A STUDY ON THE USE OF PERSONALIZED FEATURES …ijmrr.com/admin/upload_data/journal_Varsha 2nov15mrr.pdf · is focusing not only on short term sales but on increasing customer loyalty

ISSN: 2249-7196

IJMRR/Nov. 2015/ Volume 5/Issue 11/Article No-2/1045-1065

Varsha Agarwal / International Journal of Management Research & Review

*Corresponding Author www.ijmrr.com 1045

A STUDY ON THE USE OF PERSONALIZED FEATURES IN ONLINE TRAVEL

SHOPPING WEBSITES

Varsha Agarwal*1

1Research Scholar, Christ University, Institute of Management, Bangalore, India.

ABSTRACT

Personalization in online travel shopping is the process of providing the products and services

on websites according to the travelers’ preferences. Personalization plays an important role in

increasing attractiveness of consumers for online shopping of travel related products and

services. This study is conducted to examine how consumers’ perception toward

personalization features of online travel shopping websites influence consumer intentions to

purchase online tickets by identifying the important factors behind consumer intention to

make bookings online. The result of this study shows the effect of personalization factors on

consumers’ perception and purchase intentions for online travel shopping websites and their

implications for marketers in the travel industry. The variables tested in the empirical study

are gender, age, occupation, income and education and personalization features of online

ticket booking websites. In order to find out the perception and purchase intentions of online

shoppers a closed ended structured questionnaire was designed for primary data collection.

Data was analyzed using the following statistical tools namely Descriptive Statistics, Factor

Analysis and Linear Regression Analysis. This study is the first study which examines the

effect of personalization features on the perception of consumers and their purchase intention

with regard to the online travel shopping. Online marketers need to focus on adoption of

attractive personalized features and strategies to attract potential consumers.

Keywords: Online travel shopping, Online shopper, Perception, Purchase Intentions

INTRODUCTION

In e-commerce travel is the leading competitive sector and it is improving with a faster rate.

The increased use of internet is encouraging independent travel, and travelers search

information and deals online. Social media is playing important role in travel industry and it

is focusing not only on short term sales but on increasing customer loyalty also for longer

term and development of brand reputation. Smart phones and tablets are huge success and it

is making mobile applications an important channel of booking travel services. Travelers’

geo-localization also allowing real time sales for products that are location based. The

revolution in travel industry is giving central role to the consumers. And with this companies

are also getting benefits. Because of these developments in the market, travel companies are

thinking about their old models of business and focusing on acquiring the knowledge about

the expectations of customers. They are starting fruitful communications with consumers. In

the advanced economy online transactions are becoming the integral part of sales in travel


Copyright © 2012 Published by IJMRR. All rights reserved 1046

industry. And this is continuing to show stronger growth rates in comparison with traditional

channels in the travel sector.

According to Sahney et, al. (2014) for many Indians, booking of tickets via internet for travel

in train was their first introduction with the online shopping. The government portal

irctc.co.in for booking of railway tickets and travel firm Makemytrip.com, which is also

listed in NASDAQ, created revolution in the travel industry. It changed the scenario where

buying train tickets meant waiting in long queues at railway booking counters. Technology

has an important role in online travel industry and providing exponential growth. Earlier this

industry was having high opaqueness. Due to the penetration of internet scenario has been

changed. At global level lot of advanced information with communication technology has

been incorporated in online travel industry. These technologies are used for the development

of travel product, marketing, distribution and employee training in the travel sector. They are

indispensable for knowing and satisfying the changing demands of the consumers. In the

environment of ecommerce, personalization is playing important role in improving the

service levels as well as improving customer loyalty according to Shaw (2003). Many online

marketers are now offering highly personalized products as well as services in a wider range

of categories. This is transforming the practice of retailing into consumer oriented from

retailer oriented. This process of retailing involves customization of products and services for

individual customer needs. Etailers are allowing the consumers in choosing their own

services according to their preferences by adoption of the new technologies in

personalization.

Online travel industry created revolution in India for planning and buying the travel product

and service. Travel websites have introduced the ease and convenience of the operators and

expanding the choices for consumers. Now travelers can simply search on internet for

destinations of their choice. They can evaluate the available options and can take decisions.

These travel portals are emerging as one stop shop for all travel related needs in place of

mere ticket agents. The future of the online travel industry has been marked by consolidation

and new players are crating ventures into this sector. These collaborations will certainly lead

to success of the online travel industry. It has been revealed that when a customer shops

online from companies those offer personalized products and services than companies can get

the information about consumers very easily and at cheaper cost. It helps companies to gather

more information about users. And it helps them in predicting users’ preferences and online

choice pattern. This personalized information can help company to formulate further business

strategies and designing of interface and communication with the potential customers. Numes

et, al. (2001) described the process of personalization as a way of artificial intelligence use. It

helps in the analysis of demographic profile of consumers. Companies can give further

recommendations about the preference patterns of consumers.

REVIEW OF LITERATURE

The review of literature gives a fair idea about the work done in the subject area, the views

and observations made by different researchers and the gaps which need to be filled. The

literature review covers the basic concepts of personalization features in e-tailing, and

perception and intentions starting with a general review of ecommerce and e-tailing.



Studies regarding personalization in ecommerce

Pauline de Pechpeyrou (2009) conducted a study to know the value given by consumers to

the online personalization. The objective of the study was comparing the behaviour and

attitudes of consumers with respect to personalized selling and random selling. It was found

out that personalized items got more clicks than random ones. Additionally, a flavour of

personalization was added up to the positive attitude towards the website.

This study examined the attributes of service available on the websites of women apparel and

difference from attributes available on the websites of men’s apparel with regard to the nine

dimensions of e-service quality. The websites differ in providing online services in a manner

that women’s apparel websites offered more services that better the e-service quality than that

of men’s websites.

Studies regarding consumers’ perception and purchase intentions in an e-commerce

context

Sorce et, al (2005) conducted a study to investigate the buying behaviour of younger as well

as older and effect of their attitude on online shopping. The conclusion of the study made it

clear that older online buyers found lesser products in comparison with younger consumers.

There existed variance in the behaviour of shoppers on the basis of their attitudes.

Thamizhvanan et, al. (2013) conducted a study to determine the purchase intentions of Indian

consumers for online shopping.Along with previous online buying familiarity and online

belief, product positioning, impulse buying alignment and superiority positioning were found

to be significant buying orientation aspects for buyer online buying intention, as per the

detailed literature analysis. The research concluded that prior online purchase experience,

impulse purchase orientation and online trust have noteworthy influence on the customer

purchase intention.

Studies regarding e-commerce in travel industry

Sahney et, al. (2014) conducted a study to know the motivation of buyers towards online

shopping in the context of Indian railway. This study was theoretical and it conceptualizes the

motivation as an example with respect to the online shopping and tested it empirically. The

main aim of the study was to find out the important factors of motivation those are affecting

the decisions of people for online shopping and to make an integrated model.

Leica et, al. (2012) conducted a study for generalization of user behaviour for online travel

shopping websites and developed a model for website acceptance. The study focused on

analysis of tourist behaviour for travel websites. It clarified the users’ intentions for these

websites use on the basis of their determinants. As a result of concentration on blog the

relationship between reasoning as well as behavioural variables was found out and it was

concluded that it can differ according to particular website. A study was done by Law et, al.

(2008) to find the difference between perceptions of browsers who had browsed a websites

and buyers who had completed online shopping. Empirical results suggested that quality

factors were considered significant by website users and they were usually contented with

travel websites. Purchase intention in the users of these websites had a positive view.

Research conclusions suggested that customer satisfaction was positively related with travel



website quality, and that it was related to purchase intention. Woodside et, al. (2011)

conducted a study to find dominance of the tourism destination and the usefulness of the

marketing websites. The dominance of the tourism destination could be defined as number of

tourists visiting each year to residential population of the destination. A multi item metric

was created for the checking of the usefulness of the website of destination marketing. 40

destination marketing websites were judged based upon the tools, as a part of the study. The

conclusions of the study also indicated a noteworthy relationship between marketing website

usefulness and tourism destination dominance.

PURPOSE OF THE STUDY

This study is conducted to examine how consumers’ perception toward personalization

features of online travel shopping websites influence consumer intentions to purchase online

tickets by identifying the important factors behind consumer intention to make bookings

online. Thus, the focus of the study is measuring consumer perception and matching to

intentions to purchase online using personalization features by determining whether

consumer perceptions of personalized services in an online travel shopping website is a key

determinant predicting consumers’ intentions to purchase.

RESEARCH OBJECTIVES

Objectives of the study are as follows:

1) To identify the personalization features of selected online shopping websites;

2) To find the demographic profiles of online shopping consumers;

3) To analyze the perceptions of consumers towards online shopping websites;

4) To analyze the impact of factors of personalization features of the online travel shopping

websites on the purchase intentions of consumers;

RESEARCH HYPOTHESES

This research proposes the following hypothesis to be tested empirically based on the

literature review.

H11:There exist significant differences in the perception of consumers of different

demographics towards personalized features of selected online travel shopping websites.

H12:Personalization factors have a significant impact on consumers purchase intentions

towards online travel shopping websites.

RESEARCH METHODOLOGY

In order to find out the perception and purchase intentions of online shoppers a closed ended

structured questionnaire was designed for primary data collection with sample size of 650.

The location chosen for survey was Bangalore. Statistical tools such as descriptive analysis,

factor analysis and regression analysis were used to analyze the data and meeting the

objectives. This section provides a description of the methods, tools and techniques used to

conduct the research.

Data Collection: Data has been collected from primary sources for the purpose of this study,

from respondents who book tickets online. The data was obtained by using the survey method



through the administration of structured questionnaires to the respondents. Non probabilistic

convenience sampling was used for the primary data collection.

Statistical technique: Data was analyzed using the following statistical tools namely

Descriptive Statistics, Factor Analysis and Linear Regression Analysis. Descriptive statistics

was used to capture the demography of consumers. Cross tabulation was used to compare and

analyze the perceptions of consumers towards personalization features of selected online

shopping websites. Factor analysis was performed to identify important personalization factor

of online travel shopping websites. Regression Analysis was used to analyze the impact of

personalization factors of the online shopping websites on the purchase intentions of

consumers.

ANALYSIS AND INTERPRETATION

Demographics of the Respondents

This section provides an understanding about the demographic characteristics of the sample.

Table 1 gives information about gender, marital status, education, income, occupation and

age. Results in table 1 shows that majority 56.3 respondents are male who use online

shopping for booking travel related services. 58.3 percent of the respondents are married and

41.7 percent of them are single. 26.6 percent has cleared their HSC, 29.6 percent are

undergraduates and rest is 23.6 percent post graduates and 20.1 above post graduates.

Majority of respondents 45.7 percent have family income of Rs 30,000 to 50,000 per month

followed by 38.2 percent, who earn above 50,000. Majority 32.7 percent of respondents are

salaried people followed by 26.6 percent students. 16.1 percent of respondents are

housewives who shop online. It can be inferred from the table that the majority of the

respondents fall under the age group of 21-30 with 37.7 percent. After that 26.1 percent of

respondents belong to 31-40 age groups.

Table 1: Demographic Characteristics of respondents

Frequency Percent

Male 336 56.3

Female 261 43.7

Total 597 100.0

Frequency Percent

Single 249 41.7

Married 348 58.3

Total 597 100.0

Frequency Percent

HSC 159 26.6

UG 177 29.6

PG 141 23.6

Above PG 120 20.1

Total 597 100.0

Frequency Percent

Below 30,000 96 16.1

30,000-50,000 273 45.7

Above 50,000 228 38.2

Total 597 100.0

Frequency Percent

Student 159 26.6

Salaried People 195 32.7

Self Employed 147 24.6

House Wife 96 16.1

Total 597 100.0

Frequency Percent

Less than 20 135 22.6

20-30 225 37.7

30-40 156 26.1

Above 40 81 13.6

Total 597 100.0

Occupation

Age

Education

Income

Gender

Marital Status



Use of internet and online shopping websites usage

To get an understanding of perception of consumers towards online shopping websites, the

responses from online shoppers were analyzed with the help of descriptive statistics. The

analyzed data has been presented in a graphical form followed by an interpretation. The

demography of consumers for online shopping websites can be understood by analyzing the

comfort level of respondents with internet, no. of times they purchased products online in last

one year, no. of shopping websites used in last one year and most frequently used online

shopping travel websites.

Comfort level with internet use

This table 2 represents respondents comfort level with the internet use.

Table 2: Comfort level with internet use

Frequency Percent

Very Uncomfortable 18 3.0

Somewhat uncomfortable 25 4.2

Neutral 29 4.9

Somewhat Comfortable 284 47.6

Very Comfortable 241 40.4

Total 597 100.0

It can be understood from table 2that majority of the respondents 47.6 feel somewhat

comfortable with the use of internet, followed by 40.4 percent who feel very comfortable in

using internet.

No. of times products purchased online in last 1 year

Table 3 represents no. of times products purchase online in last one year by respondents.

Table 3: No. of times products purchased online in last 1 year

Frequency Percent

0-5 72 12.1

6-10 268 44.9

More than 10 257 43.0

Total 597 100.0

From Table 3 it can be understood that majority of the respondents 44.9 percent shop online

frequently and bought 6 to 10 products online in past one year followed by 43 percent

respondents who shop above 10 products over internet in last one year. It shows the

consumers perception towards online shopping.

No. of online shopping websites used in last 1 year

This table 4 represents no. of online shopping websites used in last 1 year by respondents.

Table 4: No. of online shopping websites used in last 1 year

Frequency Percent

Less than 3 71 11.9

3-5 275 46.1

More than 5 251 42.0

Total 597 100.0

From Table 4 it can be understood that majority of the respondents 46.1 percent has used 3 to

5 online shopping websites for online shopping of products and services, followed but 42

percent who used more than 5 websites in last one year for shopping over internet. It clearly

shows the perception of consumers towards online retailers.



Most frequently used online shopping website for travel bookings

This table 5 represents most frequently used travel websites for travel booking by

respondents.

Table 5: Most frequently used online shopping website for travel bookings

Frequency Percent

Yatra.com 102 17.1

Makemytrip.com 249 41.7

Irctc.co.in 246 41.2

Total 597 100.0

From table 5 it can be understood that majority of the respondents 41.7 percent use

Makemytrip.com website for booking their travel related services online. Similarly 41.2

percent of respondents use Irctc.co.in website for their ticket booking needs. Only 17.1

percent respondents use Yatra.com website for their travel related needs. This shows the

perception of consumers towards each online travel shopping website.

Personalization Factors behind Consumers’ Purchase Intentions

Factor analysis was performed to reduce the 18 independent variables into 7 important

personalization factors. To know the important personalization factors that influence

purchase intentions of consumers for online travel shopping websites factor analysis was

performed. The variables were formed as questions on a five point scale and respondents

were asked to answer them. These eighteen statements are the independent variables which

then get reduced to seven factors. Factor analysis was used to reduce dimensions of the

eighteen independent variables into seven factors using Principal Component Analysis.

Table 6: KMO and Barlett’s Test

.579

Approx. Chi-Square 4703.624

df 153

Sig. 0.000

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

Bartlett's Test of Sphericity

Significant value 0.000 indicates that this is not an identity matrix. Hence factor analysis can

be performed. Based on the above output, the KMO = 0.579. This shows that this model is

adequate for performing factor analysis.

Table 7: Total Variance Explained

Total % of

Variance

Cumulati

ve %

Total % of

Variance

Cumulati

ve %

Total % of

Variance

Cumulati

ve %

1 4.032 22.400 22.400 4.032 22.400 22.400 3.119 17.330 17.330

2 2.623 14.573 36.973 2.623 14.573 36.973 2.385 13.250 30.580

3 1.805 10.030 47.003 1.805 10.030 47.003 1.867 10.373 40.953

4 1.510 8.388 55.391 1.510 8.388 55.391 1.831 10.172 51.126

5 1.299 7.216 62.607 1.299 7.216 62.607 1.652 9.178 60.303

6 1.241 6.896 69.503 1.241 6.896 69.503 1.421 7.894 68.197

7 1.092 6.068 75.571 1.092 6.068 75.571 1.327 7.375 75.571

8 .779 4.330 79.901

9 .705 3.915 83.815

10 .617 3.429 87.245

11 .471 2.617 89.861

12 .420 2.332 92.194

13 .363 2.018 94.212

14 .345 1.915 96.127

15 .222 1.234 97.361

16 .202 1.123 98.483

17 .165 .915 99.399

18 .108 .601 100.000

Rotation Sums of Squared Component Initial Eigenvalues Extraction Sums of Squared



Table 8: Rotated Component Matrix

1 2 3 4 5 6 7

I prefer price filter option availability on websites. 0.85

Online shopping websites helps in comparing products of different

brands to a large extent.

0.82

I compare features of services when I shop online. 0.81

1This website offers me ability of personalizing a service by my

preference set.

0.70

Ratings provided by different consumers help me in choosing a

service.

0.88

Online marketers maintain robust content management and update it

on regular basis.

0.82

I can check status of my bookings online easily. 0.77

Travel websites is the one stop to shop all travel related services

like hotels, taxy , flights etc.

0.83

When I login to the websites, they offer me other supported

services also like hotels

0.75

Shopping selection aids such as recommendations, FAQs, or

expert's comments plays important role in purchase decisions.

-0.57

Online shopping of travel services provides competitive price deals. 0.90

Some e-tailors offer occasional and seasonal deals for travel related

services.

0.84

Online shopping also provides good return policy and guaranteed

cash back if I cancel my bookings or do not want to use the

services.

0.72

I like options to save my personal information. 0.63

This website offers good customer services such as a phone

number, e-mail, or chatting.

0.82

Etailers offer good and responsive enquiry services. 0.73

1 like options to save my financial information such as credit card

number.

0.80

Online travel retailers offer more reward programs such as bonus

points or miles.

0.54 -0.60

Component

The Rotated Component Matrix indicates, based on factor loadings that these eighteen

components were reduced into seven factors. Details of the factors are given in below table

9.



Table 9: List of Factors

S.N. Factor Name Variable Factor Reliability

I prefer price filter option availability on websites. 0.85

Online shopping websites helps in comparing products of

different brands to a large extent.

0.82

I compare features of services when I shop online. 0.81

1This website offers me ability of personalizing a service by

my preference set.

0.70

Ratings provided by different consumers help me in

choosing a service.

0.88

Online marketers maintain robust content management and

update it on regular basis.

0.82

I can check status of my bookings online easily. 0.77Travel websites is the one stop to shop all travel related

services like hotels, taxy , flights etc.

0.83

When I login to the websites, they offer me other

supported services also like hotels

0.75

Shopping selection aids such as recommendations, FAQs,

or expert's comments plays important role in purchase

decisions.

-0.57

4 Competitive

Deals

Online shopping of travel services provides competitive

price deals.

0.90

3Some e-tailors offer occasional and seasonal deals for

travel related services.

0.84

5 Guaranteed

Cash Back

Online shopping also provides good return policy and

guaranteed cash back if I cancel my bookings or do not

want to use the services.

0.72

I like options to save my personal information. 0.63

6 Customer

Responsivenes

This website offers good customer services such as a

phone number, e-mail, or chatting.

0.82

Etailers offer good and responsive enquiry services. 0.73

7 Loyalty

Programmes

1 like options to save my financial information such as

credit card number.

0.80

Online travel retailers offer more reward programs such as

bonus points or miles.

-0.60

3 One stop shop

0.84

0.80

0.38

0.51

0.30

0.81

0.24

1 Product

Comparison

Convenience

2 Website

Content

1. Product Comparison Convenience

This is the most important factor and captures 22.4 percent information in total. Consumers

prefer to compare the products always before making purchase. So availability of option of

comparing products online causes a great level of convenience for consumers and hence this

factor is very important to discuss. When consumers busy and have lesser time for shopping,

in that time availability of comparison of products sitting at home is proving very

comfortable, useful and helpful to them. It saves both time and energy. These days are large

number of websites which offer products from different brands in one online store. Hence

consumers are getting attracted towards such features of online shopping websites. Hence

marketers should focus in this dimension to attract large number of consumers.

2. Website Content

This factor alone contributes to 14.573 percent in total information. Content of website and

its timely management is very important and necessary to stay competitive. These days online



e-tailers are focusing on regular content management of websites to provide updated and

timely information to consumers. It increases the reliability of online seller and buyer gets

desired information about the product and service.

3. One Stop Shop

This factor captures 10.03 percent in the total information. These days travel websites are

offering travel products along with other supported services like hotel booking, taxi and many

more. Hence they are evolving as one stop shop for all consumer needs. Marketers can make

their strategies for future enhancement in the websites.

4. Competitive Deals

Factor competitive deals captures 8.388 percent of information in total. Travel websites offer

attractive and competitive deals for consumers for special occasions and seasons. Hence

consumers go for booking their tickets online in comparison with traditional medium of ticket

booking.

5. Guaranteed Cash Back

This factor contributes to 7.216 percent in the total information. Online travel websites

provide guaranteed cash back if the booking is cancelled by consumer or by Travel

Company. Hence it produces trust among the shoppers and they go for booking their tickets

online. It is an easy process of cancellation and getting the cash back.

6. Customer Responsiveness

Customer responsiveness factor contributes to 6.896 percent information in total. Online

travel websites provide responsive enquiry services and act to customer feedbacks. Hence it

makes easier for customers to solve their queries and gives them satisfaction.

7. Loyalty Programmes

Loyalty programmes has captured 6.068 percent information in total. Online travel shopping

websites various kind of loyalty programmes to consumers such as bonus points, extra miles,

coupons and many more. Hence they try to retain their customers and offer them attractive

deals to increase their loyalty. On the basis of reliability only four factors were taken for

further analysis out of 7 factors. One stop shop, guaranteed cash back and loyalty

programmes were showing less reliability. Hence these factors were not included in further

analysis.

Consumers’ Perception towards personalization factors of online shopping travel

websites

Consumers’ perception plays very important role behind their purchase intentions towards

online travel shopping. Hence perception of consumers has been measured here with

performing ANOVA between demographic variables and personalization factors of online

shopping websites. It shows whether there is any significance difference exists or not in the

perception of consumers for personalization factors of online travel shopping websites with

regard to their demographic profile and most used travel shopping website.



Analysis of Variance between Gender and Personalization Factors

Gender wise association of Personalization factors is tested to find whether there is any

significant difference between different levels educated online shoppers in association with

Personalization factors. The test is conducted at the 5 percent significance level.

Table 10: ANOVA for difference is Personalization Factors across the Gender of the

shoppers.

Sum of

Squares

df Mean

Square

F Sig.

Between Groups 0.08 1.00 0.08 0.17 0.68

Within Groups 289.80 595.00 0.49

Total 289.88 596.00

Between Groups 0.35 1.00 0.35 1.21 0.27

Within Groups 171.56 595.00 0.29

Total 171.91 596.00

Between Groups 0.36 1.00 0.36 0.35 0.55

Within Groups 605.80 595.00 1.02

Total 606.16 596.00

Between Groups 0.56 1.00 0.56 1.14 0.29

Within Groups 293.69 595.00 0.49

Total 294.26 596.00

Product Comparison

Convinience

Website Content

Competitive Deals

Customer

Responsiveness

The above shown ANOVA table10 is the test result of whether there is any significant

difference between the two genders in perceiving personalization factors. Form the ANOVA

table it is clear that the P value is greater than 0.05 that there is no significant difference

between the perception of male and female shoppers towards the personalization factors. The

test is conducted at 5% significance level. Hence male and female shoppers have similar

perception towards online travel shopping websites.

Analysis of Variance between Marital status and Personalization Factors

Marital Status wise association of Personalization factors is tested to find whether there is any



Table 11: ANOVA for difference in Personalization Factors across the Marital Status of

the shoppers

Sum of

Squares

df Mean

Square

F Sig.

Between Groups 0.23 1.00 0.23 0.48 0.49

Within Groups 289.65 595.00 0.49

Total 289.88 596.00

Between Groups 0.12 1.00 0.12 0.41 0.52

Within Groups 171.80 595.00 0.29

Total 171.91 596.00

Between Groups 0.94 1.00 0.94 0.93 0.34

Within Groups 605.22 595.00 1.02

Total 606.16 596.00

Between Groups 2.08 1.00 2.08 4.24 0.04

Within Groups 292.17 595.00 0.49

Total 294.26 596.00

Product

Comparison

Convinience

Website Content

Competitive Deals

Customer

Responsiveness

The above shown ANOVA table11 is the test result of whether there is any significant

difference (0.05) between the two marital statuses in interpreting personalization factors.

Form the ANOVA table11 it is clear that there is no significant difference between the



perceptions single and married shoppers towards 4 personalization factors. Hence married

and single status online shoppers have similar perception towards online travel shopping

websites.

Analysis of Variance between Education and Personalization Factors

Education wise association of Personalization factors is tested to find whether there is any



Table 12: ANOVA for difference in Personalization Factors across the Education level

of the shoppers

Sum of

Squares

df Mean

Square

F Sig.

Between Groups 7.34 3.00 2.45 5.14 0.00

Within Groups 282.54 593.00 0.48

Total 289.88 596.00

Between Groups 0.22 3.00 0.07 0.25 0.86

Within Groups 171.70 593.00 0.29

Total 171.91 596.00

Between Groups 9.19 3.00 3.06 3.04 0.03

Within Groups 596.96 593.00 1.01

Total 606.16 596.00

Between Groups 2.34 3.00 0.78 1.58 0.19

Within Groups 291.92 593.00 0.49

Total 294.26 596.00

Product

Comparison

Convenience

Website Content

Competitive

Deals

Customer

Responsiveness

ANOVA table 12 shows that there is a significant difference in product comparison

convenience factor with the different education level groups. So it can be concluded that

there is significant difference in the perception of different education groups with regard to

product comparison convenience.

Post Hoc Test

The post hoc test helps to explore all the possible pair wise comparison of means and show

the significant variables in the group wise. It will test the least significant difference of the

variables under the factor. In the product comparison convenience factor the first group HSC

has significance difference only in group PG. the group UG shows significance in only PG

group. The third group shows significance in all the other three groups.

Table 13: Multiple comparisons for education groups and Product comparison

convenience

UG -0.07 0.08 0.35

PG -.27910* 0.08 0.00

Above PG 0.00 0.08 0.99

HSC 0.07 0.08 0.35

PG -.20907* 0.08 0.01

Above PG 0.07 0.08 0.40

HSC .27910* 0.08 0.00

UG .20907* 0.08 0.01

Above PG .27793* 0.09 0.00

HSC 0.00 0.08 0.99

UG -0.07 0.08 0.40

PG -.27793* 0.09 0.00

Product

Comparison

Convenience

HSC

UG

PG

Above PG

Dependent

Variable

(I) Education (J) Education Mean

Difference (I-J)

Std. Error Sig.



Analysis of Variance between Family Income and Personalization Factors

Family Income wise association of Personalization factors is tested to find whether there is

any significant difference between different income groups of online shoppers in association

with Personalization factors. The test is conducted at the 5 percent significance level.

Table 14: ANOVA for difference of Personalization Factors across the family income of

shoppers

Sum of

Squares

df Mean

Square

F Sig.

Between Groups 7.34 3.00 2.45 5.14 0.00

Within Groups 282.54 593.00 0.48

Total 289.88 596.00

Between Groups 0.22 3.00 0.07 0.25 0.86

Within Groups 171.70 593.00 0.29

Total 171.91 596.00

Between Groups 9.19 3.00 3.06 3.04 0.03

Within Groups 596.96 593.00 1.01

Total 606.16 596.00

Between Groups 2.34 3.00 0.78 1.58 0.19

Within Groups 291.92 593.00 0.49

Total 294.26 596.00

Product

Comparison

Convenience

Website

Content

Competitive

Deals

Customer

Responsiveness

ANOVA table14 shows the significant difference in competitive deals factor with the

different income groups. So it can be concluded that there is significant difference in the

perception of different income groups with regards to competitive deals.

Post Hoc Test

Table 15: Multiple Comparisons for income levels and Competitive deals

30,000-50,000 0.21 0.12 0.08

Above 50,000 .37336* 0.12 0.00

Below 30,000 -0.21 0.12 0.08

Above 50,000 0.17 0.09 0.07

Below 30,000 -.37336* 0.12 0.00

30,000-50,000 -0.17 0.09 0.07

Dependent

Variable

(I) Income (J) Income

Competitive

Deals

Below 30,000

30,000-50,000

Above 50,000

Mean Difference

(I-J)

Std. Error Sig.

In the competitive deals factor the first group (Below 30,000) has significant difference in the

group Above 50,000.

Analysis of Variance between Occupation and Personalization Factors

Occupation wise association of Personalization factors is tested to find whether there is any

significant difference between different occupations of online shoppers in association with




Table 16: ANOVA for difference of personalization factors across the Occupation of

shoppers

Sum of

Squares

df Mean

Square

F Sig.

Between Groups 7.95 3.00 2.65 5.57 0.00

Within Groups 281.93 593.00 0.48

Total 289.88 596.00

Between Groups 1.40 3.00 0.47 1.62 0.18

Within Groups 170.51 593.00 0.29

Total 171.91 596.00

Between Groups 8.90 3.00 2.97 2.95 0.03

Within Groups 597.26 593.00 1.01

Total 606.16 596.00

Between Groups 2.32 3.00 0.77 1.57 0.20

Within Groups 291.94 593.00 0.49

Total 294.26 596.00

Product

Comparison

Convenience

Website Content

Competitive

Deals

Customer

Responsiveness

ANOVA table 16 shows the significant difference in product comparison convenience factor

with the different occupation groups. So it can be concluded that there is significant

difference in the perception of different occupation groups with regards to product

comparison convenience.

Post Hoc Test

Table 17: Multiple comparisons for Occupations and Product comparison convenience

Salaried People -0.06 0.07 0.43

Self Employed -0.05 0.08 0.52

House Wife -.34493* 0.09 0.00

Student 0.06 0.07 0.43

Self Employed 0.01 0.08 0.93

House Wife -.28702* 0.09 0.00

Student 0.05 0.08 0.52

Salaried People -0.01 0.08 0.93

House Wife -.29401* 0.09 0.00

Student .34493* 0.09 0.00

Salaried People .28702* 0.09 0.00

Self Employed .29401* 0.09 0.00

Product

Comparison

Convenience

Student

Salaried

People

Self Employed

House Wife

Dependent

Variable

(I) Occupation (J) Occupation Mean Difference

(I-J)

Std. Error Sig.

In the product comparison convenience factor group house wife has significant difference

between all three occupation groups student, salaried people and self-employed.

Analysis of Variance between age and Personalization Factors

Age wise association of Personalization factors is tested to find whether there is any

significant difference between different age groups of online shoppers in association with

Personalization factors. The test is conducted at the 5 percent of significance level.



Table 18: ANOVA for difference of personalization factors across the Age of shoppers

Sum of Squares df Mean Square F Sig.

Between Groups 7.57 3.00 2.52 5.30 0.00

Within Groups 282.31 593.00 0.48

Total 289.88 596.00

Between Groups 1.01 3.00 0.34 1.17 0.32

Within Groups 170.91 593.00 0.29

Total 171.91 596.00

Between Groups 6.16 3.00 2.05 2.03 0.11

Within Groups 599.99 593.00 1.01

Total 606.16 596.00

Between Groups 5.08 3.00 1.69 3.47 0.02

Within Groups 289.18 593.00 0.49

Total 294.26 596.00

Product

Comparison

Convenience

Website

Content

Competitive

Deals

Customer

Responsiveness

ANOAV table18 shows the significant differences in product comparison convenience factor

with the different age groups. So it can be concluded that there is significant difference in the

perception of different age groups with regards to product comparison convenience.

Post Hoc Test

Table 19: Multiple Comparisons for age and product comparison convenience

20-30 -.29444* 0.08 0.00

30-40 -0.14 0.08 0.08

Above 40 -.20370* 0.10 0.04

Less than 20 .29444* 0.08 0.00

30-40 .15128* 0.07 0.04

Above 40 0.09 0.09 0.31

Less than 20 0.14 0.08 0.08

20-30 -.15128* 0.07 0.04

Above 40 -0.06 0.09 0.52

Less than 20 .20370* 0.10 0.04

20-30 -0.09 0.09 0.31

30-40 0.06 0.09 0.52

Product

Comparison

Convenience

Less than 20

20-30

30-40

Above 40

Dependent

Variable

(I) Age (J) Age Mean Difference

(I-J)

Std. Error Sig.

In the product comparison convenience factor group less than 20 shows significant difference

only in the group 20-30.

Analysis of Variance between most used travel websites and personalization factors

Website used wise association of Personalization factors is tested to find whether there is any

significant difference between particular travel websites online shoppers in association with




Table 20: ANOVA for difference of Personalization factors across the most used travel

websites by shoppers

Sum of Squares df Mean Square F Sig.

Between Groups 0.93 2.00 0.47 0.96 0.38

Within Groups 288.95 594.00 0.49

Total 289.88 596.00

Between Groups 0.13 2.00 0.07 0.22 0.80

Within Groups 171.78 594.00 0.29

Total 171.91 596.00

Between Groups 1.30 2.00 0.65 0.64 0.53

Within Groups 604.86 594.00 1.02

Total 606.16 596.00

Between Groups 8.30 2.00 4.15 8.62 0.00

Within Groups 285.95 594.00 0.48

Total 294.26 596.00

Product

Comparison

Convenience

Website Content

Competitive

Deals

Customer

Responsiveness

ANOVA table20 shows the significant difference in Customer Responsiveness with the

different website groups. So it can be concluded that there is significant difference in the

perception of different website groups with regard to Customer Responsiveness.

Post Hoc Test

Table 21 Multiple Comparisons for most used travel websites and customer

responsiveness

Makemytrip.com .26347* 0.08 0.00

Irctc.co.in .33752* 0.08 0.00

Yatra.com -.26347* 0.08 0.00

Irctc.co.in 0.07 0.06 0.24

Yatra.com -.33752* 0.08 0.00

Makemytrip.com -0.07 0.06 0.24

Customer

Responsiveness

Yatra.com

Makemytrip.com

Irctc.co.in

Dependent

Variable

(I) Most used

Travel Websites

(J) Most used

Travel Websites

Mean Difference

(I-J)

Std. Error Sig.

In the customer responsiveness factor website group Yatra.com has significance difference

between the website group Makemytrip.com and itctc.com.

Personalization Factors and impact on Consumers’ purchase intentions

Correlation and Regression analysis is performed to analyze the impact of personalization

factors of selected online travel shopping websites on consumers’ purchase intentions. Here

relationship between purchase intentions and personalization factors is also found out.

Regression analysis performed to know the level of impact on consumers’ purchase

intentions due to personalization factors.

Relationship between Consumers’ purchase intentions and Personalization Factors

Correlation analysis is conducted to find out the relationship between the dependent variable

purchase intentions and all the four personalization factors. This analysis will interpret the

relevance of study by analyzing the relation between variable. Correlation is conducted

among all the independent variables (personalization factors) to know the inter correlation.



Table 22: Correlation Analysis

Purchase

Intentions

Product

Comparison

Convinience

Website

Content

Competitive

Deals

Customer

Responsiveness

Pearson

Correlation

1

Sig. (2-tailed)

N 597

Pearson

Correlation.571

** 1

Sig. (2-tailed) .000

N 597 597

Pearson

Correlation-.445

**-.227

** 1

Sig. (2-tailed) .000 .000

N 597 597 597

Pearson

Correlation.333

**.380

** -.075 1

Sig. (2-tailed) .000 .000 .067

N 597 597 597 597

Pearson

Correlation.425

**.115

**-.323

** .010 1

Sig. (2-tailed) .000 .005 .000 .811

N 597 597 597 597 597

Website Content

Competitive Deals

Customer

Responsiveness

Purchase

Intentions

Product

Comparison

Convinience

The correlation table22 indicates that correlation analysis is significant at 5 percent

significance level. There is a positive correlation between the dependent variable purchase

intentions and three independent personalization factors. The only personalization factor

website content has negative correlation with the dependent variable. It is found that purchase

intentions are positively correlated to product comparison convenience (r=0.571),

competitive deals (r=0.333), customer responsiveness (r=0.425) and. Product comparison

convenience shows the high level of correlation with r=0.571. The correlation table 22 also

shows that there is an inter correlation among the independent variables and most them are

positively correlated to each other. It is found that product comparison is positively correlated

with competitive deals (r=0.380), customer responsiveness (r=0.115). Website content has

negative correlation with three independent variables.

Impact of Personalization Factors on Consumers’ purchase intentions

Regression analysis is conducted to find out the impact of personalization factors on

consumer purchase intentions. In the regression analysis consumer purchase intention is the

dependent variable and independent variables are product comparison convenience, website

content, competitive deals, and customer responsiveness. The four personalization factors

which are considered as independent variables are used to test if personalization factors

significantly influenced consumers’ purchase intentions.

Table 23: Analysis of Variance for “Purchase Intentions” of Consumers

Sum of

Squares

df Mean

Square

F Sig.

Regression 519.50 4.00 129.87 164.15 .000b

Residual 468.39 592.00 0.79

Total 987.89 596.00

Model

1



In the ANOVA table 23 P=.000 indicates that overall the model applied is significantly good

enough to predict consumer purchase intention. It indicates that the study is relevant and it

has got a significant importance.

Table 24: Model Summary for “Purchase Intentions” of Consumers

R R Square Adjusted

R Square

Std. Error of

the Estimate

1 .725a 0.53 0.52 0.89

Model

The model summary table 24 of the regression indicates that the 4 predictors of consumer

purchase intention (the independent variable product comparison convenience, website

content, competitive deals, customer responsiveness) represent 53 percent of the variance. It

means that 53 percent of the purchase intention of the consumers is affected by

personalization factors, while the rest may be due to other variables.

Table 25: Coefficients of Regression for “Purchase Intentions” of Consumers

Standardized

Coefficients

B Std. Error Beta

(Constant) 0.23 0.50 0.46 0.64

Product

Comparison

Convenience

0.78 0.06 0.42 13.53 0.00

Website

Content

-0.58 0.07 -0.24 -7.90 0.00

Competitive

Deals

0.19 0.04 0.15 4.93 0.00

Customer

Responsiveness

0.54 0.05 0.30 9.89 0.00

Model Unstandardized

Coefficients

t Sig.

1

From the coefficient table25 it is clear that there is significant relations (at 5 percent

significance level) of consumer purchase intentions with all personalization factors

(independent variables). Hence it is concluded that all the four personalization factors product

comparison convenience, website content, competitive deals, and customer responsiveness

influenced consumers’ purchase intentions. All the personalization factors significantly

impacts consumers’ purchase intentions.

The regression equation:

y = a + b1 x1+ b2 x2 + b3 x3 ………………+ bn xn + standard error

‘y’ = dependent variable (consumer purchase intentions)

x1 = Product Comparison Convenience

x2 = Website Content

x3 = Competitive Deals

x4 = Customer Responsiveness

a = 0.23 (constant)

b1 = .78

b2= -.58



b3= .19

b4= .54

In the above table25 product comparison convenience has got the high coefficient value of

.78, so product comparison convenience has got more impact on consumer purchase

intentions.

The regression equation is given by:

IMPLICATIONS OF RESEARCH

The finding of this study proves that personalization is a part of online shopping and it cannot

be separated. It has more positive effect on consumers in comparison with products and

services alone. This study is the first study which examines the effect of personalization

features on the perception of consumers and their purchase intention with regard to the online

travel shopping. This study also indicates that proper implementation of personalization

features can bring more positive and powerful results and can increase the consumers’

purchase intentions. Web portals have to include the new and preferred personalization

features in their websites to become competitive in travel industry. This will be helpful in

increasing the competitiveness. The results of this study have some important implications.

The findings of this study suggest that use of personalization features in the online travel

websites can definitely encourage the positive perception of consumers and can make higher

the intentions of consumers. Implementation of personalization features in the online travel

shopping websites and personalization factors will influence the purchase intentions of

consumers. It can be implicated from this study that online travel companies and their users

are focusing on the adoption of the personalization features and their implementation in the

online travel shopping websites. Both government and public sector service providers are

incorporating the innovative personalization features in online travel industry for attracting

potential consumers.

SUGGESTIONS AND RECOMMENDATIONS

This study is conducted to investigate the perception of consumers and their purchase

intentions with respect to personalization features in the travel websites. Product comparison

convenience is the most important personalization factor behind the purchase intentions of

consumers. Hence all the three website designers Yatra.com, Makemytrip.com and Irctc.co.in

should focus on other personalization factors like competitive deals and customer

responsiveness. These website marketers should focus on providing more competitive deals

to consumers according to their preferences. For example these online travel shopping

websites can offer special discounts for students in their summer holidays. It will encourage

consumers to plan their trip in that season and companies can get benefit from it as well as

consumers will also feel to get advantages. Also they can offer some special packages for

senior citizens to travel to their holy destinations. Yatra.com website should provide more



customer responsive approach and it can improve on it by giving dedicated customer care no.

and online support. Makemytrip.com should focus on website content management and

should try to make it more informative for customers. Personalization factor of Irctc.co.in has

highest impact on consumers purchase intentions. Hence other online shopping websites can

refer to it and can improve on accordingly. In this study it has been revealed that

demographic profile of consumers also plays major role on perception of consumers.

SCOPE FOR FURTHER RESEARCH

There is a scope for conducting further research study for solving the managerial level

problems for helping the managers. So that can use the personalization in a better way and

can adopt it. It will help them in formulating new marketing strategies. In the same field

further studies can be conducted with use of random sampling with scientific selection for

compensating the shortcomings of this study. Research in this subject can also include the

combination of personalized services in online travel industry and personalized features

provided by other industries in the industry of online shopping.

LIMITATIONS OF THE STUDY

The major limitations of this study are with respect to the number of websites chosen for

survey. Only three websites Yatra.com, Makemytrip.com and Irctc.co.in were chosen to

conduct the study. Another major limitation is location chosen. Only Bangalore has been

chosen for study. Hence this study may not reflect the perception and purchase intentions of

all the online travel shoppers.

CONCLUSION

Analysis shows that online shoppers prefer personalized services in online travel websites.

Although, consumers have started using online shopping websites for booking of tickets, but

it needs to go long way to find considerable market share for companies in the field of online

marketing. To get them out of this traditional way of booking tickets and other travel related

services, can be a challenge. Online marketers need to focus on adoption of attractive

personalized features and strategies to attract potential consumers.

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A STUDY ON THE USE OF PERSONALIZED FEATURES …ijmrr.com/admin/upload_data/journal_Varsha 2nov15mrr.pdf · is focusing not only on short term sales but on increasing customer loyalty