THE IMMEDIATE IMPACT OF INSTAGRAM POSTS ON CHANGING THE
VIEWERS’ PERCEPTIONS TOWARDS TRAVEL DESTINATIONS
Saleh Shuqair a, Philip Cragg b
ab Bahrain Polytechnic, Manama, Bahrain
Corresponding email: saleh.shuqair@polytechnic.bh
Abstract
This study aims to measure the immediate impact of User- Generated- Contents (UGC) in
forms of Instagram images on changing the viewer’s perceptions towards a travel
destination.
By using an experimental design and subsequent t-Test in SPSS, the viewers’ perceptions of a
destination (Lebanon) pre- and post-exposure to selected Instagram images were
investigated.
The findings show that Instagram posts were effective in changing the viewers’ perceptions
and it can influence viewers’ ` behavioural intentions during the pre-visitation stage. The
study discusses implications for the strategic place of UGC in promotional strategies for
destinations by means of Instagram, but equally will assert – where appropriate – more
general guidelines and areas for future research towards the use of other social media in this
context. The research around destination image formation have been focusing on
Destination –Marketing -Organization (DMO) marketing activities, therefore, this is one of
the few studies that have addressed the impact of Instagram images on changing the viewers’
perceptions towards travel destinations.
Keywords: Destination –Marketing -Organization (DMO), Destination Image (DI), User
Generated Contents (UGC), Instagram, Lebanon.
1. Introduction
Destination Image (DI) has been widely discussed in the literature yet, in spite of the
considerable amount of studies around DI, the studies around the impact of User-
Generated–Content (UGC) on DI formation is still limited and would benefit from further
exploration. The process of DI formation has shifted since the arrival of the digital era and is
no longer controlled solely by Destination Marketing Organizations (DMO) as todays
internet users are exposed to a wide range of posts in various forms of videos, images, texts
and stories, particularly posts around vacation and holiday experience from Social Network
Sites (SNS) users. As a result, companies and scholars alike have observed that SNSs are not
simply used as interaction or communication tools but also as an active component in the DI
formation process (Fatanti & Suyadnya, 2015). The literature presents strong evidence
around the ability of marketing messages on the DI formation, all be it that different
information sources influence tourists’ perceptions to a different degree, this warrants the
need for further investigation into the role of UGC on DI formation (Beerli & Martin, 2004).
Instagram images/posts and UGC are commonly used on an interchangeable basis as they
are often used in the same context and for the purposes of this research they shall be
considered to have the same generalised meaning and as such should be considered as
interchangeable.
Asia Pacific Institute of Advanced Research (APIAR) DOI : 10.25275/apjabssv3i2bus1
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2. Literature Review
2.1 Users Generated Contents in Tourism
There is a growing body of literature around the impact of UGC on the travel decision
process, yet the relative importance of each social media platform on travellers’ purchase
decisions still has scope for further investigation (Leung, et al., 2013).
The term UGC was defined as “any form of content being generated online by platform
users” (Moens , Li & Chua,2014). The arrival of SNSs has challenged companies as sole
creators of marketing messages (Hautz, et al., 2014) as the marketing messages are no longer
exclusively generated by organizations but equally by users, highlighting the potential
relative impact of UGC towards DI formation in a travel or tourism context (Munar, 2011).
In an online context, information sources available to an audience can furthermore be
divided into formal sources, for example, paid advertising (Frías, et al., 2011) and informal
sources of information, such as friends, family and relatives (Beerli & Martin, 2004;
Crompton, 1979). UGC is considered as a third party on SNSs in a form of “text, images and
videos etc.” In a DI context, Instagram has been identified as an important social media
platform towards building an image about tourist destinations (Fatanti & Suyadnya, 2015).
Some researchers further stressed the value of Instagram as an effective communication
medium for tour operators and travel agents to promote a destination (Hanna & Puitit,
2014). Not only has Instagram has become a vital tool in the promotion strategies for many
destinations (Bath, 2015) but one of the most recently emerging marketing practices by
DMO’s is their formal engagement of travel bloggers and social media influencers in tourism
promotion strategies, e.g. Dubai, Jordan, Scotland & Australia, etc. For example, it was
stated in an interview that was conducted by Go future Media with high profile Instagram
influencer Lauren Bath in 2015, “Instagram is a very important part of the strategy because
there is a whole generation of people who are using social media who aren’t reading the
paper anymore and Instagram is a great place to reach them as it’s such a hot platform right
now” (Holly, 2015). This has become a growing practice within the travel and tourism
industry, with companies like Turkish Airline launching a campaign using 10 famous
YouTubers and National Geographic working closely with many professional photographers
in order to engage a larger audience (Delevingne, 2016) and using bloggers to promote Japan
to new target markets (Frary, 2015). All this further emphasizes the power of SNSs in travel
and tourism marketing and their perceived influence upon consumer behavior.
Earlier studies have focused on the impact of electronic word of mouth (eWOM) on travel
decisions or image formation, however, specific websites in the travel context, such as Trip
Advisor, formed the basis of these studies (Miguéns, et al., 2008; O’Connor, 2008). Most of
their findings revealed that users of SNSs collaborate in generating online content about
several tourism products and in shaping images about destinations through the information
they provide based on data relevance provided during the travel planning process. Yet, the
attention to Instagram is limited (Miguéns, et al., 2008). The work of Fatanti & Suyadnya,
(2015) contends the process of promotion through Instagram to be complex and different
from other SNS sites. Marchiori & Cantoni, (2015) found that UGC is more likely to alter DI
formation in the case of exposure to users who haven’t visited a particular destination or
have little to no prior knowledge in contrast to those who have had a real experience with the
destination. It was also noted by Frías, et al, (2008) that the influence of the web on DI is
negative and less effective compared to the DI formation by viewers who gathered the travel
information from both the travel agents and the internet. Earlier findings were also observed
in the study by Govers, et al, (2007) which indicated that the tourism promotion from
marketers does always have a major impact on viewers’ perceptions and that other sources of
information such as WOM, television and travel agents have a much greater influence on DI
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formation. Mercille, (2005) therefore recommends travelers to evaluate both UGC and DMO
messages before travelling in order to avoid any sort of communication or perception gaps.
A further complication with Instagram is that its users tend to edit the images by means of
filters in order to make their posts more visually appealing, unique and engaging to their
audience (Bakhshi, et al, 2015) This was further outlined by the founder of the Instagram
account ‘beautiful destination’ Jauncey as the tapping into specific visual elements that make
the photos on Instagram more appealing (Avakian, 2016) raising important questions i.e.
Are Instagram posts biased? Is its content appeal enjoying trustworthiness issues such as
those of DMO’s? Frías, et al, (2011) noted that the use of the internet to research a
destination taints the DI since the information available on the internet might be associated
with the above highlighted risk, although in contrast many scholars stated that UGC is a
trustworthy source of information (Marchiori & Cantoni,2015; Bastida & Huan 2014;
Fernández-Cavia, et al., 2014 ;Lueng, et al, 2013; Qiang, Ye et al, 2011) and furthermore
tends to enrich SNSs users’ perceptions towards travel destinations. Burgess, et al (2009),
for example, found that UGC can be subjective as it builds trust on the basis of real travellers
sharing real experiences, yet some UGC creation has been generated by fake (robot) accounts
or by users who have vested interest in the destination, therefore, bringing into question the
trustworthiness of such contents.
Destination Image (DI): is widely accepted as an important aspect in successful tourism
development and destination marketing Tasci & Denizci, (2009) and it is defined as an
attitudinal concept constituting the sum of beliefs, ideas, and impressions that a tourist
holds of a destination (Ankomah & Crompton, 1993). DI should be considered a multi-
dimensional phenomenon integrated by several cognitive and affective dimensions (Martín
& Bosque, 2008). The cognitive image components of destination image are the beliefs,
impressions, ideas, perceptions and knowledge that people hold with respect to different
objects (Crompton ,1979) whereas the affective components of DI are the physiological
feelings toward the destination (Uysal, 2002; Kim & Richardson, 2003). Martín & Bosque,
(2008) suggested that measuring destination image should consider a multi-dimensional
phenomenon that consists of both cognitive and affective attributes by considering the
individual’s feelings toward the tourist destination. Several scholars stressed the importance
of DI in reducing the perceived risks, creating awareness and increasing knowledge i.g.
(Gartner and Konecnik, 2011,) moreover the strong positive images of destination influence
visitors travel choices, particularly when they have limited knowledge or experience of a
destination (Fakeye & Crompton, 1991).
2.2 Research Questions
The research around the influence of UGC on DI formation needs further exploration
(Mariussen, 2014). Therefore; this research aims to answer the following question: To what
extent does UGC through Instagram change the viewer’s perceptions towards a travel
destination?
2.3 Methodology
The objective of this research was to measure the immediate impacts of UGC through
Instagram on viewers’ perceptions on DI formation towards a travel destination. We used the
Hash Tag #Visitlebanon in order to search for images on Instagram by authentic users and
to avoid any projected images by DMO. Thirty images were selected of the most important
touristic attractions and sites of Lebanon including; Cities, Beaches, Hotels, Restaurants,
Night Markets, Churches, Historical Sites, Restaurants, Natural Scenery, Local Architecture
and Cultural attractions etc. These reflected the different aspects of Lebanon destinations
attributes. A research announcement was sent via e-mail to Bahrain Polytechnic staff and
students inviting them to participate in an experimental research, the announcement were
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Asia Pacific Institute of Advanced Research (APIAR) DOI : 10.25275/apjabssv3i2bus1
also shared on SNSs. The only criteria for selecting the respondents being they must not
have visited Lebanon before, the experiment took place in October 2016 inside Bahrain
Polytechnic campus.
By using an experimental design that measures a respondent’s perception of a destination
pre-exposure and post-exposure to selected Instagram posts/images relating specifically to
Lebanon. The respondents were asked to rank 38 image items on a 7 point Likert Scale
adapted from (Hudson, et al., 2011; Gong & Tung, 2016). Both studies have used the same
scale which was appropriate for South America, but for this research we have customized
and adjusted a few of the survey questions items to make them appropriate fit with
Lebanon’s unique attributes.
The procedure: during the pre exposure survey the respondents were asked to rank the
survey items, once completed the respondents moved to the exposure stage of the
experiment.
The exposure: during this stage, the participants were exposed to 30 pre -selected images
relating to Lebanon. The exposure took place over a period of 5 minutes and the participants
were informed that these images are generated by SNSs users. The post exposure survey was
implemented immediately after the exposure stage, the respondents were asked to rank the
same survey questions
Participants: A simple random sampling, where respondents were recruited through a
direct e-mail and SNS posts, total of (151) participants participated in this experiment.
Nearly 57% of respondents are females and nearly 43% males. Half of respondents are from
young age groups: 50% are aged 18–25, 14% are aged 26–34 years old, 25% are aged from
35-44 and 11% are above 45 years old. Nearly 60% of the respondents are from Kingdom of
Bahrain, 15% are Irish, 5% from Greece, 13% from the UK and 8% are from other
nationalities.
Measurement: The survey utilised 7-point Likert scale ranging from 7 (Strongly Agree) to 1
(Strongly Disagree) with 4 as a midpoint (Neutral). This scale was utilised as it helps to
increase the variance in the measurement and leads to greater differentiation in the
judgments made (Krosnick & Pressers, 2009).
Table 1. Results of factor loading, paired sample t-test and reliability test for destination
image change
3. Results
Image Scale Item
Factor 1: Tourist
facilitation
Good tourist information is
readily available
Tours and excursions are
readily available
Many packaged vacations
available to Lebanon
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Facto
r
loadi
ng
Mean
pre
exposur
e
Mean
Post
expos
ure
d.f
Sig.
(2-
tailed)
.787
4.77
4.97
-1.603
147
.111
t
.784
4.59
4.99
-2.911
148
.004
.733
4.45
4.66
-1.430
147
.155
Alph
a
( Pre
–
Post)
Asia Pacific Institute of Advanced Research (APIAR) DOI : 10.25275/apjabssv3i2bus1
Lebanon is a safe place to
visit
There is political stability in
Lebanon
Lebanon is a modern
country
The environment in
Lebanon is clean and tidy
Lebanon is an ideal place to
visit
Factor 3: Comfort
Lebanon offers good quality
restaurants
Lebanon offers a good
choice of food
Lebanon offers a good
choice of music
It is easy to get good service
in restaurants and hotels
It’s easy to travel around
Lebanon
Tourists attractions are well
known and famous
.555
4.90
5.21
-2.093
147
592
-3.865
Composite Mean
4.51
5.23
Factor 2: Safety
.038
.000
.832/
.852
.697
4.32
4.52
-1.411
145
.160
.673
3.48
3.87
-2.407
147
.017
.636
5.12
5.49
-2.927
149
.004
.558
4.59
5.17
-3.408
148
.001
.548
5.23
5.54
-1.928
146
.056
Composite Mean
4.55
4.92
-4.752
739
.000
.772/
.759
.672
5.76
5.95
-1.682
148
.095
.634
6.24
5.46
6.005
148
.000
.603
5.22
5.26
-.233
149
.816
.501
4.99
5.21
-1.720
148
.088
.490
4.71
5.06
-2.441
-3.450
5.04
5.40
-2.359
.792
149
745
149
.016
.001
.020
.746/
.719
.674
4.85
5.23
-2.733
148
.007
Composite Mean
4.74
5.54
Factor 4:
Interest/adventure
A holiday in Lebanon is
adventurous
Lebanon has plenty of
places to get away from the
crowd
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.514
4.85
5.27
-3.239
146
.001
.510
4.50
5.02
-3.690
149
.000
.474
5.28
5.77
-3.969
-8.140
Composite Mean
5.14
5.36
Factor 5: Affordability
Prices are low in Lebanon
.774
3.96
4.10
-1.168
.632
4.82
4.96
-1.082
149
.579
4.78
5.02
-1.807
147
.073
.503
4.49
4.83
-2.873
145
.005
Composite Mean
4.51
4.72
-3.453
591
.001
Lebanon offers a luxury
holiday
In Lebanon, everything is
different and fascinating
Cities in Lebanon are
attractive
Lebanon offers affordable
activities
Goods and services are
affordable in Lebanon
Lebanon offers a wide
choice of budget
accommodation
Factor 6: Attractions
and Entertainment
Lebanon has a rich culture
Lebanon is a good
destination for a learning
and educational experience
Factor 7: Atmosphere
Lebanon has a good night
life
Lebanon has a pleasant
weather
Lebanon has natural
attractions
.780
.647
.727
.692
5.46
4.57
5.61
5.77
5.80
4.86
-2.908
-2.051
-3.444
3.78
8.504
5.84
-.605
148
744
147
148
147
296
147
146
.000
.000
.245
.281
.004
.042
.001
.000
.546
.767/
.850
.697/
.762
.610/
.559
.651/
.741
Composite Mean
4.55
5.00
.634
5.97
6.26
-2.488
149
.014
Composite Mean
5.79
6.00
-3.100
444
.002
Factor 8: Sightseeing
and Activities
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Asia Pacific Institute of Advanced Research (APIAR) DOI : 10.25275/apjabssv3i2bus1
.646/
.598
.535/
.470
.000
.001
There are many places of
interests in Lebanon
Lebanon provides
interesting sporting events
.572
5.28
5.57
-2.191
148
.030
.535
4.31
4.77
-3.619
147
Composite Mean
3.54
5.17
-3.355
296
Factor 9: Cultural
similarity
Lifestyle and customs are
similar to us
Local architectural styles
are similar to us
.715
4.03
4.37
-2.209
147
.029
.715
4.05
4.44
-2.353
146
.020
Composite Mean
4.04
4.41
-3.233
294
.001
Table one reports factor loading for 38 survey items, Confirmatory Factor Analysis utilising
Varimax rotation was performed to group 38 DI scale items into correlated dimensions,
among 38 image scale items, 32 survey items were loaded cleanly into nine core DI factors
including; Tourist facilitation, Safety, Comfort, Interest/adventure, Affordability, Tourist
Varieties, Atmosphere, Attractions and Entertainment, Sightseeing and Activities, Cultural
Similarity. The findings were supported by the literature, particularity studies around the
immediate impact of information sources on changing the viewers’ perceptions (Hudson, et
al., 2011; Gong & Tung, 2016). As reported in Table 1, The reliability test Cronbach’s Alpha
was performed on both surveys in order to support the structure, the post exposure survey
shows improvement in Cronbach’s Alpha in the following factors “tourist facilitation” from
.832 to .852 . “Interest and adventure” from .767to .850 “affordability” from .697 to .762
“atmosphere” from .651 to .741 and “cultural similarity”.535 to .773. Alpha score of the
following factors “tourist facilitation”, “safety” , “comfort”, “adventure” and “affordability”
were reported a score above .70 which is quite acceptable (Churchill, 1995; Nunnally, 1978)
and it was close to .70 in the following factors, “Sightseeing and Activities” , “atmosphere”
and reported below .50 in the cultural similarity factor. In order to test the sampling
adequacy KMO test was performed on survey items. The KMO value obtained was .735 which
is above the acceptable level of value (George & Mallery, 2001).
The highest difference in the reported mean scores among the nine factors as reported in the
table above was in “factor 8 “Sightseeing and Activities” with a mean difference of (1.63). on
a 7 point Likert Scale. There were two factors that report the same mean score of (0.37)
relating to “cultural similarity” and “safety”. The lowest observed changes in the mean
scores were reported in factor 7 “Atmosphere” with a mean difference (0.3) followed by
factor 5 “Affordability” (0.21) and finally factor 3 “Interest and adventure” (0.22). The t test
of the nine combined factors obtained a P value below (0.05). However, if we investigate
these items separately through t test without considering factor analysis we notice a decrease
in the significance levels as reported in the 9 DI attributes including; tourist information,
packaged vacations, safe place, ideal place, pleasant weather, quality of services in
restaurants and hotels, prices, affordable activities and prices of goods and services.
4. Discussion
In recent years we noticed a shift of customers’ roles from being a passive audience to active
participants in the brand experience (Prahalad & Ramasvamy, 2004). This has presented an
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opportunity for organisations to create closer connections with their customers, thus
increasing their involvement and consequently yielding greater value for both (Agrawal, et
al., 2015). As Instagram provides individuals with the opportunity to share their experience
with others, several destinations collaborate with SNSs influencers as part of their
promotional campaigns to create favourable DI, increase the exposure to their destinations
and attract prospective travellers (Holly, 2015). Our findings imply that Instagram images
were effective in changing the viewers’ perceptions in some of DI aspects. The highest
improvement in the mean score was reported in the eighth factor “Sightseeing and Activities”
with a mean score of (1.63) on a 7 point Likert scale, interestingly, the items under this factor
are “places of interests and sporting activities” as Instagram images provide a medium that
better communicate the functional attributes of a destination and this together with a low
viewers’ organic perspective towards these attributes. Gunn, (1972) assists in explaining such
findings. Even though the results show positive changes in all of the nine factors, the mean
scores for most of the recorded changes were below (1) on a 7 point Likert scale, indicating
that Instagram posts modify the viewers’ image into an induced image at best. Such findings
support the work of Gunn, (1972) which presents the seven stage theory of DI modification
and contribute to the existing literature in regards to the effectiveness of UGC in DI
formation (Marchiori & Cantoni, 2015; Alcázar, et al., 2014).
The t test of the nine combined factors obtained a P value below (0.05) and signified that the
UGC has positively changed the viewer’s perceptions. However, when the t-test was applied
to the survey items in isolation the significance was reduced on the following items “tourist
information, packaged vacations, safe place, ideal place, pleasant weather, quality of services
in restaurants and hotels, prices, affordable activities and prices of goods and services”.
Therefore, suggesting that one factor alone will not impact upon DI perceptions, but an
accumulation of cognitive and affective impressions of a destination (Baloglu & McCleary,
1999). Although, when images are presented that represent these are viewed as a set or
package they have a greater impact upon DI perception. A potential reason behind this is the
intangible nature of the underlying dimensions of this factor (i.e. psychological
characteristics) which are difficult to measure (Echtner, 1991). As may not be effective in
modifying the image (Jenkins, 1999). This considered as a further challenge in promoting
intangible DI attributes through Instagram due to the difficulty of visualizing the customer
experience in advance ( Lovelock, Wirtz & Chew, 2011). As the emerging trend for DMO is to
integrate professional bloggers in their campaigns, however, it’s safe to argue that
Instagram bloggers cannot fully relay the holistic experiential aspects of DI and therefore,
should not be solely relied upon to deliver DMO marketing messages.
Additionally t -test results show that UGC did not have a significant changing impact upon
the perceptions of the items “tourist information”, “safe place”, “beaches”, “prices”,
“affordable activities” and “weather”. It would be safe to assume that DI attributes
concerning price or holiday information might be difficult to communicate through UGC as
Instagram users often tend to share more of authentic experiential moments rather than
functional attributes and such posts regarding price or holiday information are usually
generated by DMO. Therefore, the only viable option for DMOs is to enrich the use of the
visual clues that can be directly linked to quality and price as some of the service providers
use metaphors and visuals clues to help communicate the benefits of service offering
(Lovelock, Wirtz & Chew, 2011). Another important aspect in this context is the degree of
influence the information source has on the viewers’ perceptions as discussed in the work of
Beerli & Martín, (2004). This study shows that Instagram has potential as a tool for
modifying the DI, yet the results show only slight influence on viewers’ perceptions as
Instagram images do not communicate the holistic DI. Our findings do not contradict the
current literature which states that the exposure to UGC can influence the viewers’
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