EXECUTIVE SUMMARY

The project focused on finding out the Online Buying Behaviour of consumers between the age

group of 18-30 years. The stated objective of the study was further broken down to secondary

objectives which aimed at finding information regarding the popular product categories,

frequency of purchases, average spending, factors affecting buying decision process etc.

The exploratory research was carried out with 20 respondents with a set of 12 open ended

questions. The exploratory findings helped us in determining the key factors which needed to be

further explored for research. The secondary research questionnaire designed had 9 questions

and was administered to 100 respondents. Each of the questions was designed to satisfy at least

one of the secondary objectives of the research. The response format was of a mixed variety

which also helped in better determination of outcomes.

Post data reduction, Cross tabulation was used for analyzing the causal relationship between

different pairs of factors. ANOVA was also applied to a pair of factors.

The Regression Analysis between the dependent variable “Average Amount spent per purchase

made online” and the independent variables of Frequency of Purchase of products and services

online, owning a Credit Card, Marital Status, Education and Age, was done..

Then, Cluster Analysis was done on the data and based on the responses; we could divide the

respondents in three clearly distinct groups. We named them: Confident Online Buyer, Unsure

surfer and Mall Shopper.

We performed Factor Analysis to find the major factors. We could identify six factors: Value for

Money, Trust, Connected and Up to date, Problems Faced, and Traditionalism.

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INTRODUCTION

India has the world’s 4th largest Internet user base, which crossed the 100 million mark recently.

Better connectivity, booming economy and higher spending power helped the Indian e-

commerce market revenues to cross $500 million with a CAGR of 103% over last 4 years. This

may not be a significant number, averaging to only around $5 per user per year.

With the above background in mind, this research has been conducted to gain an insight into the

online buying behaviour of consumers. The objective is to explore the factors which influence

online purchase, the psychographic profile of the consumer groups and understanding the buying

decision process.

Our findings should help an Internet Marketer to determine the product/service categories to be

introduced or to be used for marketing for a specific segment of consumers. This would also

allow them to add or remove services/features which are important in the buying decision

process. This study however does not aim to identify newer areas to introduce new services, nor

should it be used to predict the success or failure of internet ventures.

Internet is changing the way consumers shop and buy goods and services, and has rapidly

evolved into a global phenomenon. Many companies have started using the Internet with the aim

of cutting marketing costs, thereby reducing the price of their products and services in order to

stay ahead in highly competitive markets. Companies also use the Internet to

convey, communicate and disseminate information, to sell the product, to take feedback

and also to conduct satisfaction surveys with customers. Customers use the Internet not

only to buy the product online, but also to compare prices, product features and after sale

service facilities they will receive if they purchase the product from a particular store.

Many experts are optimistic about the prospect of online business.

In addition to the tremendous potential of the E-commerce market, the Internet provides a

unique opportunity for companies to more efficiently reach existing and potential

customers. Although most of the revenue of online transactions comes from business-

to-business commerce, the practitioners of business-to-consumer commerce should not lose

confidence.

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It has been more than a decade since business-to-consumer E-commerce first evolved. Scholars

and practitioners of electronic commerce constantly strive to gain an improved insight into

consumer behavior in cyberspace. Along with the development of E-retailing, researchers

continue to explain E-consumers’ behavior from different perspectives. Many of their

studies have posited new emergent factors or assumptions which are based on the traditional

models of consumer behavior, and then examine their validity in the Internet context.

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OBJECTIVES

Primary Research Objective

To determine the factors and attributes which influence online buying behavior of

consumers between the age group of 18-30 years.

Secondary Research Objectives

To determine the psychographic profile of consumers who purchase over the Internet.

To determine the key product or service categories opted for, by consumers depending on

their profile.

To determine the average spending and frequency of purchase over the internet by a

consumer.

The exploratory research, conducted on over 20 respondents (Annexure I), focused on further

analysing the research objectives and also determining various factors which would impact the

primary research objective. Through a set of 12 open-ended questions, we could finally conclude

on some of the key factors to be further explored in the research, these included frequency of

purchase, safety issues, amount per purchase, payment methods etc…

Secondary Research was based on researches done by Zinnov LLC on Internet Penetration in

India, Changing Consumer Perceptions towards Online shopping in India – IJMT. Both of the

researches stressed on the consumer profiles, popular services and payments methods as

important factors

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L I TE RA T URE

R E VI E W

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L I TE RA T URE R E VI E W

The current literature on consumer online purchasing decisions has mainly concentrated

on identifying the factors which affect the willingness of consumers to engage in Internet

shopping. In the domain of consumer behaviour research, there are general models of

buying behaviour that depict the process which consumers use in making a purchase decision.

These models are very important to marketers as they have the ability to explain and predict

consumers’ purchase behaviour. The classic consumer purchasing decision-making theory can

be characterized as a continuum extending from routine problem-solving behaviours,

through to limited problem- solving behaviours and then towards extensive problem-

solving behaviours [Schiffman et al., 2001].

The traditional framework for analysis of the buyer decision process is a five-step model. Given

the model, the consumer progresses firstly from a state of felt deprivation (problem

recognition), to the search for information on problem solutions. The information

gathered provides the basis for the evaluation of alternatives. Finally, post-purchase behaviour

is critical in the marketing perspective, as it eventually affects consumers’

perception of satisfaction/dissatisfaction with the product/service. This classic five stage

model comprises the essence of consumer behaviour under most contexts. Nevertheless,

the management of marketing issues at each stage in the virtual environment has to be

resolved by individual E- marketers. Peterson et al. [1997] commented that it is an early

stage in Internet development in terms of building an appropriate dedicated model of

consumer buying behaviour. Decision sequences will be influenced by the starting point of the

consumer, the relevant market structures and the characteristics of the product in question.

Consumers' attitude towards online shopping is a prominent factor affecting actual buying

behaviour.

Source: Jarvenpaa Journal of Electronic Commerce Research, VOL. 6, NO.2, 2005

Todd [1997] proposed a model of attitudes and shopping intention towards Internet shopping in

general. The model included several indicators, belonging to four major categories; the value of

the product, the shopping experience, the quality of service offered by the website and the risk

perceptions of Internet retail shopping. In the research conducted by Vellido et al. [2000], nine

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factors associated with users' perception of online shopping were extracted. Among those factors

the risk perception of users was demonstrated to be the main discriminator between

people buying online and people not buying online. Other discriminating factors were; control

over, and convenience of, the shopping process, affordability of merchandise, customer service

and ease of use of the shopping site. In another study, Jarvenpaa et al. [2000] tested a model

of consumer attitude towards specific webbase stores, in which perceptions of the store's

reputation and size were assumed to affect consumer trust of the retailer. The level of trust was

positively related to the attitude toward the store, and inversely related to the perception

of the risks involved in buying from that store. Jarvenpaa et al. [2000] concluded that the

attitude and the risk perception affected the consumer's intention to buy from the store.

Consumer risk perceptions and concerns regarding online shopping are mainly related to aspects

involving the privacy and security of personal information, the security of online

transaction systems and the uncertainty of product quality. Trust is interwoven with risk

[McAllister, 1995]. One of the consequences of trust is that it reduces the consumer’s

perception of risk associated with opportunistic behaviour by the seller [Ganesan, 1994]. Lack

of trust is frequently reported as the reason for consumers not purchasing from Internet

shops, as trust is regarded as an important factor under conditions of uncertainty and risk in

traditional theories.

Mayer et al. [1995] developed a model which combines traditional marketing philosophy

on consumer motivation to buy and the trust model. In this model, trust propensity;

which is a personality trait possessed by buyers; is an important antecedent of trust. In

Internet shopping, there is not much information available to the buyer regarding the seller,

prior to purchase. A buyer with a high propensity to trust will more likely be a potential

customer than a buyer with a lower propensity. Mayer et al. [1995] proposed that ability,

benevolence and integrity constitute the main elements of trustworthiness. Ability refers to

skills, competencies and characteristics that a seller has in a specific domain. In this

context, sellers need to convince buyers of the competence of their companies in the

Internet shopping business. Benevolence is the extent to which the seller is perceived by the

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buyer as wanting to ‘do good’. Sellers have to convince buyers that they genuinely want to

do good things for buyers, rather than just maximize profit.

RESSEARCH METHODOLOGY

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RESEARCH METHODOLOGY

The research was administered both online and in person during a 2 months period in Jan 2010 to

Feb 2010. The location of in-person administration was Noida, Over 32 responses are from the

online survey and the rest 68 from in-person survey conducted.

Survey Administration

The questionnaire comprised of 9 questions (Annexure II) which measured responses for

different factors of frequency of purchase, payment methods, preferred products, average

spending, hours spent on the internet etc…

The questions measuring respondent attitudes used Likert Scale (1-5), 18 statements were given

to respondents to measure their attitudes towards online buying, and a few factual questions had

dichotomous responses.

The methods used for survey was questionnaire administration with respondents filling out the

responses themselves and online survey through mail posting.

Sampling

The survey was conducted on 100 respondents; sample was based on affordability criteria

especially on time constraints. Email invitations were sent to invite respondents on the Internet,

and employee, students were contacted for responses.

Gender

65%

35%

Male

Female

Occupation

40%

60%

Student

Working Professional

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Data Reduction

The key steps of data processing which were implemented were Editing, Coding, Transcribing,

and Summarizing statistical calculations.

EDITING: For some of the item non-response errors like frequency of purchase, product

category or websites. The data was interpreted and assigned to the known categories wherever

possible.

CODING: For questions involving qualitative values the responses were codified using

numerical categories or values. For example; Online shopping is more convenient, the response

of “strongly agree” was coded as 1 and “strongly disagree” was coded as 5.

TRANSCRIBING: The data collected from all 100 questionnaires was edited, codified and

finally transferred on MS Excel on computer.

Data Analysis

Post Data Reduction, the data was further used for analyzing the impact of various factors on

each other as well the correlation amongst them using SPSS. The factors as well as their

correlation were studied with the help of the following techniques:

CROSS-TABS WITH CHI-SQUARE: The factors were grouped into 5 pairs based on the

responses from the questionnaire. These were studied using Chi-Square as that would help us to

know the interdependency between them. Chi-square in general studies causal relationship and

thus the hypotheses were created for each of them was done at 95% significance level. By

conducting the test and interpreting the results through the p-value, we can either accept or not

accept the null hypothesis.

REGRESSION ANALYSIS: In regression analysis, we create a model wherein we determine the

correlation between the dependent variable and multiple independent variables. By conducting

the tests and interpreting the results, we can determine the adjusted R2 value which tells us how

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good the regression model fits to the data. If the value is high, then the model fits well to the data

and that there is a high correlation between the variables. On the other hand, if the value is low,

then the model does not fit very well to the data and there is no significant correlation between

the variables.

ANOVA: Analysis of variance, better known as ANOVA, helps us to group the data into various

population samples and then check their relationship with an independent variable, which we

consider to be significant depending on the responses from the questionnaire. The null

hypothesis for this is also created at a 95% significant variable and then depending on the

significant value from the results, the hypothesis is accepted or not accepted.

FACTOR ANALYSIS: This is a technique to reduce data complexity by reducing the number of

variables being studies. It helps identify latent or underlying factors from an array of seemingly

important variables. This procedure helps gaining insight into psychographic variables.

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THEORITICAL FRAMEWORK

ONLINE SHOPPING IN INDIA

It is a fact that a great online shopping revolution is expected in India in the coming years. There

is a huge purchasing power of a youth population aged 18-40 in the urban area.

2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-090

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Rupeees in Crores

Info by: IAMAI

If we observe the growth of Indian online transactions from the above graph, it is

getting doubled year by year.

The usage of internet in India is only 4% of the total population. This is also getting increased

day by day as the costs of computers are decreasing and net penetration is increasing. The cost of

internet usage is also getting lower, with good competition among the providers. Wi-Fi

& Wimax is also getting tested in Bangalore and other cities in India. This will increase the

usage as it goes more on wireless internet.

Indians are proving everytime that they can beat the world when it comes to figures of online

shopping. More and more Indians are going to online shopping and the frequency of

India’s online buying is crossing the overall global averages.

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Factors That Boost Online Shopping in India

Rapid growth of cybercafés across India Access to Information The increase in number of computer users Reach to net services through broadband

Middle-class population with spending power is growing. There are about 200 million of middle-

class population good spending powers. These people have very little time to spend for shopping.

Many of them have started to depend on internet to satisfy their shopping desires.

Few Facts About Online Shopping

The figures from IAMAI show that the internet users in India will grow to 200 million by 2010.

Around 25% of regular shoppers in India are in the 18-25 age groups, and 46% are in the 26-35-

year range.

Indian online matrimonial sector is worth around $230 million.

Worldwide e-commerce is only growing at the rate of 28%, since India being a younger

market, the growth of e-commerce is expected at 51% in the coming years.

Inline with global trends finally India has also started shopping online these days. As per

the study by IAMAI online shopping in India has rose from $11million in 1999-2000 to

$522 million in 2007 and it is expected to rise above $700 million by end March 2010.

Indians are also Shopaholics like other Asians. There is a strong booming young adult

population in India with good levels of disposable income.

INDIA - Over $50 Billion and growing rapidly - Most popular online shopping products

include: books (45%), electronic gadgets (42%), railway tickets (38%), accessories

apparel (35%), apparel (35%), gifts (34%), computer and peripherals (32%), airline

tickets (28%), music downloads (21%), movie downloads (21%), hotel rooms (22%),

magazines (18%), tools (16%), home appliances (16%), toys (16%), jewelry (17%), movie

ticket (15%), beauty products (12%), health and fitness products (12%), apparel gift

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certificates( 11%) and sporting goods (7%). There are over 120 million people online in

India and this is expected to grow to 200 Million by the end of 2010

CHANGING ATTITUDE TOWARDS ONLINE SHOPPING

Malls malls springing up everywhere and yet people are e-shopping! And not in small numbers

either. E-commerce figures are going through the roof, according to Assocham

(Associated Chambers of Commerce & Industry of India). Today (2007-08) the figures

are touching Rs. 2200 crore, but are expected to increase by 150 percent by 2008-09 - to Rs

5,500 crores! And two metros - Delhi and Mumbai are driving the growth:

It was never thought that Indians would go in for e-shopping in such a big way.Ticketing, travel

bookings and even books and movies seem fine to buy online. Knowing that in India sizes vary

from brand to brand and quality is inconsistent, even of some electronic items, how is it

that there are people buying these items online?

Well, Assocham says that books are the hottest selling item on the internet. In fact most products

bought and sold off online are: books, electronic gadgets and railway tickets. However, people

are also buying clothes, gifts, computer and peripherals, and a few are buying home tools and

products, home appliances, toys, jewelry, beauty products and health and fitness products.

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TRAFFIC FOR E-COMMERCE SITES IS MOSTLY COMING FROM THE

TWO METROS OF DELHI AND MUMBAI.

Here are few reasons for this :

1. Convenience

It is the major reason. Both the cities are spread out over a large area and the best stores in both

these cities are often concentrated in certain ‘posh’ areas. In Mumbai for example there

are certain items you get only in Crawford market which is at the other end of town in South

Mumbai. And demographics show that the population of Mumbai is now concentrated in

the suburbs. Ofcourse, huge malls have come up in the suburbs as well, and India’s biggest

mall Nirmal Lifestyle is in far-flung Mulund but often you find a better choice of sizes and styles

choice in other malls, say Phoenix (central Mumbai). And though both Mumbai and Delhi have

transport system,few people like to travel for two hours just to get to a shop at the other end of

town. Clearly the transport systems leave much to be desired. In Delhi, safety is also an issue for

women traveling alone in the evenings.

2. Literacy Rate and the Cities’ Internet Savvy Population

Most cities in India have a higher literacy rate as compared to the national average of 64.8

percent. In fact Mumbai has a highest literacy even amongst the cities (86 per cent). Delhi too

has a high literate population (81.2 per cent). Oddly, although Bangalore has a higher literacy

rate than Delhi, at 83 per cent, the city’s share of e-commerce is not very high. Kolkatta too

has a literacy rate (80.8 per cent) and so does Chennai (80.1 percent.) If one compares these rates

to literacy rates of cities like Patna (62.9 percent), Jaipur (67 percent), Indore (72 percent) or

Warangal (73 percent) its clear why its the metros which are going to continue to lead e-

shopping.

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3. Home delivery concept

In any case, home delivery is a concept that Indians are familiar with and love. The mall craze

has started only now.Earlier it was a choice between sweating it out in small crowded markets, or

asking a friendly neighbourhood kirana (grocer) to deliver groceries home and this system is still

thriving.

4. Increase in the Internet users

Increasing penetration of Internet connectivity and PCs has led to an increase in the

Internet users across India. The demographic segments that have witnessed maximum

growth comprise college going students and young persons. These segments are the users of

advanced applications and technologies online and are most likely to be heavy E-

Commerce users.

5. Increase in the number of buyers and sellers

The success of a marketplace depends on the presence of a large number of buyers and a large

number of sellers. In addition to online buyers, many offline stores have begun to sell their

products in the online marketplace. The greater the number of sellers and buyers, the faster the

market grows.

PRODUCT PREFERENCES CITY WISE

Bangalore loves to buy books, electronic gadgets, computer peripherals, gifts

movies, bookings,actually just about everthing.

Well, Kolkatta prefers to buy music and movies online

Mumbai leads in all categories, except jewellery.

Delhites seem to prefer buying jewellery online as compared to any other city

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ATTITUDE TOWARDS ONLINE SHOPPING

Consumer’s attitudes toward online shopping have gained a great deal of attention in

the empirical literature. It is believed that consumer attitudes will affect intention to shop online

and eventually whether a transaction is made. It refers to:

1) The consumer’s acceptance of the Internet as a shopping channel

2) Consumer attitudes toward a specific Internet store (i.e., to what extent consumers think

that shopping at this store is appealing).

INTENTION TO SHOP ONLINE

Consumer’s intention to shop online refers to their willingness to make purchases in an

Internet store. Commonly, this factor is measured by consumer’s willingness to buy and

to return for additional purchases. The latter also contributes to customer loyalty.

Consumer’s intention to shop online is positively associated with attitude towards Internet

buying, and influences their decision-making and purchasing behavior. In addition, there is

evidence of reciprocal influence between intention to shop online and customer satisfaction.

ONLINE SHOPPING DECISION MAKING

Online shopping decision-making includes information seeking, comparison of alternatives, and

choice making. The results bearing on this factor directly influence consumer’s

purchasing behavior. In addition, there appears to be an impact on user’s satisfaction.

Though it is important, there are only five studies that include it.

According to Haubl and Trifts (2000), potential consumers appear to use a two-stage process in

reaching purchase decisions.

Initially, consumers typically screen a large set of products in order to identify a subset

of promising alternatives that appears to meet their needs. They then evaluate the subset in

greater depth, performing relative comparisons across products based on some desirable

attributes and make a purchase decision.

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ONLINE PURCHASING

This is the most substantial step in online shopping activities, with most empirical research using

measures of frequency (or number) of purchases and value of online purchases as measures

of online purchasing; other less commonly used measures are unplanned purchases

Online purchasing is reported to be strongly associated with the factors of

personal characteristics, vendor/service/product characteristics, website quality, attitudes

toward online shopping, intention to shop online, and decision making (Andrade 2000; Bellman

et al. 1999)

CONSUMER SATISFACTION

It can be defined as the extent to which consumer’s perceptions of the online shopping

experience confirm their expectations. Most consumers form expectations of the

product, vendor, service, and quality of the website that they patronize before engaging

in online shopping activities. These expectations influence their attitudes and intentions to

shop at a certain Internet store, and consequently their decision-making processes and

purchasing behavior. If expectations are met, customers achieve a high degree of

satisfaction, which influences their online shopping attitudes, intentions, decisions, and

purchasing activity positively. In contrast, dissatisfaction is negatively associated with these

four variables (Ho and Wu 1999; Jahng et al. 2001; Kim et al. 2001).

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DATA INTERPRETATION AND ANALYSIS

CROSS TABULATIONSa) Credit Card- Frequency of Purchase

Null Hypothesis: At 95% significance level, owning a credit card does not have any impact on the frequency of purchase.

Alternate Hypothesis: At 95% significance level, owning a credit card has an impact on the frequency of purchase.

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Own Credit Card V/S Freq of Purchase Cross tabulation Count

Freq of Purchase Total

Once a Month

2 -3 Times a Month

Once in 3 Months

Once in 6 Months

Never Tried

Once a Month

Own Credit Card

Yes 23 14 28 11 1 71

No 3 2 8 7 09 29

Total 26 16 34 18 10 100

As the p-value from the table is lesser than 0.05, which is our assumed level of significance, we do

not accept the null hypothesis, that is, for the sample population, owning a credit card has an

impact on the frequency of purchase.

b) E-banking-Frequency of Purchase

Null Hypothesis: At 95% significance level, e-banking does not have any impact on the frequency of purchase.

Alternate Hypothesis: At 95% significance level, e-banking has an impact on the frequency of purchase.

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E-banking V/S Freq of Purchase Cross tabulation Count

Freq of Purchase Total

Once a month

2-3 times a month

Once in 3 months

Once in 6 months

Never tried

Once a month

E-banking

Yes 22 15 27 9 0 73

No 3 1 6 10 7 27

Total 25 16 33 19 7 100

As the p-value from the table is lesser than 0.05, which is our assumed level of significance,

we do not accept the null hypothesis, that is, for the sample population, E-banking has an

impact on the frequency of purchase.

c) Gender-Amount Spent

Null Hypothesis: At 95% significance level, gender does not have any impact on the average amount spent per purchase made online.

Alternate Hypothesis: At 95% significance level, e-banking has an impact on the average amount spent per purchase made online.

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Gender * AmountSpent Crosstabulation Count

AmountSpent Total

Less than 500

500 - 1000

1000 - 2000

2000 - 5000

Greater than 5000

Less than 500

GenderMale 11 13 9 24 09 66

Female 7 4 6 9 8 34

Total 18 17 15 33 17 100

As the p-value from the table is greater than 0.05, which is our assumed level of

significance, we accept the null hypothesis, that is, for the sample population; gender does

not have any impact on the average amount spent per purchase made online.

d) Gender-Frequency of Purchase

Null Hypothesis: At 95% significance level, gender does not have any impact on the frequency of purchase of online products and services.

Alternate Hypothesis: At 95% significance level, gender has an impact on the frequency of purchase of online products and services.

As

the

p-

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Gender V/s Freq of Purchase Cross tabulation Count

FreqofPurchase Total

Once a Month

2-3 Times a Month

Once in 3 Months

Once in 6 Months

Never Tried

Once a Month

GenderMale 20 14 27 9 3 73

Female 4 2 10 8 3 33

Total 24 16 37 17 6 100

value from the table is lesser than 0.05, which is our assumed level of significance, we do

not accept the null hypothesis, that is, for the sample population; gender has an impact on

the frequency of purchase of online products and services.

e) Income-Frequency of Purchase

Null Hypothesis: At 95% significance level, income of respondents does not have any impact on the frequency of purchase of online products and services.

Alternate Hypothesis: At 95% significance level, income of respondents has an impact on the frequency of purchase of online products and services.

Income V/s Freq of Purchase Cross tabulation Count

Freq of Purchase Total

Once a Month

2-3 Times a Month

Once in 3 Months

Once in 6 Months

Never Tried

Once a Month

Income Less than 10000 2 0 1 0 0 3

10000-20000 1 0 2 5 1 9

20000-30000 2 2 11 2 0 17

30000-50000 5 0 3 1 0 9

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50000-100000 2 1 0 1 0 4

Greater than 100000 1 1 2 0 0 4

Total 13 4 19 9 1 46

As the p-value from the table is greater than 0.05, which is our assumed level of

significance, we do not accept the null hypothesis, that is, for the sample population;

income does not have an impact on the frequency of purchase of online products and

services.

REGRESSION ANALYSIS

The Regression Analysis between the dependent variable “Average Amount spent per purchase

made online” and the independent variables of Frequency of Purchase of products and services

online, owning a Credit Card, Marital Status, Education and Age, was done using SPSS. The

details are as below:

Variables Entered/Removed(b)

Model Variables EnteredVariables Removed

Method

1Marital Status, Freq of Purchase, Education, Credit Card, Age(a)

. Enter

2 AgeBackward (criterion: Probability of F-to-remove >= .100).

3 . EducationBackward (criterion: Probability of F-to-remove >= .100).

4 . Marital Status Backward (criterion: Probability of F-

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to-remove >= .100).

a All requested variables entered.

b Dependent Variable: Amt Spent

Coefficients(a)

ModelUnstandardized Coefficients Standardized Coefficients t Sig.

B Std. Error Beta B Std. Error

1

(Constant) 1.696 1.954 .868 .388

FreqofPurchase .402 .122 .330 3.305 .001

Age .054 .078 .083 .696 .489

CreditCard -.695 .318 -.234 -2.186 .032

Education -.152 .202 -.076 -.753 .454

MaritalStatus .384 .464 .096 .828 .410

2 (Constant) 2.897 .912 3.178 .002

FreqofPurchase .403 .121 .331 3.323 .001

CreditCard -.755 .305 -.254 -2.477 .015

Education -.134 .199 -.067 -.671 .504

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MaritalStatus .534 .409 .134 1.304 .196

3

(Constant) 2.564 .762 3.364 .001

FreqofPurchase .415 .120 .341 3.467 .001

CreditCard -.772 .303 -.259 -2.547 .013

MaritalStatus .561 .406 .140 1.380 .171

4

(Constant) 3.366 .496 6.781 .000

FreqofPurchase .408 .120 .335 3.393 .001

CreditCard -.882 .294 -.297 -3.005 .003

a Dependent Variable: AmtSpent

Excluded Variables(d)

ModelBeta In t Sig. Partial Correlation Collinearity Statistics

Tolerance Tolerance Tolerance Tolerance Tolerance

2 Age .083(a) .696 .489 .077 .682

3Age .071(b) .606 .546 .067 .694

Education -.067(b) -.671 .504 -.074 .962

4

Age .122(c) 1.164 .248 .127 .874

Education -.080(c) -.798 .427 -.087 .971

MaritalStatus .140(c) 1.380 .171 .150 .926

a Predictors in the Model: (Constant), MaritalStatus, FreqofPurchase, Education, CreditCard

b Predictors in the Model: (Constant), MaritalStatus, FreqofPurchase, CreditCard

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c Predictors in the Model: (Constant), FreqofPurchase, CreditCard

d Dependent Variable: AmtSpent

As can be seen from the above table, the independent variables can be gradually removed in the

regression model as they don’t have any significant impact on the value of R2. The value of R2 is

quite low and so it can be said that the regression model does not fit into the data very well. Also,

the sum of squares of regression is lesser than the sum of squares of residuals and this reiterates

the findings of R2. This is because if the sum of squares of regression is lesser than the sum of

squares of residuals, then the independent variables do not explain the variation in the dependent

variable well. While cross tabs suggest a positive relationship between multiple pairs of factors,

the linear correlation model, with all factors together, does not fit in with the outcomes.

ANOVANull hypothesis: At 95% confidence interval for the population taken, income does not have any impact on the frequency of purchase of online products and services.

Alternate Hypothesis: At 95% confidence interval for the population taken, income has an impact on the frequency of purchase of online products and services.

N MeanStd. Deviation

Std. Error

95% Confidence Interval for Mean

Minimum

Maximum

Lower Bound

Upper Bound

.00 64 2.3125 1.29560 .16195 1.9889 2.6361 .00 4.00

Less than 10000

3 2.3333 .57735 .33333 .8991 3.7676 2.00 3.00

10000-20000 7 2.8571 1.46385 .55328 1.5033 4.2110 .00 4.00

20000-30000 16 2.7500 .85635 .21409 2.2937 3.2063 1.00 4.00

30000-50000 7 2.7143 .75593 .28571 2.0152 3.4134 2.00 4.00

50000-100000 5 2.0000 1.22474 .54772 .4793 3.5207 1.00 4.00

27 | P a g e

Greater than 100000

7 2.4286 1.27242 .48093 1.2518 3.6054 .00 4.00

Total 109 2.4312 1.19696 .11465 2.2039 2.6584 .00 4.00

ANOVA

Frequency

Sum of Squares df

Mean Square F Sig.

Between Groups

5.317 6 .886 .605 .726

Within Groups 149.417 102 1.465

Total 154.734 108

Means Plots

Income

Greater than 100000

50000-100000

30000-5000020000-3000010000-20000Less than 10000

.00

Me

an

of

Fre

qu

en

cy

3.00

2.80

2.60

2.40

2.20

2.00

The p-value from the ANOVA table is greater than the significance value of 0.05 assumed by us.

Thus, at this significance level we accept the null hypothesis. So we can conclude that income

does not have an impact on the frequency of purchase of online products and services for these

28 | P a g e

respondents. Do remember that the same conclusion was arrived at when Cross tabulation of

location and usage rate was performed earlier.

CLUSTER ANALYSISThe cluster analysis was run where people were surveyed about their attitudes towards internet

shopping. The preferences indicated by respondents were used to find out the consumer

segments that react differently to different parameters related to online shopping. The segments

obtained would give an understanding as to how the consumers are placed in terms of their

attitudes.

Hierarchical clustering was done to determine the initial cluster solution. While the initial cluster

solution by SPSS gives us 2 clusters, we take a difference of coefficients greater than or equal to

2.72 form another cluster. Now we execute the K-Mean cluster to get the final cluster solution

and through ANOVA table we get that all the variables bear significance at 95% confidence

level.

ANOVA

Cluster Error F Sig.

Mean Square df Mean Square DfMean

Square dfInternetvsMall 27.190 2 .515 103 52.814 .000LatestInfo 1.744 2 .341 103 5.116 .008Accesibility 6.364 2 .536 103 11.874 .000Convenience 27.439 2 .367 103 74.819 .000Savetime 7.485 2 .608 103 12.312 .000AnywhereAnytime 2.935 2 .559 103 5.255 .007CreditCardSafe 3.441 2 .619 103 5.562 .005SpecificDateTime 6.393 2 .553 103 11.554 .000GuaranteedQuality 4.378 2 .469 103 9.334 .000Discounts 9.581 2 .710 103 13.500 .000Hasslefree 8.649 2 .691 103 12.518 .000CashonDelivery 1.011 2 .791 103 1.278 .283EasyFind 5.585 2 .838 103 6.668 .002FacedProblems .866 2 .730 103 1.186 .309Continue 6.682 2 .823 103 8.121 .001TouchandFeel 5.330 2 .728 103 7.320 .001DeliveryProcess 8.284 2 .499 103 16.612 .000NoCreditCard 12.382 2 .950 103 13.035 .000

The F tests should be used only for descriptive purposes because the clusters have been chosen to maximize the differences among cases in different clusters. The observed significance levels are not corrected for this and thus cannot be interpreted as tests of the hypothesis that the cluster means are equal.

29 | P a g e

Final Cluster Centers

Cluster

1 2 3InternetvsMall

3.38 2.09 4.00

LatestInfo1.58 1.78 2.05

Accesibility1.37 1.94 2.18

Convenience2.62 1.81 3.86

Savetime2.33 1.59 2.55

AnywhereAnytime1.88 2.09 2.50

CreditCardSafe2.52 2.78 3.18

SpecificDateTime2.10 2.28 3.00

GuaranteedQuality2.98 3.56 3.55

Discounts2.15 2.72 3.23

Hasslefree2.54 2.03 3.18

CashonDelivery2.15 2.34 2.50

EasyFind2.00 2.63 2.68

FacedProblems2.52 2.81 2.59

Continue2.40 2.94 3.27

TouchandFeel1.81 2.53 1.95

DeliveryProcess2.42 2.66 3.45

NoCreditCard4.08 3.81 2.82

Variable Description Cluster 1 (52) Cluster 2(32) Cluster 3(22)

I prefer making a purchase from internet than using local malls or stores

Disagree Strongly Agree Strongly Disagree

I can get the latest information from the Internet regarding different products/services that is not available in the market.

Strongly Agree NAND Strongly Disagree

30 | P a g e

I have sufficient internet accessibility to shop online. Strongly Agree Mildly Disagree Strongly Disagree

Online shopping is more convenient than in-store shopping.

Mildly Agree Strongly Agree Strongly Disagree

Online shopping saves time over in-store shopping. Disagree Strongly Agree Strongly Disagree

Online shopping allows me to shop anywhere and at anytime.

Strongly Agree Mildly Agree Strongly Disagree

It is safe to use a credit card while shopping on the Internet.

Strongly Agree NAND Strongly Disagree

Online shopping provides me with the opportunity to get the products delivered on specific date and time anywhere as required.

Strongly Agree Moderately Agree Strongly Disagree

Products purchased through the Internet are with guaranteed quality.

Strongly Agree Strongly Disagree Strongly Disagree

Internet provides regular discounts and promotional offers to me.

Strongly Agree NAND Strongly Disagree

Internet helps me avoid hassles of shopping in stores. NAND Strongly Agree Strongly Disagree

Cash on Delivery is a better way to pay while shopping on the Internet.

Strongly Agree NAND Strongly Disagree

Sometimes, I can find products online which I may not find in-stores.

Strongly Agree Strongly Disagree Strongly Disagree

I have faced problems while shopping online. Strongly Agree Strongly Disagree Agree

I continue shopping online despite facing problems on some occasions.

Strongly Agree Mildly Disagree Strongly Disagree

It is important for me to touch and feel certain products before I purchase them. So I cannot buy them online.

Strongly Agree Strongly Disagree Agree

I trust the delivery process of the shopping websites. Strongly Agree Agree Strongly Disagree

I do not shop online only because I do not own a credit card.

Strongly Disagree Disagree Strongly Agree

On the basis of the above scales, obtained by rating the relative results, we can name our clusters

as:

Cluster 1 : Confident Online Buyer

Cluster 2 : Unsure surfer

Cluster 3 : Mall Shopper

31 | P a g e

FACTOR ANALYSISThe responses are put into SPSS for data reduction through Factor Analysis. The details of the above are provided below:

Total Variance Explained

Component

Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %

1 3.854 21.410 21.410 3.854 21.410 21.410 2.559 14.219 14.219

2 2.175 12.082 33.491 2.175 12.082 33.491 2.318 12.879 27.098

3 1.652 9.175 42.666 1.652 9.175 42.666 2.160 11.997 39.095

4 1.514 8.413 51.080 1.514 8.413 51.080 1.727 9.593 48.688

5 1.277 7.096 58.176 1.277 7.096 58.176 1.422 7.901 56.589

6 1.098 6.101 64.276 1.098 6.101 64.276 1.243 6.903 63.492

7 1.066 5.924 70.200 1.066 5.924 70.200 1.207 6.708 70.200

8 .875 4.859 75.059

9 .771 4.283 79.342

10 .737 4.095 83.438

11 .545 3.030 86.467

12 .529 2.939 89.407

13 .384 2.134 91.541

14 .377 2.092 93.633

15 .358 1.988 95.621

16 .293 1.626 97.247

17 .278 1.546 98.793

32 | P a g e

18 .217 1.207 100.000

Extraction Method: Principal Component Analysis.

We see from the Cumulative Percentage column that there have been seven components or factors extracted which explain 70.2% of the total variance (information contained in the original 18 variables). This is an acceptable solution as generally, 70% of the total variance should be explained by the factors for the solution to be accepted.

Rotated Component Matrix(a)

Component

1 2 3 4 5 6 7

InternetvsMall .714 -.279 .149 .056 .046 -.197 -.241

LatestInfo .099 .322 -.146 .804 -.072 -.125 -.105

Accesibility -.043 .157 .158 .859 .105 .084 .020

Convenience .796 .045 .202 .206 .030 -.078 -.109

Savetime .726 .270 -.040 -.015 .017 -.120 .065

AnywhereAnytime .314 .571 .153 .109 .251 .025 -.034

CreditCardSafe .023 -.046 .657 -.047 .090 -.271 -.416

SpecificDateTime .272 -.198 .466 .404 -.197 .321 .107

GuaranteedQuality .023 .412 .647 .016 .071 -.124 .309

Discounts .069 .714 .208 .233 .095 -.095 -.158

Hasslefree .694 .225 -.022 -.153 -.129 .320 -.017

CashonDelivery -.121 -.024 -.022 .008 .123 .882 -.126

EasyFind .013 .869 .018 .143 -.043 .055 .012

FacedProblems -.203 .020 -.091 .084 .788 -.025 .049

Continue .071 .379 .518 -.040 .555 .015 -.077

33 | P a g e

TouchandFeel -.351 -.060 -.006 .079 -.546 -.193 -.065

DeliveryProcess .112 .135 .796 .065 -.101 .126 -.059

NoCreditCard -.142 -.130 -.047 -.053 .084 -.147 .885

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a Rotation converged in 8 iterations.

Looking at the rotated component matrix, we see that the loadings of variables-Internet preferred

over mall, Convenience and Saves Time, on factor 1 are high (greater than 0.7) and thus factor 1

is made up of these variables. Similarly, we can interpret for the other factors as well and a table

for the same has been provided at the end of this section.

Factor Variables Label for the Factor1 Internet over Mall

ConvenienceSaves Time

Time-bound and comfort seeking

2 DiscountsEasy Find

Value for Money

3 Delivery ProcessCredit Card Safe to UseGuaranteed Quality

Trust

4 Latest InformationAccessibility

Connected and Up to date

5 Faced Problems Problems Faced6 Cash on Delivery

Don’t Own a credit cardTraditionalism

7 Don’t Own a credit card N/A

We have combined the sixth and the seventh factor as they go hand-in-hand. A person who does

not own a credit card but shops online would invariably prefer to pay by cash on delivery.

PERCEPTUAL MAPS

34 | P a g e

-1 -0.5 0 0.5 1

-1

-0.5

0

0.5

1

Factor 2 vs Factor 3

Value for Money

Tru

st

-1 -0.5 0 0.5 1

-1

-0.5

0

0.5

1

Factor1 vs Factor3

Time-bound & Comfort-seeking

Va

lue

fo

r M

on

ey

Delivery Process

Guaranteed QualityCredit Card Safe

Internet over Mall

Saves Time

Convenience

35 | P a g e

-1 -0.5 0 0.5 1

-1

-0.5

0

0.5

1

Factor 1 vs Factor 4

Time-bound & Comfort-Seeking

Fa

cto

r 4

36 | P a g e

PRODUCT CATEGORIES PURCHASE ONLINE

Food an

d Bevera

ges

Electr

onic Pro

ducts

Gifts/Flowers

Car or H

otel Ren

tal

Apparels

Books

Ticke

ts

Pharmace

utical Pro

ducts

020406080

100120

7

5546

36

14

73101

9

Product Categories Bought Online

37 | P a g e

FINDINGS

We found a strong inter-dependence between a few variables affecting online buying

behaviour. For example, we found that owning a credit card has a significant impact on

the frequency of online purchases as credit card is the most popular mode of payment on

the Internet. Apart from the credit card, E-Banking is also slowly becoming a popular

mode of payment and we found a relationship between people who use E-Banking and

their frequency of online purchases too.

Interestingly, we found that gender does not have any major impact on the average

amount spent over the Internet in a month, but it does have a relationship with the

frequency of purchases. Also, the income of an individual does not have show any

significant relationship with the frequency of purchases. These findings are starkly

similar to the findings of Changing Consumer Perceptions towards Online shopping in

India – IJMT, which was a part of our secondary data.

Based on the responses, we could divide the respondents in three clearly distinct groups.

We named them: Confident Online Buyer, Unsure surfer and Mall Shopper. We were

also able to successfully able to create a discriminate model which could predict cluster

membership of users.

We could also arrive at six factors which can explain the data with 70% significance,

these factors could be categorized into Time-bound and comfort seeking Value for

Money, Trust, Connected and Up to date, Problems Faced, and Traditionalism.

We also found that the most popular product category sold online is Air/Rail Tickets.

This forms a major chunk of the average amount spent by our respondents on the internet,

Books come a close second. It must be noted that both the above products have a

relatively low touch-and-feel need. These findings depict almost the same ranking as

found by ACNielson on popular services on the Internet.

The most popular websites for these were found to be Makemytrip.com and Yatra.com.

Apart from Air Tickets, Books, Gifts and Electronic Products are also very popular with

the Online Shoppers and they are spending, on an average, Rs2000-Rs5000 per month on

online purchases.

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SUGGESIONS AND RECOMMENDATIONS

1. CONSUMER BIAS

Consumers often display a bias for brands that they know well and have had a good

experience in the past. Thus products of brands with a favorable bias will score over the

products of less popular brands. A few would risk to buy expensive jewelry from an

unknown jeweler online.

Lack of ‘Touch –Feel-Try’ Experience

The customer is not sure of the quality of the product unless it is delivered to him and

post delivery of the product, it is sometimes a lengthy proces to get a faulty or the

unsuitable product changed. Thus, unless the deliverables are as per the customers

expectations, it is hard to infuse more credibility in the e-Tailing market.

2. MOUNTING COMPETITIVE PRESSURES

To attract customers, the competing online players are adopting all means to provide

products and services at the lowest prices. This has resulted in making the consumers

choice-spoilt, who in turn surf various websites to spot the lowest price for the product.

Thus, although the number of transactions is increasing, the value of the products sold is

continuously falling owning to high competition and leaner margins.

3. SEASONALITY

eTailing Market is faced by seasonal fluctuations. As told by an Industry player, “August

to February is the peak seasons for sale, while March to July is the dry seasons for sale”.

During the peak season, occasions that drive the sales are Diwali, Rakhi, Valentines Day,

New Year, Christmas, Mother’s Day, Friendship Day etc are. On these occasions younger

generations prefers buying and sending gifts online.

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4. CREDIBILITY IN PAYMENT SYSTEM

Online frauds and breach are the biggest barriers to online sales. As a result, prospective

buyers prefer staying away from revealing their credit card and bank details.

Untimely Delivery of Products:- It might take a few minutes to search, book and pay for

products and services online, but the delivery of the product may take unreasonable time.

5. UNTIMELY DELIVERY OF PRODUCTS

It might take a few minutes to search, book and pay for products and services online, but

the delivery of the product may take unreasonable time.

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CONCLUSION

Increased Internet penetration, a hassle free shopping environment and high levels of Net

savvies see more and more Indians shopping online. But at the same time the companies need

to reduce the risks related to consumer incompetence by tactics such as making purchase

websites easier to navigate, and introducing Internet kiosks, computers and other aids in stores.

The goal is not to convert all shoppers to online purchasing, but to show them it’s an option.

In addition to above, efforts need to be taken to educate the online buyers on the steps that need

to be undertaken while making an online purchase. Moreover, the feedback of an online

buyer should be captured to identify flaws in service delivery. This can be done through

online communities and blogs that serve as advertising and marketing tools and a source of

feedback for enterprises. We found that it is a challenge for E-marketers to convert low

frequency online buyers into regular buyers through successful website design and by

addressing concerns about reliable performance. Thus, the online retailing raises more issues

than the benefits it currently offers. The quality of products offered online and procedures for

service delivery are yet to be standardized. Till the same is done, the buyer is at a higher risk of

frauds.

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IMPLICATIONS AND RECOMMENDATIONS FOR FUTURE

RESEARCH

As the model indicates, three out of the five dependent variables (consumer attitudes, intentions,

and purchasing behavior) and three out of the five independent variables (perceived usefulness,

perceived ease of use, perceived enjoyment, information on online shopping, security and

privacy, quality of internet connection ) receive the most attention. This seems to constitute the

main stream of research in this area. It is found that personal characteristics such as

vender/service/product characteristics and website quality significantly affect online shopping

attitudes, intention, and behavior.

The role of the external environment, demographics, online shopping decision making,

and consumer satisfaction are less well represented in the proposed model.. Any number of

factors, including vender/service/product characteristics, website quality, attitude towards

online shopping, intention to online shopping, online shopping decision making, and online

purchasing, may influence consumer’s satisfaction. More importantly, the extent to which

customers are satisfied is directly related to attitudes toward online shopping or toward specific

Internet stores. The relative importance of this factor in determining such consumer

behavior as repeat purchases suggests that further research on consumer satisfaction with online

shopping needs to be conducted.

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LIMITATIONS OF THE RESEARCH

1. The research is confined to a certain parts of Delhi & Noida and does not necessarily

shows a pattern applicable to all of Country.

2. Some respondents were reluctant to divulge personal information which can affect the

validity of all responses.

3. In a rapidly changing industry, analysis on one day or in one segment can change very

quickly. The environmental changes are vital to be considered in order to assimilate the

findings.

4. Limitation of the study is the selection of the existing studies. Owing to time limitation, we

only searched a few number of journals. This may leave some other prominent empirical studies

out. In addition, owing to the multidisciplinary nature of online shopping, it would be

very interesting to compare IS literature to other disciplines that study online shopping attitudes

and behavior.

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BIBLIOGRAPHY

http://ezinearticles.com/?Online-Consumer-Behavior&id=1947357

knowledge.emory.edu/papers/793.pdf

www.questionpro.com/.../online-survey-research-Analysis-of-online-buying-behaviour-of-Internet-users.html

portal.acm.org/citation.cfm?id=1456670

http://zendlife.info/browse.php?u=Oi8vd3d3LnF1ZXN0aW9ucHJvLmNvbS9ha2lyYS9zdXJ2ZXlUZW1wbGF0ZUluZm8uZG8%2Fc3VydmV5SUQ9MjI5NjYyJm1vZGU9Mg%3D%3D&b=13

http://ezinearticles.com/?Online-Consumer-Behavior&id=1947357

https://mr.pricegrabber.com/Economic_Climate_Shifts_Consumers_Online_March_2009_CBR.pdf

http://www.ntcresearch.org/pdf-rpts/Bref0603/S02-AC23-03e.pdf

http://apps.business.ualberta.ca/ghaeubl/papers/ConsumerDecisionMakingInOnlineShoppingEnvironments.pdf

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1422171

http://www.strangecorp.com/downloads/strange_online_marketing_trends_2009.pdf

http://faculty.washington.edu/rajgopal/files/job_KRV.pdf

44 | P a g e

ANNEXURE

45 | P a g e

Annexure 1a: Questionnaire for Exploratory Research

Hi,

We are conducting a research on consumer behavior on the internet. As a part of our exploratory

research we request you to spare a few minutes in answering the questions below. Do feel free to

ask us for any clarifications or doubts.

Your responses would serve as a base to our research; and would guide us in discovering

important facts about the buying process.

Thanks.

1. What are the online services that you generally use while surfing the net (answer as many)?

2. Which are the websites that you visit frequently for the following purposes:

a. E-mail:

b. Chat:

c. Shopping:

d. Job Search:

e. Social Networking:

f. Banking and other Financial Services(Stock Trading):

g. Education:

h. General Browsing:

2. How frequently do you shop online?

3. If you do not shop online, then what are the reasons behind not shopping online?

4. What are the occasions when you buy online?

5. While purchasing online, what are the goods and services that you generally buy?

6. What are the payment methods you generally use for online purchases?

7. What are the different types of payment options that you have come across?

46 | P a g e

8. Do you feel it is safe to buy online?

9. What are the features that make a website more attractive than the others while buying

online?

10. On an average, how much do you spend while buying online?

11. What is your general experience of buying online as compared to conventional shopping?

12. What additional features, do you feel, would enhance your buying experience on the

internet?

Any additional insights:

Name: Age:

Profession: Sex: M/F

Years since you started using the Internet:

Do you own a credit card? Y/N

Do you use Internet Banking? Y/N

47 | P a g e

Annexure 1b: Final Questionnaire

QUESTIONNAIRE

We would be thankful for your cooperation if you spare a few minutes to answer the

following questions:

1. Do you use the Internet regularly?

Yes No

2. On an average, how much time (per week) do you spend while surfing the Net?

a) 0-2 hours b) 2-6 hours

c) 6-10 hours d) 10-15 hours

e) Greater than 15 hours

3. Do you use E-banking?

Yes No

4. Do you own a credit card?

Yes No

5. I am/would be comfortable buying the following categories of products online:

a) Food & Beverages b) Apparels

c) Electronic Products d) Books

e) Gifts f) Tickets

g) Car or hotel rental h) Pharmaceutical Products

48 | P a g e

Any other product, please specify

6. How frequently do you purchase products/services online?

a) Once a month b) 2-3 times a month

c) Once in 3 months d) Once in 6 months

Any other, please specify

7. What is the average amount that you spend per purchase while shopping online?

a) < Rs 500 b) Rs 500-1000

c) Rs 1000-Rs 2000 d) Rs 2000-Rs 5000

e) > Rs 5000

8. Which of the following web sites do you shop at?

a) www.rediff.com

b) www.indiaplaza.com

c) www.indiatimes.com

d) www.amazon.com

e) www.makemytrip.com

f) www.yatra.com

g)www.ebay.com

Any other, please specify

9. Recall your earlier online buying/shopping experience and please indicate you degree of

agreement with the following statements:

Strongly

Agree

Agre

e

Neither

Agree nor

Disagre

e

Strongly

Disagree

49 | P a g e

Disagree

I prefer making a purchase from

internet than using local malls or

stores

I can get the latest information from

the Internet regarding different

products/services that is not available

in the market.

I have sufficient internet accessibility

to shop online.

Online shopping is more convenient

than in-store shopping.

Online shopping saves time over in-

store shopping.

Online shopping allows me to shop

anywhere and at anytime.

It is safe to use a credit card while

shopping on the Internet.

Online shopping provides me with the

opportunity to get the products

delivered on specific date and time

anywhere as required.

Products purchased through the

Internet are with guaranteed quality.

Internet provides regular discounts

and promotional offers to me.

50 | P a g e

Internet helps me avoid hassles of

shopping in stores.

Cash on Delivery is a better way to

pay while shopping on the Internet.

Sometimes, I can find products online

which I may not find in-stores.

I have faced problems while shopping

online.

I continue shopping online despite

facing problems on some occasions.

It is important for me to touch and

feel certain products before I

purchase them. So I cannot buy them

online.

I trust the delivery process of the

shopping websites.

I do not shop online only because I do

not own a credit card.

Name: Gender: Occupation:

Monthly Income: Education: Marital Status:

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