Heliyon 7 (2021) e07613
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Heliyon
journal homepage: www.cell.com/heliyon
Research article
Destination image's mediating role between perceived risks, perceived
constraints, and behavioral intention
Muhammad Umair Nazir *, 1, Ida Yasin 2, Huam Hon Tat 2
Putra Business School, Malaysia
A R T I C L E I N F O
A B S T R A C T
Keywords:
Destination image
Perceived constraints
Perceived risks
Revisit intention
Pakistan
This study aims to examine the effect of destination image, perceived risk, and perceived constraints on the
behavioral intention of international tourists to revisit Pakistan. The study also seeks to assess the destination
image's mediating role in the relationship between perceived risks, perceived constraints, and behavioral intention. A quantitative study with Partial least square structural equation modeling was used to investigate the
research Hypothesis. The data was collected from international tourists who were in Pakistan or who had visited
Pakistan. The findings revealed that perceived risks and perceived constraints negatively impact destination
image and behavioral intention. On the other hand, destination image has a positive impact on behavior.
Moreover, the study also proved the mediating effect of destination image among the relations of perceived risks,
perceived constraints, and behavioral intention. These findings indicate that sometimes it is difficult for destinations to overcome constraints, so destination managers should provide value-added services for substitutes. A
positive destination image can overcome risks and constraints, so destination managers should also promote
destinations besides mitigating risks. Literature has discussed the mediating effect of destination image in
different contexts. However, studies are scarce investigating destination image's effect in alleviating perceived
constraints and perceived risks through negotiation mechanisms.
1. Introduction
Risk perception is another important factor in travel decision-making
(Khan et al., 2017). With natural threats (i.e., natural disasters, epidemic
diseases), safety and security issues have also become threatening tourists (Tavitiyaman and Qu, 2013). Epidemic diseases like COVID 19 and
natural disaster incidences may exaggerate perceived risks and obstruct
international tourists' arrival (Estrada et al., 2015). Travelers perceive
several risks such as physical, health, social, financial, satisfaction, psychological, time, political, and terrorism risks which might affect their
traveling behavior (Chew and Jahari, 2014; Khan et al., 2019; Perpi~
na
et al., 2019; Rittichainuwat and Chakraborty, 2009).
Besides destination image and perceived risks, travel constraints are
essential in travel decision-making (Chew and Jahari, 2014; Khan et al.,
2019). Perceived constraints are hinders in performing a particular
behavior (Samdahl and Jekubovich, 2018). Constraints are not just the
hinders but also attached with benefits and opportunities (Tan, 2017).
Destination image can be both positive and negative in tourism research.
A strong destination image can overcome perceived risks and perceived
constraints (Beerli and Martín, 2004). Past research has revealed that
Understanding travelers' preferences, behaviors, and interests are
necessary for the global tourism industry (Al-Ansi and Han, 2019). Like
many other service industries, the nature of the tourism product is
intangible (Park et al., 2016). The experience is vulnerable to constraints,
threats, and risks (i.e., terrorism, political turmoil, epidemic diseases,
psychological trauma, less information, and language barriers). Such
vulnerability and susceptibility can destroy the destination image (Chew
and Jahari, 2014). Tourists make decisions based on destination image
perception rather than reality (Beerli and Martín, 2004; Beritelli and
Laesser, 2018; Kani et al., 2017). Tourist destinations compete on
destination image perceptions (Baloglu et al., 2014). The destination's
actual attribute is closely represented through the destination image
(Martín-Santana et al., 2017). Destination image combines different
cognitive, affective, and conative destination images (Pike, 2002).
Moreover, affective and cognitive destination images result in a holistic
image of the destination (Baloglu and McCleary, 1999).
* Corresponding author.
E-mail address: umairizan@hotmail.com (M.U. Nazir).
1
Current Address: Putra Business School, Level 3, Office Building of the Deputy Vice-Chancellor (Research & Innovation) UPM, 43400 Seri Kembangan, Selangor.
2
Co-Authors.
https://doi.org/10.1016/j.heliyon.2021.e07613
Received 1 March 2021; Received in revised form 29 April 2021; Accepted 15 July 2021
2405-8440/© 2021 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
M.U. Nazir et al.
Heliyon 7 (2021) e07613
Slak Valek and Williams (2018) elaborated on Abu Dhabi's perception
by residents and tourists to extend knowledge on destination image.
Country and destination images are different for international tourists
(Zhang et al., 2018b). Visitors, non-visitors, experiencers, and authors
perceive destination images differently (Stylidis and Cherifi, 2018).
Destination image significantly impacts perceived values and perceived
quality (Dmitrovic et al., 2009). The loyalty of tourists towards the
destination and satisfaction are affected by destination image attributes
(Mlozi, 2014). Cognitive and affective destination images significantly
affect the overall image (Chiu et al., 2016). Beerli-Palacio and Martín-Santana (2017) revealed a significant impact of cognitive image on affective and global images. Lindblom et al. (2018) stated that cognitive
destination image is the predecessor of the affective destination image.
Intention to travel is significantly affected by cognitive and functional
images (Huang and Petrick, 2009). Intent to visit Taiwan is affected
substantially by destination uniqueness and image, whereas negative
image and intention to visit were insignificant (Huang et al., 2013).
Positive destination image through visual media significantly impacts
attitude (Quintal and Phau, 2015). Intention to visit is affected considerably by destination image (Terzidou et al., 2018). A cognitive and affective image has a significant positive impact on visiting the reality tv
show destination (Fu et al., 2016). Cognitive, affective, and unique
destination image has a substantial effect on the overall destination
image. Moreover, overall destination image has a significant effect on
attitude and behavioral intention (Jalilvand and Heidari, 2017).
City image significantly affects revisit intention, whereas event image
does not substantially affect the revisit intention (Li et al., 2020). Country
image influences destination image positively, and both destination
image and country image significantly impact intention to visit (Chaulagain et al., 2019). It is necessary for destination management organizations to use Twitter for communication and overcome post-terror crises
(Oliveira and Huertas-Roig, 2019). Photos create the destination image
and echo with text to contribute to interactive interpretation (Zhao et al.,
2018).
Destination image attributes covariates with place identity and place
attachment; it also influences intention to revisit a sports event significantly (Ninomiya et al., 2019). Iceland's cognitive, affective, and conative
images are consistent with tourism characteristics and young viewer's
travel intention (Hao et al., 2019). In contrast with environmental image,
ecological image significantly affects preservative and intrinsic behavior
(Lee and Jeong, 2018). Political factors, nuclear radiation, personal
safety issues, and perceived risk negatively affects the destination image
of North Korea, which in return reduced revisit intention (F. Li et al.,
2018).
The impact of a cognitive image on the behavioral intention for visitors who have experience is significant, whereas, for non-visitors, it is
insignificant (Tan and Wu, 2016). The country's image has a significant
impact on intention (Alvarez and Campo, 2014). Cognitive and affective
destination images significantly impact behavioral intention (Souiden
et al., 2017). Destination image significantly affects the intention and
attitude of tourists (Kaur et al., 2016). Destination image significantly
impacts revisiting or recommending travel experience involvement
(Hahm and Severt, 2018). Positive or negative destination image results
in positive or negative behavioral intention, respectively.
tourists revisit the destination despite perceived risks and constraints
(Chen et al., 2013; Lepp and Gibson, 2008; Tavitiyaman and Qu, 2013).
Revisiting the destination and repeat visitors is the source of advertising
through word of mouth and sales generation (Chi and Qu, 2008).
Therefore, it is necessary to know about the revisit intention of tourists.
Besides the direct relation of destination image, various studies have
used destination image as the mediator in different contexts, i.e., event
image Li et al. (2020), destination image on the relationship between
couch surfing involvement and behavioral intention (Kuhzady et al.,
2020).
Despite the importance of destination image, perceived risks, and
perceived constraints, very few studies are conducted to understand the
interrelations. Formation of destination image through perceived constraints and perceived risks is the least study area (Beerli and Martín,
2004). The mediating role of destination image between the constructs
has not been investigated previously.
Studies that have examined the relationship between destination
image and behavioral intention have mixed findings (Huang et al., 2013;
Isa and Ramli, 2014; Jalilvand and Heidari, 2017; Stylidis and Cherifi,
2018). The relation between perceived risk and behavioral intention has
mixed findings (Al-Ansi et al., 2019; Kani et al., 2017; Sanchez et al.,
2018). Recent research that tourists revisit the destination despite
perceived risks and constraints contradicts various other research that
tourists avoid revisiting risky destinations (Hung and Petrick, 2012; Lee
et al., 2012; Tan, 2017).
Based on mixed findings, scarcity of knowledge, and escalating
vulnerability of tourist destinations to travel risks, travel constraints, and
adverse destination images, it is needed to address this research gap.
Considering the substantial role of destination perceptions in evaluating
revisit behavior, further investigation of revisit intention and travel
perception is necessary. Thus, based on existing knowledge, the objectives of this study are to investigate (1) the effect of perceived risk,
perceived constraints, and destination image on behavioral intention, (2)
the effect of perceived risks and perceived constraints on destination
image, and (3) the mediating role of destination image in the relation
between perceived constraints, perceived risks and behavioral intention
of international tourists.
2. Literature review
2.1. Destination image
Destination image is the combination of emotions, beliefs, ideas, and
tourists' impressions regarding the destination (Crompton, 1979).
Destination image combines concepts, beliefs, mental representation,
and assertions regarding a tourist's destination not being physically there
(Baloglu and McCleary, 1999). Destination image is categorized as affective, cognitive, and conative (Agapito et al., 2013). Later on, the
destination image comprised the organic, induced, and complex images
(Chiu et al., 2016). Destination image is different and changes pre-visit,
post-visit, and travel phases (Martín-Santana et al., 2017). International
and national tourists and residents perceive destination image differently
(Abodeeb et al., 2015). Furthermore, leisure attractions, history, and
accommodations are the key attractions for competitiveness and unique
destination images (Vinyals-Mirabent, 2019).
Length of stay, experience effects, and time passage might cause
changes in the destination image (Pike et al., 2019). Similarly, couch
surfing involvement also improves the destination image (Kuhzady et al.,
2020). In line with previous studies, Pike et al. (2018) revealed that
destination image attributes might change accordingly in the short nature of stopover. Warmth and competence dimensions of stereotype
content model can indicate destination image's content with a significant
relationship with visit intention (Shen et al., 2019). Marketing communication and cognitive image have a substantial impact on overall
destination image; besides direct effect, the visitation mediates the relationships (Albert da Silva et al., 2018).
Hypothesis 1. Destination image is positively related to behavioral
intention.
2.2. Perceived constraints
Constraints are barriers or blockages (Crawford et al., 1991). Constraints are the hurdles and hinder to perform a particular activity
(Jackson et al., 1993). Constraints do not always work negatively, but
they also help improve the quality and destination attributes (Carroll and
Alexandris, 1997). Constraints affect types of tourism activities, destination choices, and frequencies. Moreover, the tourism industry can
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M.U. Nazir et al.
Heliyon 7 (2021) e07613
risks were added as perceived risk dimensions (S€
onmez and Graefe,
1998). Rush, instead of risk, motivates tourists for adventurous activities
(Buckley, 2012). Equipment failure, health, physical, crime, weather,
political issues, and cultural risks are the seven risks identified by
(Schroeder et al., 2017). Tourists are categorized based on risk perceptions such as risk-averse, risk avoider, safe novelty seeker, adventurous
novelty seeker, and risk-takers (Karl, 2016).
First-time visitors and repeaters both perceive travel and terrorism
risk (Rittichainuwat and Chakraborty, 2009). Social risk, language barrier, support of local, evacuation, and treatment in case of terrorism are
the risks tourists from Israel face while crossing the Egyptian border
(Fuchs et al., 2013). Similarly, terrorism also affected tourist arrivals in
Greece (Samitas et al., 2018). Perceived travel risks for first-timers are
different as compared to repeaters (Fuchs and Reichel, 2011). Tourists set
a benchmark for traveling and destinations above that benchmark
compared to safety and security (Koo et al., 2018). Sexual harassment,
getting lost, theft, discrimination, social disapproval, and physical abuse
are the risks perceived by solo female travelers (Yang et al., 2017).
Individual drifters prefer places with higher risks, whereas tourists in
groups are not risk-takers (Williams and Balaz, 2013). Tourists perceive
more risks from places with more incidents of terror (Desivilya et al.,
2015). Offensive and aggressive street behavior is the predictor of safety
and security concern, whereas non-offensive and distracting street
behavior do not affect safety and security concern (Millar et al., 2017).
Attachment with the homeland, gender, and age make international
travel riskier and receptive to threats which result in Russians turning to
domestic tourism. In contrast, higher income makes Russians less susceptible to threats (Stepchenkova et al., 2018). In comparison, Promsivapallop and Kannaovakun (2018) did not support the argument that
young female adults perceive more risk than young males.
Experience of riskier activities results in riskier behavior
(Pr€
obstl-Haider et al., 2016). Tourist demographics, such as age and
gender, substantially impact risk perception (Schroeder and
Pennington-Gray, 2014). Experiencers perceive fewer risks as compared
to first-timers (Russel and Prideaux, 2014). Tourist with victimization
experience perceives more risk (Sharifi-Tehrani and Esfandiar, 2018).
Environmental health and psychological risks significantly impact satisfaction with destination attributes (Olya & Al-ansi, 2018).
Violent crime is the major security concern for tourists; for some
tourists, violent crime might not affect the travel intention, but it can
cause (re)visit intention (Hua et al., 2020). Terrorism has a long-term
impact on tourists' minds compared to social and political incidents
(Lanouar and Goaied, 2019). Tourists tend to change their tour plans due
to terrorism (Walters et al., 2018). Political turmoil also affects the length
of stay negatively (Hateftabar and Chapuis, 2020). Nuclear testing has
damaged the destination image of North Korea for Chinese tourists, so
the perceived risk, along with the tense regional situation, affected the
destination image negatively (F. Li et al., 2018).
Familiarity seekers perceive health and communication risks higher
than novelty seekers; young adults with experience have lower risk
perception. In comparison, risk dimensions such as crime and false
practice risk, health, communication, and over-commercialization risks
do not influence travel intention except political risk (Promsivapallop
and Kannaovakun, 2018). Perceived risk negatively affects attitude, but
the perceived risk does not affect behavioral intention (Hsieh et al.,
2016).
Perceived risks, particularly terrorism, negatively impact visiting
internationally (S€
onmez and Graefe, 1998). Perceived risks have a significant adverse effect on behavioral intention (Floyd et al., 2004).
Perceived risk has a significant impact on local food consumption
intention (Zhang et al., 2018a, 2018b). Perceived risks are different for
tourists from peaceful places; they perceive less risk and usually are
risk-takers (Desivilya et al., 2015). Financial risk, environmental risk,
social risk, health, and psychological risks have a significant negative
impact on using a product and the intention to recommend (Olya &
overcome some constraints, but some constraints are more challenging
due to their dependence on constraints negotiation (Mei and Lantai,
2018). Travel constraints also vary from tourist to tourist; others' constraints could be an opportunity for others (Barreira and Cesario, 2018).
Disinterest, personal safety, and institutional barriers are the constraints
for students to visit the US-Mexico border (Canally and Timothy, 2007).
At the attraction stage, skiers are more vulnerable to constraints (Alexandris et al., 2017). Spending on consumer products is affected by leisure
constraints (Chen, 2018).
There are four types of constraints for senior citizens: intrapersonal,
interpersonal, microstructural, and macrostructural (Huber et al., 2018).
Bozic et al. (2017) identified a lack of recommendation, structural, lack
of time, inter/intrapersonal constraints a tourist must face while traveling. There is a significant negative relation between preference level
and intrapersonal constraints. However, structural constraints, participation, and interpersonal constraints have insignificant relation with
calligraphic landscape (Zhang et al., 2012). Chinese female travelers
perceive eight types of travel constraints, and they negotiate these constraints using advertising, group travel, donkey travel, children's support,
and being motivated (Gao and Kerstetter, 2016). Lack of interest, safety
issues, money, and time constraints are more effective for high temporal
distance than low distance; similarly, personality influences constraints
more for high temporal distance (Tan, 2020).
Interest constraints are the greatest barrier for both males and females
to visit museums; comparatively, women report more structural and
interpersonal constraints (Mullens and Glorieux, 2019). Intrapersonal
constraints are more effective in precontemplation stage than interpersonal, and structural constraints are more effective in preparation, action,
and maintenance (Qiu et al., 2018). The dimensions of interpersonal
constraints such as escape, incuriousness, emotion, and culture partially
affect the revisit intention. Intrapersonal constraints also mediate the
relationship between travel experiences and revisit intention (Zhang
et al., 2016).
Interpersonal constraints and school constraints are important for
travelers at planning and actual excursion behavior stages; however,
constraints do not influence the number of trips and length of stay (Dale
and Ritchie, 2020). Leisure constraints do not influence intention to visit
theme parks in the future, whereas leisure constraints partially influence
existential authenticity and motivation to a theme park (Tan and Huang,
2020). In line with literature revealed that just perceived incapability
dimension of constraints significantly impacts learned helplessness,
whereas other dimensions of constraints have no impact on helplessness.
Moreover, negotiation does not moderate the relationship between
constraint dimensions and helplessness (Wen et al., 2020).
Health constraints significantly affect travel intention, following a
larger influence of financial and family constraints, slight effect of time,
travel stress, travel companion, and pet constraints. In contrast, no work
constraints affect travel intention (Karl et al., 2020). Intrinsic, environmental, and interactional constraints do not affect the travel intention of
disabled persons (Lee et al., 2012). In line with previous studies, Uatay
et al. (2019) identified interpersonal constraints affect both attitude and
travel intention towards solo travel. In contrast, structural constraints do
not affect attitude and travel intention. Intention to travel is significantly
affected by travel constraints, whereas travel intention is positively
influenced by negotiating constraints (Hung and Petrick, 2012). Mainland Chinese tourists' revisit intention to Hong Kong is not influenced by
structural and interpersonal constraints (Huang and Hsu, 2009).
Hypothesis 2. Perceived constraints are negatively related to behavioral intention.
2.3. Perceived risks
Cheron and Ritchie (1982) added satisfaction risk and five dimensions of perceived risks: psychological, financial, performance, social
and physical risks. Later, political instability, health risks, and terrorism
3
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Heliyon 7 (2021) e07613
do not predict destination image (Millar et al., 2017). Overall, risk
perception moderates the relationship between destination image and
travel intention (Caber et al., 2020).
Social quality risk and travel constraints significantly negatively
affect both destination image medical and non-medical attributes. In
contrast, physical health and destination risks influence just destination
image medical attributes (Khan et al., 2020). Security gaps also affect the
perceptions of destination image, as Terrah et al. (2020) identified a
significant difference between expectations and perceptions of the
airport and hotel security. Online identity and sense of community
constraints have no impact on affective image and emotion, and they do
not moderate the relationship between emotion and affective image. In
contrast, information search constraint has a significant negative impact
on emotion (Fu and Timothy, 2021).
Structural and intrapersonal constraints have a significant impact on
the destination image. A strong destination image could dominate cultural constraints. Destination image also mediates the relation between
perceived constraints and behavioral intention (Chen et al., 2013). Travel
constraints and destination image have negative relation for repeaters,
whereas a positive relation for first-timers (Tan, 2017). There is also a
significant relationship between destination image and perceived constraints (Chen et al., 2013). Gender affects enemy image with a significant impact of gender and generation on barbarian and ally images
(Chung and Chen, 2019).
Figure 1 shows the study's conceptual framework; the study adapted
the reflective model because, first, results are more powerful and less
biased in reflective specifications (Chang et al., 2016). The causality direction is from construct to items. Change in indicator did not cause a
change in the construct. All the indicators shared a common theme; the
constructed concept did not change with dropping an indicator (Jarvis
et al., 2003). Second, past studies (Khan et al., 2017, 2019; Parrey et al.,
2018; Park et al., 2017) used reflective models for their studies.
Al-ansi, 2018). Besides the perception of risk, risk knowledge also affects
risk aversion attitude and travel intention (Zhu and Deng, 2020).
Hypothesis 3. Perceived risks are negatively related to behavioral
intention.
2.4. The mediating role of destination image
Cognitive and affective destination images have a significant impact
on the overall image and intention to recommend. Moreover, the overall
destination image also mediates the relation between affective and
cognitive destination images and overall destination image (Stylidis
et al., 2017). Destination image mediates the relationship between
intention to visit and consumption value (G
omez et al., 2018). Holistic
destination image mediates the relationship between revisit intention
and affective and conative image nut insignificant between cognitive
image and revisits intention (Stylos et al., 2016). City image mediates the
relationship between event image, satisfaction, and revisits intention (Li
et al., 2020). Destination image partially mediates the relationship between couch surfing involvement and behavioral intention (Kuhzady
et al., 2020). Similarly, destination image mediates the relationship between travel motivation and destination travel intention (Caber et al.,
2020).
Perceived risks such as cultural, health, psychological, financial, and
political risks negatively affect destination image (Parrey et al., 2018).
Perceived risks have a significant negative impact on destination image
(Kani et al., 2017). Tourists perceive more risk from the destinations with
negative destination image (Carballo et al., 2017). Cognitive and affective destination images are affected by financial and socio-psychological
risks. In contrast, physical risk has no significant impact on cognitive and
affective destination images; moreover, the relationship between
perceived risks and behavioral intention is significantly mediated by
destination image (Chew and Jahari, 2014). Perpi~
na et al. (2021) integrated the behavior model by merging affective and cognitive components of destination image and perceived risk in a single construct and
identified that both cognitive and affective components influence overall
evaluations.
The consumption value and intention to visit are significantly mediated by destination image (G
omez et al., 2018). For a riskier continent,
regardless of national or regional variability, regardless of knowledge
about a place, people apply sweeping generalization over the entire
continent, especially in Uganda (Lepp et al., 2011). Perceived risk
pre-travel and post-travel significantly negatively affect destination
image perception (Xie et al., 2020). Perceived risks moderate the relation
between destination image and overall satisfaction. Moreover, risks also
moderate the link between satisfaction and behavioral intention (Tavitiyaman and Qu, 2013). Crime-related safety & security concerns are the
predictors of destination image, whereas panhandling-related concerns
Hypothesis 4. Perceived constraints are negatively related to the
destination image.
Hypothesis 5. Perceived risks are negatively related to the destination
image.
Hypothesis 6a. Destination image mediates the relation between
perceived constraints and behavioral intention.
Hypothesis 6b. Destination image mediates the relation between
perceived risks and behavioral intention.
2.5. Research method
This study used a positivist, deductive approach, in which the Hypothesis is deduced based on general theory (Sekaran and Bougie, 2016),
and a quantitative technique with questionnaire survey method to collect
Figure 1. Conceptual framework.
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M.U. Nazir et al.
Heliyon 7 (2021) e07613
data that is easy to explain, efficient in time, and low in money consumption (Saunders et al., 2009). A cross-sectional data collection
method was used, as it is easier to approach research targets, personal
identities are not required, and respondents feel free to respond in less
time (Spector, 2006). The questionnaire was distributed with cover letter
elaborating the purpose of the survey assuring the confidentiality to the
respondents. In addition, researchers conducted the study involving
human participants with institutional committee's ethical standards
(Putra Business School Research Ethical Committee headed by Prof. Dr.
Zulkornain Yusop, Reference Number: PBS/PhD/PBS18122250 dated
June 29, 2020).
Table 1. Respondent's profile.
Demographic characteristics
%
Gender
Male
53
Single
50
Marital Status
Age
Employment Status
Monthly Income
Ethnicity
Married
47
Separated
3
18–24
14
25–34
34
35–44
23
45–54
14
55–64
8
Employed full time
64
Employed part-time
12
Housewife/House worker
10
Temporarily unemployed/Looking forward Retired.
3
Student
11
Less than 2000
37
2000–3999
24
4000–6999
16
7000–9999
12
more than 9999
11
Asian
44
European
38
Australian
3
North American
10
South American
5
2.6. Research instrument
The study used well-established survey scales developed by previous
studies. All the items were measured on a five-point Likert scale ranging
from 1 (strongly disagree) to 5 (strongly agree) because fully labeled
provides benefits to the researchers (Eutsler and Lang, 2015). Moreover,
five and seven-point Likert scales produce the same mean scores on
rescaling (Dawes, 2008). The other reason for using the Five-point scale
was the past researchers, i.e., (Khan et al., 2017, 2019; Park et al., 2017;
& Parrey et al., 2018) who used five-point Likert scale. The destination
image was measured on eleven items scale developed by (Khan et al.,
2017; Park et al., 2017). Three items scale of behavioral intention was
adapted from (Lam and Hsu, 2004). Perceived risks were measured on
eight items scale developed by (Parrey et al., 2018). Ten items scale of
perceived constraints was adapted from (Huang and Hsu, 2009; Khan
et al., 2019). Tourists' demographics such as age, gender, marital status,
region, occupation, and income were also measured.
Table 2. Validity and reliability for constructs.
Constructs
Items
Alpha
Loading
AVE
CR
Destination Image (DI)
Pakistan has a quality tourism infrastructure
0.919
0.621
0.556
0.932
0.505
0.889
0.606
0.924
0.909
0.968
Pakistan has a good climate
0.819
Pakistan is safe and stable
0.776
Pakistan has a good quality of life
0.643
Pakistan has appealing local cuisine
0.689
Pakistan has a variety of unique attractions
0.768
Pakistan is rich in cultural heritage
0.810
Pakistan is a good place for shopping
0.728
Pakistani people are interesting and friendly
0.803
Pakistan is a pleasant place to visit
0.825
Pakistan has several springs
Perceived Constraints (PC)
0.685
You do not have enough money to revisit Pakistan
0.870
You do not have enough holidays to revisit Pakistan
Perceived Risk (PR)
No information about places to visit in Pakistan
0.758
The weather is not favorable in Pakistan
0.810
Areas you want to visit are too far away in Pakistan
0.619
No one to travel with you
0.743
Family and friends are not interested in traveling to Pakistan
0.689
Pakistan is not novel to you anymore
0.806
You feel overall the experience of vacation will not be a good value of money
0.907
You feel the threat of becoming sick while traveling or at the destination
0.698
0.847
You feel psychological trauma because of others' negative comments about the destination
0.827
You feel there is a chance of physical danger to my health while on vacation
0.852
You feel that you might get caught up in political turmoil while vacationing
0.811
You perceive language barriers while vacationing
0.530
You perceive the risk of a terrorist attack while vacationing
0.806
You feel that you will not receive enough personal satisfaction from this vacation
Behavioral Intention (BI)
0.647
0.572
You intend to revisit Pakistan
0.804
0.950
0.958
You intend to recommend Pakistan to others
0.957
You plan to revisit Pakistan
0.946
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Table 3. Cross-loading.
Table 5. Heterotrait-monotrait (HTMT).
Destination
Image
Perceived
Constraints
Perceived
Risks
Behavioral
Intention
Constructs
Di1
0.621
-0.248
-0.061
0.328
Destination Image
Di2
0.819
-0.332
-0.216
0.483
Perceived Constraints
0.411
0.443
Di3
0.776
-0.347
0.047
0.343
Perceived Risks
0.198
0.131
Di4
0.643
-0.303
-0.06
0.337
Di5
0.689
-0.238
-0.062
0.31
Di6
0.768
-0.333
-0.036
0.354
Di7
0.81
-0.32
0.001
0.328
Di8
0.728
-0.268
-0.032
0.325
Di9
0.803
-0.378
-0.018
0.307
Di10
0.825
-0.308
-0.082
0.363
Di11
0.685
-0.31
-0.173
0.387
Pc1
-0.229
0.647
-0.261
-0.248
Pc2
-0.171
0.572
-0.189
-0.091
Pc3
-0.231
0.758
-0.305
-0.272
Pc4
-0.425
0.81
-0.232
-0.395
Pc6
-0.239
0.619
-0.271
-0.166
Pc7
-0.357
0.743
-0.237
-0.213
-0.37
Pc8
-0.28
0.689
-0.117
Pc9
-0.323
0.806
-0.251
-0.378
Pr1
-0.141
-0.254
0.698
-0.156
Pr2
-0.099
-0.265
0.847
-0.214
Pr3
-0.098
-0.228
0.827
-0.191
Pr4
-0.017
-0.247
0.852
-0.114
Pr5
-0.035
-0.247
0.811
-0.079
Pr6
0.087
-0.28
0.53
-0.099
Pr7
-0.057
-0.232
0.806
-0.171
Pr8
-0.052
-0.282
0.804
-0.122
Bi1
0.427
-0.346
-0.194
0.958
Bi2
0.454
-0.379
-0.17
0.957
Bi3
0.483
-0.43
-0.213
0.946
1
2
3
4
Behavioral Intention
0.504
0.379
(Facebook messenger, YouTube, and Email) and face-to-face. First, the
targeted responses were identified through Facebook pages and YouTube
blogs; then, the google form link was sent through Facebook messenger,
YouTube video comments, and emails. Face to Face responses was
collected from tourists at the tourist places. The data was collected from
June to September, as it is the peak time of tourism in Pakistan.
Two hundred forty-three respondents (224 online, 19 face to face)
sent the questionnaire back. Responses with missing values higher than
5% should be excluded (Hair et al., 2017). Forty-two responses with
higher missing values were excluded. Two hundred-one respondents
were used for further analysis, which meets the required sample size
criteria, i.e., 119 by G*Power.
The sample size was determined on the following basis:
There should not be too small or too large a sample size, but it should
be moderate (Kothari, 2016). The sample size should be enough to
identify the relationship effects (Fink et al., 2010). A sample size between
200 to 400 is critical (Hair et al., 2014). Sample-to-variable ratio
preferred 15:1 or 20:1 are appropriate to determine sample size (Hair
et al., 2018; Memon et al., 2020). There are three independent variables;
according to the 20:1 ratio, the sample size for this study is 60. A power
analysis was conducted to obtain an appropriate sample size using
G*Power software (Memon et al., 2020). The research identified a sample
size of 119 respondents obtained by three predictors, the probability of
null Hypothesis rejection 0.05, the medium effect size and the Value of
0.15, and the power value (1- error probability) 0.95. Out of 201 respondents, 53% were male, and the remaining were female. Most of the
respondent's 44%, were Asian, backed by 38% European. Most of the
respondents, 64%, were full-time employed, and 50% were single.
Table 1 provides the demographics of respondents.
Bold values are representing the values of representing variable.
2.7. Data collection procedure and sampling
3. Data analysis & results
This study aimed to know to revisit international tourists' intentions
based on destination image, perceived risks, and perceived constraints.
So, the international tourists in Pakistan were the target population.
Pakistan was chosen as Pakistan stands at 121st number out of 140
countries (Calderwood and Soshkin, 2019). The tourism sector's contribution to Pakistan's GDP is just 8.83 billion US dollars, which is far less
for a country with the potential to be the number one tourist destination.
Wilson (2019) ranked at 3rd potential adventurous destination by British
Backpackers (Ahmed, 2019). This lacks infrastructure, less coordination
within departments, and safety and security issues (Arshad et al., 2018).
In the probability sampling technique, every element of the population has an equal chance of selection, and it is used when the sampling
frame is available (Veal, 2006). There was no sampling frame available,
so the study used a non-probability purposive sampling technique (Chen
and Tsai, 2007). The data was collected through online sources
3.1. Measurement model
A measurement model and structural model were measured using
Partial Least Square (PLS) – Structural Equation Modeling (SEM) and the
software used in SmartPLS 3.3.2 (Hair et al., 2012). PLS-SEM is useful for
theory development (Hair et al., 2017). PLS-SEM can handle a small
sample size. The 201 sample size used in this study can be considered
small; moreover, it is unnecessary to report each indicators' standard
deviation and mean values (Hair et al., 2014).
This study used internal consistency reliability, indicator reliability
(outer loadings), convergent and discriminant validity to analyze the
measurement model. For internal consistency reliability, Cronbach's
alpha and composite reliability (CR) were used. Cronbach's Alpha value
0.7 and composite reliability (CR) 0.8 are considered satisfactory
(Hair et al., 2018). Table 2 shows that Cronbach's Alpha and CR values
are satisfactory, so the study constructs have internal consistency
Table 4. Fornell-larker criterion.
Constructs
1
Behavioral Intention
0.954
Destination Image
0.479
2
3
Table 6. Collinearity statistics VIF.
4
Constructs
0.746
1
2
Behavioral Intention
Perceived Constraints
-0.406
-0.416
0.710
Perceived Risks
-0.203
-0.094
-0.318
0.778
Bold values are representing the values of representing variable.
6
Destination Image
1.298
Perceived Constraints
1.431
1.112
Perceived Risk
1.194
1.112
3
4
M.U. Nazir et al.
Heliyon 7 (2021) e07613
Table 7. Structural estimates (direct relation Hypothesis testing).
Hypothesis
Std.
Beta
Std.
Error
t-value
Decision
R2
0.354
PC - > BI
-0.378
0.080
4.753
Supported
PR - > BI
-0.295
0.069
4.297
Supported
DI - > BI
0.294
0.076
3.879
Supported
PC - > DI
-0.496
0.074
6.657
Supported
PR - > DI
-0.251
0.086
2.911
Supported
f2
Q2
0.154
0.309
0.113
0.103
0.230
0.287
0.117
0.074
Table 8. Structural estimates (mediation Hypothesis testing).
Hypothesis
Std. Beta
Std. Error
t-value
Decision
Confidence Interval (BC)
LL
UL
PC - > DI - > BI
-0.146
0.049
2.954
Supported
-0.259
-0.067
PR - > DI - > BI
-0.074
0.037
1.989
Supported
-0.153
-0.012
For structural model bootstrapping with 5000, resample was used to
assess beta value, std. error, t-value, effect size, R2, and Q2 values. First
direct relations between variables were evaluated. Based on the calculation of path coefficients as displayed in Table 7, all the relations have tvalues larger than 1.645, thus significant at the 0.05 significance level.
Destination image has significant positive effect on behavioral intention
(β ¼ 0.294; p < 0.05), perceived constraints have significant negative
impact on behavioral intention (β ¼ -0.378; p < 0.05), similarly
perceived risks has also significant negative impact on behavioral
intention (β ¼ -0.295; p < 0.05). Explaining 35% variation in behavioral
intention. Moreover, perceived risks (β ¼ -0.251; p < 0.05), and
perceived constraints (β ¼ -0.496; p < 0.05), have significant negative
impact on destination image, which explains 23% percent variation in
destination image. Thus, all the direct relation hypotheses H1, H2, H3,
H4, H5 were supported. P-value guides the audience either the relationship exists or not. It does not guide with the effect size. f2 value indicates the effect size, a value larger than 0.02 indicates a small effect, a
value greater than 0.15 shows medium, and higher than 0.35 shows the
substantial effect (Cohen, 1992). As the values of f2 in Table 7 show that
perceived constraints have a medium effect on behavioral intention,
perceived risk, and destination image have small effects on behavioral
intention, perceived risk is small. In contrast, perceived constraints have
a medium effect on destination image.
Besides, through the blindfolding procedure, the predictive relevance
of the model was examined. Value of Q2 larger than 0 indicates predictive
relevance of model (Hair et al., 2017). All two Q2 values for behavioral
intention (Q2 ¼ 0.309) and destination image (Q2 ¼ 0.117) are larger
than 0, which indicates that the model has predictive relevance.
Moreover, the study also examined the mediation effect of destination
image between perceived constraints, perceived risks, and behavioral
intention. The study used a segmentation approach in which three hypotheses are made to investigate the mediation effect. Based on the
calculation of path coefficients through bootstrapping as displayed in
Table 8, all the two indirect effects, β ¼ - 0.146, β ¼ -0.074, are significant
with t values 2.954 and 1.989. there is mediation, and the relations are
statistically significant if 0 is not straddled in between the limits of
confidence interval (Preacher & Hayes, 2004, 2008). Confidence interval
values in Table 8 (LL ¼ - 0.259, UL ¼ -0.067), (LL ¼ -0.153, UL ¼ -0.012)
do not straddle 0 in between, so the mediation effects are statistically
significant. So, the mediation Hypothesis H6a and H6b are significant.
reliability. For indicator reliability and convergent validity factor loadings, average variance extracted (AVE) and composite reliability (CR)
were analyzed. 0.708 is the recommended Value of factor loading, with
the AVE value higher than 0.5 (Hair et al., 2018). Factor loadings less
than 0.708 can be kept if the AVE value is higher than 0.5 (Chin et al.,
2008). PC5 and PC10 with loadings were between 0.524 and 0.557,
respectively, as the AVE value was less than 0.5. ten items Di1, Di4, Di5,
Di11, Pc1, Pc2, Pc6, Pc8, Pr1, Pr6 with the loadings 0.621, 0.643, 0.689,
0.685, 0.647, 0.572, 0.619, 0.689, 0.698, 0.530 respectively were kept
despite of their low factor loadings, because the AVE value was higher
than 0.5. Table 2 represents the convergent validity by showing factor
loadings ranging from 0.530 to 0.958. AVE value of variables ranging
from 0.505 to 0.909 and composite reliability ranged from 0.889 to
0.968.
Discriminant validity ensures that the constructs are not reflected and
unrelated to each other. There are three ways to measure discriminant
validity; Cross-loadings, Fornell-Larker Criterion, and HeterotraitMonotrait (HTMT) ratio. For cross-loading, indicators on the designated latent variable should be higher than the loadings of other variables. If the indicators are higher than the loadings of other constructs,
constructs are not interchangeable. Table 3 shows that indicator's loadings on assigned variables are higher than the loadings on all other
constructs, establishing discriminant validity. It indicates that there is no
cross-multiplication between the constructs. And there is no crossinfluence between perceived risk and perceived constraints.
For Fornell-Larker Criterion, AVE's square root on diagonal values
should be greater than the corresponding correlations' values. The study
meets the criteria, as shown in Table 4.
Henseler et al. (2016) recommended HTMT as the alternative
approach to measure discriminant validity. HTMT.₈₅ should be less than
0.85 Ringle et al. (2018). Table 5 shows that the value of HTMT is less
than 0.85, which meets the required value of discriminant validity.
3.2. Structural model
On the validation of the measurement model, at the second step
structural model was tested. Normal distribution of data is not required
for PLS-SEM, as it uses nonparametric statistical techniques (Hair et al.,
2017). VIF value should be less than 5, and in some cases, it should be less
than 3.3 to avoid multicollinearity (Hair et al., 2018). There is no multicollinearity between independent constructs (destination image,
perceived constraints, and perceived risks) and in mediation cases
(perceived risks and perceived constraints) constructs. Table 6 shows that
all the VIF values are less than 5 or 3.3. Therefore, there is no relationship
or cross multiplication between perceived constraints and perceived
risks.
4. Discussion and conclusion
This study contributes to the existing knowledge of tourism literature
in two ways. First, this study verified the relation between perceived
constraints, perceived risks, and destination image. Few studies address
7
M.U. Nazir et al.
Heliyon 7 (2021) e07613
other reason to collect data through online resources was due to epidemic
COVID 19. There are fewer issues with method biases in data collection
through online resources (Chew and Jahari, 2014; De Beuckelaer and
Lievens, 2009).
destination image formation through perceived risks and perceived
constraints (Chew and Jahari, 2014; Khan et al., 2017; Lepp et al., 2011).
To confirm relationships among these constructs, authors call for further
investigation (Chen et al., 2013; Chew and Jahari, 2014; Khan et al.,
2017). When risks and constraints are examined in this study, the relations are relevant to international tourists to Pakistan in re-forming
their destination image. Second, this study contributes to the literature
by integrating risks, constraints, and destination image. Investigating the
mediating role of destination image between the constructs as well. Our
findings support the argument that destination image mediates the
relation among constructs significantly. The perception of risks, destination image, and constraints by international tourists for the riskier
destination were understood.
This study identifies the relationship between perceived risks,
perceived constraints, and destination image. The study investigates
constraints, risks, and destination image as an individual construct to
understand international tourists' behavior. This study found a significant relationship between perceived risks, perceived constraints, and
destination image. Moreover, this study also found a significant impact of
perceived constraints, perceived risks, and destination image on behavioral intention. the results constraints to intention (β ¼ 0.294; p < 0.05),
risk to intention (β ¼ -0.378; p < 0.05) indicates that international
tourists may not intend to revisit the riskier destination if they perceive
constraints and risks. Results also indicate that tourists intend to revisit
the destination if they perceive a positive destination image. The results
align with previous research (Huber et al., 2018; Kani et al., 2017; Khan
et al., 2019; Lepp et al., 2011; Olya & Al-ansi, 2018; Parrey et al., 2018).
As the perceived risk and perceived constraints have a significant negative impact on behavioral intention and destination image, this might be
good for repeat visitors as the travel costs are lower in such a situation. In
such a situation, there should be promotions by destination management
organizations to reduce negative perceptions. Destination managers
should provide quality infrastructure, safety and security, quality food,
and a better environment. Value for money services such as airfare,
family tours, and accommodation should be provided to tourists for risk
substitutes.
This study's second finding is the mediating role of destination image
between perceived risk, perceived constraints, and behavioral intention.
Past researchers revealed that destination image, if properly managed
has a significant influence on behaviors. Existing findings illustrate that
managers should mitigate risks and alleviate constraints to improve
destination image and behavioral intention.
Declarations
Author contribution statement
Muhammad Umair Nazir: Analyzed and interpreted the data;
Contributed reagents, materials, analysis tools or data; Wrote the paper.
Ida Yasin, Huam Hon Tat: Analyzed and interpreted the data.
Funding statement
This research did not receive any specific grant from funding agencies
in the public, commercial, or not-for-profit sectors.
Data availability statement
Data will be made available on request.
Declaration of interests statement
The authors declare no conflict of interest.
Additional information
Supplementary content related to this article has been published
online at https://doi.org/10.1016/j.heliyon.2021.e07613.
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The research investigated destination image, perceived constraints,
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