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.. 1 | Page
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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
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.
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.
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.