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

Generation Y (Born between 1981 and 2000) 14 Female 3 Male 11

3 29

Generation X (Born between 1965 and 1980) 3 Female 1 Male 2

Generation Y (Born between 1981 and 2000) 26 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

Within Groups 38.889 55 .707

Total 42.431 57

X3 Variety Between Groups 2.845 2 1.422 2.292 .111

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Within Groups 34.138 55 .621

Total 36.983 57

X4 Cheap online product

Between Groups 5.335 2 2.668 2.785 .070

Within Groups 52.682 55 .958

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

Within Groups 98.453 55 1.790

Total 158.069 57

X7 Debit Card

Between Groups 22.366 2 11.183 6.417 .003

Within Groups 95.858 55 1.743

Total 118.224 57

X8 E wallet

Between Groups 8.346 2 4.173 2.813 .069

Within Groups 81.585 55 1.483

Total 89.931 57

X9 Cash

Between Groups 31.551 2 15.776 10.224 .000

Within Groups 84.862 55 1.543

Total 116.414 57

X10 Price

Between Groups .527 2 .264 .562 .573

Within Groups 25.817 55 .469

Total 26.345 57

X11 Discounts

Between Groups .508 2 .254 .282 .755

Within Groups 49.509 55 .900

Total 50.017 57

X12 Availability

Between Groups 1.426 2 .713 .862 .428

Within Groups 45.471 55 .827

Total 46.897 57

X13 Season

Between Groups .782 2 .391 .332 .719

Within Groups 64.873 55 1.180

Total 65.655 57

X14 Home dilivery

Between Groups .269 2 .134 .204 .816

Within Groups 36.162 55 .657

Total 36.431 57

X15 Variety

Between Groups .236 2 .118 .248 .782

Within Groups 26.246 55 .477

Total 26.483 57

X16 Gadgets

Between Groups 8.236 2 4.118 4.042 .023

Within Groups 56.040 55 1.019

Total 64.276 57

X17 Clothes Between Groups 53.242 2 26.621 27.635 .000

Within Groups 52.982 55 .963

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Total 106.224 57

X18 Groceries

Between Groups 20.876 2 10.438 8.314 .001

Within Groups 69.055 55 1.256

Total 89.931 57

X19 Apparel

Between Groups 71.506 2 35.753 56.193 .000

Within Groups 34.994 55 .636

Total 106.500 57

X20 Online Booking

Between Groups .470 2 .235 .166 .848

Within Groups 78.099 55 1.420

Total 78.569 57

X21 Jewellery

Between Groups 20.201 2 10.101 6.826 .002

Within Groups 81.385 55 1.480

Total 101.586 57

X22 Books

Between Groups .671 2 .336 .303 .740

Within Groups 60.846 55 1.106

Total 61.517 57

X23 Gender

Between Groups .803 2 .401 2.248 .115

Within Groups 9.818 55 .179

Total 10.621 57

X25 Future scope

Between Groups 6.487 2 3.244 4.253 .019

Within Groups 41.944 55 .763

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.

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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

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“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

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“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.

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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