Online vs Brick and Mortar

ONLINE RETAIL V/S BRICK
AND MORTAR RETAIL

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ABSTRACT:
We have seen the way trade has been conducted over the years. We have seen it evolved from
the age old method of exchange of goods (barter system) to the exchange of currency for
equal value of goods. History has seen the evolution of market places from roadside shops to
the malls we see today. This story of evolution, however, might not have reached its final
chapter. A completely new way of conducting trade has taken the market by storm.
Conducting trade online has become the new way of connecting to the masses. A lot of
companies have taken their entire business online with a view of increasing their reach
globally. Some companies have their main centre of operations online. Even though this
boom in the IT industry has increased the reach of the retail sector and various other
businesses globally we are still unaware of the basic demographics of consumers that are
being catered to.
The research that follows hereof does just that. It also entails the basic factors that affect the
consumer before he purchases a product online. It explores the possibility of how likely are
the consumers willing to completely shift to online retail. This research will help companies
that are at the cross roads of either taking the business online of to continue the tradition way
of doing business come to a decision. This research primarily revolves around the consumer
and his preferences.

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CONTENT:
Sr No. Topic Page Number
1 Introduction 2
2 Methodology
1. Quantitative
2. Sampling
3. Qualitative
4
3 Analysis and results
1. Quantitative
a. Focus group discussion analysis
b. In- depth interview analysis
2. Qualitative
a. Regression
b. Factor Analysis
c. Cluster Analysis
7
4 Discussions 19
5 Limitations 21
6 Future scope 21
7 Literature review 22
8 Bibliography 26

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INTRODUCTION:
Retail is one of the most important pillar in the Indian economy. It contributes 10% to the
GDP of the country and employs 8% of the total population. Out of the total retail industry
the organised sector only contributes to 8% of the total industry while the remaining 92%
belongs to the unorganised sector. As of 2012 India was ranked as the 5th
most lucrative
country to invest in for retail by A T Kearney. Even though the increase in population does
make India lucrative for retail, the dire lac of infrastructure does not act in the favour of the
country.
“Statistically over 14 million outlets operate in the country and only 4 percent of them are
larger than 500sq ft in size. India has 11 shops outlet for every 1000 people. These are
typically family owned and operated stores, which lack the scale to grow. Hence this sector is
in dire need of modernisation.” - Manisha Bapna, Images group
With the boom in the ecommerce sector this scenario is changing. As ecommerce portals
don’t require a physical shop to sell its product the dependency on the infrastructure
availability has reduced. Ecommerce portals like flipkart or amazon has managed to increase
their reach to the Indian population. The development of the internet in the country has
ushered this boom. Ecommerce has not restricted itself to B2C but has also explored into the
C2C (e.g. OLX) business.
A SBI research report has indicated that the ecommerce sector is one of the fastest growing
sectors with a CAGR of 56%. The increase in 3G/4G usage in the country indicated the
increase in the customer base. In addition to this various other factors have contributed to the
growth of the ecommerce sector in India. The disposable income of the Indian population has
increased which has led to an increase in the buying power of an individual. The amount of
time spent online has increased which has in-turn created an awareness among consumers.
Increase in the volume of transaction that occur though plastic money such as credit cards
and debit cards has shown the shifting dependencies from paper money to cashless
transactions.
Our nation is one of the youngest nation in the world. We have a very high percentage of
youth population with nearly 50% of the population below the age of 25 yrs. and 65% below

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the age of 35 yrs. We are a very technology savvy country. In addition to tangible goods
costumers have also begun focusing on the purchase of intangible goods such as Insurances,
travel packs online. Standing in lines for booking tickets have now become a thing of the
past. Success stories of start-ups like RedBus, goIbibo.com and so on are proof to the high
acceptance of consumers for various products that have made their life more convenient. The
consumers are now willing to experiment not only with the type of product but also with the
way it is presented to them.
With an annual growth rate of upwards of 56% it has the potential of growing exponentially
in the future. Inspite of this however online retails have not exactly cemented their foundation
in the minds of the Indian consumers. The huge variance in the mind-sets of the consumer
has been one of the main reasons, why the online retail sector has not been able to gain trust
that the brick and mortar retail establishment have enjoyed. Since the brick and mortar or
traditional retail establishments have been with us since ages. They have only changed forms
from small shops to huge malls. In India we still find a blend of both organised and
unorganised retailors. The physical nature of these retail outlet have since worked in their
favour as they instil a sense of legitimacy in the eyes of the consumers. The Online retail
sector needs to find a way of understanding the customer better. They need to understand
what drives them. They need to understand what the traditional retail outlets are unable to
provide and how can it use it in its favour. Understanding the customer`s preference is crucial
for ecommerce to thrive in the country, without which the boom in the ecommerce sector
would be nothing but a bubble.
METHODOLOGY:
The research objective:
‘To analyse the relevance of various factors that drive consumer behaviour towards or away
from E-commerce along with the degree of their relevance’
QUALITATIVE:
We have conducted a Focused group discussion among student of roughly the same age
group. The FGD consisted of 8 members all of whom are well familiar with online shopping.
Prior to the FGD ground rules were set that mainly proposed that the inputs of every member
in the FGD was important. The moderator made it a point that she could get the participation

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from every individual in the FGD. The entire FGD was recorded and the VTR analysis was
done. The analysis and the results of the same are present in the analysis section of this
report.
One in-depth interviews were also conducted, the findings of which have been recorded in a
tabulated form. The record of the same is also present in the analysis section of this report.
The video of the in-depth interview could not be taken at the discretion of the respondent.
The demographics of the respondents for both the FGD and the In-depth analysis have also
been noted in order to check the consistency of the data recorded through the survey.
SAMPLING:
The population of interest: As our research mainly entails the shopping preferences of the
population and how does it affect the buying behaviour when it comes to online shopping, the
primary population of interest is the educated and working professionals in the country. This
is due to the fact that we mainly needed to survey the population that are aware of what
ecommerce is all about. This will reduce biasness as they would have tried purchasing good
online at-least once. It is based on their experience would we be able to record the response in
our survey. We however did not limit ourselves to a single age group as we are well aware
that the advent of E-tailers is contemporary and would be perceived differently by different
age groups.
Sampling method: As mentioned above our population of interest are the population bellow
the age of 65. This nearly account to approximately 70% of the population of the country. In
order to scale down we had shot out the survey for a limited period of time. We had
undertaken a probabilistic sampling approach. The surveys were shot not only within the
college but also was shot out on the social media was mailed to some corporates as well.
MEASUREMENTS AND SCALING:
The two main types of scales that we used was mainly the Likert scale and the Dichronous
scale. Likert scale was used mainly with the view of ease of analysis on SPSS. Dichronous
scale was mainly used for the Gender of the respondent with 0 for male and 1 for female.

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QUANTITATIVE:
A questionnaire has been designed that revolve around the research objective. The survey of
the same has been floated. The major factors that we found out through the FGD and in-depth
interviews that were:
1. The demographics of the consumer
2. The mode of payment
3. The type of product
4. The perception the consumer had with respect to online retail
The survey is shown below in the appendix. We limited the number of questions of the
survey to 11. We noticed that a higher number of questions would lead the respondent to
respond to the later part of the survey with a reduced focus as compared to the initial part of
the survey. We had later removed all the unnecessary questions from the survey and had
compressed the important ones in a concise manner. The question pertaining to the
demographics of the respondent were placed at the end of the survey. The dependent variable
in the survey would be the response to the question ‘would you in the future completely shift
to online shopping?’ while the remaining would act as independent variables. The major
factor that affect the consumer’s decision to buy the product can also be deduced form this
survey. The analysis and result of the same is present in the analysis and result section of this
report.

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ANALYSIS AND RESULTS
QUANTITATIVE ANALYSIS:
Focus Group Discussion – Analysis
Number of member: 8
Number of males: 7
Number of Females: 1
Age group: Generation Y
Profession: Students
QUESTION RESPONSE
Do you trust online shopping Mixed response- half did and half did not
Are the product descriptions online
accurate
Depends on the product category
Do products online have a greater
variety?
Depends on the product category
The products online are cheaper
than retail outlets. Do you agree?
May or may not be. It depends on various factors
like type of product and season.
Is it easier to return products online
or at the retail shops?
It depends on what online shopping portal are we
talking about i.e. it is brand specific
The most used mode of payment
for online purchases?
Most use cash on delivery or debit cards. Very few
would go for online wallets
What are the major factors
affecting your buying behaviour?
Price is the most important factor followed by
variety and discounts. It is a rare occurrence that
anyone would return to the site for completing a
purchase in case item was unavailable before. Home
delivery of course is a must.

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What are you most likely to
purchase online?
It will somewhat depend on the online web site
brand they are purchasing from. Items of high value
like jewellery or expensive electronic items would
not be a suitable choice for online purchase. Books
and low cost gadgets are a go ahead for most. In
case of apparel, the retail sector has a strong edge
because of the physical presence of the product.
Groceries weren’t even considered for an online
purchase.
Would you in future completely
shift to online shopping?
For all types of purchases none of the respondents
were comfortable in doing so. They felt a strong
need of existence of retail stores.
Observations
The key observations through the FGD were as follows:
 A lot of emphasis lay on the brand image of the website that is being chosen for an
online purchase. New or relatively unknown websites do not even make it to the list of
to-visit websites before making an online purchase.
 Every response shall vary w.r.t the type of product. For example- in case of books,
online purchases shall be easier to carry out and consumers are more inclined towards
it. On the other hand, they shall be highly reluctant in buying precious items like
jewellery.
 The physical absence of the product is a major drawback of E-commerce and the
biggest advantage for retail which makes the survival of retail sector a must for the
consumers.
 Lack of trust on E-commerce has been observed.

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In-depth interview analysis
Interview
Name: Arun Reddy
Gender: Male
Age Group: Generation X
Profession: Service sector
Question Response Body language
How was your day? “My day went fine, there was not much
work load in the office. Things went on
smoothly today”
Calm
Can you tell me some of
your hobbies?
“I don’t have very fancy hobbies. I like
watching cricket, and resting whenever I
get the time. From today I guess I might
add giving interviews to my list”
Joyful
Does shopping appeal to
you?
“No not as much. I hate going to malls
and do window shopping. I always have
a pre-fixed plan before I go to shop”
Calm
Have you heard of
ecommerce sites like
flipkart, snapdeal etc?
“Who hasn’t?!” Amused
Have you purchased
anything from these sites?
“Ya I have. The things I buy from these
sites are mostly electronics. Pen drives
and other low cost electronics are my
primary choice of product.
Reflecting
Do you trust these sites? “Considering what I normally buy online
it is anytime better than going out to buy
a pen drive. It’s more convenient.”
Calm
Have you had to replace any
product?
“So far no.” Thankful
How do you imagine the
process would be?
“Tiresome, in short. I guess I would have
to call the call centre from there they
might send someone to replace the
product.”
Clam with a
slight sense of
irritation
What mode of payment do
you use?
“Now that depends on the price of the
product. Something below 1000 rupees I
would use the debit card. On the other
hand for something above that I would
use COD”
Clear in thought
How much do you trust
Ecommerce?
“It has made life convenient. I’m no
computer Guru but the cybercrimes that
we hear on the news now-a-days doesn’t
instil confidence either.”
Cautious

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What goes through your
mind once you receive your
pakage?
“It feels like Diwali has come early this
year. No matter how many times I buy
online I still have some level of
excitement when I unwrap the product”
Happy
Observations:
 It is pretty apparent that professionals are well aware of E-taillers.
 They are however unaware of the functioning of the same.
 They are still paranoid when it comes to transacting huge amounts online.
 E-tails appeal to working professionals because of their convenience.
 They are preferred for buying cheap electronic rather than going out to the store to
get the same.
SURVEY ANALYSIS:
The sample size off the analysis is 58 respondents. The pool of respondents were both from
the college and the social media. Some of these respondents were also part of corporations
with 20+ years of work experience in their respective fields.
Demographics: Out of the pool or respondents 44 were male while 14 were female. In terms
of their age group 51 belonged to Generation Y (those born in between 1980 and 2000), 3
belonged to Generation X (those born in between 1965 and 1980) while the remaining 4
belonged to Baby boomers (those born in between 1946 and 1965). The graphs bellow better
explain the segmentation of the sample.

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REGRESSION: There are 25 variables in all as stated bellow:
Variable Description
X1 The trust that people have in Ecommerce
X2 The belief in the accuracy of the product description online
X3 Variety in online product offerings
X4 Comparatively cheaper than retail outlet
X5 Ease of return of damaged goods
X6 Mode of payment -Debit Card
X7 Mode of payment -Credit Card
X8 Mode of payment - E-wallets
X9 Mode of payment - Cash
X10 Factors affecting decision - Price
X11 Factors affecting decision - Discounts
X12 Factors affecting decision - Availability
X13 Factors affecting decision - Season
X14 Factors affecting decision - Home delivery
X15 Factors affecting decision - Variety
X16 Product most likely to be purchased online -Electronics
X17 Product most likely to be purchased online - Clothes
X18 Product most likely to be purchased online - Groceries

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X19 Product most likely to be purchased online - Jewellery
X20 Product most likely to be purchased online - Apparels
X21 Product most likely to be purchased online - Travel packs
X22 Product most likely to be purchased online - Books
X23 Gender
X24 Generation
X25 Future scope of usage
The variables defined above are based on the responses of the survey conducted. The
responses were based on a 5-point Likert scale with an exception of X23 (Gender) and X24
(Generation)
The regression analysis was done between the perception based variables and the future
scope of using online retail. Here X25 is the Dependent Variable while variables X1, X2, X3,
X4 and X5 are the Independent Variables. A linear regression of the above variable reviled
the following.
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 22.978 5 4.596 9.389 .000b
Residual 25.453 52 .489
Total 48.431 57
a. Dependent Variable: X25 Future scope
b. Predictors: (Constant), X5 Return of damaged goods, X3 Variety, X2 Description, X4 Cheap online product,
X1 Trust factor
Here the null Hypothesis: H0 = There is no linear correlation
Alternate Hypothesis: Ha = There is some linear correlation
As we can see that the significance is less than 0.005 our hypothesis is accepted and the null
hypothesis has been rejected. This shows that there is some linear correlation between the
selected variables which is further supported by our analysis below.

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Correlations
X25
Future
scope
X1 Trust
factor
X2
Descriptio
n
X3
Variety
X4 Cheap
online
product
X5 Return
of
damaged
goods
Pearson
Correlation
X25 Future scope 1.000 .573 .476 .247 .357 .527
X1 Trust factor .573 1.000 .361 .406 .352 .431
X2 Description .476 .361 1.000 .114 .264 .358
X3 Variety .247 .406 .114 1.000 .188 .260
X4 Cheap online
product
.357 .352 .264 .188 1.000 .458
X5 Return of
damaged goods
.527 .431 .358 .260 .458 1.000
Sig. (1-tailed)
X25 Future scope . .000 .000 .031 .003 .000
X1 Trust factor .000 . .003 .001 .003 .000
X2 Description .000 .003 . .196 .023 .003
X3 Variety .031 .001 .196 . .079 .024
X4 Cheap online
product
.003 .003 .023 .079 . .000
X5 Return of
damaged goods
.000 .000 .003 .024 .000 .
N
X25 Future scope 58 58 58 58 58 58
X1 Trust factor 58 58 58 58 58 58
X2 Description 58 58 58 58 58 58
X3 Variety 58 58 58 58 58 58
X4 Cheap online
product
58 58 58 58 58 58
X5 Return of
damaged goods
58 58 58 58 58 58
As viewed above its pretty apparent that the consumers that have a high trust factor will be
less reluctant to completely switch to online retail in the future. It can also be noted that those
with a high perception on the return of goods of online retail, are less reluctant to completely
shift to online retail completely. Cheap online products and accurate description of the
products also form an important parameter on the basis of which the consumer would tend to
shift completely too online trade. Variety however as compared to the rest has a relatively
low correlation with the future prospect of the consumer.
Based on the following table we can also determine the regression equation for X25

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Coefficients
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) .365 .608 .601 .550
X1 Trust factor .365 .127 .356 2.874 .006
X2 Description .256 .119 .240 2.151 .036
X3 Variety -.003 .127 -.003 -.027 .978
X4 Cheap online product .043 .106 .047 .405 .687
X5 Return of damaged
goods
.206 .095 .267 2.177 .034
a. Dependent Variable: X25 Future scope
X25 = 0.365 + 0.365*X1 + 0.256*X2 - 0.003*X3 + 0.043*X4 + 0.206*X5
The above table further cements our analysis that X1, X2, X3, X4 and X5 are the variables
that form a major parameter that will affect the consumer’s decision to completely shift to
online retail in the long run.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .689a
.474 .424 .700
a. Predictors: (Constant), X5 Return of damaged goods, X3 Variety, X2
Description, X4 Cheap online product, X1 Trust factor
Further based on the above table we can say that 47.4% of the variability in the DV can be
explained by the IV`s selected.
FACTOR ANALYSIS: Every variable that has been defined in the survey has some common
factors with other variables. The use of factor analysis will help us determine the number of
common factors within the variables. It will also help us understand the role of these factors
with all these variables. We carried out a factor analysis or also known as a “hopper analysis”
from variables X1 through X22.
Based on the results below we can infer that out of 22 possible factors only 9 factors were
extracted.

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Total Variance Explained
Compon
ent
Initial Eigenvalues Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total % of
Variance
Cumulativ
e %
Total % of
Variance
Cumulativ
e %
Total % of
Variance
Cumulativ
e %
1 3.807 17.303 17.303 3.807 17.303 17.303 2.490 11.318 11.318
2 2.700 12.275 29.578 2.700 12.275 29.578 2.323 10.560 21.878
3 1.826 8.298 37.876 1.826 8.298 37.876 1.970 8.956 30.834
4 1.708 7.764 45.639 1.708 7.764 45.639 1.905 8.661 39.495
5 1.601 7.276 52.915 1.601 7.276 52.915 1.822 8.284 47.779
6 1.327 6.033 58.948 1.327 6.033 58.948 1.607 7.303 55.082
7 1.209 5.494 64.442 1.209 5.494 64.442 1.504 6.837 61.919
8 1.108 5.035 69.477 1.108 5.035 69.477 1.354 6.154 68.072
9 1.035 4.703 74.180 1.035 4.703 74.180 1.344 6.107 74.180
10 .953 4.331 78.510
11 .787 3.578 82.089
12 .637 2.896 84.985
13 .571 2.595 87.580
14 .517 2.352 89.932
15 .487 2.212 92.144
16 .389 1.770 93.914
17 .350 1.593 95.506
18 .296 1.344 96.850
19 .260 1.183 98.033
20 .186 .846 98.879
21 .141 .643 99.522
22 .105 .478 100.000
Extraction Method: Principal Component Analysis.
The factors were later rotated (Varimax rotation) in order to reduce the load on any one
single factor.
CLUSTER ANALYSIS: The entire sample was divided into 3 clusters using Ward’s
technique. We mainly carried out the cluster analysis to determine the demographic
distribution within the newly formed clusters. This would later prove as a guide while
selecting samples for future research on the same subject.
The following table displays the segregation of each cluster:

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Cluster Distribution Count
1 11
Generation Y (Born between 1981 and 2000) 11
Male 11
2 18
Baby boomers (Born between 1946 and 1964) 4
Female 2
Male 2
Female 3
Male 11
3 29
Generation X (Born between 1965 and 1980) 3
Female 1
Male 2
Female 8
Male 18
Grand Total 58
Cluster1: Consists of 11 members, all of whom are Male and belong to Generation Y.
Cluster2: Consists of 18 members, 4 of which belong to Baby Boomers while the rest belong
to Generation Y. The baby boomers consists of 2 males and 2 Females, while Generation Y
Consists of 3 Males and the remaining female.
Cluster3: Consists of 29 members 3 of which belong to Generation X while the remaining
belong to Generation Y. Generation X consists of 1 female and 2 males while Generation Y
consists of 8 Female and 18 males.
Further analysis of the cluster reveals the following results:
Based on the table below we can interpret that variables X1, X5, X6, X7, X9, X17, X18,
X19, and X21 have played a significant role in the determination of the clusters.
ANOVA
Sum of Squares df Mean Square F Sig.
X1 Trust factor
Between Groups 9.987 2 4.994 7.608 .001
Within Groups 36.099 55 .656
Total 46.086 57
X2 Description
Between Groups 3.542 2 1.771 2.505 .091
Total 42.431 57
X3 Variety Between Groups 2.845 2 1.422 2.292 .111

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Total 36.983 57
X4 Cheap online product
Between Groups 5.335 2 2.668 2.785 .070
Total 58.017 57
X5 Return of damaged goods
Between Groups 14.288 2 7.144 5.879 .005
Within Groups 66.833 55 1.215
Total 81.121 57
X6 Credit Cards
Between Groups 59.616 2 29.808 16.652 .000
Total 158.069 57
X7 Debit Card
Between Groups 22.366 2 11.183 6.417 .003
Total 118.224 57
X8 E wallet
Between Groups 8.346 2 4.173 2.813 .069
Total 89.931 57
X9 Cash
Between Groups 31.551 2 15.776 10.224 .000
Total 116.414 57
X10 Price
Between Groups .527 2 .264 .562 .573
Total 26.345 57
X11 Discounts
Between Groups .508 2 .254 .282 .755
Total 50.017 57
X12 Availability
Between Groups 1.426 2 .713 .862 .428
Total 46.897 57
X13 Season
Between Groups .782 2 .391 .332 .719
Total 65.655 57
X14 Home dilivery
Between Groups .269 2 .134 .204 .816
Total 36.431 57
X15 Variety
Between Groups .236 2 .118 .248 .782
Total 26.483 57
X16 Gadgets
Between Groups 8.236 2 4.118 4.042 .023
Total 64.276 57
X17 Clothes
Between Groups 53.242 2 26.621 27.635 .000

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Total 106.224 57
X18 Groceries
Between Groups 20.876 2 10.438 8.314 .001
Total 89.931 57
X19 Apparel
Between Groups 71.506 2 35.753 56.193 .000
Total 106.500 57
X20 Online Booking
Between Groups .470 2 .235 .166 .848
Total 78.569 57
X21 Jewellery
Between Groups 20.201 2 10.101 6.826 .002
Total 101.586 57
X22 Books
Between Groups .671 2 .336 .303 .740
Total 61.517 57
X23 Gender
Between Groups .803 2 .401 2.248 .115
Total 10.621 57
X25 Future scope
Between Groups 6.487 2 3.244 4.253 .019
Total 48.431 57
DISCUSSIONS:
Based on the FGD we found out that
 Where most have trust issues with E-commerce, they would still look at price variations
across different websites and also between retail and online, in case of the latter being
cheaper, they would gladly go for it.
 If a product is unavailable on a particular website, the consumers tend to look up to
other websites and buy the product from elsewhere even at small price variations
implying that brand loyalty isn’t much in E-commerce.
 Consumers tend to trust some brands over the others and resort to only those selected
web sites making brand recognition and trust establishment a major task in E-
commerce.
The regression analysis carried out based on the consumer survey led us to some of the
following observations:

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 The perception of the consumer plays a vital role in the consumer`s decision to
completely shift to the online retail sector in the long term.
 In a developing country like India price has always played a major factor in selection
of products. The same is true even for the online market place. If online retail giants
manage to continue to keep the price low they will be able to gain continuous support
from the consumers.
 However the FGD had brought to light the importance of the variety in products that
the online retail portals provide, the role of the variety in the product is
overshadowed by various other factors such as ease of replacement, cost of the
product and the description of the product online.
 Since the online retail outlet is a completely intangible marketplace, the integrity of
the description needs to be maintained at all times. Lac of physical evidence of the
product that needs to be purchased is one of the major drawback in the E-tail sector.
The only way to overcome this disadvantage is to maintain a high level of accuracy
in the description of the product. The description of the product is as close as the
customer can get to its physical evidence.
 Another main factor that has to be addressed with caution is the timely replacement
of goods. Traditional outlets due to their physical presence create a perception of ease
in the buyers mind when it comes to replacement of products. In the E-tail world the
shopkeeper who addresses the issues the customers face has been replaced by a
lifeless screen or a voice on the phone. In order to gain the customer`s trust and to
last and thrive in the business, prompt replacement of the damaged goods should be
given high importance.
The Factor and cluster analysis has led us to the following observations:
 The common factor among the variables have reduced to 9 from 22. There are 9
primary common factors among the 22 variables in question.
 Take for instance, the trust that a consumer has in a particular online retail portal and
the decision of purchasing clothes and apparel online have the highest weights in
factor1. Their high loads on factor1 show that change in the value of the factor will
lead to a great change in the 3 variables.

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 This can also be interpreted in a more simple way. Lac of physical evidence on the
online retail marketplace need a high level of trust on the side of the consumer to
purchase clothes and apparels.
 Similarly we can see the high load of availability, home delivery and variety on
factor2, thus explaining the high shift in the value of the variable for a minor shift in
the factor value. This factor thus can be taken to revolve around the convenience of
shopping.
 Availability of a variety of products that are ready to be delivered at your door-step
is also one of the major factor that could pull consumers towards the online retail
sector.
 Based on the cluster analysis one can say that the trust that a consumer has, the ease
of return of damaged goods and the mode of payment have proved to be a significant
parameter in segregating the population into clusters.
 Throughout the entire analysis that has been carried out the individual trust and the
ease of replacement of damaged good’s have proved to be of paramount importance
to the customer.
 E-tailers need to acknowledge the importance of service for the consumer and the
importance of the trust that they need to gain.
 The Indian consumer has not yet explored the possible usage of E-wallets. They still
rely on cash and plastic money for their day to day transaction. Since E-wallets have
not been fully explored by the Indian consumers E-tailers can bank on this
opportunity and propagate the same for their own benefit.
 The Indian population at large is still quite apprehensive of trusting online purchases
completely and also believe strongly in the physical presence of products posing a
huge challenge for the growth of E-commerce.
LIMITATIONS:
Time was one of the major factor due to which the number of responses to the survey that we
received have been limited to 58. Increase in the number of response would reduce the
variance in the responses.
The effect of advertisements on the buying behaviour of the consumer has not been covered
in the survey. This variable was not included based on the FGD and the in-depth interview
that was carried out.

P a g e | 22
Another important factor that we failed to include was the effect of the appearance of the site
on the shopping behaviour of the consumers.
FUTURE SCOPE:
This research has covered the various factors that one needs to consider before entering into
the E-tail sector. Those who are already in the Ecommerce sector can use this research as a
reference. They can use this research to revamp their supply chain. They can also use this to
back up some of their claims that would otherwise have no foundation. For example the
increase in trust on the online sector can help increase the sale of clothes and apparel. Based
on the analysis shown above E-tailers can now focus on building their image as one of the
most trust-worthy brand in the market. They can increase their investment in CSR activities.
They can also market their products based on the demographics of the customers. As seen
above low prices are one of the main advantages of online retailers. In order to maintain their
low prices the online retailers will have to re-engineer their operations and make it as
efficient as possible.
This article can form the basis of future research in the Ecommerce sector. Ecommerce is
relatively new and the respondents of the survey today may in the future answer the same
survey in an entire different manner. This research can form a benchmark based on the data
of the present.
LITERATURE REVIEW:
 “Trust and TAM in online shopping: an integrated model” – by David Gefen, Elenna
Karahanna and Detmar W. Straub
The online purchase intentions are the product of both consumer assessments of the
IT itself-specifically its perceived usefulness and ease-of-use (TAM)-and trust in the e-
vendor. The study is based on assessing the trust that the consumer has on the e-
vendor.
 “Developing and Validating Trust Measures for e-Commerce: An Integrative
Typology” – by D. Harrison McKnight, Vivek Choudhury and Charles Kacmar
Nearly 95% of consumers avoid filling their personal data online due to lac of trust on
the ones collecting data. The perceived benefits that technology bring is not enough
for the consumer to comply with the function of the e-vendor. Trust in the e-vendor is
of paramount importance for the customer.
 “Acceptance of E-Commerce Services: The Case of Electronic Brokerages” – by Anol
Bhattacherjee

P a g e | 23
 “Customer Loyalty in E-Commerce” – by David Gefen
Creating online customer loyalty or retaining existing customers is a necessity for
online vendors. This study examines whether this goal can be achieved to some
degree through increased customer trust to the feeling of assurance brought about
through superior service quality. The study also examines which aspects of service
quality contribute to this trust in an online environment
 “A Trust Model for Consumer Internet Shopping” – by Mathew K.O. Lee
This article revolves around developing a model to gauge the consumer`s trust
towards the ecommerce vendors. E-commerce success, especially in the business-to-
consumer area, is determined in part by whether consumers trust sellers and products
they cannot see or touch, and electronic systems with which they have no previous
experience.
 “"Trust me, I'm an online vendor": towards a model of trust for e-commerce system
design” – by Florian N. Egger
Consumers' lack of trust has often been cited as a major barrier to the adoption of
electronic commerce (e-commerce). To address this problem, a model of trust was
developed that describes what design factors affect consumers' assessment of online
vendors' trustworthiness. Six components were identified and regrouped into three
categories: Prepurchase Knowledge, Interface Properties and Informational
Content. This model also informs the Human-Computer Interaction (HCI) design of e-
commerce systems in that its components can be taken as trust-specific high-level
user requirements.
 “A Web assurance services model of trust for B2C e-commerce” – by SE Kaplan
and RJ Nieschwietz
The results show that Web assurance services create trust both through the assurances
they attest to and their individual provider attributes. The formation of trust is
important, as it is shown to influence various outcomes, including consumers'
willingness to purchase products. Additionally, both assurances and provider
attributes have some residual effect on outcomes beyond that shown through the
formation of trust.
 “Consumer trust in e-commerce in the United States, Singapore and China” – by S. H.
Tio and Jing Lui
This research was conducted in the US, Singapore and China. The findings of this
research indicates that reputation and system assurance of an Internet vendor and
consumers’ propensity to trust are positively related to consumer trust. Consumers’
trust has a positive relationship with attitude and a negative relationship with
perceived risk

P a g e | 24
 “An Extended Privacy Calculus Model for E-Commerce Transactions” – by Tamara
Dinev and Paul Hart
While privacy is a highly cherished value, few would argue with the notion that
absolute privacy is unattainable. Individuals make choices in which they surrender a
certain degree of privacy in exchange for outcomes that are perceived to be worth the
risk of information disclosure. The results suggest that although Internet privacy
concerns inhibit e-commerce transactions, the cumulative influence of Internet trust
and personal Internet interest are important factors that can outweigh privacy risk
perceptions in the decision to disclose personal information when an individual uses
the Internet.
 “Reputation and e-commerce: eBay auctions and the asymmetrical impact of positive
and negative ratings” – by Stephen S. Standifird
Positive reputational ratings emerged as mildly influential in determining final bid
price. However, negative reputational ratings emerged as highly influential and
detrimental. Thus, we find strong evidence for the importance of reputation when
engaging in e-commerce and equally strong evidence concerning the exaggerated
influence of negative reputation.
Based on the articles above and the other researches that have taken place over the years one
major factor that comes out is the trust that the consumer has on the E-Tailers. Trust is the
basic criterion that determines the consumer`s loyalty towards ecommerce.
The consumer trust is so volatile that a positive response from the consumer may not affect
the trust of other consumer as would a negative feedback. The influence of a negative
influence on the mind-set of the consumer is way too substantial.
Our research is on the lines of researches that have taken place in the past. Trust does stem
out as a major factor when it comes to customer loyalty. Our research is however based
mainly on the factors that influence the consumers while shopping online and the facilities
that are provided by the E-tailer. The ease of replacement is another factor that had stemmed
out of our research to be one of the major factors that would influence the trust of the
customer and thus indirectly influence their loyalty.
Even though the prior researches that are carried out are based outside India and among
different demographics, trust is one of the major influencer for all the consumers.

P a g e | 25
BIBLIOGRAPHY
 http://www.qualtrics.com/university/researchsuite/research-resources/data-
analysis-guides/advanced-analysis-methods/factor-analysis/
 https://www.hawaii.edu/powerkills/UFA.HTM
 http://www.jblearning.com/samples/0763755486/55485_CH14_Walker.pdf
 http://academic.udayton.edu/gregelvers/psy216/spss/reg.htm
 https://www.quora.com/What-is-the-difference-between-factor-and-cluster-
analyses
 https://statistics.laerd.com/spss-tutorials/linear-regression-using-spss-
statistics.php
 https://statistics.laerd.com/features-overview.php
 https://people.richland.edu/james/ictcm/2004/weight.html
 https://statisticsbyrachel.wordpress.com/2012/02/19/sampling-techniques/
 http://economictimes.indiatimes.com/industry/services/retail/indias-ecommerce-
market-to-breach-100-billion-mark-by-fy20-goldman-
sachs/articleshow/49532128.cms
 http://www.ey.com/IN/en/Industries/Technology/Re-birth-of-e-Commerce-in-
India
 http://yourstory.com/2015/10/e-commerce-sbi/
 http://www.economywatch.com/business-and-economy/indian-retail-
contribution.html

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