impact of online (e-shop, teleshopping) &physical shopping ... · declaration i hereby declare...

Impact of Online (E-Shop, Teleshopping) &Physical

Shopping Patterns of Select Fast Moving Consumer

Goods (FMCG) on Working Women in Select

Tier 1 Cities of India.

Thesis Submitted To The

D. Y. Patil University,

School of Management

In Partial Fulfilment of the

requirements for the award of the Degree of

DOCTOR OF PHILOSOPHY

IN

BUSINESS MANAGEMENT

Submitted by :

ROSHNI SAWANT

(Enrolment No: DYP-PHD0702030)

RESEARCH GUIDE:

PROFESSOR. Dr. PRADIP MANJREKAR

D .Y .PATIL UNIVERSITY,

SCHOOL OF MANAGEMENT

NAVI MUMBAI – 400 614

SEPTEMBER-2015

Impact of Online (E-shop, Teleshopping) & Physical

shopping pattern of select Fast moving Consumer

goods (FMCG) on working women in select Tier 1

cities of India.

DECLARATION

I hereby declare that the thesis titled “Impact of Shopping patterns Online(E-shop,

Teleshopping)&Physical of select Fast moving Consumer goods (FMCG) on

working women in select Tier 1 cities of India.” submitted for the Award of Doctor

of Philosophy (PhD) in Business Management at D.Y.Patil University, School of

Management is my original work and the thesis has not formed the basis for the award

of any degree, associate ship, fellowship or any other similar titles.

The material borrowed from other sources are incorporated in the thesis has been duly

acknowledged. I understand that I myself could be held responsible for plagiarism, if

any detected later on.

The research papers published based on the research conducted out of and in the

course of study are also based on the study and not borrowed from other sources.

Date: 23rd

September 2015

Place: Navi Mumbai. _________________________

Signature of Candidate

Roshni Sawant

Ph.D. Scholar

(Enrolment no: DYP-PHD-0702030)

CERTIFICATE

This is to certify that the thesis entitled “Impact of Shopping patterns Online(E-

shop, Teleshopping) & Physical of select Fast moving Consumer goods (FMCG)

on working women in select Tier 1 cities of India.” is a bonafide research work

carried out and submitted by Ms. Roshni Sawant, PhD Scholar at the D. Y. Patil

University School of Management, Navi Mumbai in partial fulfilment for the award

of the Doctor of Philosophy in Business Management and that the dissertation has not

formed the basis for the award previously of any degree, diploma, associate ship,

fellowship or any other similar title of any University or Institution.

Also certified that the dissertation represents an independent work on the part of the

candidate.

__________________________

Signature

Prof. Dr. Pradip Manjrekar)

Research Guide

School of Management

D.Y.Patil University, Navi Mumbai

Date: 23rd

September 2015

Place: Navi Mumbai

ACKNOWLEDGEMENT

I sincerely thank those who provided me support throughout my life, especially during

the years of my association with D.Y. Patil University, Navi Mumbai for my Doctoral

studies.

I am indebted to D.Y. Patil University, Navi Mumbai and the School of Management

for giving me this great opportunity to pursue my doctoral studies under its protective

wings. Firstly would like to thank our beloved Dada Saheb (Padmashree Dr D.Y.Patil)

and Aaji Saheb (Late Smt. Pushpalatatai D Patil) for giving me platform I would

further thank

Dr Ajeeknya D Y Patil & Mrs. Pooja Patil for being strong pillars. My deepest thanks

to beloved Dr.Priya Patil Cholera who was my motivator and strong supporter at every

step.

I thank Dr. Pradip Manjrekar, Guide who inspired and encouraged me to complete my

work. My heartfelt gratitude is due for his scholarly guidance, constant availability. I

am highly indebted to him for this work of mine and the personal growth in me. I

would like to convey special thank to Prof. Venkatramani, Registrar of D.Y.University

Navi Mumbai for his unmatched human concern and wholehearted support. I also

thank Dr R.Gopal, my colleagues from D Y Patil School of Management for helping

me directly and indirectly.

I express my thanks to my beloved family members Mrs. Ishwari Sawant (Mother),

Mr.Prakash Sawant (Father), Mrs. Nirmala Devi(Mother in law), Mr.Arjun Prsasd

(GrandFather) , Mr. Abhishek (Husband), Ms.Naisha (Daughter) , Mrs. Richa &

Mr.Aatish Kumar for being source of inspiration and continuous support in my

success.

Date: 23rd September 2015

Place: Navi Mumbai ______________________

Signature of candidate

Roshni Sawant

(Enrolment no: DYP-PHD-0702030)

I

CONTENTS Chapter

no.

Sub

sections

Chapter

Page

no.

TABLE OF CONTENTS I

LIST OF TABLES IV

LIST OF CHARTS AND DIAGRAMS VII

LIST OF ABBREVIATIONS 1X

EXECUTIVE SUMMARY 1

1 INTRODUCTION 26

1.1 Shopping patterns of working women 27

1.2 Working women and FMCG 29

2 SHOPPING PATTERNS OF WORKING

WOMEN

31

2.1 Demand drivers of changing shopping pattern of

working women 32

2.2 Types of shopping patterns 36

2.3 Physical shopping pattern 37

2.4 Steps in the physical shopping pattern 37

2.5 Physical shopping formats 39

2.6 Online / E-shopping 44

2.7 Advantages of online shopping 45

2.8 Impact online shopping on India shoppers 48

2.9 Telephonic shopping 49

2.10 Psychology of working women during telephonic

shopping 51

2.11 Shopping behaviour of working women 52

2.12 Working women’s decision making process 53

II

3 WORKING WOMEN 60

3.1 Economic status of working women 61

3.2 Problems faced by working women 62

4 FAST MOVING CONSUMER GOODS (FMCG ) 64

4.1 Introduction of FMCG 64

4.2 Characteristics of FMCG 69

4.3 Outlook of FMCG 70

4.4 FMCG during recession 71

4.5 Tier 1 cities of India 71

5 LITERATURE REVIEW 73

5.1 Physical shopping 73

5.2 Attributes of shopping approach 77

5.3 Online shopping 81

5.4 Important aspects of online shopping 85

5.5 Benefits of online shopping 86

5.6 Uniqueness of online shopping 87

5.7 Online shopping pattern 88

5.8 Working women 91

5.9 Working women shopping pattern 94

5.10 Fast moving consumer goods (FMCG) 99

5.11 FMCG product cosmetic 100

5.12 Research gap 118

6 OBJECTIVES & HYPOTHESIS OF STUDY

6.1 Objectives of study

119

6.2 Hypothesis of study 119

III

7 RESEARCH METHODOLOGY & DATA

COLLECTION 121

7.1 Demographic factors considered 121

7.2 Sample technique 122

7.3 Sample size calculation 123

7.4 Reliability statistics 123

7.5 Limitations of study 124

8

DATA ANALYSIS & VALIDATION OF

HYPOTHESIS 125

8.1 Classification of demographic factors 126

8.2 Analysis of online shopping pattern for five FMCG 130

8.3 Analysis of physical shopping pattern for five

FMCG 138

8.4 Descriptive statistics online & physical shopping 144

8.5 Testing of Hypothesis - 1 147

8.6 Testing of Hypothesis - 2 155

8.7 Testing of Hypothesis – 3 161





8.12 Summary of hypothesis validation 189

9 RESULTS & DISCUSSIONS 192

10 CONCLUSIONS 198

11 RECOMMENDATIONS 201

12 BIBLIOGRAPHY 204

Annexure – 1 Questionnaire 210

Annexure – 2 SPSS output 217

IV

LIST OF TABLES

Table No. Description

Page

no.

2.1 Shoppers Buying Process 54

4.1 FMCG considered in study, which women shop online

(e-shopping and teleshopping) and physical.

66

4.2 Classification of population city (tier-wise) 71

7.1. Population of working women in tier 1 cities 123

7.2 Reliability statistics 123

8.1.1 Respondents on basis of City 126

8.1.2 Respondents on basis of Age 127

8.1.3 Respondents on basis of qualification 128

8.1.4 Respondents as per income of working women 129

8.2.1 Respondents for dairy Products (Online)

131

8.2.2 Respondents for toiletries product (Online)

132

8.2.3 Respondents for packed grocery product (Online)

134

8.2.4 Respondents for cosmetic product (Online)

135

8.2.5 Respondents for packed frozen product (Online)

140

8.3.1 Respondents for dairy products (Physical)

138

8.3.2 Respondents for toiletries product (Physical)

140

8.3.3 Respondents for packed grocery product (Physical)

141

8.3.4 Respondents for cosmetic product (Physical)

142

8.3.5 Respondents for packed frozen product (Physical)

143

8.4.1 Descriptive Statistics (Online Shopping)

144

8.4.2 Descriptive statistics (Physical Shopping)

146

8.5.1 Overall online shopping mean score city wise

147

V

8.5.2 Overall online shopping level

153

8.5.3 City wise online shopping level cross tabulation 149

8.5.4

Chi-Square tests for online shopping city wise

150

8.5.5 ANOVA overall online shopping score

150

8.6.1 Overall physical shopping mean score city

151

8.6.2 Overall physical shopping level

152

8.6.3 City wise overall physical shopping level cross

tabulation

153

8.6.4 Chi-Square tests for physical shopping city wise

154

8.6.5 ANOVA overall physical shopping score

164

8.7.1 Monthly income overall online shopping level cross

tabulation

155

8.7.2 Chi-Square tests

156


157

8.7.4 Overall online shopping score

157

8.8.1 Monthly income overall physical shopping level cross

tabulation

158

8.8.2 Chi-Square Tests 159

8.8.3 ANOVA overall physical shopping score 160

8.8.4

Overall physical shopping score

160

8.9.1 Correlations

161

8.10.1 Overall online shopping level cross tabulation 163



165

8.10.4 Mean score of overall online shopping 166

8.11.1 Nature of working industry overall online shopping

level cross tabulation

168

VI


8.11.3 ANOVA overall online shopping score 170

8.11.4 Mean score overall online shopping 171

8.12.1 Nature of working industry overall physical shopping

level Cross tabulation

172

8.12.2 Chi-Square tests 173

8.12.3 ANOVA Overall physical shopping score 174

8.12.4 Mean score Overall physical shopping 175

8.13.1 Age group overall online shopping level cross

tabulation

176




8.14.1 Age group overall physical shopping level cross

tabulation

179



8.14.4 Mean score overall physical shopping 181

8.15.1 Qualification overall online shopping level cross

tabulation

182




8.16.1 Qualification physical shopping level crosstab 186



8.16.4 Mean score overall physical shopping 188

8.17 Summary of Hypothesis 189

VII

LIST OF CHARTS / DIAGRAMS

Chart

Number

Description Page

no

8.1.1 Respondents city wise 127

8.1.2 Respondents age wise 128

8.1.3 Respondents qualification wise 129

8.1.4 Respondents income wise 130

8.2.1 Respondents for dairy products (Online) 132

8.2.2 Respondents for toiletries product (Online) 133

8.2.3 Respondents for packed grocery product (Online) 135

8.2.4 Respondents for cosmetic product (Online) 136

8.2.5 Respondents for packed frozen product (Online) 137

8.3.1 Respondents for dairy products (Physical) 143

8.3.2 Respondents for toiletries product (Physical) 139

8.3.3 Respondents for packed grocery product (Physical) 140

8.3.4 Respondents for cosmetic product (Physical) 141

8.3.5 Respondents for packed frozen product (Physical) 142

8.4.1 Mean Score of online shopping 145

8.4.2 Mean Score of physical shopping 146

8.5.1 Overall online shopping mean score city wise 147

8.5.2 Overall online shopping level 148

8.5.3 City wise overall online shopping level 149

8.6.1 Overall physical shopping score 151

8.6.2 Overall physical shopping level 152

8.6.3 Overall physical shopping level city wise 153

8.7.1 Overall online shopping level monthly income wise 155

8.7.2 Overall online shopping score 156

8.8.1 Overall physical shopping level monthly income wise 158

8.8.2 Overall physical shopping score 159

8.9.1 Scattered plot for online shopping w.r.t cost 161

VIII

effectiveness

8.10.1 Overall online shopping on basis of quality 164

8.10.2 Overall online shopping mean score on basis of quality 165

8.10.3 Scattered plot for quality of product w.r.t online

shopping

166

8.10.4 Scattered plot for quality of product w.r.t physical

shopping

167

8.11.1 Overall online shopping level on basis of nature of

working industry

168

8.11.2 Overall online shopping mean score industry wise 170

8.12.1 Overall physical shopping level industry wise 173

8.12.2 Overall physical shopping mean score industry wise 174

8.13.1 Overall online shopping age wise 176

8.13.2 Overall online shopping score age wise 177

8.14.1 Overall physical shopping level age wise 179

8.14.2 Overall physical shopping score age wise 180

8.15.1 Qualification overall online shopping 182

8.15.2 Overall online shopping mean score qualification wise 184

8.16.1 Overall physical shopping level qualification wise 186

8.16.2 Overall physical shopping mean score qualification wise 187

IX

LIST OF ABBREVIATIONS

FMCG Fast Moving Consumer Goods

AMA American Marketing Association

CPG Consumer Packed Goods

FDA Federal Drug Administration

IMRB Indian Market Research Bureau

SEM Search Engine Marketing

BCG Boston Consultancy Group

ICT Information and Communication Technologies

GDP Gross Domestic Product

KFC Kentucky Fried Chicken

C&C Cash & Carry

SKU Stock Keeping Units

MBO Multi Brand outlets

EBO Exclusive Brand Outlets

MNC Multi National Company

IRCTC Indian Railway Catering and Tourism Corporation

GPS Global Positioning System

HRA House Rent Allowance

ANOVA Analysis of Variances

1

EXECUTIVE SUMMARY

The thorough study reveals that there have been concentrated studies on shopping

pattern of consumer goods and working women. The intention of the researcher is to

study the impact of types of shopping patterns Online (E-shop, Teleshopping)

&Physical of select Fast moving Consumer goods (FMCG) on working women in

select Tier 1 cities of India like Mumbai, Delhi, Bangalore and Hyderabad.

Working women‟s level of participation in the work force have focused attention on

changing life-styles and consumption patterns. A set of intervening variables

reflecting today‟s working women's attitudes toward food preparation explains their

food shopping behaviour better than either a working/nonworking classification or

general role orientations. It is not possible to pick up a magazine or newspaper

without finding at least one article describing professional women's changing

attitudes, life-styles, and behaviour with respect to their traditional household roles.

Many of these roles are linked to consumption pattern, any changes in role attitudes or

behaviour should be of substantial interest to marketers.

A recent study (2014) conducted by IMRB states that “The working woman is the

most important customer for retailers. She's the largest spender, and she influences

how the family spends their money, it‟s a position most retailers agree with. ''The

working woman carries a lot of clout with us,'' Despite her liberty and working

outside the home, women today still do most of the grocery shopping. However still

they all shop alike. Shopping is probably one of the oldest terms used by all and have

been doing over the years. The researchers of today state that feminine roles are of

great concern today to consumer analysts and marketers. A role specifies what the

typical occupant of a given position is expected to do in that position in a particular

social context.

One of the challenges professional women face today is balancing their roles as a

wife, mother, wage-earner and consumer. Married professional women experience

time constraint and pressures dealing with household responsibilities and their jobs in

the marketplace. Professional women could be part of several groups and

organizations, a member of a family, working in a certain firm, member of a

2

professional forum, a part of a political group, a member of Rotary club of the city,

active worker of a trade union, regular participant in local social activities etc.

The Classification of Indian cities viz., comprise of Tier 1 Tier2 and Tier 3 etc. a

ranking system used by the Government of India‟s Income Tax Depts. to allocate

House Rent Allowance (HRA) to public servants employed in different cities in the

country. Tier 1 cities include Mumbai, Delhi, Chennai, Kolkata, Hyderabad and

Bangalore. Tier 2 includes Pune, Cochin etc. and Tier 3 includes Nashik, Baroda and

Madurai etc.

Today‟s women have liberty to work outside the home, but still women do most of the

grocery shopping. A survey conducted by Indian Market Research Bureau found that

professional women had three predominant approaches to shopping pattern of fast

moving consumer goods. First is the "executive" mother. These women, who

comprise about 40% of all female shoppers who plan ahead and coordinate their trip

to the supermarket. They know what they need to purchase, these professional women

are well organized and likely to use a shopping list and stick to it.

Next are the "minimalist" mothers, who collectively account for one third of all

female food shoppers. These are high income mothers who hold high professional

jobs. These women have busy schedules and have diverse priorities but still they want

to keep their grocery shopping and meal preparation to a bare minimum and go for

online shopping.

The third type are professional women who do not have prepared a shopping list.

Instead they will select goods based on their convenience, ease of preparation and

visibility in the store. Finally, there are the "give-it-away" mother who look for a

helping hand with both the grocery shopping and meal preparation. In total they

account for about 10 to 15% of all female shoppers. These professional women

actively seek out assistance. Shopping is a shared activity and family helpers may be

discharged to other aisles to pick up items. All professional women are grouped

according to their own occupation. The assumption was that professional women‟s

occupation determines family standard of living and therefore family health status.

http://en.wikipedia.org/wiki/Government_of_India

http://en.wikipedia.org/wiki/India

3

Occupational status includes professional women working in education industry,

Banks, IT Company.

Indian professional women are embracing the concept of buying online consumer

goods like grocery items, frozen food ,dairy products and cosmetics which they did

not do so far offline. These products are some of the retail categories which have seen

exponential growth in Indian e-commerce in last two years.

Smart Devices like Smartphone, Pads and Tabs are taking more professional women

towards e-commerce. This was more relevant to private purchase categories like

lingerie, which is shifting online in a big way. These smart devices also provide them

to indulge in recreational and relaxed shopping.

E-shopping is a recent occurrence in the field of E-Business and is definitely going to

be the future of shopping. Most of the companies are running their on-line portals to

sell their products/services online. Online shopping is very common outside India, its

growth in Indian Market, which is a large consumer market, is still not in line with the

global market. The growth of on-line shopping has triggered on-line shopping

phenomena in India. Factors w.r.t professional women on-line shopping parameters

are satisfaction with on-line shopping, future purchase intention, frequency of on-line

shopping, numbers of items purchased, and overall spends on on-line shopping.

Shopping has got a new definition since the arrival of the internet. Any person or

company from any part of the world who is able to post and sell goods on the internet

via a website is able to sell. Consumer has various means to exchange monetary paper

by not just online banking but can pay through different payment methods. These

days, it is easy to find the most difficult of all products, by easily typing in the product

or item. Online companies are making logistics also easily available by joining the

bandwagon and helps in making sure that their products would be available to any and

all destinations in the world. Today there are more and more advantages and benefits

to online shopping equal to traditional physical shopping. Teleshopping indicates

buying consumer products using a telephone connection. Developments in

teleshopping offer many possible uses like as a supplement and alternative to

transportation. The use of time (by point in time and by time budget) and the use of

http://www.differencebetween.net/business/difference-between-consumer-goods-and-industrial-goods/

4

space (location and infrastructure) will change. Teleshopping saves time of

professional women. Some shopping trips could be scheduled to avoid the rush hour.

Teleshopping also has effects on the use of space. Scientific studies states that

touching things that one love before they buy them results to a physical effect like a

euphoric state which leads many to associate shopping as a feel-good experience. So

the best way is to experience physically touching merchandise.

Beyond the physical aspects, shopping in a retail store gives customers the

opportunity to inspect the merchandise they buy for quality. If consumer chooses to

buy big items like furniture, they can try out the product and see if they are

comfortable with it. The human contact also creates a bond between seller and buyer,

initiating trust and guarantee which can make most customers feel good about a

purchase.

Physically walking in Store from rack to rack, checking out the display, putting a

dress over and trying to check ones reflection on full-view mirrors that are placed all

around the store is traditional shopping Having the ability to physically choose and

check out what an item or product is like, would look like, and what its features are.

Some professional women still prefer the traditional type of shopping over online

shopping as it allows them to meticulously check out an item. Some professional

women are not quite certain with their own size, sometimes fitting a size that would

normally be bigger or smaller than their actual size so there are still conventional

shoppers who like to check out the product that they are interested in buying.

Traditional shopping still allows for more ground to the consumer in terms of being

able to.

Neil H. Borden(1965) in 'The Concept of the Marketing Mix' states that fast moving

consumer products that are sold quickly and at relatively low cost. Examples include

non-durable goods such as dairy product, Frozen food, Grocery, toiletries, Cosmetics,

Dairy products. Some FMCG have a short shelf life, as they have high consumer

demand or because the product deteriorates rapidly. Some FMCGs such as meat,

fruits and vegetables, dairy products, and baked goods are highly perishable. Other

http://en.wikipedia.org/w/index.php?title=Neil_H._Borden&action=edit&redlink=1

http://en.wikipedia.org/wiki/Low_cost

http://en.wikipedia.org/wiki/Shelf_life

5

goods such as alcohol, toiletries, pre-packaged foods, soft drinks, and cleaning

products have high turnover rate

Though the profit margin on FMCG products is relatively small (more so for retailers

than the producers/suppliers), they are generally sold in large quantities; the

cumulative profit on such products can be substantial. FMCG is probably the most

classic case of low margin and high volume business.

List of FMCG considered in study, which workingwomen shop Online (E-

shopping and Teleshopping) and go for physical buying

Dairy Products

Tofu

Flavored milk

Curd

Paneer

Cheese

Lassi / Butter Milk

Toiletries

Serums

Shampoos

Conditioner

Shower gel/Soap

Sanitizer

Frozen Food

Peas

French Fries

Cut veggie/ Fruits

Ready to cook & Serve food

http://en.wikipedia.org/wiki/Soft_drink

http://en.wikipedia.org/wiki/Inventory_turnover

6

Frozen raw Non-Veg (Chicken /Meat/Fish) Grocery

Cereals

Pulse

Salts & Seasonings

Edible oil

Sugar

Cosmetics

Face Powder

Hair gel

Body lotion

Nail Polish

Lipstick

In the1st chapter introduction all the information about working women shopping

pattern and behaviour towards FMCG products are mentioned and to understand how

shoppers follows information process w.r.t FMCG to study the applications of shopper

buying manner. This chapter will discuss how the study will help the marketers in

understanding the shoppers behaviour applications in marketing and finally to study

the step or process adopted by Shoppers in their decision making. This chapter also

discusses the low involvement and high-Involvement shopping decisions making.

The 2nd

chapter discusses about shopping pattern of working women. The researchers

of today state that feminine roles are of great concern today to consumer analysts and

marketers. A role specifies what the typical occupant of a given position is expected

to do in that position in a particular social context. Today‟s women have liberty to

work outside the home, but still women do most of the grocery shopping. A survey

conducted by Indian Market Research Bureau found that professional women had

three predominant approaches to shopping pattern of fast moving consumer goods.

First is the "executive" mom. These women, who comprise about 40% of all female

shoppers who plan ahead and coordinate their trip to the supermarket. They know

what they need to purchase, these professional women are well organized and likely

7

to use a shopping list and stick to it. Indian professional women are embracing the

concept of buying online consumer goods like grocery items, frozen food ,dairy

products and cosmetics which they did not do so far offline. These products are

some of the retail categories which have seen exponential growth in Indian e-

commerce in last two years. Smart Devices like Smartphone, IPads and Tabs are

taking more professional women towards e-commerce. This was more relevant to

private purchase categories like lingerie, which is shifting online in a big way. These

smart devices also provide them to indulge in recreational and relaxed shopping,

E-shopping is a recent occurrence in the field of E-Business and is definitely going to

be the future of shopping. Most of the companies are running their on-line portals to

sell their products/services online. Online shopping is very common outside India, its

growth in Indian Market, which is a large consumer market, is still not in line with the

global market. The growth of on-line shopping has triggered on-line shopping

phenomena in India.

The 2nd chapter also discusses about physical shopping pattern. This studies states

that touching things that one love before they buy them results to a physical effect like

a euphoric state which leads many to associate shopping as a feel-good experience.

The best way is to experience physically touching merchandise. Beyond the physical

aspects, shopping in a retail store gives customers the opportunity to inspect the

merchandise they buy for quality. If consumer chooses to buy big items like furniture,

they can try out the product and see if they are comfortable with it. The human

contact also creates a bond between seller and buyer, initiating trust and guarantee

which can make most customers feel good about a purchase. Some professional

women still prefer the traditional type of shopping over online shopping as it allows

them to meticulously check out an item. The 2nd chapter also discusses about

teleshopping shopping pattern. Teleshopping indicates buying consumer products

using a telephone connection. Developments in teleshopping offer many possible uses

like as a supplement and alternative to transportation. The use of time (by point in

time and by time budget) and the use of space (location and infrastructure) will

change. Teleshopping saves time of professional women. Some shopping trips could

8

be scheduled to avoid the rush hour. Teleshopping also has effects on the use of

space.

Fast-moving consumer goods (FMCG)

The term was coined by Neil H. Borden(1965) in 'The Concept of the Marketing Mix'

and are products that are sold quickly and at relatively low cost. Examples include

non-durable goods such as dairy product, Frozen food, Grocery, toiletries, Cosmetics,

Dairy products .Some FMCG have a short shelf life, as they have high consumer

demand or because the product deteriorates rapidly. Some FMCGs such as meat,

fruits and vegetables, dairy products, and baked goods are highly perishable. Other

goods such as alcohol, toiletries, pre-packaged foods, soft drinks, and cleaning

products have high turnover rates. Though the profit margin on FMCG products is

relatively small (more so for retailers than the producers/suppliers), they are generally

sold in large quantities, the cumulative profit on such products can be substantial.

FMCG is probably the most classic case of low margin and high volume business.

The 5th

chapter is on review of literature. The review of literature in chapter 5 is

divided into various types of Shopping Pattern of Working women and fast moving

consumer goods. Hareem, Rashid, Javeed (2011) states that the Influence of Brands

on female consumer‟s buying behaviour in Pakistan attempted to examine Pakistani

female consumer‟s buying behaviour and understand the key factors of branded

clothing which influence female consumer‟s involvement towards trendy branded

clothing.

Sriparna Guha (2013) states that the changing perception and buying behaviour of

women consumer in Urban India”. The working women segment has significantly

influenced the modern marketing concept. The author further states that women due

to their multiple roles influence their own and of their family members‟ buying

behaviour. The study also reveals that working women are price, quality and brand

conscious and highly influenced by the others in shopping.

Ashwin Kumar (2011) states that the buying behaviour of Indian women & their

values for the market. Women as a consumer were also participating in buying the

http://en.wikipedia.org/w/index.php?title=Neil_H._Borden&action=edit&redlink=1





9

goods. Indian women were dominating the market by making her presence in every

purchase decision. the author further states that Indian women are playing a new role

as a facilitator. Swarna Bakshi(2009) states that the Impact of gender on Consumer

Purchase Behaviour”. Men and women due to their different upbringing and

socialization along with various other social, biological and psychological factors

depict different types of behaviour at various situations. Women seem to have

satisfaction and find pleasure while they shop whereas men appear to be more disdain

towards shopping.

Shainesh (2004) presents that buying behaviour in a business market is characterized

by long cycle times, group decision making, participants from different functional

areas and levels and sometimes divergent objectives, and changing roles of the

participants during the buying cycle. The high levels of market and technological

uncertainty of services is the complexity in the buying process. Despite, marketers

have remarkably ignored on women as a separate segment. Mehta& Sivadas, (1995)

states that e-shopping buyers, gender, marital status residential location, age,

education, and household income were frequently found to be important predictors of

Internet purchasing. The consumer‟s willingness and preference for adopting the

Internet as his or her shopping medium was also positively related to income,

household size, and innovativeness.

Akhter & Hausman (2002) indicated that more educated, younger females, and

wealthier people in contrast to less educated, older, females, and less wealthier are

more likely to use the Internet for purchasing. It further states that the professional

woman is the most important customer. Working women is the largest spender, and

she influences how the family spends their money. Sharma & Boby (2013) states that

,Indian women will fuel Rs.2.17 crore e-shopping in next 5years Indian women

fuelled online shopping worth over half-a-billion dollars last calendar and that figure

is galloping five-fold to Rs.2.17 crore in the next three years. Women-influenced sales

would be 35% of Indian e-commerce market estimated at Rs.5.28 crore by 2016,

Venture capital firm Accel Partners , one of the prolific backers of start-ups, said that

These projections come in the backdrop of a frenetic growth in internet penetration

http://search.proquest.com/business/indexinglinkhandler/sng/au/Kurian,+Boby/$N?accountid=36076

10

through smart phones and professional Women lapping up the convenience of

shopping online.

Crawford & Melewar (2003) in their study was done to examine the difference in

the impulsive buying behaviour of men and women and also to determine the

important factors which influence the impulsive buying behaviour of customer. The

response showed that working men and women of younger age purchase the product

more impulsively than the older working women population and spent more amount

on impulse purchase. Although men buy the product impulsively but there is also a

rational thinking involved in the decision making which lacks in case of women up to

a certain extent. Andrews &Currim(2004)states that uncertainties about products

and shopping processes, trustworthiness of the online seller, or the convenience and

economic utility she wishes to derive from electronic shopping determine the costs

versus the benefits of this environment for consumers.

Katy & Dipika (1997) in their study attempted to analyse consumer‟s purchase

behaviour over two periods in the cities of Mumbai, Kolkata and Delhi. The study

showed that Kolkata seemed to be opting for reduced consumption as a way of

economizing rather than downgrading on product quality. Skinner (1990) notes that

when a consumer purchases an unfamiliar expensive product he/she uses a large

number of criteria to evaluate alternative brands and spends a great deal of time

seeking information and deciding on the purchase. The type of decision making used

varied from women to women and from product to product .Hate (1978) states that

there is positive change in shopping pattern of Bengali women living in big cities in

Maharashtra with the advent of independence. Sultan &Henrichs (2000) states that

women represent the major e-shopping holiday season buyer. Rainne (2002) states

that the number of women (58%) who bought online exceeded the number of men

(42%) by 16%. Among the woman who bought, 37% reported enjoying the

experience “a lot” compared to only 17% of male shoppers who enjoyed the

experience a lot. Bearden W. (1982) states that Influence of social reference group on

the purchase of products on professional women. They further reviewed research

available on reference groups with special focus on professional women on the

purchase of products. This study added to people's knowledge of how the influence of

11

society vary across different product categories consumed by professional women.

Specifically this study focuses on social reference groups of professional working

product purchase decisions. Peter & Simon (2001) studied the women‟s involvement

in purchase making decisions they further studied the relationship between

demographic & geographic variables of professional women and their involvement in

purchase making decisions of family and they also measured the level of involvement

of women in these decisions.

Sheikh & Aizen (1990) studied the changing status of professional women in India

and their impact of urbanization and development. The study further argues that legal

and constitutional rights in themselves do not change social attitudes. In the longer

term these attitudes are conditioned by economic pressures, which would ultimately

lead to improvement in the status of professional women.

Miyazaki and Fernandez(2001) states that in the Indian context, identifying pre-

purchase intentions of professional women is the key to understand why they

ultimately do or do not shop from the Web market. A compilation of some of the

determinants researchers have examined are: transaction security, vendor quality,

price considerations, information and service quality, system quality, privacy and

security risks, trust, shopping enjoyment, online shopping experience and perceived

product quality. These lists of factors having a positive or negative impact on

professional women propensity to shop do not seem to be very different from the

considerations encountered in offline environments. However, the sensitivities

individuals display for each variable might be very different in online marketplaces.

Factors like price sensitivity, importance attributed to brands or the choice sets

considered in online and offline environments can be significantly different from each

other. Eastlick and Feinberg (1999) & Lennon (2003) found that motives s were

often higher among professional women than among professional men. They found a

negative relationship between education and shopping motivations. Additionally,

these researchers found that the motive were often higher among professional women

than among professional men shoppers. Verma & Munjal (2003) identified the major

factors in making a brand choice decision namely quality, price, and availability,

packaging and advertisement w.r.t professional women. The brand loyalty is a

12

function of behavioural and cognitive patterns of a customer. The age and

demographic variables affect significantly the behaviour and cognitive patterns of the

customers while other demographic characteristics such as gender and marital status

are not significantly associated with these behaviour and cognitive patterns of the

consumers.

Woodard (1999) in their study in consumer behaviour among professional women in

United States by the National Foundation of Women Business Owners found that57%

of women business owners, who used the Internet, had purchased online, compared to

40% of female employees who used the Internet had purchased online. Also, 30% of

women business owners/executives, compared to 23% of other working women, had

ordered from a catalogue.

Henley (1979) stated that the feminine stereotype depicts Kolkata women as being

more concerned than men about their bodies, their clothing, and their appearance in

general. Professional Women are subject to a great deal more observation than

professional men; their figures and clothing; their attractiveness is the criteria by

which they most often are judged. Not surprisingly, then professional women are

more conscious than men of their visibility. This difference translates into both a

power and a sex difference. In a situation where one person is observing and the other

is being observed, the observer dominates the situation. Kapur (1979) states that the

twin roles of women cause tension and conflict due to her social structure which is

still more dominant. In her study on professional women in Delhi, the author has

shown that shown that traditional authoritarian set up of Hindu social structure

continues to be the same basically and hence women face problem of role conflict

change in attitudes of men and women according to the situation can help to

overcome their problem.

Fast Moving consumer goods

Isa Kokoi (2011) states that the female buying behaviour related to facial skin care

products. The results indicated that 20-35 and 40-60 year-old Finnish women were

rather similar in terms of the factors affecting their buying behaviour related to facial

skin care products. Kristen Wig & Chery Smith(2008)states that the main objective

13

of the research study was to examine the grocery shopping behaviour and food stamp

usage of low income women with children to identify factors influencing their food

choices on a limited budget.

Nagunuri Srinivas (2013) states that the study was conducted to examine the

“women consumer‟s preferences towards branded and unbranded grocery items in

Organized/Unorganized Retail Environment” and also aim to study the changing

market scenario i.e. transition from unorganized sector to an organized one, Due to

increasing self-service and changing consumers‟ lifestyle the interest in branding and

stimulator of impulsive buying behaviour is growing increasingly. Madalena

Pereira, Joao Ferreira & Vilma Pedroso (2008) states that” Consumer behaviour

research is the scientific study of the processes consumers use to select, secure, use

and dispose of Fashion Retailing products and services that satisfy their needs. The

study is on the gender differences in consumer buying behaviour of a Portuguese

population when they go shopping to buy apparel products. The author finds

differences between women and men especially in terms of What, Where, When, and

How they buy.

Research Gap:

Considering the fact that most of the purchases are in some form managed by women

(Professional or non-professional) and since majority professional women are

entering the workforce area, these professional women segments are of prime

importance for the marketers today. Studies on the impact of shopping patterns (E-

shop, Teleshopping& physical buying) of select Fast moving Consumer (FMCG)

products. On Professional women in select Tier 1 cities of India help managers to

understand the manner in which professional women buy certain product or services.

Professional women are the upcoming focus of marketers in the country due to their

affluent and spending power and decision making ability there is no study done so far

on Impact of Shopping patterns (E-shop, Teleshopping& physical buying) of select

Fast moving Consumer (FMCG) products on Professional women in select Tier 1

cities of India.

14

Objectives

To study E-shopping, teleshopping and physical shopping patterns of select FMCG

products by Professional women in select tier1 cities.

To study the impact of income level of working women on shopping patterns in select

tier1 cities.

To study the correlation between costs effectiveness of shopping patterns of FMCG

products in select tier1 cities.

To study the significance of quality of products in shopping pattern of FMCG


To study the significance of type of working women‟s occupation on shopping pattern

of FMCG products in select tier 1 cities.

To study the significance of demographic factor Vis –a-Vis age on shopping pattern

of working women of FMCG products in select tier1 cities.

To study the significance of demographic factor Vis –a-vis qualification on shopping

pattern of working women of FMCG products in select tier1 cities.

Hypothesis of study:

H01: There is no significant difference in proportion of Online (E shopping,

Teleshopping and physical shopping pattern of working women for FMCG products.

H11: There is significant difference in Online (E-shopping, Teleshopping) and

physical shopping pattern of working women for FMCG products.

H02: There is no association between level of income and proportion of Online (E

shopping, Teleshopping) and physical shopping pattern of FMCG products.

H12: There is association between level of income and proportion of Online (E

shopping, Teleshopping) and physical shopping pattern of FMCG products.

H03: There is no correlation between cost effectiveness and proportion of Online (E

shopping, Teleshopping) shopping pattern FMCG products.

H13: There is correlation between cost effectiveness and proportion of Online (E

shopping, Teleshopping) shopping pattern of FMCG products.

15

H04: There is no association between quality of product and proportion of Online (E


H14: There is association between quality of product and proportion of Online (E


H05: There is no association between working women‟s occupation and proportion of

Online (E shopping, Teleshopping) and physical shopping pattern of FMCG products.

H15: There is association between working women‟s occupation and proportion of


H06: There is no association between age of working women and proportional of


H16: There is association between age of workingwomen and of Online (E shopping,

Teleshopping) and physical shopping pattern of FMCG products.

H07: There is no association between qualifications of working women and

proportion Online (E shopping, Teleshopping) and physical shopping pattern of

FMCG products.

H17: There is association between qualifications of working women and proportion


Research Methodology and Data Collection

Data collection was done in two stages: in the first stage a pilot survey was

conducted to ascertain the research parameters and to test the validity and reliability

of the instruments i.e. Questionnaire used in the study. Pilot Study was conducted in

two cities out of four cities of India namely Mumbai &Bangalore to test the reliability

of the instruments. The study was conducted with a sample of 100 respondents

(working women).In the second stage the primary source of information was

collected through using the instruments in the study. Instruments used to administer

the respondent were Questionnaire.

The Secondary source of information here includes library resources, articles in

various newspapers and magazines, research papers, companies‟ brochure and online

resources like company websites, online reports and articles.

16

Demographic factors:

City: Information is collected through four different cities. These are Mumbai, Delhi,

Bangalore and Hyderabad. There are 200 respondents from each city. Age group:

Age of respondents is divided in to three groups. Respondents of age 25-30yrs are

classified in to „Young „age group, respondents of age 30 to 45 are classified as

„Middle‟ age group and respondents of age above 45 -60yrs are classified in to

„Elderly‟ group.

Qualification: Respondents are classified in to four groups according to their

qualification. These groups are „under graduates‟, „graduates‟, „post graduates‟ and

„professional‟.

Monthly Income: Respondents are classified in to 3 groups according to their

monthly income. Respondents of monthly income Rs.10,000 Rs 15,000 are

considered as „Low income‟ group, respondents of income between Rs 15,000 to

35,000 are considered as „Middle income‟ group, respondents of income between Rs

36,000 to 50,000 and classified as „High income‟ group. Occupation : Respondents

from IT industry ,Banking & Insurance ,Academic and others are considered .In case

of others professional women respondents from Fashion industry, Media ,BPO ,

Marketing & Sales etc. are taken into consideration.

Sampling Technique: The study was conducted in four Tier 1 cities of India like

Mumbai, Delhi, Bangalore and Hyderabad. In these cities working environment and

ecology are different. The sampling survey was done based on stratified Random

Sampling. The sample unit was working women of different organisations of different

age group and different levels of management.

Population and Samples Size

Name of the Cities Population of Professional

women

Number

of respondents

Mumbai 1,423,922 270

Delhi 1,250,000 250

Bangalore 4,81,077 160

Hyderabad 3,40,498 120

Total 800

(Source: Indian Market Research Bureau IMRB, Mumbai 2014)

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The sample size was finally fixed after knowing the population of all four cities.

Above table indicate that total sample size is of 800 respondents.

Sample Size Calculation:

Sample size is decided using formula as given below.

Consider z = 1.96 (it is standard for 95% level of confidence)

Standard deviation calculated from pilot study = 10.75 (app)

Margin of error = 0.75

Sample size = (1.96 * 10.75/0.75)^2 = 789 (approximate)

Reliability Statistics

Cronbach’s Alpha

Value

No of Items

0.744 68

It is more than 0.7 therefore the reliability test is satisfied

Limitations of study:

The Study was only restricted towards working women‟s of select Tier 1 cities of

India namely Mumbai, Delhi, Bangalore and Hyderabad.

The Selected FMCG Product in the study were limited to frozen foods, toiletries,

cosmetics, packed dairy products and packed grocery products.

Demographic factors are restricted to age ,income ,occupation and qualification

18

Data Analysis & Findings

Information collected through structured questionnaire was first entered in to excel

sheet. For statistical analysis of data and validation of hypothesis SPSS version 20

was used. Information was divided according to demographic factors. Information

was presented using tables, pie chart and bar diagram.

Descriptive statistics was obtained for each variable. This descriptive statistics was be

used for the analysis of data which consist of „Arithmetic mean‟ and „standard

deviation‟. For testing of hypothesis Chi-square test was applied. Thus Chi-square test

was applied to test association between tow attributes.

ANOVA and F-test was applied to test significance between mean scores. T-test was

applied to test significance of difference in mean scores of 2 variables. Karl Pearson‟s

coefficient of correlation was obtained to understand correlation between 2 variables.

Results and Discussions

Following are the results on basis of Hypothesis:

As per study there is significant difference in proportion of online buying pattern of

working women on FMCG products among four cities. Mean score of online

shopping for Mumbai is 40.54, for Delhi is 40.47, for Bangalore is 40.58 and for

Hyderabad is 31.96. This clearly justifies the project growth of online shopping in the

country. However, the frequency of online shopping is relatively less in the country.

There is significant difference in proportion of physical buying pattern of working

women on FMCG products among four cities. Mean score of physical shopping for

New Delhi is 63.02, for Mumbai is 60.6, for Bangalore is 62.24 and for Hyderabad is

61.97.

The study says that there is association between level of income and shopping pattern

of FMCG in tier 1 cites .Study says middle income go for maximum online and high

income go for physical shopping Online shopping mean per cent scores for each level

of income are calculated. For low income group respondents score is 38.81, for

middle income group is 41.89, for high income group is 38.71 and for very high

income group respondents score is 31.69.Physical shopping mean per cent scores for

19

each level of income are calculated. For low income group respondents score is 61.76,

for middle income group is 62.59, for high income group is 60.23 and for very high

income group respondent‟s score is 62.30.

Physical buying has no relation with cost effectiveness as it is mandatory whereas E-

shopping is alternate for physical .Conclusion is online shopping is cost effective.

There is no association between quality of product and proportion of shopping pattern

as shopping patterns have no effect on quality of product. There is association

between working women occupation and shopping pattern of FMCG products.

Working women from IT industry go for more online and physical shopping Mean

online shopping for each category of respondents are calculated. Mean for IT sector

women is 46.98 which is highest. It is followed by mean score of academics is37.09

and others category is 37.16. For banking and insurance group of women mean score

is 35.20Mean physical shopping scores for each category of respondents are

calculated. Mean score for IT sector women is 62.23 which is highest. It is followed

by mean score of academics is 61.31 and others category is 62.68. For banking and

insurance group of working women mean score is 60.90.

The study shows more middle age working women go for online, elderly age go for

teleshopping and young enjoy visiting the malls so they go for physical shopping.

Mean online shopping scores for each category of age group are calculated. Mean

score for young age group respondents is 36.21, for middle age group respondents is

43.06 and for elderly group is 36.71 mean physical shopping scores for each category

of age group are calculated. Mean score for young age group respondents is 62.72 for

middle age group respondents is 62.08 and for elderly group is 60.40.There is

association between age of working women and shopping pattern of FMCG products

.

There is association between qualification of working women and shopping for

FMCG products in tier 1 cities in India. It is observed from study that more doctoral

go for online and post-graduates go for physical shopping in Tier-1 cities. Mean

online shopping scores for each level of qualification are calculated. Mean score for

undergraduate respondents is 40.00, for graduates is 38.31, for post graduates is 39.47

20

and for doctoral is 40.58 mean physical shopping scores for each level of qualification

are calculated. Mean score for undergraduate respondents is 58.76, for graduates is

62.28, for post graduates is 62.76 and for doctoral is 60 respondents.

Conclusions

There is significant difference in proportion of Online (E-shopping, Teleshopping)

and physical shopping pattern of working women for FMCG products in select Tier 1

Cities The Data analysis and interpretation reflects to the fact that the mean score of

online shopping is highest in Bangalore and lowest in Hyderabad, which shows that in

Bangalore there is high level of support for connectivity and accessibility of online

shopping .In Bangalore there are many working women from various states of India

working in sectors like IT ,BPO etc. Today‟s women are working late in evening and

find it difficult to do physical shopping. It has been observed that many women who

work in corporate gets leave on Sundays only. Many working women who shops on

weekends face problems of long queue and waste time, so they prefer to shop Online.

In Hyderabad it is found that there is good access to public transportation. Hence

working women in Hyderabad go for more physical shopping. The other reason for

working women to shop physically is, as there is no problem of traffic so they prefer

going to malls and departmental store for shopping. On discussion with certain

working women in Tier 1cities was found that they believe in physically touching

product and buying.

Mean score of physical shopping for working women in Delhi is highest and lowest in

Mumbai .it‟s been observed that in Delhi many stores and local kirana shops are open

for longer time. On basis of data analysis it was found that more working women go

for physical shopping as compared to online shopping.

This study shows that there is association between level of income and proportion of

online shopping pattern (E-shopping, Teleshopping) of FMCG products. Arithmetic

mean of online shopping for working women in middle income group is highest and

for very high income group is lowest in all four tier1 of India .Physical shopping

mean percent for middle income group working women is highest and lowest for

high income group in all four Tier1 cities of India .The study shows that middle

21

income working women go for online shopping and high income go for physical

shopping in all four Tier 1 cities of India because they purchase high end and branded

products which need to be touch and felt before they buy.

There exist the correlation between cost effectiveness and online shopping of FMCG

products in Tier1 cities of India. This study states that there is negative correlation

between cost of online shopping and buying proportion which means if cost will

reduce the buying proportion of online shopping will further increase.

There is an association between quality of product and shopping pattern of FMCG

products in tier1 cities of India. The study shows that working women who shop

online in all four tier 1 cities of India are concern about quality. As per study working

women has stated that quality is of prime concern to them irrespective of Cost. Online

product selling companies have made provision for easy exchange of spoilt or

damaged products.

There is an association between industry of working women (academics /IT/banking

/others) and (Online and Physical) shopping pattern of FMCG products. The study

was significant because it has included working women from diverse backgrounds

from major tier 1 cities of India.

The study has shown there was association between occupation of working women

and shopping pattern of FMCG .The study shows that more online shopping was done

by respondents from IT sector and least by Banking and Insurance. It was observed

that respondents from banking and insurance was less tech savvy .Many women

working with Banks are very busy dealing with client so they do not get time to shop

online. In case of physical shopping women working in others industry does more and

is least in case of IT sector .It been observed that working women in IT sector have

rigid schedule which makes them difficult to go for physical shopping.

There is an association between age of working women and online buying pattern of

FMCG products in select tier1 cities of India. The study shows that elderly women go

for less online shopping and middle income women go for more online shopping. It‟s

been observed that elderly working women are not very internet friendly and they

believe more in buying products by touching and seeing them. In case of physical

22

shopping it‟s found that young working women go for high physical shopping and

lowest by elderly lady .The survey states that young women enjoy shopping at malls

and departmental stores. The study has found that elderly women go more for

telephonic (Online) shopping pattern.

This study shows that the qualification and overall shopping pattern are interrelated.

Online shopping level is highest for post graduate in all tier 1 cities and lowest among

Doctoral working women. In this study doctoral working women who are at very high

post are very busy and do not enjoy online shopping pattern. On the other hand Post

graduate women enjoy buying online as many working women are not bound by time

limit. On the other hand it‟s observed that graduates working women go for more

physical shopping and they enjoy physical shopping as they are not bound by time

limit.

Recommendations

E-shopping is one of the online shopping pattern done by working women in four tier

-1 cities of India .There are 90% of working women who are tech savvy and are heavy

online shoppers. The study states that the working women in Delhi are the largest

consumers of FMCG. Considering this fact it is highly recommended to the marketer

that working women do more online shopping as compared to non-working women.

Hence the company‟s likes Bigbasket.com, localbaniya.com, Grofers who sell their

products online etc. should aggressively concentrate on promoting their products

through electronic and print media.

The companies selling product online should try to retain their current customers and

focus on attracting the non-users by making them aware of benefits like convenience

and authenticity of products delivered to them online. The study states that still people

in India are reluctant to buy products online w.r.t authenticity. The companies should

make people believe that the products sold to them are genuine and if in case,

products delivered to them are damaged or spoilt, they would immediately get it

exchanged or replaced .The customer should be made aware of other benefits of

shopping online like on time delivery and discounted products than local retailer. In

other cities like Bangalore, Hyderabad and Mumbai the marketer has to attract

23

working women where presently the online shopping percent is low as compared to

Delhi. Hence to attract working women towards online shopping the marketer needs

to advertise about cash back offers, distribution of free sample on first purchase ,free

home delivery at door step as per convenient time of working women and return or

exchange policy of damaged products.

In case of telephonic shopping there is element of saving time and cost of travelling. It

involves order on telephone to kirana store or departmental store. Teleshopping is

most preferred by working women as it is convenient and facilitates prompt delivery.

In case of Physical shopping, it is more preferred by working women in Mumbai and

less in Delhi. In Mumbai physical shopping is done more in local kirana store and

department store which are open late in evening. For marketers it is recommended to

retain and increase the footfalls of working women by giving them cash discount

,special benefits to loyal customers ,product on product offer ,inform customer about

arrival of new product, distribution of free sample for same and gifting them during

festivals like Diwali ,Eid or Christmas.

There is association between level of income and proportion of online shopping

pattern (E-shopping, Teleshopping) of FMCG products. Working women with very

high income level go for physical shopping. The marketer should retain the loyal

customer, as these working women belong to high society and has snob appeal.

Marketer should directly communicate them about new product arrival. Other

marketing methods to retain them are relationship marketing and word of mouth.

The middle income working women go for more online shopping in tier 1 cities of

India. Online marketer should take more efforts to pull non user and retain current

customer who are middle and low income working women. The task of marketer

should be to focus on cost effectiveness through online advertising or personal mail.

Marketer should regularly update it customer about discount or price fall on FMCG.

This study shows that correlation between cost effectiveness and online shopping of

FMCG products in tier 1 cities of India which means working women will buy online

if price is lower than marked price. Considering this fact the online FMCG companies

should lower the marked up price of products so as to convince net savvy working

24

women amongst the all income level. This study has shown that product quality has

positive impact on shopping pattern amongst working women .attractive design may

help to increase the excitement among working women and generate positive word of

mouth. Thus will benefit the company to generate the feedback of their products

without much expenditure.

This study there is association between industry of working women and shopping

pattern. Working women from IT sector do more online shopping as compared to

banking, academics and other sector. In case of working women from other industry

/sector they go for more physical shopping. To promote more and keep current

shoppers the marketer needs to make the customer aware about convenience of online

shopping and other benefits they can enjoy.

There is association between age and shopping pattern of working women to retain

and attract the young working women the marketer should stock more imported

products of multiple brand of various patterns. In case of middle age working women

go for 45% online shopping and 55% for physical shopping. In case women of this

age prefer convenience and on time delivery and look out for more discounted

products as free samples.

In case of elderly age working women below 60 yrs. go for more telephonic shopping

.the marketer has focus on how he pull them towards the store. As many elderly

women are not tech savvy and also do not believe in products from E-shopping. As

marketer his job is to convince this women to visit store. If she visit store she might

buy more products than her required list. On visiting store she can avail current

discounts and offers which can further generate her need for those products.

This study shows that working women of all qualification because of their working

schedule needs to save time from it. Their shopping pattern is focussed and strategic.

Hence to attract working women the marketer especially kirana store which is oldest

form of physical shopping pattern should go for extensive visual merchandising i.e. as

it is an effective way to attract and convert the working women shoppers.

25

Future Scope of Study

The study aims at understanding the impact of shopping pattern of working women

on FMCG viz. Dairy, grocery, Cosmetics ,Soap and raw frozen food in cities like

Mumbai, Delhi ,Bangalore and Hyderabad. The scope of the study has been limited to

certain demographic characters of working women like age ,qualification ,gender

,income ,industry wise The study broadly aims at understanding advantages of online

and physical shopping on parameters like time saving ,convenience ,shopping

24*7,cost effectiveness ,privacy in shopping and comparison of various products

.Studying the perceptions of the women buyers of FMCG mainly in terms of sources

of information, location where the purchase is made, influence of communication and

promotional mix and the ultimate purchase decision factors.

Further study can be conducted on various bases of segmentations like demographic

segmentation which includes family size, Income and religion, on basis of

geographical segmentation like Tier II, Tier III & Tier IV Cities. On basis of

behavioural segmentation like usage rate etc. and on basis of psychographic

segmentation like personality and lifestyle. Study can be further conduced on other

FMCG like detergents, Beverages, Oils etc.

26

CHAPTER 1

INTRODUCTION

1.1 Introduction

Today‟s modern working women have independence to work outside the home, but

still women do most of the consumer goods shopping. Working women's level of

participation in the work force have focused attention on changing life-styles and

consumption patterns. A set of intervening variables reflecting today‟s working

women's attitudes toward food preparation explains their food shopping behaviour

better than either a working/nonworking classification or general role orientations.

Today there is change in working women's changing attitudes, life-styles, and

behaviour with respect to their traditional household roles. Many of these roles are

linked to consumption pattern; any changes in role attitudes or behaviour are of

substantial interest to marketers.The working woman is considered an important

customer for retailers. She's the largest spender, and influences how the family spends

their money. Despite working women‟s liberty and working outside the home, she

still does most of the grocery shopping. However still all women shop alike.

Working women actively seek out assistance. Shopping is a shared activity and family

helpers may be discharged to other aisles to pick up items. As per official statistics, all

working women are grouped according to their own occupation. The assumption was

that working women‟s occupation determines family standard of living and therefore

family health status. In the past decade, the way working women shop has

dramatically changed. Besides shopping at physical stores, with the aid of information

and communication technologies (ICT), consumers are able to shop via the Internet.

This new type of shopping mode, coming in different names like e-shopping, online

shopping, network shopping, online shopping, or Web-based shopping, featuring in

freeing consumers from having to personally visit physical stores, is anticipated to

greatly change people‟s everyday lives.

The researchers of today state that feminine roles are of great concern today to

consumer analysts and marketers. A role specifies what the typical occupant of a

27

given position is expected to do in that position in a particular social context. One of

the challenges working women face today is balancing their roles as a wife, mother,

wage-earner and consumer. Married working women experience time constraint and

pressures dealing with household responsibilities and their jobs in the marketplace.

Working women could be part of several groups and organizations, a member of a

family, working in a certain firm, member of a professional forum, a part of a political

group, a member of Rotary club of the city, active worker of a trade union, regular

participant in local social activities etc.

The modern working women have realized now that they have a personality of their

own as a human being and that their mission in life does not end with becoming

merely a wife and a good mother but also in realizing that they are also a member of

the civic community. Thus, the modern women are not having a passive life. They are

prepared to express and show their individuality in various walks of life. Education is

a catalytic agent for social change. Changes in life and position of women have been

greatly accelerated by the spread of education. As a result, women organizations and a

strong women‟s movement took place. The necessity for work on the part of the

women is not due to their enlightenment alone. The women work either because of

economic necessity which force them to do so, or because they want to derive

psychological satisfaction out of it.

1.2 Shopping patterns of working women

There are three patterns of shopping pattern on which a study was conducted viz

Physical shopping, Online (E-shop & Telephonic). Physical type of shopping pattern

is usually walking in Store from rack to rack, checking out the display, putting a dress

over and trying to check ones reflection on full-view mirrors that are placed all around

the store is physical shopping having the ability to physically choose and check out

what an item or product is like, would look like and what its features.

Some working women still prefer the traditional type of shopping over online



normally be bigger or smaller than their actual size so there are still physical shoppers

28

who like to check out the product that they are interested in buying. Physical

shopping still allows for more ground to the consumer in terms of being able to

physically check out and even try out what merchandise they want.

Online shopping is one of the most popular ways to make purchases, but it's not

something that everyone is comfortable doing. As with most things, there are

positives and negatives associated with this approach to shopping. Consider the

advantages and disadvantages carefully so one can make an informed decision about

what's best for them. E-shopping is a recent occurrence in the field of E-Business and

is definitely going to be the future of shopping. Most of the companies are running

their on-line portals to sell their products/services on-line. Online shopping is very

common outside India, its growth in Indian Market, which is a large consumer

market, is still not in line with the global market. The growth of on-line shopping has

triggered on-line shopping phenomena in India. Factors w.r.t working women on-line

shopping parameters are satisfaction with on-line shopping, future purchase intention,

frequency of on-line shopping, numbers of items purchased, and overall spends on on-

line shopping.

Other pattern of online shopping is telephonic shopping. Telephonic shopping saves

time of working women. Some shopping trips could be scheduled to avoid the rush

hour. Teleshopping also has effects on the use of space. Telephone shopping is in

many ways the easiest and most convenient mode of shopping ever devised.

Telephone shopping can contribute substantially to the sales and profits of department

and specialty stores. Although a telephone sales trans-action may cost the store 50%

more to service than the average floor transaction average telephone sale is probably

substantially higher than the average floor transaction. One of the very few aspects

common is that all women who are working and non-working are all consumers thus

the reason for a business firm to come into being is the presence of consumers who

have unfulfilled, or partially fulfilled needs and wants. Working women‟s behaviour

is an extremely important and complex subject for any marketer. Buyer remains an

enigma and her mind is viewed as a black box. Before businesses can develop

marketing strategies, they must understand what factors influence women‟s buying

behaviour and how they make purchase decisions to satisfy their needs and wants and

29

also understands women‟s shopping pattern and knowing they are not that simple. It is

almost impossible to predict with one hundred per cent accuracy how she will behave

in a certain situation. Women buyers are moved by a complex set of deep and subtle

emotions shopping decision-making generally involves five stages or steps: Problem

or need recognition, information search, alternatives evaluation, purchase, and post-

purchase evaluation.

1.3 Working women and FMCG

The taste of women as consumer is wide ranging and constantly changing. The correct

prediction for fast moving consumer goods decisions is difficult while the final

purchasing decision of her will differ between decision styles and profiles which

cannot be directly applied to unique purchase situations wherein the level of

involvement of the every women varies. The personal factors and situational factors

make it difficult to predict decisions beforehand. The personal factors embrace self-

image, lifestyle and sub cultural aspects shaping the women‟s beliefs and influencing

the purchase attitude. Lifestyle is a psychographic variable of values/tastes which

manifest as needs/preferences and specific purchase behaviour. The purchase decision

made by her can alter/reinforce their lifestyle. Consumers are free to select products

that reinforce their definitions of self-image and their perceived unique lifestyle in the

family/society so as to acquire satisfaction in life and express self-confidence. Women

perceive products as an extension of their personality and hence deliberate the product

choice that matches some aspect of the self-image and communicates a desired image.

Women attach symbolic meaning to FMCG in order to define themselves through the

attitude functions served. The consumer purchase decision is individualistic the

complexity of the decision depends on the degree of information search the evaluation

of alternatives and choice of products.

Personal factors like situational/marketing/environmental factors and post purchase

behaviour factors simultaneously interact each other to influence the consumer‟s

purchase decision. Working women purchase goods in response to a recognized

specific need. The purchasing behaviour is also diverse in style as per the taste/values

of the consumer. It is illustrated that the complexity of the purchase decision depends

30

on the extent of consumer‟s information search which depends on the working

women‟s buying power and buying pattern rather than the consumer goods.

The criteria which a women utilize during information search and when selecting a

fast moving consumer goods have generally the following attributes: Product Quality:

Consumer packed goods are quality driven. The consumer's choice today depends on

the premium quality and technology provided; Brand Image: The perception of the

consumer about the brand name is becoming critical on account of the huge

investment made in buying a consumer packed goods . With the fast approaching

disparity in both technology and prices, brand image is becoming a key purchase

influencer; Price: The market has been very price-sensitive. The intensity has

increased as one moved down from the premium segment to the mass consumption

range. However, of late consumers have started showing an inclination to buy high

price range quality products as opposed to low priced products.

This factor is assuming a key role in the minds of the consumers, as the consumer

goods are becoming more and more in number. Consumer involvement is defined as

consumer‟s perceived relevance of an object (e.g. Product or brand, advertisement, or

purchase situation) based on the inherent needs, values, and interests of the person.

Previous research has shown several ways in which consumers become involved with

products and the effect that product involvement has on various purchasing and

consuming behaviour.

31

CHAPTER 2

SHOPPING PATTERNS OF WORKING WOMEN

Today‟s women have liberty to work outside the home, but still women do most of the

grocery shopping. A survey conducted by Indian Market Research Bureau found that

working women had three predominant approaches to shopping pattern of fast moving

consumer goods. First is the "executive" mom. These women comprise about 40% of

all female shoppers who plan ahead and coordinate their trip to the supermarket. They

know what they need to purchase, these working women are well organized and likely

to use a shopping list and stick to it. Next are the "minimalist" moms, who

collectively account for one third of all female food shoppers. These are high income

mothers who hold high professional jobs. These women have busy schedules and

have diverse priorities but still they want to keep their grocery shopping and meal

preparation to a bare minimum and go for online shopping.

The third types of working women are who do not have prepared a shopping list.

Instead they will select goods based on their convenience, ease of preparation and

visibility in the store. Finally, there are the "give-it-away" moms who look for a

helping hand with both the grocery shopping and meal preparation. In total they

account for about 10 to 15% of all female shoppers. These working women actively

seek out assistance. Shopping is a shared activity and family helpers may be

discharged to other aisles to pick up items. As per official statistics, all working

women are grouped according to their own occupation. The assumption was that

working women‟s occupation determines family standard of living and therefore

family health status. Occupational status includes working women working in

education industry, Banks, IT Company. Indian working women are embracing the

concept of buying online consumer goods like grocery items, frozen food, dairy

products and cosmetics which they did not do so far offline. These products are some

of the retail categories which have seen exponential growth in Indian e-commerce in

last two years. Smart Devices like Smartphone, IPads and Tabs are taking more

professional women towards e-commerce. This was more relevant to private purchase

32

categories like lingerie, which is shifting online in a big way. These smart devices

also provide them to indulge in recreational and relaxed shopping.

The challenge lies in identifying the key drivers that steer the Indian consumers‟

perception and shopping behaviour. The reality is that every retailer has to

understand his customers‟ more discerningly than ever before and make strategic

choices to pursue the right target (customer) with the right proposition. The five main

values sought by shopper are convincing value for money, product quality,

fashion attributes and time saving. To understand the Indian shopper one need to

analyze his/her changing socio-economic and demography.

In the past decade, the way people shop has dramatically changed. Besides shopping

at physical stores, with the aid of information and communication technologies (ICT),

consumers are able to shop via the Internet. This new type of shopping mode, coming

in different names like e-shopping, online shopping, network shopping, Internet

shopping, or Web-based shopping, featuring in freeing consumers from having to

personally visit physical stores, is anticipated to greatly change people‟s everyday

lives.

2.1 Demand Drivers of changing shopping pattern of working women

Socio Economic Factors

India is today a nation which has a large middle class, a youth population which is

happy spending and a steady rate of growth of GDP. The changes that have been

visible in India over a period of time w.r.t working women. The primary indicator of

socio-economic change w.r.t working women is the increase in the life expectancy

from 58 years in the 1991-92 to an average of 67 years in 2013-14(Source: NCAER

National Centre for Applied Economic Research,2014).

Basic amenities like drinking water and electricity are also commonly available. So

in last 20 years there has been a tremendous change in the basic quality of life of an

average India. With the basic necessities being taken care of, there is a good chance

that the demand for product or services will increase.

33

Changing Income Profiles

Steady economic growth has fuelled the increase in personal income in India. The

middle- class forms the backbone of the Indian market story and it is the rising

incomes in the young middle class of working women population that is fuelling its

growth.

The proportion of the major consuming class (population that has an annual income

that is higher than Rs. 100,000) has risen from 20 percent in 1996-97 to 56 percent

by 2013-2014(Source: NCAER National Centre for Applied Economic

Research).This translates into a resulting in higher spending capacity and larger

consumption. This is reflective of the growth in the consuming class. An increase

in the spending class implies an expanding opportunity for shopping pattern.

As per NCAER the share of households falling in super rich, sheer rich, near rich and

almost rich is seen to be increasing, which is reflective of an increasing affluent

society and this is also an indicator of consumption levels and the products

consumed. This increase in incomes has happened in both urban and rural India,

giving rise to what is now popularly termed as the „Great Indian Middle class‟ of

working women who are beholden for shopping pattern especially of online

shopping.

Increasing Nuclear Families and Working Women:

Liberalization of the economy and the incentives to private sector development have

led to a rise in new trade formats and increased employment creation. This has

translated into the migration of both the skilled and unskilled working women

workforce from rural areas to major cities resulting in an increasing proportion of

nuclear families Combined with higher employment possibilities for women. The

rural-to-urban migration trend coupled with other factors such as increased exposure

to the media and paucity of time has not only led to changes in awareness of gender

equality and rights but also changes in the habits of people towards traditional

household chores such as grocery-shopping which is done now a days by online

shopping.

34

There has been a major shift in food habits in the metropolitan cities about 86% of

households prefer to have instant food due to steep rise in dual income level and

standard of living, convenience, influence of western countries etc, according to a

survey ( Source :ASSOCAM 2014) .

It has been found that nuclear families with children or without children in metros

lead time-pressured lifestyles and has less time available for formal meals, as a result

of which demand remains high for products which can be eaten on the go. Hence

online shopping is growing

The Age Factor

Compared with several advanced countries, where the overall population is aging,

India is very young nation with more than 70 percent of its population below the age

of 40, and more than 47 percent below the age of 20. The median age of Indians is

about 24 years (Source: UN-HABITAT 2014).

This age distribution is of significance to the marketers of goods and services and

also understanding their shopping patterns. It explains the boom in all T i e r - 1

Indian cities in consumption of FMCG products and leisure related expenditure

in general. The increasing working women population which also started earning

early also increases the overall purchasing capacity in the country, and has

implications for productivity of employment. The projected increase in the

economically active population of working women holds the key to India‟s

prosperity and its economic potential over the next 20 years.

The Changing Role of Women and the Evolving family Structure The increased

economic independence of women has redefined the rules of social behaviour. Apart

from an increased family income, it has led to a change in the kind of products and

services which are demanded.

The purchasing habit of working woman is different from that of a housewife, since

the former has lesser time to devote to the household tasks. Working women would

prefer a one-stop shop for purchasing their regular products. Also a working

woman‟s propensity for spending is higher than that of a house wife. The increase in

http://www.unhabitat.org/

35

the number of working women will hence drive the need for convenience and will

play a major role in the success of many modern retail formats in the country. With

more and more nuclear families proliferating, it is to be reasonably expected that

time poverty is setting in nearly 1.5 - 2 percent of joint families \give rise to

nuclear families every year. The rise in the number of nuclear families typically, is

seen as a factor which will translate in to higher spending on retail goods and works

in favour of organized retailing. In fact, it is estimated that nuclearization would

account for 3 to 4 percent increase in aggregate spending over the next five years.

Thus, nuclearization of families and working women are driver of shopping pattern

w.r.t working women.

The Changing Consumption Basket

Occupational changes and the expansion of media have made a significant change to

the way the consumer lives and spends his money. The increase in the contribution

made by the services is also a reflection of the new opportunities that are available

to the women in terms of job opportunities. The Indian population today is

characterized by young women who also have spending power.

There is also an easier acceptance of luxury and an increased willingness to

experiment with mainstream fashion by working women, resulting in an increased

willingness towards disposability and casting out, from apparels to cars to mobile

phones to consumer durables. Occupational changes and the expansion of media

have made significant change to the way working women lives and spend her

money. The changes in income brought about changes in the aspirations and the

spending patterns of the consumers. The traditional shopping pattern in most

developing economies shows that as income rise, working women tend to spend

proportionally less on basic necessities and more on discretionary items. A similar

change is underway in India.

Increased Credit Friendliness

There is a radical change in the working women‟s mindset regarding credit. There

has been a dramatic shift in terms of how a working women defines capital

expenditure and revenue expenditure. Many capital expenditures, i.e. Money spent

36

on buying house, vehicle, jewellery or consumer durables have transformed into

revenue expenditure because of easy availability of finance. Credit cards are a

means of spending or for that matter, increased spending, and this auger well for

the development of shopping pattern.

Geographical Dispersion of Market Potential w.r.t Tier 1 cities

There is a considerable variance in economic prosperity levels among Tier 1 cities

of Indian states, which linked to the overall wealth creation from trade and

industrial development. Accordingly, there are affluent and less affluent areas in tier

1 cities , classified according to their market potential. Herein the top 150 areas in

tier 1 cities account for 78 percent, while the next 150 areas account for 15 percent

of the national market potential for a wide category of goods.

The spread of affluent and non-affluent districts is uniform. However, the Eastern,

North eastern and central regions of India have the largest share of backward

districts. Urbanization has increased considerably in last two decades of

liberalization Urbanization marks an increased growth in consumption and spending.

2.2 Types of Shopping Pattern

Traditional Physical shopping pattern

Online (E shop ) shopping pattern

Telephonic shopping pattern

Working women are attracted towards the following 3 shopping pattern and most of

the women are from middle and high income group.

2.3 Physical Shopping Pattern

Scientific studies states that touching things that one love before they buy them

results to a physical effect like a euphoric state which leads many to associate

shopping as a feel-good experience. So the best way is to experience physically

touching merchandise.

37

Beyond the physical aspects, physical shopping tier 1 cities gives customers the

opportunity to inspect the merchandise they buy for quality. If consumer chooses to

buy big items like furniture, they can try out the product and see if they are

comfortable with it. The human contact also creates a bond between seller and buyer,

initiating trust and guarantee which can make most customers feel good about a

purchase.

Physically walking in store from rack to rack, checking out the display, putting a dress

over and trying to check ones reflection on full-view mirrors that are placed all

around the store is physical shopping Having the ability to physically choose and

check out what an item or product is like, would look like and what its features are.

Some professional women still prefer the traditional type of shopping over online



normally be bigger or smaller than their actual size so there are still physical

shoppers who like to check out the product that they are interested in buying.

Physical shopping still allows for more ground to the consumer in terms of being able

to physically check out and even try out what merchandise they wants.

2.4 Five Steps in the Physical Shopping pattern:

Discovery

Trial/test

Purchase

Pickup/delivery

Return

The study found that at nearly all ages and nearly all stages, the majority of

consumers preferred the in-store experience to the online one. Overall, stores play a

key role even in online purchases. Some two-thirds of customers who buy something

online visit a physical store at some point before or after the purchase.

38

Discovery

The only stage along the transactional journey where shoppers prefer online for a

select few categories, such as computers/electronics. Most consumers prefer in-store

Shopping pattern for popular retail categories including furniture, apparel and

accessories and health and beauty products.

Trial/Testing

The stage where in-store matters most. A whopping 80 percent of all consumers

prefer to test products in a physical store. For some products, such as furniture or

health and beauty, the percentage was even higher at 85 percent. “Immediacy, ease

and accuracy” were some of the reasons people cited for preferring to test products in-

store.

Purchase

Surprisingly despite all one hear about show rooming, 70 percent of consumers prefer

to make purchases in-store, especially for products such as furniture, fine jewellery

and electronics. They tend to believe physical stores offer better customer service than

online shopping pattern.

Pickup/Delivery

Overall, about 55 percent of consumers prefer to pick up products in a store rather

than have them delivered. This may offer more instant gratification.

Returns

Nearly three-fourths of consumers on average prefer to return items to a physical

store.

39

Principle of Physical shopping

The first of the three basic principles is namely location. This is same as it was a

hundred years ago. Having a physical shop or store means that shoppers are situated

in one street, one town and one country. This is also the most expensive part of

running business. Leasing a building can be very expensive. The importance of

location becomes clearer when one looks at the second principle. The second basic

principle being customers. Without a steady supply of customers and the introduction

of new ones from time to time no company will be able to survive or show any sign of

growth whatsoever. So ones store must be easily accessible by his customers.

The third principle is workforce. It includes everyone from the housekeeping to top

manager. If treated unfairly they could single headedly destroy ones business as

customers come directly in contact with them. Physical shop needs lot of expenses

and to make a profit all these expenses gets added to the price tags of final products.

This also gives competitors a bigger sniff of the market.

2.5 Physical shopping Formats

Economic liberalization, competition, and foreign investment since 1990s led to the

proliferation of brands, with both foreign and Indian companies acquiring strong

brand equity for their products. Hence, franchising emerged as a popular mode of

physical shopping format. Over the last 15 years, franchising as a format of physical

shopping, its expansion has gradually matured. International franchising is also in an

interesting phase in India as global organizations like Pizza Hut, Marks and Spencer,

McDonald‟s, Subway, HP, Holiday Inn, Medicine Shoppe, Domino‟s, Gold‟s Gym

and Kentucky Fried Chicken (KFC) have set up franchises in India. In India at present

there are 40,000 franchisees, with an annual turnover anywhere between Rs.8000-

Rs.10,000cr from franchising. It is estimated that total investment made by

franchisees is over Rs.5000cr and over 300,000 are directly employed by franchised

business (Economic Times, Feb, 2014).

The franchisee showrooms of various readymade garments manufacturers like

Arvind Mills, Madura Garments, Raymonds and Titan are perhaps the most

40

visible successes of franchising in India. One of the pioneers in this field, in the

area of beauty and personal care products has been Shahnaz Hussian. Today the

chain of Shannaz Hussian parlours has more than 200 franchisees in India (Source:

www.shahnaz-hussian.com). The other major physical shopping format or organised

format in India is „chain stores‟. More and more new or established companies in

other trade are coming in to the retail business in India, contributing to the

introduction of new formats like malls, supermarkets, hypermarkets, discount stores,

specialty stores and departmental store.

Hypermarket

A hypermarket is a very large physical shopping format offering merchandise at low

prices. Hypermarkets are characterized by large store size, low operating costs and

margins, low prices, and a comprehensive range of merchandise. Typically varying

between 50,000 sq. ft. and 1, 00,000 sq. ft., hypermarkets offer a large basket of

products, ranging from grocery, fresh and processed food, beauty and household

products, clothing and appliances, etc. Reliance Hyper, Big Bazaar, Star India

Bazaar, Spencer's Hyper (formerly Giant), Hyper City, Choupal Sagar (rural

hypermarket) are the major hypermarkets in India.

Cash-and-carry

These are large B2B focused physical shopping formats, buying and selling in bulk

for various commodities. At present, due to legal constraints, in most states they are

not able to sell fresh produce or liquor. Cash-and-carry (C&C) stores are large (more

than 75,000 sq. ft.), carry several thousand stock-keeping units (SKUs) and generally

have bulk buying requirements. In India an example of this is Metro, the Germany-

based C&C, which has outlets in Bangalore and Hyderabad. Wal-Mart has ventured

with cash and carry format with Bharti and has opened its first outlet in 2009 in

Amritsar, Punjab.

http://www.shahnaz-hussian.com/

41

Supermarket

Supermarkets, generally large in size and typical in layouts, offer not only household

products but also food as an integral part of their services. The family is their target

customer and typical examples of this retailing format in India are Apna Bazaar,

Sabka Bazaar, Haiko, Nilgiri's, Spencer‟s from the RPG Group, Food Bazaar from

Pantaloon Retail, etc.

Shop-in-Shop

There is a proliferation of large shopping malls across major cities. Since they are

becoming a major shopping destination for customers, more and more retail brands

are devising strategies to scale their store size in order to gain presence within

the large format, department or supermarket, within these malls. For example,

Infinity, a retail brand selling international jewellery and crystal ware from

Kolkata's Magma Group, has already established presence in over 36 department

chains and exclusive brand stores in less than five years. Shop-in-shops have to rely

heavily on a very efficiently managed supply chain system so as to ensure that stock

replenishment is done fast, as there is limited space for buffer.

Specialty Store

Specialty stores stress on one or limited number of complementary product categories

and extend a high level of service to their customers. In India, the traditionally

independent physical shopping format in the specialized market centre operate in a

particular product category, as these centres attract large crowd. Such specialized

physical shopping format operations provide expertise, economies of scale, bargain

and image to the particular stores. Specialty stores are single-category, focusing on

individuals and group clusters of the same class, with high product loyalty. Typical

examples of such physical shopping format are: footwear stores, music stores,

electronic and household stores, gift stores, food and beverages retailers, and even

focused apparel chain or brand stores. Besides all these formats, the Indian market is

flooded with formats labelled as multi-brand outlets (MBOs), exclusive brand outlets

(EBOs), kiosks and corners, and shop-in-shops. Recently, with the advent of

42

organized physical shopping format , many companies and retail chains have opted

for this retail format such as furniture (Gautier), durables (Vivek‟s), watches (Titan),

etc. In particular, this kind of retail format appeals to lifestyle product categories such

as apparel, watches, home fashions, and jewellery.

Discount Store

A discount store is a physical shopping format store offering a wide range of

products, mostly branded, at discounted prices. The physical shopping format offer a

broad variety of merchandise mix, limited or no service, and low prices are

characterized by low margins, heavy advertising, low investments on fixtures,

limited support from sales people, etc. discount stores prefer shopping centres that

provide space at lower rents as they attract customers from other adjoining stores in

the shopping centre. The average size of such stores is 1,000 sq. ft. In India, the

„discount stores‟ concept works with a difference. Indian consumers are price

conscious, interested in the best of the offerings, that is, the brands at the least price.

One needs to classify the stores on the basis of perpetual discount stores, extent of

discount, category wise discount, item/ brand wise discount, GP-based discount,

general discount, loyalty discount, special discount, festival discount, stock clearance

discount, and fixed amount discount. Vendor partnership is an essential element in

the success of the operations. A store‟s operations and inventory management need

to be very efficient and effective to keep the running expenditure under control.

Display should be self explanatory to guide the customer in his buying decision.

Price tags should be pasted depending on the commodity so that they are visible

irrespective of the category of the merchandise, etc. Typical examples of such stores

in India are ; Food and grocery stores offering discounts are D Mart ,Margin free store

, the factory outlets of apparel and footwear brands, namely, Levi‟s factory outlet,

Nike‟s factory outlet, Koutons etc

Convenience Store

A convenience store is a relatively small retail store located near a residential area

(closer to the consumer), open long hours, seven days a week, and carrying a limited

43

range of staples and groceries. Some Indian examples of convenience stores include:

In & Out, Safal etc. The average size of a convenience store is around 800 sq. ft.

Department Store

Department stores generally have a large layout with a wide range of merchandise

mix, usually in cohesive categories, such as fashion accessories, gifts and home

furnishings, but skewed towards garments. These stores are focused towards a

wider consumer audience catchment, with in-store services as a primary differentiator.

Usually, department stores are located within a planned shopping centres or

traditional up-market downtown centres. The department stores usually have 10,000 -

60,000 sq. ft. of physical shopping format recently many leading independent retailers

of the cities and even new entrants are indicating preference for autonomous

department stores. For example, Appeal, a leading fashion store in west Delhi and the

baniya store in Jammu offers a wide range of products such as gifts, dry fruits, sports

material, apparel, home fashion, curtain, bed sheets, etc. Customers are free to move

around the store unlike the traditional counter set- ups in India.

Various departments within the store have a designated selling space allocated to

them, including a point-of-sales terminal to transact and record sales, and salespeople

to assist customers. A majority of the department stores in India possess women‟s,

men‟s, kids‟, fashion accessories, and kitchenware and home fashion departments.

Some departments do provide convenience to their customers in the browsing and

selection of the merchandise. Department stores provide a distinctive shopping

experience to customers on account on account of services (home delivery, credit

card, restaurants, cloakroom, and changing room etc.) extended along with core

offerings and atmospherics . Pricing of the merchandise offered is relatively high due

to trained sales staff, range of merchandise offered and high capital investments.

Department stores, generally, opt for centralized buying taking in to consideration

the preferences and tastes of the consumers. In case of multiplicity of departments

within stores, each department carries out its own buying in accordance with the

demand patterns of their customers.

44

2.6 Online / E-shopping

Online shopping is one of the most popular ways to make purchases, but it's not

something that everyone is comfortable doing. As with most things, there are

positives and negatives associated with this approach to shopping. Consider the

advantages and disadvantages carefully so one can make an informed decision about

what's best for them. E-shopping is a recent occurrence in the field of e-business and

is definitely going to be the future of shopping. Most of the companies are running

their on-line portals to sell their products/services on-line. Online shopping is very

common outside India, its growth in Indian Market, which is a large consumer

market, is still not in line with the global market. The growth of on-line shopping has

triggered on-line shopping phenomena in India. Factors w.r.t working women on-line

shopping parameters are satisfaction with on-line shopping, future purchase intention,

frequency of on-line shopping, numbers of items purchased, and overall spends on on-

line shopping.eg.bigbasket.com, localbaniya.com, aaramshop.com,

hypercityindia.com .

Shopping has got a new definition since the arrival of the internet. Any individual or

company from any part of the world who is able to post and sell goods on the internet

via a website is able to sell. Consumers have various means to exchange monetary

paper by not just online banking but can pay through different payment methods.

These days, it is easy to find the most difficult of all products, by easily typing in the

product or item. Online companies are making logistics also easily available by

joining the bandwagon and helps in making sure that their products would be

available to any and all destinations in the world. Today there are more and more

advantages and benefits to online shopping equal to traditional physical shopping .

Grocery E-shopping Portals in India :

www.localbaniya.com

www.aaramhop.com

www.bigbasket.com

www.naturebasket.com

http://www.differencebetween.net/business/difference-between-consumer-goods-and-industrial-goods/

http://www.localbaniya.com/

http://www.aaramhop.com/

http://www.bigbasket.com/

http://www.naturebasket.com/

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www.infibeam.com

www.zopnow.com

www.rationhut.com

www.mygrahak.com

www.omart.com

www.atmydoorstep.com

www.ekstop.com

Principle of Online shopping

The first principle being location. The internet has allowed one to have a store in each

home, in every town in every country on every continent. Now one can market their

product or services globally. Since if ones business is online they do not have to lease

a building and can save money.

The second principle being customers stays the same like physical shopping pattern

The difference here being that target market has increases in numbers. One can say

that the playground just got bigger with more toys to play with. Target audience will

now comprise of a multitude of nationalities. Time has shown that if something is a

trend in one country it will with time spill over to another and become a trend there.

The third principle being workforce decreases dramatically. Since online shopping is

the opposite of physical, one can safely assume that expenses will be much less. So

one can sell their products at a reduced price making the same profit. For the

consumer this means lower price.

2.7 Advantages of Online Shopping for working women

Convenience:

There is no doubt that shopping online can be very convenient for busy people. One

can shop from their home or office or any other location where one have access to a

computer, tablet device or smartphone and Internet access. One can browse and make

purchases any time of the day or night from any location that is convenient rather

http://www.infibeam.com/

http://www.zopnow.com/

46

than having to take time out of once day or evening to go to local stores in person

during their hours of operation.

Ease of Comparison Shopping:

When one shop online, she can compare offerings and pricing at different stores with

the simple click of a button rather than having to get in her car and spend their

precious time and hard-earned gas money running from one store to another to see

what stores carry what product lines and how much each one is charging. With the

help of shopping comparison sites like NexTag.com , one can go to a central place to

narrow down to the online retailers that are likely to have the best deal on the items

one want without even having to run key words through search engines to find out

where to look.

Extensive Product Mix Availability:

When one shop online, one might find that there are more options available than

focusing the product search only on items available in ones local areas. As store

buyers have to make decisions about what items to carry in their physical stores, and

those decisions are impacted by local market demand, past purchasing success and

failures and shelf- space constraints.

Global choice

Since the boundaries of online marketing are not defined by geography or national

borders, consumer will benefit from a wide selection of vendors and products

including a wider availability of hard-to-find products.

Online delivery

For digital products, the whole commercial cycle, including distribution, can be

conducted via a network, providing instant access to products immediately when a

need arises.

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The real time nature of the medium

The internet can provide consumers with up-to-the minute information on prices;

availability.

Time savings

Consumers may benefit from the shopping process being faster in the market space

than in the market place as a result of the rapidity of the search process and the

transactions.

Access to extensive information

An important consumer benefit is the access to greater amounts of dynamic

information to support queries for consumer decision-making.

Privacy and anonymity

The internet has the potential to offer consumers benefits with respect to a partial, or

even a total privacy and anonymity throughout the purchasing process.

Competitive prices

By embracing online marketing consumers may benefit from price reductions as a

result of increased competition as more suppliers are able to compete in an

electronically open market place as a result of reduced selling prices due to reduction

in operational/transaction costs and manufacturers internalizing activities traditionally

performed by intermediaries.

Availability of personalized offers

Consumers can benefit from IT enabled opportunities for personalized interactions

and one-to-one relationships with companies, which allow for products, services and

web content to be, customized more easily.

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The social nature of purchasing process

Since consumers differ in their social disposition, many consumers may find an

impersonal purchasing situation desirable for social reasons or simply because they

find the verbal contact with a seller time-consuming. Moreover, the lack of physical

sellers creates sales setting where there is virtually no pressure to buy

2.8 Online shopping and Indian shoppers

India has large, dynamic consuming class and has rising levels of urbanization, rapid

growth in its consumer base, and one of the most youthful demographic profiles

worldwide. By 2020, India is likely to have acquired an additional 1120 lakhs urban

residents, and an urbanization ratio of 36.4 percent. Nonetheless India‟s “consuming

class” at more than 453 lakhs households is sizable, and is projected to grow to

943 lakhs households by 2020. India‟s economy has grown rapidly, at 6.8 percent real

growth per year between 2000 and 2010, supported by increasing foreign investment,

growth in infrastructure investments, and the liberalization of sectors such as telecom

and insurance. Online travel, growing at more than 25 percent per year, has been

driven by diverse online players ranging from the IRCTC (the online ticketing arm of

the Indian Railways) to indigenous travel aggregator sites such as Makemytrip,

Cleartrip, and Yatra. More recently, international travel aggregators such as Expedia

and Kayak, as well as review sites such as Tripadvisor, have begun to make a strong

push into India. Consumer traction has been driven by ease, convenience, lower

prices, and better customer offerings.

Online players (for example, Flipkart, Amazon) provides large assortments, powerful

product comparisons, and attractive pricing which are the key value propositions for

Indian consumers. Apparel is expected to be the fastest-growing online category in

e-commerce in India,. Online players (for example, Myntra, Jabong) are dominant,

along with online store-fronts of offline retailers (for example, Shoppers Stop,

Central). Convenience, cash-on-delivery, limited-period free returns, and attractive

offers are driving the fast-growing consumer interest in the online purchase of

apparel.

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Consumers likely to derive more value in future

Low levels of online activity (20 to 25 hours per month), including online shopping,

correlate with India‟s low consumer activity from the Internet. Consumers in most

Southeast Asian countries spend far more time online than those in India, India‟s

consumer surplus from the Internet is estimated to be Rs. 900 crore per year, but the

ratio of its annual aggregate consumer spends to GDP is only 0.5 percent, which is

lower than in many developed and aspiring countries. This is in line with the low

share of private consumption in India‟s GDP. India‟s consumer surplus is likely to

grow more rapidly in the future, given that emerging trends indicate that online

commerce, including online research for offline purchases, is a significant source of

value for Indian consumers.

As India‟s working women population of early adopters takes to the Internet, usage is

increasing and usage patterns are shifting dramatically, with more time spent online

and increasing sophistication in the Internet activity.Digital Consumer Research

indicates that consumers below the age of 35 represent around 85 percent of the

smartphone, VOIP, and social network markets in India, compared with about

60 percent in developed countries and 75 percent in aspiring countries. India‟s young

Internet users are displaying an increasing appetite for online research, transactions,

social networking and entertainment. Time spent on the Internet by users in India rose

44 percent from 2010 to 2014, and more sophisticated categories of Internet use, such

as e-mail/chat, social networking, and entertainment, grew more quickly than reading

and browsing downloads of applications for mobile phones have multiplied eight

times in two years, with social networking and music being the major categories.

Social networking is the single biggest use for smart phones, after voice, with the

number of Facebook users in India jumping from less than 100 lakhs in 2009 to in

excess of 900 lakhs in 2015.

2.9 Telephonic shopping:

Teleshopping indicates buying consumer products using a telephone connection.

Developments in teleshopping offer many possible uses like as a supplement and

50

alternative to transportation. The use of time (by point in time and by time budget)

and the use of space (location and infrastructure) will change. Teleshopping saves

time of working women. Some shopping trips could be scheduled to avoid the rush

hour. Teleshopping also has effects on the use of space. Telephone shopping is in

many ways the easiest and most convenient mode of shopping ever devised.

Telephone shopping is in many ways the easiest and most convenient mode of

shopping. Instead of the dressing, travelling, walking, looking, waiting, and carrying

which characterize an in-person shopping expedition, whereas in the telephonic

shopping a working women just picks up the phone, dials, orders, and awaits

delivery. Telephone shoppers themselves are nearly unanimous on this point over

90% of working women surveyed stated that the major attraction of telephone

shopping is its convenience.The question is of more than academic interest.

Telephone shopping can contribute substantially to the sales and profits of department

and specialty stores. Although a telephone sales trans-action may cost the store 50%

more to service than the average floor transaction average telephone sale is probably

substantially higher than the average floor transaction. Furthermore, many store

executives have expressed concern that encouragement of telephone orders might

inhibit in-store traffic and damage sales. It was found that women who shopped quite

often in the stores also tended to shop frequently by phone.

In addition, telephone sales can contribute "plus" sales volume which otherwise might

not be obtained by the store. Slightly more than half of the telephone shoppers

surveyed who named a favourite store for in-store shopping (often a discount store),

named a different store (a department or specialty store) as their favourite for

telephone shopping. Similarly, to the particular advantage of the downtown store,

phone orders from suburban customers temporarily tied down at home, or unable to

get into town for special promotions, represent an important source of extra sales

volume. In sum, telephone shopping, despite its higher selling costs, can contribute

importantly to department and specialty store sales and profits. As one have seen,

however, there remains substantial doubt whether department and specialty stores are

currently realizing the full profit potential of telephone sales. The fact is that the

majority of women do not shop by telephone.

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2.10 Psychology of working women during telephonic shopping

When shopping in a department store the working women has the opportunity to

reduce uncertainty by personally inspecting or testing the merchandise; by comparing

two or more brands of the same item for product characteristics, price and quality; by

comparing different sizes, colors, or styles; and by referring to a salesperson. Working

women may consult with a salesperson, as she has access only to a telephone order

clerk who is not a specialist in a particular merchandise line. Working women is

limited to essentially two means of uncertainty reduction: reliance on past experience

with the store, product, or brand; or reliance on a newspaper advertising which may or

may not picture the article. Particularly in the case of products or brands new to

women, working women must make decisions based upon little information. Many

women consider telephone shopping to be a highly risky venture. The real issue is the

extent to which perceived risk affects telephone shopping.

In-home spending profile working women estimates of their catalogue, direct mail,

and telephone spending over the previous January through November period are

aggregated into the in-home spending women locked in away from store shopping

were more likely to order by mail or phone than other women. Driving time to stores,

availability of transportation for shopping, shopper employment status, and shopper

age and presence of preschool children at home were selected as proxy measures of

locked-in shopping conditions and compared against in-home buying totals. Other

factors such as bad weather or illness in the family also would appear likely to

discourage store shopping plans. However, their effects on shopping were thought to

be transitory and random; thus they were impractical for delineating potentially

locked-in shoppers and predicting their in-home spending behaviour. Since the

validity of the proxy variables as measures of locked-in shop-ping was not clearly

established, respondents' evaluations of the shopping difficulty posed by each factor

were also obtained and compared against in-home spending totals.

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2.11 Shopping performance of Working Women

Shopping is probably one of the oldest terms and have been over the years. Working

women‟s shopping behaviour refers to “the mental and emotional processes and the

observable behaviour of consumers during searching for, purchasing and post

consumption of a product or service. Shopper‟s behaviour has two aspects: the final

purchase activity which is visible to us and the decision process which may involve

the interplay of a number of complex variables not visible to us. In fact, purchase

behaviour is the end result of a long process of consumer decision-making. The study

involves what working women as consumer‟s buy, why they buy it, how they buy it,

when they buy it, where they buy it, how frequently they buy it and how they dispose

of the product after use. Consumer behaviour is defined as "the totality of consumers´

decisions with respect to the acquisition, consumption, and disposition of goods,

services, time, and ideas by (human) decision-making units. It includes consumers´

actions, and their feelings and thoughts experienced during the consumption process.

Additionally, all other aspects in the environment, which may influence these actions,

feelings, or thoughts, are counted as consumer behaviour.

The behaviour of consumer groups and their environment are continuously changing

and therefore marketers regularly conduct consumer research and analysis in order to

follow trends. Marketers can gain understandings of how consumer behaviour is

affected by thoughts, feelings, actions and environment, in order to comprehend

consumers´ meaning of products and brands. This is also helpful in understanding

consumer behaviour in relation to consumer shopping, purchase and consumption

habits. By comprehending the interactions´ effect on individual consumers, similar

target markets and society, marketers can better satisfy needs and wants,

subsequently creating value for consumers. Another aspect of consumer behaviour

involves exchanges between people when something of value is sacrificed and

replaced, such as money and products. In summary, understanding of consumer

behaviour contributes to companies' success in developing marketing strategies that

in turn increase profitability.

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The principles of shopping behaviour are applied in many areas of marketing such as

Analysing market opportunity shopping pattern study helps in identifying the

unfulfilled needs and wants of consumers. This requires examining the trends and

conditions operating in the marketplace, consumers lifestyles, income levels and

emerging influences. This may reveal unsatisfied needs and wants. The trend towards

increasing number of dual income households and greater emphasis on convenience

and leisure have led to emerging needs for household gadgets such as washing

machine, mixer grinder, vacuum cleaner and childcare centres etc. Mosquito

repellents have been marketed in response to a genuine and unfulfilled consumer

need.Selecting target market: A review of market opportunities often helps in

identifying distinct consumer segments with very distinct and unique wants and

needs. Identifying these groups, learning how they behave and how they make

purchase decisions enables the marketer to design and market products or services

particularly suited to their wants and needs. For example, consumer studies revealed

that many existing and potential shampoo users did not want to buy shampoo packs

priced at Rs. 60 or more and would rather prefer a low-priced sachet containing

enough quantity for one or two washes. This finding led companies to introduce the

shampoo sachet, which became a good seller. In case of consumer durables market in

India marketers are targeting the higher income class with special features in the

equipments as well as longer warranty period and of course world class quality. In

case of semi urban and rural areas consumers who prefer the basic offerings or

slightly modern version of the product are targeted.

2.12 Working women’s decision making process

One of the very few aspects common to all is that all consumers and the reason for a

business firm to come into being is the presence of consumers who have unfulfilled,

or partially fulfilled needs and wants. Buyer behaviour is an extremely important and

complex subject for any marketer. At the same time, it is important to appreciate that

there is no unified, tested, and universally established theory on this subject. Buyer

remains an enigma and her/his mind is viewed as a black box. Before businesses can

develop marketing strategies, they must understand what factors influence buyer

behaviour and how they make purchase decisions to satisfy their needs and wants.

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Understanding buyer behaviour and “knowing buyers” is the most difficult task. It is

almost impossible to predict with hundred per cent accuracy how buyers will behave

in a certain situation. Buyers are moved by a complex set of deep and subtle emotions

consumer decision-making generally involves five stages: Problem or need

recognition, information search, alternatives evaluation, purchase, and post-purchase

evaluation.

Table no 2.1 Shoppers shopping Process

(Source: Schiffman L G & Kanuk L C, Consumer Behaviour)

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Problem Recognition

Purchase decision-making process begins when a buyer becomes aware of an

unsatisfied need or a problem. Problem recognition is a critical stage in consumer

decision-making process because without it, there is no deliberate search for

information. One common problems faced such as the need to replenish items of

everyday consumption. The process of problem recognition combines some highly

relevant consumer behaviour concepts such as information processing and the

motivation process. Consumers must become aware of the problem through

information processing arising as a result of internal or external stimuli. This leads to

motivating consumers; they are aroused and activated to engage in some goal directed

activity (purchase decision making). This kind of action in response to recognising

problems and finding solutions to problems depends on the magnitude of the

discrepancy between the current state and the desired or ideal state and secondly, the

importance of the problem for the concerned consumer. The discrepancy and/or

importance should be of sufficient magnitude to start the purchase process. Without

perception of a problem by the consumer, there is no recognition of an existing

problem and hence there is actually no need to engage in the process of decision-

making. Since the consumer does not perceive any discrepancy between her/his

current state and the desired state, the current state for the concerned consumer is

apparently quite satisfactory and does not need decision-making. It is important to

appreciate that it is actually the consumer‟s perception of the actual state that

stimulates problem recognition and not some “objective” reality. Also, the relative

importance is a critical concept in several purchase decisions because almost all

consumers have budgetary or time constraints.

Information Search

After problem or need recognition, consumers generally take steps to gather adequate

information to select the appropriate solution. Information search refers to what

consumer surveys in her/his environment for suitable information to make a satisfying

purchase decision. Problem recognition is an ongoing process for consumers and they

use internal and external searches to solve these problems. Consumers may also be

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involved in ongoing search activities to acquire information for possible future use.

No sooner does a consumer recognises a problem, than she/he in a reflexive manner

first thinks or tries to remember how she/he usually solves this kind of problem. The

recall may be immediate or occur slowly as a result of conscious effort. This recall

from long-term memory might produce a satisfactory solution in case of many

problems, and no further information search is likely to occur.

Sources of external information include:

• Relatives, friends, neighbors and chat groups.

• Professional information from handouts, pamphlets, articles, magazines,

journals, books, professional contacts, and the Internet.

• Direct experience through trial, inspection, and observation.

• Marketer initiated efforts included in advertisements, displays, and sales

people.

The information collection yields an awareness set of brands/products. Awareness or

consideration set is composed of recalled and learned about solutions. Awareness set

contains evoked set, inept set, and inert set. Evoked set is composed of those brands

the consumer will evaluate to choose the solution of a particular problem or need.

Inept set includes those brands that the consumer finds unworthy of consideration.

Inert set is composed of alternatives that the consumer is aware of but would not

consider buying and would treat with indifference.

Store Selection and Purchase Decision

Making a purchase is often a simple, routine matter of going to a retail outlet where

the consumer looks around and quickly picks out something needed. All consumers

like to view themselves as intelligent shoppers and make decisions regarding the retail

outlet choice in which they will shop. Generally, consumers decide about the make of

the computer first then choose the dealer to buy it from. Frequently, it happens that

consumers choose the retail outlet first and this influences their choice of the brand.

57

For example, when consumers shop for clothes, they generally decide about a retail

outlet first, or go to a market area where several such stores exist

Similarly, they often make a brand decision in the retail store when women shop for

appliances. Increasingly, consumers are exposed to product introductions and their

descriptions in direct-mail pieces and catalogues, in various print media vehicles, on

television and on the Internet and buy them through mail, telephone, or computer

orders. In case of some product categories, Internet offers greater selection,

convenience and lower prices than other distribution outlets for at least some

consumers. So far, this in-home shopping is not so common in India but is on the

increase. A large number of companies with websites are encouraging consumers to

buy products through computer orders. Retail outlet image and location has an

obvious impact on store patronage and consumers‟ outlet choice often depends on its

location. Consumers generally will choose the store that is closest. Similarly, the size

of the store is also an important factor that influences consumers‟ outlet choice. For

minor shopping goods or convenience items, consumers are unwilling to travel very

far. However, for high-involvement purchases, consumers do not mind travelling to

distant shopping areas. Retail outlets are also perceived as having varying degrees of

risk. Consumers perceive less risk with traditional retail outlets compared to more

innovative outlets such as the Internet. Once the consumer has chosen a brand and

selected a retail outlet. Traditionally, this would involve offering the cash to acquire

the rights to the product. In developed and many developing countries, credit often

plays an important role in completing the purchase transaction.

Credit cards are popular in developed economies and are increasingly becoming

popular in India and many other developing countries, as a convenient way of

financing many purchases. Many retail outlets overlook the fact that the purchase

action is generally the termination of last contact that the customer will have with the

store on that shopping trip. This presents the business an opportunity to create a

lasting impression on the customer

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Post-Purchase Action

Consumers‟ favourable post-purchase evaluation leads to satisfaction. Consumers

choose a particular brand or retail outlet because they perceive it as a better overall

choice than other alternatives that were evaluated while making the purchase decision.

They expect a level of performance from their selected item that can range from quite

low to quite high. Expectations and perceived performance are not independent and

consumers tend to perceive performance in line with their expectations.

After using the product, service, or retail outlet, the consumer will perceive some

level of performance that could be noticeably more than the expected level, noticeably

below expectations, or match the expected level of performance. Thus, satisfaction

with a purchase is basically a function of the initial performance level expectations,

and perceived performance relative to those expectations. Consumers engage in a

constant process of evaluating the things that they buy as these products are integrated

into their daily consumption activities. In case of certain purchases, consumers

experience post-purchase dissonance. This occurs as a result of the consumer

doubting her/his wisdom of a purchase. After purchase, most products are put to use

by consumers, even when they experience dissonance. Consumers experience post

purchase dissonance because making a relatively longer commitment to a selected

alternative requires one to forgo the alternative not purchased. Thus, in case of

nominal-decisions and most cases of limited-decisions, consumers are unlikely to

experience post-purchase dissonance because in such decisions consumers do not

consider attractive attributes in a brand not selected .As one may expect, a positive

post-purchase evaluation results in satisfaction and the negative evaluation causes

dissatisfaction.

In case the consumer‟s perceived performance level is below expectations and fails to

meet the expectations, this will definitely cause dissatisfaction and the product or the

outlet will be most likely pushed in the inept set and dropped from being considered

on future occasions. Thus, the consumer is also likely to initiate complaint behaviour

and spread negative word-of-mouth. The consumer generally experiences satisfaction

when the performance level meets or exceeds the minimum performance

59

expectations. Similarly, when the performance level far exceeds the desired

performance level, the consumer will not only be satisfied but also will most likely be

delighted. Such an outcome tends to reduce the consumer‟s decision-making efforts

on future purchase occasions of the same product or service to accomplish need

satisfaction. Thus, rewarding purchase experience encourages consumers to repeat the

same behaviour in future. A delighted consumer is likely to be committed and

enthusiastic about a particular brand and usually unlikely to be influenced by

competitors actions. A delighted consumer is also inclined to spread favourable word-

of-mouth.

Over the years, Indian economy is undergoing through certain changes. Competition

has ushered in an altogether new marketing environment in the country. Marketing

has become a necessity for survival of business firms. Price, competitiveness, quality

assurance and customer service has become vital components of marketing and most

business firms are realizing that if they do not have competitive strength, they cannot

survive. A business cannot succeed by supplying products and services that are not

properly designed to serve the needs of the customers. The entire business has to be

seen from the point of view of the customer. A company‟s business therefore,

depends on its ability to create and retain its customers. Thus, a company, which

wants to enhance its market share, has to think of customers and act customer.

Understanding the buying behaviour of the target market is the essential task of

marketing managers in marketing concept. The term consumer behaviour refers to the

behaviour that consumers display in searching for, purchasing, using evaluating and

disposing of products and services that they expect will satisfy their needs.

.

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CHAPTER 3

WORKING WOMEN

The study conducted in recent year‟s states that feminine roles are of great concern to

consumer analysts and marketers. A role specifies what the typical occupant of a

given position is expected to do in that position in a particular social context. One of

the challenges working women face today is balancing their roles as a wife, mother,

wage-earner and consumer. Married working women experience time constraint and

pressures dealing with household responsibilities and their jobs in the marketplace.

Working women could be part of several groups and organizations, a member of a

family, working in a certain firm, member of a professional forum, a part of a political

group, a member of Rotary club of the city, active worker of a trade union, regular

participant in local social activities etc.

The modern working women have realized now that they have a personality of their

own as a human being and that their mission in life does not end with becoming

merely a wife and a good mother but also in realizing that they are also a member of

the civic community. Thus, the modern women are not having a passive life. They are

prepared to express and show their individuality in various walks of life. Education is

a catalytic agent for social change. Changes in life and position of women have been

greatly accelerated by the spread of education. As a result, women organizations and a

strong women‟s movement took place. The necessity for work on the part of the

women is not due to their enlightenment alone. The women work either because of

economic necessity which force them to do so, or because they want to derive

psychological satisfaction out of it. The reasons that prompt women to work apart

from economic necessity are manifold. The women may work in order to raise the

standard of living of their household or to have an independent income or by the

compulsion of the family members. Modern women do not like to stay idle and

stagnate at home, but rather aspire to utilize their education and mental abilities in a

constructive and creative manner. They prefer to work because they find plenty of

time after their household chores is taken care of, or because they can use their job as

an „escape-mechanism‟ from the drudgery of life. They can also gain self-confidence

61

within themselves by working, establish themselves a status and gain significant role

in the family affairs. These are some of the reasons that motivate women to venture

into the men‟s world, leaving behind the monotony of home.

The obstacles to thei r success are many; ceilings to thei r aspiration are made of

more than glass. Traditional social attitudes and cultural patterns have not changed

overnight. Over discrimination may be receding, but the “old boys‟ network‟s may still

be operational. The skills and confidence to push for career advancement are not

instantly acquired. Practical infrastructure challenges can be the most determined of

women as they try to make lives that embrace both work and family.

3.2 Economic Status of working women

Appraisal of women‟s economic roles and opportunities for participation in economic

activities cannot be done in isolation of the society‟s state of development, the socio-

cultural attitudes towards women's role in the family and in society and the social

ideology concerning the basic components of status. Socio-economic advancement of

a country can be judged by the status and position, which it can bestow on its women.

So the levels of economic equality and independence are the real indicators to

measure the status of women in any society. In India, the general economic situation

is far from satisfactory, the situation of women is worse than that of men. There is no

doubt that, over the years there has been sea of changes in social perception of issues

that relates to women in rural areas. They remain the most deprived and long

neglected segment of the society, despite constitutional guarantee for equal rights and

privileges for men and women.

Their contribution to the economic growth of the society is quite substantial although

it is a fact that the labour put in by women in discharging the economic and domestic

duties hardly gets its due recognition. Women are considered as secondary citizens

with no independence of any sort. Since centuries, known and unknown women were

the targets of social exploitation and subordination, women work for as many hours as

men do, if not more; yet their labour is counted as “shadow work” giving them neither

the due credit nor equal pay for the work done. Women play a critical role in the

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family and community as major contributors to family income both in rural areas and

urban areas.

3.3 Problems of Working Women

Social:

Once the women are on a job either on economic grounds or on personal reasons, it

becomes a matter of routine and virtue of regular income. While women pull

themselves up to men‟s life, they find themselves in the midst of responsibilities and

eventually end up in discharging the obligations of men. Each women have problems

which are different in nature. They have problems of adjusting to time schedules with

other working adults in the family, wanting privacy in freedom and a greater

participation in the financial management and a desire for a balanced life. Though

Indian constitution has given equal rights and opportunities, their problems remain

unsolved and these cannot be solved by legislations alone.

Nature of other problems varies with the nature of category to which the working

women belong, their personality dimensions, their capacity to work, their motivation

ability to work and to adjust to the family conditions. Challenges faced by

workingwomen are that husband and wife both are working. This gives rise to

problems. Essentially, it is a woman‟s problem because the working wife, when

working women returns from her work, has to ensure that her family does not face

any deficiency. The family has to be fed and looked after. For a happy home, it is

essential that the job timings of women do not coincide with those of the husband and

children.

Psychological Problem:

Various problems which working women face every day, make them apart mentally.

The tolerance level of this strain bears some relationship with personality of the role

player. If the problem is deeply felt by the women, it may result in lack of adjustment

either in the family or in their social and emotional life or in their job setting. Many of

these working women suffer from a guilt feeling, due to the non-fulfilment of their

legitimate duties. This psychological reaction may be mostly subjective in nature. The

household workload has become a problem for working women as the joint family

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system is dying out and servants are not available today to assist them. The strains of

work at home and office coupled with lack of household amenities and vanishing

domestic help, contributing to the gravity of problems among working women.

Having less time and more incongruent demands of conflicting roles, the working

women are experiencing more and more adjustment problems in the modern society.

A careful observation indicates that most of the husbands seem to be selfish to have

additional income and hence they permit their marital partner to seek a gainful

employment. They also tend to tolerate for economic reasons but they do not actively

assist and share the family responsibilities of their employed wives. However, there is

a mild transition in modern India in certain families, where the husbands also share or

assist in the performance of family responsibilities. But, this is at the peripheral level

which manifests their hesitation to share role performance which was culturally and

traditionally assigned for women alone.

The working woman is considered an important customer for retailers and the largest

spender, and influences how the family spends their money. Despite working

women‟s liberty and working outside the home, she still do most of the grocery

shopping. However still all women shop alike.

64

CHAPTER 4

FAST MOVING CONSUMER GOODS (FMCG)

4.1 Introduction:

Fast Moving Consumer Goods (FMCG) goods are popularly named as consumer

packaged goods. Items in this category include all consumables like groceries/pulses,

Toiletries, Frozen food, Dairy products, Cosmetics etc. which people buy at regular

intervals. These items are meant for daily of frequent consumption and have a high

return. The Indian FMCG sector with a market size of Rs 1.48 crore is the fourth

largest sector in the economy. The FMCG market is set to double in 2018. FMCG

sector will witness more than 70 per cent growth in rural and semi-urban India by

2016.Hair care, household care, male grooming, female hygiene, and the chocolates

and confectionery categories are estimated to be the fastest growing segments. At

present, urban India accounts for 66% of total FMCG consumption, with rural India

accounting for the remaining 34%. However, rural India accounts for more than 40%

consumption in major FMCG categories such as personal care, fabric care, and hot

beverages.

In urban areas, home and personal care category, including skin care, household care

and feminine hygiene, will keep growing at relatively attractive rates. Within the

foods segment, it is estimated that processed foods, bakery, and dairy are long-term

growth categories in both rural and urban areas. The growing inclination of rural and

semi-urban people for FMCG products will be mainly responsible for the growth in

this sector, as manufacturers will have to deepen their concentration for higher sales

volumes. Major Players in this sector include Hindustan Unilever Ltd., ITC (Indian

Tobacco Company), and Nestlé India, GCMMF (AMUL), Dabur India, Asian Paints

(India), Cadbury India, Britannia Industries, Procter & Gamble Hygiene and Health

Care, Marico Industries, Nirma, Coca-Cola, Pepsi and others. As per the analysis by

Associated Chambers of Commerce and Industry of India (ASSOCHAM), companies

like Hindustan Unilever Ltd, Dabur India originates half of their sales from rural

India. While Colgate Palmolive India and Marico constitutes nearly 37% respectively,

65

however Nestle India Ltd and Glaxo Smith Kline Consumer drive 25 per cent of sales

from rural India. A rapid urbanization, increase in demands, presence of large number

of young population, a large number of opportunities is available in the FMCG sector.

The growth of consumption, production, and employment is directly proportionate to

reduction in indirect taxes, this reduction in indirect tax was incorporated by BJP led

Indian Govt ,which constitute no less than 35% of the total cost of consumer products

- the highest in Asia.. The bottom line is that Indian market is changing rapidly and is

showing unprecedented consumer business opportunity.

Fast-moving consumer goods (FMCG) or consumer packaged goods (CPG) are

products that are sold quickly and at relatively low cost. Examples include soft drinks,

toiletries, over-the-counter drugs, toys, processed foods and many other consumables.

In contrast, durable goods or major appliances such as kitchen appliances are

generally replaced over a period of several years. FMCG have a short shelf life, either

as a result of high consumer demand or because the product deteriorates rapidly.

Some FMCG such as meat, fruits and vegetables, dairy products, and baked goods are

highly perishable. Other goods such as alcohol, toiletries, pre-packaged foods, soft

drinks, and cleaning products have high turnover rates.

Though the profit margin made on FMCG products is relatively small, they are

generally sold in large quantities; thus, the cumulative profit on such products can be

substantial. FMCG is probably the most classic case of low margin and high volume

business.



http://en.wikipedia.org/wiki/Toiletries

http://en.wikipedia.org/wiki/Over-the-counter_drug

http://en.wikipedia.org/wiki/Toy

http://en.wikipedia.org/wiki/Processed_food

http://en.wikipedia.org/wiki/Consumables






66

Table 4.1.FMC Goods considered in this study, which women shop Online (E-

shopping and Teleshopping) and Physical.

Toiletries

Serums

Shampoos

Conditioner

Shower gel/Soap

Sanitizer

Frozen Food

Peas

French Fries

Cut veggie/ Fruits

Ready to cook & Serve food

Frozen raw Non-Veg

(Chicken /Meat/Fish )

4.1.1 Dairy product

Dairy Product or milk product is food produced from the milk of mammals. Dairy

products are usually high energy-yielding food products. A production plant for the

processing of milk is called a dairy or a dairy factory. Apart from breastfed infants,

the human consumption of dairy products is sourced primarily from the milk of cows,

buffaloes, goats, sheep, yaks, horses, camels, domestic buffaloes, and other mammals.

Dairy products are commonly found in European, Middle Eastern, and Indian cuisine.

Grocery

Cereals

Pulse

Salts & Seasonings

Edible oil

Sugar

Cosmetics

Face Powder

Hair gel

Body lotion

Nail Polish

Lipstick

Dairy Products

Tofu

Flavored milk

Curd

Paneer

Cheese

Lassi / Butter Milk

http://en.wikipedia.org/wiki/Milk

http://en.wikipedia.org/wiki/Mammals

http://en.wikipedia.org/wiki/Dairy

http://en.wikipedia.org/wiki/Cattle

http://en.wikipedia.org/wiki/Water_buffaloes

http://en.wikipedia.org/wiki/Dairy_goat

http://en.wikipedia.org/wiki/Domestic_sheep

http://en.wikipedia.org/wiki/Yak

http://en.wikipedia.org/wiki/Horse

http://en.wikipedia.org/wiki/Camel

http://en.wikipedia.org/wiki/European_cuisine

http://en.wikipedia.org/wiki/Middle_Eastern_cuisine

http://en.wikipedia.org/wiki/Indian_cuisine

67

Types of Dairy Product:

Clotted and thick cream made by heating milk , single cream, double cream

and whipping cream.

Cultured milk resembling buttermilk, but uses different yeast and bacterial

cultures.

Powdered milk (or milk powder), produced by removing the water from

(usually skim) milk :

o Whole milk products

o Buttermilk products

o Skim milk

o High milk-fat and nutritional products (for infant formula)

o Cultured and confectionery products

Condensed milk, milk which has been concentrated by evaporation, with sugar

added for reduced process time and longer life in an opened can.

Khoa, milk which has been completely concentrated by evaporation, used in

Indian cuisine including gulab jamun, peda, etc.

Evaporated milk, (less concentrated than condensed) milk without added sugar

Infant formula, dried milk powder with specific additives for feeding human

infants.

Buttermilk, the liquid left over after producing butter from cream.

Ghee, clarified butter, by gentle heating of butter and removal of the solid

matter anhydrous milk fat (clarified butter).

Cheese, produced by coagulating milk, separating from whey and letting it

ripen, generally with bacteria and sometimes also with certain moulds.

Curds, the soft, curdled part of milk (or skim milk) used to make cheese.

Paneer /Cottage cheese the liquid drained from curds and used for further

processing or as a livestock feed.

http://en.wikipedia.org/wiki/Fermented_milk_products

http://en.wikipedia.org/wiki/Powdered_milk

http://en.wikipedia.org/wiki/Skim_milk

http://en.wikipedia.org/wiki/Condensed_milk

http://en.wikipedia.org/wiki/Khoa

http://en.wikipedia.org/wiki/Indian_cuisine

http://en.wikipedia.org/wiki/Gulab_jamun

http://en.wikipedia.org/wiki/Peda

http://en.wikipedia.org/wiki/Evaporated_milk

http://en.wikipedia.org/wiki/Infant_formula

http://en.wikipedia.org/wiki/Buttermilk

http://en.wikipedia.org/wiki/Ghee

http://en.wikipedia.org/wiki/Anhydrous

http://en.wikipedia.org/wiki/Clarified_butter

http://en.wikipedia.org/wiki/Cheese

http://en.wikipedia.org/wiki/Bacteria

http://en.wikipedia.org/wiki/Mold

http://en.wikipedia.org/wiki/Curd

68

4.1.2 Toiletries

Toiletries is the industry which manufactures consumer products used in personal

hygiene and for beautification. Subsectors of personal care include personal hygiene

and cosmetics. There some small distinction between personal hygienic items and

cosmetics, which are luxury goods solely used for beautification, but in practice such

sundries are most often intermixed in retail store aisles.

Small bar of soap

Disposable shower cap

Small bottle of moisturizer

Small bottles of shampoo and conditioner

Toilet paper

Box of tissue

Face towels

Disposable shoe polishing cloth

4.1.3 Frozen food

Preserves it from the time it is prepared to the time it is eaten. Since early times,

farmers, fishermen, and trappers have preserved their game and produce in unheated

buildings during the winter season. Freezing food slows down decomposition by

turning residual moisture into ice, inhibiting the growth of most bacterial species.In

the food commodity industry, there are two processes: mechanical and cryogenic (or

flash freezing).

4.1.4 Grocery

A marketplace where groceries are sold .it an area in a town where a public mercantile

establishment is set. It is a support that consists of a horizontal surface for holding

objects. Supermarket which is a large self-service grocery store selling groceries and

dairy products and household goods.

http://en.wikipedia.org/wiki/Industry

http://en.wikipedia.org/wiki/Manufacture

http://en.wikipedia.org/wiki/Consumer

http://en.wikipedia.org/wiki/Product_%28business%29

http://en.wikipedia.org/wiki/Personal_hygiene



http://en.wikipedia.org/wiki/Beautification

http://en.wikipedia.org/wiki/Subsector


http://en.wikipedia.org/wiki/Cosmetics

http://en.wikipedia.org/wiki/Personal_hygienic

http://en.wikipedia.org/wiki/Luxury_good

http://en.wikipedia.org/wiki/Flash_freezing

http://www.thefreedictionary.com/supermarket

69

4.1.5 Cosmetics

Cosmetics are care substances used to enhance the appearance or odour of the human

body. They are generally mixtures of chemical compounds, some being derived from

natural sources and many being synthetics. The word cosmetics derives from the

Greek (kosmetikē tekhnē), meaning "technique of dress and ornament", from "skilled

in ordering or arranging “and that from meaning amongst others "order" and

"ornament".

According to one source, early major developments include:

Castor oil used by ancient Egypt as a protective balm.

Skin creams made of beeswax, olive oil, and rosewater, described by Romans.

Vaseline and lanolin in the nineteenth century.

Nivea

4.2 Characteristics of FMCG

From the consumers' perspective:

o Frequent purchase

o Low involvement (little or no effort to choose the item)

o Low price

From the marketers' angle:

o High volumes

o Low contribution margins

o Extensive distribution networks

o High stock turnover

Product which has a quick turnover and relatively low cost are known as Fast Moving

Consumer Goods (FMCG). FMCG products are those that get replaced within a year.

Examples of FMCG generally include a wide range of frequently purchased consumer

products such as toiletries, soap, cosmetics, tooth cleaning products, shaving products

and detergents, as well as other non-durables such as glassware, bulbs, batteries, paper

products, and plastic goods. FMCG may also include pharmaceuticals, consumer

http://en.wikipedia.org/wiki/Human_physical_appearance

http://en.wikipedia.org/wiki/Odor

http://en.wikipedia.org/wiki/Human_body



http://en.wikipedia.org/wiki/Chemical_compound

http://en.wikipedia.org/wiki/Ancient_Greek

http://en.wikipedia.org/wiki/Castor_oil

http://en.wikipedia.org/wiki/Liniment

http://en.wikipedia.org/wiki/Cream_%28pharmaceutical%29

http://en.wikipedia.org/wiki/Beeswax

http://en.wikipedia.org/wiki/Olive_oil

http://en.wikipedia.org/wiki/Rosewater

http://en.wikipedia.org/wiki/Vaseline

http://en.wikipedia.org/wiki/Lanolin

http://en.wikipedia.org/wiki/Nivea

http://en.wikipedia.org/wiki/Contribution_margin

http://en.wikipedia.org/wiki/Distribution_%28business%29

http://en.wikipedia.org/wiki/Stock_turnover

70

electronics, packaged food products, soft drinks, tissue paper, and chocolate bars .A

subset of FMCGs is Fast Moving Consumer Electronics which include innovative

electronic products such as mobile phones, MP3 players, digital cameras, GPS

Systems and Laptops. These are replaced more frequently than other electronic

products. White goods in FMCG refer to household electronic items such as

Refrigerators, T.Vs, Music Systems etc. The Fast Moving Consumer Goods (FMCG)

industry in India is one of the largest sectors in the country and over the years has

been growing at a very steady pace. The sector consists of consumer non-durable

products which broadly consists, personal care, household care and food & beverages.

The Indian FMCG industry is largely classified as organized and unorganized. This

sector is also buoyed by intense competition. Besides competition, this industry is also

marked by a robust distribution network coupled with increasing influx of MNCs

across the entire value chain. This sector continues to remain highly fragmented.

The FMCG industry is volume driven and is characterized by low margins.

The products are branded and backed by marketing, heavy advertising, slick

packaging and strong distribution networks. The FMCG segment can be classified

under the premium segment and popular segment. The premium segment caters

mostly to the higher/upper middle class which is not as price sensitive apart from

being brand conscious. The price sensitive popular or mass segment consists of

consumers belonging mainly to the semi-urban or rural areas who are not particularly

brand conscious. Products sold in the popular segment have considerably lower

prices than their premium counterparts.

4.3 Outlook of FMCG

There is a huge growth potential for all the FMCG companies as the per capita

consumption of almost all products in the country is amongst the lowest in the world.

The demand or prospect could be increased further if these companies can change the

consumer's mind-set and offer new generation products. Earlier, Indian consumers

were using non-branded apparel, but today, clothes of different brands are available

and the same consumers are willing to pay more for branded quality clothes. It's the

quality, promotion and innovation of products, which can drive many sectors.

71

4.4 FMCG during Recession

At a time when the economy and industry sectors such as automobiles, aviation and

financial services are reeling from the global slowdown, the consumer goods sector in

India has managed to jump the trend with most companies posting double-digit

growth in net profits in the first half of fiscal 2015 backed by healthy sales.

India's fast moving consumer goods industry has so far been resilient to the slowdown

in the economy and a dip in consumer sentiment.

4.5 Tier 1 cities of India

The Classification of Indian cities comprises of Tier 1 ,Tier2 and Tier 3 etc a ranking

system used by the Government of India‟s Income Tax Depts. to allocate House Rent

Allowance (HRA) to Govt. employees employed in different cities in the country.

Tier 1 cities include Mumbai, Delhi, Chennai, Kolkata, Hyderabad and Bangalore.

Tier 2 includes Pune, Cochin etc. and Tier 3 includes Nasik, Baroda &Madurai etc.

Table 4.2 Classification of Population City (tier-wise)(Source: Economic

Times, 2015

Population classification Population

Tier-1 100,000 and above

Tier-2 50,000 to 99,999

Tier-3 20,000 to 49,999

Tier-4 10,000 to 19,999

Tier-5 5,000 to 9,999

Tier-6 less than 5000

Mumbai: Mumbai is the capital city of the Indian state of Maharashtra. It is the most

populous city in India, most populous metropolitan area in India, and the eighth most

populous city in the world, with an estimated city population of 18.4 million and

metropolitan area population of 20.7 million as of 2014 Along with the urban areas,

including the cities of Navi Mumbai, Thane, Bhiwandi, Kalyan. Mumbai lies on the

west coast of India and has a deep natural harbour. In 2009, Mumbai was named an

alpha world city. It is also the wealthiest city in India and has the highest GDP of any

city in South, West or Central Asia. Mumbai has the highest number of billionaires

and millionaires than any other city in India.



http://en.wikipedia.org/wiki/Capital_city


http://en.wikipedia.org/wiki/States_and_Territories_of_India

http://en.wikipedia.org/wiki/List_of_most_populous_cities_in_India



http://en.wikipedia.org/wiki/List_of_metropolitan_areas_in_India

http://en.wikipedia.org/wiki/List_of_cities_proper_by_population



http://en.wikipedia.org/wiki/Mumbai_metropolitan_area

http://en.wikipedia.org/wiki/Navi_Mumbai

http://en.wikipedia.org/wiki/Thane

http://en.wikipedia.org/wiki/Bhiwandi

http://en.wikipedia.org/wiki/Kalyan,_India

http://en.wikipedia.org/wiki/Konkan

http://en.wikipedia.org/wiki/Alpha_world_city

http://en.wikipedia.org/wiki/List_of_cities_by_GDP#Asia.2C_Central_.26_South

http://en.wikipedia.org/wiki/South_Asia

http://en.wikipedia.org/wiki/West_Asia

http://en.wikipedia.org/wiki/Central_Asia

http://en.wikipedia.org/wiki/Billionaires

http://en.wikipedia.org/wiki/Millionaires

72

Delhi: Delhi is capital of India and seat of the executive, legislative, and judiciary

branches of the Government of India. It is also the centre of the Government of the

National Capital Territory of Delhi. New Delhi is situated within the metropolis of

Delhi and is one of the eleven districts of Delhi National Capital Territory. The

metropolitan area has population of around 2.3 crore and city population is around 1.1

million.

Bangalore: Officially known as Bengaluru is the capital of the South Indian state of

Karnataka. It has a population of about 84.2crores, making it the third most populous

city and fifth most populous urban agglomeration in India located in southern India on

the Deccan Plateau, at a height of over 900 m (3,000 ft.) above sea level, Bengaluru is

known for its pleasant climate throughout the year. Its elevation is the highest among

the major large cities of India.

Hyderabad: Hyderabad is the capital of the southern Indian state of Telangana and

capital of Andhra Pradesh. Occupying 650 square kilometres along the banks of the

Musi River, it has a population of about68 lakhs making it the fourth most populous

city and sixth most populous urban agglomeration in India.




http://en.wikipedia.org/wiki/Government_of_Delhi



http://en.wikipedia.org/wiki/Delhi

http://en.wikipedia.org/wiki/List_of_districts_of_Delhi

http://en.wikipedia.org/wiki/National_Capital_Territory

http://en.wikipedia.org/wiki/List_of_renamed_Indian_cities_and_states


http://en.wikipedia.org/wiki/South_India

http://en.wikipedia.org/wiki/Karnataka




http://en.wikipedia.org/wiki/List_of_million-plus_urban_agglomerations_in_India


http://en.wikipedia.org/wiki/Southern_India

http://en.wikipedia.org/wiki/Deccan_Plateau

http://en.wikipedia.org/wiki/States_and_union_territories_of_India

http://en.wikipedia.org/wiki/Telangana

http://en.wikipedia.org/wiki/Andhra_Pradesh

http://en.wikipedia.org/wiki/Musi_River_%28India%29




http://en.wikipedia.org/wiki/List_of_million-plus_urban_agglomerations_in_India


73

CHAPTER 5

LITERATURE REVIEW

The literature review was undertaken to develop and justify the research work. An

overview of literature highlighting the impact of shopping pattern on working

women for FMCG products, understanding the consumer behaviour for FMCG in

tier 1 cities of India. It was observed while going through literature review that

many researchers highlighted on various shopping behaviour, consumer behaviour

and research in various areas w.r.t FMCG, research studies were only in the area of

either consumer behaviour in FMCG.

5.1Physical Shopping

Jain, Singh (2007)states in their book that classification of various retailers as well

as retailer competitive analysis. Book also throws light on Retail locations; Store

planning, Design and Layout of retail stores. Product and Merchandise Management is

discussed while giving idea about branding strategies and private label brands.

Dimensions and determinants of retail consumer buying behaviour are elaborated in

detail.

Levy Michel, Weitz Barton, Ajay Pandit (2009) states in their book that though a

retail giant India has characterized by a dominant non- organized retail sector which

accounts for whopping 95% of the total retail turnover. It throws light on the various

important issues like world of retailing, Retail Strategy, Merchandise Management,

Store Management, CRM Human Resource Management and relevant case studies.

In addition to above, vital subjects such as brand development, retail site locations

and retail market strategies have been handled in a different way.

Martin, Turley (2004), researchers undertook a study on the attitudes of generation Y

(19 - 25 years) consumers towards the malls and on their consumption motivation.

Key findings include that they are more likely to be objectively rather than socially

motivated to consume. The findings also suggest that motivation predicts an

individual's perception of shopping mall's ambience, layout and involvement in

74

shopping. Managerial implications include using objective information, such as

price-oriented promotions when trying to attract older generation Y consumers.

Laxmi Prabha &Amatul Baseer (2007)states in their books strong regional and

national players are emerging across formats and product categories. Real estate

developers are also moving fast through the learning curve to provide qualitative

environment to the consumers. The shopping mall formats are fast evolving.

Partnering among brands, retailers, franchisees, investors and malls is taking place.

The demanding assertive Indian consumer is now sowing the seeds for an exciting

retail transformation that has already started bringing in larger interest from

international brands. With the advent of these players, the race is on to please the

Indian consumer and its time for the Indian consumer to sit back and enjoy the

hospitality of being treated like a king.

Gupta C.P & Mitali Chaturvedi (2010) states that the gap between living standards

of the consumers of metro and non-metro cities are narrowing down day by day. One

of the prime concerns of the retailers is the availability of space for the retailing in

India. The availability of prime space would definitely enable the retailers to deliver

better quality products and services to the consumers, resulting in increase in

operational efficiencies and decline in costs for the supply chain. This new arena will

offer new jobs, high salaries, better living conditions, world quality products and

services, a unique shopping experience and more social activities and huge business

opportunities to the world retail players.

Gupta&Tripat Kaur (2007) states in their research paper the present situation of

organized retail formats with a special reference to shopping malls. It is concluded

that understanding of our shopper's attitude towards different characteristics of the

stores and retailers response towards the shoppers' mood. The results suggested that if

proper window display and other methods of presentation of merchandising are done,

the retailers are able to attract more shoppers. Study also focuses on product

categorization, merchandise co- ordination and market segmentation.

Alliswari M N, (2003) states the peculiarity of the Indian Retail scene lies in the co-

existence of innumerable small informal retail stores alongside with modern chain

75

stores and malls. The poor and middle class constituting a major part of population,

patronize the smaller stores as they are more comfortable with them. Small local

stores still find patronage from substantial number of customers belonging to the

middle class and above because of their convenient location in residential areas.

Md. Ismail (2009)states a segmentation approach to shopping malls attractiveness in

the UAE revealed six mall attractiveness factors from the shoppers' perspective:

comfort, entertainment, diversity, mall essence, convenience, and luxury. It also

arrived at three malls shopper segment specifically relaxed shoppers, demanding

shoppers, and pragmatic shoppers. Each segment was profiled in terms of mall

attractiveness attributes, demographics and shopping behaviour.

Panandikar, Rajiv Gupte(2012)states that malls have revolutionized the concept of

retailing and they pose serious competition to their conventional counterparts in terms

of service, ambience, price, access to the brands etc. Furthermore they have created a

niche in the minds of consumer through a perception of innovation style and status.

They observed that most preferred items are food and stationary followed by toys and

beauty care products. Price was observed as influencing factor followed by product

offer, shop display and previous experience.

V. Shridhar (2007) states retail invasion taking control of the supply chain in India,

and there is growing unease among people who depend on retailing for livelihood.

There are about 15 million retail outlets in India; of this only 2 per cent are in the

organised sector. 95 per cent of the outlets occupy less than 500 sq. of space. India

has the highest density of retail outlets in the world. There are about 15 outlets per

1000 inhabitants in India compared to 4 or 5 in developed countries. About 40 million

people make living from the activities that come under retailing. Unorganized retail is

done through family-owned shops, roadside eateries, kiosks at street corners,

hawkers and street vendors plying their wares on pushcarts. They cater the need of low-

value, high frequency customers.

Freda J Swaminathan, Vani (2008)- Consumer attitudes colour growth of malls', this

paper studies growth of malls in India. The research recognizes that in an economy

where organized retailing plays important role in boosting consumptions expenditure.

76

There is need to understand consumer attitude towards these malls. Consumer attitude

towards these malls would influence kind of offerings and experience retailers need to

come up with. The research is limited to Delhi NCR region and provides directions

regarding the winning retail formats tomorrow. It brings out the result that mall have

affected consumer shopping and entertainment behaviour.

Ganguli Shrishendu, Vinod Kumar (2008) states that customer satisfaction has

strong influence on loyalty, which means satisfied customers continue shopping and

recommend retail store to others.

Singh Abhinava , Sidhartha Das, Mamta Mahapatra (2008), states that Indian

Retail Industry attempt to elucidate on the realignment tactics and strategies of Kirana

against emerging organized retailers. New retail business models are being created to

lure the Indian consumer away from the traditional Kirana. The Kirana are not playing

salient spectators to this new reality. Although current demographic indicators and

growing consumerism point positively towards the growth of organized retailers,

consumers are still loyal to Kirana. In spite of the success stories like Big Bazaar, the

Indian Kirana community which forms the hub of small business and entrepreneurs in

India is still holding ground in the extremely competitive Indian retail market.

Choudhari Himanshu, Vandana Sharma (2009) states that, it is essential to know-

how of all factors which will help retailers to sustain in the long run. It was observed

that there is significant influence of format of retail stores and location on the

operational efficiency. Location of the retail store must be central to the customers to

encourage higher footfall and combat competition.

Shrivastava Ashish Kumar, Saket Ranjan Praveer (2009) states thatin their

research paper the prospects of Organized Retail in FMCG Segment in Rural Market.

The study has been carried out on the selected categories of FMCG viz. (I) Packaged

Food and Beverages; (ii) Cosmetics; (iii) Toiletries; and (iv) Apparels through

evaluating the effectiveness of determinants of organized retail. Bhatia Hitesh

(2010)states that modern retail formats reflect a gradual evolution of trade from melas

to malls, contradicting the general theory of revolution.

77

J. Prasad, A. R. Aryasri (2010) states that emergence of hypermarkets; shopping

malls have become destination centres to cater ever-changing need of consumers. It is

imperative to understand changing trends of consumerism that led to the growth of

organized retailing in India. The study puts great focus upon overview of selected

organized retail formats like food and grocery, apparel and throws light upon changing

trends of retailing and prospects associated with it.

Sahoo Swaroop Chandra, Das Prakash Chandra (2010) states that the purchase of

goods and services include a number of factors that could affect each decision. Increase

in numbers of variety of goods and stores, shopping malls and the availability of multi-

component products have broadened the sphere of consumer choice and have

complicated process of decision making.

Saxena Nitu, (2010) states that, service orientation in retailing has come to the fore

with the emergence of organized retailing, and has spread its roots to traditional

formats as well. The changing expectations of consumers have necessitated that

services are effectively planned and executed. Successful retailers know that the

demand for their merchandise is not just price elastic, as economists would like to

believe, but also service elastic. Accordingly service orientation should be integrated

into all aspects of retailing. The goal should not be only customer satisfaction, but also

customer delight.

5.2Attributes of shopping approach : Peterson (1997)states that shopping is a

process, composed of a set of distinct components linked together in a particular

sequence and the choice of shopping mode is among them. Mokhtarian (2004) states

that the choice of shopping mode can play important role in each element of the

shopping process. Of these elements, information gathering, transaction/purchase and

delivery may be the three more noticeable ones for the shopping mode choice

between e-shopping and store shopping Rotem-Mindali & Salomon, (2007).

However, involving these three elements in the shopping process seems enough to

perplex the issue. Farag (2007) note a hybrid form is evolving across these three

elements, and cite that empirical research shows that nowadays many individuals tend

to start their shopping process with an information search on the Internet before they

78

go to the store, and many others to search for a product online, check it out in-store,

and finally buy it online. Nevertheless, this study still tries to extract the attributes

associated with time and cost expenses for further empirical use by examining the

comparative advantages of e-shopping and store shopping according to these three

major elements. As consumers reach shopping places, they start gathering

information, or shopping. A number of studies have pointed out that shopping

activities also serve social motives. Today large shopping malls and department stores

are even facilitated with cinema, coffee shops, food halls, etc., making shopping

activities even more recreational. To enjoy such shopping pleasure, store shopping is

obviously more attractive to consumers than e-shopping.

Shopping trips are mostly chained with other out-of-home activities. Specifically,

shopping is often not the only purpose as consumers go out. Bhat (1996) found that

about 18% of his sample conducted shopping activities on the way home from work.

Jou &Mahmassani (1997) also found that about a third of commuters in their sample

made at least one stop on the way home from work, and that nearly one-fifth of those

stops were for shopping. In such cases, the travel cost and travel time attributed to the

shopping activities could be very small. Physical stores, large shopping malls in

particular, have dispersed spatially in recent years. Consumers also seem willing to go

farther to a mall with more comfortable shopping environment and with more

diversified and cheaper products. Gould and Golob(1997)states that people have

desire for movement; sometimes they simply want to get out and go somewhere.

Salomon(2001)states, „„it is likely that a number of shopping trips are „invented‟ in

order to „justify‟ (often subconsciously) an urge simply to get out and go somewhere”.

As consumers reach shopping places, they start gathering information, or shopping. A

number of studies have pointed out that shopping activities also serve social motives

Koppelman (1988) provide recreational and psychological gratification.

Tauber(1972) states that today large shopping malls and department stores are even

facilitated with cinema, coffee shops, food halls, etc, making shopping activities even

more recreational. To enjoy such shopping pleasure, store shopping is obviously more

attractive to consumers than e-shopping. What‟s more, information obtained from

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direct experience of multisensory stimulation of physical stores and products is also

superior to that of e-shopping.

Jasola (2007) states that malls, specialty stores, discount stores, department stores,

hypermarkets, supermarkets, convenience stores and multi- brand outlets are the most

preferred retail formats in India. In the organized sector, super-markets contribute to

30% of all food and grocery retail sales. The share of modern retail is likely to grow

from its current 2% to 15-20% over the next decade. With the growth of malls,

multiplexes and hypermarkets, the consumer is being exposed to a new kind of

shopping experience and services that redefines the expectations from shopping. The

Food and groceries, health and beauty, apparel, jewellery and consumer durables are

the fastest growing categories of organized retailing. Currently, the fashion sector in

India commands a lion„s share in the organized retail pie. The discount stores

emerged as classless stores with consumers of all income levels shopping at these

stores. Favourable demographic and psychographic changes relating to India„s

consumer class, international exposure, availability of products and brands

communication are some of the attributes that are driving the retail in India.

Bellenger (1980) states that shopper may switch to a new format either permanently

or intermittently changing among formats. While switching to new format a satisfied

patron will be inclined to shop from favourable retail brand, also present in that

particular format. Though in a different format and shopping situation, the shopper

carries expectation of identical value proposition congruent to retailer's brand image

perception in the parent format. Buchanan, Simmons & Bickart (1999) emphasised

on retailers' ability to influence on manufacturer's brand equity, either through

physical encounter (store format) or through direct communication (non- store

format).Gerar & GurhanKok (2007) discussed the assortment planning problem

with multiple merchandise categories and basket shopping customers i.e. customers

who desire to purchase from multiple categories. They presented a duopoly model in

which retailers choose prices and variety level in each category and consumers make

their store choice between retail stores and no-purchase alternative based on their

utilities from each category.

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Goyal and Aggarwal (2009) examined the relative importance of the various

products purchased at organized retail outlets and the choice of format, the consumer

has when purchasing a product. The results of the study depict that food and grocery;

clothing, apparels and accessories; catering services; health and beauty;

pharmaceuticals, watches; mobile, accessories and services; books, music and gifts;

footwear and entertainment are the order of importance for various items for

organized retailing. The most appropriate retail formats for various items are: food

and grocery supermarket; health and beauty care services supermarket; clothing and

apparels mall; books, music and gifts-convenience store and mall; catering services

mall; entertainment mall; watches - hypermarket; pharmaceuticals- hypermarket;

mobile, accessories and services - hypermarket; foot wares - departmental store.

Jain and Bagdare (2009) reviewed the concept of women experience and identified

its major determinants in context of new format retail stores by analysing customer

expectations. Their study highlights that as compared to traditional stores, new format

stores are pre-engineered retail outlets, characterized by well-designed layout,

ambience, display, self-service, value added services, technology based operations

and many more dimensions with modern outlook and practices. They seem to attract

and influence young minds by satisfying both hedonic and utilitarian needs. Customer

experience is governed by a range of demographic, psychographic, behavioural,

socio-cultural and other environmental factors.

Surajit Ghost Dastidar and Biplab Datta (2009) tried to assess whether the

women„s demographics have any influence on their exploratory tendencies.

According to their research the males are more risk taking and innovative than

females and younger consumers are more prone to indulge in interpersonal

communication about purchases and education and income have no influence on any

of the exploratory tendencies.

Manju Rani Malik (2011) states to explore the components of women satisfaction

and also investigates the relationship between each of the women satisfaction

components and level. Product characteristics, Price factor, Physical Aspects,

Promotional Schemes and Personal interaction of retail customer satisfaction were

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studied. The author‟s study has identified that location, variety of products and

reasonable price are the major motivating factors that influence the customers to visit

the retail outlets and emphasis on facilities such as parking, physical aspects,

availability of variety of branded and non - branded products at reasonable price by

the retailer will increase the revenue. There were numerous studies in the area of

consumer satisfaction, Consumer expectations on services, comparative study on

consumer satisfaction towards organized retailing and many. This study analyses the

consumer attitude that is the basis for consumer satisfaction, towards one of the

existing and growing format among the organized retailing that is departmental stores

in Coimbatore city.

Venu Gopal & Santosh Ranganath (2012), states that modern retailing, despite its

cost effectiveness, has come to be identified with lifestyles particularly the affluent

one, thereby excluding an important and larger segment of consumers. In order to

appeal to all classes of society, organized retail stores would have to identify with

different lifestyles and socioeconomic strata and respond to their respective

requirements and shopping patterns. This trend is visible with the emergence of stores

with an essentially value for money image. While insisting on value for money and

cost effectiveness, today consumers want a better shopping experience, recreation,

friendly interactions and a wide choice of products and services. Retail stores have to

live up to these expectations in order to flourish, prosper and grow in the Indian

market.

5.3Online shopping

Alok Gupta , Bochiuan Su, Zhiping Walter(2013)states that the customers in online

shopping cannot be trusted as they have a habit from switching from one site to

another for purchasing. So, it cannot be said that if a customer is buying from a site

then next time for shopping he/she will purchase from the same. Thus, customers are

not loyal to a particular site. They say online shopping has some limitations such as

only those customers can shop if they have knowledge of operating computer and can

access internet properly. Online shopping offers a risk factor where the point comes of

touching the product physically. There is no doubt that the description of product is

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given in a properly organized form but certain customers find it difficult to purchase

until and unless they touch the product. This risk is majorly involved in certain

products such as clothes, food-products, home décor items etc.

Benedict,Dellaert &Ruyter(2014) states there are various type of customers. Some

consider online shopping as a destination for purchase; on the other hand some

consider it as a source of fun and entertainment. Those people who are serious

customers say that online shopping offers them a wide range of products and saves

their time of retail shopping where they only have few choices whereas other category

of customer take online shopping just to get a online shopping experience.

Na Wang, Dongchang Liu 1, Jun Cheng (2008)states that there are number of

factors that are responsible for shopping from online websites. They found that some

customers find online shopping as a supplement to traditional shopping. They say that

it saves them from travelling in traffic, waiting at every signal and wander from one

shop to another. They also say that they have the flexibility to shop online whenever

and wherever they want and they do not have to take out time from their working

hours and go for shopping.

Ruby (2014)states that online shopping gives the advantage of cost comparison. In

retails shops sometimes one is forced to buy a product at the marked price without

comparing its price. This drawback will overcome by online shopping as one can

compare a same product at number of sites. Online shopping also allows seeing wide

range of products and that too number of times whereas in traditional shopping one is

restricted to see from the limited shelves available in the store. Sapna Rakesh &

Arpita Khare(2013)states that there is huge difference between shopping pattern of

men and women. According to them women take time and look for varieties whereas

men concentrate on the product which they need to shop. Women have become brand

conscious as men but they give preference to products that offer discounts. Hsieh

(2013) stated that internet is influencing people‟s daily life more so as compared to

past. People‟s daily activities have gradually shifted from physical conditions to

virtual environment. According to researcher the shopping and payment surroundings

have also changed from physical store into online stores.

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Weiber & Kollmann (1998)states that online technologies provide many competitive

advantages like agility, selectivity, individuality and interactivity. Li Na & Zhang

Ping, (2002) states that online shopping has become the third most popular Internet

activity, immediately following e-mail using, instant messaging and web browsing.

Jush & Ling, (2012) defined online shopping as the process a customer takes to

purchase a service or product over the internet. A consumer may at his or her leisure

buy from the comfort of their own home products from an online store. Comscore

(2013) states that India is now the world‟s third largest internet Population. Younger

males and women aged 35-44 emerge as power users.73.8 million Indians surfed the

web via a home or work computer. BCG report, (2012) stated that there will be three

billion internet users globally, almost half the world‟s population. The internet

economy will reach $4.2 trillion in the G-20 economics. If it were a national

economy, the internet economy would rank in the world‟s top 5, behind only the

USA, China, Japan, and India, and ahead of Germany.

Kanwal Gurleen, (2012) states that India has more than 100 million internet users

out of which one half opts for online purchases and the number is rising sharply every

year. The growth in the number of online shoppers is greater than the growth in

Internet users, indicating that more Internet users are becoming comfortable to shop

online. Until recently, the consumers generally visit online to reserve hotel rooms and

buy air, rail or movie tickets, books and gadgets, but now more and more offline

product like clothes - saris, kurtis, T-shirts-shoes, and designer lingerie, consumer

durables are being purchased online. Master Card Worldwide Insights, (2008)

revealed that 47% of internet users shop online. Indian shopping community is around

28 million and Indian online shopping market is worth about $71 billion. Indian

online shoppers spend about 11% of their personal income in online shopping.

Michal Pilik (2012) states that online buying behaviour is affected by various factors

like, economic factors, demographic factors, technical factors, social factors, cultural

factors, psychological factors, marketing factors and legislative factors. Customers

choose an online-shop mainly based on references, clarity and menu navigation, terms

of delivery, graphic design and additional services. Complicated customers read

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discussions on the Internet before they spend their money on-line and when customers

are unable to find the product quickly and easily they leave online-shop.

Efthymios Constantinides(2004) states that the main constituents of the online

experience as follows: the functionality of the web site that includes the elements

dealing with the site‟s usability and interactivity, the psychological elements intended

for lowering the customer‟s uncertainty by communicating trust and credibility of the

online vendor and web site and the content elements including the aesthetic aspects of

the online presentation and the marketing mix. Usability and trust are the issues more

frequently found to influence the online consumer‟s behaviour. Karayanni, (2003)

examined that discriminating of potential determinants between web- shoppers and

non-shoppers. The most major discriminate variable between web shoppers and non-

shoppers was found to be web shopping motives concerning time efficiency,

availability of shopping on 24 hours basis and queues avoidance. Lack of trust to web

shopping affects negatively web shopping behaviour.

Bosnjak (2007) states that neuroticism, openness to experiences, and agreeableness

has small, but significant influences on the willingness to buy online. Need for

Cognition has a direct negative effect towards willingness to online purchase. Lack of

online shopping experience could emphasize the effects of personality traits on the

estimation of likelihood of future online purchases. They implied that the decision to

shop online is made with emotion rather than reasoning. Lee, (2009) in his study

states that quality of online reviews has a positive effect on the purchasing intention

of online shoppers. Attitudes of online consumers increase with the number of

reviews. Large number of reviews is perceived as an indication of product popularity

and hence increases the purchasing intention of consumers.

Kim (2002) studied that significant factors affecting the intention towards shopping

on the internet are convenient and dependable shopping, reliability of retailer,

additional information and product perception. Shipra G (2012) states that

satisfaction of online consumers can be improved by improving their satisfaction

related to shipping and returns. Free shipping is a great motivator, drawing shoppers

back to sites to make repeat purchases and causing shoppers to recommend an online

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retailer, consumers are willing to pay a nominal fee for getting their product faster.

While comparison shopping, consumers take product price and shipping charges

almost equally into consideration. There are several other things that retailers can do

to improve the experience for their online shoppers. The first is to communicate the

expected delivery date of the order, customers are willing to wait for their orders but

want to know just how long that might be. Timely arrival of shipments encourages

shoppers to recommend an online retailer. Consumers also like having tracking

updates and delivery notifications to understand when their package is arriving.

Online shoppers want flexibility in their shipping, particularly the ability to give

special delivery instructions or schedule a delivery time or select an alternate delivery

location.

Schaupp & Bélanger (2005) states that privacy (technology factor), merchandising

(product factor), and convenience (shopping factor) are three most important

attributes to consumers for online satisfaction. These are followed by trust, delivery,

usability, product customization, product quality, and security.

5.4Important aspects of online shopping

Kotlar &Keller (2009) states that consumer shop online because it is convenient.

Gordan & Bhowan (2005) studied factors that encourage online shopping. Alan &

Omar (2007) states that convenience, usefulness, eases of use and efficiency are

positive characteristics of online shopping. Jush and Ling (2012) suggested that e-

commerce experience, product perception and customer service have important

relationship with attitude towards e-commerce purchases through online shopping.

According to these researchers consumers who purchase online are more likely to buy

clothes, book and make travel booking. Delafrooz Narges (2009) states that

utilitarian orientations, convenience, price and wider selection are a significant

determinant of consumer‟s attitude toward online shopping. Consumers are looking

for more convenience (time and money saving), cheaper prices and wider selection

when they shop online. Consumers who value the convenience, prices and wider

selection of internet shopping tend to purchase more online and more often.

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Michal Pilik, (2012) states that logistics, security and privacy of information,

timeliness, availability, convenience, and customer service were criteria used by

customers while online shopping. Zhou (2007) states nine types of consumer factors,

including demographics, Internet experience, normative beliefs, shopping orientation,

shopping motivation, personal traits, online experience, psychological perception, and

online shopping experience in affect consumer online. Smith and William, (2003)

examined the factors influencing consumers towards online shopping are marketing

efforts, socio-cultural influences, psychological factors, personal questions, post-

decision behaviour and experience.

5.5Benefits of online shopping

Jush and Ling, (2012) states that customers can enjoy online shopping for 24 hour

per day and can buy any goods and services anytime at everywhere. Online shopping

is more user friendly compare to in store shopping because consumers can just

accomplish his desires just with a click of mouse without leaving their home.

Forouhandeh Behnam (2011) states that warrant, assurance and enjoyment as

factors that perceived as the online shopping benefits. Eastlick & Feinberg(1999)

states that online shopping has various advantages as compared to shopping at a

physical shop like, 24/7 shopping, saves time , Price comparison, third party

shopping sites keeping merchants competitive hence offering the best products and

prices. This encourages customer for online shopping but also helps in relationship

management and maintain consistency between advertised price and site price.

Sometimes no cost delivery even to third party receiver ease in merchandise

cancellation or return sometimes tracking of shipping available large online shopping

site offering store comparison and sometimes no taxes

Kim Kyung (2002) states that shopping malls and internet are major competitor,

providing multiple dimensions of consumer value .The consumer value includes four

components- efficiency, excellence, play and aesthetics. Consumer value analysis

sheds light on the complex issues surrounding the viability of shopping malls against

the competition from internet. Online shopping enhances the experience of shopping,

area of shopping, comfort level and products variety. It widens the customer‟s

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imagination towards products and inducing them to looking for varieties and

satisfying their hunger for fun and pleasure.

5.6 Uniqueness of Online Shoppers

Burke(1997)states that the typical Internet user of the twentieth century is young,

professional, and affluent with higher levels of income and higher education Palumbo

and Herbig (1998)states that women value time more than money which

automatically makes the working population and dual-income or single-parent

households with time constraints better candidates to be targeted by non-store retailers

Actually, both demographics and personality variables such as opinion leadership or

risk evasiveness are very important factors that are considered in studies trying to

determine the antecedents of Internet purchases. Kwak (2002)states that confirmatory

work shows that income and purchasing power have consistently been found to affect

consumers‟ propensity to shift from brick-and-mortar to virtual shops. Comor

(2000)states that internet usage history and intensity also affect online shopping

potential. Consumers with longer histories of Internet usage, educated and equipped

with better skills and perceptions of the Web environment have significantly higher

intensities of online shopping experiences and are better candidates to be captured in

the well known concept of flow in the cyber world. Hoffman & Novak (1996) states

that those consumers using the Internet for a longer time from various locations and

for a higher variety of services are considered to be more active users. Bellman

(1999) states that the demographics are not so important in determining online

purchasing potential. Whether the consumer has a wired lifestyle and the time

constraints the person has are much more influential. Risk taking attributed to brands

or the choice sets considered in online and offline environments can be significantly

different from each other. Andrews & Currim (2004)states that uncertainties about

products and shopping processes, trustworthiness of the online seller, or the

convenience and economic utility to derive from electronic shopping determine the

costs versus the benefits of this environment for consumers.

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5.7 Online Shopping pattern

Identifying pre-purchase intentions of consumers is the key to understand why they

ultimately do or do not shop from the Web market. One stream of research under

online consumer behaviour consists of studies that handle the variables influencing

these intentions. A compilation of some of the researchers have examined are:

transaction security, vendor quality, price considerations, information and service

quality, system quality, privacy and security risks, trust, shopping enjoyment, valence

of online shopping experience, and perceived product quality. Liao and Cheung

(2001)states that there are lists of factors that have a positive or negative impact on

consumers‟ propensity to shop do not seem to be very different from the

considerations encountered in offline environments. However, the sensitivities

individuals display for each variable might be very different in online marketplaces.

However factors like price sensitivity, importance attributed to brands or the choice

sets considered in online and offline environments can be significantly different from

each other

Online Shopping process

Mayer(2002) states that many studies frequently mention that there is a vast amount

of window shopping taking place online but the number or the rate of surfers who turn

into purchasers or regular buyers are very low to lack of consumer intention to

purchase an offering from the online environment at the outset. It might also happen

because of various problems that arise during online shopping driving the consumer to

abandon the task in the middle. Therefore, while one stream of research should

identify the reasons behind the purchase reluctance of consumers, another area of

concentration should be why people abandon their shopping carts and stop the

purchasing process in the middle. Such attempts can help to understand how to turn

surfers into inter actors, purchasers, and finally, repeat purchases by making them

enter into continuous interaction with this environment. Berthon(1996)states that

common reasons for purchase reluctance are the difficulties and costs of distance

shipping, inadequate amount of purchase related information, troubles experienced

after the purchase such as delivery or refund problems, general security fear, and

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various perceived risks such as financial, product-related or psychological risks. Chen

(2003)states that the reasons of abandoning purchases are much more technical such

as unexpected shipping costs or transaction complexity. In other words, some

consumers accept to shop from the Internet in principle but technical complexities or

ineffective systems discourage them. Regardless of the pessimistic state of events,

marketers should not be hopeless about the future. Once the risks consumers perceive

about shopping through the Web are reduced, the environment still promises a high

potential for selected consumer segments. Shim (2001) in his studies show that

consumers who search for product related information through the Web have stronger

intentions to make purchases online Therefore, building on the information advantage

can be expected to pay off in the future. Constructing effective decision support

systems and assisting consumers with interactive decision tools are also successful

attempts that need to be developed further Barber (2001)states that investing on the

pre-purchase stages of the decision making process is not adequate.

Redmond (2002)states that the developing and testing the effectiveness of specific

“selling” strategies and tactics for the cyber market are also crucial. Studies that focus

on currently unavailable but possible tools of cyber shopping in the future, such as the

use of artificial shopping agents that work on behalf of consumers in the online

market are also very valuable efforts enlightening the road for future studies.

Abdelmessih and Stanger (2001) states that the risk of delivering consistent

experience is high as dissatisfaction in one channel can be carried out to other

channels also. He found that many retailers who become frustrated with an online site,

for functional failure, blame the retailer not the Internet In the year 1999 itself, at least

6 percent of shoppers switched their patronage habit in the off-line store, due to

dissatisfaction in online experience. The number of switchers increased to 9 percent in

the year 2000. In absence of complete information about a store, shopper makes

inferences from available information cues before forming perceptions of the store

Monroe &Krishnan, (1985) states that experience in online version of the store acts

as a cue that helps the shoppers to form impression about the on land store. The

situation warrants more attention, as valuable premium segment customers are more

exposed to multi-channel shopping and very sensitive towards the brand image of the

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retailer they patronize. To achieve multi-channel offering, retail need to understand

shopper's attitude behind each shopping situation across channels and how these

situation shapes shopping behaviour.

Rosen and Howard (2000) state that the new area of retail business ushered lots of

opportunity both for the shoppers and retailers. However shoppers acquire benefits

from savings in terms of time price and searching effort, expanded information on

goods and services, shopping convenience and greater availability of customized

products, uninterrupted accessibility and smooth flow of transaction choosing any of

the available formats. To the retailers e-commerce offers greater efficiencies in

market and information access, providing scope of better services, reduced operating

and product procurement cost. Calkins, Farello and Smith, (2000) state that

traditional store based retailers only need spend about Rs.500 a person to bring their

existing customer online, which is as high as Rs.5000 case of pure e- retailers.

According to the retailer with strong brand equity enjoys shoppers' preference and

loyalty, and extracts either price premium or volume advantage (in case of price

parity), or both.

Henderson and Mihas (2000)states new multi category retailers have emerged that

combined functional benefits like price, convenience and service, with the emotional

relationship that gives a retail brand true personality. The Authors cited example of

office supply industry in the U.S, where retail players have started opening smaller

stores, giving the shoppers the killer assortment of goods through whatever format or

channel best suits a given transaction. The culmination of this trend is emergence of

electronic commerce through WWW. They point out the retailer's challenge of multi-

channel management and the need to provide a consistent brand statement across each

channel. Wileman &Jery (1997) states that retail formats appear to vary substantially

in their potential for supporting for the development of strong retail brands. While

segmentation is easy for repertoire retailers, proximity retailers occupy the opposite

end. Thus the task of retailers to bundle their retail strategy mix in a way that builds

and maintain loyalty across formats is particularly challenging.

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5.8Working Women

Klein (1968) states that men and women due to their different upbringing and

socialization along with various other social, biological and psychological factors depict

different types of behaviour at various situations. Whether it is decision making in

personal life or professional life, whether it is about shopping or eating, and both the

genders behave differently. As a result of education, women‟s economic horizon

expanded considerably and they have begun to feel that they must earn their own

living. They have made their first response to the call for teachers. More than hundred

years ago itself, they took this profession. With the establishment of hospitals and

health centres, women have qualified themselves as doctors, nurses, health visitors

and mid-wives. When law, agricultural, engineering and other professional

institutions were opened, they invaded these fields too. Now there is scarcely any

venue of employment in which women have not entered. Various American studies

have shown that there is a definite correlation between the educational level of

women and their employment .Woodard (1999) states that consumer behaviour

among women in US by the National Foundation of Women Business Owners found

that 57% of women business owners, who used the Internet, had purchased online,

compared to 40% of female employees who used the Internet had purchased online.

However women contributed more than $ 3.6 trillion in revenues from their purchases

online. Also, 30% of women business owners/executives, compared to 23% of other

working women, had ordered from a catalogue.

Dr.M.Subrahmanian (2011) states that in his study “buying behaviour of the new

aged Indian women” in the city of Chennai” with respect to the age, marital status,

occupation, professional status factors, etc. to identify the decision maker and the

influencer for the purchase made by the women. A sample of 200 women from the

few distinct geographical areas of the Chennai city was collected. According to this

study the women‟s value perception is multi-faceted and they are more quality

oriented. When it comes to the price attribute women do not opt for the products even

if it is heavily priced or low priced but to the maximum prefer when it is reasonably

priced within the affordable range.

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Etzel, M., Bearden W. (1982) states that Influence of social reference group on the

purchase of products on professional women .They further reviewed research

available on reference groups with special focus on professional women on the

purchase of products. This study further adds to people's knowledge of how the

influence of society varies across different product categories consumed by

professional women. Specifically this study focuses on social reference groups of

professional working product purchase decisions. Peter & Simon (2001) studied the

women‟s involvement in purchase making decisions they further studied the

relationship between demographic & geographic variables of professional women and

their involvement in purchase making decisions of family and they also measured the

level of involvement of women in these decisions.

Sheikh & Aizen (1990) stated the changing status of professional women in India

and their impact of urbanization and development The study further argues that legal

and constitutional rights in themselves do not change social attitudes. In the longer

term these attitudes are conditioned by economic pressures, which would ultimately

lead to improvement in the status of professional women. Miyazaki &

Fernandez(2001) states thatin the Indian context, Identifying pre-purchase intentions

of professional women is the key to understand why they ultimately do or do not shop

from the Web market .

A compilation of some of the determinants researchers have examined are: transaction

security, vendor quality, price considerations, information and service quality, system

quality, privacy and security risks, trust, shopping enjoyment, valence of online

shopping experience and perceived product quality. These lists of factors having a

positive or negative impact on professional women propensity to shop do not seem to

be very different from the considerations encountered in offline environments.

However, the sensitivities individuals display for each variable might be very

different in online marketplaces. Factors like price sensitivity, importance attributed

to brands or the choice sets considered in online and offline environments can be

significantly different from each other.

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Eastlick and Feinberg (1999) states that motives s were often higher among

professional women than among professional men. They found a negative relationship

between education and shopping motivations. Further researchers found that the

motive were often higher among professional women than among professional men

shoppers. Verma and Munjal (2003) identified the major factors in making a brand

choice decision namely quality, price, availability, packaging and advertisement w.r.t

working women. The brand loyalty is a function of behavioural and cognitive patterns

of a customer. The age and demographic variables affect significantly the behaviour

and cognitive patterns of the customers while other demographic characteristics such

as gender and marital status are not significantly associated with these behaviour and

cognitive patterns of the consumers.

Rajesh Singh (1979) stated that the feminine stereotype depicts Kolkata women as

being more concerned than men about their bodies, their clothing, and their

appearance in general. Working women are subject to a great deal more observation

than professional men; their figures and clothing; their attractiveness is the criteria by

which they most often are judged. Kapur (1979) states that the twin roles of

workingwomen cause tension and conflict due to her social structure which is still

more dominant .In her study on professional women in Delhi, the author has shown

that shown that traditional authoritarian set up of Hindu social structure continues to

be the same basically and hence, working women face problem of role conflict change

in attitudes of men and women according to the situation can help to overcome their

problem. Once the women are out on a job either on economic grounds or purely

personal reasons, they tend to become a matter of routine and by virtue of regular

income. While they pull themselves up to share tribulations of men‟s life, they soon

find themselves in the midst of responsibilities and eventually end up in discharging

the obligations which normally are those of men. The social problems faced by

working women are varied. Many problems have remained unsolved in their domestic

as well as working place, from the time they stepped out of the four walls of their

home for the first time. Their problems are different. They have problems of adjusting

to time schedules with other working adults in the family, wanting privacy in freedom

and a greater participation in the financial management and a desire for a balanced life

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Kalhan (1972)states on problems of working women, that husband and wife both

going for work is common today. This naturally gives rise to problems. Essentially, it

is a woman‟s problem because the working wife, when she returns from her work, has

to ensure that her family does not face any deprivation. The family has to be fed and

looked after. The author further states that “The Indian working woman‟s luck in this

respect is much harder than that of her counterpart in many other countries, where

entire industries are geared to take drudgery out of house work.

The above cited divergent problems which the working women have to face every

day, pull them apart mentally. The tolerance level of this strain bears some

relationship with personality of the role player. If the problem is deeply felt by the

women, it may result in lack of adjustment either in the family or in their social and

emotional life or in their job setting. Many of these working women suffer from a

guilt feeling, due to the non-fulfilment of their legitimate duties.

5.09 Working Women’s shopping Pattern

Harding(2003); Hancock & Tyler(2007); Tyler & Cohen(2008)states that study

have been carried out in order to develop a general understanding of what influences

and performs gender in organizations analysing practice requires a shift in focus

Gender scholars favours a social constructionist approach to understanding and

explaining gender Gary Mortimer &Peter Clarke(2011)states that the overriding

research objective was to identify which store characteristics male and female grocery

shoppers consider as important and what differences exist between the levels of

importance and the shopper‟s gender. The study results demonstrate that male and

female grocery shoppers consider important store characteristics differently and there

are specific characteristics that men and women consider more important. Male

shoppers considered speed, convenience and efficiency to be the most important

factors. Female shoppers, in contrast, prefer pricing, cleanliness and quality.

Mintel, (2008) initiates that 20-24 and 25-34 age groups of working women are of

utmost importance to the marketers as women are less anxious about quality than

style in their clothing. Euromonitor, (2007) insists that in terms of spending on

clothing, age is a stronger determinant of women‟s budget than their socio-economic

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status Zeb, Hareem; Rashid, Kashif; Javeed, M. Bilal (2011) states that Pakistani

female consumer‟s shopping patter and understand the key factors of branded clothing

which influence female consumer‟s involvement towards trendy branded clothing. In

their research the prime focus is on females of age20-35 years to analyse and evaluate

their perception and behaviour, when they purchase their clothing brands. The results

show that all the factors discussed in the literature account for their impact on the

consumer involvement in fashion clothing.

Ashwin Kumar (2011) conducted a research on “Indian Women‟s Buying

Behaviour& Their Values for the Market” This paper examined the buying behaviour

of Indian women & their values for the market. To achieve the objectives of the study

total 500 women respondents had been selected from Delhi-National Capital Region

NCR. A well-structured questionnaire had been drafted to get the information

regarding buying behaviour of women. As we know that market cannot operate

without the consumer so, the consumer is known as God for the market, as one

behaves market work accordingly. Women as a consumer were also participating in

buying the goods. Indian women were dominating the market by making her presence

in every purchase decision. So, it is also required to know that how women behave

during purchasing & it is also required that what is the value of women for the

market. An effort has been made to judge the Indian women buying behaviour& their

values for the market in this paper. Analyses of the study found that Indian women are

playing a new role as a facilitator.

Gary Mortimer (2011),states that family grocery shopping was the accepted domain

of women; however, modern social and demographic movements challenge traditional

gender roles within the family structure. Men were engaged in grocery shopping more

freely and frequently, yet the essence of male shopping behaviour and beliefs present

an opportunity for examination. This research identifies specific store characteristics,

investigates the perceived importance of those characteristics and explores gender,

age and income differences that may exist. A random sample collection methodology

involved 280 male and female grocery shoppers was selected. Results indicated

significant statistical differences between genders based on perceptions of importance

of most store characteristics. Overall, male grocery shoppers considered super market

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store characteristics less important than female shoppers. Income did not affect

shoppers‟ level of associated importance; however respondents‟ age, education and

occupation influenced perceptions of price, promotions and cleanliness.

Sriparna Guha (2013) states that the working women segment has significantly

influenced the modern marketing concept. This work identified the changing

perception and comparison of buying behaviour for working and non-working women

in Urban India. It suggests that women due to their multiple roles influence their own

and of their family members‟ buying behaviour. The study also reveals that working

women are price, quality and brand conscious and highly influenced by the others in

shopping.

Varadaraj & S. Kumar (2013) states that the shopping behaviour of women

customer‟s towards jewellery products with special reference to Tirupur city. The

objective of the study is to get the feedback about various factors affecting Buying

behaviour of Jewellery products, Evaluate the brand awareness and buying attitude of

the women customer‟s in purchasing of gold at the various jewellery retail stores. The

research design used in this study is descriptive research design. Data was collected

from around 200 customers from the retail jewellery like Sri Kumaran, Joyalukkas,

Kalyan jewellery.

Isa Kokoi (2011) states that the buying behaviour of Finnish women related to facial

skin care products. The primary purpose of the study is to discover the similarities and

differences in the buying behaviour of young and middle-aged women when

purchasing facial skin care products. The objective is to study what kinds of factors

affect the buying behaviour of both young (20 to 35 years old) and middle-aged (40 to

60 years old) women and then compare the findings from both groups. The results

indicated that 20-35 and 40-60 year-old Finnish women were rather similar in terms

of the factors affecting their buying behaviour related to facial skin care products.

Although existing literature suggests that factors such as age have an impact on

buying behaviour, the results showed that it does not have that big of an impact on the

purchasing behaviour of Finnish women related to facial skin care products. However,

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the research findings of this study can definitely benefit the case company lumene in

their business actions.

Kristen Wig & Chery Smith (2008) conducted a research study on “The art of

grocery shopping on a food stamp budget: factors influencing the food choices of

low-income women as they try to make ends meet” in his journal Public Health

Nutrition: 12(10), 1726–1734. The main objective of the research was amidst a

hunger–obesity paradox, the purpose of the present study was to examine the grocery

shopping behaviour and food stamp usage of low income women with children to

identify factors influencing their food choices on a limited budget. Focus groups,

which included questions based on Social Cognitive Theory constructs, examined

food choice in the context of personal, behavioural and environmental factors. A

quantitative grocery shopping activity required participants to prioritize food

purchases from a 177-item list on a budget of Rs. 3000 for a one week period, an

amount chosen based on the average household food stamp allotment in 2005. Efforts

to improve food budgeting skills, increase nutrition knowledge, and develop meal

preparation strategies involving less meat and more fruits and vegetables, could be

valuable in helping low-income families nutritionally make the best use of their food

dollars.

Nagunuri Srinivas (2013) states that the purpose of this study is to examine the

“women consumer‟s preferences towards branded and unbranded grocery items in

Organized/Unorganized Retail Environment” and also aim to study the changing

market scenario i.e. transition from unorganized sector to an organized one, Due to

increasing self-service and changing consumers‟ lifestyle the interest in branding and

stimulator of impulsive buying behaviour is growing increasingly. In India according

to many research Surveys there is huge growth potential for all the FMCG companies

as Well-established distribution networks and intense competition between the

organized and unorganized retailers. Gain the demand or prospect could be increased

further if these companies can change the consumer's mind-set and offer new

generation products. Earlier, Groceries were usually purchased by the housewife from

small neighbourhood grocery stores with an average size of about 250 square feet.

Her loyalty was strong, based on convenience and added services such as credit and

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free home delivery, but today, Different brands are available and the same consumers

are gradually shifting towards branded quality Products.

Swarna Bakshi(2009)explained that men and women due to their different

upbringing and socialization along with various other social, biological and

psychological factors depict different types of behaviour at various situations.

Whether it is decision making

in personal life or professional life, whether it is about shopping or eating, both the

genders are completely different at every stage of decision making. Right from need

recognition through the evaluation of alternatives to the post purchase behaviour, men

and women work differently with different types of stimuli and different parameters

of evaluations. Women seem to have satisfaction and find pleasure while they shop

whereas men appear to be more disdain towards shopping. In this paper an attempt is

made to study these differences at various levels of purchase decision. Drake (1987)

explained that gender can be explained with the terms gender distinctiveness and the

role it plays. Gender identity can be explained as to which degree a man or a woman

identifies with masculine and feminine behaviour traits. Gender differences refer to

difference in their responsibilities, roles, and privileges of men and women, this

makes them different and they respond to all stimuli and products offered by the

marketer differently Fischer & Arnold, (1994)states that demographics & household

structures, desires, emotions, ethics and personality, group influences, information

processing are considered some of the key factors responsible for buying purchase

behaviour. Consumer‟s purchases are sturdily influenced by the factors like cultural,

social, personal and psychological characteristics. Thomson, & Locander (1994)

states that the marketers find it very difficult to formulate a different strategy for both

males and females. There is no economic viability also to formulate strategies

separately. This difference of gender gap is not considered good and extremely

unwelcomed by the marketers as efforts have to be raised by them. Some marketers

believe that a common measure is good enough to handle the issue where as some feel

it is workable to formulate separate strategy for both.

Kaur & Singh (2007)states that youth are an important consuming class and owing to

time pressures in dual career families with high disposable incomes. This study

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enlightens the important dimensions of motivation for the youth when they shop. The

results reveal that young consumers, interestingly, lend to shop not from a utilitarian

perspective but from a hedonistic perspective. Their key indulgence includes getting

product ideas or meeting friends. They also view shopping as a means of diversion to

alleviate depression or break the monotony of daily routine. In addition to this, they

also go shopping to have fun or just browse through the outlets.

5.10Fast Moving Consumer Goods(FMCG)

Fast Moving Consumer Goods are also known as Consumer Packaged Goods (CPG).

FMCGs are products that have a quick turnover, and relatively low cost. FMCG

products are those that get replaced within a year and they constitute a major part of

consumers‟ budget in many countries. The FMCG sector primarily operates on low

margin and therefore success very much depends on the volume of sales Sarangapani

& Mamatha (2008).

Paragi kuntal shah & Bijalnishantmethta (2012)stated that today‟s personal care

customers are greatly influence of advertisement. The sales promotions immediately

hit the sales volume and face the competitions. The sales promotion stimulate to

consumers buying behaviour in such as sales promotions advertisement, buy one get

one free and store communications. Gopaldas (2011) stated that price promotions are

increasing consumers buying behaviours. This paper highlighted sales promotion such

as direct price discount, buy one get one free, buy one get another product free, media

advertisement, store publicities are stimulate consumers buying decision in FMCG

products.

Abhigyan Bhattacharjee (2011)stated FMCG products influenced to Medias are

both visual and print media. Advertisement and Medias as well as publicities are

creating new demand of products. It is suitable for both rural and urban areas.

Garima malik (2011)states that strong distribution and affordability also road shows

are use of customer retention and stimulate to buy Dabur products in rural market.

Robert P.Hamlin & Andreainshch (2011)stated that food industry customers are

like price promotions. The price cut immediate hit the sales as well as create demand

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in food products. The researcher said that manufactures and retailers are may have

power relationships.

5.11FMCG Product Cosmetic

Duff (2007) states the niche market in women‟s cosmetics and observed that

cosmetics buyers were becoming more fashion conscious and were demanding

products with more attractive design; furthermore, consumers have a tendency to use

different makeup designs for different occasions. It is further argued that design or

visual appearance is the important part of the product, which includes line, shape and

details affecting consumer perception towards a brand.

Guthrie, Kim & Jung (2008) states that women's perceptions of brand personality in

relation to women's facial image and cosmetic usage. This study sought to develop a

better understanding of how various factors influence perceptions of cosmetic brands

in the USA. The survey included items measuring facial image, cosmetic usage, brand

personality and brand attitude. The findings showed that an effective brand

personality was important across all three brands, although consumer perceptions

pertaining to the remaining brand personality traits differed. The study found that

consumers' facial image influenced the total quantity of cosmetics used. Results also

indicated that a relationship existed between facial image and brand perceptions.

Demographics include characteristics such as language, educational level, occupation,

income, age, geographic location, family structure, ethnic background, marital status

and gender.

Hawkins (2004); Schiffman & Kanuk, (2007) states that demographics are objective

and measurable characteristics and are likely to be used in consumer descriptions.

Demographics influence consumer behaviour by directly influencing consumer

attributes, for example values and decision-making styles. Hyllegard, Eckman,

Descals & Borja, (2005)states that education influences people‟s occupations and

their occupations greatly determine their income. Hellenger, Robertson and

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Greenberg (1977)stated that the consumers‟ level of education also influences

shopping centre patronage factors as it relates to store image.

Choi & Park (2006) states that consumers‟ occupation and education influence

preferences in products, media and activities, while income provides the necessary

means for consumption behaviour. Paulins &Geistfeld (2003) focused on identifying

attributes that affect store image preference. They found that consumers are more

critical of store image attributes when they have a higher education, but that

consumers from different income levels tend to perceive store image similarly. The

influence of age on store image perception is frequently investigated. Lumpkin

(1985) studied the needs of elderly or mature consumers and their findings concluded

that age groups within the elderly market differed regarding their preference for store

image attributes. Vaugt (1996) indicated those elderly consumers‟ perceptions of

store image do not differ significantly. Janse van Noordwyk (2002) did a qualitative

study of large-size female apparel consumers which indicated that the perceived

importance of store attributes differs by age. Therefore it is apparent that age

influences customers‟ perception of store image. Demographic variables in isolation

cannot provide a complete picture of the consumer. Studied in isolation,

demographics hamper the segmentation process, while demographical characteristics

such as age, income and employment status can be misleading. A person‟s biological

age is of less consequence than his/her psychological age, according to Joyce and

Lambert (1996). Furthermore, even though income can be tied to spending

behaviour, it reveals very little about consumer‟s personal interest, health or

discretionary time Oates et al., (1996). Consumers‟ lifestyle is therefore a necessary

variable when attempting to understand consumer behaviour.

Baiding Hu (1997) stated that the success of the economic reforms in rural China has

raised the living standards of rural households. This is reflected in households'

consuming goods and services that were not previously part of their consumption

pattern. However, because of differences in economic and demographic

characteristics, not every household has been able to increase consumption.

Consequently, it will be useful to investigate how the likelihood of consuming such

goods and services is affected by economic and demographic factors.

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Lokhande (2003) analysed that rural consumer has become enough aware about his

needs and up gradation of his standard of living. IT, government policies, corporate

strategies and satellite communication have led to the development of rural marketing.

Although income is one of the major influencing factors, caste, religion, education,

occupation and gender also influence the buyer behaviour in rural areas. Verma and

Munjal (2003) identified the major factors in making a brand choice decision namely

quality, price, availability, packaging and advertisement. The brand loyalty is a

function of behavioural and cognitive patterns of a customer. The age and

demographic variables affect significantly the behaviour and cognitive patterns of the

customers while other demographic characteristics such as gender and marital status

are not significantly associated with these behaviour and cognitive patterns of the

consumers. Emin Babakus (2004) examining individual tolerance for unethical

consumer behaviour provides a key insight in to how people behave as consumers

worldwide. In this study, consumer reactions to 11 unethical consumer behaviour

scenarios were investigated using sample data from Austria, Brunei, France, Hong

Kong, the UK, and the USA. Nationality is found to be a significant predictor of how

consumers view various questionable behaviours. Gender is not a significant

predictor, while age and religious affiliation are found to be significant predictors of

consumer ethical perception. The study identifies distinct consumer clusters based on

their perceptions of consumer unethical behaviour. Implications of the findings are

discussed and future research directions are provided.

Howard & Sheth (1969) states that people's motives for shopping are a function of

numerous variables, many of which are unrelated to the actual buying of products.

Shopping experience is a utilitarian effort aimed at obtaining needed goods and

services as well as hedonic rewards. Literature in marketing and related behavioural

sciences suggests a breadth of consumer motives for shopping. The idea that

consumers are motivated by more than simply the utilitarian motive to obtain

desired items has been acknowledged at least as far back as the 1960s.

Their consumer behaviour model, in addition to considering traditional explanatory

variables such as needs, brand attitudes, and the impact of shopping behaviour on

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promotions, also examined less explicitly utilitarian consumer motives such as

arousal seeking and symbolic communication. Skinner (1969)stated that the basic

consumer motives in selecting a supermarket for the retail food industry. His study

revealed that six variables: friendliness, selection/assortment, cleanliness, parking,

fast checkout service, and ease of shopping to increase the probability of the shopping

trip being pleasant. Tauber (1972) stated the idea that shoppers were often motivated

by a number of personal and social factors unrelated to the actual need to buy

products. He proposed that people shop not just to purchase goods, but to learn

about new trends, to make themselves feel better, to gain acceptance with their

peers, and simply to divert themselves from life's daily routine. He identified 11

hidden motives that drive people to the stores and often lead to 'impulse buys' among

consumers who initially were not planning on buying anything at all.

This included social interaction which consists of a variety of social motives, such

as, social interaction, reference group affiliation and communicating with others

having similar interests. The information-seeking motive, as proposed included

information seeking, comparison, and accessing in a retail context. Hirschman and

Holbrook (1982)suggested that a traditional emphasis on information processing

related to specific product attributes, and resultant focus on what may be termed

utilitarian shopping considerations, does not completely explain purchase and

consumption behaviour. Researchers have identified a segment of consumer 'market

experts ' who are particularly likely to provide other people with information on

obtaining the best values for particular purchases. Individuals scoring highest on the

maven scale were found not only to engage in more information search and provide

others with more information, but also to enjoy shopping more. Belch(2005) stated

hedonic and utilitarian shopping motives coexisting among consumers, although one

mode tended to dominate some consumers. Schindler (1989) suggested that while

some consumers may be strongly influenced by the utilitarian benefits of obtaining a

valued product at a good price, 'ego-expressive' desires to bolster one's self-concept

as a smart shopper may be a stronger motivator. He did not formally test this

hypothesis.

Lichtenstein (1990)stated the feelings of mastery experienced by consumers who

feel responsible for being able to obtain good deals. It is evident that consumers

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often experience an involvement in the shopping process which far exceeds a

detached effort to obtain desired products in an efficient and cost-effective manner.

This experience may be primarily recreational in nature, or may be motivated more

in terms of ego-involvement in one's shopping skills. In the retail shopping

experience, a recreational shopper is seen to be one who enjoys shopping and

appreciates the process and enjoyment of shopping. Rohm &Swaminathan (2004)

identified two concepts of retail shopping motives. On one hand, retail shopping

experience refers to the enjoyment of shopping as a leisure-based activity and

second, it taps into aspects of the enjoyment of shopping for its own sake. It is argued

as well that, in many instances, consumers may desire to obtain a higher level of

experiential consumption relative to utilitarian consumption. Kim (2001) states that

shopping enjoyment is an enduring individual trait that influences enduring shopping

style and has previously been associated with transient emotional responses.

Dawson (1990) states that is the underlying and enduring shopping enjoyment trait

impacts transient emotions that may arise during particular shopping episodes.

Kimberly (2002)states that positive emotions such as excitement, pleasure, and

satisfaction have also been identified as significant determinants of consumer

shopping behaviour (patronage, amount of time and money spent in the store). The

importance of the emotional element for successful retailing has been evidenced in

the emphasis on emotional retailing Regarding the emotional responses of

consumers to the textile/apparel product offerings at stores, Consumers in Shanghai

gave higher ratings to utilitarian responses, i.e. efficient, timesaving, convenient (4

on the five-point semantic differential scale) than to hedonic responses, i.e. excited,

surprised, interested

Lennon (2003)states that Korean consumers rated utilitarian and hedonic responses

approximately equally (3.6 and 3.5 respectively). This result reflects how consumers

at discount stores in the two country markets responded to their present

textile/apparel offerings at the stores. It was also suggested that satisfying shoppers

in the discount store format with utilitarian attributes (quality, price, variety of

products) of textile/apparel products is critically important to eliciting positive

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hedonic emotions (e.g., surprised, interested) as well as utilitarian emotions (e.g.,

efficient, convenient). Consumers in China who generally believe that shopping is

very important to their life rated high in both utilitarian and hedonic responses. Also,

Chinese consumers who go shopping for the purpose of getting away from daily

routines (i.e. diversion) exhibited stronger utilitarian responses. In other words,

shopping at a discount store is an important leisure activity to the Chinese consumer.

However, Korean consumers' responses to textile/apparel products were not affected

by either individual consumers shopping involvement or shopping motives. In China,

the shopping excitement consumers experienced at discount stores was positively

affected by store ambiance, facility convenience, brand/fashion, consumer shopping

involvement, and socialization shopping motives.

Haanpa (2005) states that comparison of different motives and shopping styles. Her

study revealed that Finnish consumers were very functionally oriented; they valued

ease and convenience and very tangible elements of shopping, such as having the

possibility to buy alimentary concurrently when going shopping for other purposes

than daily consumer goods. The factor dimensions produced with principal

component analysis formed two experiential and gratification type factors, labelled

as Hedonistic and Recreational motives. The other two factors were named as

Economic and Convenience motive. The analysis of variance revealed that there

were, to a certain extent, differences among different consumer groups. Consumers

that were demanding enjoyable experiences in their shopping trips were typically

young females especially when it came to shopping are hedonic and escapist

elements. Young consumers looked for interesting shopping experiences that were a

mixture of social and emotional needs and wants and related to interaction and

communication with other people.

Parsons (2002)states that many of the hidden motivations uncovered by Tauber 30

years prior are relevant to internet shopping today. His findings revealed that online

shoppers are commonly driven by personal motives such as diversion, self-

gratification, and learning about new trends; and social motives, including social

experiences outside the home, communications with others having a similar interest,

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peer group attraction, and status and authority. Eastlick and Feinberg (1999) found

that motive scores were often higher among women than among men. The researcher

found a negative relationship between education and shopping motivations.

Additionally, the researchers found that the motive scores were often higher among

women than among men shoppers.

Lennon (2003)states that consumers' motivations for shopping from television

shopping channels; to determine if motivations differed as a function of clothing

purchase frequency when controlling for personal characteristics. Respondents were

motivated to shop from television due to convenience, the amount of information

available on the shopping channels, and the return policies. Regular apparel shoppers

agreed that they were somewhat motivated by the prices offered on television. Ray

and Walker (2004) reported that college students' motivation to purchase from non-

store based retailers was not related to personal characteristics (age, gender,

employment, etc.).The foregoing review illustrates that shopping motives for people

vary from being utilitarian to purely hedonic. They are also expected to operate

simultaneously in a particular shopping situation.

Birtwistle(1999) state that defining market segments through behavioural aspects

supply a more concrete foundation for a marketing strategy. By understanding the

characteristics of the segments, effective communication can be developed. Du

Preez (2001) chose demographics, family life cycle, lifestyle, cultural

consciousness, patronage behaviour, shopping orientation, and place of distribution

to form clusters of female apparel shoppers. Some variables chosen by other

researchers to investigate shopping behaviour were information sources, situational

influences, shopping orientation, product-specific variables, media usage, store-

specific variables, socio-psychological attributes, clothing involvement,

demographics, socio- cultural, clothing store dimensions, clothing orientation,

psychographics, personal characteristics and self-concept Gutman & Mills (1982)

states that there are three broad groups of variables most often included in store

image research, namely demographics, lifestyle and shopping orientation.

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Kenneth (1980) analysed the consumer search for information and explored that a

consumer often weighs between the cost and value of search. The information does

not come free. It involves costs in the form of time, psychological discomfort and

financial expenditure. The value of search depends on consumer experience,

urgency of making purchase, satisfaction derived from search, perceived risk and

value placed on the product.Oliver (1980) compared the pre-purchase expectations

and post purchase satisfaction and found that even good performance does not

ensure satisfied customers. This was because customer satisfaction typically depends

on more than actual performance. According to his expectancy disconfirmation

model, it was identified that satisfaction depends on a comparison of pre-purchase

expectations to actual outcomes.

Kent and Allen (1994) explained that brand familiarity captures consumer's brand

knowledge structures, that is, the brand associates that exist within a consumer's

memory. Although many advertised products are familiar to consumers, many others

are unfamiliar, either because they are new to the market place or because consumers

have not yet been exposed to the brand. Consumers may have tried or may use a

familiar brand or they may have family or friends who have used the brand and told

them something about it. Jarvis (1998) identified that a purchase decision requires a

subset of decisions associated with information search. At some point in time,

consumers acquire information from external sources that gets stored in long-term

memory. For most consumers, usually this stored information, referred to as internal

information, serves as the primary source of information most of the time as is

evident in nominal or limited decision making.

Krishna Mohan Naidu (2004)states that an attempt had been made to analyze the

awareness level of rural consumers. It was found from the study that awareness of

the rural consumers about the consumer movements were qualitative in character

and cannot be measured directly in quantitative terms. There is no fixed value or

scale which will help to measure the awareness. But the awareness had been studied

with the help of their responses to various questionnaires relating to consumer

movements, cosmetics, banking services, drugs, food products, tooth pastes and hair

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oil. Awareness levels were higher in the above said segments in Ranga Reddy of Andhra

Pradesh.Sharma and Kasturi (2004) observed that rural consumers do experience

tension due to dissonance and exhibit defensive behaviour and use attribution in

support of their behaviour. They were worse hit by non-availability of quality

alternatives. This forces them to accept low quality products. As advertisements

were not reaching the rural sector effectively, there is need to strengthen the hands

of information agents to remove the ill effect of post purchase dissonance.

Anandan (2007)examined that quality is the major driver to prefer a particular

brand in washing soaps in the rural market. Power soaps are ruling the rural market.

If the preferred brands are not available, customers buy the available brands. It is

found that there is a significant relationship between the age of the respondents and

the factors influencing the customers' brand preferences. IT is also found that there is

no significant relationship between the type of income of the respondents and the

factors influencing the customers' brand preferences. Higher price and non-

availability are the key reasons for dissatisfaction of the rural customers. Marketers

should target the customers with high qualitative soaps at affordable prices. They

should concentrate on distribution strategies, as non- availability had been an

important factor for dissatisfaction.

John Mano Raj (2007) states that attractions for the FMCG marketers to go to rural

and the urban markets and uses a suitable marketing strategy with the suitable

example of companies and their experience in going rural. Thus the rural marketing

has been growing steadily over the years and is now bigger than the urban market

for FMCG. Globally, the FMCG sector has been successful in selling products to

the lower and middle income groups and the same is true in India. Over 70% of sales

are made to middle class households today and over 50% of the middle class is in

rural India. But the rural penetration rates are low. This presents a tremendous

opportunity for makers of branded products who can convert consumers to buy

branded products. The marketers need to develop different strategies to treat the

rural consumers since they are economically, socially and psycho-graphically

different from each other. This paper covers the attractions for the FMCG marketers

to go to rural, the challenges, the difference between the rural and the urban market

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and the suitable marketing strategy with the suitable customers. Rajesh Shinde (2007)

states that rural India has more than 70% population in 6.27 lakhs villages, which is

a huge market for FMCG products. All the income groups purchase the FMCG

product but their brands differ from each other. The place of purchase, which the

rural consumer prefers, is the weekly market, which is a good channel of

distribution of FMCG. Moreover the youth who visit the taluka place or district place

are influenced by the city culture and it is reflected in their purchasing decision.

Overall the marketer should understand the customer before taking up the road to the

rural market.

Aditya Prakash Tripathi (2008) states that the Indian rural market has a tremendous

potential that is yet to be tapped. A small increase in rural income results in an

exponential increase in buying power. However, the marketing strategy for rural

market has to be different from that adopted for the urban market, because of

different social environment. Appropriate advertising and personal selling to meet

the demand and integrated outlets have become the essential elements of the

marketing strategy for the rural market. the success of marketing in rural areas

depends on how effectively the marketing skills are applied in the number of

complex activities of marketing, beginning with the assessment of the need of the

rural consumers, organizing the production to match the demand, pricing,

advertising and publicity, culminating in the sale of the product at a profit.

David Griffith (2008)states consumers' reaction towards the advertising market by

incorporating the use of information sources and perceived source credibility into

the advertising effectiveness literature. The results show that rural Chinese

consumers utilise a variety of information sources when making their purchase

decision, and for different product categories different information sources are

preferred. Although perceived source credibility is a reliable predictor for

information sources use, the most trusted information source might not always be the

most used source. Sarangapani (2008) pointed out the essence of modern marketing

concept is to satisfy the customer, and naturally all the marketing activities should

revolve around the customers and their buying behaviour. The key to ensure

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consumer satisfaction lies in understanding the customer, his likes, dislikes, buying

behaviour, buying motives and buying practices. In the light of this, rural consumer

behaviour provides a sound basis for identifying and understanding consumer

needs. Knowledge of customer behaviour is important for effective marketing efforts

and practices.

Jyothsna Priyadarsini (2009)states that many rural men feel delicate to use

cosmetics. Rural males have a feeling that cosmetics are mainly meant for females.

The social stigmas against male grooming products persist a lot. These male

respondents consider their use as feminine. Now it is the job of marketers to create a

cosmetic sense among the masculine breed. The present empirical study shows that a

majority of the customers are unaware of the importance of male grooming and

exclusive male grooming brands. Henceforth, marketers should attempt to create

product awareness and drive the customers through brand awareness. Zeb, Hareem;

Rashid, Kashif; Javeed, M (2011) states that the Influence of Brands on female

consumer‟s buying behaviour in Pakistan attempted to examine Pakistani female

consumer‟s buying behaviour and understand the key factors of branded clothing

which influence female consumer‟s involvement towards trendy branded clothing.

Sriparna Guha (2013) states that the changing perception and buying behaviour of

women consumer in Urban India”. The working women segment has significantly

influenced the modern marketing concept. The author further states that women due

to their multiple roles influence their own and of their family members‟ buying

behaviour. The study also reveals that working women are price, quality and brand

conscious and highly influenced by the others in shopping. Ashwin Kumar (2011)

states that the buying behaviour of Indian women & their values for the market.

Women as a consumer were also participating in buying the goods. Indian women

were dominating the market by making her presence in every purchase decision. The

author further states that Indian women are playing a new role as a facilitator.

Swarna Bakshi(2009) states that the Impact of Gender on Consumer Purchase

Behaviour”. Men and women due to their different upbringing and socialization along

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with various other social, biological and psychological factors depict different types

of behaviour at various situations. Women seem to have satisfaction and find pleasure

while they shop whereas men appear to be more disdain towards shopping. Shainesh

(2004) states that buying behaviour in a business market is characterized by long

cycle times, group decision making, participants from different functional areas and

levels and sometimes divergent objectives, and changing roles of the participants

during the buying cycle. The high levels of market and technological uncertainty of

services is the complexity in the buying process. Despite all this, marketers have been

remarkably remiss in not looking at women as a separate segment.

Mehta & Sivadas, (1995) states that e-shopping buyers, gender, marital status

residential location, age, education, and household income were frequently found to

be important predictors of Internet purchasing. The consumer‟s willingness and

preference for adopting the Internet as his or her shopping medium was also

positively related to income, household size, and innovativeness. Akhter &

Hausman(2002)states that more educated, younger females, and wealthier people in

contrast to less educated, older, females, and less wealthier are more likely to use the

Internet for purchasing. It further states that the professional woman is the most

important customer we have. She's the largest spender, and she influences how the

family spends their money.

Sharma Samidha&Kurian Boby (2013) states that ,Indian women will fuel Rs.2.17

crore e-shopping in next 5years Indian women fuelled online shopping worth over

half-a-billion dollars last calendar and that figure is galloping five-fold to Rs.2.17

crore in the next three years. Women-influenced sales would be 35% of Indian e-

commerce market estimated at Rs.5.28 crore by 2016, Venture capital firm Accel

Partners , one of the prolific backers of start-ups, said that These projections come in

the backdrop of a frenetic growth in internet penetration through smartphones and

professional Women lapping up the convenience of shopping online .Crawford and

Melewar (2003) states that to examine the difference in the impulsive buying

behaviour of men and women and also to determine the important factors which

http://search.proquest.com/business/indexinglinkhandler/sng/au/Sharma,+Samidha/$N?accountid=36076


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influence the impulsive buying behaviour of customer. The response showed that

working men and women of younger age purchase the product more impulsively than

the older population and spend more amount on impulse purchase. Although men buy

the product impulsively but there is also a rational thinking involved in the decision

making which lacks in case of women up to a certain extent. Andrews and

Currim(2004)states that uncertainties about products and shopping processes,

trustworthiness of the online seller, or the convenience and economic utility she

wishes to derive from electronic shopping determine the costs versus the benefits of

this environment for consumers.

Katy & Dipika (1997)states that consumer‟s purchase behaviour over two periods in

the cities of Mumbai, Kolkata and Delhi. The study showed that Kolkata seemed to be

opting for reduced consumption as a way of economizing rather than downgrading on

product quality. Skinner (1990) states that when a consumer purchases an unfamiliar

expensive product he/she uses a large number of criteria to evaluate alternative brands

and spends a great deal of time seeking information and deciding on the purchase.

The type of decision making used varied from women to women and from product to

product.

Hate (1978) states that there is positive change in shopping pattern of Kolkata women

living in big cities in Maharashtra with the advent of independence. Sultan &

Henrichs (2000) states that women represent the major e-shopping holiday season

buyer. Rainne,(2002) states that the number of women (58%) who bought online

exceeded the number of men (42%) by 16%. Among the woman who bought, 37%

reported enjoying the experience “a lot” compared to only 17% of male shoppers who

enjoyed the experience “a lot.

Mowen (1988)state that the focus of many consumer decisions was on the feelings and

emotions associated with acquiring or using the brand or with the environment in

which it was purchased or used than it's attributes. Whether consumer decision was

attribute-based or driven by emotional or environmental needs, the decision process

discussed helps to gain insights into all types of purchases. Narayan Krishnamurthy

(1999) states that semiotics primarily works best for products that have low -

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involvement at the time of purchase, and had very frequent usage. Fast moving

consumer goods (FMCG) such as soaps, shampoo, types goods and tea were the one that

fit the bill best Mnemonics also became crucial to nurture and retain place in mind

space. The shelf - life of FMCG products was short enough for most to remember

those products by their symbols, colours and names, or a combination of those

elements. The low level of literacy in rural India acts positively for signs and

symbols along with visual looks, to succeed.

Upadhyay (1999) identified significant differences between rural and urban areas on

the basis of the role played by different members of a family in purchase decision of

non-durable goods. As initiators, husbands and kids are more prominent in rural

areas, while wife is more prominent in the urban areas. Leszezye & Timmerman

(2000)analysed that the store choice is a dynamic decision which can be

conceptualized as a problem of deciding, when and where to shop. The first decision

is the traditional store location choice problem whereas the second is the shopping trip

incidence problem relating to the timing of shopping trips. The two decision

processes are correlated. Store choice is dependent on the timing of shopping trips as

consumers may go to a local store for short fillin trips and go to a more distant

grocery store for regular shopping trips.

Keshav Sharma (2002)states that rural customer in the urban analogous villages

wants to acquire the urban life style but when it comes to buying, decision making is

entirely different from its urban counterpart. Culture has a great influence on their

buying decisions.

a) Equal status of female in buying decision making.

b) The rural customer up holds his traditions and customs in high esteem.

c) They hate the way their culture is being diluted through ads.

d) Only a very small proportion of the younger segment is willing to change and keep

only the good that their culture has.

The Rural customer is simple and virgin. Upholding the dictum that customer is the

king, if marketers try to approach them through his culture, they will feel respected

and honoured and will be forever companies.

Nillo Home (2002) states that the relationship between consumers and grocery stores

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in the countryside. More attention must be paid towards retailing and consumer

behaviour in rural areas since a lot of studies have focused on urban consumers'

buying behaviour while paying rather little attention to that of rural inhabitants,

especially in sparsely populated areas. The buying behaviour of rural consumers and

the positive and negative features connected with the product and service supply of

rural stores are examined. The study ideates the most relevant store choice factors of

an ideal grocery store and the most important features which best describe the rural

store. Factor analysis revealed the dimensions according to which rural consumers

evaluate grocery purchasing, and homogeneous customer groups with different

shopping orientation and were formed using cluster analysis.

Sarwade (2002)states that marketing and consumer behaviour aspects in rural areas

with reference to three villages namely Adul, Paithan and Sangri (s) from the

Marathwada region. The study revealed that the role of a husband in the family

purchasing decisions in various items was comparatively less than of a housewife. It

was found in the study that most of the consumers from rural area developed brand

familiarity with brand names such as Lipton, international Lux, Keokarpin, Brahmi

Amla, and Pantene which were heavily used in urban areas. An interesting finding of

the study was that overall consumption pattern of the rural consumers had changed.

Consumption expenditure for non - durable items had increased considerably during

the study period. Farmers should like risk bearing capabilities and self-dependence.

Keshav Sharma (2002) states that the rural consumers believed in joint buying

decision making in consultation with the elders and the ladies of the house for their

personal use according to their own independent buying decisions. Advertisement

with rural culture and regional/local language attracted the audience. The entire

respondent felt strongly about their customs and traditions. The respondents were

aware of the availability of the products. They preferred quality to price. Rajnish Tuli

and Amit Mooherjee (2004)states that the rural consumer prefers to meet his

immediate and day-to-day needs from village shops and avoid a comparatively higher

transportation cost at the same time; bulk purchase will drive them to the periodic

markets to avail the bargain and promotional incentives which will negate the impact

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of shopping cost incurred. Rural consumers patronize village shops to meet their

credit-based impulsive requirements. On the other hand, cash rich consumers with no

urgency, prefer to purchase from periodic markets to avail the benefits of low prices,

discounts and varieties ets, which in turn motivate rural consumers.

Archana Kumar (2009) states that Indian consumers examines the effects of

individual characteristics (i.e., consumer's need for uniqueness and attitudes toward

American products) and brand-specific variables (i.e., perceived quality and

emotional value) on purchase intention toward a U.S. retail brand versus a local

brand. A total of 411 college students in India participated in the survey. Using

Structural Equation Modeling (SEM), this study finds that Indian consumers' need

for uniqueness positively influences attitudes toward American products. Attitudes

toward American products positively affect perceived quality and emotional value for

a U.S. brand while this effect is negative in the case of a local brand. Emotional value

is an important factor influencing purchase intention towards a U.S. brand and a local

brand as well. Implications for both U.S. and Indian retailers are provided. Estiri

(2010) tried to evaluate and compare the effects of packaging elements on consumer

behaviour in the pre- purchase, purchase and post-purchase stages. The questionnaires

filled by participants which were analysed qualitatively to examine the importance of

different packaging elements on consumer behaviour in the three stages of purchase

decision. Results show that all packaging elements are highly important for food

products buyers and these elements can highly influence their purchasing decision

Joyce Xin Zhou (2010)states that China is rapidly becoming an important market for

consumer goods, but relatively little is known about variations in consumer shopping

patterns in different regions of China. We employ a cultural materialism perspective

in understanding decision-making styles of inland and coastal shoppers. Our

findings reveal that consumers in the two regional markets do not differ in

utilitarian shopping styles but they do in hedonic shopping styles. Marketers need to

understand these differences to be able to market effectively to consumers in

different regional markets within China.

Post-purchase attitude of shoppers

Venkatesan (1973) states that the result of satisfaction to the consumer from the

purchase of a product or service was that more favourable post purchase attitudes,

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higher purchase intentions and brand loyalty are likely to be exhibited that is, the

same behaviour was likely to be exhibited in a similar purchasing situation. Thus, as

long as positive reinforcement takes place, the consumer will tend to continue to

purchase the same brand.

Kapoor (1976)states that the emerging lifestyles of 47 rural families living in the

villages of Delhi, Haryana, Punjab and Uttar Pradesh. It revealed that rural

consumers were not satisfied with the services rendered by village retailers. This

includes product availability, price charged, after sale service and credit availability.

Geva & Goldman (1991) states that the possible inconsistencies in consumer's

post-purchase attitude when faced with disconfirmed expectations. The main

argument, based on an extension of cognitive dissonance theory was that post-purchase

attitude may be characterized by duality. Satisfaction with post purchase may not be

closely related to intentions to repurchase because of the different functions they

may fulfil. Whereas satisfaction reflects the need to justify post purchase behaviour,

intentions to repurchase, which are of instrumental importance, reflect learning

from experience. This approach contrasts the prevalent satisfaction- intention

paradigm which assumes a causal link from satisfaction with the purchase, to

intentions to repeat it. Vasudeva (1999) states that the proportion of households, which

are brand loyal to one or more brands, are similar in urban market and rural markets.

Toothpaste is the only product for which rural market shows greater brand loyalty

than the urban market. The rural brand loyal consumers were found to be

comparatively more price conscious than the urban brand loyal for detergent powder

and toilet soaps. Lokhande (2004) states that illiteracy to be a major hindrance in

rural marketing and thus audio-visual aids can enable the marketers to take their

message effectively to rural areas. It was found that brand does not matter to the rural

consumers; they just want to fulfil their needs. Some consumers were brand loyal also

and didn't make brand shifts. Thus, marketers should focus on brand value.

Distribution channel should be made effective so that rural retailers are not deficient

of necessary goods. Although barter system was found to be prevalent notably in the

rural areas, daily wage earners were purchasing commodities on payment basis only.

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Archna Shukla (2006) states that residents of at least four villages visit saunda Heat in

Meerut district of Uttar Pradesh every Thursday, as do merchants from the same

villages. There are around 60 stalls in Hat selling everything from groceries to apparel to

kitchenware to fresh produce. Few of the brands which are familiar are parlea, Tiger,

Parachute and lifebuoy she further adds that saunda Haat is one of 47,000 that is serving

the needs of 742 million. She concludes that despite constraints, the rural market

especially for Fast Moving Consumer Goods (FMCG), apparel, footwear and fuel is

bigger than the urban market. Yuping (2007) states that consumers who were heavy

buyers at the beginning of a loyalty program were most likely to claim their

qualified rewards, but the program did not prompt them to change their purchase

behaviour. For light buyers, the loyalty program broadened their relationship with the

firm into other business areas.

Wen-bao Lin (2008)states that study is attempted to combine the decomposition

theory of planned behaviour with the theories of relationship quality and product

involvement to establish a complete model for the explanation of factors influencing

online investment and post-purchase behaviour. The SEM causal model was used to

verify the capability of the model to explain the online investment and post-

purchase behaviour of consumers. Consumers in the top four largest cities in Taiwan

who invest in financial products via banks were selected for the study. In the

preliminary fit, the financial support of family members has the highest influence on

the decision of consumers (subjective norm), the incorrectness of product information

announced by service providers is perceived by consumers as the highest risk

(perceived risk), and the attractiveness of products is the most important variable to

arouse the interest of consumers to buy (product involvement). As for the internal

fit, the subjective norm to actual behaviour, perceived risk to actual behaviour,

subjective norm to post- purchase behaviour, and gap of perceived service quality to

post- purchase behaviour reach the significant level and the overall goodness- of-fit of

the research model was satisfactory.

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2.12Research Gap

After going through several literature it was noticed that many research w.r.t shopping

pattern were conducted for understanding consumer and working women shopping

pattern. However there was no study conducted by any researcher on all format like

online and physical which this research is focused on. There is no study done so far

on Impact of Shopping patterns (E-shop, Teleshopping& physical buying) of select

Fast moving Consumer (FMCG) products on working women in select Tier 1 cities of

India like Mumbai ,Delhi ,Bangalore and Hyderabad. This study can help marketers

to adopt marketing mix strategies while targeting working women for mentioned

categories. The same is mentioned in conclusion and suggestion part and it is proved

in data analyses and data result. Considering the fact that most of the purchases are in

some form managed by women (working or non-working) and since majority working

women are entering the workforce area, these working women segments are of prime

importance for the marketers today. Studies on the impact of Shopping patterns (E-

shop, Teleshopping& physical buying) of select Fast moving Consumer (FMCG)

products On working women in select Tier 1 cities of India help managers to

understand the manner in which working women buy certain product or services.

Working women are the upcoming focus of marketers in the country due to their

affluent and spending power and decision making ability.

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CHAPTER 6

OBJECTIVE & HYPOTHESIS OF STUDY

6.1 Objectives

To study the proportion of E-shopping, teleshopping and physical shopping patterns

of select FMCG products by Professional women in select tier1 cities.

To study the impact of income level of working women on shopping patterns in select

tier1 cities.

To study the correlation between costs effectiveness of shopping patterns of FMCG


To study the significance of quality of products in shopping pattern of FMCG


To study the significance of demographic factors vis-à-vis working women‟s

occupation on shopping pattern of FMCG products in select tier 1 cities.

To study the significance of demographic factor Vis -a-Vis age on shopping pattern of

working women of FMCG products in select tier1 cities.

To study the significance of demographic factor Vis -a-Vis qualification on shopping

pattern of working women of FMCG products in select tier1 cities.

6.2Hypothesis of study:

H01: There is no significant difference in proportion of online (E shopping,

Teleshopping )and physical shopping pattern of working women for FMCG products.

H11: There is significant difference in online (E-shopping, Teleshopping) and physical

shopping pattern of working women for FMCG products.

H02: There is no association between level of income and proportion of online (E

shopping, Teleshopping )and physical shopping pattern of FMCG products .

H12: There is association between level of income and proportion of online (E

shopping, Teleshopping )and physical shopping pattern of FMCG products.

H03: There is no correlation between cost effectiveness and proportion of online (E

shopping, Teleshopping ) shopping pattern FMCG products.

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H13: There is correlation between cost effectiveness and proportion of online (E

shopping, Teleshopping ) shopping pattern of FMCG products .

H04: There is no association between quality of product and proportion of online (E

shopping, Teleshopping ) shopping pattern of FMCG products.

H14: There is association between quality of product and proportion of online (E


H05: There is no association between working women‟s occupation and proportion of

online (E shopping, Teleshopping )and physical shopping pattern of FMCG products.

H15: There is association between working women‟s occupation and proportion of

online(E shopping, Teleshopping )and physical shopping pattern of FMCG products.

H06: There is no association between age of working women and proportional of


H16: There is association between age of working women and of online (E shopping,

Teleshopping )and physical shopping pattern of FMCG products.

H07: There is no association between qualifications of working women and

proportion online (E shopping, Teleshopping )and physical shopping pattern of

FMCG products.

H17: There is association between qualifications of working women and proportion


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CHAPTER 7

RESEARCH METHODOLOGY & DATA COLLECTION

Data collection was done in two stages: in the first stage a pilot survey was

conducted to ascertain the research parameters and to test the validity and reliability

of the instruments i.e. Questionnaire used in the study. Pilot Study was conducted in

two cities out of four cities of India namely Mumbai &Bangalore to test the reliability

of the instruments. The study was conducted with a sample of 100 respondents

(working women).In the second stage the primary source of information was

collected through using the instruments in the study. Instruments used to administer

the respondent were Questionnaire.

The Secondary source of information here includes library resources, articles in

various newspapers and magazines, research papers, companies‟ brochure and online

resources like company websites, online reports and articles. The source is gathered

from National Council of Applied Economic Research NCAER and Indian Market

Research Bureau.

7.1 Demographic factors:

City: Information is collected through four different cities. These are Mumbai, Delhi,

Bangalore and Hyderabad. Out of 800 respondents, 270 were surveyed from Mumbai,

250 respondents from New Delhi, 160 respondents from Bangalore and 120

respondents from Hyderabad. In Mumbai, 270 respondents were selected from 6

Parliamentary Constituencies like Mumbai North, Mumbai North West, Mumbai

North East, Mumbai North Central, Mumbai South Central, Mumbai South. In each

parliamentary constituency of Mumbai46 respondents were surveyed in Delhi 250

respondents were selected from7 parliamentary constituencies. Delhi constituency

includes New Delhi, North West, Chandni Chowk, West Delhi, South Delhi, East

Delhi and North East Delhi. 39 respondents were surveyed from each of these

constituencies. In Bangalore out of 160 respondents, 52 respondents were surveyed

from each of North, South and Central parliamentary constituencies. In Hyderabad

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out of 120 respondents, 43 respondents were surveyed from each of 3 parliamentary

constituencies.

Age group: Age of respondents is divided in to three groups. Respondents of age

below 30yrs are classified in to „Young „age group, respondents of age 30 to 45 are

classified as „Middle‟ age group and respondents of age above 45 are classified in to

„Elderly‟ group. Qualification: respondents are classified in to four groups according

to their qualification. These groups are „under graduates‟, „graduates‟, „post

graduates‟ and „professional‟.

Monthly Income: Respondents are classified into 3 groups according to their

monthly income. Respondents of monthly income below Rs. 15,000 are considered as

„Low income‟ group, respondents of income between Rs. 15,000 to 35,000 are

considered as „Middle income‟ group, respondents of income between Rs. 36,000 to

50,000 and classified as „High income‟ group .

Occupation : Respondents from IT industry ,Banking & Insurance ,Academic and

others are considered .In case of others professional women respondents from

Fashion industry, Media ,BPO , Marketing & Sales , etc. are taken into consideration.

7.2 Sample Technique

The study was conducted in four Tier 1 cities of India like Mumbai, Delhi, Bangalore

and Hyderabad. In these cities working environment and ecology are different. The

sampling survey was done based on stratified Random Sampling. The sample unit

was working women of different organisations of different age group and different

levels of management. The sample size was fixed after knowing the population of all

four cities. Below table indicate that total sample size is of 800 respondents. Selection

of sample size based on following formula.

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Table 7.1 Population of working women in Tier1 cities (Source: International

Market Research Bureau, Mumbai 2014)

Name of the Cities Population of Professional

women

Number

of respondents

Mumbai 1,423,922 270

New Delhi 1,250,000 250

Bangalore 4,81,077 160

Hyderabad 3,40,498 120

Total 3,495,497 800

7.3Sample Size Calculation

: Sample size is decided using formula as given below.

Consider z = 1.96 (it is standard for 95% level of confidence)

Standard deviation calculated = 10.75

Margin of error = 0.75

Sample size = (1.96 * 10.75/0.75)^2 = 789 (approximate)

Minimum requirement of total number of respondents is of 789 respondents..

7.2 Reliability Statistics

Cronbach’s Alpha

Value

No of Items

0.744 68

It is more than 0.7 therefore the reliability test is satisfied

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7.5Limitations of study:

The Study was only restricted towards working women‟s of select Tier 1 cities of

India namely Mumbai, Delhi, Bangalore and Hyderabad.

The Selected FMCG Product in the study were limited to frozen foods, toiletries,

cosmetics ,packed dairy products and packed grocery products .

Demographic factors are restricted to age ,income ,occupation and qualification

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CHAPTER 8

DATA ANALYSIS AND VALIDATION OH HYPOTHESIS

Information collected through structured questionnaire was first entered in to excel

sheet. For statistical analysis of data and validation of hypothesis SPSS version 20

was used. Information was classified according to demographic factors. Classified

information was presented using tables, pie chart and bar diagram. Descriptive

statistics was obtained for each variable. Descriptive statistics will be used for the

analysis of data which consist of „Arithmetic mean‟ and „standard deviation‟.

For testing of hypothesis Chi-square test is applied. Chi-square test is applied to test

association between 2 variables: i) working women and ii) inclination of buying

pattern towards online shopping and physical shopping for FMCG in four Tier1 cities

of India .ANOVA and F-test was applied to test significance between mean scores.

Paired T-test: t-test (also known as z-test for large sample) was applied to test

significance of difference in mean scores of above mention 2 variables.

Karl Pearson‟s coefficient of correlation was obtained to understand correlation

between two variables viz working women and inclination of buying pattern towards

online shopping and physical shopping for FMCG in four Tier1 cities of India. This

chapter consists of response collected from working women in select Tier 1 cities of

India namely Mumbai, Delhi, Bangalore & Hyderabad. Information is collected

through a questionnaire. To study online (E-shopping, Teleshopping) and physical

shopping pattern for select five FMCG in select Tier -1 cities of India like Mumbai,

Delhi, Bangalore and Hyderabad. 1200 questionnaire was distributed to sample

respondent working women out of which 950 was obtained. Out of 950 questionnaires

obtained 800 were found to be in order.

126

8.1 Classification of demographic factors is as follows:

City of respondent: Information is collected from four different cities like Mumbai,

Bangalore, Delhi and Hyderabad. The information is presented in the table no 8.1.1.

Table No: 8.1.1 Respondents City wise

Out of 800 respondents, 270 respondents were surveyed from Mumbai, 250

respondents from New Delhi, 160 respondents from Bangalore and 120respondents

from Hyderabad. In Mumbai, 270 respondents were selected from 6 Parliamentary

Constituency like Mumbai North, Mumbai North West, Mumbai North East, Mumbai

North Central, Mumbai South Central, Mumbai South. In each constituency of

Mumbai 46 respondents were surveyed. In Delhi 234 respondents were selected from

7 parliamentary constituencies. Delhi constituency includes New Delhi, North-West,

Chandni Chowk, West Delhi, South Delhi, East Delhi and North East Delhi. 39

respondents were surveyed from each of these constituencies. In Bangalore out of 160

respondents, 52 respondents were surveyed from each of North, South and Central

parliamentary constituencies. In Hyderabad out of 120 respondents,43 respondents

were surveyed from each of 3 parliamentary constituencies. This information is

presented using pie diagram as shown below chart no 8.1.1

CITIES Number of

respondents

Percent

Mumbai 270 33.75

Delhi 250 31.25

Bangalore 160 20

Hyderabad 120 15

Total 800 100.0

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Chart No: 8.1.1 Respondents City wise

Age group: Information about age of respondent in 4 cities is collected. This

information is classified in to three groups. Age of respondents is divided in to three

groups. Respondents of age below 30yrs are classified in to „Young „age group,

respondents of age 30 yrs. to 45 yrs. are classified as „Middle‟ age group and

respondents of age above 45 yrs. are classified in to „Elderly‟ group. The information

is presented in the following table no 8.1.2.

Table No. 8.1.2: Respondents Age wise

Age group Frequency Percent

Elderly 190 23.8

Middle 340 42.5

Young 270 33.8

Total 800 100.0

Above table no 8.1.2 indicates that there are total 800 respondents in 4 cities out of

which 190 belongs to „Elderly‟ age group, 340 belong to Middle age group and 270

belongs to Young age group. Above information is presented in using pie-chart as

shown below chart no 8.1.2

34%

31%

20%

15%

Diagram of respondents according to city

Mumbai

Delhi

Bangalore

Hyderabad

128

Chart No. 8.1.2: Respondents Age wise

Qualification of respondent: Information about Qualification of respondent is

collected. This information is classified in to four groups according to their

qualification. These groups are „under graduates‟, „graduates‟, „post graduates‟ and

„Doctoral‟.

Table No. 8.1.3: Respondents Qualification wise

Qualification Frequency Percent

Graduate 300 37.5

Post graduate 310 38.8

Doctoral 110 13.8

Undergraduate 80 10.0

Total 800 100.0

Above table no 8.1.3 indicate that there are total 800 respondents out of which 300 are

graduates, 310 are Post-graduate ,110 are Doctoral and 80 are Undergraduate. Above

information is presented by using pie-chart as shown below in chart no 8.1.3

24%

42%

34%

Diagram of respondents according to age

Elderly

Middle

Young

129

Chart no 8.1.3: Respondents Qualification wise

Monthly income of respondent: Information about monthly income of respondent is

collected. This information is classified in to three groups according to their monthly

income. Respondents of monthly income below Rs 15,000 are considered as „Low

income‟ group, respondents of income between Rs 15,000 to 35,000 are considered as

„Middle income‟ group, respondents of income between Rs 36,000 to 50,000 are

classified as „High income‟ group. Respondents of income above Rs.50000 are

classified as „Very High income‟ group.

Table 8.1.4: Respondents Income wise

Monthly

income

Frequency Percent

Low 300 37.5

Middle 300 37.5

High

Very High

120

80

15.0

10.0

Total 800 100.0

Above table no 8.1.4 indicate that there are total 800 respondents out of which 120

are High income group , 300 are low income group ,300 are Middle income group

37%

39%

14%

10%

Diagarm of respondents according to qualification

Graduate

Post graduate

Doctoral

Undergraduate

130

and 80 are very High income group . Above information is presented by using pie-

chart as shown in chart no 8.1.4.

Chart no 8.1.4: Respondents Income wise

8.2 PARAMETERS OF STUDY: Online shopping pattern: For this study Online

shopping pattern is considered E-shopping and telephonic shopping both. To

study online shopping pattern, information is collected for five types of FMCG

products. These five FMCG products are :

I (A) = Online shopping pattern for Dairy products

I (B) = Online shopping pattern for Toiletries

I(C) = Online shopping pattern for Grocery

I (D) = Online shopping pattern for Cosmetics

I (E) = Online shopping pattern for Frozen food.

I (A)Dairy products: To understand online shopping pattern of „dairy products‟,

seven products are considered. Response of all 800 respondents for these seven

products is recorded and classified. The Diary product mention below are from

branded as well as non-branded companies in India. Table of classification of

response is presented in the following table no 8.2.1.

15%

37%

38%

10%

Diagram of respondents according to monthly income

High

Low

Middle

Very High

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Table 8.2.1: Respondents (Working women) buying Dairy Products (Online)in 4

cities.

Sr

no

Dairy Product Never

buy

Sometimes

buy

Mostly

buy

Always

buy

1 Strained Yogurt 582 168 50 0

2 Flavored milk 580 63 157 0

3 Curd 195 185 270 150

4 Paneer 430 153 157 60

5 Cheese 320 125 290 65

6 Lassi 284 316 120 80

7 Milk 434 220 136 10

Above table indicate that there are total 800 respondents out of which 582

respondents never buy strained yogurt online,168 respondents sometimes buy and

50respondents nearly buy online .In case of flavoured milk out of total respondent

,580 respondents never buy,63respondents sometimes and 157respondents mostly

buy online . It‟s been observed that tofu and flavoured milk are not regularly

consumed by respondents whereas products like curd cheese lassi and milk are

generally never bought online as these products are readily available and people

prefer buying them fresh .In case of curd 195 respondent never buy ,185 respondent

sometime buy and 270respondents mostly and 150respondents always buy online .In

case of paneer 430 respondent never buy ,153 sometimes ,157 respondents mostly and

60respondents always buy online .In case of cheese out of total respondents , 320

respondents never buy ,125respondents sometimes buy ,290 respondents mostly buy

and 65 respondents always buy online .In case of lassi out of total respondents

284respondents never buy ,316 respondents sometimes buy,120 respondents mostly

buy and 80 respondents always buy lassi online .In case of milk out of total

respondents 434respondents never buy ,220respondents sometimes buy ,136

respondents mostly buy and 10respondents always buy online . Above information is

presented by using bar diagram as shown below in chart no 8.2.1

132

Chart no 8.2.1: Respondents for Dairy Products (Online)in 4 Cities

I (B) Toiletries: To understand online shopping pattern of „Toiletries‟, five products

are considered. Response of all 800 respondents from 4 cities for these five products

are recorded and classified. The Toiletries product mentions below are from branded

as well as non-branded companies in India. Table of classification of response is

presented in the following table no 8.2.2

Table 8.2.2: Respondents for Toiletries product (Online)in 4 Cities

Sr no Toiletries

product

Never

buy

Sometimes

buy

Mostly

buy

Always

buy

1 Serums 542 220 28 10

2 Shampoo 240 158 290 12

3 Conditioner 348 262 180 10

4 Shower gel /soap 150 92 338 220

5 Sanitizer 408 392 0 0

Above table indicate that there are total 800 respondents out of which in case of

Serum 542 respondents never buy, 220respondentssometimesbuy,28 respondents

mostly buy and 10respondentsalways buy online. As serum is a product recommended

0

100

200

300

400

500

600

700

Strained Yogurt

Flavored milk

Curd Paneer Cheese Lassi Milk

Respondents for Diary Products(Online) Never

Sometimes

Mostly

Always

133

by the hair stylist only after physical examination of hair, so the online buying is low

amongst the respondents. In case of shampoo 240respondentsnever buy,

158respondentssometimesbuy, 290 respondents mostly buy and 12respondents always

buy shampoo online. In case of conditioner 348respondentsnever buy

262respondentssometimesbuy, 180 respondents mostly buy and 10respondents always

buy conditioner online. In case of shower gel /soap 150 respondents never

buy,92respondentssometimesbuy, 338respondents mostly buy and 220respondents

always buy shower gel /soap online .In case of sanitizer 408respondentsnever buy

392respondentssometimes buy sanitizer online. Above information is presented by

using Bar diagram as shown below chart no 8.2.2

Chart no.8.2.2: Respondents for Toiletries product (Online)in 4 Cities

I(C) Packed Grocery product:

To understand online shopping pattern of Packed Grocery, five products are

considered. Response of all 800 respondents for these five products is recorded and

classified. Table of classification of response is presented in the following table.

Packed grocery products are available in local, state and national brands

.

0

100

200

300

400

500

600

Serums Shampoo Conditioner Shower gel /soap

Sanitizer

Nu

mb

er

of

Re

spo

nd

en

ts

Respondents for Toiletiers Products online Never

Sometimes

Mostly

Always

134

Table 8.2.3: Respondents for Packed Grocery Product (Online)in 4 Cities

Sr

no

Packed Grocery product Never

buy

Sometimes

buy

Mostly

buy

Always

buy

1 Rice (Cereal) 38 190 318 250

2 Pulse 178 380 208 30

3 Salt & Seasonings 232 178 310 80

4 Edible Oil 272 160 280 88

5 Sugar 99 230 310 159

Above table no 8.2.3 indicates that there are total 800 respondents. In case of Rice

(Cereal) 38respondentsNever buy, 190 respondents sometimes buy,

318respondents mostly buy and 250 respondents always buy online .In case of

pulse 178 respondents never buy,380 respondents sometimes buy208 respondents

mostly buy and 30respondents always buy Pulse online. In case of Salt &

Seasonings 232 respondents never buy 178respondentssometimes buy, 310

respondents mostly buy and 80 respondents always buy salt & seasonings online.

In case of edible oil 272 respondents never buy , 160respondentssometimesbuy,

280 respondents mostly buy and 88 respondents always buy edible oil online .In

case of sugar 99respondentsnever buy 230respondentssometimes

buy,310respondentsmostly buy, 159 respondents never buy sugar online .Above

information is presented by using Bar diagram as shown below in chart no 8.2.3.

135

Chart no.8.2.3: Respondents for Packed Grocery Product (Online)in 4 Cities.

I (D) Cosmetics product:

To understand online shopping behaviour of „Cosmetics‟, five products were


classified./The products mentioned below are branded and non-branded. Table of

classification of response is presented in the following table 8.2.4

Table 8.2.4: Respondents for Cosmetic Product (Online)in 4 Cities

Sr no Cosmetic product Never

buy

Sometimes

buy

Mostly

buy

Always

buy

1 Face Powder 172 148 280 200

2 Kohl (Kajal ) 150 112 278 220

3 Lipstick 99 170 359 170

4 Nail and Hand products 381 280 129 10

5 Body lotion 284 186 230 100

0

50

100

150

200

250

300

350

400

Rice (Cereal) Pulse Salt & Seasonings

Edible Oil Sugar

resp

on

de

nts

Respondents for Packed Grocery Product (Online)

Never

Sometimes

Mostly

Always

136

Above table indicate that there are total 800 respondents .In case of Face Powder 172

respondents never buy, 148 respondents sometimes buy, 280 respondents mostly buy

and 200respondents always buy face powder online .In case of kohl(kajal ) 150

respondents never buy,112respondentssometimesbuy, 278respondents mostly buy and

220 respondents always buy kohl (kajal ) online. In case of lipstick

99respondentsnever buy 178respondentssometimes, buy, 359respondents mostly buy

and 170 respondents always buy lipstick online. In case of nail and hand products 381

respondents never buy , 280respondentssometimesbuy, 129 respondents mostly buy

and 10 respondents always buy online .In case of Body lotion 284 respondents never

buy 186respondentssometimes buy 230respondentsmostly buy 100respondents never

buy online . Above information is presented by using bar diagram as shown below in

chart no 8.2.4

Chart no.8.2.4: Respondents for Cosmetic Product (Online)in 4 Cities

I(E) Packed Frozen product :

To understand online shopping behaviour of „Packed Frozen, five products are


classified. Table of classification of response is presented in the following table:

0

100

200

300

400

500

Face Powder Kohl (Kajal ) Lipstick Nail and Hand

products

Body lotion

NU

mb

er

of

resp

om

de

nts

Respondents for Cosmetics (Online) Never

Sometimes

Mostly

Always

137

Table 8.2.5: Respondents for Packed Frozen Product (Online) in 4 Cities

Sr no Packed Frozen product Never

buy

Sometimes

buy

Mostly

buy

Always

buy

1 Green Peas 214 196 270 120

2 Ready to cook products 172 350 168 110

3 Fresh Cut Vegetables/Fruits 313 220 217 50

4 Ice cream 140 182 220 258

5 Raw Non-veg Products 274 260 230 36


respondents never buy, 196 respondents sometimes, 270respondents mostly and 120

respondents always green peas online .In case of ready to cook &serve 172

respondents never buy,350 respondents sometimes, 168 respondents mostly and 110

respondents always buy ready to cook &serve online. In case of fresh cut veggies /

fruits 313 respondents never buy 220 respondents sometimes, 217 respondents mostly

and 50respondents always buy fresh cut veggies /fruits online. In case of ice cream

140 respondents never buy , 182respondents sometimes, 220 respondents mostly and

258respondents always buy ice cream online .In case of raw non-veg 274 respondents

never buy 260respondentssometimes buy 230respondentsmostly buy 36respondents

never buy raw non-veg online. Above information is presented by using bar diagram

as shown below in chart no 8.2.5

Chart No.8.2.5: Respondents for Packed Frozen Product (Online)in 4 Cities

050

100150200250300350400

Green Peas Ready to cook &serve Fresh Cut Veggies/Fruits Ice cream Raw Non-veg

NU

mb

er

of

resp

on

de

nt

Respondents for Packed frozen products(Online) Never

Sometimes

Mostly

Always

138

8.3 Parameter of study: Physical shopping pattern

To study physical shopping pattern information is collected for same five types of

FMCG products. Overall physical shopping mean score is 61.90 percent. It is also

calculated for each type of five FMCG products:

II(A)=Physical shopping pattern for Dairy products in 4 Cities

II(B) =Physical shopping pattern for Toiletries in 4 Cities

II(C) =Physical shopping pattern for Grocery in 4 Cities

II(D) =Physical shopping pattern for Cosmetics in 4 Cities

II(E) =Physical shopping pattern for Frozen food in 4 Cities

II A) Dairy products: To understand physical shopping pattern of dairy products,

seven products are considered. Response of all 800 respondents for these seven

products is recorded and classified. The Diary product mentions below are from

branded as well as non-branded companies in India. Table of classification of

response is presented in the following table.

Table 8.3.1: Respondents for Dairy Products (Physical)in 4 Cities

Sr no Dairy product Never buy

Sometimes

buy

Mostly

buy

Always

buy

1 Strained yogurt 488 240 72 0

2 Flavoured milk 318 82 330 0

3 Curd 118 282 400 0

4 Paneer 12 98 310 380

5 Cheese 30 25 345 400

6 Lassi 0 155 375 370

7 Milk 0 22 258 520

Above table indicate that there are total 800 respondents. In case of strained

yogurt488 respondents never buy,240 respondents sometimes buy and 72 respondents

mostly buy products physically .In case of flavoured milk out of total respondents

139

,318 respondents never buy,82respondents sometimes buy and 330 respondents

mostly buy products physically. It‟s been observed that strained yogurt and flavoured

milk are not regularly consumed by respondents. In case of curd 118 respondents

never buy 282 respondent sometime buy and 400 respondents mostly buy it physically

.In case of paneer 12 respondent never buy ,98 respondents sometimes buy ,310

respondents mostly buy and 380respondents always buy physically. In case of cheese

out of total respondents, 30respondents never buy, 25respondents sometimes buy ,345

respondents mostly buy and 400 respondents always buy physically In case of lassi

out of total respondents 155respondents sometimes buy, 375respondents mostly buy

and 370 respondents always buy lassi physically. In case of milk out of total

respondents, 22 respondents sometimes buy 258respondents mostly buy and

520respondents always buy physically. Above information is presented by using bar

diagram as shown below in chart no 8.3.1

Chart no 8.3.1 Respondents for Dairy Products (Physical)in 4 Cities

II(B) Toiletries :To understand physical shopping pattern of „Toiletries‟, five

products are considered. Response of all 800 respondents for these five products is

considered and classified. The toiletries product mentions below are from branded as

well as non-branded companies in India. Table of classification of response is


0

100

200

300

400

500

600

Strained yogurt

Flavored milk

Curd Paneer Cheese LassiMilk

Never

Sometimes

Mostly

Always

Respondents for Dairy Products (Physical)

140

Table 8.3.2: Respondents for Toiletries product (Physical)in 4 Cities

Sr no Toiletries product Never buy Sometimes buy Mostly buy

Always

buy

1 Serums 320 78 332 70

2 Shampoo 0 182 88 530

3 Conditioner 130 78 230 362

4 Shower gel /soap 0 42 248 510

5 Sanitizer 330 203 257 10

Above table indicate that there are total 800 respondents. In case of serums 320

respondents never buy, 78 respondents sometimes buy, 332 respondents mostly buy

and 70 respondents always buy products physically. As serum is a product

recommended by the hair stylist only after physical examination of hair. In case of

shampoo 182 respondents sometimes buy,88 respondents mostly buy and 530

respondents always buy products physically. In case of conditioner 130 respondents

never buy 78respondentssometimes buy, 230respondentsmostly buy and 362

respondents always buy products physically. In case of shower gel /soap

42respondentssometimes, buy, 248respondentsmostly buy and 510respondentsalways

buy products physically. In case of sanitizer 330 respondents never buy,203

respondents sometimes buy 257respondentsmostly and 10 respondents always buy

sanitizer physical. Above information is presented by using bar diagram as shown

below chart no.8.3.2

Chart no.8.3.2: Respondents for Toiletries product (Physical)in 4 Cities

0

100

200

300

400

500

600

Serums Shampoo Conditioner Shower gel /soap

Sanitizer

Nu

mb

er

of

Re

spo

nd

en

ts

Respondents for Toileteries Product (Physical )

Never

Sometimes

Mostly

Always

141

II(C) Packed Grocery: To understand physical shopping behaviour of „Packed

Grocery „, five products are considered. Response of all 800 respondents for these

five products is recorded and classified. Table of classification of response is


Table 8.3.3: Respondents for Packed Grocery Product (Physical)in 4 Cities

Sr no Packed Grocery

product Never buy

Sometimes

buy

Mostly

buy

Always

buy

1 Rice (Cereal) 0 54 346 400

2 Pulse 0 44 530 236

3 Salt & Seasonings 0 137 373 290

4 Edible Oil 0 217 343 240

5 Sugar 0 439 261 100

Above table indicate that there are total 800 respondents. In case of Rice (Cereal) 54

respondents Sometimes buy, 346respondentsmostly buy and 400 respondents always

buy product physically. In case of pulse 44 respondents sometimes buy,

530respondents mostly buy and 236 respondents always buy product physically. In

case of salt & seasonings 137respondents sometimes buy, 373 respondents mostly buy

and 290respondents always buy product physically. In case of edible oil 217

respondent‟s sometimes buy, 343respondents mostly buy and 240respondents always

buy product physically. In case of sugar 439respondents sometimes buy,261

respondents mostly buy 100respondents never buy product physically. Above

information is presented by using Bar diagram as shown below chart no 8.3.3

Chart No.8.3.3: Respondents for Packed Grocery Product (Physical)in 4 Cities

0

100

200

300

400

500

600

Rice (Cereal) Pulse Edible Oil Sugar

Respondents for Packed Grocery (Physical) Never

Sometimes

Mostly

Always

142

I (D) Cosmetics: To understand physical shopping behaviour of „Cosmetics‟, five

products are considered. Response of all 800 respondents for these five products is

recorded and classified. Table of classification of response is presented in the

following table no 8.3.4

Table 8.3.4: Respondents for Cosmetic Product (Physical)in 4 Cities

Sr.no Cosmetic Never buy Sometimes buy Mostly buy Always buy

1 Face Powder 0 120 300 380

2 Kohl (Kajal ) 40 263 347 150

3 Lipstick 0 123 427 250

4 Nail/Hand 40 173 357 230

5 Body lotion 0 0 246 554


respondents sometimes, 300 respondents mostly and 380respondents always face

powder physical. In case of kohl(kajal) 40respondents never buy,263 respondents

sometimes, 347 respondents mostly and 150respondents always buy kohl (kajal)

physical. In case of lipstick 123respondents sometimes, 427respondents mostly and

250 respondents always buy lipstick physical. In case of nail and hand products 40

respondents never buy , 173 respondents sometimes, 357respondents mostly and

230respondents always buy nail and hand products physically .In case of body lotion

256respondents mostly buy 554 respondents never buy body lotion physically. Above

information is presented by using bar diagram as shown below in chart no 8.3.4

Chart no.8.3.4: Respondents for Cosmetic Product (Physical)in 4 Cities

0

200

400

600

Face Powder Kohl (Kajal ) Lipstick Body lotion Nu

mb

er

of

Re

spo

nd

en

ts

Respondents for Cosmentics (Physical) NeverSometimesMostlyAlways

143

II (E) Packed Frozen product :

To understand physical shopping behaviour of „Packed Frozen „, five products are


classified. Table of classification of response is presented in the following table

Table 8.3.5: Respondents for Packed Frozen Product (Physical)in 4 Cities

Sr no Packed Frozen

product

Never

buy

Sometimes

buy

Mostly

buy

Always

buy

1 Green Peas 37 330 283 150

2 Ready to cook &serve 78 380 230 112

3 Fresh Cut Veg /Fruits 355 230 116 89

4 Ice cream 229 379 142 50

5 Frozen Raw Non-veg 398 202 140 60

Above table indicate that there are total 800 respondents. In case of Green Peas

37respondents never buy, 330respondents sometimes buy, 283respondents mostly buy

and 150respondents always buy physically. In case of ready to cook & serve

78respondents never buy, 380respondentssometimes, 230respondents mostly and

112respondentsalways buy physically. In case of fresh cut veggies /fruits 355 never

buy 230respondents sometimes, 116 respondents mostly buy and 89respondents

always buy physically. In case of ice cream 229respondents never buy,

379respondentssometimes buy, 142 respondents mostly buy and 50 respondents

always buy physically. In case of raw non-veg 398 respondents never buy

202respondents sometimes buy 140 respondents mostly buy 60respondents never buy

physically. Above information is presented by using bar diagram as shown below

8.3.5

144

Chart no .8.3.5: Respondents for Packed Frozen Product (Physical)in 4 Cities

8.4 Analysis of data: After classification of data responses are rate as follows:

Never = 0

Sometimes = 1

Mostly = 2

Always = 3

Using rating of these questions, score of online shopping is calculated for each

respondent using formula given below.

Score of online shopping = Sum of scores of all questions * 100

Maximum score of all questions

Results of mean and standard deviations for online shopping are as follows:

Table No: 8.4.1 Descriptive Statistics (Online Shopping)

Online Shopping Number of

respondents

Mean Std. Deviation

Dairy 800 27.3810 11.92412

Toiletries 800 32.0833 14.38852

Packed Grocery 800 47.5000 17.34338

Cosmetics 800 47.6667 16.47913

Packed Frozen food 800 41.5833 17.75131

Overall online shopping score 800 39.2429 9.05072

0

100

200

300

400

500

Green Peas Ready to cook &serve Fresh Cut Veggies /Fruits Ice cream Frozen Raw Non-vegNu

mb

er

of

Re

spo

nd

en

ts

Respondents For Packed Frozen Product (Physical) NeverSometimesMostlyAlways

145

From the above table out of 800 respondents 39 percent of respondents go for online

shopping of FCMG. Amongst the five segments of FMCG, in case of Online

shopping,27 percent of respondents go for dairy product 32 percent go for toiletries

47.5 percent go for packed grocery 47.6 go for cosmetics and 41 percent go for

packed frozen food Above information is presented by using Bar diagram as shown

below in chart no 8.4.1

Chart no .8.4.1 Mean Score of Online Shopping

After classification of data of physical shopping responses are rate as follows:

Never = 0

Sometimes = 1

Mostly = 2

Always = 3

Using rating of these questions, score of online shopping is calculated for each

respondent using formula given below.

Score of physical shopping = Sum of scores of all questions X 100

Maximum score of all questions

Results of mean and standard deviations for physical shopping are as follows in table

no 8.4.2

27.3832.08

47.50 47.6741.58 39.24

0.00

10.00

20.00

30.00

40.00

50.00

60.00

Dairy Toiletries Packed Grocery

Cosmetics Packed Frozen food

Overall

Me

an s

core

in p

er

cen

t

Digram of mean scores of online shopping

146

Table No: 8.4.2 Descriptive Statistics Physical Shopping

Physical Shopping N Mean Std. Deviation

Dairy shopping 800 66.6667 8.55691

Toiletries score 800 61.1667 16.09231

Packed Grocery 800 69.5000 7.80623

Cosmetics 800 72.8333 6.89835

Packed Frozen Food 800 41.5833 17.75131

Overall physical shopping 800 61.9000 5.22054

From the above table out of 800 respondents 61 percent of respondents go for

physical shopping of FCMG. Amongst the five segments of FMCG, in case of

Physical shopping 66 percent of respondents go for dairy product 61 percent go for

toiletries 69 percent go for packed grocery 72 percent go for cosmetics and 41

percent go for packed frozen food Above information is presented by using Bar

diagram as shown below chart no 8.4.2

Chart no .8.4.2 Mean Score of Physical Shopping

66.6761.17

69.5072.83

41.58

61.90

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

Dairy Toiletries Packed Grocery

Cosmetics Packed Frozen food

Overall

Me

an s

core

in p

er

cen

t

Diagram of mean score of physical shopping

147

Hypothesis:

Note: ‘A’ is w.r.t to online shopping

‘B’ is w.r.t to physical shopping

8.5 Hypothesis 1A:H01A: There is no significant difference in proportion of online

shopping pattern of working women of FMCG products among four cities.

H11A: There is significant difference in proportion of online shopping pattern of

working women of FMCG products among four cities.

Table No: 8.5.1 Overall online shopping score

City N Mean Std. Deviation

Bangalore 160 40.5810 7.42305

Delhi 250 40.4747 9.89243

Hyderabad 120 31.9683 8.74882

Mumbai 270 40.5425 7.64972

Total 800 39.2429 9.05072

From the above table the overall score of online shopping is highest in Bangalore

which is followed by 40.54 percent inMumbai, 40.47 percent in Delh, 31 percent in

Hyderabad. Above information is presented by using bar diagram as shown below

chart no 8.5.1

Chart No: 8.5.1 Mean percent of Online shopping City wise

40.58 40.47

31.97

40.54

0.00

10.00

20.00

30.00

40.00

50.00

Bangalore Delhi Hyderabad Mumbai

Nu

mb

er

of

resp

on

dn

ets

Mean percent of Online shopping Citywise

148

Respondents are classified in to three groups according to score of online shopping.

Respondents of score below 30.26 are classified as „Low‟ level of online shopping.

Respondents of score between 30.26 and 48.36 are classified as „Medium‟ level.

Respondents of score more than 48.36 are classified as „High‟ level. Classified table

of respondents is presented as given below table no 8.5.2

Table No: 8.5.2 Overall online shopping level

Frequency Percent

High 150 18.8

Low 120 15.0

Medium 530 66.3

Total 800 100.0

From the above table the overall score of online shopping level is more at medium

level where there are 530 respondents followed by 150 respondents at high level and

120 respondents at low level. Above information is presented by using pie diagram as

shown below chart No: 8.5.2

Chart No: 8.5.2 Online shopping level

19%

15%

66%

Pie diagram of respondents according to level of online shopping

High

Low

Medium

149

Table No:8.5.3 City wise Online shopping level Cross tabulation

City Overall Online Shopping

level

Total

High Low Medium

Bangalore 40 8 112 160

Delhi 53 32 165 250

Hyderabad 13 54 53 120

Mumbai 44 26 200 270

Total 150 120 530 800

From the above table out of total 800 respondents the overall score of online

shopping in Bangalore are as follows: 112 respondents go for medium level of

online shopping followed by 40respondentsfrom high level and 8respondents from

low level does online shopping. In Delhi 165 respondents from medium level

followed by 53 respondents from high level and 32respondents from low level does

online hopping. In Hyderabad 53respondents from medium level and 13 respondents

from high level followed by 54 respondents from low level does online shopping. In

Mumbai which is Commercial capital of India 200 respondents from medium level

44respondents from high level and 26 respondents from low level does online

shopping. Above information is presented by using bar diagram as shown below chart

no: 8.5.3

Chart No: 8.5.3 City wise overall online shopping level

4053

13

44

832

54

26

112

165

53

200

0

50

100

150

200

250


Nu

mb

er

of

resp

on

dn

ets

Respondnets Citywise and level of online shopping.High

Low

Medium

150

To test null hypothesis Chi-square test is applied. Results of test are as follows

Table No: 8.5.4 Chi-Square Tests for online shopping

Calculat

ed Value

Degree

of

freedom

Table value

(5% level of

significance )

Results

Pearson Chi-Square 109.347 6 12.591 Rejected.

Above results indicate that Chi-square calculated value is 109.347 which is greater

than table value 12.591 for 6 degree of freedom at 5% level of significance. Therefore

null hypothesis is rejected and alternate hypothesis is accepted. Conclusion of test is

significant difference in proportion of shopping pattern of working women of FMCG

products among four cities. Since Chi-square test is rejected for further study

ANOVA is obtained and F-test is applied. Results are presented in the following table

No: 8.5.5

Table No: 8.5.5 ANOVA

Sum of Squares Degree of

freedom

Mean Square F value Significance

Between Groups 7472.253 3 2490.751 34.196 0.000

Within Groups 57978.250 796 72.837

Total 65450.503 799

Above results indicate that p-value is 0.000 which is less than standard value 0.05

(5% level of significance). Therefore F-test is rejected. Conclusion is there is

significant difference in mean scores of online shopping of four cities. Hence H1A is

accepted.

151

8.6 Hypothesis 1B:

H01B: There is no significant difference in proportion of physical shopping

pattern of working women for FMCG products.

H11B: There is significant difference in physical shopping pattern of

workingwomen for FMCG products.

To test above hypothesis mean scores of physical shopping for all four cities is

obtained and presented in the following table.

Table No: 8.6.1 Overall physical shopping score


Bangalore 160 62.2476 5.51328

Delhi 250 63.0248 5.21233

Hyderabad 120 61.9714 4.36332

Mumbai 270 60.6208 5.14994

Total 800 61.9000 5.22054

From the above table no 8.6.1the mean of physical shopping is 63 percent which is

highest in Delhi followed by 62 percent in Bangalore, 61 percent in Hyderabad and

60 percent in Mumbai. Above information is presented by using bar diagram as

shown below chart no: 8.6.1

Chart No: 8.6.1 Physical shopping Mean city wise

62.247663.0248

61.9714

60.6208

596061626364

Bangalore Delhi Hyderabad MumbaiNo

of

resp

on

dan

ts

City

Physical shopping mean city wise

152

Respondents are classified in to three groups according to score of physical shopping.

Respondents of score below 56.67 are classified as „Low‟ level of physical shopping.

Respondents of score between 56.67 and 67.12 are classified as „Medium‟ level.

Respondents of score more than 67.12 are classified as „High‟ level. Classified table

of respondents is presented as given below table no: 8.6.2

Table No: 8.6.2 Physical shopping level wise

Level of shopping Frequency Percent

High 110 13.8

Low 90 11.3

Medium 600 75.0

Total 800 100.0

From the above table 8.6.2 the overall score of physical shopping level is more at

medium level with 75 percent followed by 13.8 percent at high level a and

11.3percent respondents at low level. Above information is presented by using pie

diagram as shown below chart no 8.6.2

Chart no 8.6.2 Overall physical shopping level

13.8

11.3

75

Physical shopping level

High

Low

Medium

153

From the above table no: 8.6.3out of total 800 respondents. In case of Bangalore 112

respondents from medium level 32 respondents from high level and 16fromlow level

does physical shopping. In Delhi, 197respondents from medium level 38 respondents

from high level and 15respondents from low level does physical shopping. In

Hyderabad 90respondents from medium level 18respondents from high level and

12respondents from low level does physical hopping. In Mumbai which is

Commercial capital of India, 201 respondents from medium level 47respondents from

low level and 22respondents from high level does physical shopping. Above

information is presented by using bar diagram as shown below 8.6.3

Chart No: 8.6.3 Overall physical shopping level city wise

32 3818 2216 15 12

47

112

197

90

201

0

50

100

150

200

250


Leve

l o

f P

hys

ical

sh

op

pin

g

City

Overall physcial shopping level city wise High

Low

Medium

Table No: 8.6.3 Physical Shopping Level Cross tabulation

City Overall physical shopping

level

Total

High Low Medium

Bangalore 32 16 112 160

Delhi 38 15 197 250


Mumbai 22 47 201 270

Total 110 90 600 800

154

For testing of hypothesis, Chi-square test is applied results of test are as follow.

Table No: 8.6.4 Chi-Square Tests

Calcula

ted

Value

Degree

of

freedom

Table value

(5% Level of

significance.)

Results

Pearson Chi-Square 27.865 6 12.591 Rejected

.

Above results indicate that Chi-square calculated value is 27.86 which is greater than

table value for 6 degree of freedom at 5% level of significance. Therefore null

hypothesis is rejected and alternate hypothesis is accepted. Since Chi-square test is

rejected for further study ANOVA is obtained and F-test is applied. Results are



Sum of

Squares

Degree

of

freedom

Mean

Square

F-Value Significant

Between

Groups 778.026 3 259.342 9.831 0.000

Within Groups 20997.919 796 26.379

Total 21775.946 799


(5% level of significance).Therefore F-test is rejected. Conclusion is there is

significant difference in mean scores of online shopping of four cities H1B is accepted.

155

8.7Hypothesis 2 A

H02A: There is no association between level of income and proportion of online

shopping pattern (E-shopping, Teleshopping) of FMCG products.

H12A: There is association between level of income and proportion of online

shopping pattern (E-shopping, Teleshopping) of FMCG products.

To test above hypothesis mean scores of online shopping for all monthly income level

is obtained and presented in the following table

Table No: 8.7.1 Overall online shopping income wise

Monthly income N Mean

High 120 38.7143

Low 300 38.8190

Middle 300 41.8921

Very High 80 31.6905

Total 800 39.2429

From the above table no 8.7.1 the overall mean score of online shopping of middle

income group is high with 41percent followed by 38 percent of respondents from

low income level & high income group and 31 percent respondents from very high

income level is lowest. Above information is presented by using Bar diagram as


Chart No: 8.7.1 Overall online shopping score

38.7143 38.81941.8921

31.6905

0

10

20

30

40

50

High Low Middle Very High

Me

an

Income level

Overall online shopping mean score

156

Table No: 8.7.2 Overall online shopping income wise cross tabulation

Monthly income Online shopping level Total

High Low Medium

High 0 10 110 120

Low 50 30 220 300

Middle 100 40 160 300

Very High 0 40 40 80

Total 150 120 530 800

From the above table 8.7.2out of total 800 respondents in case of high monthly

income 110 respondents does medium level of online shopping followed by 10

respondents from low level do physical shopping? In case of low monthly income

group 220respondents from medium level, 50 respondents from high level and 30

respondents from low level does physical shopping. In case of middle monthly

income group 160 respondents from medium level 100 respondents from high level

and 40 respondents from low level does physical shopping. In case of very high

monthly income group 40 respondents from medium level and 40 respondents from

low level does physical shopping. Above information is presented by using bar

diagram as shown below chart no. 8.7.2

Chart no. 8.7.2 Overall online shopping level monthly income wise

For testing of hyposthesis ,Chi square test is applied results of test are as follows :

1030 40 40

110

220

160

40

0

50

100

150

200

250


Nu

mb

er

of

resp

on

de

nts

Income level

Overall online shopping level Income wise High

Low

Medium

157

Table No.8.7.2 Chi Squae Test

Calculated

Value

Degree

of

freedom

Table value

(5% Level of

Significance)

Results

Pearson Chi-

Square 171.384 6 12.591

Rejected


than table value for 6 degree of freedom at 5% level of significance. Therefore null



presented in the following table no: 8.7.3

Table No: 8.7.3ANOVA Overall online shopping score

Sum of

Squares

Degree

of

freedom

Mean

Square

F-

calculate

d

p-

value

Result

Between

Groups 6755.976 3 2251.992 30.541 0.000

Significant

Within Groups 58694.528 796 73.737

Total 65450.503 799




accepted.

158

8.8 Hypothesis 2B:

H02B: There is no association between level of income and physical shopping of

FMCG products in four cities.

H12B: There is association between level of income and physical shopping of

FMCG products in four cities

Table No: 8.8.1Overallphysicalshoppingscore

Monthly income N Mean

High 120 60.2381

Low 300 61.7651

Middle 300 62.5905

Very High 80 62.3095

Total 800 61.9000

From the above table no 8.8.1 the overall mean score of physical shopping of middle

income group and very high income group is high with 62 percent followed by 61%

of low income and 60 percent by high group which is lowest. Above information is

presented by using bar diagram as shown below chart no: 8.8.1

Chart No: 8.8.1 Overall physical shopping score

60.24

61.77

62.5962.31

59.00

59.50

60.00

60.50

61.00

61.50

62.00

62.50

63.00

Banking/Insurance IT sector Others Very High

Me

an s

core

in p

er

cen

t

Income level

Overall physical shopping mean score income wise

159

Table No: 8.8.2 Monthly Income and Physical shopping Pattern Cross

Tabulation

Monthly income Overall physical shopping level Total

High Low Medium

High 10 20 90 120

Low 30 40 230 300

Middle 60 30 210 300


Total 110 90 600 800

From the above table no 8.8.2 out of total 800 respondents. In case of High monthly

income 90 respondents from medium level followed by 20 respondents from low level

and 10 respondents from high level does physical shopping. In case of Low monthly

income group 230respondents from medium level followed by 40 respondents from

low level and 30 from high level does physical shopping. In case of Middle monthly

income group 210 respondents from medium level followed by 60 respondents from

high level and 30 respondents from low level does physical shopping. In case of very

high monthly income group 70 respondents from medium and 10 from high level does

physical shopping. Above information is presented by using bar diagram as shown

below 8.8.2

Chart No: 8.8.2 Overall physical shopping level monthly income wise

1030

60

102040 30

0

90

230210

70

0

50

100

150

200

250


Nu

mb

er

of

resp

on

de

nts

Income level

Overall physical shopping level monthly income wise

High

Low

Medium

160

Table No: 8.8.3 Chi-Square Tests

Calculate

d Value

Degree

of

freedom

Table value

(5% level of

significance )

Results

Pearson Chi-

Square 30.724 6 12.591

Rejected

Above results indicate that Chi-square calculated value is 30.74 which is greater than


hypothesis is rejected and alternate hypothesis is accepted.

Since Chi-square test is rejected for further study ANOVA is obtained and F-test is

applied. Results are presented in the following table no 8.8.4.



Sum of

Squares

Degree

of

freedom

Mean

Square

F value Significance Result

Between

Groups 493.336 3

164.44

5 6.150 .000

Significant

Within Groups 21282.609 796 26.737

Total 21775.946 799



significant difference in mean scores of physical shopping of four cities. Hence H2B is

accepted

161

8.9 Hypothesis 3:

H03: There is no correlation between cost effectiveness and online shopping of

FMCG products.

H13: There is correlation between cost effectiveness and online shopping of

FMCG products.

To test above hypothesis Karl Pearson‟s coefficient of correlation is calculated.

Results are as follows

Table No: 8.9.1Correlations

It is cost

effective

than

physical

shopping

Overall

online

shopping

score

It is cost effective than

physical shopping

Pearson

Correlation 1 -.264

**

Sig. (2-tailed) .000

N 800 800

Overall online

shopping score

Pearson

Correlation -.264

** 1


N 800 800

**Correlation is significant at the 0.01 level (2-tailed).

Above results indicate that coefficient of correlation is -0.264 which is negative and

significant at 1 per cent level of significance. Therefore null hypothesis is rejected and

alternate hypothesis is accepted.

Conclusion is there is negative correlation between cost of online shopping and

buying proportion. This means if cost will reduce the buying proportion of online

shopping will further increase.

162

Diagram No: 8.9.1 Scattered Diagram on Overall online shopping score and cost

effectiveness w.r.t physical shopping score

163

8.10 Hypothesis 4:

H04: There is no association between quality of product and shopping pattern of

FMCG products.

H14: There is association between quality of product and shopping pattern of

FMCG products.

To test above hypothesis mean scores of online shopping patter for quality of product

is obtained and presented in the following table

From the above table no: 8.10.1its been observed that44 percent of respondents agree

that quality of product in online shopping is reliable.41 percent of respondents

strongly believe that quality of product in online shopping is reliable.38 of

respondents percent disagree that quality of product in online shopping is reliable and

34 percent of respondents strongly disagree that they do not believe in product of

online shopping. Above information is represented using bar diagram in chart no

8.10.1

Table No: 8.10.1 Overall online shopping score

Quality of online shopping is

reliable

N Mean Std. Deviation

Agree 130 44.8498 6.72939

Strongly agree 50 41.4095 10.69260

Strongly disagree

Disagree

110

510

34.3203

38.6629

5.57740

9.24000

Total 800 39.2429 9.05072

164

Chart No: 8.10.1 Overall online shopping mean score on basis of Quality

Table No: 8.10.2Overallonlineshoppinglevel Cross tabulation

Quality of online shopping is

reliable

Overall online shopping

level

Total

High Low Medium

Agree 90 80 340 510

Strongly Agree

Disagree

20

40

10

0

20

90

50

130

Strongly disagree 0 30 80 110

Total 150 120 530 800

From the above table there are total 800 respondents‟ .In case of respondents who

agree that quality of product in online shopping is reliable are 510 from high level 340

respondents from medium level80 respondents from low level and 90 respondents

from high level. In case of respondents who disagree that quality of product in online

shopping is reliable are 90 respondents from medium level and 40 respondents from

high level. In case of respondents who strongly agree are 20 respondents from high

and medium level and 10 respondents from low level. In case of respondents who

strongly disagree 80 respondents from medium level and 30from low level. Above

information is presented by using Bar diagram as shown below chart no: 8.10.2

44.849841.4095

34.320338.6629

0

10

20

30

40

50

Agree Strongly agree Strongly disagree Disagree

Me

an

Overall online shopping mean score on basis of Quality

165

Chart No: 8.10.2 Online shopping on basis of Quality

To test the null hypothesis, Chi square test is applied Results of the test are as follows

Table No: 8.10.3Chi-Square Tests

Calculated

Value

Degree

of

freedom

Table value

(5% level of

significance.)

Results

Pearson Chi-

Square 80.637 6 12.591

Rejected

Above results indicate that Chi-square calculated value is 80.637 which is less than




applied. Results are presented in the following table no: 8.10.3

90

2040

0

80

10 030

340

20

90 80

0

50

100

150

200

250

300

350

400

Agree Strongly Agree Disagree Strongly disagree

sho

pp

ing

leve

lOverall online shopping on basis of Quality

High

Low

Medium

166

Table No: 8.10.4 ANOVA Overall online shopping score

Sum of

Squares

Degree

of

Freedom

Mean

Square

F-

value

Significanc

e

Result

Between Groups 7158.604 3 2386.2 32.58 .000 Significant

Within Groups 58291.899 796 73.231

Total 65450.503 799



significant difference in mean scores of online shopping of four cities. Hence H4 is

accepted.

Diagram No: 8.10.5 Scatter diagram

167

Diagram No: 8.10.6 Scatter diagram

168

Hypothesis 5A:

H05A: There is no association between Occupation of working women sector wise

(academics/IT/banking/others) and Online buying pattern of FMCG products.

H15A: There is association between Occupation of working women in Sector wise

(academics/IT/banking/others) and Online buying pattern of FMCG products

Table No: 8.11.1 Overall online shopping sector wise

Occupation Sector

wise

N Mean Std.

Deviation

Academics 130 37.0989 9.64388

Banking/Insurance 200 35.2095 8.16165

IT sector 210 46.9841 7.25619

Others 260 37.1648 6.70220

Total 800 39.2429 9.05072

From the above table no 8.11.1 the overall mean score of online shopping by the

respondents from IT sector is 46percent which is highest followed by Academics and

others which is 37 percent .the overall online shopping level is seen lowest by

respondents from Banking and insurance sector which is 35 percent Above

information is presented by using Bar diagram as shown below chart no: 8.11.1

Chart No: 8.11.1Online shopping Mean Sector wise

01020304050

Academics Banking/Insurance IT sector Others

Me

an

Online shopping Mean Sector wise

169

Table No: 8.11.2 Nature of occupation Sector wise and overall online

shopping level cross tabulation

Nature of

Occupation sector

wise

Overall online shopping level Total

High Low Medium

Academics 20 40 70 130

Banking/Insurance 10 50 140 200

IT sector 110 0 100 210

Others 10 30 220 260

Total 150 120 530 800

From the above table no 8.11.2 there are total 800 respondents .In case of

academician 70 respondents from medium level 40 respondents from low level and

20respondents from high level does online shopping. In case of banking and

insurance industry 140 respondents from medium level 50 respondents from low level

and 10 respondents from high level does online shopping. In case of the respondents

from IT sector 110 respondents from medium level and 100 respondents from high

level does online shopping. In case of respondent from other sector 220 respondents

from medium level 30 respondents from low level and 10 respondents from high

level does online shopping Above information is presented by using Bar diagram as


170

Chart No: 8.11.2 Online shopping level on basis of Sector of working women

Table No: 8.11.3 Chi Square test

Calculated

Value

Degree

of

freedom

Table value

(5% Level of

Significance )

Pearson Chi-

Square 274.575 6 12.591





applied. Results are presented in the following table.

2010

110

10

4050

0

30

70

140

100

220

0

50

100

150

200

250


leve

l

Overall online shopping level and Sector High

Low

Medium

171

Table No: 8.11.4ANOVA

Overall online shopping score

Sum of

Squares

Degree

of

freedom

Mean

Square

F Significant Result

Between

Groups 17558.557 3 5852.852 97.279 .000

Significant

Within Groups 47891.947 796 60.166

Total 65450.503 799




accepted.

172

8.12 Hypothesis 5B:

H05b: There is no association between occupation of working women sector wise

(academics/IT/banking/Others) and physical shopping pattern of FMCG

products.

H15b: There is association between occupation of working women sector wise

(academics/IT/banking/Others) and physical shopping pattern of FMCG

products.

Table No: 8.12.1 Overall physical shopping score

Occupation Sector wise N Mean Std. Deviation

Academics 130 61.3187 3.64461


IT sector 210 62.2313 5.29293

Others 260 62.6886 5.69792

Total 800 61.9000 5.22054

From the above table no 8.12.1 the overall mean score of physical shopping by the

respondents from IT sector and others is 62 percent which is highest followed by

academics which is 61 percent the overall online shopping level is seen lowest by

respondents from banking and insurance sector which is 60 percent Above

information is presented by using Bar diagram as shown below Chart no 8.12.1.

173

Chart No: 8.12.1 Overall physical shopping mean score sector wise

Table No: 8.12.2Occupation Sector wise and Physical shopping level Cross

tabulation

Nature of Occupation

Sector wise

Overall physical shopping level Total

High Low Medium

Academics 0 20 110 130

Banking/

Insurance 30 30 140 200

IT sector 30 10 170 210

Others 50 30 180 260

Total 110 90 600 800

From the above table no 8.12.2thereare total 800 respondents In case of academician

110 respondents from medium level, 20 respondents from low level does physical

shopping. In case of banking and insurance industry 140 respondents from medium

level,30 respondents from low level and high level does physical shopping. In case of

the respondents from IT sector 170 respondents from medium level 30 respondents

from high level and 10 respondents from low level does physical shopping. In case of

respondent from other sector 180 respondents from medium level 50 respondents

from high level and 30 respondents from low level does physical shopping. Above

information is presented by using bar diagram as shown below 8.12.2

61.318760.9048

62.2313

62.6886

60

60.5

61

61.5

62

62.5

63


leve

l

Overall physical shopping Mean score Sector wise

174

Chart No 8.12.2 Overall physical shopping level Industry wise

To test the null hypothesis Chi square test is applied. Results of the test are as follows

Table No: 8.12.3Chi-Square Tests

Calculated

Value

Degree

of

freedom

Table value

(5% level of

significant)

Results

Pearson Chi-Square 40.594 6 0.000 Rejected






2030

10

30

110

140

170180

0

20

40

60

80

100

120

140

160

180

200re

spo

nd

en

ts

Overall physical shopping level Sector wise

High

Low

Medium

175



Sum of

Squares

Degree

of

freedom

Mean

Square

F-

value

Significance Result

Between

Groups 426.789 3 142.263 5.304 0.001

Within Groups 21349.157 796 26.821 Significant

Total 21775.946 799



significant difference in mean scores of physical shopping of four cities. Hence H5B is

accepted.

176

8.13 Hypothesis 6A:

H06: There is no association between age of working women and online shopping

pattern of FMCG products.

H16: There is association between age of working women and online shopping

pattern of FMCG products

Table No: 8.13.1 Overall online shopping

score

Age

group

N Mean Std.

Deviation

Elderly 190 36.7118 8.89512

Middle 340 43.0644 8.39386

Young 270 36.2116 8.16826

Total 800 39.2429 9.05072

From the table no. 8.13.1 the overall mean score of online shopping of middle age

group is high with 43 percent followed by 36% of elderly and young age respondents.

Above information is presented by using bar diagram as shown below chart no 8.13.1

Chart No: 8.13.1 Overall online shopping score age wise

36.7118

43.0644

36.2116

32

34

36

38

40

42

44

Elderly Middle Young

Overall online shopping score age wise

177

From the table no 8.13.2 there are total 800 respondents .In case of respondents from

elderly age group it was found that 100 respondents from medium level 60

respondents from low level and 30 respondents from high level does online shopping.

In case of respondents from middle age group 230 respondents from medium level

100 respondents from high level and 10 from low level does online shopping. In case

of respondents from young age group 200 respondent from medium level 50

respondent‟s from low level and 20 respondents from high level does online


no 8.13.2

Chart No: 8.13.2 Online shopping age wise


0

50

100

150

200

250


LEV

EL

Overall online shopping Age wise High

Low

Medium

Table No: 8.13.2 Age group and overall online shopping level

Cross tabulation

Age Overall online shopping level Total

High Low Medium

Elderly 30 60 100 190

Middle 100 10 230 340

Young 20 50 200 270

Total 150 120 530 800

178

Table no 8.13.3 Chi square Test for


than table value for 6 degree of freedom at 5% level of significance. Therefore null



presented in the following table.



Sum of

Squares

Degree

of

freedom

Mean

Square

F-value Result

Between

Groups 8663.533 2 4331.767 60.796 0.000

Within Groups 56786.970 797 71.251

Total 65450.503 799



significant difference in mean scores of online shopping of four cities. Hence

Hypothesis 6A is accepted.

Calculat

ed Value

Degree

of

freedom

Table value

(5% level of

significance)

Results

Pearson Chi-

Square 117.946 4 9.49

Rejected

.

179

8.14 Hypothesis 6B:

H06B: There is no association between age of working women and physical

shopping pattern of FMCG products.

H16B: There is association between age of working women and physical shopping

pattern of FMCG products

Table No. 8.14.1Overallphysicalshoppingscore

Age group N Mean Std. Deviation

Elderly 190 60.4010 4.15140

Middle 340 62.0840 4.75835

Young 270 62.7231 6.16433

Total 800 61.9000 5.22054

From the table no8.14.1 the overall mean score of physical shopping of middle age

group and age is high with 62 percent followed by 60% of elderly respondents.

Above information is presented by using bar diagram as shown below chart no. 8.14.1

Chart No. 8.14.1Overall physical shopping score age wise

60.401

62.084

62.7231

59

59.5

60

60.5

61

61.5

62

62.5

63


Me

an

Mean score of overall Physical shopping

180

From the above table no. 8.14.2there are total 800 respondents. In case of respondents

from elderly age group it was found that 150 respondents from medium level 30

respondents from low level 10 respondents does high level does physical shopping. In

case of respondents from middle age group 280 respondents from medium level 30

respondents from high level and low level from does physical shopping. In case of

respondents from young age group 170 respondents from medium level 70

respondents „from high level and 30 respondents from high level does physical


no. 8.14.2

Chart No. 8.14.2 Overall physical shopping level age wise

1030

70

30 30 30

150

280

170

0

50

100

150

200

250

300


leve

l

High

Low

Medium

Table No. 8.14.2Age and physical shopping level cross tabulation

Age group Overall physical shopping

level

Total

High Low Medium

Elderly 10 30 150 190

Middle 30 30 280 340

Young 70 30 170 270

Total 110 90 600 800

181


Table No. 8.14.3 Chi-Square Tests

Calculated

Value

Degree

of

freedom

Table value

(5% level of

Significance)

Results

Pearson Chi-

Square 58.392 4 0.000

Rejected





presented in the following table no. 8.14.4

Table No. 8.14.4ANOVA


Sum of

Squares

Degree

of

freedom

Mean

Square

F Significant

Between

Groups 621.369 2 310.685

11.70

5 .000

Within Groups 21154.576 797 26.543

Total 21775.946 799



significant difference in mean scores of physical shopping of four cities. Hence H06Bis

accepted.

182

8.15 Hypothesis 7A:

H07a: There is no association between qualification of working women and online

shopping pattern of FMCG products.

H17a: There is association between qualification of working women and online

shopping buying pattern of FMCG products.

Table No. 8.15.1Overall online shopping score

Qualification N Mean Std.

Deviation

Graduate 300 38.3111 7.12794

Post graduate 310 39.4716 10.79496

Doctoral 110 40.5887 8.14868

Undergraduate 80 40.0000 9.06477

Total 800 39.2429 9.05072

From the above table the overall mean score of online shopping by doctoral and

undergraduate are high with 40 percent followed by post graduate with 39 percent and

38 percent which is lowest by graduate. Above information is presented by using bar

diagram as shown below chart no. 8.15.1

Chart No. 8.15.1Overall online shopping mean score qualification wise

38.3111

39.4716

40.5887

40

37

37.5

38

38.5

39

39.5

40

40.5

41

Graduate Post graduate Doctoral Undergraduate

183

Table No. 8.15.2 Qualification and overall online shopping

level Cross tabulation

Qualification Overall online shopping

level

Total

High Low Mediu

m

Graduate 30 20 250 300

Post graduate 70 80 160 310

Doctoral 20 10 80 110

Undergraduate 30 10 40 80

Total 150 120 530 800

From the table no 8.15.2 there are total 800 respondents. In case of graduates 250

respondents from medium level 30 respondents from high level and

20respondentsfrom low level does online shopping. In case of postgraduates 160

respondents from medium level, 80 respondents from low level and 70 respondents

from high level does online shopping. In case of doctoral 80 respondents from

medium level 20 respondents from high level and 10 respondents from low level does

online shopping. In case of undergraduates 40 respondents from medium level

30respondentsfromhigh level and 10 respondents from low level does online shopping

Above information is presented by using Bar diagram as shown below chart no.

8.15.2

184

Chart No. 8.15.2 Qualification and overall online shopping level


Table no 8.15.3 Chi Square

Calculated

Value

Degree of

freedom

(5% Level of

significance)

Results

Pearson Chi-

Square 97.738 6 12.591

Rejected






0

50

100

150

200

250

300


High

Low

Medium

185

Table No. 8.15.4ANOVA


Sum of

Squares

Degree

of

freedom

Mean

Square

F-value p-value Result

Between

Groups 521.780 3 173.927 2.132 .095

Non-

significant

Within Groups 64928.724 796 81.569

Total 65450.503 799

Since p-value is 0.095 which is greater than standard value 0.05 F-test is accepted.

Conclusion is there is no significant difference in mean scores of online shopping of

different qualification group. Hence the H07A is rejected.

186

8.16 Hypothesis 7B:

H07B: There is no association between qualification of working women and

physical shopping pattern of FMCG products.

H17B: There is association between qualification of working women and physical

shopping buying pattern of FMCG products.

Table No. 8.16.1 Overall physical shopping score

Qualification N Mean Std. Deviation

Graduate 300 62.2857 5.62094

Post graduate 310 62.7650 5.30355

Doctoral 110 60.6926 3.28222


Total 800 61.9000 5.22054

From the table no.8.16.1 the overall mean score for physical shopping by post -

graduate and graduate are high with 62 percent followed by doctoral with 60 percent

and 58 percent by undergraduate, which is lowest. Above information is presented by

using bar diagram as shown below chart no 8.16.1

Chart No. 8.16.1Overall physical shopping mean score qualification wise

62.285762.765

60.6926

58.7619

56

57

58

59

60

61

62

63

64


187

Table No. 8.16.2Qualification and overall physical shopping level Cross

tabulation

Qualification Overall physical shopping

level

Total

High Low Medium

Graduate 50 40 210 300


Doctoral 0 10 100 110


Total 110 90 600 800

From the table no. 8.16.2 there are total 800 respondents. In case of graduates 210

respondents from medium level 50respondentsfrom high level and 40respondents

from low level does physical shopping. In case of postgraduates 230 respondents from

medium level 60 respondents from low level and 20 respondents from high level does

physical shopping. In case of doctoral 100 respondents from medium level 10

respondents from high level does physical shopping. In case of undergraduates 60

respondents from medium level and 20 respondents from low level does physical

shopping .Above information is presented by using bar diagram as shown below chart

no. 8.16.2

Chart No. 8.16.2 Overall physical shopping level qualification wise

0

50

100

150

200

250

300


High

Low

Medium

188


Table No. 8.16.3Chi-Square Tests

Value Degree of

Freedom

Significant Result

Pearson Chi-

Square 61.205 6 12.591

Rejected





presented in the following table.

Table No. 8.16.4Overallphysicalshoppingscore

Sum of

Squares

Degree of

Freedom

Mean

Square

F-value Significant

Between

Groups 1224.730 3 408.243 15.812 .000

Within Groups 20551.215 796 25.818

Total 21775.946 799



significant difference in mean scores of physical shopping of four cities. Hence the

H07B is accepted.

189

8.17 Summary of Hypothesis

Sr No Null Hypothesis

Alternative Hypothesis

Hypothesis

1A

There is no significant

difference in proportion of

Online (E-shopping,

Teleshopping) shopping pattern

of working women for FMCG

products in select Tier 1 Cities-

Rejected

There is significant difference in

proportion of Online (E-

shopping, Teleshopping)

shopping pattern of working

women for FMCG products in

select Tier 1 Cities-Accepted

Hypothesis

1B

There is no significant

difference in proportion of

physical shopping pattern of

working women for FMCG

products in select Tier 1 Cities-

Rejected

There is significant difference in

proportion of physical shopping

pattern of working women for

FMCG products in select Tier 1

Cities- Accepted

Hypothesis

2A

There is no association between

level of income and proportion

of Online(E shopping,


of FMCG products - Rejected

There is association between



Teleshopping ) shopping pattern

of FMCG -Accepted

Hypothesis

2B



of physical shopping pattern of

FMCG products - Rejected



of physical shopping pattern of

FMCG -Accepted

Hypothesis

3

There is no correlation between

cost effectiveness and

proportion of Online

(E shopping, Teleshopping)

shopping pattern FMCG

products. -Rejected

There is correlation between cost

effectiveness and proportion of

Online(E shopping,


of FMCG products-Accepted

190

Hypothesis

4


quality of product and

proportion of Online(E

shopping, Teleshopping )

shopping pattern of FMCG

products-Accepted


quality of product and proportion



of FMCG products -Rejected

Hypothesis

5A


working women‟s occupation

and proportion of Online(E



products -Rejected



and proportion of Online(E



products. -Accepted

Hypothesis

5B



and proportion of physical


products -Rejected



and proportion of physical


products. -Accepted

Hypothesis

6A


age of working women and of

Online (E shopping,


of FMCG products.- Rejected

There is association between age

of working women and of

Online (E shopping,


of FMCG products.-Accepted

Hypothesis

6B


age of working women and

proportional of physical


products - Rejected

There is association between age

of working women and physical


products.-Accepted

191

Hypothesis

7A


qualifications of working

women and proportion

Online(E shopping,

Teleshopping )and physical


products.–Accepted



women and proportion Online(E

shopping, Teleshopping )and


FMCG products - Rejected

Hypothesis

7B



women and proportion of


FMCG products. - Rejected



women and proportion of


FMCG products -Accepted

192

CHAPTER 9

RESULTS & DISCUSSIONS

Information was collected from four different cities of India namely Mumbai,

Bangalore, Delhi and Hyderabad. There are 270 respondents from Mumbai, 250 from

New Delhi, 160 from Bangalore and 120 from Hyderabad. There were total 800

respondents out of which 190 belong to „Elderly‟ age group, 340 belong to Middle

age group and 270 belong to Young age group. Out of total 800 respondents 300 are

graduates, 310 are Post-graduate, 110 are Professionals and 80 are Undergraduate.

Out of total 800 respondents 120 are High income group, 300 are low income group,

300 are Middle income group and 80 are very High income group.

For this study, online shopping consists of E-shopping and telephonic shopping

both: To study online shopping behaviour information is collected for five types

of FMCG products (i) Dairy products (ii) Toiletries (iii) Grocery (iv) Cosmetics

and (v) Frozen food in all 4 cities of India.

In case of Dairy products :There are total 800 respondents out of which 582

respondents never buy tofu online, 168 sometimes buy and 50 nearly buy online .In

case of flavoured milk out of total respondent, 580 respondents never buy, 63

sometimes and 157 mostly buy online. Items like Curd, Cheese, Lassi and Milk are

generally never bought online, as these products are readily available and mostly

people prefer buying them fresh. In case of curd, 195 respondent never buy ,185

respondent sometime buy , 270 mostly and 150 always buy online .In case of Paneer

,430 respondent never buy ,153 sometimes buy,157 mostly buy and 60 always buy

online .In case of cheese, out of total respondents , 320 never buy ,125 sometimes

buy ,290 mostly buy and 65 always buy online .In case of Lassi ,out of total

respondents 284 never buy ,316 sometimes buy ,120 mostly buy and 80 always buy

Lassi online .In case of milk ,out of total respondents 434 never buy ,220 sometimes

buy ,136 mostly buy and 10 always buy online.

In case of toiletries there are total 800 respondents out of which 542 never buy, 220

sometimes buy, 28 mostly and 10 always Serums online. Serum is a product which is

recommended by the hair stylist only after physical examination of hair , it is

193

observed that many people buy it directly from salon , as a result online buying of

serum is low amongst the respondent .In case of shampoo 240 never buy, 158

sometimes buy ,290 mostly buy and 12 always buy shampoo online. In case of

conditioner 348 never buy, 262 sometimes, 180 mostly buy and 10 always buy

conditioner online. In case of Shower gel /Soap 150 never buy, 92 sometimes buy,

338 mostly buy and 220 always buy Shower gel /soap online .In case of Sanitizer 408

never buy, 392 sometimes buy sanitizer online.

In Packed Grocery product there are total 800 respondents, out of which 38 never buy,

190 sometimes, 318 mostly buy and 250 always buy Rice (Cereal) online .In case of

Pulse, 178 never buy, 380 sometimes buy, 208 mostly buy and 30 always buy Pulse

online. In case of Salt & Seasonings, 232 never buy, 178 sometimes buy, 310 mostly

buy 80 always buy Salt & Seasonings online. In case of Edible Oil, 272 never buy,

160 sometimes buy, 280 mostly buy and 88 always buy Edible Oil online .In case of

Sugar, 99 never buy, 230 sometimes buy, 310 mostly buy and 159 never buy Sugar

online.

In case of Cosmetics product there are total 800 respondents, out of which 172 never

buy, 148 sometimes, 280 mostly and 200 always buy face powder online .In case of

Kohl(Kajal) ,150 never buy ,112 sometimes buy , 278 Mostly buy and 220 always

buy Kohl (Kajal) online. In case of Lipstick, 99 never buy, 178 sometimes buy 359

mostly and 170 always buy Lipstick online. In case of Nail and Hand products 381

never buy , 280 sometimes, 129 mostly and 10 always buy nail and hand products

online .In case of Body lotion, 284 never buy ,186 sometimes buy 230 mostly buy

,100 never buy body lotion online .

In case of Packed Frozen product there are total 800 respondents out of which 214

never buy, 196 sometimes, 270 mostly and 120 always green Peas online .In case of

Ready to cook &serve,172 Never buy,350 sometimes, 168 mostly and 110 always

buy ready to cook &serve online. In case of Fresh Cut Veggies /Fruits, 313 never buy,

220 Sometimes buy, 217 mostly and 50 always buy Fresh Cut Veggies /Fruits online.

In case of Ice cream 140 never buy ,182 sometimes, 220 mostly and 258 always buy

194

Ice cream online .In case of raw non-veg ,274 Never buy ,260 sometimes buy, 230

mostly buy and 36 never buy raw non-veg online.

Physical shopping pattern: To study physical shopping pattern, information is

collected for five types of FMCG products. Overall physical shopping mean score

is 61.90 percent. It is also calculated for each type of five FMCG products:

In case of Dairy products there are total 800 respondents, out of which 488

respondents never buy tofu physically, 240 sometimes buy and 72 mostly buy

physically .In case of flavoured milk, 318 respondents never buy, 82 sometimes buy

and 330 mostly buy physically. It has been observed that tofu and flavoured milk are

not regularly consumed by respondents. In case of curd, 118 respondent never

buy,282 respondent sometime buy and 400 mostly buy physically .In case of

Paneer,12 respondent never buy ,98 sometimes buy ,310 mostly buy and 380 always

buy physically .In case of cheese, out of total respondents , 30 never buy ,25

sometimes buy ,345 mostly buy and 400 always buy physically .In case of lassi out of

total respondents 155 sometimes buy ,375 mostly buy and 370 always buy lassi

physically .In case of milk ,out of total respondents, 22 sometimes buy 258 mostly

buy and 520 always buy physical

In case of toiletries there are total 800 respondents out of which, 320 never buy, 78

sometimes buy ,332 mostly and 70 always buy serums physically .Serum is

recommended by the hair stylist only after physical examination of hair. In case of

shampoo 182 sometimes buy, 88 mostly and 530 always buy shampoo physically. In

case of conditioner, 130 never buy, 78 sometimes, 230mostly and 362 always buy

conditioner physically. In case of Shower gel /soap, 42 sometimes, 248 mostly buy

and 510 always buy Shower gel /soap physically .In case of Sanitizer, 330never buy,

203 sometimes buy, 257 mostly buy and 10 always buy sanitizer physically.

In case of Packed Grocery product there are total 800 respondents, out of which 54

Sometimes buy , 346mostly buy and 400 always buy Rice (Cereal) physically .In

case of pulses, 44 sometimes buy ,530 mostly buy and 236 always buy pulse

physically. In case of Salt & Seasonings 137 sometimes buy, 373 mostly buy and 290

195

always buy Salt &Seasonings physically. In case of Edible Oil 217 sometimes, 343

mostly and 240 always buy Edible Oil physically. In case of Sugar, 439 sometimes

buy, 261 mostly buy and 100 never buy sugar physically.

In case of Cosmetics product there are total 800 respondents, out of which 120

sometimes buy, 300 mostly buy and 380 always buy face powder physically. In case

of Kohl (Kajal) 40 Never buy, 263 sometimes buy, 347mostly buy and 150 always

buy Kohl (Kajal) physically. In case of Lipstick 123 sometimes, 427 mostly and 250

always buy Lipstick physically. In case of Nail and Hand products 40 never buy, 173

sometimes buy 357 mostly and 230 always buy Nail and Hand products physically. In

case of Body lotion 256 mostly buy 554 never buy Body lotion physically.

In case of Packed Frozen product there are total 800 respondents out of which 37

never buy, 330 sometimes, 283 mostly and 150 always green peas physically .In case

of ready to cook & serve 78 never buy, 380 sometimes buy , 230 mostly buy and 110

always buy ready to cook &serve physically. In case of Fresh Cut Veggies /Fruits 355

never buy, 230 sometimes, 116 mostly buy and 89 always buy Fresh Cut Veggies

/Fruits physically. In case of Ice cream 229 never buy , 379 sometimes buy , 142

mostly buy and 50 always buy Ice cream physically .In case of Raw Non-veg,398

never buy,202 sometimes buy,140 mostly buy 60 never buy Raw Non-veg physically.

On basis of Hypothesis there are following results:

As per study there is significant difference in proportion of online buying pattern of

working women on FMCG products among four cities. Mean score of online

shopping for Mumbai is 40.54, for Delhi is 40.47, for Bangalore is 40.58 and for

Hyderabad is 31.96. This clearly justifies the project growth of online shopping in the

country. However, the frequency of online shopping is relatively less in the country

.There is significant difference in proportion of physical buying pattern of working

women on FMCG products among four cities. Mean score of physical shopping for

New Delhi is 63.02, for Mumbaiis 60.6, for Bangalore is 62.24 and for Hyderabad is

61.97.

The study says that there is association between level of income and shopping pattern

of FMCG in tier 1 cites .Study says middle income go for maximum online and high

196

income go for physical shopping Online shopping mean per cent scores for each level

of income are calculated. For low income group respondents score is 38.81, for

middle income group is 41.89, for high income group is 38.71 and for very high

income group respondents score is 31.69.Physical shopping mean per cent scores for

each level of income are calculated. For low income group respondents score is 61.76,

for middle income group is 62.59, for high income group is 60.23 and for very high

income group respondent‟s score is 62.30.

Physical buying has no relation with cost effectiveness as it is mandatory whereas E-

shopping is alternate for physical. Conclusion is online shopping is cost effective.

There is no association between quality of product and proportion of shopping pattern

as shopping patterns have no effect on quality of product.

There is association between working women occupation and shopping pattern of

FMCG products. Working women from IT industry go for more online and physical

shopping Mean online shopping for each category of respondents are calculated.

Mean for IT sector women is 46.98 which is highest. It is followed by mean score of

academics is37.09 and others category is 37.16. For banking and insurance group of

women mean score is 35.20Mean physical shopping scores for each category of

respondents are calculated. Mean score for IT sector women is 62.23 which is highest.

It is followed by mean score of academics is 61.31 and others category is 62.68. For

banking and insurance group of workingwomen mean score is 60.90.

The study shows more middle age working women go for online, elderly age go for

teleshopping and young enjoy visiting the malls so they go for physical shopping.

Mean online shopping scores for each category of age group are calculated. Mean

score for young age group respondents is 36.21, for middle age group respondents is

43.06 and for elderly group is 36.71 mean physical shopping scores for each category

of age group are calculated. Mean score for young age group respondents is 62.72 for

middle age group respondents is 62.08 and for elderly group is 60.40.There is

association between age of working women and shopping pattern of FMCG products

.There is association between qualification of working women and shopping for

FMCG products in tier 1 cities in India. It is observed from study that more doctoral

197

go for online and post-graduates go for physical shopping in Tier-1 cities. Mean

online shopping scores for each level of qualification are calculated. Mean score for

undergraduate respondents is 40.00, for graduates is 38.31, for post graduates is 39.47

and for doctoral is 40.58 mean physical shopping scores for each level of qualification

are calculated. Mean score for undergraduate respondents is 58.76, for graduates is

62.28, for post graduates is 62.76 and for doctoral is 60.

198

CHAPTER 10

CONCLUSIONS

There is significant difference in proportion of Online (E-shopping, Teleshopping)

and physical shopping pattern of working women for FMCG products in select Tier 1

Cities. The Data analysis and interpretation reflects to the fact that the mean score of

online shopping is highest in Bangalore and lowest in Hyderabad ,which shows that

in Bangalore there is high level of support for connectivity and accessibility of online

shopping .In Bangalore there are many working women from various states of India

working in sectors like IT ,BPO etc. Today‟s women are working late in evening and

find it difficult to do physical shopping. It has been observed that many women who

working gets leave on Sundays only. Many working women who shops on weekends

face problems of long queue and waste time, so they prefer to shop Online. The other

reason for working women to shop physically is, as there is no problem of traffic so

they prefer going to malls and departmental store for shopping on discussion with

certain working women in Tier1cities was found that they believe in physically

touching product and buying.

Mean score of physical shopping for working women in Delhi is highest and lowest in

Mumbai. It‟s found in this study that in Delhi many stores and local kirana shops are

open for longer time. On basis of data analysis it was found that more working

women go for physical shopping as compared to online shopping in all Tier 1 cities.

This study shows that there is association between level of income and proportion of

online shopping pattern (E-shopping, Teleshopping) of FMCG products. Arithmetic

mean of online shopping for working women in middle income group is highest and

for very high income group is lowest in all four tier1 of India. Physical shopping

mean percent for middle income group working women is highest and lowest for

high income group in all four Tier1 cities of India .The study shows that middle

income working women go for online shopping and high income go for physical

shopping in all four Tier 1 cities of India because they purchase high end and branded

products which need to be touch and felt before they buy. There exist the Correlation

between cost effectiveness and online shopping of FMCG products in Tier1 cities of

199

India .This study states that there is negative correlation between cost of online

shopping and buying proportion which means if cost will reduce the buying

proportion of online shopping will further increase. There is an association between

quality of product and shopping pattern of FMCG products in tier1 cities of India. The

study shows that working women who shop online in all four tier 1 cities of India are

concern about Quality .As per study working women has stated that quality is of

prime concern to them irrespective of cost. Online product selling companies have

made provision for easy exchange of spoilt or damaged products.There is an

association between industry of working women (academics /IT/banking/others) and

(Online and Physical) shopping pattern of FMCG products. The study was significant

because it has included working women from diverse backgrounds from major tier 1

cities of India.

The study has shown there was association between occupation of working women

and shopping pattern of FMCG .The study shows that more online shopping was done

by respondents from IT sector and least by Banking and Insurance. It was observed

that respondents from banking and insurance was less tech savvy .Many women

working with Banks are very busy dealing with client so they do not get time to shop

online. In case of physical shopping women working in others industry does more and

is least in case of IT sector .It been observed that working women in IT sector have

rigid schedule which makes them difficult to go for physical shopping.

There is an association between age of working women and online buying pattern of

FMCG products in select tier1 cities of India. The study shows that elderly women go

for less online shopping and middle income women go for more online shopping. It‟s

been observed that elderly working women are not very internet friendly and they

believe more in buying products by touching and seeing them. In case of physical

shopping it‟s found that young working women go for high physical shopping and

lowest by elderly lady. The survey states that young women enjoy shopping at malls

and departmental stores. The study has found that elderly women go more for

telephonic (Online) shopping pattern.

200

This study shows that the qualification and overall shopping pattern are interrelated.

Online shopping level is highest for post graduate in all tier 1 cities and lowest

among Doctoral working women .In this study doctoral working women who are at

very high post are very busy and do not enjoy online shopping pattern. On the other

hand Post graduate women enjoy buying online as many working women are not

bound by time limit. On the other hand its observed that graduates working women go

for more physical shopping and they enjoy physical shopping as they are not bound

by time limit.

201

CHAPTER 11

RECOMMENDATIONS

E-shopping is one of the online shopping pattern done by working women in four tier

-1 cities of India .There are 90% of working women who are tech savvy and are heavy

online shoppers. The study states that the working women in Delhi are the largest

consumers of FMCG. Considering this fact it is highly recommended to the marketer

that working women do more online shopping as compared to non-working women.

Hence the company‟s likes bigbasket.com, localbaniya.com, grofers who sell their

products online etc. should aggressively concentrate on promoting their products

through electronic and print media.

The companies selling product online should try to retain their current customers and

focus on attracting the non-users by making them aware of benefits like convenience

and authenticity of products delivered to them online. The study states that still people

in India are reluctant to buy products online w.r.t authenticity. The companies should

make people believe that the products sold to them are genuine and if in case,

products delivered to them are damaged or spoilt, they would immediately get it

exchanged or replaced .The customer should be made aware of other benefits of

shopping online like on time delivery and discounted products than local retailer.

In other cities like Bangalore, Hyderabad and Mumbai the marketer has to attract

working women where presently the online shopping percent is low as compared to

Delhi. Hence to attract working women towards online shopping the marketer needs

to advertise about cash back offers, distribution of free sample on first purchase, free

home delivery at door step as per convenient time of working women and return or

exchange policy of damaged products. In case of telephonic shopping there is

element of saving time and cost of travelling .It involves order on telephone to kirana

store or departmental store. Teleshopping is most preferred by working women as it is

convenient and facilitates prompt delivery. In case of Physical shopping it is more

preferred by working women in Mumbai and less in Delhi. In Mumbai physical

shopping is done more in local kirana store and department store which are open late

in evening. For marketers it is recommended to retain and increase the footfalls of

202

working women by giving them cash discount ,special benefits to loyal customers

,product on product offer ,inform customer about arrival of new product, distribution

of free sample for same and gifting them during festivals like Diwali ,Eid or

ChristmasThere is association between level of income and proportion of online

shopping pattern (E-shopping, Teleshopping) of FMCG products. Working women

with very high income level go for physical shopping. The marketer should retain the

loyal customer, as these working women belong to high society and has snob appeal.

Marketer should directly communicate them about new product arrival. Other

marketing methods to retain them are relationship marketing and word of mouth.

The middle income working women go for more online shopping in tier 1 cities of

India. Online marketer should take more efforts to pull non user and retain current

customer who are middle and low income working women. The task of marketer

should be to focus on cost effectiveness through online advertising or personal mail.

Marketer should regularly update it customer about discount or price fall on

FMCG.This study shows that Correlation between cost effectiveness and online

shopping of FMCG products in tier 1 cities of India which means working women

will buy online if price is lower than marked price .Considering this fact the online

FMCG companies should lower the marked up price of products so as to convince net

savvy working women amongst the all income level. This study has shown that

product quality has positive impact on shopping pattern amongst working women

.attractive design may help to increase the excitement among working women and

generate positive word of mouth. Thus will benefit the company to generate the

feedback of their products without much expenditure.

This study there is association between industry of working women and shopping

pattern .Working women from IT sector do more online shopping as compared to

banking, academics and other sector. In case of working women from other industry

/sector they go for more physical shopping. To promote more and keep current

shoppers the marketer needs to make the customer aware about convenience of online

shopping and other benefits they can enjoy. There is association between age and

shopping pattern of working women. to retain and attract the young working women

the marketer should stock more imported products of multiple brand of various

203

patterns .In case of middle age working women go for 45% online shopping and 55%

for physical shopping. In case women of this age prefer convenience and on time

delivery and look out for more discounted products as free samples. In case of elderly

age working women below 60 yrs. go for more telephonic shopping .the marketer has

focus on how he pull them towards the store .As many elderly women are not tech

savvy and also do not believe in products from E-shopping .As marketer his job is to

convince this women to visit store .If she visit store she might buy more products than

her required list .On visiting store she can avail current discounts and offers which

can further generate her need for those products.This study shows that working

women of all qualification because of their working schedule needs to save time from

it. Their shopping pattern is focussed and strategic .hence to attract working women

the marketer especially kirana store which is oldest form of physical shopping pattern

should go for extensive visual merchandising i.e. as it is an effective way to attract

and convert the working women shoppers.

Future Scope of Study

The study aims at understanding the impact of shopping pattern of working women

on FMCG viz. Dairy, grocery, Cosmetics ,Soap and raw frozen food in cities like

Mumbai, Delhi ,Bangalore and Hyderabad. The scope of the study has been limited to

certain demographic characters of working women like age ,qualification ,gender

,income ,industry wise The study broadly aims at understanding advantages of online

and physical shopping on parameters like time saving ,convenience ,shopping

24*7,cost effectiveness ,privacy in shopping and comparison of various products

.Studying the perceptions of the women buyers of FMCG mainly in terms of sources

of information, location where the purchase is made, influence of communication and

promotional mix and the ultimate purchase decision factors. Further study can be

conducted on various bases of segmentations like demographic segmentation which

includes family size, Income and religion, on basis of geographical segmentation like

Tier II, Tier III & Tier IV Cities. On basis of behavioural segmentation like usage rate

etc. and on basis of psychographic segmentation like personality and lifestyle. Study

can be further conduced on other FMCG like detergents, Beverages, Oils etc.

204

CHAPTER 12

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210

ANNEXURE 1

QUESTIONAIRRE

Dear Madam,

I have enrolled for PhD. program at D Y Patil University, Nerul, NaviMumbai.

As a part of my research work I am collecting information about “Impact of

Online (E-shop, Teleshopping) &Physical Shopping patterns of select Fast

moving Consumer goods (FMCG) on working women in select Tier 1 cities of

India. I will be grateful if you could spare some valuable time to fill this

questionnaire. I assure that the response will be kept strictly confidential and

will be used only for academic purpose.

Thank You for the support.

Name: Roshni Sawant

Designation: Asst. Professor

Note:

The information is collected only for academic purpose.

The information given shall be strictly held in confidence.

Giving the name is optional.

Tick in the appropriate box.

1. Name of Respondent(Optional ) : ___________ ____________________

2. Age :

Below 30yrs

30yrs -45yrs

Above 45yrs

3. Qualification

Undergraduate

Graduate

Post-graduate

Doctoral

211

4. City :

Mumbai

Delhi

Bangalore

Hyderabad

5. Income (per month):

< 15000 (Low Income Group)

15000-35000 (Middle Income Group)

36000-50000 (High Income Group )

>50000 (Very Income Group)

6. Industry type :

IT

Education /Academic

Banking /Insurance

Others

7. (A) What is the frequency of online shopping of following dairy product?

(please tick only one appropriate option)

Sr no Category of Dairy product Never Sometimes Mostly Always

1 Tofu /Paneer

2 Flavoured Yogurt

3 Condensed Milk

4 Infant Formula Milk

5 Toned Milk

6 Lassi /Butter milk

7 Ghee

212

(B) What is the frequency of physical shopping of following dairy product?


Sr

no Category of Dairy product Never Sometimes Mostly Always

1 Tofu /Paneer

2 Flavored Yogurt

3 Condensed Milk

4 Infant Formula Milk

5 Tonned Milk

6 Lassi /Butter milk

7 Ghee

8. (A) What is the frequency of online shopping of following Toiletries products?


Sr

no

Category of Toiletries

product Never Sometimes Mostly Always

1 Shower gel /Soap

2 Shampoo /Conditioner

3 Serums/Oils

4 Facewash /Scrubs

5 Sanitary napkins

213

8 (B) What is the frequency of physical shopping of following Toiletries product?


9. (A) What is the frequency of online shopping of following Packed Grocery

products? (please tick only one appropriate option)

Sr

no

Category of Packed Grocery


1 Salts & Seasonings

2 Cereals

3 Sugar

4 Edible Oil

5 Pulses

Sr

no

Category of Toiletries


1 Shower gel /Soap

2 Shampoo /Conditioner

3 Serums/Oils

4 Facewash /Scrubs

5 Sanitary napkins

214

(B) What is the frequency of physical shopping of following Packed Grocery

product? (Please tick only one appropriate option)

10. (A) What is the frequency of online shopping of following Cosmetics? (please

tick only one appropriate option)

Sr no Category of Cosmetics Never Sometimes Mostly Always

1 Face powder/Compaq

2 Lipgloss

3 Eyeliner /Kajal

4 Nail polish

5 Mascara

Sr

no

Category of Packed Grocery


1 Salts & Seasonings

2 Cereals

3 Sugar

4 Edible Oil

5 Pulses

215

(B) What is the frequency of physical shopping of following Cosmetics? (Please


Sr no Category of Cosmetics Never Sometimes Mostly Always

1 Face powder/Compaq

2 Lip-gloss

3 Eyeliner /Kajal

4 Nail polish

5 Mascara

11. (A) What is the frequency of online shopping of following Frozen food? (please tick

only one appropriate option)

Sr no Category of Frozen Food Never Sometimes Mostly Always

1 Peas

2 Cut veggies /Fruits

3 Tortilla/Parathas

4 Veg /Non veg fries

(B) What is the frequency of physical shopping of following Frozen food? (Please


Sr no Category of Frozen Food Never Sometimes Mostly Always

1 Peas

2 Cut veggies /Fruits

3 Veg /Non veg fries

4 Tortilla /Parathas

216

12(A) Please give your opinion about advantages of online shopping. (Please tick

only one appropriate option)

(B) Please give your opinion about advantages of physical shopping. (Please tick only

one appropriate option)

Sr no Factors of Advantages of Physical

shopping Never Sometimes Mostly Always

1 Comparison of quality is possible

2 Comparison between brand is

possible

3 Get information about latest scheme

4

I am not sure what exactly I want to

purchase

5 Bargaining is possible

6 Exchange facility is easy

Sr no Factors of Advantages of online

shopping

Strongly

Disagree Disagree Agree

Strongly

Agree

1 It is Time saving

2 It is Less Physical efforts

3 24x7 shopping is possible

4 Shopping is possible from any

place (traveling/office)

5 I know what I want to purchase

6

It is cost effective than physical

shopping

7 Quality of online shopping is

reliable

217

ANNEXURE 2

SPSS OUTPUT

Frequency Table

Frequency Percent

Bangalore 160 40.5810

Delhi 250 40.4747

Hyderabad 120 31.9683

Mumbai 270 40.5425

Total 800 100.0

Age_group

Frequency Percent Valid Percent Cumulative

Percent

Elderly 190 23.8 23.8 23.8

Middle 340 42.5 42.5 66.3

Young 270 33.8 33.8 100.0

Total 800 100.0 100.0

Qualification


Percent

Graduate 300 37.5 37.5 37.5

Post graduate 310 38.8 38.8 76.3

Professional 110 13.8 13.8 90.0

Undergraduate 80 10.0 10.0 100.0

Total 800 100.0 100.0

218

Level_of_working


Percent

Lower level 150 18.8 18.8 18.8

Middle level 510 63.7 63.7 82.5

High level 140 17.5 17.5 100.0

Total 800 100.0 100.0

Nature_of_working_industry


Percent

Academics 130 16.3 16.3 16.3

Banking/Insurance 200 25.0 25.0 41.3

IT sector 210 26.3 26.3 67.5

Others 260 32.5 32.5 100.0

Total 800 100.0 100.0

Monthly_income


Percent

High 120 15.0 15.0 15.0

Low 300 37.5 37.5 52.5

Middle 300 37.5 37.5 90.0

Very High 80 10.0 10.0 100.0

Total 800 100.0 100.0

Output Created 18-JAN-2015 07:50:08

Comments

Input

Active Dataset DataSet1

Filter <none>

Weight <none>

Split File <none>

N of Rows in Working

Data File 800

219

Missing Value Handling

Definition of Missing

User-defined missing

values are treated as

missing.

Cases Used

Statistics for each table are

based on all the cases with

valid data in the specified

range(s) for all variables in

each table.

Syntax

CROSSTABS

/TABLES=City

Age_group

Nature_of_working_indust

ry Monthly_income BY

overall_online_shopping_l

evel

/FORMAT=AVALUE

TABLES

/STATISTICS=CHISQ

/CELLS=COUNT

/COUNT ROUND CELL.

Resources

Processor Time 00:00:00.02

Elapsed Time 00:00:00.03

Dimensions Requested 2

Cells Available 174762

Overall_online_shopping_level Citywise

Crosstab

Count

overall_online_shopping_level Total

High Low Medium

City

Bangalore 50 10 140 200

Delhi 50 10 140 200

Hydarabad 20 90 90 200

Mumbai 30 10 160 200

Total 150 120 530 800

220

Chi-Square Tests

Value df Asymp. Sig.

(2-sided)

Pearson Chi-Square 198.189a 6 .000

Likelihood Ratio 172.400 6 .000

N of Valid Cases 800

a. 0 cells (.0%) have expected count less than 5. The minimum

expected count is 30.00.

Age group overall online shopping level

Crosstab

Count


High Low Medium

Age_group

Elderly 30 60 100 190

Middle 100 10 230 340

Young 20 50 200 270

Total 150 120 530 800

Chi-Square Tests


(2-sided)






221

Overall online shopping levelindustry wise

Nature of working industry overall_online_shopping_level T

o

t

a

l

High Low Medium

Academics 20 40 70

1

3

0

Banking/Insurance 10 50 140

2

0

0

IT sector 110 0 100

2

1

0

Others 10 30 220

2

6

0

Total 150 120 530

8

0

0

Chi-Square Tests


(2-sided)






222

Monthly income overall online shopping level

Crosstab

Count


High Low Medium

Monthly_income

High 0 10 110 120

Low 50 30 220 300

Middle 100 40 160 300


Total 150 120 530 800

Chi-Square Tests


(2-sided)






.

Mean


Comments

Input


Filter <none>

Weight <none>

Split File <none>


Data File 800

Missing Value Handling Definition of Missing

For each dependent

variable in a table, user-

defined missing values for

the dependent and all

grouping variables are

treated as missing.

223

Cases Used

Cases used for each table

have no missing values in

any independent variable,

and not all dependent

variables have missing

values.

Syntax

MEANS

TABLES=Overall_online_

shopping_score BY City

Age_group


ry Monthly_income

/CELLS COUNT MEAN

STDDEV.

Resources Processor Time 00:00:00.02




Bangalore 160 40.5810 7.42305

Delhi 250 40.4747 9.89243

Hydarabad 120 31.9683 8.74882

Mumbai 270 40.5425 7.64972

Total 800 39.2429 9.05072


Age group

Age_group N Mean Std. Deviation

Elderly 190 36.7118 8.89512

Middle 340 43.0644 8.39386

Young 270 36.2116 8.16826

Total 800 39.2429 9.05072

224


Nature of working industry

Nature_of_working_indus

try


Academics 130 37.0989 9.64388


IT sector 210 46.9841 7.25619

Others 260 37.1648 6.70220

Total 800 39.2429 9.05072

Overall online shopping scorew.r,t Monthly income

Monthly income N Mean Std. Deviation

High 120 38.7143 5.99402

Low 300 38.8190 7.79264

Middle 300 41.8921 10.19789

Very High 80 31.6905 8.08743

Total 800 39.2429 9.05072

Crosstabs

Output Created

Comments

Input


Filter <none>

Weight <none>

Split File <none>


Data File 800





missing.

Cases Used





each table.

225

Syntax

CROSSTABS

/TABLES=City

Age_group


ry Monthly_income BY

overall_physical_shopping

_level

/FORMAT=AVALUE

TABLES

/STATISTICS=CHISQ

/CELLS=COUNT

/COUNT ROUND CELL.

Resources





City wise overall physical shopping level

Crosstab

Count

overall_physical_shopping_level Total

High Low Medium

City

Bangalore 40 8 112 160

Delhi 53 32 165 250

Hydarabad 13 54 53 120

Mumbai 44 26 200 270

Total 110 90 600 800

Chi-Square Tests

Value df Sig. (2-sided)




a. 0 cells (.0%) have expected count less than 5. The

minimum expected count is 22.50.

226

Age group overall physical shopping level

Count


High Low Medium

Age_group

Elderly 10 30 150 190

Middle 30 30 280 340

Young 70 30 170 270

Total 110 90 600 800

Chi-Square Tests







Nature of working_industry and overall physical shopping level

overall_physical_shopping_level T

o

t

a

l

High Low Medium


try

Academics 0 20 110

1

3

0


2

0

0

IT sector 30 10 170

2

1

0

Others 50 30 180

2

6

0

Total 110 90 600

8

0

0

227

Chi-Square Tests


(2-sided)






Monthly income overall physical shopping level

Crosstab

Count


High Low Medium

Monthly_income

High 10 20 90 120

Low 30 40 230 300

Middle 60 30 210 300


Total 110 90 600 800

Chi-Square Tests







228

Means

Notes


Comments

Input


Filter <none>

Weight <none>

Split File <none>


Data File 800



For each dependent





treated as missing.

Cases Used






values.

Syntax

MEANS

TABLES=Overall_physica

l_shopping_score BY City

Age_group


ry Monthly_income

/CELLS COUNT MEAN

STDDEV.



229

Overall physical shopping score City wise


Bangalore 160 62.2476 5.51328

Delhi 250 63.0248 5.21233

Hyderabad 120 61.9714 4.36332

Mumbai 270 60.6208 5.14994

Total 800 61.9000 5.22054

Overall physical shopping score Age group


Elderly 190 60.4010 4.15140

Middle 340 62.0840 4.75835

Young 270 62.7231 6.16433

Total 800 61.9000 5.22054

Overall physical shopping score Nature of working industry


try


Academics 130 61.3187 3.64461


IT sector 210 62.2313 5.29293

Others 260 62.6886 5.69792

Total 800 61.9000 5.22054

Overall physical shopping score and Monthly income

Monthly_income N Mean Std. Deviation

High 120 60.2381 4.76982

Low 300 61.7651 5.39174

Middle 300 62.5905 5.31300

Very High 80 62.3095 4.27364

Total 800 61.9000 5.22054

Descriptive Statistics

230

N Minimum Maximum Mean Std.

Devia

tion

Dairy online shopping

score 800 .00 52.38 27.3810

11.92

412

Soaps and Det OS score 800 .00 60.00 32.0833 14.38

852

Grocery OS score 800 13.33 73.33 47.5000 17.34

338

Cosmetics OS score 800 13.33 80.00 47.6667 16.47

913

Frozen food OS_ score 800 6.67 73.33 41.5833 17.75

131

Valid N (listwise) 800

\.

Descriptive

Notes


Comments

Input


Filter <none>

Weight <none>

Split File <none>

N of Rows in

Working Data File 800


Definition of

Missing

User defined missing values are

treated as missing.

Cases Used All non-missing data are used.

Syntax

DESCRIPTIVES

VARIABLES=Dairy_physical_s

hopping_score

Soaps_and_Det_PS_score

Grocery_PS_score

Cosmetics_PS_score

Frozen_food_PS_score

/STATISTICS=MEAN

STDDEV MIN MAX.



231

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

Dairy_physical_shopping_

score 800 47.62 80.95 66.6667 8.55691

Soaps_and_Det_PS_score 800 33.33 93.33 61.1667 16.09231

Grocery_PS_score 800 40.00 80.00 69.5000 7.80623

Cosmetics_PS_score 800 60.00 86.67 72.8333 6.89835

Frozen_food_PS_score 800 6.67 73.33 39.3333 14.01670

Valid N (listwise) 800

Correlations

It_is_cost_effe

ctive_than_phy

sical_shopping

Overall_online

_shopping_sco

re

It_is_cost_effective_than_

physical_shopping

Pearson Correlation 1 -.264**


N 800 800

Overall_online_shopping_

score

Pearson Correlation -.264**

1


N 800 800

**. Correlation is significant at the 0.01 level (2-tailed).

Correlation

Notes


Comments

Input


Filter <none>

Weight <none>

Split File <none>


Data File 800





missing.

Cases Used

Statistics for each pair of

variables are based on all

the cases with valid data

for that pair.

232

Syntax

CORRELATIONS

/VARIABLES=Quality_of

_online_shopping_is_relia

ble

Overall_online_shopping_s

core

/PRINT=TWOTAIL

NOSIG

/MISSING=PAIRWISE.



Correlations

Quality_of_onl

ine_shopping_i

s_reliable

Overall_online

_shopping_sco

re

Quality_of_online_shoppi

ng_is_reliable

Pearson Correlation 1 .080*


N 800 800


score

Pearson Correlation .080* 1


N 800 800

*. Correlation is significant at the 0.05 level (2-tailed).

Mean

Case Processing Summary

Cases

Included Excluded Total

N Percent N Percent N P

e

r

c

e

n

t

233

Overall_physical_shoppin

g_score * City 800 100.0% 0 0.0% 800

1

0

0

.

0

%

Report



Bangalore 160 62.2476 5.51328

Delhi 250 63.0248 5.21233

Hyderabad 120 61.9714 4.36332

Mumbai 270 60.6208 5.14994

Total 800 61.9000 5.22054

Frequencies

Notes

Output Created 24-FEB-2015 16:46:58

Comments

Input

Data C:\Users\User\Desktop\Ro

shni PH D\Data800.sav


Filter <none>

Weight <none>

Split File <none>


Data File 800





missing.

Cases Used Statistics are based on all

cases with valid data.

Syntax

FREQUENCIES

VARIABLES=overall_phy

sical_shopping_level

/ORDER=ANALYSIS.



234

Overall_physical_shopping_level


Percent

Valid

High 110 13.8 13.8 13.8

Low 90 11.3 11.3 25.0

Medium 600 75.0 75.0 100.0

Total 800 100.0 100.0

]Crosstabs

Notes


Comments

Input

Data C:\Users\User\Desktop\Roshni PH

D\Data800.sav


Filter <none>

Weight <none>

Split File <none>

N of Rows in

Working Data

File

800


Definition of

Missing

User-defined missing values are

treated as missing.

Cases Used

Statistics for each table are based on

all the cases with valid data in the

specified range(s) for all variables in

each table.

Syntax

CROSSTABS

/TABLES=City BY

overall_physical_shopping_level

/FORMAT=AVALUE TABLES

/STATISTICS=CHISQ

/CELLS=COUNT

/COUNT ROUND CELL.

Dimensions

Requested 2

Cells

Available 174762

235

Cases

Valid Missing Total


e

r

c

e

n

t

City *

overall_physical_shoppin

g_level

800 100.0% 0 0.0% 800

1

0

0

.

0

%

City overall physical shopping level Cross tabulation

Overall physical shopping level Total

High Low Medium

City

Bangalore 32 16 112 160

Delhi 38 15 197 250


Mumbai 22 47 201 270

Total 110 90 600 800

Chi-Square Tests


(2-sided)




a. 0 cells (.0%) have expected count less than 5.

The minimum expected count is 22.50.

236

Notes


Comments

Input




Filter <none>

Weight <none>

Split File <none>


Data File 800





missing.

Cases Used

Statistics for each analysis

are based on cases with no

missing data for any

variable in the analysis.

Syntax

ONEWAY

Overall_physical_shopping

_score BY Coded_city

/MISSING ANALYSIS.



ANOVA

Overall_physical_shopping_score

Sum of

Squares

df Mean Square F Sig.

Between Groups 1092.009 3 364.003 14.008 .000

Within Groups 20683.937 796 25.985

Total 21775.946 799

237

Crosstabs

Notes


Comments

Input




Filter <none>

Weight <none>

Split File <none>


Data File 800





missing.

Cases Used





each table.

Syntax

CROSSTABS

/TABLES=Quality_of_onli

ne_shopping_is_reliable

BY


evel

/FORMAT=AVALUE

TABLES

/STATISTICS=CHISQ

/CELLS=COUNT

/COUNT ROUND CELL.

Resources





238


Cases

Valid Missing Total


e

r

c

e

n

t


ng_is_reliable *


evel

800 100.0% 0 0.0% 800

1

0

0

.

0

%

Quality_of_online_shopping_is_reliable * overall_online_shopping_level

Crosstabulation

Count


High Low Medium


ng_is_reliable

1.00 0 30 80 110

2.00 40 0 90 130

3.00 90 80 340 510

4.00 20 10 20 50

Total 150 120 530 800


(2-sided)






239

Crosstabs

Notes


Comments

Input

Data C:\Users\User\Desktop\Roshni

PH D\Data800.sav


Filter <none>

Weight <none>

Split File <none>

N of Rows in



Definition of Missing User-defined missing values

are treated as missing.

Cases Used





each table.

CROSSTABS

/TABLES=Quality_of_online_

shopping_is_reliable BY

overall_online_shopping_level

/FORMAT=AVALUE

TABLES

/STATISTICS=CHISQ

/CELLS=COUNT

/COUNT ROUND CELL.



Dimensions

Requested 2


240

Cases

Valid Missing Total


e

r

c

e

n

t

Quality of online

shopping reliable overall

online shopping level

800 100.0% 0 0.0% 800

1

0

0

.

0

%

Quality of online shopping is reliable overall online shopping level Crosstabulation

Count


High Low Medium

Agree 90 80 340 510

Disagree 40 0 90 130

Strongly agree 20 10 20 50

Strongly disagree 0 30 80 110

Total 150 120 530 800





b. 0 cells (.0%) have expected count less than 5.

The minimum expected count is 7.50.

241


Comments

Input




Filter <none>

Weight <none>

Split File <none>


Data File 800





missing.

Cases Used





Syntax

ONEWAY


score BY Coded_Quality

/MISSING ANALYSIS.



ANOVA

Overall_online_shopping_score

Sum of

Squares


Between Groups 7158.604 3 2386.201 32.585 .000

Within Groups 58291.899 796 73.231

Total 65450.503 799

242


Comments

Input




Filter <none>

Weight <none>

Split File <none>


Data File 800



For each dependent





treated as missing.

Cases Used






values.

Syntax

MEANS


shopping_score BY

Quality_of_online_shoppin

g_is_reliable

/CELLS COUNT MEAN

STDDEV.



243


Cases



e

r

c

e

n

t


ng_is_reliable 800 100.0% 0 0.0% 800

1

0

0

.

0

%



ng_is_reliable


Agree 510 38.6629 9.24000

Disagree 130 44.8498 6.72939

Strongly agree 50 41.4095 10.69260

Strongly disagree 110 34.3203 5.57740

Total 800 39.2429 9.05072


Comments

Input




Filter <none>

Weight <none>

Split File <none>


Data File 800

244





missing.

Cases Used





each table.

Syntax

CROSSTABS

/TABLES=Nature_of_wor

king_industry BY


evel

/FORMAT=AVALUE

TABLES

/STATISTICS=CHISQ

/CELLS=COUNT

/COUNT ROUND CELL.

Resources






Cases

Valid Missing Total


e

r

c

e

n

t


try *

overall_online_shopping_

level

800 100.0% 0 0.0% 800

1

0

0

.

0

%

245

Nature_of_working_industry * overall_online_shopping_level Crosstabulation

Count

Nature_of_working_industry overall_online_shopping_level T

o

t

a

l

High Low Medium

Academics 20 40 70

1

3

0

Banking/In

surance 10 50 140

2

0

0

IT sector 110 0 100

2

1

0

Others 10 30 220

2

6

0

Total 150 120 530

8

0

0

Chi-Square Tests


(2-sided)






246

Notes


Comments

Input


PH D\Data800.sav


Filter <none>

Weight <none>

Split File <none>

N of Rows in



Definition of Missing User-defined missing values

are treated as missing.

Cases Used

Statistics for each analysis are

based on cases with no missing

data for any variable in the

analysis.

Syntax

ONEWAY


BY Coded_working_pattern

/MISSING ANALYSIS.



ANOVA


Sum of

Squares


Between Groups 17558.557 3 5852.852 97.279 .000

Within Groups 47891.947 796 60.166

Total 65450.503 799

247

Notes


Comments

Input


D\Data800.sav


Filter <none>

Weight <none>

Split File <none>

N of Rows in



Definition of

Missing

For each dependent variable in a

table, user-defined missing values

for the dependent and all grouping

variables are treated as missing.

Cases Used

Cases used for each table have no

missing values in any independent

variable, and not all dependent

variables have missing values.

Syntax

MEANS

TABLES=Overall_online_shoppin

g_score BY


/CELLS COUNT MEAN

STDDEV.




Cases



er

c

e

nt

248


score *


try

800 100.0% 0 0.0% 800

1

0

0.

0

%



try


Academics 130 37.0989 9.64388


IT sector 210 46.9841 7.25619

Others 260 37.1648 6.70220

Total 800 39.2429 9.05072


Comments

Input




Filter <none>

Weight <none>

Split File <none>


Data File 800





missing.

Cases Used





each table.

249

Syntax

CROSSTABS

/TABLES=Nature_of_wor

king_industry BY


_level

/FORMAT=AVALUE

TABLES

/STATISTICS=CHISQ

/CELLS=COUNT

/COUNT ROUND CELL.

Resources






Cases

Valid Missing Total


e

r

c

e

n

t


try *


g_level

800 100.0% 0 0.0% 800

1

0

0

.

0

%

250

Nature_of_working_industry * overall_physical_shopping_level

Nature_of_working_industry overall_physical_shopping_level T

o

t

a

l

High Low Medium

Academics 0 20 110

1

3

0


2

0

0

IT sector 30 10 170

2

1

0

Others 50 30 180

2

6

0

Total 110 90 600

8

0

0

Chi-Square Tests


(2-sided)







Comments

Input




Filter <none>

Weight <none>

Split File <none>


Data File 800

251





missing.

Cases Used





Syntax

ONEWAY


_score BY

Coded_working_pattern

/MISSING ANALYSIS.



ANOVA


Sum of

Squares


Between Groups 426.789 3 142.263 5.304 .001

Within Groups 21349.157 796 26.821

Total 21775.946 799

Means


Comments

Input


D\Data800.sav


Filter <none>

Weight <none>

Split File <none>

N of Rows in


Missing Value Handling Definition of

Missing


table, user-defined missing values

for the dependent and all grouping


252

Cases Used





Syntax

MEANS

TABLES=Overall_physical_shopp

ing_score BY


/CELLS COUNT MEAN

STDDEV.




Cases



e

r

c

e

n

t


g_score *


try

800 100.0% 0 0.0% 800

1

0

0

.

0

%



try


Academics 130 61.3187 3.64461


IT sector 210 62.2313 5.29293

Others 260 62.6886 5.69792

Total 800 61.9000 5.22054

253


Comments

Input




Filter <none>

Weight <none>

Split File <none>


Data File 800





missing.

Cases Used





each table.

Syntax

CROSSTABS

/TABLES=Age_group

BY


_level

/FORMAT=AVALUE

TABLES

/STATISTICS=CHISQ

/CELLS=COUNT

/COUNT ROUND CELL.

Resources





254

Age_group * overall_physical_shopping_level Crosstabulation

Count


High Low Medium

Age_group

Elderly 10 30 150 190

Middle 30 30 280 340

Young 70 30 170 270

Total 110 90 600 800

Chi-Square Tests


(2-sided)






Notes


Comments

Input




Filter <none>

Weight <none>

Split File <none>


Data File 800





missing.

Cases Used





each table.

255

Syntax

CROSSTABS

/TABLES=Age_group

BY


evel

/FORMAT=AVALUE

TABLES

/STATISTICS=CHISQ

/CELLS=COUNT

/COUNT ROUND CELL.

Resources






Cases

Valid Missing Total


e

r

c

e

n

t

Age_group *


level

800 100.0% 0 0.0% 800

1

0

0

.

0

%

256

Age_group * overall_online_shopping_level Crosstabulation

Count


High Low Medium

Age_group

Elderly 30 60 100 190

Middle 100 10 230 340

Young 20 50 200 270

Total 150 120 530 800

Chi-Square Tests


(2-sided)







Comments

Input




Filter <none>

Weight <none>

Split File <none>


Data File 800





missing.

Cases Used





257

Syntax

ONEWAY


score BY Age_coded

/MISSING ANALYSIS.



ANOVA


Sum of

Squares


Between Groups 8663.533 2 4331.767 60.796 .000

Within Groups 56786.970 797 71.251

Total 65450.503 799

Mean


Comments

Input




Filter <none>

Weight <none>

Split File <none>


Data File 800



For each dependent





treated as missing.

Cases Used






values.

258

Syntax

MEANS


shopping_score BY

Age_group

/CELLS COUNT MEAN

STDDEV.




Cases


N Percent N Percent N Percent

Overall_online_shoppi

ng_score * Age_group 800 100.0% 0 0.0% 800 100.0%

Report



Elderly 190 36.7118 8.89512

Middle 340 43.0644 8.39386

Young 270 36.2116 8.16826

Total 800 39.2429 9.05072

Notes


Comments

Input




Filter <none>

Weight <none>

Split File <none>


Data File 800

259





missing.

Cases Used





each table.

Syntax

CROSSTABS

/TABLES=Age_group

BY


_level

/FORMAT=AVALUE

TABLES

/STATISTICS=CHISQ

/CELLS=COUNT

/COUNT ROUND CELL.

Resources






Cases

Valid Missing Total


e

r

c

e

n

t

Age_group *


g_level

800 100.0% 0 0.0% 800

1

0

0

.

0

%

260

Age_group * overall_physical_shopping_level Crosstabulation

Count


High Low Medium

Age_group

Elderly 10 30 150 190

Middle 30 30 280 340

Young 70 30 170 270

Total 110 90 600 800

Chi-Square Tests


(2-sided)







Comments

Input




Filter <none>

Weight <none>

Split File <none>


Data File 800





missing.

Cases Used





261

Syntax

ONEWAY


_score BY Age_coded

/MISSING ANALYSIS.



ANOVA


Sum of

Squares


Between Groups 621.369 2 310.685 11.705 .000

Within Groups 21154.576 797 26.543

Total 21775.946 799


Comments

Input




Filter <none>

Weight <none>

Split File <none>


Data File 800



For each dependent





treated as missing.

Cases Used






values.

262

Syntax

MEANS


l_shopping_score BY

Age_group

/CELLS COUNT MEAN

STDDEV.




Cases



e

r

c

e

n

t


g_score * Age_group 800 100.0% 0 0.0% 800

1

0

0

.

0

%

Report



Elderly 190 60.4010 4.15140

Middle 340 62.0840 4.75835

Young 270 62.7231 6.16433

Total 800 61.9000 5.22054

263

Crosstabs

Notes


Comments

Input




Filter <none>

Weight <none>

Split File <none>


Data File 800





missing.

Cases Used





each table.

Syntax

CROSSTABS

/TABLES=Qualification

BY


_score

/FORMAT=AVALUE

TABLES

/STATISTICS=CHISQ

/CELLS=COUNT

/COUNT ROUND CELL.

Resources





264


Cases

Valid Missing Total


e

r

c

e

n

t

Qualification *


g_score

800 100.0% 0 0.0% 800

1

0

0

.

0

%

Chi-Square Tests


(2-sided)




a. 203 cells (86.0%) have expected count less than 5. The


265

Notes


Comments

Input




Filter <none>

Weight <none>

Split File <none>


Data File 800





missing.

Cases Used





each table.

Syntax

CROSSTABS


BY


evel

/FORMAT=AVALUE

TABLES

/STATISTICS=CHISQ

/CELLS=COUNT

/COUNT ROUND CELL.

Resources





266


Cases

Valid Missing Total


e

r

c

e

n

t

Qualification *


level

800 100.0% 0 0.0% 800

1

0

0

.

0

%

Qualification * overall_online_shopping_level Crosstabulation

Count


High Low Medium

Qualification

Graduate 30 20 250 300


Professional 20 10 80 110


Total 150 120 530 800

267


Comments

Input


PH D\Data800.sav


Filter <none>

Weight <none>

Split File <none>

N of Rows in



Definition of Missing User-defined missing values are

treated as missing.

Cases Used

Statistics for each analysis are

based on cases with no missing

data for any variable in the

analysis.

Syntax

ONEWAY


BY CODED_Qualification

/MISSING ANALYSIS.



ANOVA


Sum of

Squares


Between Groups 521.780 3 173.927 2.132 .095

Within Groups 64928.724 796 81.569

Total 65450.503 799

268

Mean


Comments

Input


D\Data800.sav

Active

Dataset DataSet1

Filter <none>

Weight <none>

Split File <none>

N of Rows

in Working

Data File

800


Definition

of Missing

For each dependent variable in a table,

user-defined missing values for the

dependent and all grouping variables are

treated as missing.

Cases

Used

Cases used for each table have no missing

values in any independent variable, and

not all dependent variables have missing

values.

Syntax

MEANS

TABLES=overall_online_shopping_level

BY Qualification

/CELLS COUNT MEAN STDDEV.

Resources

Processor

Time 00:00:00.02

Elapsed

Time 00:00:00.01

269

Cases



e

r

c

e

n

t


level * Qualification 800 100.0% 0 0.0% 800

1

0

0

.

0

%

Report

overall_online_shopping_le

vel

Qualification N

Graduate 300

Post graduate 310

Professional 110

Undergraduate 80

Total 800

Chi-Square Tests


(2-sided)






270


Comments

Input


D\Data800.sav


Filter <none>

Weight <none>

Split File <none>

N of Rows in

Working Data

File

800


Definition of

Missing


table, user-defined missing values for

the dependent and all grouping


Cases Used





Syntax

MEANS

TABLES=Overall_online_shopping_s

core BY Qualification

/CELLS COUNT MEAN STDDEV.



271

Cases



e

r

c

e

n

t

Overall_online_shopping

_score * Qualification 800 100.0% 0 0.0% 800

1

0

0

.

0

%



Graduate 300 38.3111 7.12794

Post graduate 310 39.4716 10.79496

Professional 110 40.5887 8.14868


Total 800 39.2429 9.05072

272


Comments

Input




Filter <none>

Weight <none>

Split File <none>


Data File 800





missing.

Cases Used





each table.

Syntax

CROSSTABS


BY


_level

/FORMAT=AVALUE

TABLES

/STATISTICS=CHISQ

/CELLS=COUNT

/COUNT ROUND CELL.

Resources





273


Cases

Valid Missing Total


e

r

c

e

n

t

Qualification *


g_level

800 100.0% 0 0.0% 800

1

0

0

.

0

%

Qualification * overall_physical_shopping_level Crosstabulation

Count


High Low Medium

Qualification

Graduate 50 40 210 300


Professional 0 10 100 110


Total 110 90 600 800

Chi-Square Tests


(2-sided)






274

Notes


Comments

Input




Filter <none>

Weight <none>

Split File <none>


Data File 800





missing.

Cases Used





Syntax

ONEWAY


_score BY

CODED_Qualification

/MISSING ANALYSIS.



ANOVA


Sum of

Squares


Between Groups 1224.730 3 408.243 15.812 .000

Within Groups 20551.215 796 25.818

Total 21775.946 799

275

Mean

Notes


Comments

Input




Filter <none>

Weight <none>

Split File <none>


Data File 800



For each dependent





treated as missing.

Cases Used






values.

Syntax

MEANS


l_shopping_score BY

Qualification

/CELLS COUNT MEAN

STDDEV.



276

Case Processing

Summary

Cases



e

r

c

e

n

t


g_score * Qualification 800 100.0% 0 0.0% 800

1

0

0

.

0

%

Report



Graduate 300 62.2857 5.62094

Post graduate 310 62.7650 5.30355

Professional 110 60.6926 3.28222


Total 800 61.9000 5.22054

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