
with abundant 3. China has famous historical .87 .02
cultural sites
heritage
Value 1. China's traveling cost is .84 .02
reasonable
Image as a place 2. China's accommodation cost .91 .02
worth its in reasonable
traveling cost 3. China's facility and .84 .02
attraction ticket price are
reasonable
Safety 1. China is safe to travel .61 .03
Image as a 2. China's cities and tourist .87 .02
secured and site are clean
sanitary place 3. China is safe from disease .84 .02
to travel
Climate 1. China is a place to enjoy .88 .02
good weather
Image as a place 2. China has pleasant weather .92 .02
with good
weather
Convenience 1. China's hotels and trans- .77 .02
portation is well-developed
Image as a place 2. China has infrastructure for .88 .02
providing entertainment and leisure
traveler- 3. China provides good quality .81 .02
friendly service service
Country Image
People 1. Chinese people are .68 .04
trustworthy
Image of the 2. Chinese people are kind .69 .03
general public 3. Chinese people are diligent .66 .03
Political 1. China is a capitalist country .51 .04
Image as a 2. China is a democratic country .90 .02
country with 3. China is a civilized country .76 .03
political
civilization
Social 1. China has a stable society .76 .03
Image as a 2. China is well-ordered .77 .03
country with 3. China is peaceful .74 .03
social
stability
Economic 1. Chinese living standard .71 .03
are high
Image as a 2. Chinese products has good .67 .04
country with quality
economic power 3. Chinese business environment .62 .03
is well-developed
Cultural 1. Chinese culture is authentic .78 .03
Image as a 2. Chinese culture is diverse .82 .03
country with 3. Chinese culture is unique .73 .03
abundant
cultural
heritage
Behavioral Intentions
Purchase Intention 1. Will you purchase "made in .85 .04
China" products?
2. Do you intend to purchase .95 .04
"made in China" products?
Visit Intention 1. Are you planning to visit .90 .03
China for tourism in 5 years?
2. Do you intend to visit China .96 .03
for tourism in 5 years?
Dimensions Scale Items [rho] AVE
Destination Image
Urban 1. China has urbanized cities .75 .49
Image as a 2. China has a developed
modernized business industry
place 3. China has modern streets
and buildings
Nature 1. China has many opportunities .90 .74
to enjoy nature
Image as a place 2. China has a beautiful natural
with natural scenery
scenic beauty 3. China has many natural
spectacles
Culture 1. China has a rich cultural .82 .76
heritage
Image as a place 2. China has a unique culture
with abundant 3. China has famous historical
cultural sites
heritage
Value 1. China's traveling cost is .90 .75
reasonable
Image as a place 2. China's accommodation cost
worth its in reasonable
traveling cost 3. China's facility and
attraction ticket price are
reasonable
Safety 1. China is safe to travel .81 .61
Image as a 2. China's cities and tourist
secured and site are clean
sanitary place 3. China is safe from disease
to travel
Climate 1. China is a place to enjoy .89 .81
good weather
Image as a place 2. China has pleasant weather
with good
weather
Convenience 1. China's hotels and trans- .86 .67
portation is well-developed
Image as a place 2. China has infrastructure for
providing entertainment and leisure
traveler- 3. China provides good quality
friendly service service
Country Image
People 1. Chinese people are .72 .46
trustworthy
Image of the 2. Chinese people are kind
general public 3. Chinese people are diligent
Political 1. China is a capitalist country .76 .55
Image as a 2. China is a democratic country
country with 3. China is a civilized country
political
civilization
Social 1. China has a stable society .80 .57
Image as a 2. China is well-ordered
country with 3. China is peaceful
social
stability
Economic 1. Chinese living standard .71 .45
are high
Image as a 2. Chinese products has good
country with quality
economic power 3. Chinese business environment
is well-developed
Cultural 1. Chinese culture is authentic .82 .60
Image as a 2. Chinese culture is diverse
country with 3. Chinese culture is unique
abundant
cultural
heritage
Behavioral Intentions
Purchase Intention 1. Will you purchase "made in .90 .82
China" products?
2. Do you intend to purchase
"made in China" products?
Visit Intention 1. Are you planning to visit .93 .87
China for tourism in 5 years?
2. Do you intend to visit China
for tourism in 5 years?
Dimensions Scale Items Mean SD
Destination Image
Urban 1. China has urbanized cities 5.45 1.36
Image as a 2. China has a developed 4.28 1.21
modernized business industry
place 3. China has modern streets 4.73 1.32
and buildings
Nature 1. China has many opportunities 5.58 1.25
to enjoy nature
Image as a place 2. China has a beautiful natural 5.41 1.26
with natural scenery
scenic beauty 3. China has many natural 5.43 1.21
spectacles
Culture 1. China has a rich cultural 5.58 1.15
heritage
Image as a place 2. China has a unique culture 5.57 1.16
with abundant 3. China has famous historical 5.80 1.14
cultural sites
heritage
Value 1. China's traveling cost is 4.59 1.24
reasonable
Image as a place 2. China's accommodation cost 4.37 1.27
worth its in reasonable
traveling cost 3. China's facility and 4.22 1.26
attraction ticket price are
reasonable
Safety 1. China is safe to travel 2.96 1.23
Image as a 2. China's cities and tourist 2.47 1.14
secured and site are clean
sanitary place 3. China is safe from disease 2.16 1.05
to travel
Climate 1. China is a place to enjoy 3.54 1.23
good weather
Image as a place 2. China has pleasant weather 3.47 1.20
with good
weather
Convenience 1. China's hotels and trans- 3.09 1.13
portation is well-developed
Image as a place 2. China has infrastructure for 2.99 1.08
providing entertainment and leisure
traveler- 3. China provides good quality 2.87 1.08
friendly service service
Country Image
People 1. Chinese people are 3.02 1.24
trustworthy
Image of the 2. Chinese people are kind 3.31 1.19
general public 3. Chinese people are diligent 3.52 1.38
Political 1. China is a capitalist country 3.17 1.50
Image as a 2. China is a democratic country 2.49 1.22
country with 3. China is a civilized country 2.55 1.35
political
civilization
Social 1. China has a stable society 2.99 1.30
Image as a 2. China is well-ordered 2.81 1.42
country with 3. China is peaceful 2.97 1.22
social
stability
Economic 1. Chinese living standard 2.77 1.16
are high
Image as a 2. Chinese products has good 2.17 1.17
country with quality
economic power 3. Chinese business environment 3.09 1.37
is well-developed
Cultural 1. Chinese culture is authentic 5.22 1.33
Image as a 2. Chinese culture is diverse 5.28 1.31
country with 3. Chinese culture is unique 4.91 1.41
abundant
cultural
heritage
Behavioral Intentions
Purchase Intention 1. Will you purchase "made in 3.36 1.43
China" products?
2. Do you intend to purchase 2.97 1.39
"made in China" products?
Visit Intention 1. Are you planning to visit 4.68 1.60
China for tourism in 5 years?
2. Do you intend to visit China 4.54 1.57
for tourism in 5 years?
Table 3
Estimated Correlation Matrix for the Latent Variables
1 2 3 4 5 6 7
1. People
2. Political .39
3. Social .59 .47
4. Economic .50 .58 .68
5. Cultural .19 -.11 .09 .09
6. City -.01 -.04 -.09 -.10 -.08
7. Nature -.01 -.07 -.06 -.06 -.08 .72
8. Culture -.04 -.07 -.07 -.07 -.10 .65 .75
9. Value -.08 -.08 -.05 -.04 -.01 .34 .25
10. Safety .01 .05 .00 -.02 -.05 .06 -.04
11. Climate .06 .00 -.01 .01 .00 .25 .27
12. Convenience -.03 -.02 -.05 -.08 -.02 .27 .12
13. Purchase -.02 .01 -.06 -.08 -.04 .36 .18
14. Visit -.01 -.08 -.07 -.06 -.05 .47 .37
8 9 10 11 12 13 Mean SD
1. People 3.29 1.01
2. Political 2.75 1.11
3. Social 2.93 1.12
4. Economic 2.68 0.98
5. Cultural 5.13 1.16
6. City 4.83 1.06
7. Nature 5.49 1.13
8. Culture 5.67 1.06
9. Value .36 4.40 1.15
10. Safety -.05 .14 2.53 0.97
11. Climate .23 .19 .44 3.50 1.16
12. Convenience .13 .25 .68 .51 2.99 0.97
13. Purchase .16 .21 .34 .20 .38 3.17 1.34
14. Visit .33 .28 .21 .27 .32 .34 4.61 1.54
Table 4
Parameter Estimates for the Structural Equation Modeling
Image [right arrow] Visit Intention
Est. S.E. Est./S.E p-value
Destination Image Urban * .33 .08 3.95 <.01
Nature .09 .08 1.20 .23
Culture -.02 .07 -0.32 .75
Value * .10 .05 2.19 .03
Safety .08 .07 1.25 .21
Climate .05 .05 0.99 .32
Convenience .12 .07 1.75 .08
Country Image People .04 .06 0.65 .52
Political -.07 .06 -1.31 .19
Social -.03 .07 -0.40 .69
Economic .03 .08 0.41 .68
Cultural -.03 .04 -0.67 .50
Image [right arrow] Purchase
Intention
Est. S.E. Est./S.E p-value
Destination Image Urban * .38 .08 4.94 <.01
Nature -.07 .08 -0.89 .37
Culture -.05 .07 -0.66 .51
Value .05 .05 1.11 .27
Safety * .22 .06 3.39 <.01
Climate -.05 .05 -0.98 .33
Convenience * .16 .07 2.26 .02
Country Image People .01 .07 0.09 .93
Political .04 .06 0.65 .52
Social -.01 .08 -0.12 .90
Economic -.05 .08 -0.62 .53
Cultural .00 .04 0.09 .93
* p < .05.
