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IJRAR1BFP005 International Journal of Research and Analytical Reviews (IJRAR) www.ijrar.org 27
Buying Behavior of Indian Tourists towards Holiday
Packages: An Analysis of Tourist Online Applications
Deepak Pandey1 Amit Kakkar2
1 Assistant Professor, Mittal School of Business, Lovely Professional University 1 Assistant Professor, Mittal School of Business, Lovely Professional University
Abstract
India has always been a tourist destination because of its diversified culture and interesting history. Every region of India caries
some ancient story. Indians believe in mythological stories of Gods and so they also started travelling across globe to explore
more on this. This study has focused on the aspect of Indian travelers who are interested in exploring various places and in this
due course they take services of holiday packages offered by various tour and travel firms. Investigation was conducted for the
travel packages booked through online application providers and in this process 200 regular travelers were approached.
Responses were collected and analysed based upon the structured scale used for collecting data. Smart PLS 2.0 was used to
analyse the responses which has shown a strong relationship among the antecedents of intention to buy these packages and
further was having an impact on thebehaviour of these online application users.
This study is useful for the organization which are trying to understand the behaviour of the customers for travel package
bookings.
Keywords: Travel packages, Buying behavior, Smart PLS and Intention to Buy
Introduction
Tourism Industry has shown a phenomenon growth in the past decades. Everyone loves roaming across globe and
spending some time at new places for enjoying their food, understanding their culture, looking at their clothing styles,
lifestyle etc.
Twenty first century is the era of digitalization where a growing number of customers are making purchases online. It
has been found that customers have started behaving in a different manner and reflecting altogether a different pattern
of going about the holidays. Planning holidays is an integral part of our life, people work hard for their livelihood and
after a certain span of time they want to celebrate their vacations with their friends and family members. Holidays
planning involve family members who are the major stakeholders for this event. Many at times it has been noticed
that friends plan vacations together for their own reasons. Marriage parties, destination wedding, bachelor party, kid’s
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summer vacation, leave travels, official meetings, business trips, exploring world, etc. are the major travel reasons.
Gone are those days when people used to plan their holidays on their own. Earlier they use to take one to one
feedback from the well-travelled persons from their circle and based on that they used to make a plan, now people are
more exposed to social media, public forum, information communication and technology, etc. and they take online
feedbacks, look for the reviews posted on various portals and blogs(Kaynama and Black, 2000).
Over a period of time it has been noticed that various new reasons have emerged for going for vacations. People have
started adopting services over internet, e-Travel Portals has strongly emerged as a new tool for booking vacations.
There are lots of new service providers have entered into this space and grown over a period of time. These service
providers started offering various services to the travelers like travel guidance, ticketing services, hotel booking,
travel arrangements, online money transfer etc(Ip et. al, 2012).
The best way to encourage the customer to plan a trip is heavy discounts and offers through websites and about 36%
of the customer in India makes plans through the same. Apart from discount and offers there can be other factors
responsible, this study focusing on those reasons, researchers have found that there are other benefits for using online
services of e-portals. Benefits can include the role of social and functional knowledge. With every kind of benefits to
any service there is always a cost attached to it, hence in this case also people have to make lot of effort to search for
relevant information online. Apart from efforts made there is also an important aspect of privacy, whenever we online
services we share lot of personal data over internet and hence everyone want the data shared by them should be kept
confidential and hence the privacy protection should work.
Usage incentive is big and so people are using the services of e-Travel Portals, incentives included the trust and its
role in this business, access to all the relevant information and services through this services also become the part of
incentive.
These various benefits and incentives offered by e-Travel Portals have played the key role in the development of this
new business and hence there were cost too attached to it. The prior studies show that buying behaviour of these users
are affected by various factors. This study have tried looking for all decisional reasons impacting this
behaviour.(Chen and Barnes, 2007).
Literature Review
Internet plays an important role for the buying behavior of travel packages. (Beldona et. al, 2011),determine the
factors for buying air travel tickets through various means. This study discussed that various demographic factors and
internet based services will have different buying behavior on both the channels weather e-service or physical. The
outcome demonstrates that old travelers prefer offline channels as compare to young travelers who chooses online
mode for booking packages. Chiam et. al, (2009)studied the effects of one of these components (values, bundle
qualities, travel operators and a seal of endorsement) in on the web and disconnected conditions were analyzed
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IJRAR1BFP005 International Journal of Research and Analytical Reviews (IJRAR) www.ijrar.org 29
utilizing conjoint examination. It was discovered that cost had the greatest effect, although movement specialist and
aircraft notoriety and dependability likewise affected on individual’s inclinations. Strikingly, there were no
significant contrasts in the qualities effects in the on the web and disconnected condition. As the study done
byMandal et. al, (2017)empirically showcased that website attractiveness is the most important component for travel
and tourism because customers will utilize service of website only when relying upon the online services and the
other requirements. However, web attractiveness benefits to the evolution of these e-Travel Portals as it has
influenced the people to revisit the website again. The Internet has likewise highlighted the development and
advancement of the market by encouraging more noteworthy straightforwardness of costs joined with omnipresent
reach. Online portals offer attractive packages when compared to offline as technology cut the cost and the saving in
cost is transferred to customers in the form of packages. Discounting, Time Saving and Advance Booking are the
various attributes that influence the decision making while choosing the online packages (Kumar et. al, 2017).
Attitude sometimes affect the consumer behavior towards online shopping which is the most important concern
nowadays. Wen (2009) developed a conceptual framework based on theory of planned behavior and related literature
reviews which was empirically tested to check the bond between the factors and the framework, results that the
factors such as customer satisfaction, consumer attitude and e-Travel Portal design has a major role on online
medium to make purchases. Wen(2012) investigated that theory of attitude behavior consistency, communication
theory and related theories gives theoretical foundation for understanding factors such as information quality, service
quality, and system quality, which influence the customer for purchases. In addition, customer’s attitude and trust are
strong mediators that link the quality of website with the buying decision. Budeanu (2007)contemplated that despite
the pronounced uplifting attitude towards feasible travel industry, vacationers do not act alike by purchasing mindful
the travel industry items. The low help from clients is one of the fundamental boundaries for advancement towards
reasonable travel industry. Enlightening instruments are irreplaceable for making a move to practical vacation
conduct, which might be accomplished.
The key challenge that is face by the websites for its growth and development nowadays is Trust. Research analyses
six dimensions including order facilitation effort, prior knowledge of vendor and most importantly enhancing
consumer trust within the online travel marketplace (Austin et. al, 2006).Belief system coming from consumer
behavior for making purchases through online medium. It investigates how trust can be developed in customers
initially by the online channel and their purchase intentions. They found that customers are concernedabout online
security, privacy, policy of websites and willingness to customise and are the important antecedents to online initial
trust. The demographic features such as income and age group influence the online purchase attitude of travelers as
examined by Datta et. al, (2018). It concluded that gender do not affect the purchases through online websites but
trust, service and awareness seems to be major concerns for the travelers but convenience and network play a major
role in online travel purchase.
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All the companies and hotels need to adopt and make a website which is a platform for getting all the information
regarding the customer’s hotel reservations or any other information that customers are looking for. Many companies
and hotels are maintaining their websites by using various technology with the help of internet by providing online
transactions and many others features which can attract customers, thus hotel websites are replacing the traditional
mode of hotel reservations (Morosan and Jeong, 2006).
The most important thing which should be considered is that people are doing research from information by
experienced tourists who are contributing information regarding vacations and holidays trip by uploading photos,
videos and comments. Then, people who are about to go their vacations evaluating according to this information. And
also, the intention of tourist to used social media tools are also increased because of experience tourists are put
forward and endorse to other to use the social media tools in order to gather information related to their holiday’s
packages, prices, planning destinations, etc.Datta et. al, (2018) focused on understanding the consumer behaviour on
the buying decision making process where the studied was confined in North India while booing the holidays
vacations through e-Portals. Business travelers are investigating on various online travel portals for booking their
trips. According to this study, it was identified the nine fundamental values that people are looking for when they are
about to purchase: quality of the product,congeniality, business awareness. Business travelers are taking keener about
the confidentiality, security and product quality the most when they are booking their holidays trip. Pappas, N. (2016)
focused on vacations planned though internet.
India is a place which having a large variety of tourist’s attractions but it has failed due to 90% of the total
international tourists who are coming here does not go for holidays packages booking (Chaudhary, 1996).Due to
advancement in technology, modern travelers across the world needs various information of good quality about the
services offered which are related to their travel plan and which in turn helps tourists to take their decision. Tourists
are giving less importance towards online transactions due to lack of trust and security aspects in online financial
transactions than other services provided by the websites (Khare and Khare, 2010).Many tourists are not buying their
holiday packages from e-commerce company due to lack of trusts on their system like providing personal information
(Grabner-Krauter and Kaluscha, 2003).
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Proposed Conceptual Model
The hypotheses proposed in the study are as follows
H1: Access for customers will positively influence incentive to use online tourist application.
H2: Altruism of customers will positively influence incentive to use online tourist application.
H3: Customer’s Trust will positively influence incentive to use online tourist application.
H4: Customer’s functional knowledge will positively influence benefits of using online tourist application.
H5: Customer’s social knowledge will positively influence benefits of using online tourist application.
H6: Customer’s hedonic knowledge will positively influence benefits of using online tourist application.
H7: Customer’s effort will influence cost of using online tourist application.
H8: Customer’s usage difficulty will influence cost of using online tourist application.
H9: Customer’s privacy concerns will influence cost of using online tourist application.
H10: Incentives of using online applications hasan effect on the purchase intention of online tour package booking.
H11: Benefits of using online applications will be related to thepurchase intention of online tour package booking.
H12: Costs of using online applications will affectpurchase intention of online tour package booking.
H13: Purchase intention of online tour package booking will positively affect the actual buying behaviour towards
tour package booking.
Benefits of Use
Costs of Use
Incentives of Use
Intention of Online
Tour Packages Booking
Buying Behaviour of
Tour Packages Booking
Effort
Social Functional
Access Altruism
Trust
Hedonic
Usage
difficulty
Privacy
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Analysis and Results
The proposed hypotheses in the present study were tested through PLS-SEM technique. The validity of the scale was
established once the convergent validity among the individual construct (relationship between indicators/items and
construct) and discriminant validity among the constructs was proved. Internal reliability of the scale (Cronbach
alpha) was also established. The internal reliability, construct validity (convergent validity as well as discriminant
validity) and relationship establishment between items and construct and between different constructs were confirmed
using Smart PLS 2. The detailing of every stage of PLS-SEM approach to be followed was provided by (Hair et al.,
2014).
1) Outer Model Specification (Measurement Model): Once the inner and outer models were developed at the
model specification step, the analysis of the measurement model was done by establishing the reliability as well as
the validity of the constructs through PLS-SEM algorithm (Henseler et al., 2012).
a) Composite reliability of all the constructs was checked as the first step. Table 1 showcased the composite
reliability as well as the internal consistency (Cronbach alpha) of all the constructs. As the values of
composite reliability and Cronbach alpha (internal reliability) for all the constructs were more than 0.5, the
internal reliability for all the constructs was established through both the reliability measurements.
b) Validity of the constructs was established by confirming the convergent validity and discriminant validity of
the constructs. If the AVE (average variance extracted) of the individual construct is more than 0.5
(Fornell&Larcker, 1981), the convergent validity of that construct is established (Hair et al., 2014). The value
of convergent validity (AVE) for all the constructs are given in table 1.
Table 1 (Measurement Model: Composite & Internal Reliability
and Convergent Validity)
Construct Items Item
Loadings Composite Reliability
Cronbachs Alpha
AVE
Access
Acc1 0.7676
0.7981 0.6208 0.5686 Acc2 0.7449
Acc3 0.7494
Actual Behavior
Act_Beh1 0.7847
0.832 0.7388 0.5562 Act_Beh2 0.7853
Act_Beh3 0.5971
Act_Beh4 0.7974
Altruism
Alt1 0.7906
0.8023 0.6296 0.5755 Alt2 0.7037
Alt3 0.7787
Benefits of Use
Ben1 0.7997
0.8613 0.7584 0.6743 Ben2 0.8427
Ben3 0.8205
Cost of Use
Cost1 0.7606 0.8453 0.7247 0.646
Cost2 0.851
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Cost3 0.7971
Usage Difficulty
Diff1 0.7716
0.8243 0.6806 0.6102 Diff2 0.8138
Diff3 0.757
Efforts EFF1 0.7996
0.8135 0.5439 0.6858 EFF2 0.8558
Functional
Func1 0.6594
0.7735 0.5609 0.5335 Func2 0.7645
Func3 0.7623
Hedonic
Hed1 0.762
0.814 0.6574 0.5935 Hed2 0.7442
Hed3 0.8038
Incentives of Use
Inc1 0.6229
0.8215 0.6757 0.6757 Inc2 0.8671
Inc3 0.8306
Intention to Use
Int1 0.7755
0.8434 0.7526 0.5742 Int2 0.7311
Int3 0.7945
Int4 0.7279
Privacy
Priv1 0.7721
0.8205 0.672 0.6038 Priv2 0.7759
Priv3 0.7831
Social
Soc1 0.7304
0.8268 0.685 0.6146 Soc2 0.8122
Soc3 0.8065
Trust Trst1 0.8618
0.8644 0.6867 0.7612 Trst2 0.883
Discriminant validity showcases that how much one construct is empirically different from all other constructs.
Fornell and Larcker (1981) provided one criterion for assessing the discriminant validity wherein the AVE of the
construct should be higher than the squared correlation with any other construct. The value of discriminant validity
for all the constructs are given in table 2. The discriminant validity of all the constructs is established as per the
criterion suggested by Fornell and Larcker (1981) (Table 2)
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Table 2 (Discriminant Validity)
ACC ACT_BEH ALT BEN COST DIFF EFF FUNC HED INC PINT PRIV SOC TRST
ACC 0.754
ACT_BEH 0.378 0.746
ALT 0.608 0.483 0.759
BEN 0.626 0.570 0.649 0.821
COST 0.544 0.653 0.512 0.608 0.804
DIFF 0.416 0.626 0.427 0.522 0.758 0.781
EFF 0.460 0.471 0.423 0.427 0.757 0.509 0.828
FUNC 0.502 0.473 0.463 0.711 0.433 0.340 0.311 0.730
HED 0.533 0.490 0.582 0.724 0.555 0.485 0.462 0.534 0.770
INC 0.706 0.501 0.710 0.697 0.614 0.479 0.496 0.511 0.588 0.781
PINT 0.717 0.473 0.623 0.632 0.570 0.439 0.430 0.478 0.567 0.703 0.758
PRIV 0.303 0.585 0.322 0.430 0.734 0.679 0.369 0.344 0.366 0.397 0.302 0.777
SOC 0.564 0.479 0.589 0.734 0.569 0.515 0.407 0.524 0.603 0.602 0.579 0.375 0.784
TRST 0.311 0.449 0.456 0.393 0.407 0.383 0.284 0.312 0.270 0.631 0.322 0.389 0.347 0.872
ACC: Access, ACT_BEH: Actual Behavior, ALT: Altruism, BEN: Benefits to Use, Cost: Cost to Use, DIFF: Usage
difficulty, EFF: Efforts, FUNC: Functional, HED: Hedonic, INC: Incentive to Use, PINT: Intention to Use, PRIV:
Privacy, SOC: Social, TRST: Trust.
c) Evaluation of Inner model: After establishing the reliability and validity of the outer models, significance of
the hypothesized relationships of the inner models was tested. Assessment of the inner model was done by
determining the values of estimation of path coefficients, coefficient of determination (R2) and cross-validated
redundancy (Q2). But before undergoing the above steps, the issue of collinearity among the constructs of the
inner models was checked. Since the tolerance value was ‘>’ 0.2 and the VIF value was ‘<’ 5, there was no
issue of collinearity among the constructs in the inner model. (table 3a, 3b, 3c and 3d).
Table 3a: Tolerance and VIF Values for Collinearity
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
T Sig.
Collinearity Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) -6.908E-06
.018 .000 1.000
FUNC .345 .023 .345 15.231 .000 .651 1.537
SOC .421 .024 .421 17.539 .000 .579 1.727
HED .385 .024 .385 15.932 .000 .571 1.753
a. Dependent Variable: BEN
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Table 3b: Tolerance and VIF Values for Collinearity
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
T Sig.
Collinearity Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) -1.038E-05
.026 .000 1.000
EFF .456 .030 .456 15.299 .000 .739 1.352
DIFF .355 .038 .355 9.411 .000 .461 2.167
PRIV .324 .035 .324 9.279 .000 .538 1.858
a. Dependent Variable: COST
Table 3c: Tolerance and VIF Values for Collinearity
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.
Collinearity Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) -1.953E-05
.020 -.001 .999
ACC .443 .025 .443 17.516 .000 .629 1.589
ALT .449 .027 .449 16.629 .000 .552 1.812
TRST .288 .023 .288 12.745 .000 .790 1.265
a. Dependent Variable: INC
Table 3d: Tolerance and VIF Values for Collinearity
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.
Collinearity Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) 6.337E-06 .048 .000 1.000
BEN .220 .071 .220 3.107 .002 .462 2.164
COST .158 .064 .158 2.461 .015 .560 1.786
INC .453 .071 .453 6.371 .000 .457 2.190
a. Dependent Variable: PINT
d) Coefficient of determination (R2). The predictive accuracy of the model is established by the value of R2.
The collective effect of exogenous variable(s) on the endogenous variable(s) was represented by the value of
R2. For the following study, the mentioned criterion (rule of thumb) wherein R2 value above the values of
0.75, 0.50, 0.25, respectively, illustrating substantial, moderate, or weak levels of predictive accuracy was
used (Hair et al., 2011). Table 4 provides the R2 value mentioning the collective effect of exogenous variables
on endogenous variables. Since, the value of R2 in all the cases is more than 0.4, it is confirmed that the
predictive accuracy for one of the relations is from weak to moderate and for the rest of the relations, it is
substantial.
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e) Cross-validated redundancy (Q2). Q2value highlighted the predictive relevance of inner model. More the
value of Q2, higher is the predictive accuracy of the model. Greater than zero value of Q2 for an endogenous
construct establishes the predictive relevance of the path model for that construct. As the value of Q2 (Table 4)
was more than zero for all the endogenous variables, predictive relevance of the inner models was proved
(Ringle et al., 2012).
Table 4: Values of R2 (co-efficient of Determination) and Q2 (Predictive Relevance)
Total R^2 Relationship SSO SSE 1-
SSE/SSO
Predictive
Relevance
ACT_BEH 0.414 Weak to
Moderate 800 603.5034 0.24562 Medium
BEN 0.9346 Substantial 600 223.522 0.6275 High
COST 0.871 Substantial 600 264.8614 0.5586 High
INC 0.921 Substantial 600 270.0775 0.5499 High
PINT 0.5474 Moderate 800 559.8274 0.3002 Medium
f) Path Co-efficients: After running a PLS model, estimates (T-stats) were studied for the path coefficients. The
value of the t-stats (estimates) determines the significance of the relationship between the endogenous and
exogenous and constructs or the hypothesis proposed for the different relationships. The estimate value of
more than 1.96 signifies that the relationship between the constructs is significant at 95% level of confidence.
Table 5 illustrates the path-coefficients of all the relationships of the conceptual model.
Table 5: Path Co-efficient (Path Model)
Original
Sample (O)
Sample
Mean (M)
Standard
Deviation
(STDEV)
Standard
Error
(STERR)
T Statistics
(|O/STERR|) Relationship
ACC -> INC 0.4433 0.4439 0.0278 0.0278 15.9301* Significant
ALT -> INC 0.4495 0.4485 0.0305 0.0305 14.7506* Significant
BEN -> PINT 0.2196 0.2181 0.0761 0.0761 2.8873* Significant
COST -> PINT 0.1581 0.1587 0.0657 0.0657 2.4055* Significant
DIFF -> COST 0.3554 0.3587 0.0442 0.0442 8.0378* Significant
EFF -> COST 0.4564 0.4499 0.0456 0.0456 10.0026* Significant
FUNC -> BEN 0.3449 0.3433 0.024 0.024 14.3694* Significant
HED -> BEN 0.3853 0.3837 0.0291 0.0291 13.2272* Significant
INC -> PINT 0.4531 0.4566 0.0694 0.0694 6.525* Significant
PINT -> ACT_BEH 0.4733 0.483 0.0703 0.0703 6.7278* Significant
PRIV -> COST 0.3245 0.3274 0.0388 0.0388 8.3654* Significant
SOC -> BEN 0.4211 0.4225 0.0302 0.0302 13.9469* Significant
TRST -> INC 0.2879 0.2852 0.0407 0.0407 7.0791* Significant
*Significant at 95%
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ACC: Access, ACT_BEH: Actual Behavior, ALT: Altruism, BEN: Benefits to Use, Cost: Cost to Use, DIFF:
Difficulty, EFF: Efforts, FUNC: Functional, HED: Hedonic, INC: Incentive to Use, PINT: Intention to Use,
PRIV: Privacy, SOC: Social, TRST: Trust.
Conclusion
The results have shown thataccess,altruism andcustomer’s trust influences positively for incentive to use online
tourist application. Whereas customer’s functional, social and hedonic knowledge has influenced benefits of using
online tourist application positively. At the same time customer’s effort, usage and privacy concern has influenced
cost of using online tourist application. This study has also proved that incentives and benefits of using online
applications will positively affect purchase intention towards online tour package booking and at the same time cost
of using online applications has also affected purchase intention of online tour package booking. Moving forward the
purchase intention of online tour package booking will positively affect the actual buying behaviour towards tour
package booking.
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