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.
1 | P a g e
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.
2 | P a g e
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.
3 | P a g e
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
4 | P a g e
L I TE RA T URE
R E VI E W
5 | P a g e
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
6 | P a g e
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
7 | P a g e
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
8 | P a g e
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
9 | P a g e
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
10 | P a g e
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.
11 | P a g e
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.
12 | P a g e
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
13 | P a g e
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.
14 | P a g e
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.
15 | P a g e
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
16 | P a g e
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.
17 | P a g e
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).
18 | P a g e
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.
19 | P a g e
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.
20 | P a g e
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.
21 | P a g e
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-
22 | P a g e
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
23 | P a g e
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-
24 | P a g e
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
25 | P a g e
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
26 | P a g e
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.
38 | P a g e
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.
39 | P a g e
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.
40 | P a g e
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.
41 | P a g e
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.
42 | P a g e
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.
43 | P a g e
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:
51 | P a g e
Top Related