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© 2018 IJRAR December 2018, Volume 5, Issue 4 www.ijrar.org (E-ISSN 2348-1269, P- ISSN 2349-5138)

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




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.




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).




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




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




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)




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




Table 3b: Tolerance and VIF Values for Collinearity

Coefficientsa

Model



T Sig.



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



t Sig.



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



t Sig.



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.




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%




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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