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MILLENNIAL TRAVELERS DECISION MAKING
INFLUENCED THROUGH USER-GENERATED CONTENTS
AND PSYCHOLOGICAL ATTRIBUTES ON DESTINATION
LOYALTY TO A TROPICAL ISLAND
Mazlina Jamaludin1&2,*
Azlizam Aziz1,
Manohar Mariapan1
1Department of Recreation and Ecotourism, Faculty of Forestry, Universiti Putra Malaysia.
2Department of Tourism and Hospitality, Politeknik Sultan Idris Shah, Selangor, Malaysia.
*Corresponding author: mazlinajamaludin1973@gmail.com
Accepted date: 25 February 2018 Published date: 3 April 2018
To cite this document: Jamaludin, M., Aziz, A., & Mariapan, M. (2018). Millennial Travelers
Decision Making Influenced Through User-Generated Contents and Psychological Attributes
on Destination Loyalty to A Tropical Island. Journal of Tourism, Hospitality and Environment
Management, 3(8), 44-55.
__________________________________________________________________________________________
Abstract: An investigation into the decision-making process of millennial travellers influenced
by, using user-generated contents, and other psychological factors such as pull motivation,
cognitive image on destination loyalty, took place at a small tropical Island in Malaysia called
Pangkor. A total of 170 respondents were selected using purposive sampling. Social cognitive
theory was used to describe the phenomenon of this research. Results revealed that user-
generated contents had a significant relationship on push motivation and explained 11.3% of
the variance in push motivation. Push motivation had a significant relationship, but with a low
influence, with destination loyalty, it had a high significant relationship with cognitive image.
Meanwhile, cognitive image had a significant relationship with destination loyalty. Cognitive
image and pull motivation play important roles in explaining 46.3% of the variance in
destination loyalty, while push motivation alone explains 52.2% of variance in cognitive image.
These findings indicate that cognitive image plays the most important role in the millennial
travellers’ decision making process to visit an isolated tropical island. This proves that
millennial travellers use user-generated contents as an information source for guidance rather
than solely using it for making decisions.
Keywords: Social media, Millennial, User-generated content
___________________________________________________________________________
Introduction
The millennial generation is expected to escalate on travel spending by the year 2020 globally
as compared to other generations. FutureCast, reported that global travel spending among the
millennials in 2016 is around $200 billion annually. This makes them a very marketable
business generation in the future. Therefore, destination management organisation (DMO)
world-wide needs to tap in and re-evaluate their online marketing business strategies as the
millennial will become the biggest generation contributing in the new trend of travel tourism
Volume: 3 Issues: 8 [March, 2018] pp.44-55
Journal of Tourism, Hospitality and Environment Management
eISSN: 0128-178X
Journal Website: www.jthem.com
45
industry (Morrison, 2017), along with their family members. Thus, the industry needs to
prepare their tourism resources, especially island tourism that is dependent on nature’s
environment and society’s heritage digitally to increase destination loyalty. These resources
need to be preserved, but at the same time, shared and passed down to new generations through
tourism.
Hence, DMO has a huge obligation to create an appealing market electronically for this
millennial. Additionally, DMO must diversify and develop more facilities and create stunning
artificial man-made attractions to complement natural environment attraction on this small
island. This is to ensure that DMO could sustain in a competitive tourism market particularly
island destinations that are far away or isolated from the mainland.
New trends in the tourism industry by the DMO’s and the millennial travellers involve the use
of and reliance on travel apps to acquire travel related information in addition to gain price
competitiveness. The notion is to create a more enjoyable and comfortable visit with less. In
doing so, both would benefit profits and gain valuable visits by inducing longer island holidays.
The millennial share their personal experiences, comments, and destination suggestions among
friends on digitally devices via user-generated content (UGC) (Nusair, Bilgihan, Okumusa, &
Cobanoglu, 2013). Ideally, UGC would push visitors’ motivation, cognitive image and enhance
loyalty.
The millennial are individuals born between 1980 and 1999, and they are also known as the
‘net generation’. They grow up in an era of rapid technological changes. These millennial
travellers are extremely curious, and take educated risks and reviews online to explore the
wonders of the world (Barton, Haywood, Jhunjhunwala, & Bhatia, 2013) before taking
selecting an ideal destination to their liking. They prefer authentic experiences. A self-guided
booking is likely to shape the future of these travellers. They are regarded as technologically
savvy users and are more occupied in online engagements such as text messaging, social
networks, podcasts and blogs to search for information before making decisions (Barton et al.,
2013). They have their travel brand preference with specific travel habits (Morrison, 2017).
Pulau Pangkor: A Small Tropical Island of Malaysia
Pulau Pangkor, a small beautiful Island, was previously a favourite resting place for fishermen,
sailors, merchants and pirates. It was an important site to control trading in the Strait of Melaka,
Malaysia. In the 17th century, the Dutch built a fort here in their bid to monopolise the tin trade
Perak but they were driven out by a local ruler. The historical event of Pangkor Treaty of
1874 was a treaty signed between the British and the Sultan of Perak. It was signed on 20
January 1874, on the HMS Pluto, anchored off the island of Pangkor (off the coast of Perak).
The treaty was a significant event on the Malay states as it legitimised the British control of
the ruler and flagged the way for British imperialism in Malaya (The Straits Times, 1962). A
letter expressed by Raja Abdullah's desire to place Perak under the British protection, and "to
have a man of sufficient abilities to show (him) a good system of government" became the “key
to the door” that led to the Pangkor Treaty and British domination over the Malay States and to
strengthen its monopoly on the tin industries. As a result, the Pangkor Treaty of 1874 was signed
(The Straits Times, 1962). A Dutch Fort located in Teluk Gedang was built in the 1670 and it
served as a safekeeping place for tin manufacturer.
This significant event has also attracted foreign tourist to visit and learn about the island leading
to the signing of the Pangkor Treaty. It has a deep history in culture, economic and political
struggles between the Malay palace and the British. It also led to active the British military and
46
also political intervention in the Malay states. The British Resident system was attached to the
Sultan’s court. Similar agreements and systems were later signed with other Malay states,
achieving de facto British rule of the Malay Peninsula by 1914. These historical and
geographical attractions were fully advertised through various social media using UGC to
trigger economic growth of the island and encourage repeat visits especially among the
millennial visitors to learn about historical culture. Every visitor to Pangkor can feel that a
second visit is, without doubt, necessary.
Literature
Social Cognitive Theory
The theoretical foundation for this study is derived from social cognitive theory (Miller, 2002).
This theory claims that individual behaviour and character interacts within its setting. Cognitive
powers and knowledge are used to create and change cognitive constructs and schemas.
Individuals act according to their acquired thinking of the world. Using this theory, millennial
visitors’ individual habits and character beliefs communication using social media are crucial
in their routine life (Bandura, 2003).. Their dynamic behaviour and involvement in the UGC
social media play an important role in thinking, responses and values which create a new culture
in their life. The cognitive process that is created via the use of internet environment influences
their daily life including travelling (Bandura, 2001). Through interactive responses in UGC
using the social media network as sources of personal references, they are able to make decision
wisely and quickly. Traveling is part of social cognitive process. Visitors are able to be very
active, aggressive and dynamic. Their external social environment is borderless making them
very well informed globally and more personalised in their decision making (Bandura, 2003).
We could see that this generation’s social environment interaction uses a lot of mobile
communication devices to create to plan for their trip, during the trip and even after the trip.
U.S. Travel Association (2010) reported that they reviewed and evaluated UGC before making
travel purchases. At present, this social media is becoming a culture setting. Thus, DMO
needs to fulfil customers’ requirements and ensure constant communication engagement to
enhance market influence. This is because the millennial visitors carry along their mobile
communication devices. They use them actively while travelling to seek for advice and receive
advice instantly to enjoy their vacation all the time. These millennial visitors are able to create
for themselves personal travel arrangement and recreation activities based on their needs and
wants. This makes them active travellers.
Cognitive Destination Image
The topic of cognitive destination image has received substantial attention in tourism research
(Chen & Hsu, 2000; Gartner & Hunt, 1987; Oppermann, 1996). However, due to its complexity
(Smith, 1994), multidimensionality (Gartner, 1989), subjectivity (Gallarza, Saura, & García
2002), and elusiveness (Fakeye & Crompton, 1991), no consensus has been reached for a
universally accepted and reliable scale in different respondents and scenarios (Beerli & Martin,
2004). The reason to look at cognitive destination images, rather than affective destination
images is because the former are directly observable, descriptive and measurable (Walmsley &
Young, 1998), and thu provide more concrete and interpretive meaning regarding the
uniqueness of a destination. Hence, cognitive destination image receives support from an
increasing number of scholars on its priority in characterising the destination (Baloglu &
Brinberg, 1997; Dann, 1996; Echtner & Ritchie, 1991). According to Dibb, Simkin and
Bradley’s (1996) product theory, cognitive destination image has been split across images of
“natural environment”, “built environment”, “socially responsible environment”, plus “local
people” to thread the ring.
47
Çoban (2012) claimed that destination image formation comprises of a number of tough factors
which are difficult to be classified as cognitive, affective or conative. However, cognitive image
is the most highly recognised due to its correlation to cognitive thinking in decision making and
related to social cognitive theory. A research work by Jamaludin, Johari, Aziz, Kayat, and
Yusof (2012) revealed that destination images do influence destination loyalty through tourists’
satisfaction during Perak Visit Year 2012. Tourists who had visited Perak made their decision
based on cognitive images rather than affective image. Nevertheless, destination image
perception is highly impacted by information sources that were projected to visitors
(Jamaludin, Aziz, Yusof, & Idris, 2013). Thus, favourable image is viewed as the most crucial
aspect in marketing a destination. Therefore, cognitive destination image concept developed for
visitors is in line with the research interest of this study.
Push Motivation
‘Push and pull’ motivation concept is among the most outstanding concept used in tourism
related studies that remains relevant and pertinent (Chen & Chen, 2015). This theory tells us
that visitors are ‘pushed’ by individual or internal needs towards destinations where their needs
can be satisfied. In general, the push factors are related to: (1) personal motivation such as
escape, rest and relaxation, self-esteem, adventure, social interaction, personal interests,
benefits expectations; (2) socio-economic factors such as income, education, occupation; (3)
demographic ones such as age, gender, family life-cycle, and (4) market knowledge. Dann
(1977), however, classified the push travel motivation factors into two, namely (1) anomie and
(2) ago-enhancement. Anomie refers to craving beyond the feeling of isolation accomplished
in daily life. The tourist wishes to be away from daily routine life, while ego-enhancement is
the need to gaining recognition or status after travel (Fodness, 1994).
A past research by Yuan and McDonald (1990) identified five push motivation factors: (1)
escape, (2) novelty, (3) enhancement of kinship relationships, (4) prestige, and (5)
relaxation/hobbies. Out of the five, (1) novelty and (2) escape are the most forceful factors that
push individual to travel abroad. Many researchers categorise the push motivation in a very
unique way due to the complexity of human nature in travel. However, all illustrations believe
that push motivation has a strong emotional power to force individuals in their travel decision
(Baniya & Paudel, 2016). Mohammad and Som (2010) claimed that motivation factors must
be integrated with other factors such image and loyalty to attract visitors to a destination.
Therefore, it is necessary for this study to examine the millennial visitors who travel to this
tropical island using factors such as UGC and its relationship on push motivation whose roles
and impacts on repeat visits remain unclear.
Destination Loyalty
Loyalty is an extension of visitors’ satisfaction. It is a concept that measures recommendations
to other potential tourists in travel destination. Destination loyalty can be divided into three
approaches, which are: (1) behavioural approach, (2) attitudinal approach, and (3) composite
approach (Jacoby & Chestnut, 1978). Behavioural approach is related to a series of purchases.
However, this approach was criticised because it did not explain how revisit would occur among
tourists. Attitudinal approach expresses loyalty in terms of psychological commitment or
statement of preferences. Tourists who favour a particular destination express their intention to
revisit. Finally, composite loyalty approach integrates attitudinal and behavioural factors. Chi
and Qu (2008) also ascertained that destination loyalty had a causal relationship with image but
it must be controlled intelligently to ensure that the bonding would be lasting and beneficial to
the visitors, local people and DMO. Composite approach was viewed as the most appropriate
48
to ensure that both elements are not neglected when measuring loyalty visitors (Jamaludin et
al., 2012). This is considered as more appropriate to measure repeat visitors for a destination in
Asian countries.
Conceptual Model
Based on the review of related literature, this study fills the gap by proposing a research
objective to understand the relationships between constructs. The main objective is to examine
the effect of UGC, push motivation, cognitive image on destination loyalty. Given the
importance of understanding the millennial visitors’ decision making to select a tropical island
as their travel destination, this study has proposed a research model (figure 1). The following
hypotheses were put forward in this study:
H1: Cognitive Image has a positive effect on loyalty.
H2: Push motivation has a positive effect on loyalty.
H3: Push motivation has a positive effect on cognitive image.
H4: User-generated content has a positive effect on push motivation.
H3 H1
H4 H2
Figure 1. Conceptual Framework
Methodology
A self-administrated questionnaire was distributed and collected from domestic visitors who
came to Pangkor Island during the school holidays. A non-probability purposive sampling
technique was used since the population of tourists who visited the Island was unknown. Only
departing tourists were approached for the survey, and they were briefed about the purpose of
the research. The respondents who were in the age group of 18- 37 at the time of this study were
chosen to fulfil the requirement for generation Y group for the purpose of this study. The
respondents responded to the survey using an online Google survey form questionnaire. The
questionnaire survey was passed to respondents using mobile phones using the WhatsApp
application as this was convenient for the respondents to quickly answer the questions. A total
of 170 completed questionnaires were collected from respondents.
Smart PLS 3.0 was used as it is the best software to explore constructs. Further, PLS-SEM was
able to maximise the variance explaining the endogenous latent constructs (Hair, Sarstedt,
Hopkins, & Kuppelwieser, 2014; Ramayah, Cheah, Chuah, Ting, & Memon, 2017) in this
study. The endogenous latent construct for this study is destination loyalty. Meanwhile,
cognitive image, motivation and user-generated content are the exogenous latent constructs.
The research instrument was reviewed by two panel experts to ensure that the content meet the
study. A pre-test was done to ensure that the questionnaire was reliable and valid before
distributing it to the actual respondents. All the research variables were measured using multiple
item scales adopted from previous research (Table 1). Minor amendment to words and phrases
Cognitive
Image
Loyalty
Push
Motivation
UGC
49
was done to tackle the culture of the local context. The Instrument consists of (a) Cognitive
Image – eight items, (b) Push Motivation – three items, (c) User-generated content – two items
and (d) Loyalty – three items (Table 2). A 7-point Likert scale was used in this survey. Each
item on the instrument requires respondents to define their degree of perception. The scale was
ranged from a response of “1” to indicate “Strongly Disagree” to “7” which represents
“Strongly Agree” (Lee & Lings, 2008). A 7-point Likert scale was chosen because it gives the
respondents a wider range of the likelihood to response and escape clutter in the data set (Eutsler
& Lang, 2015).
Table 1. Sources of Instrument Development
Constructs
Sources of measurement
Item
Destination Loyalty
Grappi & Montanari, (2011)and
Loi, So, Lo, & Fong, (2017)
3
User-generated content
Del Chiappa, Alarcón-Del-Amo,and
Lorenzo-Romero, (2016)
2
Motivation
Yoon & Uysal, (2005)
3
Cognitive Image
Chi & Qu, (2008)and Baloglu, (2001)
8
Data Analysis and Results
Assessment of the Reflective Measurement Model
A confirmatory factor analysis (CFA) was conducted to test the reliability, convergent validity,
and discriminant validity of the scales. As indicated in Table 2, all item loadings were larger
than 0.5 (significant at p < 0.01). All Average Variance Extracts (AVEs) exceeding 0.5 are
considered as satisfactory (Bagozzi & Yi, 1988). Further, composite reliability (CR) for all the
variables exceeding 0.7 is considered as satisfactory (Ramayah et al., 2017). Table 2 indicates
that all the Cronbach alpha values exceeded 0.7 (Nunnally & Bernstein, 1978). Composite
reliability and Cronbach’s alpha have predominantly and widely been used in quantitative
research. The two reliability measures use sum scores rather than construct scores (Henseler,
Ringle, & Sarstedt, 2016). In particular, Cronbach’s alpha is regarded as a lower boundary to
reliability (Sijtsma, 2009), while composite reliability is regarded as an upper boundary to
reliability (Cepeda Carrión, Henseler, Ringle, & Roldán, 2016). Therefore, this measurement
model has achieved the requirement needed to proceed for structural model.
Table 2. Measurement Model and Convergent Validity
Model Construct
Measurement Item
Loading
α
Rho A
CRᵃ
AVEᵇ
Cognitive Image
AI1
0.898
0.930
0.934
0.944
0.705
AI2
0.855
AI3
0.903
AI4
0.944
Loyalty
L1
0.955
0.944
0.945
0.964
0.900
L2
0.964
L3
0.927
Motivation
M1
0.847
0.871
0.878
0.921
0.796
M2
0.901
M3
0.925
UGC
UGC1
0.946
0.864
0.873
0.936
0.880
UGC2
0.930
Note. ᵃ Composite Reliability (CR) = (square of the summation of the factor loadings) / {(square of the
summation of the factor loadings) + (square of the summation of the error variance)}
50
ᵇ Average Variance Extracted (AVE) = (summation of the square of the factor loadings)/
{(summation of the square of the factor loadings) + (summation of the error variances)}
A discriminant validity is displayed in Table 3 using Fornell-Lacker Criterion result. It was
recorded that all the indicators loaded much higher on their hypothesised factor than on other
factors. Meanwhile, the squared roots of AVEs on the diagonal are higher than the values of
the inter-construct on the same columns and rows (own loading are higher than cross loadings)
(Chin, 1998; Ringle, Sarstedt, & Straub, 2012). In addition, the square root of the AVE was
tested against the inter-correlations of the construct with the other constructs in the model to
ensure the discriminant square root of the AVE exceeded the validity (Chin & Dibbern, 2010;
Fornell & Larcker, 1981), and all the correlations with other variables (Table 3). Secondly,
Table 4 depicts a method of discriminant analysis using cross loading between the constructs.
Each indicator of cognitive image, loyalty, push motivation, UGC load is high on its own
construct but low on other constructs. This indicates that discriminant validity is achieved as
the constructs are distinctly different from each other (Ramayah et al., 2017).
Table 3. Discriminant Validity using Fornel-Larcker Criterion.
Discriminant Validity
C. Image
Loyalty
Motivation
UGC
Cognitive image
0.840
Loyalty
0.668
0.949
Motivation
0.722
0.571
0.892
User-generated content(UGC)
0.497
0.423
0.336
0.938
Note. Diagonals represent the square root of the average variance extracted while the other entries represent
the correlations.
Table 4. Cross loading
Items
Cognitive image
Loyalty
Push Motivation
UGC
I1
0.839
0.654
0.666
0.394
I2
0.861
0.559
0.684
0.354
I3
0.848
0.513
0.526
0.431
I5
0.832
0.542
0.498
0.358
I6
0.819
0.512
0.547
0.511
I7
0.829
0.596
0.634
0.461
I8
0.850
0.529
0.651
0.423
L1
0.614
0.953
0.495
0.406
L2
0.646
0.963
0.555
0.409
L3
0.640
0.929
0.573
0.389
M5
0.614
0.424
0.850
0.274
M6
0.650
0.599
0.894
0.340
M7
0.667
0.491
0.930
0.279
UGC1
0.467
0.392
0.333
0.946
UGC2
0.467
0.403
0.295
0.930
The third method of assessing discriminant validity is by using HTMT technique developed by
Henseler, Ringle, & Sarstedt, 2015). Table 5 presents all the values that have fulfilled the
criterion of HTMT.90 (Gold, Malhotra, & Segars, 2001) and HTMT.85 (Kline, 2016). All the
items are less than 0.85 showing that the model that has established reliability and validity. In
other words, it indicates that discriminant validity has been ascertained. Besides, the results of
HTMT inference also revealed that the confidence interval does not show a value of 1 on any
of the constructs (Hensler et al., 2015). Thus, the measurement model for this study was
51
measured satisfactory with confirmation of adequate reliability, convergent validity, and
discriminant validity exit. After establishing the validity and reliability of this reflective
measurement model, a structural model assessment was analysed, and hence, discriminant
validity was confirmed.
Table 5. Discriminanty Validity using HTMT
Discriminant Validity
C. Image
Loyalty
Motivation
Loyalty
0.708
CI
(0.645, 0.767)
Motivation
0.794
CI
(0.715, 0.855)
0.623
CI
(0.521, 0.717)
User-generated content(UGC)
0.556
CI
(0.455, 0.614)
0.469
CI
(0.340, 0.614)
0.384
CI
(0.215,0.516)
Note. CI is confident interval.
Assessment of the Structural Model
Assessment of structural model needs to be evaluated based on the five steps (Ramayah et al.,
2017) of assessing for (1) collinearity issues, (2) significance and relevance of relationships,
(3) level of R² (Hair et al., 2014), (4) effect sizes f² (Chin, 1988), and (5) predictive relevance
Q² (Hair et al., 2014).
Based on the data given in Table 3, the Fornel-Larcker criterion results showed that there was
no collinearity problem in the construct. The significant and relevant relationship of the model
was assessed using the bootstrapping procedure. It is a non-parametric analysis that does not
make postulation about the distribution of the data. A bootstrapping procedure of 500 samples
was taken from the original sample with replacement to determine bootstrap standard errors.
This process provides approximate t-values for the significance testing of the structural path in
this study (Wong, 2013). The bootstrap result approximates the normality of data (Ramayah,
2014; Wong, 2013) which makes this study relevant. Consequently, the researcher evaluates
the model’s predictive accuracy using the coefficient of determination score (R²). Then, the
level of effect size was determined using f² (Cohen, 1988). The effect size is a measure used to
weigh the relative impact of a predictor construct on an endogenous construct (Ramayah et al.,
2017). Finally, the predictive relevance (Q²) of the path model was evaluated using
blindfolding procedure, which is a resampling technique that systematically deletes and predicts
every data point of the indicators in the reflecting measurement model of endogenous construct.
Interpretation of the Model
Figure 2 presents the results of the four hypotheses testing. In order to test the significance
level, t-statistics for all paths were generated using bootstrapping function. Based on the
assessment of the coefficient shown in table 6, hypothesis 1,3 and 4 relationships were found
to have t-value > 1.645, thus significant at 0.05 level of significance. Exception was for
hypothesis 2 which was statistically not supported.
52
Figure 2. Results of the Path Analysis
Specifically, the predictors of H1: cognitive image -> loyalty (β=0.535, p<0.01), cognitive
image are positively related to loyalty. Cognitive image has a large effect size (f² = 0.255) in
producing R² for loyalty and a large predictive relevance (Q²) of 0.356. Subsequently, H2: push
motivation -> loyalty (β = 0.185, p<0.119), push motivation are positively related to destination
loyalty. Motivation has a medium effect size (f² = 0.030) in producing R²=0.463 and a predictive
relevance (Q²) of 0.412 for loyalty. Cognitive image and pull motivation are positively related
to destination loyalty, which explain 46.3% of the variance in destination loyalty. The R² value
of 0.302 is above the 0.26 value, which Cohen (1988) suggest as indicating a substantial model.
Then, H3: push motivation -> cognitive image (β = 722, p<0.01), push motivation is positively
related to cognitive image, which explains 52.2% of variance in cognitive image. The R² value
of 0.522 is above the 0.26 value which is suggested by Cohen (1988) as indicating a substantial
model. Motivation has large effect size (f² = 1.090) in producing R² for cognitive image and a
predictive relevance (Q²) of 0.072. Finally, H4: UGC-> push motivation (β = 0.336, p<0.01).
UGC has medium effect size (f² = 0.127) in producing R² for motivation.
The results revealed that the all the constructs have a value larger than 0 which indicates that
exogenous constructs have predictive relevance (Q²) of the path model over endogenous
construct, i.e. destination loyalty. Further, all the inner VIF values for the independent variables
(user-generated content, push motivation, cognitive image) that examined for lateral
multicollinearity were less than 5 indicating lateral multicollinearity is not a concern (Hair et
al., 2014). All the result of t-values, coefficient of determination scores (R²), effect size (f²)
and predictive relevance (Q²) are presented in Table 6. All the four hypotheses are supported.
53
Table 6. Path Coefficients and Hypothesis Testing
Hypo
Std.
β
Std.
Error
t-
value
p-
value
Decision
R²
f²
Q²
VIF
H1
0.535
0.110
4.842
0.000
Supported
0.463
0.255
0.356
2.062
H2
0.185
0.128
1.439
0.119
Not Supported
0.463
0.030
0.412
2.062
H3
0.722
0.052
13.908
0.000
Supported
0.522
1.090
0.072
-
H4
0.336
0.093
3.623
0.000
Supported
0.113
0.127
-
-
Conclusion
In the current environment, it is important to understand how millennial travellers perceive
cognitive images, push motivation and destination loyalty towards island destination. This
generation will mature and their behaviour changes with age. This millennial generation has a
huge market and so do their families. Indeed, the outcome of this research will provide a more
rigorous psychometric result compared to the previous research which commonly uses
traditional information sources. Cognitive image is seen a better predictor in decision making
among the millennial generation even though UGC plays a minimal role in push motivation.
This result proves that internal desire motivation plays an insignificant role in decision making
as compared to cognitive image. DMOs are advice to look into this matter when developing e-
marketing plan.
Future researchers are encouraged to test additional antecedents to loyalty and other UGC
factors that lead to motivation. This may lead to a new discovery of research or a
misrepresentation of the relationships tested in the study. Further theoretical refinement and
addition might lead to a better result in the future. Meanwhile, other types of analysis such as
thematic analysis should be considered for use and combined with quantitative analysis to
provide a more rigorous result. Replicating this model is encouraged in other setting to
understand their travel behaviour. Testing this model at a different point of time and comparing
the changes will be more beneficial to understand their behavioural changes over time. Clearly,
DMO and local authorities should not lead to a negative impact of the result. Rather, they should
genuinely look to improve the benefits of using UGC to promote island tourism, its culture and
the local people who will directly benefit from tourism development.
References
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of
the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1007/BF02723327
Baloglu, S. (2001). Image variations of Turkey by familiarity index: informational and
experiential dimensions. Tourism Management, 22(2), 127–133.
https://doi.org/10.1016/s0261-5177(00)00049-2
Bandura, A. (2001). Social cognitive theory of mass communication. Media Psychology, 3(3),
265–299.
Bandura, A. (2003). Social cognitive theory for personal and social change by enabling media.
In Entertainment-Education and Social Change. Routledge., 97–118.
Baniya, R., & Paudel, K. (2016). An Analysis of Push and Pull Travel Motivations of Domestic
Tourists in Nepal. Journal of Management and Development Studies, 27, 16–30.
Barton, C., Haywood, J., Jhunjhunwala, P., & Bhatia, V. (2013). Traveling with millennials.
Cepeda Carrión, G., Henseler, J., Ringle, C. M., & Roldán, J. L. (2016). Prediction-oriented
modeling in business research by means of PLS path modeling: Introduction to a JBR
special section. Journal of Business Research, 69(10), 4545–4551.
54
https://doi.org/10.1016/j.jbusres.2016.03.048
Chi, C., & Qu, H. (2008). Examining the structural relationships of destination image, tourist
satisfaction and destination loyalty: An integrated approach. Tourism Management, 29(4),
624–636. https://doi.org/10.1016/j.tourman.2007.06.007
Chin, W. W. (1998). Issues and Opinion on Structural Equation Modeling. MIS Quarterly,
22(March), vii–xvi. https://doi.org/Editorial
Chin, W. W., & Dibbern, J. (2010). Handbook of Partial Least Squares. Handbook of Partial
Least Squares, (July), 171–193. https://doi.org/10.1007/978-3-540-32827-8
Çoban, S. (2012). The Effects of the Image of Destination on Tourist Satisfaction and Loyalty :
The Case of Cappadocia. European Journal of Social Sciences, 29(2), 222–232.
Cohen, J. (1988). Statistical Power analysis for the behavioral science (2nd.). Hillsdale, NJ:
Lawrence Erlbaum Associates.
Dann, G. (1977). Anomie, ego-enhancement and tourism. Annals of Tourism Research., 4, 184–
194.
Del Chiappa, G., Alarcón-Del-Amo, M.-D.-C., & Lorenzo-Romero, C. (2016). Internet and
User-Generated Content Versus High Street Travel Agencies: A Latent Gold
Segmentation in the Context of Italy. Journal of Hospitality Marketing & Management,
0(0), 1–21. https://doi.org/10.1080/19368623.2014.1001933
Eutsler, J., & Lang, B. (2015). Rating Scales in Accounting Research: The Impact of Scale
Points and Labels. Behavioral Research in Accounting, 27(2), 35–51.
https://doi.org/10.2308/bria-51219
Fodness, D. (1994). Measuring Tourist Motivation. Social Sciences Journal, 21(3), 555–581.
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable
Variables and Measurement Error. Journal of Marketing Research, 18(1), 39.
https://doi.org/10.2307/3151312
Gold, A. H., Malhotra, A., & Segars, A. H. (2001). Knowlwedge Management: An
Organizational Capabilities Perspective. Journal of Management, 18(1), 185–214.
Grappi, S., & Montanari, F. (2011). The role of social identification and hedonism in affecting
tourist re-patronizing behaviours: The case of an Italian festival. Tourism Management,
32(5), 1128–1140. https://doi.org/10.1016/j.tourman.2010.10.001
Hair, J. F., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014). Partial least squares
structural equation modeling (PLS-SEM). European Business Review, 26(2), 106–121.
https://doi.org/10.1108/EBR-10-2013-0128
Henseler, J., Ringle, C. M., & Sarstedt, M. (2016). Using PLS path modeling in new technoogy
research: updated guidelines. Industrial Management & Data Systems, 1(2), 2–20.
Jamaludin, M., Aziz, A., Yusof, A. R. M., & Idris, N. H. (2013). Information Source Influence
Destination Image. International Journal of Independent Research and Studies, 2(4),
146–155.
Jamaludin, M., Johari, S., Aziz, A., Kayat, K., & Yusof, A. R. M. (2012). Examining structural
relationship between destination image, tourist satisfaction and destination loyalty.
International Journal of Independent Research and Studies, 1(3), 89–96.
Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling. (Todd D. Little,
Ed.) (4th. ed.). New York, N.Y.: Guildord Press.
Lee, N., & Lings, I. (2008). Doing business research: A guide to theory and practice. Sage.
Loi, L. T. I., So, A. S. I., Lo, I. S., & Fong, L. H. N. (2017). Journal of Hospitality and Tourism
Management Does the quality of tourist shuttles influence revisit intention through
destination image and satisfaction ? The case of Macao. Journal of Hospitality and
Tourism Management, 32, 115–123. https://doi.org/10.1016/j.jhtm.2017.06.002
Miller, P. H. (2002). Theories of developmental psychology. Macmillan.
Mohammad, B. A. M. A.-H., & Som, A. P. M. (2010). An Analysis of Push and Pull Travel
55
Motivations of Foreign Tourists to Jordan. International Journal of Business and
Management, 5(12), 41–50. https://doi.org/10.5539/ijbm.v5n12p41
Morrison, G. (2017). Millennial traveller report 2016 by Brand Expedia Worldwide.
Nunnally, J. C., & Bernstein, I. H. (1978). The assessment of reliability. McGraw Hill (2nd ed.,
Vol. 3). New York: McGraw.
Nusair, K. ‘Khal’, Bilgihan, A., Okumusa, F., & Cobanoglu, C. (2013). Generation Y travelers’
commitment to online social network websites. Tourism Management, 35, 13–22.
https://doi.org/10.1016/J.TOURMAN.2012.05.005
Ramayah, T. (2014). SmartPLS 2.0 (1st ed.). Malaysia: Institute of Post Graduate Studies USM,
Penang, Malaysia.
Ramayah, T., Cheah, J., Chuah, F., Ting, H., & Memon, M. A. (2017). Partial Least Squares
Structural Equation Modeling Using SmartPLS 3.0 (1 st Editi). Pearson Malaysia.
Ringle, C. M., Sarstedt, M., & Straub, D. (2012). A critical look at the use of PLS-SEM in MIS
Quarterly. MIS Quarterly (MISQ), 36(1), iii–xiv. https://doi.org/10.3200/JOEB.79.4.213-
216
Sijtsma, K. (2009). NOn the use, the misuse, and the very limited usefulness of Cronbach’s
alpha.o Title. Psychometrika, 74(1), 107–120.
The Straits Times. (1962). When the door to Malaysia was opened, at Pangkor in 1874,. The
Straits Times, p. 14. The Straight Times.
Wong, K. K. (2013). Partial Least Squares Structural Equation Modeling (PLS-SEM)
Techniques Using SmartPLS. Marketing Bulletin, 24(1), 1–32.
https://doi.org/10.1108/EBR-10-2013-0128
Yoon, Y., & Uysal, M. (2005). An examination of the effects of motivation and satisfaction on
destination loyalty: A structural model. Tourism Management, 26(1), 45–56.
https://doi.org/10.1016/j.tourman.2003.08.016
Yuan, S., & McDonald, C. (1990). Motivational determinants of international pleasure time.
Journal of Travel Research, 24(1), 42–44.
