Post on 22-Mar-2020
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
8.8 Testing of Hypothesis – 4 163
8.9 Testing of Hypothesis – 5 168
8.10 Testing of Hypothesis – 6 176
8.11 Testing of Hypothesis – 7 182
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
8.7.3 ANOVA overall online shopping score
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
8.10.2 Chi-Square Tests 164
8.10.3 ANOVA overall online shopping score
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
8.11.2 Chi-Square Tests 169
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
8.13.2 Chi-Square tests 177
8.13.3 ANOVA overall online shopping score 178
8.13.4 Mean score overall online shopping 178
8.14.1 Age group overall physical shopping level cross
tabulation
179
8.14.2 Chi-Square Tests 180
8.14.3 ANOVA overall physical shopping score 181
8.14.4 Mean score overall physical shopping 181
8.15.1 Qualification overall online shopping level cross
tabulation
182
8.15.2 Chi-Square tests 183
8.15.3 ANOVA overall online shopping score 184
8.15.4 Mean score overall online shopping 185
8.16.1 Qualification physical shopping level crosstab 186
8.16.2 Chi-Square tests 187
8.16.3 ANOVA overall physical shopping score 188
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.
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
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
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
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
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
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
products in select tier1 cities.
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
shopping, Teleshopping) shopping pattern of FMCG products.
H14: There is association between quality of product and proportion of Online (E
shopping, Teleshopping) shopping pattern of FMCG products.
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
Online (E shopping, Teleshopping) and physical shopping pattern of FMCG products.
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
Online (E shopping, Teleshopping) and physical shopping pattern of FMCG products.
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)
17
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
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 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
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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.
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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.
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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.
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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
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.
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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
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 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.
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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.
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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.
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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.
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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
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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
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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
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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.
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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
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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
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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.
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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,
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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.
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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
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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.
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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.
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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
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.
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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.
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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.
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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
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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
114
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
115
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,
116
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.
117
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.
118
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.
119
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
products in select tier1 cities.
To study the significance of quality of products in shopping pattern of FMCG
products in select tier1 cities.
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.
120
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
shopping, Teleshopping) shopping pattern of FMCG products.
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
online (E shopping, Teleshopping )and physical shopping pattern of FMCG products.
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
online (E shopping, Teleshopping )and physical shopping pattern of FMCG products.
121
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
122
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.
123
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
124
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
125
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
127
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
131
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
considered. Response of all 800 respondents for these five products is recorded and
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
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:
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
Above table indicate that there are total 800 respondents out of which 214
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
presented in the following table no 8.3.2
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
presented in the following table no 8.3.3
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
Above table indicate that there are total 800 respondents out of which 120
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
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
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
Bangalore Delhi Hyderabad Mumbai
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
City N Mean Std. Deviation
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
Bangalore Delhi Hyderabad Mumbai
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
Hyderabad 18 12 90 120
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
presented in the following table no 8.6.5
Table No: 8.6.5 ANOVA
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
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 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
shown below chart no: 8.7.1
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
High Low Middle Very High
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
Above results indicate that Chi-square calculated value is 171.384 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
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
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 H2A is
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
Very High 10 0 70 80
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
High Low Middle Very High
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
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 presented in the following table no 8.8.4.
Table No: 8.8.4 ANOVA
Overall physical shopping score
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
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 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
Sig. (2-tailed) .000
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
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 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
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 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
shown below chart no: 8.11.2
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
Above results indicate that Chi-square calculated value is 274.575 which is less 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 presented in the following table.
2010
110
10
4050
0
30
70
140
100
220
0
50
100
150
200
250
Academics Banking/Insurance IT sector Others
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
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 H5A is
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
Banking/Insurance 200 60.9048 5.19552
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
Academics Banking/Insurance IT sector Others
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
Above results indicate that Chi-square calculated value is 40.594 which is less 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 presented in the following table.
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
Table No: 8.12.4 ANOVA
Overall physical shopping score
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
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 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
shopping. Above information is presented by using bar diagram as shown below chart
no 8.13.2
Chart No: 8.13.2 Online shopping age wise
To test the null hypothesis Chi square test is applied. Results of the test are as follows
0
50
100
150
200
250
Elderly Middle Young
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
Above results indicate that Chi-square calculated value is 117.946 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
presented in the following table.
Table No: 8.13.4 ANOVA
Overall online shopping score
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
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
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
Elderly Middle Young
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
shopping. Above information is presented by using bar diagram as shown below chart
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
Elderly Middle Young
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
To test the null hypothesis Chi square test is applied. Results of the test are as follows
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
Above results indicate that Chi-square calculated value is 30.74 which is less 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
presented in the following table no. 8.14.4
Table No. 8.14.4ANOVA
Overall physical shopping score
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
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 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
To test the null hypothesis Chi square test is applied. Results of the test are as follows
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
Above results indicate that Chi-square calculated value is 97.738 which is less 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 presented in the following table.
0
50
100
150
200
250
300
Graduate Post graduate Doctoral Undergraduate
High
Low
Medium
185
Table No. 8.15.4ANOVA
Overall online shopping score
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
Undergraduate 80 58.7619 3.95967
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
Graduate Post graduate Doctoral Undergraduate
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
Post graduate 60 20 230 310
Doctoral 0 10 100 110
Undergraduate 0 20 60 80
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
Graduate Post graduate Doctoral Undergraduate
High
Low
Medium
188
To test the null hypothesis Chi square test is applied. Results of the test are as follows
Table No. 8.16.3Chi-Square Tests
Value Degree of
Freedom
Significant Result
Pearson Chi-
Square 61.205 6 12.591
Rejected
Above results indicate that Chi-square calculated value is 30.74 which is less 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
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
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 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,
Teleshopping) shopping pattern
of FMCG products - Rejected
There is association between
level of income and proportion
of Online(E shopping,
Teleshopping ) shopping pattern
of FMCG -Accepted
Hypothesis
2B
There is no association between
level of income and proportion
of physical shopping pattern of
FMCG products - Rejected
There is association between
level of income and proportion
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,
Teleshopping ) shopping pattern
of FMCG products-Accepted
190
Hypothesis
4
There is no association between
quality of product and
proportion of Online(E
shopping, Teleshopping )
shopping pattern of FMCG
products-Accepted
There is association between
quality of product and proportion
of Online(E shopping,
Teleshopping ) shopping pattern
of FMCG products -Rejected
Hypothesis
5A
There is no association between
working women‟s occupation
and proportion of Online(E
shopping, Teleshopping )
shopping pattern of FMCG
products -Rejected
There is association between
working women‟s occupation
and proportion of Online(E
shopping, Teleshopping )
shopping pattern of FMCG
products. -Accepted
Hypothesis
5B
There is no association between
working women‟s occupation
and proportion of physical
shopping pattern of FMCG
products -Rejected
There is association between
working women‟s occupation
and proportion of physical
shopping pattern of FMCG
products. -Accepted
Hypothesis
6A
There is no association between
age of working women and of
Online (E shopping,
Teleshopping) shopping pattern
of FMCG products.- Rejected
There is association between age
of working women and of
Online (E shopping,
Teleshopping) shopping pattern
of FMCG products.-Accepted
Hypothesis
6B
There is no association between
age of working women and
proportional of physical
shopping pattern of FMCG
products - Rejected
There is association between age
of working women and physical
shopping pattern of FMCG
products.-Accepted
191
Hypothesis
7A
There is no association between
qualifications of working
women and proportion
Online(E shopping,
Teleshopping )and physical
shopping pattern of FMCG
products.–Accepted
There is association between
qualifications of working
women and proportion Online(E
shopping, Teleshopping )and
physical shopping pattern of
FMCG products - Rejected
Hypothesis
7B
There is no association between
qualifications of working
women and proportion of
physical shopping pattern of
FMCG products. - Rejected
There is association between
qualifications of working
women and proportion of
physical shopping pattern 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?
(please tick only one appropriate option)
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?
(please tick only one appropriate option)
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?
(please tick only one appropriate option)
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
product Never Sometimes Mostly Always
1 Salts & Seasonings
2 Cereals
3 Sugar
4 Edible Oil
5 Pulses
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
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
product Never Sometimes Mostly Always
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
tick only one appropriate option)
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
tick only one appropriate option)
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
Frequency Percent Valid Percent Cumulative
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
Frequency Percent Valid Percent Cumulative
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
Frequency Percent Valid Percent Cumulative
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
Frequency Percent Valid Percent Cumulative
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
overall_online_shopping_level Total
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
Value df Asymp. Sig.
(2-sided)
Pearson Chi-Square 117.946a 4 .000
Likelihood Ratio 128.628 4 .000
N of Valid Cases 800
a. 0 cells (.0%) have expected count less than 5. The minimum
expected count is 28.50.
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
Value df Asymp. Sig.
(2-sided)
Pearson Chi-Square 274.575a 6 .000
Likelihood Ratio 280.817 6 .000
N of Valid Cases 800
a. 0 cells (.0%) have expected count less than 5. The minimum
expected count is 19.50.
222
Monthly income overall online shopping level
Crosstab
Count
overall_online_shopping_level Total
High Low Medium
Monthly_income
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
Chi-Square Tests
Value df Asymp. Sig.
(2-sided)
Pearson Chi-Square 171.384a 6 .000
Likelihood Ratio 178.328 6 .000
N of Valid Cases 800
a. 0 cells (.0%) have expected count less than 5. The minimum
expected count is 12.00.
.
Mean
Output Created 18-JAN-2015 07:51:44
Comments
Input
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
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
Nature_of_working_indust
ry Monthly_income
/CELLS COUNT MEAN
STDDEV.
Resources Processor Time 00:00:00.02
Elapsed Time 00:00:00.02
Overall online shopping score
City N Mean Std. Deviation
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
Overall online shopping score
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
Overall online shopping score
Nature of working industry
Nature_of_working_indus
try
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
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
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
Data File 800
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.
225
Syntax
CROSSTABS
/TABLES=City
Age_group
Nature_of_working_indust
ry Monthly_income BY
overall_physical_shopping
_level
/FORMAT=AVALUE
TABLES
/STATISTICS=CHISQ
/CELLS=COUNT
/COUNT ROUND CELL.
Resources
Processor Time 00:00:00.03
Elapsed Time 00:00:00.02
Dimensions Requested 2
Cells Available 174762
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)
Pearson Chi-Square 39.717a 6 .000
Likelihood Ratio 41.907 6 .000
N of Valid Cases 800
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
overall_physical_shopping_level Total
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
Value df Sig. (2-sided)
Pearson Chi-Square 58.392a 4 .000
Likelihood Ratio 56.264 4 .000
N of Valid Cases 800
a. 0 cells (.0%) have expected count less than 5. The
minimum expected count is 21.38.
Nature of working_industry and overall physical shopping level
overall_physical_shopping_level T
o
t
a
l
High Low Medium
Nature_of_working_indus
try
Academics 0 20 110
1
3
0
Banking/Insurance 30 30 140
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
Value df Asymp. Sig.
(2-sided)
Pearson Chi-Square 40.594a 6 .000
Likelihood Ratio 59.538 6 .000
N of Valid Cases 800
a. 0 cells (.0%) have expected count less than 5. The
minimum expected count is 14.63.
Monthly income overall physical shopping level
Crosstab
Count
overall_physical_shopping_level Total
High Low Medium
Monthly_income
High 10 20 90 120
Low 30 40 230 300
Middle 60 30 210 300
Very High 10 0 70 80
Total 110 90 600 800
Chi-Square Tests
Value df Sig. (2-sided)
Pearson Chi-Square 30.724a 6 .000
Likelihood Ratio 38.895 6 .000
N of Valid Cases 800
a. 0 cells (.0%) have expected count less than 5. The
minimum expected count is 9.00.
228
Means
Notes
Output Created 18-JAN-2015 07:53:46
Comments
Input
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
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.
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_physica
l_shopping_score BY City
Age_group
Nature_of_working_indust
ry Monthly_income
/CELLS COUNT MEAN
STDDEV.
Resources Processor Time 00:00:00.03
Elapsed Time 00:00:00.03
229
Overall physical shopping score City wise
City N Mean Std. Deviation
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
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
Overall physical shopping score Nature of working industry
Nature_of_working_indus
try
N Mean Std. Deviation
Academics 130 61.3187 3.64461
Banking/Insurance 200 60.9048 5.19552
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
Output Created 18-JAN-2015 07:58:06
Comments
Input
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in
Working Data File 800
Missing Value Handling
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.
Resources Processor Time 00:00:00.03
Elapsed Time 00:00:00.02
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**
Sig. (2-tailed) .000
N 800 800
Overall_online_shopping_
score
Pearson Correlation -.264**
1
Sig. (2-tailed) .000
N 800 800
**. Correlation is significant at the 0.01 level (2-tailed).
Correlation
Notes
Output Created 18-JAN-2015 08:01:32
Comments
Input
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
Data File 800
Missing Value Handling
Definition of Missing
User-defined missing
values are treated as
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.
Resources Processor Time 00:00:00.02
Elapsed Time 00:00:00.02
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*
Sig. (2-tailed) .023
N 800 800
Overall_online_shopping_
score
Pearson Correlation .080* 1
Sig. (2-tailed) .023
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
Overall physical shopping score
City N Mean Std. Deviation
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
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
Data File 800
Missing Value Handling
Definition of Missing
User-defined missing
values are treated as
missing.
Cases Used Statistics are based on all
cases with valid data.
Syntax
FREQUENCIES
VARIABLES=overall_phy
sical_shopping_level
/ORDER=ANALYSIS.
Resources Processor Time 00:00:00.00
Elapsed Time 00:00:00.01
234
Overall_physical_shopping_level
Frequency Percent Valid Percent Cumulative
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
Output Created 24-FEB-2015 16:47:45
Comments
Input
Data C:\Users\User\Desktop\Roshni PH
D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in
Working Data
File
800
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 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
N Percent N Percent N P
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
Hyderabad 18 12 90 120
Mumbai 22 47 201 270
Total 110 90 600 800
Chi-Square Tests
Value df Asymp. Sig.
(2-sided)
Pearson Chi-Square 39.717a 6 .000
Likelihood Ratio 41.907 6 .000
N of Valid Cases 800
a. 0 cells (.0%) have expected count less than 5.
The minimum expected count is 22.50.
236
Notes
Output Created 24-FEB-2015 16:50:32
Comments
Input
Data C:\Users\User\Desktop\Ro
shni PH D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
Data File 800
Missing Value Handling
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
Overall_physical_shopping
_score BY Coded_city
/MISSING ANALYSIS.
Resources Processor Time 00:00:00.02
Elapsed Time 00:00:00.01
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
Output Created 24-FEB-2015 17:15:34
Comments
Input
Data C:\Users\User\Desktop\Ro
shni PH D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
Data File 800
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=Quality_of_onli
ne_shopping_is_reliable
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 00:00:00.01
Dimensions Requested 2
Cells Available 174762
238
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N P
e
r
c
e
n
t
Quality_of_online_shoppi
ng_is_reliable *
overall_online_shopping_l
evel
800 100.0% 0 0.0% 800
1
0
0
.
0
%
Quality_of_online_shopping_is_reliable * overall_online_shopping_level
Crosstabulation
Count
overall_online_shopping_level Total
High Low Medium
Quality_of_online_shoppi
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
Value df Asymp. Sig.
(2-sided)
Pearson Chi-Square 80.637a 6 .000
Likelihood Ratio 114.730 6 .000
N of Valid Cases 800
a. 0 cells (.0%) have expected count less than 5. The
minimum expected count is 7.50.
239
Crosstabs
Notes
Output Created 24-FEB-2015 17:18:59
Comments
Input
Data C:\Users\User\Desktop\Roshni
PH D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in
Working Data File 800
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.
CROSSTABS
/TABLES=Quality_of_online_
shopping_is_reliable BY
overall_online_shopping_level
/FORMAT=AVALUE
TABLES
/STATISTICS=CHISQ
/CELLS=COUNT
/COUNT ROUND CELL.
Processor Time 00:00:00.03
Elapsed Time 00:00:00.02
Dimensions
Requested 2
Cells Available 174762
240
Cases
Valid Missing Total
N Percent N Percent N P
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
overall_online_shopping_level Total
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
Value df Sig. (2-sided)
Pearson Chi-Square 80.637a 6 .000
Likelihood Ratio 114.730 6 .000
N of Valid Cases 800
b. 0 cells (.0%) have expected count less than 5.
The minimum expected count is 7.50.
241
Output Created 24-FEB-2015 17:21:32
Comments
Input
Data C:\Users\User\Desktop\Ro
shni PH D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
Data File 800
Missing Value Handling
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
Overall_online_shopping_
score BY Coded_Quality
/MISSING ANALYSIS.
Resources Processor Time 00:00:00.02
Elapsed Time 00:00:00.02
ANOVA
Overall_online_shopping_score
Sum of
Squares
df Mean Square F Sig.
Between Groups 7158.604 3 2386.201 32.585 .000
Within Groups 58291.899 796 73.231
Total 65450.503 799
242
Output Created 24-FEB-2015 17:22:07
Comments
Input
Data C:\Users\User\Desktop\Ro
shni PH D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
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.
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
Quality_of_online_shoppin
g_is_reliable
/CELLS COUNT MEAN
STDDEV.
Resources Processor Time 00:00:00.02
Elapsed Time 00:00:00.02
243
Case Processing Summary
Cases
Included Excluded Total
N Percent N Percent N P
e
r
c
e
n
t
Quality_of_online_shoppi
ng_is_reliable 800 100.0% 0 0.0% 800
1
0
0
.
0
%
Overall_online_shopping_score
Quality_of_online_shoppi
ng_is_reliable
N Mean Std. Deviation
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
Output Created 24-FEB-2015 17:25:49
Comments
Input
Data C:\Users\User\Desktop\Ro
shni PH D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
Data File 800
244
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=Nature_of_wor
king_industry 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 00:00:00.02
Dimensions Requested 2
Cells Available 174762
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N P
e
r
c
e
n
t
Nature_of_working_indus
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
Value df Asymp. Sig.
(2-sided)
Pearson Chi-Square 274.575a 6 .000
Likelihood Ratio 280.817 6 .000
N of Valid Cases 800
a. 0 cells (.0%) have expected count less than 5. The
minimum expected count is 19.50.
246
Notes
Output Created 24-FEB-2015 17:29:57
Comments
Input
Data C:\Users\User\Desktop\Roshni
PH D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in
Working Data File 800
Missing Value Handling
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
Overall_online_shopping_score
BY Coded_working_pattern
/MISSING ANALYSIS.
Resources Processor Time 00:00:00.02
Elapsed Time 00:00:00.02
ANOVA
Overall_online_shopping_score
Sum of
Squares
df Mean Square F Sig.
Between Groups 17558.557 3 5852.852 97.279 .000
Within Groups 47891.947 796 60.166
Total 65450.503 799
247
Notes
Output Created 24-FEB-2015 17:30:37
Comments
Input
Data C:\Users\User\Desktop\Roshni PH
D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in
Working 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.
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
Nature_of_working_industry
/CELLS COUNT MEAN
STDDEV.
Resources Processor Time 00:00:00.02
Elapsed Time 00:00:00.02
Case Processing Summary
Cases
Included Excluded Total
N Percent N Percent N P
er
c
e
nt
248
Overall_online_shopping_
score *
Nature_of_working_indus
try
800 100.0% 0 0.0% 800
1
0
0.
0
%
Overall_online_shopping_score
Nature_of_working_indus
try
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
Output Created 24-FEB-2015 17:31:47
Comments
Input
Data C:\Users\User\Desktop\Ro
shni PH D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
Data File 800
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.
249
Syntax
CROSSTABS
/TABLES=Nature_of_wor
king_industry BY
overall_physical_shopping
_level
/FORMAT=AVALUE
TABLES
/STATISTICS=CHISQ
/CELLS=COUNT
/COUNT ROUND CELL.
Resources
Processor Time 00:00:00.02
Elapsed Time 00:00:00.02
Dimensions Requested 2
Cells Available 174762
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N P
e
r
c
e
n
t
Nature_of_working_indus
try *
overall_physical_shoppin
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
Banking/Insurance 30 30 140
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
Value df Asymp. Sig.
(2-sided)
Pearson Chi-Square 40.594a 6 .000
Likelihood Ratio 59.538 6 .000
N of Valid Cases 800
a. 0 cells (.0%) have expected count less than 5. The
minimum expected count is 14.63.
Output Created 24-FEB-2015 17:33:19
Comments
Input
Data C:\Users\User\Desktop\Ro
shni PH D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
Data File 800
251
Missing Value Handling
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
Overall_physical_shopping
_score BY
Coded_working_pattern
/MISSING ANALYSIS.
Resources Processor Time 00:00:00.02
Elapsed Time 00:00:00.01
ANOVA
Overall_physical_shopping_score
Sum of
Squares
df Mean Square F Sig.
Between Groups 426.789 3 142.263 5.304 .001
Within Groups 21349.157 796 26.821
Total 21775.946 799
Means
Output Created 24-FEB-2015 17:34:06
Comments
Input
Data C:\Users\User\Desktop\Roshni PH
D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in
Working 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.
252
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_physical_shopp
ing_score BY
Nature_of_working_industry
/CELLS COUNT MEAN
STDDEV.
Resources Processor Time 00:00:00.02
Elapsed Time 00:00:00.02
Case Processing Summary
Cases
Included Excluded Total
N Percent N Percent N P
e
r
c
e
n
t
Overall_physical_shoppin
g_score *
Nature_of_working_indus
try
800 100.0% 0 0.0% 800
1
0
0
.
0
%
Overall_physical_shopping_score
Nature_of_working_indus
try
N Mean Std. Deviation
Academics 130 61.3187 3.64461
Banking/Insurance 200 60.9048 5.19552
IT sector 210 62.2313 5.29293
Others 260 62.6886 5.69792
Total 800 61.9000 5.22054
253
Output Created 24-FEB-2015 17:37:21
Comments
Input
Data C:\Users\User\Desktop\Ro
shni PH D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
Data File 800
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=Age_group
BY
overall_physical_shopping
_level
/FORMAT=AVALUE
TABLES
/STATISTICS=CHISQ
/CELLS=COUNT
/COUNT ROUND CELL.
Resources
Processor Time 00:00:00.02
Elapsed Time 00:00:00.02
Dimensions Requested 2
Cells Available 174762
254
Age_group * overall_physical_shopping_level Crosstabulation
Count
overall_physical_shopping_level Total
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
Value df Asymp. Sig.
(2-sided)
Pearson Chi-Square 58.392a 4 .000
Likelihood Ratio 56.264 4 .000
N of Valid Cases 800
a. 0 cells (.0%) have expected count less than 5. The
minimum expected count is 21.38.
Notes
Output Created 24-FEB-2015 17:40:44
Comments
Input
Data C:\Users\User\Desktop\Ro
shni PH D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
Data File 800
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.
255
Syntax
CROSSTABS
/TABLES=Age_group
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 00:00:00.02
Dimensions Requested 2
Cells Available 174762
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N P
e
r
c
e
n
t
Age_group *
overall_online_shopping_
level
800 100.0% 0 0.0% 800
1
0
0
.
0
%
256
Age_group * overall_online_shopping_level Crosstabulation
Count
overall_online_shopping_level Total
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
Value df Asymp. Sig.
(2-sided)
Pearson Chi-Square 117.946a 4 .000
Likelihood Ratio 128.628 4 .000
N of Valid Cases 800
a. 0 cells (.0%) have expected count less than 5. The
minimum expected count is 28.50.
Output Created 24-FEB-2015 17:41:26
Comments
Input
Data C:\Users\User\Desktop\Ro
shni PH D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
Data File 800
Missing Value Handling
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.
257
Syntax
ONEWAY
Overall_online_shopping_
score BY Age_coded
/MISSING ANALYSIS.
Resources Processor Time 00:00:00.02
Elapsed Time 00:00:00.03
ANOVA
Overall_online_shopping_score
Sum of
Squares
df Mean Square F Sig.
Between Groups 8663.533 2 4331.767 60.796 .000
Within Groups 56786.970 797 71.251
Total 65450.503 799
Mean
Output Created 24-FEB-2015 17:41:53
Comments
Input
Data C:\Users\User\Desktop\Ro
shni PH D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
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.
Cases Used
Cases used for each table
have no missing values in
any independent variable,
and not all dependent
variables have missing
values.
258
Syntax
MEANS
TABLES=Overall_online_
shopping_score BY
Age_group
/CELLS COUNT MEAN
STDDEV.
Resources Processor Time 00:00:00.02
Elapsed Time 00:00:00.01
Case Processing Summary
Cases
Included Excluded Total
N Percent N Percent N Percent
Overall_online_shoppi
ng_score * Age_group 800 100.0% 0 0.0% 800 100.0%
Report
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
Notes
Output Created 24-FEB-2015 17:42:32
Comments
Input
Data C:\Users\User\Desktop\Ro
shni PH D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
Data File 800
259
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=Age_group
BY
overall_physical_shopping
_level
/FORMAT=AVALUE
TABLES
/STATISTICS=CHISQ
/CELLS=COUNT
/COUNT ROUND CELL.
Resources
Processor Time 00:00:00.02
Elapsed Time 00:00:00.02
Dimensions Requested 2
Cells Available 174762
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N P
e
r
c
e
n
t
Age_group *
overall_physical_shoppin
g_level
800 100.0% 0 0.0% 800
1
0
0
.
0
%
260
Age_group * overall_physical_shopping_level Crosstabulation
Count
overall_physical_shopping_level Total
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
Value df Asymp. Sig.
(2-sided)
Pearson Chi-Square 58.392a 4 .000
Likelihood Ratio 56.264 4 .000
N of Valid Cases 800
a. 0 cells (.0%) have expected count less than 5. The
minimum expected count is 21.38.
Output Created 24-FEB-2015 17:43:32
Comments
Input
Data C:\Users\User\Desktop\Ro
shni PH D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
Data File 800
Missing Value Handling
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.
261
Syntax
ONEWAY
Overall_physical_shopping
_score BY Age_coded
/MISSING ANALYSIS.
Resources Processor Time 00:00:00.00
Elapsed Time 00:00:00.01
ANOVA
Overall_physical_shopping_score
Sum of
Squares
df Mean Square F Sig.
Between Groups 621.369 2 310.685 11.705 .000
Within Groups 21154.576 797 26.543
Total 21775.946 799
Output Created 24-FEB-2015 17:43:50
Comments
Input
Data C:\Users\User\Desktop\Ro
shni PH D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
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.
Cases Used
Cases used for each table
have no missing values in
any independent variable,
and not all dependent
variables have missing
values.
262
Syntax
MEANS
TABLES=Overall_physica
l_shopping_score BY
Age_group
/CELLS COUNT MEAN
STDDEV.
Resources Processor Time 00:00:00.02
Elapsed Time 00:00:00.01
Case Processing Summary
Cases
Included Excluded Total
N Percent N Percent N P
e
r
c
e
n
t
Overall_physical_shoppin
g_score * Age_group 800 100.0% 0 0.0% 800
1
0
0
.
0
%
Report
Overall_physical_shopping_score
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
263
Crosstabs
Notes
Output Created 24-FEB-2015 17:49:12
Comments
Input
Data C:\Users\User\Desktop\Ro
shni PH D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
Data File 800
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=Qualification
BY
Overall_physical_shopping
_score
/FORMAT=AVALUE
TABLES
/STATISTICS=CHISQ
/CELLS=COUNT
/COUNT ROUND CELL.
Resources
Processor Time 00:00:00.05
Elapsed Time 00:00:00.02
Dimensions Requested 2
Cells Available 174762
264
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N P
e
r
c
e
n
t
Qualification *
Overall_physical_shoppin
g_score
800 100.0% 0 0.0% 800
1
0
0
.
0
%
Chi-Square Tests
Value df Asymp. Sig.
(2-sided)
Pearson Chi-Square 2017.769a 174 .000
Likelihood Ratio 1737.083 174 .000
N of Valid Cases 800
a. 203 cells (86.0%) have expected count less than 5. The
minimum expected count is 1.00.
265
Notes
Output Created 24-FEB-2015 17:49:26
Comments
Input
Data C:\Users\User\Desktop\Ro
shni PH D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
Data File 800
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=Qualification
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 00:00:00.02
Dimensions Requested 2
Cells Available 174762
266
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N P
e
r
c
e
n
t
Qualification *
overall_online_shopping_
level
800 100.0% 0 0.0% 800
1
0
0
.
0
%
Qualification * overall_online_shopping_level Crosstabulation
Count
overall_online_shopping_level Total
High Low Medium
Qualification
Graduate 30 20 250 300
Post graduate 70 80 160 310
Professional 20 10 80 110
Undergraduate 30 10 40 80
Total 150 120 530 800
267
Output Created 24-FEB-2015 17:50:22
Comments
Input
Data C:\Users\User\Desktop\Roshni
PH D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in
Working Data File 800
Missing Value Handling
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
Overall_online_shopping_score
BY CODED_Qualification
/MISSING ANALYSIS.
Resources Processor Time 00:00:00.02
Elapsed Time 00:00:00.01
ANOVA
Overall_online_shopping_score
Sum of
Squares
df Mean Square F Sig.
Between Groups 521.780 3 173.927 2.132 .095
Within Groups 64928.724 796 81.569
Total 65450.503 799
268
Mean
Output Created 24-FEB-2015 17:52:55
Comments
Input
Data C:\Users\User\Desktop\Roshni PH
D\Data800.sav
Active
Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows
in Working
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.
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
Included Excluded Total
N Percent N Percent N P
e
r
c
e
n
t
overall_online_shopping_
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
Value df Asymp. Sig.
(2-sided)
Pearson Chi-Square 97.738a 6 .000
Likelihood Ratio 96.606 6 .000
N of Valid Cases 800
a. 0 cells (.0%) have expected count less than 5. The
minimum expected count is 12.00.
270
Output Created 24-FEB-2015 17:53:08
Comments
Input
Data C:\Users\User\Desktop\Roshni PH
D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in
Working 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.
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_s
core BY Qualification
/CELLS COUNT MEAN STDDEV.
Resources Processor Time 00:00:00.02
Elapsed Time 00:00:00.02
271
Cases
Included Excluded Total
N Percent N Percent N P
e
r
c
e
n
t
Overall_online_shopping
_score * Qualification 800 100.0% 0 0.0% 800
1
0
0
.
0
%
Overall_online_shopping_score
Qualification N Mean Std. Deviation
Graduate 300 38.3111 7.12794
Post graduate 310 39.4716 10.79496
Professional 110 40.5887 8.14868
Undergraduate 80 40.0000 9.06477
Total 800 39.2429 9.05072
272
Output Created 24-FEB-2015 17:54:05
Comments
Input
Data C:\Users\User\Desktop\Ro
shni PH D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
Data File 800
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=Qualification
BY
overall_physical_shopping
_level
/FORMAT=AVALUE
TABLES
/STATISTICS=CHISQ
/CELLS=COUNT
/COUNT ROUND CELL.
Resources
Processor Time 00:00:00.02
Elapsed Time 00:00:00.02
Dimensions Requested 2
Cells Available 174762
273
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N P
e
r
c
e
n
t
Qualification *
overall_physical_shoppin
g_level
800 100.0% 0 0.0% 800
1
0
0
.
0
%
Qualification * overall_physical_shopping_level Crosstabulation
Count
overall_physical_shopping_level Total
High Low Medium
Qualification
Graduate 50 40 210 300
Post graduate 60 20 230 310
Professional 0 10 100 110
Undergraduate 0 20 60 80
Total 110 90 600 800
Chi-Square Tests
Value df Asymp. Sig.
(2-sided)
Pearson Chi-Square 61.205a 6 .000
Likelihood Ratio 83.819 6 .000
N of Valid Cases 800
a. 0 cells (.0%) have expected count less than 5. The
minimum expected count is 9.00.
274
Notes
Output Created 24-FEB-2015 17:54:41
Comments
Input
Data C:\Users\User\Desktop\Ro
shni PH D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
Data File 800
Missing Value Handling
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
Overall_physical_shopping
_score BY
CODED_Qualification
/MISSING ANALYSIS.
Resources Processor Time 00:00:00.02
Elapsed Time 00:00:00.01
ANOVA
Overall_physical_shopping_score
Sum of
Squares
df Mean Square F Sig.
Between Groups 1224.730 3 408.243 15.812 .000
Within Groups 20551.215 796 25.818
Total 21775.946 799
275
Mean
Notes
Output Created 24-FEB-2015 17:54:59
Comments
Input
Data C:\Users\User\Desktop\Ro
shni PH D\Data800.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
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.
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_physica
l_shopping_score BY
Qualification
/CELLS COUNT MEAN
STDDEV.
Resources Processor Time 00:00:00.02
Elapsed Time 00:00:00.02
276
Case Processing
Summary
Cases
Included Excluded Total
N Percent N Percent N P
e
r
c
e
n
t
Overall_physical_shoppin
g_score * Qualification 800 100.0% 0 0.0% 800
1
0
0
.
0
%
Report
Overall_physical_shopping_score
Qualification N Mean Std. Deviation
Graduate 300 62.2857 5.62094
Post graduate 310 62.7650 5.30355
Professional 110 60.6926 3.28222
Undergraduate 80 58.7619 3.95967
Total 800 61.9000 5.22054