IMPACT AND EFFECTIVENESS OF SOCIAL
MEDIA ADVERTISING ON YOUNG WORKING
WOMEN’S BUYING BEHAVIOUR WITH
REFERENCE TO CONSUMER ELECTRONICS - A
STUDY OF SELECTED CITIES IN
MAHARASHTRA AND GUJARAT.
THESIS SUBMITTED TO D.Y.PATIL UNIVERSITY, DEPARTMENT
OF BUSINESS MANAGEMENT IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE AWARD OF THE DEGREE OF
DOCTOR OF PHILOSOPHY
IN
BUSINESS MANAGEMENT
SUBMITTED BY
MS. SHALAKA AYAREKAR
(ENROLLMENT NO. DYP-PhD-116100013)
RESEARCH GUIDE
DR. R. GOPAL
DIRECTOR AND HEAD OF DEPARTMENT
D.Y.PATIL UNIVERSITY,
DEPARTMENT OF BUSINESS MANAGEMENT
SECTOR 4, PLOT NO. 10,
C.B.D. BELAPUR, NAVI MUMBAI - 400614.
FEBRUARY 2015
1
Impact and effectiveness of social media advertising
on young working women’s buying behaviour with
reference to consumer electronics – A study of
selected cities in Maharashtra and Gujarat.
2
DECLARATION
I hereby declare that the thesis entitled “Impact and effectiveness of Social media
advertising on young working women’s buying behaviour with reference to
consumer electronics – A study of selected cities in Maharashtra and Gujarat”
submitted for the Award of Doctor of Philosophy in Business Management at the
D.Y.Patil University, Department of Business 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 and incorporated in the thesis has been
duly acknowledged.
I understand that I myself could be held responsible and accountable for plagiarism, if
any, detected later on.
The research papers published based on the research conducted out of an in the course
of the study are also based on the study and not borrowed from other sources.
Place : Navi Mumbai
Date :
Signature of the candidate
Enrollment No.: DYP-PhD-116100013
3
CERTIFICATE
This is to certify that the thesis entitled “Impact and effectiveness of social media
advertising on young working women’s buying behaviour with reference to consumer
electronics - A study of selected cities in Maharashtra and Gujarat” and submitted by
Ms. Shalaka Ayarekar is a bonafide research work for the award of the Doctor of
Philosophy in Business Management at the D.Y.Patil University Department Of
Business Management in partial fulfilment of the requirements for the award of the
Degree of Doctor of Philosophy in Business Management and that the thesis 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 thesis represents an independent work on the part of the
candidate.
Place : Navi Mumbai.
Date :
Signature of the Head of the Department
Signature of the Guide
4
ACKNOWLEDGEMENT
I am grateful to D.Y.Patil University, Department of Business Management for
having given me an opportunity of career enhancement by carrying out the current
research work.
I am extremely thankful to my guide, mentor, philosopher Dr. R. Gopal for having
guided me with his valuable inputs and extending all his support throughout my
research work. Without his able and valuable guidance and support this would not
have been possible.
I would also like to thank my colleagues, the IT Lab staff especially Mr. Sandeep
Surve, librarian Ms. Vanda Salgaonkar and the administration staff for helping me
wherever needed.
I would also like to thank Prof. Dr. Pradip Manjrekar, for his guidance.
I sincerely thank my mother, father and brother for their limitless support and for
always being a source of encouragement to me.
Lastly I also thank everyone who have been directly and indirectly instrumental in the
completion of my dissertation.
Place : Navi Mumbai.
Date :
Signature of the candidate
5
CONTENTS
Chapter
Section
Title
Page No.
List of Tables
10
List of Figures
35
List of Abbreviations
36
Executive Summary
37
Introduction
58
1.1
Concept of Social Media, Advertising,
Advertising on Social Networking sites and
Consumer Behaviour
58
1.2
Origin Of Social Media
60
1.3
Popularity of Social Media
60
1.4
Advertising
61
1.5
Social Media Advertising
62
1.6
Consumer Buying Behaviour
65
1.7
Online Consumer’s Buying Behaviour
67
1.8
Women and Social Network Sites(SNSs)
68
1.9
Consumer Electronics and Social Media
68
Review of Literature
71
Literature Gap
100
Objectives, Hypothesis and Research
Methodology
101
3.1.
Statement of the Problem
101
3.2.
Objectives of Study
102
3.3.
Hypothesis
102
3.4.
Research Methodology
104
3.4.1.
Type of Study
104
3.4.2.
Data Collection
104
3.4.3.
Pilot Study
105
1
2
2.1
3
6
3.4.4.
Reliability
105
3.4.5.
Questionnaire
105
3.4.6.
Size and Design of Sample
107
3.4.7.a.
Sampling Technique
107
3.4.7.b.
Sample Size Calculation
107
3.4.8.
Variables of the study
108
3.4.9.
Definition of the Variables
109
3.5.
Limitations of the Study
111
3.6.
Utility of the study
111
3.7.
Theoretical Model
111
3.8.
Analysis of Data
112
Typical Aspects of Social Media and Social
Networking sites
113
4.1
Typical Aspects of Social Media
113
4.2
Social Networking Sites
114
4.2.1.
Face-book
114
4.2.1.1.
Origin of Facebook
114
4.2.1.2.
Number of Users on Facebook
115
4.2.1.3.
Face book’s Revenue
115
4.2.1.4.
Advantages of Facebook
115
4.2.1.5.
Disadvantages of Facebook
117
4.2.2.
Twitter
118
4.2.2.1.
Origin of Twitter
118
4.2.2.2.
Number of Users on Twitter
120
4.2.2.3.
Revenue of Twitter
121
4.2.2.4.
Advantages of Twitter
121
4.2.2.5.
Disadvantages of Twitter
122
4.2.3.
LinkedIn
122
4.2.3.1
Origin of LinkedIn
122
4
7
4.2.3.2
Status of LinkedIn Today
122
4.2.3.3.
Revenue of LinkedIn
123
4.2.3.4.
Benefits LinkedIn Brings for Business
123
4.2.4.
YouTube
125
4.2.4.1.
Origin of YouTube
125
4.2.4.2.
Number of users accessing YouTube
126
4.2.5.
RSS
126
4.2.5.1.
Working of RSS
126
4.2.5.2.
Benefits of RSS
127
4.2.6.
SlideShare
127
4.2.6.1.
Users of Slideshare
128
4.2.7.
Myspace
128
4.2.8.
Friendster
128
Consumer Electronics Companies and their
presence on Social Media
129
5.1
Global Players
129
5.1.1
Samsung
129
5.1.2.
Apple
130
5.1.3.
Sony
130
5.1.4
Hewlett-Packard
132
5.1.5
LG
135
5.2
Indian Players
136
5.2.1
Hindustan Computers Limited (HCL)
136
5.2.2
TCS
136
5.2.3
Wipro
137
Study of Consumer Electronics Market
138
Market Research and Market Share of Global
138
5
6
6.1.
8
Consumer Electronics Industry
6.1.1
Geographic trends in Global Consumer
Electronics Markets
140
6.1.2
Trends based on product preferences in Global
Consumer Electronics Market
140
6.2
Market Share of Indian Consumer Electronics
Industry
140
Data Analysis and Findings
142
7.1
Tabulation and Statistical Analysis of Data
142
7.2
Summary of the Analysis
184
7.3
Summary of Hypothesis
207
8
Conclusion
213
9
Recommendation and Suggestion
225
9.1
Recommendation and Suggestion
225
9.2
Future scope of research
226
Annexure
227
1
Bibliography
227
1.1
Webliography
234
2
Questionnaire
236
3
Descriptive Analysis (SPSS Output)
244
4
Inferential Analysis in Detail
306
7
9
LIST OF TABLES
Table No.
Title
3.1.
Table showing details of questionnaire
Page
No.
106
3.2.
Table on Chosen Sample Size
108
3.3.
Table showing the variables of the study
108
4.1.
Table showing the number of users on Twitter
120
4.2
Table showing the year-wise revenue of Twitter
121
7.1.1.
Table showing the number of young working women 143
accessing or using social networking sites in Mumbai,
Nashik and Surat.
7.1.2.
Table showing the number of young working women 144
accessing or using “Facebook” in Mumbai, Nashik and
Surat.
7.1.3.
Table Showing the frequency with which the young 146
working women access SNS in a week in Mumbai,
Nashik and Surat.
7.1.4.
Table Showing the time the young working women 147
spend each time they access Facebook in Mumbai,
Nashik and Surat.
7.1.5.
Table Showing whether the young working women 148
share their opinion about a particular product or
service with your family or friends by writing reviews
or blogs in Mumbai, Nashik and Surat.
7.1.6.
Table Showing the number of times the young working 150
women provided online rating in one year in Mumbai,
10
Nashik and Surat.
7.2.1.1.m.a.
Table
Relationship
behaviour
with
the
between
factor
consumer
of
social
buying 152
media
advertisement i.e positive reactions/feelings towards
advertisements displayed on SNS in Mumbai.
7.2.1.1.m.b.
Table of Symmetric Measures to show how much 153
relationship exists in between consumer buying
behaviour and the factor of social media advertising i.e
positive
reactions/feelings
towards
advertisements
displayed on SNS in Mumbai.
7.2.1.1.n.a.
Table
Relationship
behaviour
with
the
between
factor
consumer
of
social
buying 154
media
advertisement i.e positive reactions/feelings towards
advertisements displayed on SNS in Nashik.
7.2.1.1.n.b.
Table of Symmetric Measures to determine how much 155
relationship exists in between consumer buying
behaviour and the factor of social media advertising i.e
positive
reactions/feelings
towards
advertisements
displayed on SNS in Nashik.
7.2.1.1.s.a.
Table
Relationship
behaviour
with
the
between
factor
consumer
of
social
buying 156
media
advertisement i.e positive reactions/feelings towards
advertisements displayed on SNS in Surat.
7.2.2.1.m.a.
7.2.2.1.n.a.
Table Relationship between online purchase
158
behaviour with the factor of social media advertising
i.e positive reactions/feelings towards the
advertisements displayed on SNS in Mumbai.
Table
Relationship between online purchase 159
behaviour with the factor of social media advertising
i.e
positive
reactions/feelings
towards
the
advertisements displayed on SNS in Nashik.
11
7.2.2.1.n.b.
Table of Symmetric Measures to determine how much 160
relationship
exists
in
between
online
purchase
behaviour and factor of social media advertising i.e
positive reactions/feelings towards the advertisements
displayed on SNS in Nashik.
7.2.2.1.s.a.
Table
Relationship
between
online
purchase 161
behaviour with the factor of social media advertising
i.e
positive
reactions/feelings
towards
the
advertisements displayed on SNS in Surat.
7.2.2.1.s.b.
Table of Symmetric Measures to determine how much 162
relationship
exists
in
between
online
purchase
behaviour and factor of social media advertising i.e
positive reactions/feelings towards the advertisements
displayed on SNS in Surat.
7.2.3.1.m.a.
Table
behaviour
Relationship
with
the
between
factor
complex
i.e.
buying 163
“positive
reactions/feelings towards advertisements displayed on
it” of social media advertising in Mumbai.
7.2.3.1.m.b.
Table of Symmetric Measures to determine how much 164
relationship exists between complex buying behaviour
and the factor i.e. “positive reactions/feelings towards
advertisements displayed on it”
of social media
advertising in Mumbai.
7.2.3.1.n.a.
Table
behaviour
Relationship
with
the
between
factor
complex
i.e.
buying 165
“positive
reactions/feelings towards advertisements displayed on
it” of social media advertising in Nashik.
7.2.3.1.n.b.
Table of Symmetric Measures to determine how much 166
relationship exists between complex buying behaviour
and the factor i.e. “positive reactions/feelings towards
advertisements displayed on it”
of social media
12
advertising in Nashik.
7.2.3.1.s.a.
7.2.3.1.s.b.
Table
Relationship between complex buying 167
behaviour
with
the
factor
i.e.
“positive
reactions/feelings towards advertisements displayed on
it” of social media advertising in Surat.
Table of Symmetric Measures to determine how much 168
relationship exists between complex buying behaviour
with the factor i.e. “positive reactions/feelings towards
advertisements displayed on it”
of social media
advertising in Surat.
7.2.4.1.m.
Table showing relationship between all the factors of 169
habitual buying behaviour and all the
factors
of
social media advertising in Mumbai.
7.2.4.1.n.
Table showing relationship between all the factors of 170
habitual buying behaviour and all the
factors
of
social media advertising in Nashik.
7.2.4.1.s.
Table showing relationship between all the factors of 171
habitual buying behaviour and all the
factors
of
social media advertising in Surat.
7.3.1.m.a.
Table of Model Summary for Mumbai
173
7.3.1.m.b.
Table for Anova to determine the level of significance 173
of R2
7.3.1.m.c.
Table for significance of Coefficients
175
7.4.m.1.
Table Showing effectiveness of SNSs in terms of 176
Audience in Mumbai
7.4.m.2.
Table Showing effectiveness of SNSs in terms of 177
Targeting consumers in Mumbai.
7.4.m.3.
Table Showing effectiveness of SNSs in terms of more 178
followers due to acquaintances in Mumbai.
13
7.4.m.4.
Table Showing effectiveness of SNSs in terms of more 178
unknown followers in Mumbai.
7.5.I.m.
Table Relationship between qualification of young 180
working women and impact of social media advertising
in Mumbai .
7.5.II.s.a.
Table Relationship between annual income of young 181
working women and impact of social media advertising
in Surat.
7.5.II.s.b.
Table To determine how much relationship exists 181
between annual income of young working women and
impact of social media advertising in Surat.
7.5.III.n.a.
Table Relationship between occupation of young 182
working women and impact of social media advertising
in Nashik.
7.5.III.n.b.
Table To determine how much relationship exists 183
between occupation of young working women and
impact of social media advertising in Nashik.
7.6
Tabular representation of details of Analysis and 184
results.
7.7
Tabular
representation
showing
Hypothesis 207
(Accepted/Rejected).
10.1.1.
Education
244
10.1.2.
Annual income
244
10.1.3.
Occupation
245
10.1.4.
Place
245
14
10.2.1.
Frequency of accessing internet
10.2.2.
Table
246
showing number of women using social 246
networking sites.
10.2.2.1.
Table showing number of women who use facebook.
247
10.2.2.2.
Table showing number of women who use Twitter.
247
10.2.2.3.
Table showing number of women who use LinkedIn.
248
10.2.2.4.
Table showing number of women who use other SNSs.
248
Table showing the frequency of using SNS within a 249
10.2.5.
week among working women.
10.2.5.1.
Table showing the number of women not using SNS 250
because they are not interested.
10.2.5.2.
Table showing the number of women not using SNS 250
because of security concerns.
10.2.5.3.
Table showing the number of women not using SNS 251
because of Non Availability of Enough time.
10.2.5.4.
Table showing the number of women not using SNS 251
because they prefer face to face interactions
10.2.5.5.
Table showing the number of women not using SNS 252
because of Lack of computer skills.
10.2.5.6.
Table showing the number of women not using SNS 252
because they Prefer to use phone for interaction.
10.2.6.1.
Table showing the time spent by working women for 253
using Facebook each time they access SNS.
10.2.6.2.
Table showing the time spent by working women for 253
using LinkedIn each time they access SNS.
10.2.6.3.
Table showing the time spent by working women for 254
15
using Twitter each time they access SNS.
10.2.6.4.
Table showing the time spent by working women for 255
using other SNS(other than facebook, twitter &
LinkedIn) each time they access SNS.
10.2.7.
Table showing the number of women who have 255
increased, decreased or spent about the same amount
of time using the social networking site compared to
last year.
10.2.8.
Table showing the number of women who think the 256
time that they are spending currently on the social
networking sites for product information search, is
about right, too much or not enough.
10.2.9.
Table showing the number of women who in the next 257
twelve months will be increasing, decreasing or
spending the same amount of time using social
networking sites for product information search
compared to the last year.
10.2.10.
Table showing the number of women who share their 257
opinion about a particular product or service with
your family or friends by writing reviews or blogs.
10.2.11.
Table showing the number of women who share their 258
feedback about a particular product or service with
the organization/company.
10.2.12.
Table
showing the number of women who visit 258
company website and provide a particular rating for a
particular product or service.
16
10.2.13.
Table showing the number of times women have you 259
provided online rating in one year for a particular
product or service.
10.2.14.
Table showing whether working women send the 260
company link of their favourite brand to their family
and friends.
10.3.1.
Table
showing the number of women who have 260
purchased consumer electronic items through social
media.
10.3.2.
Table showing the list of reasons due to which women 261
purchased consumer electronic items through social
media.
10.3.3.1.
Table showing the number of women providing online 262
rating to music players.
10.3.3.2.
Table showing the number of women providing online 262
rating to Television set.
10.3.3.3.
Table showing the number of women providing online 263
rating to Video Recorder.
10.3.3.4.
Table showing the number of women providing online 263
rating to DVD Players.
10.3.3.5.
Table showing the number of women providing online 264
rating to Digital Cameras.
10.3.3.6.
Table showing the number of women providing online 264
rating to Personal computers/Laptops.
10.3.3.7.
Table showing the number of women providing online 265
rating to Telephone Instruments.
17
10.3.3.8.
Table showing the number of women providing online 265
rating to Mobile Phones.
10.3.3.9.
Table showing the number of women providing online 266
rating to Video Games Console
10.3.3.10.
Table showing the number of women providing online 266
rating to Camcorders
10.3.4.
Table showing the number of women who read blogs 267
or online reviews about a product or service before
making buying decision
10.4.1.
Table showing the number of women who are 267
personally involved in making a buying decision.
10.4.2.
Table showing the number of women who think there 268
is any difference between the products of different
brands.
10.4.3.
Table showing the women’s opinion towards the price 268
of the branded product.
10.4.4.
Table showing women’s perception regarding the time 269
consumed in taking the buying decision, about a
particular product whose advertisement they have
viewed on any social networking sites.
10.5.1.
Table showing the type of product information search 270
conducted on social media by women before actual
buying.
10.5.2.
Table showing how frequently the women pay 271
attention to the advertisements of consumer electronic
products on social networking sites
18
10.5.3.
Table showing the amount of time and efforts the 272
women spend on researching
for the
product
information on the network before actual online
purchase.
10.5.4.
Table showing the number of online electronic stores 273
visited on an average by women before making the
buying decision.
10.5.5.a.
Table showing the number of women who consider the 273
Physical Appearance of the product while taking the
buying decision of consumer electronic product
through a social networking site .
10.5.5.b.
Table
showing the number of women who consider 274
the feature of Availability of a variety of functions in
the product while taking the buying decision of
consumer
electronic
product
through
a
social
networking site .
10.5.5.c.
Table showing the number of women who consider the 275
price of the product while taking the buying decision of
consumer
electronic
product
through
a
social
networking site .
10.5.5.d.
Table showing the number of women who consider the 275
quality of the product while taking the buying decision
of consumer electronic product through a social
networking site .
10.5.5.e.
Table showing the number of women who consider the 276
popularity of the product while taking the buying
decision of consumer electronic product through a
social networking site .
19
10.5.5.f.
Table showing the number of women who consider the 277
Association with a particular brand for the product
while taking the buying decision of consumer
electronic product through a social networking site .
10.5.5.g.
Table showing the number of women who consider 277
none of the mentioned features of the product while
taking the buying decision of consumer electronic
product through a social networking site .
10.5.6.
Table showing the number of women who compare 278
different electronic products available in retail store by
physically visiting the stores in the market before
making a final online purchase.
10.6.1.
Table showing the number of women who buy the 279
product because they buy it regularly
10.6.2.
Table showing the number of women who buy the 280
product because they think that the product is best fit
for them.
10.7.1.
Table showing the number of women who buy the 280
product because they wanted to try out a different
variety of product, belonging to a different brand .
10.7.2.
Table showing the number of women who like to buy a 281
new variety of product belonging to a new brand; each
time they make a purchase-decision after viewing an
advertisement on social networking site
10.7.3.
Table showing the number of women who agree that 282
20
the different brands of the same product serve, one
and the same purpose
10.8.1.
Table showing the number of women who agree that 283
taking a buying decision of an expensive electronic
product is difficult and needs a lot of thinking.
10.8.2.
Table showing the number of women who agree that 284
taking a buying decision of an expensive electronic
product is time consuming.
10.8.3.
Table showing the number of women who agree that 284
they have the feeling of anxiety that whether their
purchase decision is correct.
10.9.1.
Table showing the number of women who agree that 285
they had no plans of buying any consumer electronic
products when they logged on a social networking site.
10.9.2.
Table showing the number of women who agree that 286
the advertisement of the product on the social
networking site provokes their purchase intentions.
10.9.3.
Table showing the number of women who agree that at 287
times they buy a product just because they find the
discount scheme displayed in the advertisement on the
social networking site interesting and not available in
the retail stores.
10.10.1.A
Table showing the number of women liking anyone 288
SNS from Facebook, Twitter and LinkedIn the most.
10.10.1.B.
Table showing the number of women according to 288
whom the most useful SNS is anyone from Facebook,
Twitter and LinkedIn.
10.10.1.C.
Table showing the number of women according to 289
whom the most preferable SNS to use is anyone from
Facebook, Twitter and LinkedIn.
21
10.10.1.D.
Table showing the number of women according to 289
whom the most user-friendly SNS is anyone from
Facebook, Twitter and LinkedIn.
10.10.1.E.
Table showing the number of women according to 290
whom the most striking SNS is anyone from Facebook,
Twitter and LinkedIn.
10.10.2.A.
Table showing the Facebook ratings for having a large 290
number of groups (networks) available for any
demographics you are looking for; for instance group
of teenagers, group of kids etc provided by working
women.
10.10.2.B.
Table showing the Twitter ratings for having a large 291
number of groups (networks) available for any
demographics you are looking for; for instance group
of teenagers, group of kids etc provided by working
women.
10.10.2.C.
Table showing the LinkedIn ratings for having a large 292
number of groups (networks) available for any
demographics you are looking for; for instance group
of teenagers, group of kids etc. provided by working
women.
10.10.3.A.
Table showing the Facebook ratings according to the 293
way they are targeting the advertisements to specific
group of audience provided by working women.
10.10.3.B.
Table showing the Twitter ratings according to the 294
way they are targeting the advertisements to specific
group of audience, provided by working women.
10.10.3.C.
Table showing the LinkedIn ratings according to the 295
way they are targeting the advertisements to specific
group of audience, provided by working women.
10.10.4.A.
Table showing the number of women who select the 295
site having more followers due to acquaintances
10.10.4.B.
Table showing the number of women who select the 296
22
site having more unknown .
10.11.1.A.1.
Table showing the number of women who have 297
positive
reactions/feelings
towards
advertisements
displayed on Facebook.
10.11.1.A.2.
Table showing the number of women who have 297
positive
reactions/feelings
towards
advertisements
displayed on Twitter.
10.11.1.A.3.
Table showing the number of women who have
298
positive reactions/feelings towards advertisements
displayed on LinkedIn.
10.11.1.B.1.
Table showing the number of women who think 298
advertisements displayed on Facebook are appealing.
10.11.1.B.2.
Table showing the number of women who think
299
advertisements displayed on Twitter are appealing.
10.11.1.B.3.
Table showing the number of women who think
299
advertisements displayed on LinkedIn are appealing.
10.11.1.C.1.
Table showing the number of women who find the
300
visuals and slogans of the advertisements displayed on
Facebook memorable.
10.11.1.C.2.
Table showing the number of women who find the
300
visuals and slogans of the advertisements displayed on
Twitter memorable.
10.11.1.C.3.
Table showing the number of women who find the
301
visuals and slogans of the advertisements displayed on
LinkedIn memorable.
10.11.1.D.1.
Table showing the number of women who find the
302
product advertisement displayed on Facebook
memorable.
10.11.1.D.2.
Table showing the number of women who find the
302
product advertisement displayed on Twitter
memorable.
10.11.1.D.3.
Table showing the number of women who find the
303
23
product advertisement displayed on LinkedIn
memorable.
10.11.1.E.1.
Table showing the number of women who trust the
303
product advertisement displayed on Facebook.
10.11.1.E.2.
Table showing the number of women who trust the
304
product advertisement displayed on Twitter.
10.11.1.E.3.
Table showing the number of women who trust the
304
product advertisement displayed on LinkedIn.
10.11.2.
Table showing the number of times the working
305
women have seen an advertisement for consumer
electronics on SNS in the time they spend on Social
networking site .
10.11.3.
Table showing the number of working women who 306
were satisfied with the actual product which they
purchased after watching the advertisement on any of
the social networking sites.
10.12.1.
Table Showing the frequency with which the young
307
working women access internet in Mumbai, Nashik
and Surat.
10.12.4.
Table Showing the number of young working women
309
accessing or using “Twitter” in Mumbai, Nashik and
Surat.
10.12.5.
Table Showing the number of young working women
310
accessing or using “LinkedIn” in Mumbai, Nashik and
Surat.
10.12.6
Table Showing the number of young working women
311
accessing or using “Other SNSs” in Mumbai, Nashik
and Surat.
10.12.8.
Table Showing the number of young working women
312
not accessing SNS for the reason lack of interest in
Mumbai, Nashik and Surat.
Table Showing the number of young working women
313
24
10.12.9.
not accessing SNS for the reason of security concerns
in Mumbai, Nashik and Surat.
10.12.10.
Table Showing the number of young working women
315
not accessing SNS for the reason of Non Availability of
Enough time in Mumbai, Nashik and Surat.
10.12.11.
Table Showing the number of young working women
316
not accessing SNS for the reason of more Prefering
face to face interactions in Mumbai, Nashik and Surat.
10.12.12.
Table Showing the number of young working women
317
not accessing SNS for the reason of Lack of computer
skills in Mumbai, Nashik and Surat.
10.12.13.
Table Showing the number of young working women
319
not accessing SNS for the reason of Prefering to use
phone for interaction in Mumbai, Nashik and Surat.
10.12.15.
Table Showing the time the young working women
320
spend each time they access LinkedIn in Mumbai,
Nashik and Surat.
10.12.16.
Table Showing the time the young working women
321
spend each time they access Twitter in Mumbai,
Nashik and Surat.
10.12.17.
Table Showing the time the young working women
322
spend each time they access Other SNS in Mumbai,
Nashik and Surat.
10.12.18.
Table Showing whether the young working women has
323
increased or decreased the time they spend using SNS
in Mumbai, Nashik and Surat.
10.12.19.
Table Showing whether the amount of time spent by
325
young working women for product information search
is right, too much or not enough the time they spent
using SNS in Mumbai, Nashik and Surat.
10.12.20.
Table Showing whether the young working women 326
will be increasing, decreasing or spending the same
amount of time using social networking sites for
25
product information search as compared to the last
year in Mumbai, Nashik and Surat.
10.12.22.
Table Showing whether the young working women
328
share their feedback about a product or service with
the organization /Company in Mumbai, Nashik and
Surat.
10.12.23.
Table Showing whether the young working women
329
visit company website and provide a particular rating
for a particular product or service in Mumbai, Nashik
and Surat.
10.12.25.
Table Showing whether the young working women
331
send the company link of their favourite brand to their
family and friends in Mumbai, Nashik and Surat.
10.13.1.2.m.a. Table Relationship between consumer buying
333
behaviour with the appealing factor of social media
advertisement towards advertisements displayed on
SNS in Mumbai.
10.13.1.2.m.b. Table of Symmetric Measures to determine how much
334
relationship exists in between consumer buying
behaviour and the appealing factor of social media
advertising towards advertisements displayed on SNS
in Mumbai.
10.13.1.2.n.a.
Table Relationship between consumer buying
335
behaviour with the appealing factor of social media
advertisement towards advertisements displayed on
SNS in Nashik.
10.13.1.2.s.a.
Table Relationship between consumer buying
336
behaviour with the appealing factor of social media
advertisement towards advertisements displayed on
SNS in Surat.
10.13.1.3.m.a. Table Relationship between consumer buying
338
behaviour with the factor of memorable visuals and
slogans of the advertisements displayed on SNS in
26
Mumbai.
10.13.1.3.n.a.
Table Relationship between consumer buying
339
behaviour with the factor of memorable visuals and
slogans of the advertisements displayed on SNS in
Nashik.
10.13.1.3.s.a.
Table Relationship between consumer buying
340
behaviour with the factor of memorable visuals and
slogans of the advertisements displayed on SNS in
Surat.
10.13.1.4.m.a. Table Relationship between consumer buying
342
behaviour with the attractive factor of the
advertisements displayed on SNS in Mumbai.
10.13.1.4.n.a.
Table Relationship between consumer buying
343
behaviour with the attractive factor of the
advertisements displayed on SNS in Nashik.
10.13.1.4.s.a.
Table Relationship between consumer buying
344
behaviour with the attractive factor of the
advertisements displayed on SNS in Surat.
10.13.1.4.s.b.
Table of Symmetric Measures to determine how much
345
relationship exists in between consumer buying
behaviour and the attractive factor of social media
advertisements displayed on SNS in Surat.
10.13.1.5.m.a. Table Relationship between consumer buying
346
behaviour with the trustworthiness factor of the
advertisements displayed on SNS in Mumbai.
10.13.1.5.n.a.
Table Relationship between consumer buying
348
behaviour with the trustworthiness factor of the
advertisements displayed on SNS in Nashik.
10.13.1.5.s.a.
Table Relationship between consumer buying
349
behaviour with the trustworthiness factor of the
advertisements displayed on SNS in Surat.
10.13.1.5.s.b.
Table of Symmetric Measures to determine how much
350
relationship exists in between consumer buying
27
behaviour and the attractive factor of social media
advertisements displayed on SNS in Surat.
10.13.2.2.m.a. Table Relationship between online purchase
352
behaviour with the appealing factor of social media
advertisements displayed on SNS in Mumbai.
10.13.2.2.n.a.
Table Relationship between online purchase
353
behaviour with the appealing factor of social media
advertisements displayed on SNS in Nashik.
10.13.2.2.n.b.
Table of Symmetric Measures to determine how much
354
relationship exists in between online purchase
behaviour and appealing factor of social media
advertisements displayed on SNS in Nashik.
10.13.2.2.s.a.
Table Relationship between online purchase
355
behaviour with the appealing factor of social media
advertisements displayed on SNS in Surat.
10.13.2.2.s.b.
Table of Symmetric Measures to determine how much
356
relationship exists in between online purchase
behaviour and appealing factor of social media
advertisements displayed on SNS in Surat.
10.13.2.3.m.a. Table Relationship between online purchase
357
behaviour with the factor of social media advertising
i.e memorable visuals and slogans of the
advertisements displayed on SNS in Mumbai.
10.13.2.3.n.a.
Table Relationship between online purchase
358
behaviour with the factor of social media advertising
i.e memorable visuals and slogans of the
advertisements displayed on SNS in Nashik.
10.13.2.3.n.b.
Table of Symmetric Measures to determine how much
359
relationship exists in between online purchase
behaviour with the factor of social media advertising
i.e memorable visuals and slogans of the
advertisements displayed on SNS in Nashik.
10.13.2.3.s.a.
Table Relationship between online purchase
360
28
behaviour with the factor of social media advertising
i.e memorable visuals and slogans of the
advertisements displayed on SNS in Surat.
10.13.2.3.s.b.
Table of Symmetric Measures to determine how much
361
relationship exists between online purchase behaviour
with the factor of social media advertising i.e
memorable visuals and slogans of the advertisements
displayed on SNS in Surat.
10.13.2.4.m.a. Table Relationship between online purchase
362
behaviour with the attractive factor of social media
advertising in Mumbai.
10.13.2.4.n.a.
Table Relationship between online purchase
363
behaviour with the attractive factor of social media
advertising in Nashik.
10.13.2.4.n.b.
Table of Symmetric Measures to determine the
364
relationship between online purchase behaviour with
the attractive factor of social media advertising in
Nashik.
10.13.2.4.s.a.
Table Relationship between online purchase
365
behaviour with the attractive factor of social media
advertising in Surat.
10.13.2.4.s.b.
Table of Symmetric Measures to determine the
366
relationship between online purchase behaviour with
the attractive factor of social media advertising in
Surat.
10.13.2.5.m.a. Table Relationship between online purchase
367
behaviour with the trust factor of social media
advertising in Mumbai.
10.13.2.5.n.a.
Table Relationship between online purchase behaviour
369
with the trust factor of social media advertising in
Nashik.
10.13.2.5.s.a.
Table Relationship between online purchase behaviour 370
29
with the trust factor of social media advertising in
Surat.
10.13.3.2.m.a. Table Relationship between complex buying behaviour
372
with the appealing factor of social media advertising in
Mumbai.
10.13.3.2.n.a.
Table Relationship between complex buying
373
behaviour with the appealing factor of social media
advertising in Nashik.
10.13.3.2.n.b.
Table of Symmetric Measures to
determine how
374
much relationship exist between complex buying
behaviour and the appealing factor of social media
advertising in Nashik.
10.13.3.2.s.a.
Table Relationship between complex buying behaviour
375
with the appealing factor of social media advertising in
Surat.
10.13.3.2.s.b.
Table of Symmetric Measures to determine how much
376
relationship exists between complex buying behaviour
and the appealing factor of social media advertising in
Surat.
10.13.3.3.m.a. Table Relationship between complex buying
377
behaviour with the memorable visuals and slogans
factor of social media advertising in Mumbai.
10.13.3.3.m.b. Table of symmetric measures to determine how much
378
relationship exists between complex buying behaviour
and the memorable visuals and slogans factor of social
media advertising in Mumbai.
10.13.3.3.n.a.
Table Relationship between complex buying behaviour 378
with the memorable visuals and slogans factor of social
media advertising in Nashik.
10.13.3.3.s.a.
Table Relationship between complex buying behaviour 380
with the memorable visuals and slogans factor of social
30
media advertising in Surat.
10.13.3.4.m.a. Table Relationship between complex buying
381
behaviour with the attractive factor of social media
advertising in Mumbai.
10.13.3.4.n.a.
Table Relationship between complex buying
382
behaviour with the attractive factor of social media
advertising in Nashik.
10.13.3.4.s.a.
Table Relationship between complex buying
384
behaviour with the attractive factor of social media
advertising in Surat.
10.13.3.4.s.b.
Table of symmetric measures to determine how much
384
relationship exists between complex buying behaviour
and the attractive factor of social media advertising in
Surat.
10.13.3.5.m.a. Table Relationship between complex buying
385
behaviour with the trust factor of social media
advertising in Mumbai.
10.13.3.5.m.b. Table of symmetric measures to determine how much
386
relationship exists between complex buying behaviour
and the trust factor of social media advertising in
Mumbai.
10.13.3.5.n.a.
Table Relationship between complex buying
387
behaviour with the trust factor of social media
advertising in Nashik.
10.13.3.5.n.b.
Table of symmetric measures to determine how much 388
relationship between complex buying behaviour and
the trust factor of social media advertising in Nashik.
10.13.3.5.s.a.
Table Relationship between complex buying behaviour 389
with the trust factor of social media advertising in
Surat.
31
10.13.5.m.
Table showing relationship between all the factors of
390
variety seeking buying behaviour and all the factors
of social media advertising in Mumbai.
10.13.5.n.
Table showing relationship between all the factors of
391
variety seeking buying behaviour and all the factors
of social media advertising in Nashik.
10.13.5.s.
Table showing relationship between all the factors of
392
variety seeking buying behaviour and all the factors
of social media advertising in Surat.
10.13.6.m.
Table showing relationship between all the factors of 394
Dissonance buying behaviour and all the factors of
social media advertising in Mumbai.
10.13.6.n.
Table relationship between all the factors of
395
Dissonance buying behaviour and all the factors of
social media advertising in Nashik.
10.13.6.s.
Table showing relationship between all the factors of
396
Dissonance buying behaviour and all the factors of
social media advertising in Surat.
10.13.7.m.
Table showing relationship between all the factors of
397
Impulsive buying behaviour and all the factors of
social media advertising in Mumbai.
10.13.7.n.
Table showing relationship between all the factors of 398
Impulsive buying behaviour and all the factors of
social media advertising in Nashik.
10.13.7.s.
Table showing relationship between all the factors of
399
Impulsive buying behaviour and all the factors of
social media advertising in Surat.
10.14.n.a.
Table of Model Summary for Nashik
400
10.14.n.b.
Table of Anova to determine the level of significance
401
10.14.n.c.
Table for significance of Coefficients
402
32
10.14.s.a.
Table of Model Summary for Surat
404
10.14.s.b.
Table of Anova to determine the level of significance of 404
R2
10.14.s.c.
Table for significance of Coefficients
405
10.15.1.n.
Table Showing effectiveness of SNSs in terms of more 407
Audience groups in Nashik.
10.15.2.n.
Table Showing effectiveness of SNSs in terms of 408
targeting consumers in Nashik.
Table Showing effectiveness of SNSs in terms of more
10.15.3.n.
10.15.4.n.
408
followers due to acquaintances in Nashik.
Table Showing effectiveness of SNSs in terms of more
409
unknown followers in Nashik.
10.15.1.s.
Table Showing effectiveness of SNSs in terms of more
409
Audience groups in Surat.
10.15.2.s.
Table Showing effectiveness of SNSs in terms of
410
targeting consumers in Surat.
10.15.3.s.
Table Showing effectiveness of SNSs in terms of more
410
followers due to acquaintances in Surat.
10.15.4.s.
Table Showing effectiveness of SNSs in terms of more 411
unknown followers in Surat.
10.16.1.n.a.
Table Relationship between qualification of young
412
working women and impact of social media advertising
in Nashik.
10.16.1.n.b.
Table To determine qualification group has more or 413
less impact of social media advertisement in Nashik.
10.16.1.s.a.
Table Relationship between qualification of young 414
working women and impact of social media advertising
in Surat.
10.16.1.s.b.
Table To determine how much relationship exists
414
between qualification of young working women and
impact of social media advertising in Surat.
33
10.16.2.m.a.
Table Relationship between annual income of young 415
working women and impact of social media advertising
in Mumbai.
10.16.2.n.a.
Table Relationship between annual income of young 416
working women and impact of social media advertising
in Nashik.
10.16.2.n.b.
Table To determine how much relationship exists 417
between annual income of young working women and
impact of social media advertising in Nashik.
10.16.3.m.a.
Table Relationship between occupation of young
418
working women and impact of social media advertising
in Mumbai.
10.16.3.s.a.
Table Relationship between occupation of young
419
working women and impact of social media advertising
in Surat.
34
LIST OF FIGURES
Figure No.
Title
Page No.
3.1
Theoretical Model of the Study
111
4.1
Revenue distribution of LinkedIn
123
5.1
Three Cs of E-marketing ecosystem at HP
134
6.1.
Showing the Markets that will lead the growth in tech 138
sector in the year 2015.
35
List of Abbreviations
SNS – Social Networking Sites.
SM – Social Media.
IMC –Integrated Marketing Communications.
CRM – Customer Relationship Management.
FMCG – Fast moving consumer goods.
PR – Public Relations
eWOM – electronic word of mouth
PCAP – perceived community attitude toward a product
MAU – Monthly active users
CAGR - Compounded Annual Growth Rate.
SG&A - sales, general and administrative
SERP - search engine ranking position
CPC - cost per click
EBITDA - Earnings before Interest, Taxes, Depreciation and Amortization
USD - US Dollar
MNC - Multi-national company
36
ASSOCHAM - The Associated Chamber of Commerce and Industry of India
Executive Summary
Social media has created a huge buzz in today’s world. It is very popular in the
younger generations, but the middle and the older generations are also not untouched
by the wave of social media. On domestic front it is used for interacting with friends
and relatives and for the purpose of socialising. On professional front, it has been
widely used for acquiring markets by new business ventures. Many established
organizations are undergoing operational change in their traditional practices in order
to adapt to this online environment for promoting their products and services globally.
Social media has been the most recent and booming technological innovations. It
offers a wide range of benefits. Interest and curiosity to gain more knowledge in the
field of social media has been the main ground for selecting the topic of Social media
for the research purpose. Also much research has not been done on social media in the
Indian context and more precisely in Maharashtra, therefore Social Media has been
selected as the topic for research.
In the first part of this study the various concepts are defined like the concept of social
media, social media advertising, consumer buying behaviour etc. Moving ahead in the
second part of the study, social media and advertising, social media and consumer
behaviour, social media and consumer electronics, social media and women have
been discussed in combination. The third part of the study has talked in detail about
the different social media tools like Facebook, Twitter and LinkedIn which are the
only tools considered for the purpose of this study from the large number of existing
SNSs. In the fourth part of the study the various promotional strategies adopted by the
37
leading players in the consumer electronics segment are discussed in detail. The fifth
part of the study throws light on the objectives of the study.
The objectives of the study were :
1. To study the reason of online consumer’s Social Media usage.
2. To study the customers buying behaviour with respect to Social media advertising.
3. To study the impact of social media advertising on the buying behavior of young
working women for consumer electronics.
4. To study the effectiveness of Social Media tools like face book, twitter, LinkedIn
on the consumer behaviour.
5. To study the impact of social media advertising on working women belonging to
different demographic factors such as qualification, annual income, occupation and
place.
The Research Methodology Adopted:
Data Collection:
Primary data was collected by questionnaire survey method. Research instrument
is questionnaire, personal interviews. Single questionnaire was created and
administered in three cities. The target audience was working women in the age
group of 18-35 from Mumbai, Nashik and Surat in this study.
The cities in India have been classified on the basis of grading structure devised by
the government of India. According to this gradation, Mumbai belongs to Tier I
category of cities and Nashik and Surat belongs to Tier II category (source for
information on Tier I & II cities of India: www.maps ofindia.com). The
38
requirement of the study was comparison between the tier one and tier two cities
having different population sizes. Therefore based on convenience, Mumbai was
selected as a Tier I city or a Metro city with heterogeneous population of 12.7
million and Nashik as a tier II city having population approximately 1.5 million
from Maharashtra and Surat having population 4.5 million was selected from
Gujarat (source :Reports of Internet And Mobile Association of India [IAMAI] and
Internet Market Research Bureau [IMRB] ).
Pilot Study :
Pilot study was conducted and the questionnaire was first pre-tested on a sample of
100 respondents (working women in the age group of 18-35) from Mumbai city for
checking the reliability of the questionnaire.
Reliability
The Chronbach’s Alpha found out was 0.860. Any value of Cronbach’s Alpha above
0.6 shows that the scale is reliable.
Questionnaire
The questionnaire comprised of several sections relating to the various aspects of
social media advertising, buying behaviour etc. Essentially it comprises of :
1. Demographic aspects - 4 questions.
2. Usage of social media - 14 questions.
3. Buying behaviour including Consumer buying behaviour, complex, habitual
buying behaviour etc. - 25 questions.
4. Effectiveness of social media and impact of social media advertising - 15
questions.
39
Size and Design of Sample
The study was conducted in two cities of Maharashtra (Mumbai & Nashik) and in one
city of Gujarat (Surat). The sample unit is working women in the age group of 18-35
and having knowledge of internet.
Sampling Technique :
Random Sampling technique has been used for this study. In a Random sample from
infinite population, selection of each item is controlled by the same probabilities and
the successive selections are independent of one another. (C.R.Kothari, Research
Methodology Methods and Techniques)
Sample size Calculation :
Where,
n = Sample size
= critical value
 = Standard Deviation
E = Estimated margin of error
2
 Z  
1.96 18.19 
2


n

 1271.78
 E  
1



2
Round off sample size is required is 1272 respondents.
40
Table on Chosen Sample Size
Name of the Cities
No. of Respondents
1
Mumbai
516
2
Nashik
359
3
Surat
397
TOTAL
1272
Sr. No.
Variables of the study :
Dependent Variables - Buying Behaviour with respect to Social Media Advertising.
Independent Variables - Online Purchase Behaviour, Consumer Buying Behaviour,
Complex Buying Behaviour, Habitual Buying Behaviour, Variety Seeking Buying
Behaviour, Dissonance Buying Behaviour, Impulsive Buying Behaviour.
Description of the Variables :
1. Online Purchase Behaviour :
“Online Purchase Behaviour”, this variable primarily indicates the online
behaviour of the consumer from the purchase point of view. It throws light on
a couple of things related to the purchase taking place online through the
medium of social media, like involvement of the consumer while taking the
online purchase decision, to what extent the consumer thinks there is a
difference in the products of different brands available online, what does the
consumer think about the price of the product available on social media and
41
does the consumer think that the decision making process in case of online
products is time consuming.
2. Consumer Buying Behaviour :
“Consumer Buying Behaviour”, this variable focuses on the online behaviour
of the consumer from the reasons which lead to the purchase through social
media, point of view. It considers the reasons which lead to the purchase like
whether the consumer read the blogs / reviews or view the advertisement on
social media. It also studies the consumers behaviour by considering, to which
electronic products consumer has provided rating.
3. Complex Buying Behaviour :
Complex buying behaviour when the consumer is highly involved in the
buying then it is called complex buying behavior. In case of complex buying
behavior the consumer must collect proper information about the product
features and attributes.
4. Habitual Buying Behaviour :
In case of Habitual buying behavior there is low involvement of the consumer.
The consumer buys the product belonging to a particular brand which has been
regularly preferred by them because the consumer thinks that the product
belonging to a particular brand is best fit for them. The consumer buys the
product quickly.
5. Variety-Seeking Buying Behaviour :
Variety seeking buying behaviour takes place when the consumer has many
different product choices that serve the same purpose. In case of Variety-
42
Seeking Buying Behaviour Consumers generally buy different products
because they want to try out a new variety of product.
6. Dissonance Buying Behaviour :
In Dissonance buying behavior consumer is highly involved in the purchase.
Dissonance buying behavior occurs when the product which the consumer is
thinking of buying is expensive or there are no differences or a few differences
between the brands. The consumers experience a feeling of discomfort or
anxiety after the purchase of the product, because they fear that the expensive
product which they have bought should not be a failure.
7. Impulsive Buying Behaviour :
Impulsive buying behaviour takes place when the consumer makes an
unplanned purchase, provoked by seeing the product or upon exposure to a
lucrative advertisement or scheme.
Limitations of the Study:
The study was conducted based on the data collected from Mumbai, Nashik
and Surat and therefore findings of this study may not be applicable to other
countries of the world because of the socio-cultural and economic differences.
Utility:
The study would be very useful to markets who would now be able to use
social media as a platform for promoting their products and services.
43
Conclusion of the study :
The detailed research has lead to certain conclusions which are being
discussed in this chapter.
Association between Positive reactions or feelings towards social media
advertisements with consumer buying behaviour :
It has been concluded from the study that there is a strong positive association
between the factor of social media advertising i.e. positive reactions/feelings
with the consumer buying behaviour in Mumbai and Nashik. So if there is any
increase in the positive reactions/feelings it will positively affect the consumer
buying behaviour. However there is no association between the factor of social
media advertising i.e. positive reactions/feelings with the consumer buying
behaviour in Surat.
Association between appealing factor of social media advertisements with
consumer buying behaviour :
It has been concluded from the study that there is a strong positive association
between the appealing factor of social media advertising with the consumer
buying behaviour in Mumbai. However there is no association between the
appealing factor of social media advertising with the consumer buying
behaviour in Nashik and Surat.
Association between memorable visuals and slogans factor of social media
advertisements with consumer buying behaviour :
It has been concluded from the study that the memorable visuals and slogans
factor of social media advertising and consumer buying behaviour are
44
independent of each other and there is no association between the memorable
visuals and slogans factor of social media advertising with the consumer
buying behaviour in Mumbai, Nashik and Surat.
Association between attractive factor of social media advertisements with
consumer buying behaviour :
It has been concluded from the study that the attractiveness factor of social
media advertising and consumer buying behaviour are dependent of each other
and there is a strong association between the attractiveness factor of social
media advertising with the consumer buying behaviour in Surat. However
there is no association between the attractiveness factor of social media
advertising with the consumer buying behaviour in Mumbai and Nashik and
they are independent of each other.
Association between trustworthiness factor of social media advertisements
with consumer buying behaviour :
It has been concluded from the study that the trustworthiness factor of social
media advertising and consumer buying behaviour are dependent of each other
and there is a strong association between the trustworthiness factor of social
media advertising with the consumer buying behaviour in Surat. However
there is no association between the trustworthiness factor of social media
advertising and the consumer buying behaviour in Mumbai and Nashik and
they are independent of each other.
45
Association between Positive reactions or feelings towards social media
advertisements with online purchase behaviour :
It has been revealed from the study that there is an association between the
factor of social media advertising i.e. positive reactions/feelings with the
online purchase behaviour in Nashik and Surat. So if there is any change in the
positive reactions/feelings it will lead to change in the online purchase
behaviour. However there is no association between the factor of social media
advertising i.e. positive reactions/feelings with the online purchase behaviour
in Mumbai.
Association between appealing factor of social media advertising and
online purchase behaviour :
It has been revealed from the study that there is an association between the
appealing factor of social media advertising and the online purchase behaviour
in Nashik and Surat. So if there is any change in the appealing factor of social
media advertising it will lead to change in the online purchase behaviour.
However there is no association between the appealing factor of social media
advertising and the online purchase behaviour in Mumbai.
Association between memorable visuals and slogans factor of social media
advertising and online purchase behaviour :
It has been revealed from the study that there is an association between the
memorable visuals and slogans factor of social media advertising and the
online purchase behaviour in Nashik and Surat. So if there is any change in the
memorable visuals and slogans factor of social media advertising it will lead
46
to change in the online purchase behaviour. However there is no association
between the memorable visuals and slogans factor of social media advertising
and the online purchase behaviour in Mumbai.
Association between attractiveness factor of social media advertising and
online purchase behaviour :
It has been revealed from the study that there is an association between the
attractiveness factor of social media advertising and the online purchase
behaviour in Nashik and Surat. So if there is any change in the attractiveness
factor of social media advertising it will lead to change in the online purchase
behaviour. However there is no association between the attractiveness factor
of social media advertising and the online purchase behaviour in Mumbai.
Association between trustworthiness factor of social media advertising
and online purchase behaviour :
It has been revealed from the study that there is no association between the
trustworthiness factor of social media advertising and the online purchase
behaviour in Mumbai, Nasik and Surat. The trustworthiness factor of social
media advertising and the online purchase behaviour are independent of each
other.
Association between Positive reactions or feelings towards social media
advertisements with complex buying behaviour :
It has been revealed from the study that there is a strong relationship between
the factor of social media advertising i.e. positive reactions/feelings with
online consumer behaviour in Mumbai, Nashik and Surat. So if there is any
47
change in the positive reactions/feelings factor of social media advertising, it
will lead to change in the complex buying behaviour.
Association between appealing factor of social media advertisements with
complex buying behaviour :
It has been revealed from the study that there is a strong relationship between
the appealing factor of social media advertising with complex buying
behaviour in Nashik and Surat. So if there is any change in the appealing
factor of social media advertising, it will lead to change in the complex buying
behaviour. However there is no relationship between the appealing factor of
social media advertising and complex buying behaviour in Mumbai and they
are independent of each other.
Association between memorable visuals and slogans factor of social media
advertisements with complex buying behaviour :
It has been revealed from the study that there is a strong relationship between
the memorable visuals and slogans factor of social media advertising with
complex buying behaviour in Mumbai. So if there is any change in the
memorable visuals and slogans factor of social media advertising, it will lead
to change in the complex buying behaviour. However there is no relationship
between the memorable visuals and slogans factor of social media advertising
and complex buying behaviour in Nashik and Surat.
48
Association between attractiveness factor of social media advertisements
with complex buying behaviour :
It has been revealed from the study that there is a strong relationship between
the attractiveness factor of social media advertising with complex buying
behaviour in Surat. So if there is any change in the attractiveness factor of
social media advertising, it will lead to change in the complex buying
behaviour in Surat. However there is no relationship between the
attractiveness factor of social media advertising and complex buying
behaviour in Mumbai and Nashik and they are independent of each other.
Association between trustworthiness factor of social media advertisements
with complex buying behaviour :
It has been revealed from the study that there is a strong relationship between
the trustworthiness factor of social media advertising with complex buying
behaviour in Mumbai and Nashik. So if there is any change in the
trustworthiness factor of social media advertising, it will lead to change in the
complex buying behaviour in Mumbai and Nashik. However there is no
relationship between the trustworthiness factor of social media advertising and
complex buying behaviour in Surat and they are independent of each other.
Relationship between all the factors of Habitual Buying Behaviour with
all the factor of Social Media Advertisement in different cities :
From the study it has been concluded that all the factors of Social Media
Advertisement and all the factors of Habitual Buying Behaviour of young
working women for consumer electronics in Mumbai are independent of each
49
other. However in Nashik and Surat, all the factors of Social Media
Advertisement and all the factors of Habitual Buying Behaviour of young
working women for consumer electronics are dependent of each other.
Relationship between all the factors of Variety Seeking Buying Behaviour
with all the factor of Social Media Advertisement in different cities :
It has been concluded from the study that all the factors of Social Media
Advertisement and all the factors of Variety Seeking Buying Behaviour of
young working women for consumer electronics in Mumbai, Nashik and Surat
are dependent of each other.
Relationship between all the factors of Dissonance Buying Behaviour with
all the factor of Social Media Advertisement in different cities :
It has been concluded from the study that all the factors of Social Media
Advertisement and all the factors of Dissonance Buying Behaviour of young
working women for consumer electronics in Mumbai are dependent of each
other. However all the factors of Social Media Advertisement and all the
factors of Dissonance Buying Behaviour of young working women for
consumer electronics in Nashik and Surat are independent of each other.
Relationship between all the factors of Impulsive Buying Behaviour with
all the factor of Social Media Advertisement in different cities :
It has been concluded from the study that all the factors of Social Media
Advertisement and all the factors of Impulsive buying behaviour of young
working women for consumer electronics in Nashik and Surat are dependent
of each other. However all the factors of Social Media Advertisement and all
50
the factors of Impulsive buying behaviour of young working women for
consumer electronics in Mumbai are independent of each other.
Impact of Social Media advertising on different factors of buying
behaviour of young working women for consumer electronics in Mumbai
:
From the study it has been concluded that in Mumbai, Social media
advertising has a significant impact on the following factors of buying
behaviour - Consumer Buying Behaviour, Complex buying behaviour,
Variety-seeking buying behaviour, Dissonance buying behaviour.
Impact of Social Media advertising on different factors of buying
behaviour of young working women for consumer electronics in Nashik :
It has been revealed from the study that in Nashik, Consumer Buying
Behaviour and Variety - Seeking buying behaviour are the factors of buying
behaviour which are significantly impacted by Social media advertising.
Impact of Social Media advertising on different factors of buying
behaviour of young working women for consumer electronics in Nashik :
It has been revealed from the study that in Surat, Consumer Buying
Behaviour,
Complex
buying behaviour,
Habitual
buying behaviour,
Dissonance and Variety - Seeking buying behaviour are the factors of buying
behaviour which are significantly impacted by Social media advertising.
51
Effectiveness of social media tools like Face book, Twitter , LinkedIn on
the consumer Behaviour in Mumbai :
Audience:
From the research study it has been concluded that in Mumbai the young
working women prefer most Face book, as the Social networking sites that
have a large number of groups (networks) available for any demographics you
are looking for; for instance group of teenagers, group of kids, youth, group of
new moms, brides, sports fans, technology enthusiasts, entrepreneurs etc.
Face-book is followed by LinkedIn and Twitter is the least preferred site.
Targeting :
From the study it has been concluded that Face book is the most preferred
Social networking site that targets the advertisements to specific group of
audience according to the young working women in Mumbai. Face book is
followed by LinkedIn and the least preferred site for targeting the
advertisements to specific group of audience is Twitter in Mumbai.
More followers due to acquaintances :
From the study it has been observed that in Mumbai, Face-book has more
followers due to acquaintances, followed by Twitter and LinkedIn has the least
number of followers due to acquaintances.
52
More Unknown followers :
From the study it has been observed that in Mumbai, Face-book has more
unknown followers, followed by Twitter and LinkedIn has least number of
unknown followers.
Effectiveness of social media tools like Face book, Twitter , LinkedIn on
the consumer Behaviour in Nashik :
Audience:
From the study it is concluded that in Nashik, the young working women
prefer most Face book, followed by Twitter and least preferred is LinkedIn as
the Social networking sites that have a large number of groups (networks)
available for any demographics; for instance group of teenagers, group of kids,
youth, group of new moms, brides, sports fans, technology enthusiasts,
entrepreneurs etc.
Targeting :
From the study it has been observed that in Nashik, the young working women
prefer most Face book, followed by LinkedIn and least preferred is twitter as
the social networking sites targeting the advertisements to specific group of
audience.
More followers due to acquaintances :
From the study it has been observed that in Nashik a maximum number of
young working women agreed that Face book has more number of followers
53
due to acquaintances, followed by Twitter and LinkedIn has the minimum
number of followers due to acquaintances.
More Unknown followers :
From the research it has been revealed that in Nashik, a maximum number of
young working women said that face book has more unknown followers,
followed by Twitter and LinkedIn has minimum unknown followers.
Effectiveness of social media tools like Face book, Twitter , LinkedIn on
the consumer Behaviour in Surat :
Audience :
From the study it has been observed that in Surat the young working women
prefer Face book most, followed by Twitter and the least preferred is
LinkedIn, as the Social networking sites that have a large number of groups
(networks) available for any demographics; for instance group of teenagers,
group of kids, youth, group of new moms, brides, sports fans, technology
enthusiasts, entrepreneurs etc.
Targeting :
From the study it has been concluded that in Surat, the young working women
prefer most Face book, followed by Twitter and least preferred is LinkedIn as
the Social networking sites targeting the advertisements to specific group of
audience.
54
More followers due to acquaintances :
From the study it has been observed that in Surat, maximum number of young
working women said that Face book has more followers due to acquaintances,
followed by Twitter and LinkedIn has least number of followers due to
acquaintances.
More Unknown followers :
From the study it has been observed that in Surat, a maximum number of
respondents said that LinkedIn has more unknown followers, followed by Face
book and Twitter has minimum number of unknown followers.
Relationship between impact of social media advertising of young
working women with their qualification in different cities :
From the study it has been concluded that in Mumbai qualification of the
young working women does not have any effect on the impact of Social media
advertising. However qualification of the young working women has a direct
effect on the impact of social media advertising in Nashik and Surat. Social
media advertising has more impact on non graduate young working women
followed by post graduate and graduates in Nashik. While Social media
advertising has more impact on non graduate young working women followed
by graduates and post graduate in Surat.
Relationship between impact of social media advertising of young
working women with their Annual Income in different cities :
From the study it has been observed that annual income of young working
women does not have any relationship with the impact of social media
55
advertising in Mumbai. However in Nashik and Surat the annual income of
young working women has a substantial relationship with the impact of social
media advertising. From the findings of the study it has been observed that the
impact of social media advertising is more observed on the young working
women having annual income upto 3 lakhs followed by young working
women in the income groups of 3.1- 5, 5.1 – 10 and above 10 lakhs in Nashik.
In surat the impact of social media advertising is found to be more on young
working women having annual income upto 3 lakhs followed by young
working women in the income groups of 3.1- 5,
above 10
lakhs
and
minimum impact is observed on the young working women having annual
income in the group of 5.1 – 10 lakhs.
Relationship between impact of social media advertising and the
Occupation of young working women in different cities :
From the study it has been concluded that Occupation of young working
women does not have any effect on the impact of social media advertising in
Mumbai and Surat. However in Nashik, occupation of the young working
women does affect the impact of social media advertising. The impact of
social media advertising is more observed on business class women, followed
by women doing service and finally the self-employed women.
Recommendations & Suggestions :
The consumer electronics segment has a large scope of penetrating in smaller
cities like Nashik, where large market is still untapped. This gap should be
bridged and the awareness of Social Media should be increased in smaller
56
cities, so that organizations can directly reach more and more consumers and
can interact with them.
The Social networking sites like LinkedIn and Twitter can improve their
advertising efficiency by enhancing features like targeting the advertisements
to the right group of audience, introducing more groups for any demographics
like group of engineers, manufacturers, entrepreneurs, doctors, youth, house
wives etc., more user friendliness so that more and more audience are attracted
towards them for socialising as well as accessing product information.
The study has revealed that the impact of social media advertising is more on
undergraduates, business class and young working women having annual
income around three lakhs. Therefore there is a need for the consumer
electronics companies to find out the reasons for not accessing social media,
among the young working women belonging to other educational, economic
and occupational background and spreading awareness among them about the
Social Media tools and to reach out to them through social media in order to
tap more consumers and increase the business. So consumer electronics
segment should take social media to smaller cities and should work towards
building trust in this less explored market.
Future Scope of Study :
The study today is applicable to working women in selected cities of
Maharashtra and Gujarat and to consumer electronics product segment. In
future, the study can be carried out to the other areas of consumer markets and
also to other cities of India. Additionally the study could also be extended to
other group of women e.g. college students, house wives etc.
57
Chapter 1
Introduction
In the last few years, the trend in worldwide business has been the adoption of new
marketing strategies that utilize the ever-advancing technology applications available
today. One of the foremost technology application used in business promotion has
been the use of social media. Social media has emerged as an Internet-based platform
which is extremely dynamic and vibrant. It has proved to be an extremely useful
platform where one person can communicate with hundreds or thousands of other
people.
Social media has been the most recent and booming technological innovations. It
offers a wide range of benefits. Interest and curiosity to gain more knowledge in the
field of social media has been the main ground for selecting the topic of Social media
for the research purpose. Also much research has not been done on social media in the
Indian context and more precisely in Maharashtra, therefore Social Media has been
selected as the topic for research.
1.1 Concept of Social Media, Advertising, Advertising on Social Networking sites
and Consumer Behaviour:
Concept of Social Media :
Social Media refers to a collection of social technologies which have enabled a
revolution in user generated content, global community and publishing of consumer
opinion. It can also be defined as a group of Internet-based applications that is built on
58
ideological and technological foundations of Web 2.0 and that allow the creation and
exchange of User Generated Content (Andreas Kaplan and Michael Haenlein 2010).
Concept of Advertising :
Advertising is the non-personal communication of information usually paid for and
usually persuasive in nature about products, services or ideas by identified sponsors
through the various media (Bovee 1992,7).
Concept of Advertising on Social Networking sites:
The term Social Network Advertising is the advertising which is done online through
Social networking sites like Facebook, Friendster, twitter etc. It is a paid form of
promotion of brand or product or service and require a properly planned
communicative message and budget. This form of advertising is more customer
centric and customers play a vital role in short or long communication because they
are one who will decide the fate of the advertising communication. (P. Sri. Jothi, M.
Neelamalar and R. Shakthi Prasad, 2011)
Concept of Consumer Behaviour :
Blackwell (2001) defines consumer behaviour as the activities people undertake when
obtaining, consuming and disposing of products and services. Consumer Behaviour is
composed of five dimensions namely, perception, information/ learning, attitude,
motivation and actual behaviour.
59
1.2 Origin Of Social Media :
In the year 1979, Usenet was created by Jim Ellis and Tom Truscott from Duke
University, a platform which allowed Internet users from across the world to post
public messages. The period of Social Media started 20 years earlier when Bruce and
Susan Abelson founded “Open Diary” , a social networking site developed in the very
initial years and which
brought together all the online diary writers into one
community. The term “weblog” was first used at the same time and was truncated to
“blog” a year later. The growing availability of high speed Internet access added to
the popularity of the concept, leading to the formation of social networking sites like
MySpace(in 2003) and Facebook (in 2004) and coining of the term “Social Media”.
1.3 Popularity of Social Media:
There is a significant rise in the use of Social Media among Internet surfers. In 2007,
56% of Internet surfers used Social Media which grew in 2008 to almost 75% . The
growth of Social Media was not limited to teenagers, members belonging to the age
group of 35-44 years old, increasingly participate as joiners, spectators and critics.
The Universal McCann tracker study I which was conducted up to 2008, measured the
usage of the main social platforms across the world among 17,000 active web users.
The number of surfers reading blogs increased from 54% to 77% globally in just two
years. The number of surfers who had written and created blogs increased from 28%
to 45%. The consumer-driven multimedia platforms such as video sharing, also
increased from 32% in 2006 to 83% in 2008, making social media the fastest growing
platform in the history. Asian internet users are the most active users of blogs,
particularly South Korea and China, where blogs are accepted as a form of social
community. The next most active users are in Latin America. The well established
60
web markets of the US and Europe demonstrate a slightly lower levels of adoption
and a more passive approach towards creating and sharing content. However the
‘active participation rates’ are increasing rapidly (Tom Smith, 2010).
1.4 Advertising:
Our lives are governed by advertisements to a great extent. They have also become a
vital element of the corporate world and large amount of money is being assigned by
companies towards their advertising budget. Advertising has evolved to a great extent
over the years. In today’s world a large gamete of choices are available to the
Advertisers, of the medium, through which they can advertise their product or service.
The medium of Advertising can be categorized into Print advertising, Guerrilla
advertising, Broadcast advertising, Outdoor advertising, Public service advertising,
Product placement advertising, Cell phone and mobile advertising and Online
advertising. When an advertisement is printed it is called as Print advertising like
newspapers, magazines, booklets etc. Guerrilla advertising is a form of
unconventional advertising which mainly involves creative ideas and it allows the
consumers to interact or participate in the advertisement. It normally spreads through
word of mouth and social media. Broadcast advertising takes place through
television, radio and has the capability to reach the masses. Outdoor advertising is
any type of advertising that reaches the consumer when he or she is out of the home
e.g. Billboard, kiosks, trade shows etc. Public service advertising is advertising done
for a social cause, primarily to educate and inform people and not for sale of products
or services. Product placement advertising is the promotion of branded products or
services in context with a movie or show. Mobile advertising is a comparatively new
form of advertising and it is spreading rapidly with the help of face-book and twitter
61
applications over smart-phones. Online Advertising is another comparatively new
form of advertising. When any advertisement is displayed over a website through
internet it is called as online advertisement. It involves advertising through emails,
search engines, social media advertising and many types of display advertising like
banner advertising etc. Online advertising is a large business and is widely used
across all industry sectors. It is growing extensively. This study focuses on Social
Media Advertising.
1.5 Social Media Advertising :
Media propagation has changed the ways in which advertising messages are delivered
and received. Due to the high costs incurred in delivering a mass audience, advertisers
are moving away from television and investing in alternate media, such as social
network sites (SNSs), to reach their target customers. The emergence of Social Media
has helped organisations in engaging in a direct, efficient, cost effective and timely
end-consumer contact as compared to the traditional communication tools. Therefore
Social Media Advertising is more beneficial not only to large multinational firms, but
also to small and medium sized companies, and even non profit and governmental
agencies. “Marketing over conventional channels is four times more expensive than
marketing over the Internet” (Verity and Hof,1994). Companies communicate with
consumers through a wide range of online, word-of-mouth forums including blogs,
company-sponsored discussion boards and chat rooms, consumer product or service
rating websites and forums, internet discussion boards and forums, mo-blogs (sites
containing digital audio, images, movies or photographs) and social networking
websites to name a few. With the help of Social network sites (SNSs) consumers can
actively interact with advertising like for instance SNS gives opportunity to the
62
consumers to “like” certain ads, follow ads on twitter, share them with friends and to
know which friends like the ads.
Many consumers are turning away from the
traditional sources of advertising like radio, television, magazines, newspapers and
are using Social Media more frequently to search information about products and
make purchase decision. Therefore many researchers think that Social Media should
be included as an integral part of the organization’s Integral Marketing
Communication strategy. Integral Marketing Communication (IMC) is an important
principle organizations follow to communicate with their target markets. “Marketing
on the internet known as the world wide web results in ten times as many units sold
with one-tenth the advertising budget” (M. Potter, 1994)
The main benefit of selecting Social networking sites for advertisement is that the
advertiser can use the user’s demographic information and target their advertisement
appropriately.
On one hand Social media has given immense powers to the consumers which they
have never experienced in the marketplace before; and on the other hand, the
organizations have no direct control over the content, duration and frequency of the
social media-based conversations.
The relationship between businesses and customers is changing with the introduction
of Social Media. Various aspects of consumer behaviour are being influenced by
Social Media. The businesses are required to develop their marketing strategies in
order to generate a genuine relationship with its customers. According to a study
Face-book had a higher amount of influence; than Twitter, on the buying behaviour of
social media users. A variety of new ways and sources of online information, that are
63
created, circulated and used by consumers with the intention of educating each other
about products, services, personalities and issues have been thrown open by Social
Media. Just as consumers are becoming social, the products they purchase and the
organizations they interact with have to be social as well. The companies mere
presence on the Face book or Twitter is not going to increase it’s turnover, the
companies should use the Social networking sites to change the traditional purchasing
process.
Sony made a lot of profit through its social media campaign that incentivised people
to purchase their products. They offered to the Twitter users a chance to build a
customised Sony Vaio Laptops alongwith a 10% discount. And it worked by
increasing the company’s sales from Twitter for that period of $1.5 million. The
prime objective of this Sony campaign was to achieve more sales by giving little bit
extra to the users – by giving the consumers a customised laptop which will fit their
requirement. The Twitter users felt privileged for getting the customised laptop
according to their requirements and to get an extra discount of 10% which nobody
else got.
(Source : Evidence that Social Media Really Does Drive Sales; dated :
18/11/2014)
IBM raised millions of dollars using social media. According to Ed Linde II, Senior
Marketing Manager at IBM, they have been successful in increasing the sales by
simply ‘listening for leads’.
Dell announced that it made a sale of $3 million in sales through their Twitter account
in 2009.
64
There has been a dramatical shift in the online consumer’s buying behaviour that
shows most of them prefer to buy through social media. It has been found that the
users of Facebook and Twitter spend more money online than the average internet
users. This is partly because companies are making it easier to buy through social
networks and more online consumers have begun trusting in SN, social shopping or
group buying.
1.6 Consumer Buying Behaviour :
Consumer buying behaviour is the way in which the consumers behave or react
while purchasing a product. Consumers buying behaviour is a long process in which
the consumer has to identify the product, study its features well which involves
minutely knowing its pros and cons, and finally deciding on whether to purchase it
or not. The consumers of products or services exhibit different types of consumer
buying behaviour. Following are the different types of consumer buying behaviours:
i. Complex buying behaviour :
This type of buying behaviour involves complete involvement of the buyer.
Complex buying behaviour is normally witnessed when the product which the
buyer wants to purchase is an expensive one, or carries itself with a great risk
factor or is not purchased very often. E.g. buying a laptop, house, television etc.
65
ii. Habitual buying behaviour :
In this buying behaviour the buyer purchases the product that he has been using
previously for a long time without thinking of switching to another brand. E,g.
Habitual buying behaviour can apply to products like sugar, bread etc.
iii. Dissonance reducing buying behaviour :
This type of buying behaviour is exhibited in case of products which are
expensive or has a risk involved in its purchase and when there are number of
brands that have very less or no difference. The consumers develop a sense of
discomfort after purchasing the product and fears if the product fails to perform
when a lot of money is spent in buying that product. E.g. buying a car, mobile etc.
iv. Variety seeking buying behaviour :
This type of buying behaviour is seen when consumers have a many different
product choices that serve the same purpose. As the different brands of the same
product serve only one purpose the consumers my tend to tryout a different brand.
E.g. products like cooking oil, detergent which do not have much difference in the
different brands so the consumer may tryout different brands of these products
every time they want to purchase it.
v. Impulsive Buying Behaviour :
This type of behaviour is exhibited by the buyer when he sees the product and
cannot resist from buying it. E.g. cloths, jewellery etc.
66
1.7 Online Consumer’s Buying Behaviour :
This research intends to study the impact of social media advertising on online
consumer’s buying behaviour. The adoption of advanced technologies have changed
the manner in which people buy a product or choose a service. The Consumer
behaviour of online consumers is posing a great challenge to the marketing managers
to develop the right digital (product promotion) strategy that meets the changing
needs and retain the competitiveness in the marketplace. Various aspects of consumer
behaviour including information acquisition, awareness, attitudes, opinions, purchase
behaviour and post purchase communication and evaluation are influenced by Social
Media. Online consumers are unwilling to read large amounts of data. They prefer
brief but complete information while seeking the key benefits of a product or service.
Integrated timesaving features like pop-up descriptions, photo galleries, product
comparison etc. are always valued by the online consumers. Product/Service reviews
are more preferred over automated recommendations. Buying decisions of consumer
products, vacations and movies are more influenced by online information. It is very
important for the organizations to know online customer’s expectations and reactions
to advertisements, in order to attract and to retain them (online customers).
Knowledge of consumer behaviour is critical to develop an appropriate advertising
strategy. It is very difficult to understand the consumers; rather it is a complex and
multi-dimensional process. Consumers may say one thing and do another. They may
respond positively to influences or advertisements and may change their mind at the
last minute. Therefore gaining the correct knowledge about consumers is extremely
important before planning an advertising strategy. This research tends to throw a light
on the effect of Social media advertising on the various types of consumer buying
67
behaviours like Complex, Variety seeking, Impulsive, Habitual and Dissonance
buying behaviour.
1.8 Women and Social Network Sites(SNSs):
Women are the driving force behind the SNSs and represent a proliferating portion of
the SNS user population. SNSs have a majority of female users as compared to
males. Women are using SNSs to connect with their old friends, make new ones, as
well as maintain social ties with their family and friends across the globe using SNS.
The research suggests that the female user population of SNSs has increased since
2008, while the male user population is declining over the years. It is therefore
important for academicians and practitioners to understand that women play a vital
role in the propagation of SNSs.
1.9 Consumer Electronics and Social Media :
The consumer electronics sector can be called as an ice-breaker or the first mover in
trying out the digital marketing techniques. Consumer electronics is among the most
popular product categories that consumers purchase online (Nielson 2010).
A change has been observed in the advertising strategy of electronic items and the
electronics manufacturers are now using and rather emphasising consumer’s
experience for promoting the electronic products than the traditional product lead
method of promotion. The competition in terms of price, loyalty has lead brands to
find even more innovative and effective ways of engaging directly with the
consumers. Social media has served as a crucial weapon in this battle. In the year
2007, Philips a well known brand in Consumer electronics, launched a universal
‘experiential website’ in order to change the traditional product-led approach to an
68
approach which makes consumers aware of the experiences of the people who have
used the Philips products. This was a wider shift in the advertising strategy taken up
by Philips even though their online sale was extremely good, around 70m products
every year.
Matt McDowell, the marketing director of Toshiba states that Social media has
provided a platform where organizations can give more contents about the product
and answer more questions raised by the traditional media, specifically the TV.
According to him the medium of TV only raises questions in the minds of the
consumers and causes a craving in people to want to know more. However digital
medium provides a bridge from engagement and interest right through to purchase.
Social media has helped in giving a special element to the advertising campaigns; the
element of personal touch or user friendliness. A survey by GfK Technology UK has
shown that there has been an increase of nine points in the Consumer Confidence
index for digital media. Therefore more and more organizations are directing a hefty
chunk of their media budget towards digital marketing.
Digital Pioneers : According to Ruth Speakman, general manager at Sony Europe,
Sony was accidentally introduced to Social Media in 2005 when it was running a
campaign to promote the Bravia LCD TV. The campaign was based on a TV
advertisement however it took off in Social Media and this proved to be an eye opener
for the company to the power of this platform.
A Space chair campaign was launched by Toshiba for promoting its new LED TV and
this campaign was rooted in Social Media.
69
Panasonic also launched its first Facebook campaign to promote its latest compact
digital camera, the Lumix ZX-1 with 8x optical zoom and got a very positive
response.
Customer reviews and Online communities play a vital role in the customer-led
promotion process. The consumers have a lot of questions which they ask through
reviews and forums. The Online community has experts and customers, who build up
a dialogue with consumers and answer their queries. Online videos are also becoming
popular for educating consumers. Thus consumer electronics sector is efficiently
merging the use of videos and social media, giving its brand’s otherwise complex
products a platform where all its contents can be seen and directly read by the
consumers. “The growth in online reviews, ratings and feedback forums puts power in
the hands of consumers” (Robbie Tutt). The growth in digital marketing has provided
new opportunities to new players and has lead to the ‘reinvention’ of the established
ones and has resulted in a major shift in the market, with companies which understand
and adopt digital marketing making comparatively more profits.
For the purpose of research, selected consumer electronic items have been taken into
account such as music players, television set, video recorder, DVD players, digital
cameras, personal computers/Laptops, telephone instruments, mobile phones, video
games consoles, camcorders.
70
Chapter 2
Review of Literature
The whole essence of this study has been to study the impact of advertising done,
using social media tools like face-book, twitter, LinkedIn, on consumer buying
behaviour. The argument that Social Media played an influential role in shaping
consumer perception and ultimately affected the buying behaviour of the consumers,
has been given considerable attention. In order to get complete understanding of the
theory and practice, various International as well as National Literature Review has
been analyzed and reviewed.
1. Andreas M. Kaplan and Michael Haenlein (2010) in Users of the world, unite!
The challenges and opportunities of Social Media, has defined Social Media as a
group of Internet-based applications that is built on ideological and technological
foundations of Web 2.0 and that allow the creation and exchange of User Generated
Content. The writer of the paper has explained that although Social Media is a related
concept, with Web 2.0 and User Generated Content and has evolved from the same,
however it differs from them on technological and ideological grounds. The various
types of Social Media tools or applications like Collaborative projects, blogs, content
communities, social networking sites, virtual game worlds and virtual social worlds
are explained in detail. The author says that today everything is about social media
and that if you do not participate in Face-book, YouTube, Twitter you are no more a
part of cyberspace. Social media is a tool through which businesses can directly
contact the end-consumers, within short span of time and with great efficiency and
that too at low cost as compared to other traditional media. This paper recommends
71
companies, for developing their own Social Media strategies in order to be a part of
this new trend and gain more profits.
2. W. Glynn Mangold and David J. Faulds (2009) in Social Media : The new
hybrid element of the promotion mix, argues that Social Media is a hybrid element of
the promotion mix because in a traditional sense it enables companies to talk to their
customers, while in a non-traditional sense it enables customers to talk directly to one
another. The writers feel that Social Media being a hybrid element of the promotional
mix, should be incorporated as an integral part of the company’s Integrated Marketing
Communication (IMC). When Procter and Gamble (P&G) or General Electric (GE)
entered the arena of Social Media, they carefully framed their communications with
the market place in order to consistently reflect their organizational values and they
acknowledge the value of incorporating Social Media into their IMC strategies and
promotional efforts.
The second promotion related role of Social media is : customers can use Social
Media to communicate with one another. The organization cannot control the content,
timing, and frequency of the social media based conversations occurring between
consumers. This stands in contrast to the traditional integrated marketing
communications paradigm where organizations have a high degree of control over the
customer’s communication. The Social Media has profoundly affected all aspects of
consumer behaviour, and has bestowed consumers with power they have not
previously experienced in the marketplace (Li & Bernhoff, 2008).
3.Tom Smith (2010) in The social media revolution, says that the impact of Social
Media is being felt across the globe. Social Media has changed the manner in which
72
the communication between the organizations and the customers were taking place; it
has changed from talking through mass media to listening and conversing through
social media. Since the consumer online is a commentator, reviewer and publisher, all
the organizations have to stop talking and start listening to how they are perceived
online. Listening is just the start, after listening, actively participating in the
discussions with the consumers and engaging them is crucial. This engagement with
the consumers online will be the key way for building long-term advocates of the
brand, who not only purchase their products but also recommend them on and offline.
The writer then opines that there is a huge opportunity for research, as the need for
research outputs and knowledge will shape the consumer opinion. Research and
research companies have a great scope for research through Social Media and the
research companies that evolve with Social Media can increasingly prosper. Research
Companies can evolve in various ways :
Build community : Consumers want to share views and opinion and communities
should grab this opportunity and tap in. This means providing constant surveys,
message boards, listening permanently and not occasionally, and making the
conversation two-way by sharing results back with them.
Work with brands to build research communities: Research agencies and companies
should work together and two-way research oriented portals should be implemented
by organisations. Research agencies should constantly and carefully monitor the
consumer’s conversations and help the organizations with the latest updates.
73
Widgitise : widgets or mini applications allow you to place your site or content in an
external web environment. These offer a new way of building and maintaining
research communities and distributing surveys.
Embrace social media platforms as research platforms: Sharing information, content,
opinions through wikis, network sites, blogs, video sites, generates a large amount of
data which can be very helpful for research. Therefore the social media platforms
should be used as research platforms.
4.Yin, Sara (2008) in her research paper How Social Media and PR Connect,
writes that with the emergence of Social Media, the whole communications landscape
has transformed and the mass mobilizing power of Social Media is tremendous.
People think that Social Media is a threat to traditional PR and mainstream media,
however Social media complements traditional PR and traditional PR will exist as an
important component of any successful business. The PR and advertising agencies are
all undergoing a change and are trying to evolve their strategy, physical structure and
business models to be in tune with social media.
5. Áine Dunne, Margaret-Anne Lawlor, Jennifer Rowley (2010) in their study Young
people’s use of online social networking sites-a uses and gratifications
perspective have made an attempt to find out the reason behind young people’s use of
social networking site with special reference to bebo. The results of the study indicate
that the participants were using bebo for their personal motives and in order to
maintain a certain persona and identity in social context. The impersonal nature of the
Social media has lead to facilitate the young people where they can negotiate the
practicalities and forge the identities and maintain relationships.
74
6. Irene, falsePollach (Oct-Dec 2008) in their study on Media Richness in Online
Consumer Interactions : An Exploratory Study of Consumer-Opinion Web Sites
have exclusively discussed on Consumer Opinion websites which provide
opportunities to people to share their opinions or views about a product or service,
read others opinions and also interact with other consumers. The writers have
identified three major challenges which the consumer opinion websites face and they
are i. quality of contributions ii. motivating users to participate iii. earning reader’s
trust. The main objective of this article is to find out ways by which the quality of the
contents of these websites is enhanced so that it becomes a useful source of
information for the consumers as well as the companies. The conclusions drawn from
the study shows that the consumer opinion websites are more influential and provide
more valuable information when they separate the complex task of information search
and dissemination from the simple task of social interaction, and support each task
with appropriate levels of richness. The writers conclude that consumers should
consider both positive and negative points about a product or service before stating
their opinion.
7. Anil Bhatt (May 2012) in his paper on Blog Popularity And Activity On Social
Media : An Exploratory Research has made an attempt to find out the impact of
some social media website’s popularity on ROI. Social media provides a global
opportunity for brands to use them as an effective channel for marketing of products
and services. However the effectiveness of any marketing channel is largely
dependent on a very important entity the ROI. ROI is something that most marketers
look at when one has to determine the effectiveness of any marketing channel. The
study therefore examined ROI for weblogs and how their promotions through two
highly popular social networking sites, namely Facebook and Twitter affects their
75
popularity and in turn increases their revenue through advertisements. Page views is a
direct measure of the traffic a particular blog has and therefore a correlation between
page views and Facebook fans and twitter fans was established to understand the
effect of promotion of brands through social media. The findings of the study revealed
a positive correlation across all blog categories and hence it was concluded that a
positive change in Facebook followers and Twitter followers increases the number of
page views. It was also found that the page views increased with the increase in time
due to an increase in fans or followers.
8. Shahir Bhatt and Amola Bhatt (2012) in their research paper Factors
influencing Online Shopping : An Empirical Study in Ahmedabad writes about
the factors which influence the perceptions of consumers regarding online shopping.
The study has revealed ease/attractiveness of website, service quality of websites and
website security as the three important factors which have prominently emerged from
the study. The paper has proved that that these factors are related to specific type of
consumers classified as occasional, frequent and regular consumers. The study shows
that the regular buyers are most influenced by the ease/attractiveness and service
quality of website, whereas the occasional buyers value website security to a greater
extent.
9. Sheetal Thapar and Navneet Sharma (2013) in their study on role of social
networking sites in some key cases throws a light on the growing popularity of social
networking sites. The study showed that people have got their own media to raise
their voice and stand for their rights. Author thinks that Social Media possess the
character of true democratization of information. Study concludes that the
participatory nature of Social Networking Sites cuts through caste and class barriers.
76
10. Ambrose Jagongo, Catherine Kinyua (2013) in their study The Social Media
and Entrepreneurship Growth focused on the effect of social media on the growth
of SMEs in Nairobi. The study established that social media tools offer greater
market accessibility and CRM which in turn have a significant impact on the growth
of SMEs. This study recommends that the policy makers should come up with
favourable internet surfing rates and e-business policies to encourage the
technological adoption that would grow the SME industry.
11. Venkatesh, R(2013), examines the possibilities of different sections of society
following different trends of communication. This study talks about the usage of
product promotion on social media, by the multinational companies in India
especially in the FMCG sector.
12. The potential of Social networking sites in the field of education have been
explored by Afendi Hamat, Mohamed Amin Embi, Haslinda Abu Hassan (2012)
in the research paper “ The Use of Social Networking Sites among Malaysian
university students”. The young adults most often use social media to interact and
socialise with their peers. This study is conducted nationwide in Malaysia, where
SNSs are popular and very commonly used for interaction by the young adults,
however there is very limited data available on the patterns of its use for the wider
segment of the target population. The results show that the SNS has not penetrated
completely i.e. 100% in Malaysia as was assumed earlier. The study also shows that
the respondents are found to spend more time on interacting and socializing through
SNS than learning and they do not think that the use of SNS affects their academic
performance. It has come out from the studies that the respondents are using SNS for
the purpose of informal learning activities and nearly half i.e. 50.3% use it to get in
touch with the lecturers for informal learning purpose.
77
13. Tool for collecting brand visibility information of brands present over social
media have been identified by the authors Botha, E., Farshid, M., Pitt, L. (2011) in
their research paper “How Sociable? An exploratory study of university brand
visibility in social media”. Brands are using Social Media to acquire new customers
and to retain the existing ones. Brands need to acquire information about their
visibility on these social networking sites, as compared to the visibility of their
competitors. This is an exploratory study that has been conducted on the South
African University brands. This study has identified, positioning of the brands over
social media and the strategies followed by them to make themselves visible to the
audience, as the tools for knowing the visibility of the brands. The findings of the
study revealed that the South African University brands do not have a distinct position
over social media, nor do they have effective strategies to engage their stakeholders.
The writers concluded that the institutions should have a fair attitude towards Social
media, since social media is currently governing the internet and the media. The
people who are managing these brands can see this as an opportunity to make the
brand presence prominent.
14. Sunil Karve, Shilpa C. Shinde (March 2013) in their paper “Effectiveness of Social
Networking Sites (SNS)” have made an attempt to figure out the experiences of the
internet users regarding social media and have also tried to find out the pattern of
SNS usage of the consumers. The writers state that social media has become so
much popular, that it has surpassed the popularity of email, to become number four
after search, portals and PC software applications. The tremendous increase in the
amount of time people are spending using these SNS have changed the way people
spend their time online and this affects the way people behave, interact and share in
78
their normal daily lives. This paper has tried to analyse the overall effectiveness of
SNS.
15. An effort has been made to know the awareness of social media with respect to
business among teaching, non-teaching staff and students of a college by T. S.
Venkateswaran, B. Sowmya, R. Arun (July-Aug. 2012) in their research study
“Effective use of Social Websites towards business among academicians and
students in Namakkal District”. The research intends to investigate the following :1.
Knowledge of business through social websites.2. Uses, impact and causes of social
websites.3. Rating the various activities in Social websites.4. Impact of Social
websites in future. The study lists the benefits of social websites for business: 1. To
create brand awareness. 2. Utility of SNSs as a effective online reputation
management tool. 3. For the purpose of recruiting. 4. To learn about new
technologies and competitors. 5. As a lead generation tool to intercept potential
prospects. The results of the study reveals that most of professionals and students
are aware about business taking place through social networking sites, however
most of them are not using it for the business purpose. Most of the respondents are
using SNSs for socializing. Therefore the writers think that the social websites need
to grab the professionals and students from rural areas to concentrate on business
through Social websites.
16. The importance of Social media platform as a crucial tool for strategic marketing
has been discussed by Efthymios Constantinides, Carlota Lorenzo Romero and
Miguel A. Gomez Boria (2009) in their research study “Social Media : A New
Frontier for Retailers?” This study has proposed a number new strategies for
79
retailers implementing which will not only help the retailers to survive, but create a
competitive advantage and flourish in the new environment.
17. The rising significance of Virtual social worlds and how business can use their
potentials has been discussed by Andreas Kaplan and Michael Heinlein (Nov.-Dec.
2009) in their research work “The fairyland of Second Life : Virtual social worlds and
how to use them”. At the beginning this study the authors have discussed about the
evolution of virtual social worlds and its history. Followed by how it fits in our time
and lastly how they are different from other social media, such as content
communities (e.g. YouTube), social networking sites and blogs (e.g. Facebook),
collaborative projects (e.g. Wikipedia) and virtual game worlds. This study has
thrown light on how businesses can make use of these virtual social worlds in the
field of advertising and communication, virtual product sales (v-Commerce), human
resource, marketing research and internal process management. Along with it this
study has also discussed increasing linkages between the real and the virtual worlds,
enforcement of law and order and transformation of virtual social business as
business hubs or centres of the future.
18. Age differences have been considered in the perceptions of social communities
held by people who were not participating in these comparatively new social spaces
have been examined by Jae Eun Chung, Namkee Park,Hua Wang, Janet Fulk,
Margaret McLaughlin in their study “Age differences in perceptions of online
community participation among non-users :An extension of the Technology
Acceptance Model”. With the help of Technology Acceptance Model (TAM) An
effort has been made to investigate the factors that have an effect on future intention
80
of non users, to take part in online communities. The results of the study showed that
perceived usefulness factor has a positive relationship with the behavioural intension.
The ease of use factor did not play a significant role in predicting the perceived
usefulness. The study also found out that there exists a negative relationship between
the factors age and internet efficiency; age and perceived quality of online community
websites. The writers concluded by stating that age does not change the relationships
between perceived usefulness, ease of use and intention to participate in online
communities.
19. The effect of the News feed privacy outcry on user behaviour changes has been
examined by Christopher M. Hoadley, Heng Xu, Joey J. Lee, Mary Beth Rosson in
their research “Privacy as information access and illusory control : The case of
the Facebook News feed privacy outcry”. A survey was conducted on 172 current
Facebook users in one of the large universities of US in order to determine the reasons
and the extent to which the users were upset and to investigate the influence of the
News feed privacy outcry on the user behaviour changes. The results showed that an
easy access to information and an unreal loss of control which was alerted by the
introduction of News feed features stimulates, privacy and security concerns among
users.
20. The impact of cultural orientation of American, Chinese and Turkish non-profit
organization’s behaviour and communication patterns in the social media space has
been examined by Richard D. Waters & Kevin D. Lo (2012) in their research study
“Exploring the impact of Culture in Social Media Sphere : A content analysis of
non-profit organization’s use of facebook”. A content analysis of 225 non-profit
organization’s Facebook profiles was carried out for the research purpose. Particularly
81
the study has focused on the ways in which the organizations disclose information
about themselves and about those who manage their Facebook presence, ways of
promoting the organizational accomplishments and news, and engaging with the
stakeholders in relation to their context, performance and collectivist/individualist
natures, respectively. The findings of the study showed mixed support for the impact
of traditional cultural expectations, thus suggesting that global connectivity of social
media may be contributing to blurred cultural boundaries in favor of virtual culture
that promoted the global community.
21. The usage of social media among the destination marketing organizations (DMO)
of the top 10 most visited countries by international tourists has been examined by
Stephanie Hays, Stephen John Page & Dimitrios Buhalis (2012) in their research
study “ Social Media as a destination marketing tool: its use by national tourism
organisations”. Social media are gaining more importance in the marketing strategies
of DMOs as it helps in seeking greater value in the way marketing budgets are spent.
Social media offers DMOs with global audience at limited costs. The writer has made
an attempt to determine the impact and usage of social media marketing strategies and
has developed a model of best practices for the national tourism organizations to learn
from. The findings show that the social media usage among the top DMOs is still
experimental and the strategies differs extensively.
22. The usage of social media and customer centric management systems and its
contribution to firm-level capability of social customer relationship management
(CRM) has been investigated by Kevin J. Trainor, James (Mick) Andzulis, Adam
Rapp, Raj Agnihotri (June 2014) in their research work “Social media technology
usage and customer relationship performance : A capabilities-based examination
82
of social CRM”. This paper has made an attempt to conceptualize and measure the
capabilities of
social CRM. The second important contribution of this paper is
exploring how social CRM capabilities are influenced by customer centric
management systems and social media technologies. The results suggests that social
media technologies and customer centric management systems had a positive relation
with the customer relationship performance.
23. The social media presence and social media metrics of Indian IT companies
namely Infosys, Wipro, TCS and HCL has been discussed by Ramulu Bhukya (2012)
in the research paper “ Presence of Indian Big IT Brands on Social Media : an
Empirical Study”. The data for this study has been collected from the respective
brand’s social media websites and analyzed on a 5 point scale. The research findings
shows that HCL and Infosys have high scores of 3.75 points for their social media
presence and have their brand accounts on 7 social network sites each. Next with
score of 1.75 and presence on 7 SNS is Wipro followed by TCS with score of 1.25
and social media presence on 6 SNS. de Vries et.al. (2012) conducted a study on 355
brand posts from 11 international brands spread across six categories of products and
the result shows that positioning the brand post on top of the brand fan page helps in
increasing and enhancing the popularity of the brand post. The author opines that the
SNS are free to join because if they are not free they will not expand and earn money
from the brand or customer driven advertisements. According to the Social Media
Benchmark survey conducted by http://business.com, 2948 businesses were
undergoing the process of decentralization of marketing plan. Among them 1,197
maintained a company profile on one or more SNSs, 80% maintained a presence on
Facebook while 56% maintained an account on Twitter. Social media has opened the
doors of global markets and Infosys, Wipro, HCL and TCS are now preparing to
83
compete with the global biggies. Already Indian companies have labour availability at
a comparatively much lower costs as compared to the global companies which is one
advantage that the Indian companies have in their favour. However value created in
the fields of brand, intellectual property will take these Indian companies to new
heights. Accordingly these tech companies are working aggressively over budgets for
marketing globally, enhancing global positioning and brand valuations.
24. Whether integrated marketing communication is a new horizon or a beginning of
another failed marketing communications in marketplace experiencing economic
turmoil, has been examined by Philip J. Kitchen & Don E. Schultz (2009) in their
research work “IMC: New horizon/false dawn for a marketplace in turmoil”.
The writers argue for a totally new opinion for IMC going forward to match the
economic realities faced by the organizations. IMC will be driven by marketplace,
customer, technological changes enhanced by globalization and a shift of marketplace
power to consumers, all heavily influenced by the current economic conditions.
25. Wright, Elizabeth; Khanfar, Nile M (Nov. 2010) in “The lasting effects of
Social Media Trends on Advertising” has explored that there is no use investing
millions in traditional methods of advertising because people find new ways to block
or get away from these advertisements. The key is to target the right people with the
right messages. In order to do this, the marketers should focus on Connectors(are the
ones that move and steer people into directions and avenues of interest to them) ,
Mavens(are the ones who want to know the best deals and tell everyone about it) and
salespeople(who have the ability to convince and sell new ideas). The connectors,
mavens and salespeople have the ability to give a high return on investment, since by
targeting them the organization can acquire high sales just by targeting a small group
84
of people. Thus targeting the right people not only brings down the organization’s
advertising expenses but also drastically improves their marketing productivity. Social
Media makes it very easy for any organization to connect with these connectors,
mavens and salespeople. It is very important to use holistic, comprehensive
relationship marketing strategy to target these people, since they are key drivers in
influencing the consumers buying decision. The idea of relationship marketing is to
“build mutually satisfying long-term relationships with the key constituents in
order to earn and retain their business”(Keller & Kotier,2009,p.20). To build and
sustain such a relationship, marketers must understand and respond to customer needs
and goals. Marketers are using tailored social networking forums to reach the
consumers in a more personalised way. Marketers are finding that interactive and
targeted marketing are the key to success and are far more beneficial than the
traditional advertising.
26. Integrated Marketing Communications (IMC) must be used in social marketing is
proposed by Jacinta Hawkins, Sandy Bulmer and Lynne Eagle (2011) in their
research work “Evidence of IMC in social marketing”. This research study has
given evidence of IMC being successfully incorporated in the commuication of
school-based health promotion activities within schools that promotes health. The
findings of the research reveals that IMC principles are successfully communicted and
forms part of the HPS(health promoting school) policy of promoting health. This
research throws light on how IMC can and should be used in social marketing. This
research has provided insights for social marketing practitioners to improvise on the
communication efforts.
27. Tan, Wei Jia; Kwek, Choon Ling; Li, Zhongwei (March 2013) in paper “The
Antecedents of Effectiveness Interactive Advertising in the Social Media”, have
85
tried to find out consumer’s attitude towards interactive advertising and its impact on
purchase intention. Through their study the writers have made an attempt to share
some understandings and opinions with advertisers and companies on the
measurement of effectiveness, which they can consider when placing an interactive
advertising. In the literature review the writers states the following factors as the
determinants of the effectiveness of interactive advertising: Attitude towards
Advertising, Attitude towards Advertised Brand, Purchase Intention, Time of
exposure to advertisement(Yang, 1996). The results of the study reveals that, there is
a positive relation between attitude towards advertisement and purchase intention to
effectiveness of interactive advertising. Thus the writers concluded saying traditional
advertising could be used, but interactive advertising measures should be an add on.
28. Thirushen Naidoo (November 2011) in his research paper “The effectiveness of
advertising through the social media in Gauteng” has made an attempt to
investigate the effectiveness of advertising through the medium of social media and
has focused mainly on Facebook. The author states that social media marketing
explores and utilizes the social aspect of the web and is therefore able to connect and
interact on a much more personalised manner than the traditional marketing. The
study reveals brand engagement, brand attitude, brand image and consumer
engagement as the factors contributing to the effectiveness of advertising. The paper
also talks about brands having strong market presence automatically getting more
attention from consumers on Social media. The author concludes that inorder to be
effective, a brand needs to be established and must have strong brand reputation. The
advertisements on Facebook serve to supplement the brand and does not put the
brand up the rank in terms of its reputation.
86
29. A study has been conducted on social networking sites like Facebook, Twitter and
Orkut by authors P. Sri Jothi, M. Neelamalar and R. Shakthi Prasad (March
2011) in their research paper “Analysis of social networking strategy in developing
brand communication”, with the primary objective of determining the effectiveness
of brand communication strategy in advertising products and promoting brands on
social networking sites. The various reasons for social media being a widely used
platform, for advertising compared to the other traditional advertising mediums have
been discussed. The various ways that are being provided by social media platform
for its users to communicate with each other and interact with the brand are discussed
like chat, messaging, video, email, voice chat, file-sharing, blogging and discussion
groups. According to the writers views, the marketing communications are becoming
personal, interesting, interactive and social. Findings of the study suggest that social
media advertising has its impact on 70% of the users and half of them access these
ads i.e. games, quiz, events etc. It was found that the interaction is more in the display
banner advertisements in Face book and Orkut. Every Social networking site has a
unique communication strategy and user interaction. Face book promote and allows
user interactions, Twitter feeds posts relating the brand and Orkut promote through
click ads and promotional brand pages. Face shows accessibility because of its huge
popularity and Twitter gives more importance to the text. The writers concluded by
stating that Social networking sites have the scope to grow big for highly targeted
marketing and advertising. Social networking sites presents enormous opportunities to
build the brands and have become a branding hub.
30. The characteristics of online marketing strategies used by E-entrepreneurs have
been explored by S. Vivin. Richard, Ms. Sri. Jothi (Aug. 2012) in the research
study “ A study on online marketing strategies used by E-Entrepreneurs in
87
India”. The study has analysed E-Entrepreneurs like www. Amazon.com, www.
Flipkart.com, www. Naptool.com etc. for the purpose of studying the nature and
extent of marketing strategies used by successful online Entrepreneurs. SNSs apart
from being a fantastic medium of communication and interaction, keeps the customers
informed of the consumer market as well. Through this paper the writers have voiced
their view that there is a need to analyze and research the needs of the customers who
come online to fulfil their requirements. Internet has given an opportunity to
entrepreneurs to market their products and services across the globe and has opened
the doors of such a gigantic market, that their sales force cannot even think of
identifying. The online companies can engage in online promotional activities through
effective online marketing strategies to enhance their offerings in the online markets.
Advertising on the internet not only provides the information about the offerings but it
also encourages innovation. The study concluded by revealing the results which stated
that Social media marketing is one of the best online marketing strategies that has
been used by the E-Entrepreneurs. The results also revealed that the International
players like e.Bay.com, Amazon.com are well ahead in customer relationship building
and management and in the online marketing strategies. Indian brands are identifying
the strategies which the international websites use to improve the website and are
trying to build their brand identity.
31. The Mass media advertising is coming to an end and its chances of recovery are
grim, this has been talked about by Ronald T. Rust and Richard W. Oliver (June
2013) in their research “The Death of Advertisng”. In this paper the authors have
expressed their opinion about the traditional advertising business being directly hit by
direct marketing. The reasons for the diminishing business of traditional advertising
has been attributed to the arrival of new technologies that have empowered the
88
consumers. A new market has evolved which is more in capacity, interactive and
multimedia in place of the traditional mass media. This new media advertising results
in more producer-consumer interactions.
32. Work on building systems that would help in recognizing the spam blogs, finding
opinion on topics, identifying communities of interest, deriving trust relationships and
detecting influential bloggers with the help of models of blogosphere have been
elaborately explained by Tim Finin, Anupam Joshi, Pranam Kolari, Akshay Java,
Anubhav Kale, and Amit Karandikar (2008) in their study “The information
ecology of social media and online communities”. Social media systems such as
photo- and link-sharing sites like YouTube etc., weblogs, online forums are estimated
to generate one third of the new web contents. One prominent feature that
distinguishes the “web 2.0” sites from the other web pages is that they are interlinked
with other forms of network data. Their standard hyperlinks are enriched by social
networks, comments, trackbacks, advertisement, tags, resource description framework
(RDF) data and metadata. The writers conclude by stating that as the internet evolves,
it changes the way in which the people interact with it as content providers or content
consumers and the results will be more interesting mixture of underlying networks network of individuals, groups, opinions, beliefs, documents, advertisements and
scams. These interwoven networks will pose new opportunities and challenges for
extracting information and knowledge from them.
33. Many online social networks fail to generate sustainable revenues from
advertising, even though the usage activity is high. The means of harnessing effective
advertising strategies in online social networks has been discussed by Florian Probst
(2011) in his research work “ Predicting User’s future level of communication
89
activity in online social networks : A first step towards more advertising
effectiveness”. To enable effectiveness of advertising strategies, identifying a user
who can influence a large number of friends or acquaintances is essential. In this
regards, the user’s future level of online communication activity in online social
network plays an important role. High online activity in the past does not guarantee
high level of the future online activity. The means of predicting user’s future level of
communication activity are required. Therefore the writers have proposed a
probability based model that has been developed to primarily forecast the purchasing
behaviour of the consumers resulting from the user’s communication activity on
online social networks.
34. Applying Anthropological theories into the social marketing practices has been
advocated by the authors Guang Tian, Luis Borges (2012) in their study “The
effectiveness of social marketing mix strategy : Towards an Anthropological
Approach”. The author has described social marketing as a new science that seeks to
improve the overall life quality of human beings by adopting marketing strategies and
skills without aiming for making profits. Although the basic concepts of social
marketing and commercial marketing are similar, however their principles differ in
various fields. The author feels that the social marketers should become aware of the
anthropological aspect of social marketing and about the differences between the
social and commercial marketing theories. Social marketers should be able to apply
anthropological theories and methods into social marketing practice.
35. Discussing the impact of social media on marketing has been the main goal of this
study “Impact of Social Media on Marketing” by Rajiv Kaushik (March 2012).
Different media of marketing before social media revolution have been discussed,
90
followed by the evolution of Social media. The impact of social media on marketing
is discussed in detail with the help of standard metrics like online advertising, public
relations and search engine optimization. The paper has also discussed the concerns
and criticism of social media. Some of the important concerns were if the customers
post comments or tweets in haste it can cause severe damage to the brand image. If
the consumers find the brand’s social networking activity intrusive then there is a high
risk of losing the consumer. Since marketers are directly dealing with the public, they
cannot lurk behind the scene, but have to become more accountable for the brand. The
growing popularity of social media can lead to social media overtaking to other
functional areas of marketing. Social media is building a bridge between the
marketers and the consumers through continuous engagement, building trust and
targeting the right audience at the right time and in real-time.
36. Simona Vinerean, Iuliana Cetina, Luigi Dumitrescu & Mihai Tichindelean
(June,2013) in their exploratory research work The Effects of Social Media
Marketing on Online Consumer Behaviour, have tried to determine the students
pattern of using social media and social networking sites in relation to their reactions
to the advertisements on social media, where they have the freedom to choose the
information they engage with. The aim of this research paper is to empirically
investigate what type of social media users, have a positive outlook regarding
advertising on social networking sites. This study has contributed to the existing
knowledge, of consumer behaviour in an online environment and on developing
positive reactions to online advertisements and have also presented new ways to
classify the online consumers, which served as a basis for psychographic
segmentation, based on respondents online activities. The authors concluded stating
that in order to be successful in the social media environment, companies must
91
undergo continuous online marketing research and should be sensitive to the changes
in consumer behaviour patterns and should be able to identify new areas of customer
interest.
37. Logan, Kelty; Bright, Laura F; Gangadharbatla, Harsha (2012) in paper
Facebook versus television : advertising value perceptions among females the
writers compared the perceptions of female students regarding value of advertising on
social network sites and value of advertising on television. Advertising
trustworthiness-The study shows that consumers have become more concerned about
the factualness or trustworthiness of advertising content. It was found out that
consumer-generated product recommendations are more recommended than marketergenerated product recommendations. Involvement-SNSs provide an involving
environment for advertisers. The users of Social networking sites involve in brand
related activities and are therefore more engaged than consumers who simply read,
listen or watch advertisements about a brand. Advertising effectiveness measures-It
was found out that the advertisements having an element of entertainment and
information in them are more accepted advertisements on SNSs. The results indicated
that the young adults crowd(19-24) are more inclined towards informativeness than
entertainment and young female participants are more engaged by entertaining
advertisements on SNSs. Therefore it was concluded that informativeness and
entertainment play a significant role in assessing advertising value whereas irritation
did not play a significant role in assessing the advertising value.
38. Garima Gupta (January-June 2013) in the research paper Assessing the
Influence of Social Media on Consumer’s Purchase Intentions has made an
attempt to determine the impact of social media on product evaluation and the
resulting decision-making process of Indian consumers. The results are supportive of
92
the fact that social media does affect purchase intentions. More Specifically, there is a
positive and strong impact of three factors namely peer communication, perceived
product informativeness and the level of product involvement on consumers purchase
intentions in the context of social media. The author concludes that as the products
offered online cannot be examined, perceived information on social media and its
spread through communication among peer groups facilitates consumer’s evaluation
and purchase related decision.
39. Boris Bartikowski, Gianfranco Walsh in their research paper Attitude contagion
in consumer opinion platforms: posters and lurkers have tried to explain how
consumer’s perception about product reviews affect the product and brand attitudes
and in turn affect the consumer’s buying decision. This study also reveals how the
consumer product reviews affect the brand-related attitudes of posters than lurkers.
40. O’Brien, Clodagh (2011) in their research paper “The emergence of the social
media empowered consumer” has thrown a light upon the various platforms that has
an impact on traditional relationship marketing concepts and how this has resulted in
raising consumer expectations of the conventional business. This study also talks
about areas like word of mouth and consumer empowerment and emphasises the areas
of potential development in theory and practice as a result of social media
empowerment. The author in this study has expressed his views about social media
and CRM. He says that Social media has completely changed the manner in which
communication takes place thereby giving a new dynamics to the human
relationships. The organizations which are accepting social media must also accept
that they are losing the element of control to the consumers. For most of the
businesses social media has gained a lot of importance in establishing their web
93
presence overtaking the company website and email communication programme. This
has completely changed the manner in which the organizations are interacting with
their consumers and how they are implementing the customer relationship
management strategies. The main difference between the traditional CRM and social
CRM is that the social CRM is more customer oriented and it involves customers
proactively. The author also talks about word of mouth marketing that empowers the
organization and not the consumer.
41. falseDiffley, Sarah; Kearns, James, Bennett, William; Kawalek, Peter (2011)
in their research paper Consumer Behaviour in Social Networking Sites:
Implications for Marketers have made an attempt to investigate whether social
networking sites (SNSs) can be used as an effective tool of marketing and whether it
can engage the consumers to participate in marketing on SNSs. The authors write that
companies need to undertake a different approach that will attract consumers rather
than pushing marketing messages on them. If marketing messages are pushed onto the
consumers it will result in adverse reaction and the consumers will express their
dissatisfaction when they are communicating over SNS. This will have a negative
effect on the company and put an end to the potential of SNS to be used as a
marketing tool. This paper talks about developing the correct approach in using SNSs
as a marketing tool. In this paper the authors have drawn a conclusion that companies
need to work towards having a ‘friendship’ based approach with the consumers and
need to build relationships with them in order to have the SNSs act as a Marketing
tool for the companies.
42. The factors which affect shopping attitude on social networking sites are
identified by Jugal Kishor and Prof. V. K. Singh (August 2014) in their study “An
94
empirical study on shopping tendency through social networking sites (SNSs)”.
Different methods of payment used for shopping through SNSs are focused in this
study. This research study has revealed that social networking sites have different
targest consumers and factors; and have correlated these factors. The nature of the
study is exploratory since it focuses on new idea of virtual shopping through SNSs.
The writers opine that patrons who vary in age all through the 30s are captivating the
targets for sellers of goods and accommodations. The writer further states that due to
the unique characteristics of gregarious networks the items that are sold on the
gregarious networking sites can vary from the items that are being sold on other
virtual sites. The internet sites largely sell authentic items i.e. the items (goods or
accommodation) that can be used offline, irrespective of whether they are bought
online or offline, such as books, flight tickets, furniture, apparel etc. The gregarious
networks not only sell authentic items but they additionally sell virtual items i.e. the
items (good or accommodations) whose use and purchase are constrained by exacting
webspace, such as homepage outline, avatars, implicit gifts, music that can be utilised
only on concrete websites etc. The findings of the study shows that time spent on
social destinations is a differentiating element that influence the disposition to looking
for things on a long range interpersonal communication. The apparent fit is the
strongest factor that influence the shopping intensions on social destinations. The
study found out that the individuals who regularly use long range informal
communication locales usually accept more extra offers. Therefore the informal
community clients hesitate to shop on interpersonal communication locales. It has
been discovered through this study that different age groups have association with the
SNS shopping variable. From the managerial viewpoint the study reveals that the
target consumers and the features of SNSs should differ according to the product type
95
if SNSs look forward to expand their businesses to include the shopping services.
That is younger people with positive perceptions of usefulness, ease of use and
security of shopping services on SNSs.
43. An attempt is being made to examine how social relationship factors relate with
eWOM being transmitted via social networking sites by Shu-Chuan Chu, Yoojung
Kim(2011) in their study “Determinants of consumer engagement in electronic
word-of-mouth (eWOM) in social networking sites”. As more and more marketers
incorporates Social media in their promotional activities, there is a need to investigate
the determinants that impact the consumers engagement in eWOM via social
networks. eWOM is based on three aspects : opinion seeking behaviour, opinion
giving behaviour and opinion passing behaviour. Opnion seekers depend on others
advice to make purchase decision. Opinion givers exert a great influence on others
opinions. Opinion passers helps in the flow of information. Literature review has
revealed four social relationship variables - tie strength, homophily, trust and
interpersonal influence. Interpersonal influence is further divided into normative
influence and informational influence. The results indicate that tie strength, trust,
normative and informational influence had a positive relationship with all types of
eWOM behaviours. However homophily had a negative relationship with the eWOM
behaviour.
44. The theory of ‘factors affecting the shopping attitude on social networking site
differ with change in the type of product’ has been explored by Jiyoung Cha (2009)
in their research work “Shopping on Social networking websites ; Attitudes
toward real versus virtual items”. The study is based on two types of products
which are present on social networking sites: Real products and Virtual products. The
96
study reveals that usefulness, age, ease of use, security and fit play a significant role
in determining the attitude for shopping real products. On the other hand gender,
social networking site experience, ease of use and fit influence the attitudes for
shopping virtual products.
45. The factors affecting consumer’s attitudes towards marketing through the medium
of social media have been discussed by Erkan Akar & Birol Topcu (March 2011)
in their study “An examination of the factors influencing consumer’s attitudes
towards social media marketing”. Consumer communities on social media are new
marketplaces for marketers. The goal of this research is to identify the factors that
affect the consumer’s attitude towards marketing on a social media platform.
46. The brand communities based on social networking sites influence the elements of
customer centric model and brand loyalty has been talked about by Michel Laroche,
Mohammad Reza Habibi, Marie-Odile Richard (Feb. 2013) in their research work
“To be or not to be in social media : How brand loyalty is affected by social
media?”. The study has aimed to show how brand communities based on social
media influence the elements of customer centric model (i.e. the relationships
between focal customer and brand, product, company and other customers) and brand
loyalty. An empirical study was conducted on 441 respondents through survey
method. The results of the study revealed that brand communities present on social
media have a positive effect on customer-product, customer-brand, customercompany and customer-other customer relationships, these in turn have positive effect
on brand trust and trust has positive effect on brand loyalty. The study found that
brand trust plays an intermediary role in converting the effects of relationships in
brand community to brand loyalty.
97
47. Examining the young social media user’s responses towards social media
advertising has been focused by Shu-Chuan Chu, Sara Kamal & Yoojung Kim
(May 2013) in their research work “ Understanding consumer’s responses
toward social media advertising and purchase intention towards luxury
products. The popularity of Social media as an advertising platform is increasing
with the users interacting with each other and with the brand. In the same time period
the online luxury market experienced enormous growth due to rising number of users
in the age group of 18-35 and belonging to affluent background. This research
focused on determining young social media user’s belief, attitudes and behavioral
response towards social media advertising. Brand consciousness and awareness was
found to have its effect on user’s attitudes towards social media advertising, which
eventually affects their behavioral response towards social media advertising and
ultimately affects purchase intention of luxury products.
48. Smith, Nicola (Nov 2009) in their research paper “Consumer electronics
vertical Focus : The heights of invention” has stated that consumer electronics
sector has been the first sector in experimenting and trying out the different
techniques of digital marketing. The various product promotional campaigns taken up
on the Social Media by the digital pioneers like Sony, Toshiba, Samsung, Panasonic
and the overwhelming responses they received online form a part of this paper. It has
been put forth by the writers that the key challenge faced by the consumer electronics
brands has been to effectively communicate the complexity of their products to a wide
audience and through digital marketing they have overcome this challenge and
therefore they are turning to digital marketing. Online video and Social Media (Social
media includes Consumer reviews, online communities and forums) have been cited
as the greatest opportunities by the consumer electronics brands. The brands can
98
directly open a dialogue with the consumer, understand the consumer’s needs, answer
their questions, get feedback and have a friendly engagement with the consumer; all
because of Social Media.
49. A detailed understanding of the relationship between social media and viral
marketing has been provided by Andreas M. Kaplan and Michael Haenlein
(May-June 2011) in their research work “Two hearts in three quarter time : How
to waltz the social media/viral marketing dance”. Viral marketing has been
defined as the electronic word of mouth in which some marketing messages relating
to the product, or brand is transmitted in an exponentially growing way through
social media applications. This study has considered three conditions which need to
be fulfilled to create an epidemic of viral marketing. These three conditions are
giving the right message to the right messengers in the right environment. For any
business there are two main reasons of using the social media platform - 1.
Marketing. 2. Customer service.
50. The future possibility and the prospects of connecting consumer electronics to
the web through open API’s has been endeavoured by the writers Mariana Baca
and Henry Holtzman (2008) in their research work “Television meets Facebook :
Social Networks through Consumer Electronics”. The project consist of an IP
enabled digital video recorder in the form of advance cable television set-top box
connected to Facebook social network. The objective of this project are : a. How can
the omnipresent consumer electronics work together in a simple way, b. How can
the data available on social networking application diffuse in useful ways into the
participant’s real lives and c. How can these systems accomplish these tasks
seamlessly without adding time or complexity to the user’s experience. The results
of this project are the user can now watch on his TV, the media that his friends
99
enjoy, as well as his own explicitly requested recordings, through the system of
ratings the user can use her DVR’s enhanced interface to post back on social
network what shows have been liked by them. The user’s profile box in Facebook is
going to be the main tool for sharing the user’s upcoming viewing schedule. The
information on their profile affects their viewing habits, therefore users will be more
conscious with whom they add in their friend network and what information they
provide through their profiles. The integration of consumer electronics and social
networking is expected to benefit the content producers and distributors through the
automation of word-of-mouth (WOM) recommendation. This project is also
expected to help the users in finding the appealing new content faster than they
would otherwise, help users in sharing contents and experiences more easily and
will help the content distributors track content distribution in a social network
directly into consumer electronics.
2.1 Literature Gap
From the literature review specific to Social Media it was found out that there is no
study in Maharashtra which talks about the role of social media in shaping the
consumer’s perception and thus influencing their buying behaviour.
Also, even though many people use Social media for buying consumer electronics
however no major study has been conducted on role of social media with respect to
consumer electronics segment in Maharashtra as well as Gujarat.
There has been no study conducted so far on Social Media with special reference to
young working women of Maharashtra and Gujarat.
100
Chapter 3
Objectives, Hypothesis and Research Methodology
3.1 Statement of the Problem :
Social Media is a Buzz word today. It is extremely popular not only among the youth
but people belonging to higher age groups also seem to be catching up with this new
technological advancement to a great extent. Businesses are extensively making use
of Social media in framing their marketing strategies. The main goal of this study has
been to study the impact of social media and how it influences consumer’s perception
in turn to affect their buying behaviour. This study would be able to bring out whether
advertising on social media does influence the consumer’s buying behaviour so that
companies can decide whether to continue with traditional marketing practices or
whether to incorporate social media in their marketing strategies.
It has been revealed from the literature review that there has been no study conducted
so far on Social Media front of cities in both states, Maharashtra and Gujarat in the
consumer electronics segment. Therefore this study was conducted with the aim of
comparing the results and findings of the research in the two states.
101
3.2 Objectives of Study:
Based on the Literature Review and the gap identified from the Literature Review the
objectives of the study were framed, which are as follows:
1. To identify the Social Media Usage by young working women in different cities.
2. To study the customers buying behaviour with respect to Social media advertising.
3. To study the impact of social media advertising on the buying behavior of young
working women for consumer electronics.
4. To study the effectiveness of Social Media tools like face book, twitter, LinkedIn
on the consumer behaviour.
5. To study the impact of social media advertising on working women belonging to
different demographic factors such as qualification, annual income, occupation and
place.
3.3 Hypothesis :
From the Objectives of the study the following Hypothesis were formed:
H01: There is no specific reason of consumer’s social media usage.
H11: There is a specific reason of consumer’s social media usage.
H02: There is no significant difference in buying behaviour of customer with respect
to social media advertising.
H12: There is a significant difference in buying behaviour of customer with respect to
social media advertising.
102
H03 : There is no significant impact of social media advertising on young working
women’s buying behaviour.
H13 : There is a significant impact of social media advertising on young working
women’s buying behaviour.
H04 : There is no significant difference in effectiveness of different social media tools
on consumer behaviour.
H14: There is a significant difference in effectiveness of different social media tools
on consumer behaviour.
H05: There is no association between effect of social media advertising and education
of respondent.
H15: There is an association between effect of social media advertising and education
of respondent.
H06: There is no association between effect of social media advertising and income of
respondent.
H16: There is an association between effect of social media advertising and income of
respondent.
H07: There is no association between effect of social media advertising and occupation
of respondent.
H17: There is an association between effect of social media advertising and occupation
of respondent.
103
3.4. Research Methodology
This chapter has been an over view of the research method used for this study and has
include data collection, sample selection, type and contents of questionnaire,
processing of data and finally interpretation of the data. Quantitative and Qualitative
research approaches have been embraced for the purpose of research by this study.
3.4.1. Type of Study
The study has been conducted in two phases. Initially in Phase-I, Exploratory research
has been conducted. For the same purpose formal interactions were conducted with
those young working women who use online sites for buying consumer electronics
products. After the interactions, the variables of the study had been identified and
accordingly the questionnaire was prepared. In Phase-II, a Descriptive study had been
conducted. The secondary data had been collected from various available resources.
Review of Literature from various published reports, research journals, reference
books and online databases like Proquest, www.Googlescholar.com,www.Alexa.com,
www.statista.com, www. Statisticbrain.com, www.econsultancy.com etc.
3.4.2 Data Collection
Primary data had been collected by questionnaire survey method. Research instrument
that had been used for this research were questionnaire and personal interviews. A
single questionnaire had been created and administered in three cities by the
researcher. The target audience for this study were working women in the age group
of 18-35 from Mumbai, Nashik and Surat.
The cities in India have been classified on the basis of grading structure devised by
the government of India. According to this gradation, Mumbai belongs to Tier I
104
category of cities and Nashik and Surat belongs to Tier II category (source for
information on Tier I & II cities of India: www.maps ofindia.com). The requirement
of the study was comparison between the tier one and tier two cities having different
population sizes. Therefore based on convenience, Mumbai was selected as a Tier I
city or a Metro city with heterogeneous population of 12.7 million and Nashik as a
tier II city having population approximately 1.5 million from Maharashtra and Surat
having population 4.5 million was selected from Gujarat (source :Reports of Internet
And Mobile Association of India [IAMAI] and Internet Market Research Bureau
[IMRB] ).
3.4.3. Pilot Study
Pilot study was conducted and the questionnaire was first pre-tested on a sample of
100 respondents (working women in the age group of 18-35) from Mumbai city for
checking the reliability of the questionnaire.
3.4.4. Reliability
The Chronbach’s Alpha found out was 0.860. Any value of Cronbach’s Alpha above
0.6 shows that the scale is reliable.
3.4.5. Questionnaire
The questionnaire comprised of questions pertaining to various sections mentioned
below and each section had several questions related to the section to which it belongs
to.
105
Table 3.1 Showing details of Questionnaire
Section
Name
Questions
Total
Number
number of
questions
Sec. 1
Demographic information
Education,
Annual 4
Income, Occupation and
Place
Sec.2
Usage of Social Media.
1,2,3,4,5,6,7,8,9,10,11,12,
14
13,14.
Sec. 3
Consumer Buying Behaviour.
1,2,3,4
4
Sec. 4
Online Purchase Behaviour.
1,2,3,4
4
Sec. 5
Complex Buying Behaviour.
1,2,3,4,5,6
6
Sec. 6
Habitual Buying Behaviour.
1,2
2
Sec. 7
Variety
Seeking
Buying 1,2,3
3
Buying 1,2,3
3
Behaviour.
Sec. 8
Dissonance
Behaviour.
Sec. 9
Impulsive Buying Behavior.
Sec. 10
Effectiveness
of
1,2,3
3
Social 1(A,B,C,D,E),2,3,4
8
Media 1(A,B,C,D,E),2,3
7
Media.
Sec. 11
Impact
of
Social
Advertising.
106
3.4.6. Size and Design of Sample
The study was conducted in two cities of Maharashtra (Mumbai & Nashik) and in one
city of Gujarat (Surat). The sample unit is working women in the age group of 18-35
and having knowledge of internet.
3.4.7.a. Sampling Technique :
Random Sampling technique has been used for this study. In a Random sample from
infinite population selection of each item is controlled by the same probabilities and
the successive selections are independent of one another. (C.R.Kothari, Research
Methodology Methods and Techniques)
3.4.7.b. Sample size Calculation – The following formula was used for calculating
the sample size.
Where, n = Sample size,  = Standard Deviation, E = Estimated margin of error
= is known as the critical value. The critical value is
The margin of error
= 1 and the standard deviation
for sample size, we can calculate
= 1.96.
= 18.19. Using the formula
:
2
 Z  
1.96 18.19 
2


n

 1271.78
 E  
1



2
Round off sample size required is 1272 respondents.
107
3.2. Table on Chosen Sample Size
Place
No. of Respondents
1
Mumbai
516
2
Nashik
359
3
Surat
397
TOTAL
1272
Sr. No.
3.4.8. Variables of the study
3.3. Table showing the variables of the study
Dependent Variables
Buying Behaviour with respect to
Social Media Advertising.
Independent Variables
Online Purchase Behaviour
Consumer Buying Behaviour
Complex Buying Behaviour
Habitual Buying Behaviour
Variety Seeking Buying Behaviour
Dissonance Buying Behaviour
Impulsive Buying Behaviour
108
3.4.9. Definition of the Variables :
1. Online Purchase Behaviour :
Online Purchase Behaviour variable primarily indicates the online behaviour
of the consumer from the purchase point of view. It throws light on a couple
of things related to the purchase taking place online through the medium of
social media, like involvement of the consumer while taking the online
purchase decision, to what extent the consumer thinks there is a difference in
the products of different brands available online, what does the consumer
think about the price of the product available on social media and does the
consumer think that the decision making process in case of online products is
time consuming.
2. Consumer Buying Behaviour :
Consumer Buying Behaviour variable focuses on the online behaviour of the
consumer from the reasons which lead to the purchase through social media,
point of view. It considers the reasons which lead to the purchase like whether
the consumer read the blogs / reviews or view the advertisement on social
media. It also studies the consumers behaviour by considering, to which
electronic products consumer has provided rating.
3. Complex Buying Behaviour :
Complex buying behaviour when the consumer is highly involved in the
buying then it is called complex buying behavior. In case of complex buying
behavior the consumer must collect proper information about the product
features and attributes.
109
4. Habitual Buying Behaviour :
In case of Habitual buying behavior there is low involvement of the consumer.
The consumer buys the product belonging to a particular brand which has been
regularly preferred by them because the consumer thinks that the product
belonging to a particular brand is best fit for them. The consumer buys the
product quickly.
5. Variety-Seeking Buying Behaviour :
Variety seeking buying behaviour takes place when the consumer has many
different product choices that serve the same purpose. In case of VarietySeeking Buying Behaviour Consumers generally buy different products
because they want to try out a new variety of product.
6. Dissonance Buying Behaviour :
In Dissonance buying behavior consumer is highly involved in the purchase.
Dissonance buying behavior occurs when the product which the consumer is
thinking of buying is expensive or there are no differences or a few differences
between the brands. The consumers experience a feeling of discomfort or
anxiety after the purchase of the product, because they fear that the expensive
product which they have bought should not be a failure.
7. Impulsive Buying Behaviour :
Impulsive buying behaviour takes place when the consumer makes an
unplanned purchase, provoked by seeing the product or upon exposure to a
lucrative advertisement or scheme.
110
3.5 Limitations of the Study :
The study was conducted based on the data collected from Mumbai, Nashik and Surat
and therefore findings of this study may not be applicable to other cities in India and
the world at large because of the socio-cultural and economic differences.
3.6 Utility of the study :
The study would be very useful to markets who would now be able to use social
media as a platform for promoting their products and services.
3.7 Theoretical Model :
Figure 3.1 Theoretical Model of the Study
Factors of Social
Media
Advertising
Age
Occupation
Education
Annual Income
Place
Online Purchase
Behaviour
Consumer Buying
Behaviour
Complex Buying
Behaviour
Habitual Buying
Behaviour
Consumer
Buying
Behaviour
with respect to
Social Media
Advertising
Variety Seeking
Buying Behaviour
Dissonance Buying
Behaviour
Impulsive Buying
Behaviour
Age : 18-35.
Occupation : Service, Business & Self-employed professionals.
Education : Non-graduates, Graduates & Post Gadruates.
Annual Income : Up to Rs. 3 Lakhs, 3.1-5 Lakhs, 5.1-10 Lakhs & above 10 Lakhs.
Place : Mumbai, Nashik & Surat.
111
3.8 Analysis of Data :
The data was analyzed in SPSS version 17 using different statistical tools viz:
1. Frequency Table With percentages
2. Analysis of Variance (ANOVA)
3. Chi-Square Test
4. Regression
5. Rank Order
The raw data has been collected from primary source i.e. with the help of
questionnaire which consists of the question at two different level of measurements
i.e. nominal and interval scale. To draw the logical inferences from the data
descriptive and inferential statistics techniques had been used. The type of statistical
techniques i.e. Bivariate analysis and Multivariate analysis has been used based upon
the level of measurements of the questions pertaining to those variables. The
Multivariate procedures dealing with the analysis of variance were used to test and to
draw the inference whether the samples have been drawn from more than two
populations having the same mean; it helped the researcher to understand the
perception of the responses for all the factors in more than two groups. Then in bivariate analysis, Chi square test has been used to find the association between the two
qualitative variables. Frequency table with percentages has been used to identify the
demographics and buying behaviour perception of young working women across
different cities. Regression analysis has been used to determine the nature of
relationship (functional relationship) between the variables of the study for
forecasting or prediction. Rank Order has been used to determine which Social
networking tool from Facebook, Twitter and LinkedIn is the most effective social
networking tool. Rank Order correlation coefficient measures the strength of
association between two ranked variables.
112
Chapter 4
Typical Aspects of Social Media and Social Networking Sites
Electronic word of mouth is an important phenomenon of Social Media and it has the
potential to take the products and the brand to new heights, as well as to ruin the
existence of a product or a brand. Electronic word of mouth (eWOM) has been
defined by Hennig- Thurau et al. (2004, p.13) as “any positive or negative statement
made by potential, actual or former customers about a product or company, which is
made available to a multitude of people and institutions via the internet”. Consumers
very often refer to the eWOM as a source of information that gives them a large
variety of opinions about the product or service or the company. Consumers find the
eWOM as more persuasive, more trustworthy than the information direct from
corporate sources. Yet eWOM can cause a threat to companies, since managers don’t
have any control over the negative messages spread by the unsatisfied consumers.
Therefore it is very important for managers to effectively manage these eWOM so as
to use them to promote the products and brands.
Social media management is a new area of management which has emerged with the
growing success of social media and it is a kind of service which is associated with
Social Media. Organizations can efficiently manage their outbound and incoming
online interactions along with the small business marketing activities with the help of
Social media management solutions. Social media management solutions helps in
reorganising and fusing the organization’s conversations taking place when the
organization interacts through different channels of Social media like - blogs, social
networks like Twitter or Facebook, and other public and private Web communities
113
and sites. They also help the organizations to monitor and to know the people’s views
and opinions about their business, products or services. Social media management
helps in the automation of the process of delivering the outgoing messages through
multiple social media outlets simultaneously and also enhances the organization’s
social media presence across several social networking sites.
Social media management tools also facilitates organizations to integrate their social
networking activities with other marketing programs which include other online
activities, such as search engine marketing campaigns, Web sites, email marketing,
contact management systems, as well as offline marketing, such as events or white
paper.
4.2. Social Networking Sites :
4.2.1. Facebook
4.2.1.1. Origin of Facebook:
Face book was invented by a computer science student of Harvard University called
Mark Zukerberg in February 2004. Mark Zukerberg alongwith his classmates
Eduardo Saverin, Dustin Moskovitz, and Chris Hughes invented the Facebook.
Facebook was originally named as Facemash. Facemash was a software written by
Zukerberg when he was in his second year and it was a type of a game wherein the
students who visited this website were able to see and compare two students identity
photographs side-by-side which let them decide “Hot” or “not”. Mark Zukerberg
tapped into the Harvard university’s security network and from there he csopied the
student’s id images to populate Facemash. For the same Mark Zukerberg faced
charges of breach of security system which were later on dropped. On 4 Frebruary
2004 Mark Zukerberg launched the website “TheFacebook”. The access to The
114
Facebook site was at first restricted to Harvard students only. Zukerberg then took the
help of his friends to make his site more popular : Eduardo Saverin worked on
business, Dustin Moskovitz as a programmer, Andrew McCollum as a graphic artist,
and Chris Hughes. Together the team expanded “TheFacebook” from Harvard’s
campus to additional universities and colleges. In 2005 they purchased the domain
name Facebook.com for $20,000 and within no time Mark Zukerberg became the
world’s youngest multi-billionaire.
4.2.1.2. Number of Users on Facebook :
Active users are those users who have logged into Face book during the last 30 days.
In the second quarter of 2012, there were more than 1 billion monthly active users
(MAU). In the fourth quarter of 2013 the number of face book users had crossed 945
million mobile MAU. In the first quarter of 2014 Facebook had 1.28 billion monthly
active users.
4.2.1.3. Face book’s Revenue: Facebook’s revenue grew from 153 million in 2007
to 7.87 billion US dollars in 2013.
4.2.1.4. Advantages of Facebook :
1. Facebook is free :
Facebook has provided free services to its users. This is the biggest advantage of
Facebook because anything which comes free is more powerful. Apart from that
Facebook is a well designed website and has the capability of engaging the users for
longer time. In the recent days some paid services have been started by Facebook, but
those paid services are not made compulsory for its users and the users are given the
freedom to choose the right service.
115
2. Facebook helps in Networking :
Facebook helps in connecting with old school friends, college friends and relatives.
Also facebook gives its users opportunities to make new friends from the different
parts of the world. Facebook users can use facebook chat, poke, messages, group etc
to connect with different people and improve their relationships with them. Therefore
the users of facebook can take advantage of the various services provided by
Facebook and maintain their relationships. They can not only share videos and albums
but also write blogs, articles and share it with their acquaintances.
3. Facebook facilitates Business :
Facebook has billions of users across the world, therefore it is the best place for
businesses to promote their products or services. Using facebook, businesses can
improve their brand value in the social media network. Businesses can direct their
products or services through promotional campaigns to their target audience over
Facebook. For acquiring Business, organizations can make Facebook fan page of their
brand or company.
4. Facebook video chat :
Facebook’s video chat tool allows its users to video chat with their friends and
relatives. Facebook in partnership with skype has an in built video chat application
which offers the service of video chatting to Facebook users.
116
5. Facebook as image and video hosting site :
Facebook users can make albums of their images or collection of their videos on
Facebook and share it as public with their acquaintances or keep it private by using
Facebook privacy.
6. Facebook security :
Facebook has extremely high standard privacy policies and it provides high class
security to the users account. It has kept the privacy settings very simple so that the
users can easily use them to secure their account. Facebook is very strict as far as
spammers are concerned. The users can hide the posts of the spammers, block the
spammers or report the spammers to Facebook.
7. Free Gaming and app store on Facebook :
Free gaming services are provided by Facebook to its users, whereby the users can
play free games with their friends. Also Facebook provides free app store where you
can use thousands of Facebook application.
8.Facebook for news :
Facebook is also used by many users as a source of information and to know the
news.
4.2.1.5. Disadvantages of Facebook :
Though Facebook has an array of benefits, it also has some disadvantages.
However the advantages of Facebook outpass the disadvantages.
117
1.1.Fake profiles and ids :
Fake profiles is one of the biggest disadvantages of Facebook. Many people
create false id’s and fake profiles to cheat and trouble people they don’t like.
2.Addicting nature of Facebook :
Facebook is a well designed site with thousands of exiting and interesting
applications and services. It has the power to engage the user for long hours by
consuming their precious time. If the facebook is used to suffice the need, then it
is alright, however if one gets addicted to it, it consumes a lot of valuable time.
3.Privacy issues :
Many times due to lack of knowledge, many people don’t use the privacy features
which are offered by Facebook. This directly affects their personal information
which they provide to facebook to be accessed by anyone and for any purpose
causing serious problems.
4.2.2. Twitter :
4.2.2.1. Origin of Twitter :
Twitter is an innovation born out of necessity. Twitter is basically an sms mobile
phone-based communications platform, which eventually grew into a web
platform and it was founded by Jack Dorsey, Noah Glass, Biz Stone and Evan
Williams in 2006. This sms based platform was proposed by Jack Dorsey to cofounder of Odeo, Evan Williams and Biz Stone during a brainstorming session at
Odeo which was a podcasting company. Evan Williams and Biz Stone asked
Jack Dorsey to go ahead and develop the twitter project. Noah Glass came up
118
with the name “twttr” earlier and later on it became popular with the name of
“twitter” which was also found out by Noah Glass. During the development and
testing phase of twitter the podcasting company Odeo underwent a rough patch
because Apple released its own podcasting platform which killed Odeo’s
business model. The founders decided to buy their company back from the
investors. Jack Dorsey, Biz stone, Evan Williams and other members of Odeo
staff facilitated the buyback and by doing this they decided to acquire the rights
to the Twitter platform. There is a controversy involved in the formation of
Obvious Corporation which was formed as a formality after the investor
buyback of Odeo to house Twitter. The key members of the twitter development
team were not brought on to the new company, specifically Noah Glass.
Twitter’s user base has grown at an astonishing rate to over 200 million active
monthly users in six years and in March 2013, Jack Dorsey and Biz Stone were
awarded the patent that secures the ownership of Twitter.
4.2.2.2. Number of Users on Twitter :
Table 4.1. Showing the Number of users on Twitter
119
Twitter Company Statistics
Data
Total number of active registered Twitter users
645,750,000
Number of new Twitter users signing up everyday
135,000
Number of unique Twitter site visitors every month
190 million
Average number of tweets per day
58 million
Number of Twitter search engine queries every day
2.1 billion
Percent of Twitter users who use their phone to tweet 43 %
Percent of tweets that come from third party
60%
applicants
Number of people that are employed by Twitter
2,500
Number of active Twitter users every month
115 million
Percent of Twitters who don’t tweet but watch other
40%
people tweet
Number of days it takes for 1 billion tweets
5 days
Number of tweets that happen every second
9,100
For the period, Twitter reported 241 million monthly active users. The service
also reported monthly mobile active users of 184 million.
Source : www.techcrunch.com dated 28/11/2014
4.2.2.3. Revenue of Twitter :
Table 4.2 Showing the year-wise revenue of Twitter
120
Twitter Annual Advertising Revenue
Revenue
2013
$405,500,000
2012
$259,000,000
2011
$139,000,000
2010
$45,000,000
Source : www.statisticbrain.com dated 28/11/2014.
4.2.2.4. Advantages of Twitter :
1. Businesses can do Internet marketing free of cost by using Twitter wisely.
Twitter provides an excellent opportunity for the businesses to identify and to
understand the passion and interests of their target market. Businesses can
research their target markets by following their tweets.
2. Twitter can be used by businesses to understand the strategies of the
competitors by following their tweets.
3. Twitter allows individuals to efficiently network with large groups of people
and interact with their target markets effectively. Twitter helps in efficiently
directing the internet marketing campaigns to the relevant groups.
4. Twitter helps businesses in communicating instantly and directly with the
target market. It helps in gathering valuable feedback (real time intelligence)
from the target audience, in a very short span of time. Thus facilitating in
having a lasting relationship with the consumers.
4.2.2.5. Disadvantages of Twitter :
1. Twitter has a large number of spammers. Therefore its adivisible to filter out
the spammers from the lists frequently, to have a fair judgement of the target
market.
121
2. It is very easy to get distracted from the purpose or objective as you get
involved in the communication or receive tweets from outside the business
interests. Therefore the individuals need to be focused on their business goals.
4.2.3. LinkedIn
4.2.3.1. Origin of LinkedIn :
LinkedIn is the oldest social networking site and it is older then YouTube,
Facebook and Twitter. LinkedIn started in the living room of the co-founder
Reid Hoffman and it was officially launched on 5 May 2003 with a goal of
connecting world’s professionals and making them more productive and
successful. Reid Hoffman had an excellent track record of working on the boards
of Google, Ebay, PayPal. Along with Reid Hoffman, Allen Blue, Konstantin
Guericke, Eric Ly and Jean-Luc Vaillant are the inventers of LinkedIn.
LinkedIn has diversified business model with talent solutions, marketing
solutions and premium subscription products.
4.2.3.2. Status of LinkedIn Today :
LinkedIn operates the world’s largest professional networks on the internet with
more than 315 million users in over 200 countries. The professionals are joining
the LinkedIn at the rate of more than 2 new member per second.
4.2.3.3. Revenue of LinkedIn :
LinkedIn has earned a total revenue of $534 Million in the second quarter of
2014.
122
Figure 4.1 Revenue distribution of LinkedIn
Source : http://press.linkedin.com/about dated 20/10/2014
4.2.3.4. Benefits LinkedIn Brings for Business:
For business owners, account managers, business development managers and
anyone else who is in sales, these are the benefits LinkedIn can offer :
1. 1.Find Business Partners, Clients and Service Providers :
By giving simple searches you can connect with the experts, service providers
and prospective customers. If you want to recruit people, LinkedIn provides easy
access to potential candidates that fit in the required level of expertise. Business
can also post job ads. LinkedIn is the best solution where you can find
professionals according to your business requirement.
2. 2.Information Sharing :
LinkedIn is commonly used for knowledge sharing and the LinkedIn users can
123
use their mailbox to pose questions to contacts and the multitude of groups on
LinkedIn serves as forums for discussions on variety of products and industry
related topics. LinkedIn Answers is the tool of LinkedIn which aims at providing
online information and idea-sharing.
3. 3. A Blog Promotional Tool :
LinkedIn users are able to add a blog or website to their individual profile in
order to give it exposure. LinkedIn is a great way to promote and share blogs.
4. 4. LinkedIn Recommendations :
Another tool that has been offered by LinkedIn is recommendation feature. Once
the products or services are there on any individual’s company profile,
recommendations can be requested from the customers regarding the product.
Recommendations means people talking about an individual or a product or a
service in Discussions, mentioning them as the expert in Answers or talking
about them outside of LinkedIn. Recommendations is the way to increase the
company’s trustworthiness and win new clients.
5. 5. LinkedIn for SEO :
LinkedIn’s profiles get a high PageRanking in google and this is a good way to
influence what people see when they search for an individual or his business.
LinkedIn allows an individual’s profile information to be available for search
engines to index. Going one step further LinkedIn now provides the facility to
share the content alike Facebook and Twitter and this activity is called as the
stimulator of search engine ranking positions (SERPs).
6. 6. Starting groups:
An excellent opportunity to network and grow the business is provided by the
Groups tool. Through this tool one can put the website link in the group profile
124
for greater visibility and also include the website link in the group welcome
message and in each new discussion that is being created.
7. 7. Advertising :
LinkedIn has 120 million users global presence and excellent targeting
capabilities to attract the advertisers. LinkedIn has started a service called
“LinkedIn Ads” which has given an advertising opportunity to all it its users on a
cost per click (CPC) or impression basis (CPM).
4.2.4. Youtube :
4.2.4.1. Origin :
YouTube is the world’s most popular video site. In the year 2005 there were a
lot of content and photographs sharing sites and there were a lot of ways of
capturing photos. However there was not a single site through which one can
share the videos. That’s when Youtube was invented by Chad Hurley, Steve
Chen and Jawed Karim. Chad Hurley had studied design at Indiana University in
Pennsylvania and Steve Chen was a computer science student at the Illinois
University at Urbana Champaign. After graduation both of them started working
at Paypal in San Jose, California. In Feb. 2005 the logo and domain of YouTube
was registered by Hurley and three months later the beta test site
www.YouTube.com was launched in May 2005. YouTube received its funding
from Sequoia Capital in November 2005 and in the month of December 2005
YouTube officially became a corporation with its office in California. The first
video which flashed on the site was “Me at the zoo” which was a 19 seconds
long video. Google identified the growing potential in YouTube and YouTube
was acquired by Google for $1.65billion in October 2006. YouTube is spread
125
across 61 countries and 61 languages.
4.2.4.2. Number of Users accessing YouTube :
More than 1 billion users visit YouTube each month and more than 6 billion
hours of video are watched every month on YouTube. 100 hours of video are
uploaded to YouTube every minute.
4.5. RSS
Short form for Rich Site Summary or Really Simple Syndication. This service is
an easy way to distribute the updates of websites to a large number of people. It
is mostly used by those computer programs which organize the headlines and
updates for easy reading.
4.2.5.1. Working of RSS
The author of the website maintains a list of notifications in a standard format on
the website, which is called as the “RSS Feed”. People who are interested in
knowing the latest information or updates can check this list. “RSS Aggregators”
are specialised computer programs which access the RSS Feed of various
websites of interest, on your behalf and organize the results. The RSS feed and
RSS aggregators together form the “RSS Readers”.
4.2.5.2. Benefits of RSS :
There are some websites whose content changes on an unpredictable schedule
for e.g. product information pages, news sites, community and religious
organization information pages, medical websites, websites of educational
institutions and weblogs. Continuously scrutinizing each website for any updates
126
is very time consuming and tedious.
Earlier websites were emailing notifications to the users. But when there are
notifications from many websites they are not organised and are mistaken for
spam. RSS is a better way of receiving many updates and in an organized
manner. RSS properly handles the updates of multiple websites and the results
are presented in a well organized form.
4.2.6. SlideShare :
Slideshare is a web service meant for uploading presentations and sharing it with
everyone, so that the presentations receives more views. It is the world’s largest
community for uploading and sharing presentations. It supports all formats : ppt,
pps, pptx, odp, pdf, doc, docx, odt, keynote and iWork pages. The presentations
and documents can be linked with an individual’s LinkedIn account. One can
incorporate YouTube videos in presentations and can also add audio. Slideshare
is present on LinkedIn and Facebook. If the presentation is uploaded to anyone
from Facebook, LinkedIn, Slideshare, then it shows up in all three instantly.
Looking at the potential Google bought Slideshare platform at $119 Million.
4.2.6.1. Users of Slideshare :
According to comScore, SlideShare had 29 million unique visitors. Users have
uploaded 9 million presentations on SlideShare.
4.2.7. Myspace :
Myspace is a social networking site which allows its users to create web pages
for the purpose of interacting with friends. Myspace allows its users to create
blogs, upload photos and videos and design profiles to portray their talents and
127
interests. This site originated in the year 2003 and it became extremely popular
among people. Myspace has helped many talented music artists and actors to
kick start their flourishing careers.
4.2.8. Friendster :
Friendster is another social networking site used for interacting with friends. It
came into existence in 2002, therefore it’s a predecessor to Myspace and
Facebook. Friendster at present, has closed its social networking services.
Friendster founder Jonathan Abrahams has entered into a new business of social
news services, which is open to public and shares news and stories of successful
tech companies.
Chapter 5
Consumer Electronics Companies and their presence on Social
Media
5.1. Global Players :
5.1.1. Samsung
Samsung has efficiently mastered the social care element not just by its social
media presence, but by cleverly targeting their target consumers by its social
128
media campaigns. Samsung has a customer centric approach for its online
advertising where the consumer’s needs, their issues, queries, feedback is given
a lot of importance.
Samsung has successfully set an example of taking advantage of metrics
powered digital and social strategy to have an overwhelming number of fans
from the earlier 8 million to directly 25 million fans making it the number one
brand in Europe. Samsung has achieved maximum global reach by creating its
Facebook brand page in 25 different languages for 28 countries taking itself to
very corner of the globe. Samsung is very much happy with its strategy of
localized campaigns.
Every week it has approximately 300,000 fans joining its fan list. This success of
Samsung Europe can be attributed to analytics driven understanding of
consumers, developing localized campaigning strategy and engagement through
quality creative content that supports two-way communications between
Samsung and its fans. Engaging, listening and responding to the consumers is of
prime importance to Samsung. The main objective of their social media
participation is creating the best social content, social advertising and
segmentation and social care.
5.1.2. Apple
Apple has not made a direct appearance on Social media sites like Facebook and
twitter which means that Apple does not have its own company page or Apple
brand page on these social networking sites for their products. However it does
have accounts on facebook and twitter for its services like itunes, iBook and App
129
Store. But all these accounts which Apple has for its services are not interactive
and social. Apple does not make any attempt to respond to the consumers or
does not participate in any discussions through these accounts. It simply pushes
the marketing messages on to the consumers. Apple’s strategy was to become
famous socially by remaining completely silent and let the rumors do the work
of enhancing Public relations. Basically Apple wanted to create arouse
excitement among people for its products. However Apple finally made its social
media debut by creating its Apple brand page on Tumblr for promoting its
iPhone5C. Apple selected Tumblr because it allows users to click on several
short video animations with a short story and tagline in it. Apple’s strategy was
to create a buzz by remaining silent and let the rumors do all the promotion work
for it.
5.1.3. Sony
Sony adopts the policy of providing Social customer service which is delivering
customer centric service through social media platform. According to social
media management experts at Sony, there is no need to respond to every brand
related query of the consumers that pops up in the social world, allowing the
answers to the query to come from within the social media community.
The experts advocate that Sony’s social media strategy has been to identify the
Super Users (Super active sony fans) educate them, train them and allow them to
respond to customer queries on behalf of Sony, to become social media
moderators. The customer service provided through social media should be
personal, helpful and chatty.
130
According to Nico Henderijckx European Forums Communities Manager, Sony:
1. The best support given to Sony users is by other users through their
community.
2. It follows the principle of allowing end users to help other users in social
customer service.
3. Sony keeps their ‘super’ fans i.e super active Sony fans passionate by
training them about the brand so that these super fans become the Social
media moderators for Sony.
4. Sony has been successful in achieving an 85% solve rate of the
complaints through peer-to-peer online support and it has been observed
that the complex problems are solved faster than the support line calls.
5. The moderators or the super fans are kept abreast with the latest product
knowledge through monthly training, online meetings, super user
conferences and insights on product launches. The top management
keeps the super fans passionate and motivated by meeting them and
explaining them the company’s goals and objectives.
6. The moderators are treated such that they feel special and are also given
incentives in the form of free products.
7. According to the latest statistics there has been an increase in the number
of British user’s interaction with brands over Twitter from 12% to 36%.
8. 65% people prefer social media than the call centers for solving their
customer queries and for customer service.
9. Social customer service is often used as a (di)stress channel i.e a back
channel to release customer’s frustration when other methods of contact
fail.
131
10. Social customer service is more profitable than marketing since 1%
increase in customer coming back to website generates 10% increase in
revenues.
5.1.4. Hewlett -Packard
The growing effectiveness of digital media has changed the way marketers
interact with customers. New age customers are informed and empowered. The
challenge for organizations is to be a part of the customers’ conversations
involving their product and to influence their choices. Hence, the adoption of
digital channels such as social media, e-mail marketing and online search and
display ads is growing steadily. What helps marketers get the most out of their
digital marketing strategies is the ability to capture and mine data exhaustively
and cost effectively through advanced analytics techniques. These techniques
shed light on the effectiveness of all marketing activities, thus helping marketers
fine-tune their strategy. In the long run, this enhances customer engagement,
optimizes digital “marketing spend” [1] and has a direct impact on revenues.
Hewlett-Packard uses advanced analytics and operations research (O.R.)
applications to drive its digital marketing strategies and overcome business
challenges. HP’s in-house analytics team, Global Business Services Analytics
(GBS Analytics), directs this enterprise-wide analytics effort and plays a critical
role in developing various advanced analytics solutions. These solutions
leverage structured and unstructured data across billions of customers worldwide
to improve the effectiveness of the e-marketing model across HP.
E-Marketing at HP
132
HP is at the centre stage of this digital revolution and has been making
significant investments on digital channels to generate demand, engage
customers and provide technical support. E-marketing at HP can be summarized
into three broad themes – the three “Cs” of the e-marketing ecosystem:
1. Community deals with the aspect of engaging customers and creating
awareness about HP products and services, leading to effective lead generation.
2. Content deals with the aspect of providing customized relevant content to
customers and improving user experience.
3. Commerce deals with aspects of generating revenue through online
commerce. (See Figure 5.1.)
Figure 5.1: Three Cs of E-marketing ecosystem at HP.
Multiple levers are available to grow the online business. These levers
include:
133

driving traffic by guiding customers to the right content;

increasing conversion by offering relevant products at appropriate price
points;

cross-sell and up-sell to increase basket size and improve margins; and

increasing customer engagement to increase loyalty and repeat purchase.
HP deploys various analytical solutions to drive online commerce.
5.1.5. LG
LG intends to be present on Social media to create a closer connect with the
consumers. The objective is to develop a bonding with the brand based on the
likes and dislikes of consumers which will result in brand awareness and brand
promotion.
According to LG facebook is a much broader engagement platform as compared to
twitter. LG makes maximum use of Facebook platform for informing, engaging and
entertaining the target audiences. Therefore LG undertakes a wide variety of initiatives
related to product and technology news, contests, exploiting their sponsorships with
respect to cricket and Formula One etc. Interaction levels are high here. LG’s activities
on Twitter are limited in terms of content sharing and it is more often used as a platform
for answering the grievances. Twitter’s role for brands should be to listen more than
134
talk; to solve problems and answer questions when asked.
According to LG the marketers must understand the consumer’s reason of joining a
social community instead of bombarding its followers with the brand messages.
Therefore the content should be such that it helps people enjoy, connect with others and
entertain them.
The Smartphone Idea Camp allows our consumers to showcase their creativity
and talent and its an excellent way to gain inspiration from the imagination of
the mobile phone users and the tech savvy crowd. Through this contest LG is in
a better position to understand the demands and desires of the consumers and to
know what exactly they are looking for in a product so that it can add value to
their lives.
5.2. Indian Players :
5.2.1. Hindustan Computers Limited (HCL) :
HCL is an India based company which has a network of offices in 26 countries
with 88,000 professionals of “ diverse nationalities”. HCL is exclusively using
Social Media for hiring people. In order to provide Human resources to
employees, HCL has developed its own social network for internal use, called
Meme. The Meme platform has been used by the employees for connecting with
135
each other, for giving feedback and suggestions, for sharing photographs, for
asking queries, for enabling support functions and staffing services. HCL’s
social listening and analytics studies the consumer’s views and opinions,
delivers competitive intelligence and product research and performs customer
profiling, segmentation and influencer identification. HCL has a maximum
social media presence with its company profiles being created on Facebook,
Twitter as well as LinkedIn, Flickr, Youtube, Google+ and Scribd.
5.2.2. TCS :
TCS is an Indian multinational Information Technology (IT) company offering it
IT and Business solutions and services and providing outsourcing service. In
consumer electronics segment it focuses on audio, video and game console
products. TCS has presence on six social networking sites Facebook, Twitter,
LinkedIn, RSS feed, Slideshare, Youtube. On a 5 point scale, TCS has a rating
of 1.25 for its Social Media presence.
5.2.3. Wipro :
Wipro is extensively using the Social media platform for spreading the brand
and reaching out to customers. When wipro decided to use social media platform
in 2007, it considered social media as a business driver. A great deal of emphasis
was laid on the importance of creating an experience for its users.
136
The MyWiproWorld is Wipro’s internal social media channel which allows its
global employees to interact, engage, exchange ideas, innovations etc. Wipro has
a Council for Industry research which comprises of technology, business experts
along with academicians from institutes for analysing the market trends and
meet the customers needs. This council generates insights for the business which
help in formation of strategies. The digital team works in close collaboration
with the Council for industry research team and internal marketing team to
monitor the conversations getting exchanged over multiple channels and to drive
the online conversations. The digital team increased the frequency of
engagement on social platform by posting the daily updates on business insights,
company events, job and other related areas.
The low cost, focused and high impact branding strategy of Wipro has taken it to
new level. Wipro has its presence on Facebook, Twitter, LinkedIn, Slideshare
and Youtube. Wipro is having 4 Facebook profiles (i.e. wipro.com,
careers@Wipro, Xperience@Wipro and careers@Wipro BPO), 3 twitter profiles
(@Wipro, @Wiprocareers and @XperienceWipro), 1 LinkedIn, 1 YouTube
accounts.
137
Chapter 5
Consumer Electronics Companies and their presence on Social
Media
5.1. Global Players :
5.1.1. Samsung
Samsung has efficiently mastered the social care element not just by its social
media presence, but by cleverly targeting their target consumers by its social
media campaigns. Samsung has a customer centric approach for its online
advertising where the consumer’s needs, their issues, queries, feedback is given
a lot of importance.
Samsung has successfully set an example of taking advantage of metrics
powered digital and social strategy to have an overwhelming number of fans
from the earlier 8 million to directly 25 million fans making it the number one
brand in Europe. Samsung has achieved maximum global reach by creating its
138
Facebook brand page in 25 different languages for 28 countries taking itself to
very corner of the globe. Samsung is very much happy with its strategy of
localized campaigns.
Every week it has approximately 300,000 fans joining its fan list. This success of
Samsung Europe can be attributed to analytics driven understanding of
consumers, developing localized campaigning strategy and engagement through
quality creative content that supports two-way communications between
Samsung and its fans. Engaging, listening and responding to the consumers is of
prime importance to Samsung. The main objective of their social media
participation is creating the best social content, social advertising and
segmentation and social care.
5.1.2. Apple
Apple has not made a direct appearance on Social media sites like Facebook and
twitter which means that Apple does not have its own company page or Apple
brand page on these social networking sites for their products. However it does
have accounts on facebook and twitter for its services like itunes, iBook and App
Store. But all these accounts which Apple has for its services are not interactive
and social. Apple does not make any attempt to respond to the consumers or
does not participate in any discussions through these accounts. It simply pushes
the marketing messages on to the consumers. Apple’s strategy was to become
famous socially by remaining completely silent and let the rumors do the work
of enhancing Public relations. Basically Apple wanted to create arouse
excitement among people for its products. However Apple finally made its social
139
media debut by creating its Apple brand page on Tumblr for promoting its
iPhone5C. Apple selected Tumblr because it allows users to click on several
short video animations with a short story and tagline in it. Apple’s strategy was
to create a buzz by remaining silent and let the rumors do all the promotion work
for it.
5.1.3. Sony
Sony adopts the policy of providing Social customer service which is delivering
customer centric service through social media platform. According to social
media management experts at Sony, there is no need to respond to every brand
related query of the consumers that pops up in the social world, allowing the
answers to the query to come from within the social media community.
The experts advocate that Sony’s social media strategy has been to identify the
Super Users (Super active sony fans) educate them, train them and allow them to
respond to customer queries on behalf of Sony, to become social media
moderators. The customer service provided through social media should be
personal, helpful and chatty.
According to Nico Henderijckx European Forums Communities Manager, Sony:
28. The best support given to Sony users is by other users through their
community.
29. It follows the principle of allowing end users to help other users in social
customer service.
30. Sony keeps their ‘super’ fans i.e super active Sony fans passionate by
training them about the brand so that these super fans become the Social
140
media moderators for Sony.
31. Sony has been successful in achieving an 85% solve rate of the
complaints through peer-to-peer online support and it has been observed
that the complex problems are solved faster than the support line calls.
32. The moderators or the super fans are kept abreast with the latest product
knowledge through monthly training, online meetings, super user
conferences and insights on product launches. The top management
keeps the super fans passionate and motivated by meeting them and
explaining them the company’s goals and objectives.
33. The moderators are treated such that they feel special and are also given
incentives in the form of free products.
34. According to the latest statistics there has been an increase in the number
of British user’s interaction with brands over Twitter from 12% to 36%.
35. 65% people prefer social media than the call centers for solving their
customer queries and for customer service.
36. Social customer service is often used as a (di)stress channel i.e a back
channel to release customer’s frustration when other methods of contact
fail.
37. Social customer service is more profitable than marketing since 1%
increase in customer coming back to website generates 10% increase in
revenues.
5.1.4. Hewlett -Packard
The growing effectiveness of digital media has changed the way marketers
interact with customers. New age customers are informed and empowered. The
challenge for organizations is to be a part of the customers’ conversations
141
involving their product and to influence their choices. Hence, the adoption of
digital channels such as social media, e-mail marketing and online search and
display ads is growing steadily. What helps marketers get the most out of their
digital marketing strategies is the ability to capture and mine data exhaustively
and cost effectively through advanced analytics techniques. These techniques
shed light on the effectiveness of all marketing activities, thus helping marketers
fine-tune their strategy. In the long run, this enhances customer engagement,
optimizes digital “marketing spend” [1] and has a direct impact on revenues.
Hewlett-Packard uses advanced analytics and operations research (O.R.)
applications to drive its digital marketing strategies and overcome business
challenges. HP’s in-house analytics team, Global Business Services Analytics
(GBS Analytics), directs this enterprise-wide analytics effort and plays a critical
role in developing various advanced analytics solutions. These solutions
leverage structured and unstructured data across billions of customers worldwide
to improve the effectiveness of the e-marketing model across HP.
E-Marketing at HP
HP is at the centre stage of this digital revolution and has been making
significant investments on digital channels to generate demand, engage
customers and provide technical support. E-marketing at HP can be summarized
into three broad themes – the three “Cs” of the e-marketing ecosystem:
1. Community deals with the aspect of engaging customers and creating
awareness about HP products and services, leading to effective lead generation.
2. Content deals with the aspect of providing customized relevant content to
142
customers and improving user experience.
3. Commerce deals with aspects of generating revenue through online
commerce. (See Figure 5.1.)
Figure 5.1: Three Cs of E-marketing ecosystem at HP.
Multiple levers are available to grow the online business. These levers
include:

driving traffic by guiding customers to the right content;

increasing conversion by offering relevant products at appropriate price
points;

cross-sell and up-sell to increase basket size and improve margins; and

increasing customer engagement to increase loyalty and repeat purchase.
HP deploys various analytical solutions to drive online commerce.
143
5.1.5. LG
LG intends to be present on Social media to create a closer connect with the
consumers. The objective is to develop a bonding with the brand based on the
likes and dislikes of consumers which will result in brand awareness and brand
promotion.
According to LG facebook is a much broader engagement platform as compared to
twitter. LG makes maximum use of Facebook platform for informing, engaging and
entertaining the target audiences. Therefore LG undertakes a wide variety of initiatives
related to product and technology news, contests, exploiting their sponsorships with
respect to cricket and Formula One etc. Interaction levels are high here. LG’s activities
on Twitter are limited in terms of content sharing and it is more often used as a platform
for answering the grievances. Twitter’s role for brands should be to listen more than
talk; to solve problems and answer questions when asked.
According to LG the marketers must understand the consumer’s reason of joining a
social community instead of bombarding its followers with the brand messages.
Therefore the content should be such that it helps people enjoy, connect with others and
entertain them.
The Smartphone Idea Camp allows our consumers to showcase their creativity
144
and talent and its an excellent way to gain inspiration from the imagination of
the mobile phone users and the tech savvy crowd. Through this contest LG is in
a better position to understand the demands and desires of the consumers and to
know what exactly they are looking for in a product so that it can add value to
their lives.
5.2. Indian Players :
5.2.1. Hindustan Computers Limited (HCL) :
HCL is an India based company which has a network of offices in 26 countries
with 88,000 professionals of “ diverse nationalities”. HCL is exclusively using
Social Media for hiring people. In order to provide Human resources to
employees, HCL has developed its own social network for internal use, called
Meme. The Meme platform has been used by the employees for connecting with
each other, for giving feedback and suggestions, for sharing photographs, for
asking queries, for enabling support functions and staffing services. HCL’s
social listening and analytics studies the consumer’s views and opinions,
delivers competitive intelligence and product research and performs customer
profiling, segmentation and influencer identification. HCL has a maximum
social media presence with its company profiles being created on Facebook,
Twitter as well as LinkedIn, Flickr, Youtube, Google+ and Scribd.
145
5.2.2. TCS :
TCS is an Indian multinational Information Technology (IT) company offering it
IT and Business solutions and services and providing outsourcing service. In
consumer electronics segment it focuses on audio, video and game console
products. TCS has presence on six social networking sites Facebook, Twitter,
LinkedIn, RSS feed, Slideshare, Youtube. On a 5 point scale, TCS has a rating
of 1.25 for its Social Media presence.
5.2.3. Wipro :
Wipro is extensively using the Social media platform for spreading the brand
and reaching out to customers. When wipro decided to use social media platform
in 2007, it considered social media as a business driver. A great deal of emphasis
was laid on the importance of creating an experience for its users.
The MyWiproWorld is Wipro’s internal social media channel which allows its
global employees to interact, engage, exchange ideas, innovations etc. Wipro has
a Council for Industry research which comprises of technology, business experts
along with academicians from institutes for analysing the market trends and
meet the customers needs. This council generates insights for the business which
help in formation of strategies. The digital team works in close collaboration
with the Council for industry research team and internal marketing team to
monitor the conversations getting exchanged over multiple channels and to drive
146
the online conversations. The digital team increased the frequency of
engagement on social platform by posting the daily updates on business insights,
company events, job and other related areas.
The low cost, focused and high impact branding strategy of Wipro has taken it to
new level. Wipro has its presence on Facebook, Twitter, LinkedIn, Slideshare
and Youtube. Wipro is having 4 Facebook profiles (i.e. wipro.com,
careers@Wipro, Xperience@Wipro and careers@Wipro BPO), 3 twitter profiles
(@Wipro, @Wiprocareers and @XperienceWipro), 1 LinkedIn, 1 YouTube
accounts.
147
Chapter 7
Data Analysis and Findings
7.1 Tabulation and Statistical Analysis of Data
The data collected from questionnaire were scored and tabulated into a master
data sheet. The data was analyzed with the help of statistical package SPSS 17.
The mean scores arrived are put to various statistical analysis using various
statistical tools in order to test the research hypothesis. The statistical tools
applied included Chi-Square test, Regression, Anova, Rank Order Co-efficients
etc.
The Data Analysis has been divided into :
i)
Descriptive Analysis.
ii)
Inferential Analysis.
(I) Descriptive Analysis - The descriptive analysis has been written in the
Annexure and can be referred to in the Annexure.
(II) Inferential Analysis - Exclusive analysis has been done for the purpose of
the research and the essence of the analysis has been presented in this
chapter.
148
Objective 1 – To identify the Social Media Usage by young working
women in different cities.-
1. USAGE
OF
SOCIAL
MEDIA
-
DO
YOU
USE
SOCIAL
NETWORKING SITES ?IN DIFFERENT CITIES Table 7.1.1. Showing the number of young working women accessing or
using social networking sites in Mumbai, Nashik and Surat.
USAGE
OF
SOCIAL MEDIA DO
YOU
USE
SOCIAL
NETWORKING
SITES
YES
NO
YES
459
57
516
% of Total
36.1%
4.5%
40.6%
Count
364
33
397
% of Total
28.6%
2.6%
31.2%
Count
314
45
359
% of Total
24.7%
3.5%
28.2%
Count
1137
135
1272
% of Total
89.4%
10.6%
100.0%
PLACE MUMBAI Count
SURAT
NASHIK
Total
Total
Mumbai
149
It was found that out of total 516 respondents, 459 agreed that they use
social networking sites and 57 disagreed.
Surat
It was found that out of total 397 respondents, 364 agreed that they use
social networking sites and 33 disagreed.
Nashik
It was found that out of total 359 respondents, 314 agreed that they use
social networking sites and 45 disagreed.
2. USAGE OF SOCIAL MEDIA - WHICH OF THESE SOCIAL
NETWORKING SITES DO YOU USE- "Face book" –
Table 7.1.2. Showing the number of young working women accessing or
using “Facebook” in Mumbai, Nashik and Surat.
USAGE OF SOCIAL MEDIA WHICH OF THESE SOCIAL
NETWORKING
PLACE
MUMBAI
SURAT
NASHIK
Total
SITES
DO
YOU USE- "Face book"
Total
Yes
No
Yes
Count
408
108
516
% of Total
32.1%
8.5%
40.6%
Count
334
63
397
% of Total
26.3%
5.0%
31.2%
Count
279
80
359
% of Total
21.9%
6.3%
28.2%
Count
1021
251
1272
% of Total
80.3%
19.7%
100.0%
150
Mumbai
It was found that out of total 516 respondents, 408 agreed that they used Face
book and 108 disagreed.
Surat
It was found that out of total 397 respondents, 334 agreed that they used Face
book and 63 disagreed.
Nasik
It was found that out of total 359 respondents, 279 agreed that they used Face
book and 80 disagreed.
3. USAGE OF SOCIAL MEDIA - HOW OFTEN DO YOU USE SOCIAL
NETWORKING SITES LIKE FACE-BOOK, TWITTER, LINKEDIN -
151
Table 7.1.3. Showing the frequency with which the young working
women access SNS in a week in Mumbai, Nashik and Surat.
USAGE OF SOCIAL MEDIA - HOW OFTEN DO YOU USE
SOCIAL
NETWORKING
SITES
LIKE
FACE-BOOK,
TWITTER, LINKEDIN
PLAC
MUMB
Coun
E
AI
t
Total
4-5
2-3
DAYS
DAY
ONC
ALMOST
/
S
E
EVERYDA
WEE
WEE
WEE
RAREL
NEVE
EVERYDA
Y
K
K
K
Y
R
Y
291
116
47
28
16
18
516
22.9%
9.1%
3.7%
2.2%
1.3%
1.4%
40.6%
244
97
25
13
7
11
397
19.2%
7.6%
2.0%
1.0%
.6%
.9%
31.2%
172
79
27
24
23
34
359
13.5%
6.2%
2.1%
1.9%
1.8%
2.7%
28.2%
707
292
99
65
46
63
1272
55.6%
23.0%
7.8%
5.1%
3.6%
5.0%
100.0%
A
A
ALMOST
% of
Total
SURAT
Coun
t
% of
Total
NASHIK
Coun
t
% of
Total
Total
Coun
t
% of
Total
Mumbai
It was found that out of total 516 respondents, 291 agreed that they used
social networking sites like Face book, Twitter, LinkedIn almost every day
and 16 agreed that they used rarely.
152
Surat
It was found that out of total 397 respondents, 244 agreed that they used
social networking sites like Face book, Twitter, LinkedIn almost every day
and 7 agreed that they used rarely.
Nashik
It was found that out of total 359 respondents, 172 agreed that they used
social networking sites like Face book, Twitter, LinkedIn almost every day
and 23 agreed that they used rarely.
4. ROUGHLY HOW MUCH TIME DO YOU SPEND EACH TIME YOU
ACCESS THESE SOCIAL NETWORKING SITES - FACE BOOK –
Table 7.1.4. Showing the time the young working women spend each time
they access Facebook in Mumbai, Nashik and Surat.
ROUGHLY HOW MUCH TIME DO YOU SPEND
EACH TIME YOU ACCESS THESE SOCIAL
NETWORKING SITES - FACE BOOK
Total
MORE
THAN 2
PLACE
MUMBAI
SURAT
NASHIK
Total
15 MIN.
30 MIN.
HOUR
HOURS
HOURS
Count
159
111
127
80
39
516
% of Total
12.5%
8.7%
10.0%
6.3%
3.1%
40.6%
Count
125
82
109
60
21
397
% of Total
9.8%
6.4%
8.6%
4.7%
1.7%
31.2%
Count
107
84
78
68
22
359
% of Total
8.4%
6.6%
6.1%
5.3%
1.7%
28.2%
Count
391
277
314
208
82
1272
% of Total
30.7%
21.8%
24.7%
16.4%
6.4%
100.0%
153
Mumbai
It was found that out of total 516 respondents, 159 spent 15 min of their time
on the social networking site Face book and 39 spent more than 2 hours.
Surat
It was found that out of total 397 respondents, 125 spent 15 min of their time
on the social networking site Face book and 21 spent more than 2 hours.
Nashik
It was found that out of total 359 respondents, 107 spent 15 min of their time
on the social networking site Face book and 22 spent more than 2 hours.
5. Do you share your opinion about a particular product or service with
your family or friends by writing reviews or blogs?
Table 7.1.5. Showing whether the young working women share their
opinion about a particular product or service with your family or friends
by writing reviews or blogs in Mumbai, Nashik and Surat.
Do you share your opinion about a
particular product or service with your
family or friends by writing reviews or
blogs?
PLACE
MUMBAI
SURAT
NASHIK
Total
Yes
No
Count
285
231
516
% of Total
22.4%
18.2%
40.6%
Count
241
156
397
% of Total
18.9%
12.3%
31.2%
Count
161
198
359
% of Total
12.7%
15.6%
28.2%
154
Total
Count
687
585
1272
% of Total
54.0%
46.0%
100.0%
Mumbai
It was found that out of total 516 respondents, 285 agreed that they shared
opinion about a particular product or service with your family or friends by
writing reviews or blogs and 231 disagreed.
Surat
It was found that out of total 397 respondents, 241 agreed that they shared
opinion about a particular product or service with your family or friends by
writing reviews or blogs and 156 disagreed.
Nashik
It was found that out of total 359 respondents, 161 agreed that they shared
opinion about a particular product or service with your family or friends by
writing reviews or blogs and 198 disagreed.
155
6. How many times have you provided online rating in one year? –
Table 7.1.6. Showing the number of times the young working women
provided online rating in one year in Mumbai, Nashik and Surat.
How many times have you provided online rating in
one year?
Total
over
PLACE
MUMBAI
SURAT
NASHIK
Total
none/don’t
up to 10
11-20
21-50
50
rate
times
times
times
times
Count
259
212
35
8
2
516
% of Total
20.4%
16.7%
2.8%
.6%
.2%
40.6%
Count
265
119
6
3
4
397
% of Total
20.8%
9.4%
.5%
.2%
.3%
31.2%
Count
188
123
23
10
15
359
% of Total
14.8%
9.7%
1.8%
.8%
1.2%
28.2%
Count
712
454
64
21
21
1272
% of Total
56.0%
35.7%
5.0%
1.7%
1.7%
100.0%
Mumbai
It was found that out of total 516 respondents, 2 said that they provided online
rating in one year over 50 times and 259 did not rate.
Surat
It was found that out of total 397 respondents, 4 said that they provided online
rating in one year over 50 times and 265 did not rate.
Nashik
156
It was found that out of total 359 respondents, 15 said that they provided
online rating in one year over 50 times and 188 did not rate.
Objective 2 – To study the Different types of buying behaviour with
respect to Social Media Advertising in different cities –
(I)To study the customer buying behaviour with respect to Social Media
Advertising in different cities –
a) Relationship between consumer buying behaviour with the factor of
Social Media Advertisement i.e. “On social media do you have positive
reactions/feelings towards advertisements displayed on it” in different
cities –
(i) In Mumbai -
H 0a : There is no association between the factor i.e. “On social media do
you have positive reactions/feelings towards advertisements displayed on
it” with Consumer buying behaviour of young working women for
consumer electronics in Mumbai.
H1a : There is association between the factor i.e. “On social media do
you have positive reactions/feelings towards advertisements displayed on
it” with Consumer buying behaviour of young working women for
consumer electronics in Mumbai
157
Chi-Square Tests
Table 7.2.1.1.m.a. Relationship between consumer buying behaviour
with the factor of social media advertisement i.e positive
reactions/feelings towards advertisements displayed on SNS in
Mumbai.
Asymp.
Value
Df
sided)
Pearson Chi-Square
37.289(a)
8
.000
Likelihood Ratio
38.494
8
.000
16.218
1
.000
Sig.
(2-
Linear-by-Linear
Association
N of Valid Cases
516
From the above table, it is observed that at 5 % level of significance p < α
(0.05), so the null hypothesis is rejected and alternative is accepted, so, we
conclude that there is association between the factor i.e. “On social media do
you have positive reactions/feelings towards advertisements displayed on it”
with Consumer buying behaviour of young working women for consumer
electronics in Mumbai. This means the factor i.e. “On social media do you
have positive reactions/feelings towards advertisements displayed on it” with
Consumer buying behaviour of young working women in Mumbai for
consumer electronics are dependent of each other. Further to check how
158
much association is existing between them we will use the Contingency
Coefficient Statistics.
Symmetric Measures
Table 7.2.1.1.m.b. Table of Symmetric Measures to show how much
relationship exists in between consumer buying behaviour and the
factor of social media advertising i.e positive reactions/feelings towards
advertisements displayed on SNS in Mumbai.
Approx.
Nominal by Nominal
Value
Sig.
.760
.000
Contingency
Coefficient
N of Valid Cases
516
From the above table, it is observed that there is strong positive
reactions/feelings of young working women in Mumbai towards
advertisements displayed on social media for buying electronics products,
which will affect consumer buying behaviour by 76.0 %.
(ii)In Nashik -
H 0b : There is no association between the factor i.e. “On social media do
you have positive reactions/feelings towards advertisements displayed on
it” with Consumer buying behaviour of young working women for
consumer electronics in Nashik.
159
H1b : There is association between the factor i.e. “On social media do
you have positive reactions/feelings towards advertisements displayed on
it” with Consumer buying behaviour of young working women for
consumer electronics in Nashik.
Chi-Square Tests
Table 7.2.1.1.n.a. Relationship between consumer buying behaviour
with the factor of social media advertisement i.e positive
reactions/feelings towards advertisements displayed on SNS in
Nashik.
Value
Df
Asymp. Sig. (2-sided)
Pearson Chi-Square
47.467(a)
3
.000
Likelihood Ratio
51.331
3
.000
19.337
1
.000
Linear-by-Linear
Association
N of Valid Cases
359
From the above table, it is observed that at 5 % level of significance p < α
(0.05), so the null hypothesis is rejected and alternative is accepted.
Therefore, we conclude that there is association between the factor i.e.
“On social media do you have positive reactions/feelings towards
advertisements displayed on it” with Consumer buying behaviour of
young working women for consumer electronics in Nashik. This means
the factor i.e. “On social media do you have positive reactions/feelings
towards advertisements displayed on it” with Consumer buying
160
behaviour of young working women in Nashik for consumer electronics
are dependent of each other. Further to check how much association
exists between them, we will use the Contingency Coefficient Statistics.
Symmetric Measures
Table 7.2.1.1.n.b. Table of Symmetric Measures to determine how
much relationship exists in between consumer buying behaviour and
the factor of social media advertising i.e positive reactions/feelings
towards advertisements displayed on SNS in Nashik.
Nominal
Nominal
Value
Approx. Sig.
.642
.000
by Contingency
Coefficient
N of Valid Cases
359
From the above table, it is observed that young working women in Nashik
are having very strong positive reactions /feelings towards advertisements
displayed on social media for buying electronics products which will affect
consumer buying behaviour by 64.2 %.
(iii) In Surat H0c : There is no association between the factor i.e. “On social media do
you have positive reactions/feelings towards advertisements displayed on
it” with Consumer buying behaviour of young working women for
consumer electronics in Surat.
H1c : There is association between the factor i.e. “On social media do you
have positive reactions/feelings towards advertisements displayed on it”
161
with Consumer buying behaviour of young working women for
consumer electronics in Surat
Chi-Square Tests
Table 7.2.1.1.s.a. Relationship between consumer buying behaviour
with the factor of social media advertisement i.e positive
reactions/feelings towards advertisements displayed on SNS in Surat.
Value
Df
Asymp. Sig. (2-sided)
Pearson Chi-Square
48.567(a)
3
.077
Likelihood Ratio
46.110
3
.000
10.219
1
.001
Linear-by-Linear
Association
N of Valid Cases
397
From the above table, it is observed that at 5 % level of significance p > α
(0.05), so the null hypothesis is accepted and alternative is rejected, so, we
conclude that there is no association between the factor i.e. “On social
media do you have positive reactions/feelings towards advertisements
displayed on it” with Consumer buying behaviour of young working
women for consumer electronics in Surat. This means the factor i.e. “On
social
media
do
you
have
positive
reactions/feelings
towards
advertisements displayed on it” with Consumer buying behaviour of young
working women in Surat for consumer electronics are independent of each
other. So we can say that they do not have positive reactions/feelings
162
towards advertisements displayed on social media and there is no
association of the factor “On social media do you have positive
reactions/feelings towards advertisements displayed on it” with buying
behaviour of young working women in Surat for consumer electronics
which will not affect consumer buying behaviour.
In the same manner Relationship between consumer buying behaviour
with the various other factors of Social Media Advertisement like
appealing nature, memorable visuals and slogans, attractiveness and
trustworthiness has been studied one by one in different cities – Mumbai,
Nashik and Surat and the same can be referred in the Annexure.
(II)To study the online purchase behaviour with respect to Social Media
Advertising in different cities a)
Relationship between online purchase behaviour with the factor of Social
Media Advertisement i.e. “On social media do you have positive
reactions/feelings towards advertisements displayed on it” in different cities
–
(i) In Mumbai H0a : There is no association between the factor i.e. “On social media do you
have positive reactions/feelings towards advertisements displayed on it”
with online purchase behavior of young working women for consumer
electronics in Mumbai.
H1a : There is association between the factor i.e. “On social media do you
have positive reactions/feelings towards advertisements displayed on it”
163
with online purchase behavior of young working women for consumer
electronics in Mumbai
Chi-Square Tests
Table 7.2.2.1.m.a.
Relationship between online purchase behaviour
with the factor of social media advertising i.e positive reactions/feelings
towards the advertisements displayed on SNS in Mumbai.
Value
Df
Asymp. Sig. (2-sided)
Pearson Chi-Square
53.365(a)
36
.031
Likelihood Ratio
48.137
36
.085
.002
1
.964
Linear-by-Linear
Association
N of Valid Cases
516
From the above table, it is observed that at 5 % level of significance p > α
(0.05), so the null hypothesis is accepted and alternative is rejected and
therefore, we conclude that there is no association between the factor i.e.
“On social media do you have positive reactions/feelings towards
advertisements displayed on it” with online purchase behaviour of young
working women for consumer electronics in Mumbai. This means the factor
i.e. “On social media do you have positive reactions/feelings towards
advertisements displayed on it” with online purchase behaviour of young
working women in Mumbai for consumer electronics are independent of
each other. So, we can conclude that young working women in Mumbai do
not have positive reactions/feelings towards advertisements displayed on it.
164
So, it will not affect the online purchase behaviour of young working
women in Mumbai.
(ii) In Nashik H0b : There is no association between the factor i.e. “On social media do you
have positive reactions/feelings towards advertisements displayed on it”
with online purchase behaviour of young working women for consumer
electronics in Nashik
H1b : There is association between the factor i.e. “On social media do you
have positive reactions/feelings towards advertisements displayed on it”
with online purchase behaviour of young working women for consumer
electronics in Nashik
Chi-Square Tests
Table 7.2.2.1.n.a. Relationship between online purchase behaviour with
the
factor of social media advertising i.e positive reactions/feelings
towards the advertisements displayed on SNS in Nashik.
Value
Df
Asymp. Sig. (2-sided)
Pearson Chi-Square
147.224(a)
24
.000
Likelihood Ratio
136.279
24
.000
13.059
1
.000
Linear-by-Linear
Association
N of Valid Cases
359
165
From the above table, it is observed that at 5 % level of significance p < α
(0.05), so the null hypothesis is rejected and alternative is accepted,
therefore, we conclude that there is association between the factor i.e. “On
social media do you have positive reactions/feelings towards advertisements
displayed on it” with online purchase behaviour of young working women
for consumer electronics in Nashik. This means the factor i.e. “On social
media do you have positive reactions/feelings towards advertisements
displayed on it” with online purchase behaviour of young working women in
Nashik for consumer electronics are dependent of each other. Further to
check how much association exists the Contingency Coefficient Statistics is
used.
Symmetric Measures
Table 7.2.2.1.n.b. Table of Symmetric Measures to determine how much
relationship exists in between online purchase behaviour and factor of
social media advertising i.e positive reactions/feelings towards the
advertisements displayed on SNS in Nashik.
Approx.
Value
Sig.
.639
.000
Nominal by Nominal Contingency
Coefficient
N of Valid Cases
359
From the above table, it is observed that they are having very strong positive
/feelings of young working women in Nashik towards advertisements
166
displayed on social media for buying electronics products, which will affect
online purchase behaviour by 63.9 %.
(iii) In Surat H0c : There is no association between the factor i.e. “On social media do you
have positive reactions/feelings towards advertisements displayed on it” with
online purchase behavior of young working women for consumer electronics
in Surat.
H1c : There is association between the factor i.e. “On social media do you
have positive reactions/feelings towards advertisements displayed on it” with
online purchase behaviour
of young working women for consumer
electronics in Surat.
Chi-Square Tests
Table 7.2.2.1.s.a. Relationship between online purchase behaviour with
the
factor of social media advertising i.e positive reactions/feelings
towards the advertisements displayed on SNS in Surat.
Asymp.
Value
Df
Sig.
(2-sided)
Pearson Chi-Square
83.531(a) 24
.000
Likelihood Ratio
79.912
24
.000
2.814
1
.093
Linear-by-Linear
Association
N of Valid Cases
397
From the above table, it is observed that at 5 % level of significance p < α
167
(0.05), so the null hypothesis is rejected and alternative is accepted, so, we
conclude that there is association between the factor i.e. “On social media do
you have positive reactions/feelings towards advertisements displayed on it”
with online purchase behaviour of young working women for consumer
electronics in Surat. This means the factor i.e. “On social media do you have
positive reactions/feelings towards advertisements displayed on it” with
online purchase behaviour of young working women in Surat for consumer
electronics are dependent of each other. Further to check how much
association is existing we will use the Contingency Coefficient Statistics.
Symmetric Measures
Table 7.2.2.1.s.b. Table of Symmetric Measures to determine how much
relationship exists in between online purchase behaviour and factor of
social media advertising i.e positive reactions/feelings towards the
advertisements displayed on SNS in Surat.
Approx.
Nominal by Nominal
Value
Sig.
.617
.000
Contingency
Coefficient
N of Valid Cases
397
From the above table, it is observed that they are having very strong positive
reactions /feelings of young working women in Surat towards advertisements
displayed on social media for buying electronics products, which will affect
online purchase behaviour by 61.7 %.
In the same manner Relationship between online purchase behaviour with the
168
various other factors of Social Media Advertisement like appealing nature,
memorable visuals and slogans, attractiveness and trustworthiness has been
studied one by one in different cities – Mumbai, Nashik and Surat and the
same can be referred in the Annexure.
(III) Relationship between complex buying behaviour with the factor of Social
Media Advertisement i.e. “On social media do you have positive
reactions/feelings towards advertisements displayed on it” in different cities
–
i. In Mumbai H 0a : There is no association between the factor i.e. “On social media do you
have positive reactions/feelings towards advertisements displayed on it” with
Complex buying behaviour of young working women for consumer
electronics in Mumbai.
H1a : There is association between the factor i.e. “On social media do you
have positive reactions/feelings towards advertisements displayed on it” with
Complex buying behaviour of young working women for consumer
electronics in Mumbai
Chi-Square Tests
Table 7.2.3.1.m.a. Relationship between complex buying behaviour with
the factor i.e. “positive reactions/feelings towards advertisements
displayed on it” of social media advertising in Mumbai.
Pearson Chi-Square
Value
Df
Asymp. Sig. (2-sided)
134.020(a)
48
.000
169
Likelihood Ratio
103.374
48
.000
19.801
1
.000
Linear-by-Linear
Association
N of Valid Cases
516
From the above table, it is observed that at 5 % level of significance p < α
(0.05), so the null hypothesis is rejected and alternative is accepted, so, we
conclude that there is association between the factor i.e. “On social media do
you have positive reactions/feelings towards advertisements displayed on it”
with Complex buying behaviour of young working women for consumer
electronics in Mumbai. This means the factor i.e. “On social media do you
have positive reactions/feelings towards advertisements displayed on it” with
Complex buying behaviour of young working women in Mumbai for
consumer electronics are dependent of each other. Further to check how
much association exists we will use the Phi & Cramer’s V Statistics.
Symmetric Measures
Table 7.2.3.1.m.b. Table of Symmetric Measures to determine how
much relationship exists between complex buying behaviour and the
factor i.e. “positive reactions/feelings towards advertisements displayed
on it” of social media advertising in Mumbai.
Value
Approx. Sig.
.610
.000
Nominal Phi &
by
Cramer's V
Nominal
170
N of Valid Cases
516
From the above table, it is observed that they are having very strong positive
reactions /feelings of young working women in Mumbai towards
advertisements displayed on social media for buying electronics products,
which will affect Complex buying behaviour by 61.0 %.
ii. In Nashik H0b : There is no association between the factor i.e. “On social media do you
have positive reactions/feelings towards advertisements displayed on it” with
Complex buying behaviour of young working women for consumer
electronics in Nashik
H1b : There is association between the factor i.e. “On social media do you
have positive reactions/feelings towards advertisements displayed on it” with
Complex buying behaviour of young working women for consumer
electronics in Nashik
Chi-Square Tests
Table 7.2.3.1.n.a. Relationship between complex buying behaviour with
the factor i.e. “positive reactions/feelings towards advertisements
displayed on it” of social media advertising in Nashik.
Asymp.
Value
Sig.
Df
sided)
Pearson Chi-Square
179.455(a)
36
.000
Likelihood Ratio
186.883
36
.000
Linear-by-Linear
17.965
1
.000
(2-
171
Association
N of Valid Cases
359
From the above table, it is observed that at 5 % level of significance p < α
(0.05), so the null hypothesis is rejected and alternative is accepted, so, we
conclude that there is association between the factor i.e. “On social media do
you have positive reactions/feelings towards advertisements displayed on it”
with Complex buying behaviour of young working women for consumer
electronics in Nashik. This means the factor i.e. “On social media do you
have positive reactions/feelings towards advertisements displayed on it” with
Complex buying behaviour of young working women in Nashik for
consumer electronics because they are dependent of each other for. Further
to check how much association is exist we will use the Phi & Cramer’s V
Statistics.
Symmetric Measures
Table 7.2.3.1.n.b. Table of Symmetric Measures to determine how much
relationship exists between complex buying behaviour and the factor i.e.
“positive reactions/feelings towards advertisements displayed on it” of
social media advertising in Nashik.
Nominal
Phi &
by
Cramer's V
Value
Approx. Sig.
.707
.000
Nominal
N of Valid Cases
359
172
From the above table, it is observed that there are having very strong positive
/feelings of young working women in Nashik towards advertisements
displayed on social media for buying electronics products, which will affect
Complex buying behaviour 70.7 %.
iii. In Surat -
H0c : There is no association between the factor i.e. “On social media do you
have positive reactions/feelings towards advertisements displayed on it” with
Complex buying behaviour of young working women for consumer
electronics in Surat
H1c : There is association between the factor i.e. “On social media do you
have positive reactions/feelings towards advertisements displayed on it” with
Complex buying behaviour of young working women for consumer
electronics in Surat
Chi-Square Tests
Table 7.2.3.1.s.a. Relationship between complex buying behaviour with
the factor i.e. “positive reactions/feelings towards advertisements
displayed on it” of social media advertising in Surat.
Pearson
Value
Df
Asymp. Sig. (2-sided)
133.482(a)
36
.000
36
.000
Chi-
Square
Likelihood Ratio 116.736
173
Linear-by-Linear
21.778
1
.000
Association
N of Valid Cases 397
From the above table, it is observed at 5 % level of significance p < α (0.05),
so the null hypothesis rejected and alternative is accepted, so, we conclude
that there is association between the factor i.e. “On social media do you have
positive reactions/feelings towards advertisements displayed on it” with
Complex buying behaviour of young working women for consumer
electronics in Surat. This means the factor i.e. “On social media do you have
positive reactions/feelings towards advertisements displayed on it” with
Complex buying behaviour of young working women in Surat for consumer
electronics because they are dependent of each other for. Further to check
how much association is exist we will use the Phi & Cramer’s V Statistics.
Symmetric Measures
Table 7.2.3.1.s.b. Table of Symmetric Measures to determine how much
relationship exists between complex buying behaviour with the factor
i.e. “positive reactions/feelings towards advertisements displayed on it”
of social media advertising in Surat.
Nominal
Value
Approx. Sig.
.658
.000
by Phi &
Nominal
N of Valid Cases
Cramer's V
397
174
From the above table, it is observed that there are very strong positive
reactions/feelings
of
young
working
women
in
Nashik
towards
advertisements displayed on social media for buying electronics products,
which will affect Complex buying behaviour 65.8 %.
In the same manner relationship between complex buying behaviour and
various other factors of social media advertisement like appealing nature,
memorable visuals and slogans, attractiveness and trustworthiness has been
studied one by one in different cities - Mumbai, Nashik and Surat and the
same can be referred in the Annexure.
IV) Relationship between all the factors of Habitual Buying Behaviour with all
the factor of Social Media Advertisement in different cities –
(i) In Mumbai –
H 0a : All the factors of Social Media Advertisement and all the factors of
Habitual Buying Behaviour of young working women for consumer
electronics in Mumbai are independent of each other.
H1a : All the factors of Social Media Advertisement and all the factors of
Habitual Buying Behaviour of young working women for consumer
electronics in Mumbai are dependent of each other.
ANOVA
Table 7.2.4.1.m. showing relationship between all the factors of habitual
buying behaviour and all the factors of social media advertising in
175
Mumbai.
Sum
of
Squares
Df
Mean Square
F
Sig.
Between Groups
.595
6
.099
1.535
.165
Within Groups
32.869
509
.065
Total
33.464
515
From the above table, it is observed that p > α (0.05), so the null hypothesis is
accepted and alternative is rejected, so we can conclude that all the factors of
Social Media Advertisement and all the factors of Habitual Buying Behaviour
of young working women for consumer electronics in Mumbai are
independent of each other. So, we can say that social media advertisement
does not have any impact on Habitual Buying Behaviour of the young
working women in Mumbai.
(ii) In Nashik –
H0b : All the factors of Social Media Advertisement and all the factors of
Habitual Buying Behaviour of young working women for consumer
electronics in Nashik are independent of each other
H1b : All the factors of Social Media Advertisement and all the factors of
Habitual Buying Behaviour of young working women for consumer
electronics in Nashik are dependent of each other
ANOVA
Table 7.2.4.1.n. showing relationship between all the factors of habitual
buying behaviour and all the factors of social media advertising in
176
Nashik.
Sum
of
Mean
Squares
Df
Square
F
Sig.
Between Groups
51.632
17
3.037
11.927 .000
Within Groups
86.836
341
.255
Total
138.468
358
From the above table, it is observed that p < α (0.05), so the null hypothesis
is rejected and alternative is accepted, so we can conclude that all the factors
of Social Media Advertisement and all the factors of Habitual Buying
Behaviour of young working women for consumer electronics in Nashik are
dependent of each other. So, we can say that social media advertisements are
having impact on Habitual Buying Behaviour of the young working women
in Nashik.
(iii) In Surat –
H0c : All the factors of Social Media Advertisement and all the factors of
Habitual Buying Behaviour of young working women for consumer
electronics in Surat are independent of each other.
H1c : All the factors of Social Media Advertisement and all the factors of
Habitual Buying Behaviour of young working women for consumer
electronics in Surat are dependent of each other.
ANOVA
Table 7.2.4.1.s. showing relationship between all the factors of habitual
177
buying behaviour and all the factors of social media advertising in
Surat.
Sum
of
Squares
Df
Mean Square
F
Sig.
Between Groups
1.554
6
.259
10.677
.000
Within Groups
9.460
390
.024
Total
11.014
396
From the above table, it is observed that p < α (0.05), so the null hypothesis is
rejected and alternative is accepted, so we can conclude that all the factors of
Social Media Advertisement and all the factors of Habitual Buying Behaviour
of young working women for consumer electronics in Surat are dependent of
each other. So, we can say that social media advertisements are having impact
on Habitual Buying Behaviour of the young working women in Surat.
In the same manner Relationship between all the factors of Social Media
Advertisements and all the factors of various consumer buying behaviours
like variety seeking buying behaviour, dissonance buying behaviour and
impulsive buying behaviours has been studied one by one in different cities –
Mumbai, Nashik and Surat and the same can be referred in the Annexure.
Objective 3 – To Study the impact of social media advertising on the
buying behaviour of young working women for consumer electronics in
all the cities –
a) Impact of Social Media advertising on different factors of buying behaviour
of young working women for consumer electronics in Mumbai –
In the model, the dependent variable Y is Social Media Advertising whereas
178
independent variables X1 , X 2 , ......, X n are all buying Behaviours of young
working women i.e.
Online purchase behaviour, Consumer Buying
Behaviour, Complex Buying Behaviour, Habitual Buying Behaviour, VarietySeeking Buying Behaviour, Dissonance Buying Behaviour and Impulsive
Buying Behaviour. The estimated regression model is as follows:
Y (Social Media Advertising) = 1. 421+ (-0.045) Online purchase behaviour
+ (0.013) Consumer Buying Behaviour + (0.64) Complex Buying Behaviour
+ (-0.022) Habitual Buying Behaviour + (0.047) Variety-Seeking Buying
Behaviour + (0.038) Dissonance Buying Behaviour + (0.004) Impulsive
Buying Behaviour.
The results indicate that all the independent variables namely consumer
buying behaviour, complex buying behaviour, variety-seeking buying
behaviour, dissonance buying behaviour and impulsive buying behaviour
have a positive impact on the Social Media Advertising. The independent
variables namely Online purchase behaviour and Habitual Buying Behaviour
have a negative impact on Social Media Advertising.
Model Summary
Table 7.3.1.m.a. Table of Model Summary for Mumbai
Adjusted
R Std. Error of the
Model
R
R Square Square
Estimate
1
.859(a)
.737
.68557
.619
From the above it is observed, The R2 value for the model is 0.737 which
indicates that 73.7 % of the variations in the Social Media Advertising are
explained by Online purchase Behaviour, Consumer Buying Behaviour,
179
Complex Buying Behaviour, Habitual Buying Behaviour, Variety-Seeking
Buying Behaviour, Dissonance Buying Behaviour and Impulsive Buying
Behaviour. The significance of R2 is tested with the help of F statistic, which
is shown in below table,
2
H 0a : R is not statistically significant
2
H1a : R is statistically significant
ANOVA(b)
Table 7.3.1.m.b. Table for Anova to determine the level of significance of
R2
Sum
Model
1
Squares
of
Mean
Df
Square
F
Sig.
Regression 2.600
6
.433
12.584 .000(a)
Residual
17.529
509 .034
Total
20.129
515
From the above table it is observed that the , the p < (0.05) , so we reject null
hypothesis and alternative hypothesis is accepted, so we conclude that that at
5% level of significance R2 is statistically significant .
The significance of the individual coefficients can be tested using t-statistic,
H 0a : There is no significant impact of social media advertising on young
working women’s buying behaviour in Mumbai.
H1a : There is significant impact of social media advertising on young
working women’s buying behaviour in Mumbai.
180
Coefficients (a)
Table 7.3.1.m.c. Table for significance of Coefficients
Unstandardized Standardized
Model
Coefficients
Coefficients
T
Std.
1
(Constant)
Sig.
Std.
B
Error
Beta
B
Error
1.421
.049
-.045
.023
-.092
-1.947
.052
0.013
.088
0.044
-1.234
0.04
.064
.012
.276
5.282
.000
-.022
.017
-.062
-1.272
.204
28.830 .000
Online
purchase
behaviour
Consumer
Buying
Behaviour
Complex
Buying
Behaviour
Habitual
Buying
181
Behaviour
Variety
Seeking
.047
.017
.139
2.780
.006
.038
.015
.113
2.465
.014
.004
.014
.013
.291
.771
Buying
Behaviour
Dissonance
Buying
Behaviour
Impulsive
Buying
Behaviour
From the above table at 5 % level of significance p > α (0.05), so the null
hypothesis is accepted and alternative is rejected, so the coefficients of
Habitual, online purchase behaviour and Impulsive buying behaviour is not
statistically significant. But the coefficients of Consumer Buying Behaviour,
Complex buying behaviour, Variety - Seeking buying behaviour, Dissonance
buying behaviour is statistically significant. Therefore, we conclude that
Consumer Buying Behaviour, Complex buying behaviour, Variety - Seeking
buying behaviour, Dissonance buying behaviour has a significant impact on
influencing social media advertising amongst young working women in
Mumbai.
In the same manner impact of Social Media Advertising on different factors
of buying behaviour of young working women for consumer electronics in
Nashik and Surat has been studied one by one and the same can be referred
182
to in the Annexure.
Objective 4 – To Study the effectiveness of social media tools like Face
book, Twitter , LinkedIn on the consumer Behaviour in different cities –
a) Effectiveness of social media tools like Face book, Twitter , LinkedIn on
the consumer Behaviour in Mumbai –
(i) Rank Order – Audience –
Table 7.4.m.1. Showing effectiveness of SNSs in terms of Audience in
Mumbai
Tota
Rank
1
2
3
4
5
6
7
8
9
10
Face
book
Twitter
3726
31
19
15
20
25
33
81
106
66
120
20
19
28
45
74
75
102
62
54
37
LinkedI
n
l
3208
3246
29
30
25
47
63
54
63
92
52
61
From the above table it was found that as an audience the young working
women prefer most Face book and least preferred is prefer Twitter as the
Social networking sites that have a large number of groups (networks)
available for any demographics you are looking for; for instance group of
teenagers, group of kids, youth, group of new moms, brides, sports fans,
technology enthusiasts, entrepreneurs etc in Mumbai.
(ii) Rank Order – Targeting –
183
Table 7.4.m.2. Showing effectiveness of SNSs in terms of Targeting
consumers in Mumbai.
Rank
1
2
3
4
5
6
7
8
9
10
Face
book
Twitter
LinkedIn
Total
3670
35
16
13
24
34
37
72
98
82
105
25
41
37
64
58
63
63
75
36
54
3047
56
56
31
38
42
59
70
59
48
57
2941
From the above table it was found that as an audience the young working
women in Mumbai prefer most Face book and least preferred is twitter as the
Social networking site that targets the advertisements to specific group of
audience in Mumbai.
(iii)Social Networking Site having more followers due to acquaintances (i.e.
friends and relatives) -
Table 7.4.m.3. Showing effectiveness of SNSs in terms of more followers
due to acquaintances in Mumbai.
184
Cumulative
Valid
Frequency
Percent
Valid Percent
Percent
Face book
430
83.3
83.3
83.3
Twitter
50
9.7
9.7
93.0
LinkedIn
36
7.0
7.0
100.0
Total
516
100.0
100.0
Out of the total 516 valid respondents, a maximum of 83.3 % agreed that
Face book more followers due to acquaintances and the minimum of 7.0 %
respondents said that LinkedIn has more followers due to acquaintance.
(iv) Social Networking Site having more unknown followers Table 7.4.m.4. Showing effectiveness of SNSs in terms of more unknown
followers in Mumbai.
Cumulative
Valid
Frequency
Percent
Valid Percent
Percent
Face book
258
50.0
50.0
50.0
Twitter
140
27.1
27.1
77.1
LinkedIn
118
22.9
22.9
100.0
Total
516
100.0
100.0
Out of the total 516 respondents, a maximum percentage of 50.0 % said that
face book has more unknown followers and minimum of 22.9 % said that
LinkedIn has more unknown followers.
In the same manner effectiveness of social media tools like Face book,
185
Twitter , LinkedIn on the consumer Behaviour for consumer electronics in
Nashik and Surat are studied one by one and the same can be referred to in
the Annexure.
In the same manner effectiveness of social media tools like Face book,
Twitter , LinkedIn on the consumer Behaviour of young working women for
consumer electronics in Nashik and Surat has been studied one by one and
the same can be referred to in the Annexure.
Objective 5- To study the impact of social media advertising on people
belonging to different demographic factors such as qualification, annual
income, occupation and place –
(I) Relationship between impact of social media advertising of young working
women with their qualification in different cities –
(a) In Mumbai –
H 0a : Impact of social media advertising and the qualification of young
working women in Mumbai are independent of each other
H1a : Impact of social media advertising and the qualification of young
working women in Mumbai are dependent of each other
ANOVA
Table 7.5.I.m. Relationship between qualification of young working
women and impact of social media advertising in Mumbai .
Sum of Squares
Df
Mean Square
F
Sig.
186
Between Groups .085
2
.043
Within Groups
20.044
513
.039
Total
20.129
515
1.088
.338
From the above table, it is observed that p > α (0.05), so the null hypothesis is
accepted and alternative is rejected, so we can conclude that impact of social
media advertising and the qualification of young working women in Mumbai
are independent of each other. So, we can say that qualification of the young
working women in Mumbai has no effect on the impact of social media
advertising on young working woman’s buying behaviour in Mumbai.
In the same manner Relationship between impact of social media advertising
and the qualification of young working women in other cities like Nashik and
Surat has been studied one by one and the same can be referred to in the
Annexure.
(II) Relationship between impact of social media advertising of young working
women with their Annual Income in different cities –
(c)In Surat –
H0c : Impact of social media advertising and the Annual Income of young
working women in Surat are independent of each other
H1c : Impact of social media advertising and the Annual Income of young
working women in Surat are dependent of each other
ANOVA
Table 7.5.II.s.a. Relationship between annual income of young working
women and impact of social media advertising in Surat.
187
Sum of Squares
Df
Mean Square
F
Sig.
Between Groups
1.517
3
.506
21.306
.000
Within Groups
9.324
393
.024
Total
10.841
396
From the above table, it is observed that p < α (0.05), so the null hypothesis is
rejected and the alternative is accepted, so we can conclude that impact of
social media advertising and the Annual Income of young working women in
Surat are dependent of each other. So, we can say that social media
advertisement has an impact on Annual Income of the young working women
in Surat. So, which Annual Income group has more or less impact we can
refer descriptive statistics table which is given below:
Descriptive
Table 7.5.II.s.b. To determine how much relationship exists between
annual income of young working women and impact of social media
advertising in Surat.
N
Mean
Std. Deviation
UPTO RS. 3 LAKHS
179
1.6752
.11972
3.1- 5 LAKHS
132
1.6535
.16214
5.1-10 LAKHS
64
1.5156
.18411
ABOVE 10 LAKHS
22
1.5242
.23842
Total
397
1.6339
.16546
From the above table, we observed that income group i.e. upto Rs. 3 lakhs
earning of young working women are having more impact of social media
advertising on buying behaviour followed by other income groups i.e. 3.1- 5,
above 10 lakhs and 5.1 – 10 lakhs respectively.
188
In the same manner Relationship between impact of social media advertising
and the Annual Income of young working women in other cities like Mumbai
and Nashik has been studied one by one and the same can be referred to in
the Annexure.
(III) Relationship between impact of social media advertising of young working
women and their Occupation in different cities –
(b) In Nashik –
H0b :
Impact of social media advertising and the Occupation of young
working women in Nashik are independent of each other
H1b : Impact of social media advertising and the Occupation of young
working women in Nashik are dependent of each other
ANOVA
Table 7.5.III.n.a. Relationship between occupation of young working
women and impact of social media advertising in Nashik.
Sum of Squares
Df
Mean Square
F
Sig.
Between Groups .280
2
.140
3.049
.049
Within Groups
16.342
356
.046
Total
16.622
358
From the above table, it is observed that p < α (0.05), so the null hypothesis is
rejected and alternative is accepted, so we can conclude that impact of social
media advertising and the Occupation of young working women in Nashik
are dependent of each other. So, we can say that social media advertisement
189
has an impact on Occupation of the young working women in Nashik. So,
which Occupation group has more or less impact we can refer descriptive
statistics table which is given below:
Descriptive
Table 7.5.III.n.b. To determine how much relationship exists between
occupation of young working women and impact of social media
advertising in Nashik.
N
Mean
Std. Deviation
SERVICE
283
1.6641
.21898
BUISNESS
55
1.7418
.20207
SELF- EMPLOYED PROFESSION
21
1.6857
.17530
Total
359
1.6773
.21548
From the above table, we observed that business class working women are
having more impact of social media advertising on buying behaviour
followed by service and self – employed professionals.
In the same manner Relationship between impact of social media advertising
and the Occupation of young working women in other cities like Mumbai and
Surat has been studied one by one and the same can be referred to in the
Annexure.
190
7.2 Summary of the Analysis
7.6 Tabular representation of Summary of Analysis and results.
S.
No
.
Objecti
ve
Hypothes
is
1.
To
Nil
identify
Questionnaire
Statis
tical
Test
Used
PVa
lue
Sect
ion
Question Nos.
Sect
ion
II
1,2,3,4,5,6,7,8,9,1
0,11,12,13,14.
Nil
Nil
Consumer buying
behaviour /
Section XI (1.A.)
ChiSquar
e test
0.0
00
the
Social
Media
Usage
by
young
Result
s of
Testin
g of
Hypot
hesis
Threw
light
on
Patter
n of
Social
Media
Usage
in 3
differe
nt
cities.
workin
g
women
in
differe
nt
cities.
2.
To
study
1. H0:
There is
no
Sect
ion
H0
Reject
ed.
191
the
custom
ers
buying
behavi
our
with
respect
to
Social
media
adverti
sing.
associatio
n between
the factor
i.e.
positive
reactions/
feelings
towards
advertise
ments
displayed
with
Consumer
buying
behaviour
in
Mumbai.
2. H0:
There is
no
associatio
n between
the factor
i.e.
positive
reactions/
feelings
towards
advertise
ments
displayed
with
Consumer
buying
behaviour
in Nashik.
3. H0:
There is
no
associatio
n between
the factor
i.e.
XI
Sect
ion
XI
Sect
ion
XI
Consumer buying
behaviour /
Section XI(1.A.)
ChiSquar
e test
0.0
00
H0
Reject
ed.
Consumer buying
behaviour /
Section XI (1.A.)
ChiSquar
e test
0.0
77
H0 is
Accep
ted.
192
positive
reactions/
feelings
towards
advertise
ments
displayed
with
Consumer
buying
behaviour
in Surat
4. H0:
There is
no
associatio
n between
the
appealing
factor of
social
media
advertise
ments
with
Consumer
buying
behavior
in
Mumbai.
5. H0:
There is
no
associatio
n between
the
appealing
factor of
social
media
advertise
ments
with
Sect
ion
XI
Sect
ion
XI
Consumer buying
behaviour /
Section XI (1.B.)
ChiSquar
e test
0.0
00
H0 is
Reject
ed.
Consumer buying
behaviour /
Section XI(
1.B.)
ChiSquar
e test
0.0
99
H0 is
Accep
ted.
193
Consumer
buying
behavior
in Nashik.
6. H0:
There is
no
associatio
n between
the
appealing
factor of
social
media
advertise
ments
with
Consumer
buying
behavior
in Surat.
7. H0:
There is
no
associatio
n between
the
memorabl
e visuals
and
slogans
factor of
social
media
advertise
ments
with
Consumer
buying
behavior
in
Mumbai.
8. H0:
Sect
ion
XI
Sect
ion
XI
Consumer buying
behaviour /
Section XI (
1.B.)
ChiSquar
e test
0.7
77
H0 is
Accep
ted.
Consumer buying
behaviour /
Section XI(
1.C.)
ChiSquar
e test
0.5
66
H0 is
Accep
ted.
Consumer buying
Chi-
0.7
H0 is
194
There is
no
associatio
n between
the
memorabl
e visuals
and
slogans
factor of
social
media
advertise
ments
with
Consumer
buying
behavior
in Nashik.
9. H0:
There is
no
associatio
n between
the
memorabl
e visuals
and
slogans
factor of
social
media
advertise
ments
with
Consumer
buying
behavior
in Surat.
10. H0:
There is
no
associatio
Sect
ion
XI
Sect
ion
XI
Sect
ion
behaviour /
Section XI(
1.C.)
Squar
e test
68
Accep
ted.
Consumer buying
behaviour /
Section XI(
1.C.)
ChiSquar
e test
0.5
66
H0 is
Accep
ted.
Consumer buying
behaviour /
Section XI(
1.D.)
ChiSquar
e test
0.8
11
H0 is
Accep
ted.
195
n between
the
attractive
factor of
social
media
advertise
ments
with
Consumer
buying
behavior
in
Mumbai.
11. H0:
There is
no
associatio
n between
the
attractive
factor of
social
media
advertise
ments
with
Consumer
buying
behavior
in Nashik.
12. H0:
There is
no
associatio
n between
the
attractive
factor of
social
media
advertise
ments
XI
Sect
ion
XI
Sect
ion
XI
Consumer buying
behaviour /
Section XI(
1.D.)
ChiSquar
e test
0.7
66
H0 is
Accep
ted.
Consumer buying
behaviour /
Section XI(
1.D.)
ChiSquar
e test
0.0
0
H0 is
Reject
ed.
196
with
Consumer
buying
behavior
in Surat.
13. H0:
There is
no
associatio
n between
the
trustworth
iness
factor of
social
media
advertise
ments
with
Consumer
buying
behavior
in
Mumbai.
14. H0:
There is
no
associatio
n between
the
trustworth
iness
factor of
social
media
advertise
ments
with
Consumer
buying
behavior
in Nashik.
15. H0:
Sect
ion
XI
Sect
ion
XI
Consumer buying
behaviour /
Section XI(
1.E.)
ChiSquar
e test
0.0
89
H0 is
Accep
ted.
Consumer buying
behaviour /
Section XI(
1.E.)
ChiSquar
e test
0.4
44
H0 is
Accep
ted.
Consumer buying
Chi-
0.0
H0 is
197
There is
no
associatio
n between
the
trustworth
iness
factor of
social
media
advertise
ments
with
Consumer
buying
behavior
in Surat.
16. H0 :
There is
no
associatio
n between
the factor
i.e.
positive
reactions/
feelings
towards
advertise
ments
displayed
with
online
purchase
behavior
in
Mumbai.
Sect
ion
XI
Sect
ion
XI
17. H0 :
There is
Sect
no
ion
associatio
XI
n between
behaviour /
Section XI(
1.E.)
Squar
e test
00
Reject
ed.
Online purchase
behaviour /
Section XI(
1.A.)
ChiSquar
e test
0.0
31
H0 is
Accep
ted.
Online purchase
behaviour /
Section XI(
1.A.)
ChiSquar
e test
0.0
00
H0 is
Reject
ed.
198
the factor
i.e.
positive
reactions/
feelings
towards
advertise
ments
displayed
with
online
purchase
behavior
in Nashik.
18. H0 :
There is
Sect
no
ion
associatio
XI
n between
the factor
i.e.
positive
reactions/
feelings
towards
advertise
ments
displayed
with
online
purchase
behavior
in Surat.
Online purchase
behaviour /
Section XI(
1.A.)
ChiSquar
e test
0.0
00
H0 is
Reject
ed.
19. H0 :
There is
Sect
no
ion
associatio
XI
n between
the
appealing
factor of
Online purchase
behaviour /
Section XI(
1.B.)
ChiSquar
e test
0.2
52
H0 is
Accep
ted.
199
the
advertise
ments
displayed
with
online
purchase
behavior
in
Mumbai.
20. H0 :
There is
Sect
no
ion
associatio
XI
n between
the
appealing
factor of
the
advertise
ments
displayed
with
online
purchase
behavior
in Nashik.
Online purchase
behaviour /
Section XI(
1.B.)
ChiSquar
e test
0.0
00
H0 is
Reject
ed.
21. H0 :
There is
Sect
no
ion
associatio
XI
n between
the
appealing
factor of
the
advertise
ments
displayed
with
online
Online purchase
behaviour /
Section XI(
1.B.)
ChiSquar
e test
0.0
00
H0 is
Reject
ed.
200
purchase
behavior
in Surat.
22. H0 :
There is
Sect
no
ion
associatio
XI
n between
the
memorabl
e visuals
and
slogans
factor of
the
advertise
ments
with
online
purchase
behavior
in
Mumbai.
Online purchase
behaviour /
Section XI(
1.C.)
ChiSquar
e test
0.7
96
H0 is
Accep
ted.
23. H0 :
There is
Sect
no
ion
associatio
XI
n between
the
memorabl
e visuals
and
slogans
factor of
the
advertise
ments
with
online
purchase
behavior
Online purchase
behaviour /
Section XI(
1.C.)
ChiSquar
e test
0.0
00
H0 is
Reject
ed.
201
in Nashik.
24. H0 :
There is
Sect
no
ion
associatio
XI
n between
the
memorabl
e visuals
and
slogans
factor of
the
advertise
ments
with
online
purchase
behavior
in Surat.
Online purchase
behaviour /
Section XI(
1.C.)
ChiSquar
e test
0.0
00
H0 is
Reject
ed.
25. H0 :
There is
Sect
no
ion
associatio
XI
n between
the
attractive
factor of
the
advertise
ments
with
online
purchase
behavior
in
Mumbai.
Online purchase
behaviour /
Section XI(
1.D.)
ChiSquar
e test
0.5
24
H0 is
Accep
ted.
26. H0 :
There is
Sect
no
Online purchase
behaviour /
Section XI (1.D.)
ChiSquar
e test
0.0
00
H0 is
Reject
ed.
202
associatio ion
n between XI
the
attractive
factor of
the
advertise
ments
with
online
purchase
behavior
in Nashik.
27. H0 :
There is
Sect
no
ion
associatio
XI
n between
the
attractive
factor of
the
advertise
ments
with
online
purchase
behavior
in Surat.
Online purchase
behaviour /
Section XI(
1.D.)
ChiSquar
e test
0.0
00
H0 is
Reject
ed.
28. H0 :
There is
Sect
no
ion
associatio
XI
n between
the
trustworth
iness
factor of
the
advertise
ments
Online purchase
behaviour /
Section XI(
1.E.)
ChiSquar
e test
0.1
46
H0 is
Accep
ted.
203
with
online
purchase
behavior
in
Mumbai.
29. H0 :
There is
Sect
no
ion
associatio
XI
n between
the
trustworth
iness
factor of
the
advertise
ments
with
online
purchase
behavior
in Nashik.
Online purchase
behaviour /
Section XI(
1.E.)
ChiSquar
e test
0.1
17
H0 is
Accep
ted.
30. H0 :
There is
Sect
no
ion
associatio
XI
n between
the
trustworth
iness
factor of
the
advertise
ments
with
online
purchase
behavior
in Surat.
Online purchase
behaviour /
Section XI(
1.E.)
ChiSquar
e test
0.6
65
H0 is
Accep
ted.
204
31. H0:
There is
no
associatio
n between
the factor
i.e.
positive
reactions/
feelings
towards
advertise
ments
displayed
with
Complex
buying
behaviour
in
Mumbai.
32. H0 :
There is
no
associatio
n between
the factor
i.e.
positive
reactions/
feelings
towards
advertise
ments
displayed
with
Complex
buying
behaviour
in Nashik.
33. H0 :
There is
no
associatio
Sect
ion
XI
Sect
ion
XI
Sect
ion
Complex Buying
Behaviour /
Section XI(
1.A.)
ChiSquar
e test
0.0
00
H0 is
Reject
ed.
Complex Buying
Behaviour /
Section XI( 1.A.)
ChiSquar
e test
0.0
00
H0 is
Reject
ed.
Complex Buying
Behaviour /
Section XI( 1.A.)
ChiSquar
e test
0.0
00
H0 is
Reject
ed.
205
n between
the factor
i.e.
positive
reactions/
feelings
towards
advertise
ments
displayed
with
Complex
buying
behaviour
in Surat.
34. H0:
There is
no
associatio
n between
the
appealing
factor of
the
advertise
ments
displayed
with
Complex
buying
behaviour
in
Mumbai.
35. H0 :
There is
no
associatio
n between
the
appealing
factor i.e.
of the
advertise
XI
Sect
ion
XI
Sect
ion
XI
Complex Buying
Behaviour /
Section XI(
1.B.)
ChiSquar
e test
0.2
13
H0 is
Accep
ted.
Complex Buying
Behaviour /
Section XI(
1.B.)
ChiSquar
e test
0.0
00
H0 is
Reject
ed.
206
ments
displayed
with
Complex
buying
behaviour
in Nashik.
36. H0 :
There is
no
associatio
n between
the
appealing
factor of
the
advertise
ments
displayed
with
Complex
buying
behaviour
in Surat.
37. H0:
There is
no
associatio
n between
the
memorabl
e visuals
and
slogans
factor of
the
advertise
ments
displayed
with
Complex
buying
behaviour
Sect
ion
XI
Sect
ion
XI
Complex Buying
Behaviour /
Section XI(
1.B.)
ChiSquar
e test
0.0
00
H0 is
Reject
ed.
Complex Buying
Behaviour /
Section XI(
1.C.)
ChiSquar
e test
0.0
00
H0 is
Reject
ed.
207
in
Mumbai.
38. H0 :
There is
no
associatio
n between
the
memorabl
e visuals
and
slogans
factor of
the
advertise
ments
displayed
with
Complex
buying
behaviour
in Nashik.
39. H0 :
There is
no
associatio
n between
the
memorabl
e visuals
and
slogans
factor of
the
advertise
ments
displayed
with
Complex
buying
behaviour
in Surat.
40. H0:
Sect
ion
XI
Sect
ion
XI
Complex Buying
Behaviour /
Section XI(
1.C.)
ChiSquar
e test
0.0
97
H0 is
Accep
ted.
Complex Buying
Behaviour /
Section XI(
1.C.)
ChiSquar
e test
0.0
98
H0 is
Accep
ted.
Complex Buying
Chi-
0.7
H0 is
208
There is
no
associatio
n between
the
attractive
ness
factor of
the
advertise
ments
displayed
with
Complex
buying
behaviour
in
Mumbai.
41. H0 :
There is
no
associatio
n between
the
attractive
ness
factor of
the
advertise
ments
displayed
with
Complex
buying
behaviour
in Nashik.
42. H0 :
There is
no
associatio
n between
the
attractive
Sect
ion
XI
Sect
ion
XI
Sect
ion
XI
Behaviour /
Section XI(
1.D.)
Squar
e test
88
Accep
ted.
Complex Buying
Behaviour /
Section XI(
1.D.)
ChiSquar
e test
0.1
22
H0 is
Accep
ted.
Complex Buying
Behaviour /
Section XI(
1.D.)
ChiSquar
e test
0.0
00
H0 is
Reject
ed.
209
ness
factor of
the
advertise
ments
displayed
with
Complex
buying
behaviour
in Surat.
43. H0:
There is
no
associatio
n between
the
trustworth
iness
factor of
the
advertise
ments
displayed
with
Complex
buying
behaviour
in
Mumbai.
44. H0 :
There is
no
associatio
n between
the
trustworth
iness
factor of
the
advertise
ments
displayed
Sect
ion
XI
Sect
ion
XI
Complex Buying
Behaviour /
Section XI(
1.E.)
ChiSquar
e test
0.0
00
H0 is
Reject
ed.
Complex Buying
Behaviour /
Section XI(
1.E.)
ChiSquar
e test
0.0
00
H0 is
Reject
ed.
210
with
Complex
buying
behaviour
in Nashik.
45. H0 :
There is
no
associatio
n between
the
trustworth
iness
factor of
the
advertise
ments
displayed
with
Complex
buying
behaviour
in Surat.
46. H0 :
All the
factors of
Social
Media
Advertise
ment and
all the
factors of
Habitual
Buying
Behaviou
r in
Mumbai
are
independe
nt of each
other.
47. H0 :
All the
0.0
45
H0 is
Accep
ted.
Sect
ion
XI,
Sect
ion
VI
Section XI
Anova 0.1
(1.A,1.B,1.C,1.D,1
65
.E) / Section VI
(1,2.)
H0 is
Accep
ted.
Sect
ion
Section XI
Anova 0.0
(1.A,1.B,1.C,1.D,1
00
H0 is
Reject
Sect
ion
XI
Complex Buying
Behaviour /
Section XI (
1.E.)
ChiSquar
e test
211
factors of
Social
Media
Advertise
ment and
all the
factors of
Habitual
Buying
Behaviou
r in
Nashik
are
independe
nt of each
other.
48. H0 :
All the
factors of
Social
Media
Advertise
ment and
all the
factors of
Habitual
Buying
Behaviou
r in Surat
are
independe
nt of each
other.
49. H0 :
All the
factors of
Social
Media
Advertise
ment and
all the
factors of
Variety
XI,
Sect
ion
VI
.E) / Section VI
(1,2.)
ed.
Sect
ion
XI,
Sect
ion
VI
Section XI
Anova 0.0
(1.A,1.B,1.C,1.D,1
00
.E) / Section VI
(1,2.)
H0 is
Reject
ed.
Sect
ion
XI,
Sect
ion
VII
Section XI
Anova 0.0
(1.A,1.B,1.C,1.D,1
00
.E) / Section VII
(1,2,3).
H0 is
Reject
ed.
212
Seeking
Buying
Behaviou
r in
Mumbai
are
independe
nt of each
other.
50. H0 :
All the
factors of
Social
Media
Advertise
ment and
all the
factors of
Variety
Seeking
Buying
Behaviou
r in
Nashik
are
independe
nt of each
other.
51. H0 :
All the
factors of
Social
Media
Advertise
ment and
all the
factors of
Variety
Seeking
Buying
Behaviou
r in Surat
are
Sect
ion
XI,
Sect
ion
VII
Section XI
Anova 0.0
(1.A,1.B,1.C,1.D,1
00
.E) / Section VII
(1,2,3)
H0 is
Reject
ed.
Sect
ion
XI,
Sect
ion
VII
Section XI
Anova 0.0
(1.A,1.B,1.C,1.D,1
00
.E) / Section VII
(1,2,3)
H0 is
Reject
ed.
213
independe
nt of each
other.
52. H0 :
All the
factors of
Social
Media
Advertise
ment and
all the
factors of
Dissonan
ce
Buying
Behaviou
r in
Mumbai
are
independe
nt of each
other.
53. H0 :
All the
factors of
Social
Media
Advertise
ment and
all the
factors of
Dissonan
ce
Buying
Behaviou
r in
Nashik
are
independe
nt of each
other.
54. H0 :
All the
Sect
ion
XI,
Sect
ion
VIII
Section XI
Anova 0.0
(1.A,1.B,1.C,1.D,1
00
.E) / Section VIII
(1,2,3).
H0 is
Reject
ed.
Sect
ion
XI,
Sect
ion
VIII
Section XI
Anova 0.7
(1.A,1.B,1.C,1.D,1
78
.E) / Section VIII
(1,2,3).
H0 is
Accep
ted.
Sect
ion
Section XI
Anova 0.2
(1.A,1.B,1.C,1.D,1
34
H0 is
Accep
214
factors of
Social
Media
Advertise
ment and
all the
factors of
Dissonan
ce
Buying
Behaviou
r in Surat
are
independe
nt of each
other.
55. H0 :
All the
factors of
Social
Media
Advertise
ment and
all the
factors of
Impulsive
Buying
Behaviou
r in
Mumbai
are
independe
nt of each
other.
56. H0 :
All the
factors of
Social
Media
Advertise
ment and
all the
factors of
XI,
Sect
ion
VIII
.E) / Section VIII
(1,2,3).
ted.
Sect
ion
XI ,
Sect
ion
IX
Section XI
Anova 0.1
(1.A,1.B,1.C,1.D,1
28
.E) / Section IX
(1,2,3)
H0 is
Accep
ted.
Sect
ion
XI ,
Sect
ion
IX
Section XI
Anova 0.0
(1.A,1.B,1.C,1.D,1
00
.E) / Section IX
(1,2,3)
H0 is
Reject
ed.
215
3
Impact
of
social
media
adverti
sing on
the
buying
behavi
Impulsive
Buying
Behaviou
r in
Nashik
are
independe
nt of each
other.
57. H0 :
All the
factors of
Social
Media
Advertise
ment and
all the
factors of
Impulsive
Buying
Behaviou
r in Surat
are
independe
nt of each
other.
1
(i) H0:
Model is
not
statisticall
y fit.
Sect
ion
XI,
Sect
ion
IX
Section XI
Anova 0.0
(1.A,1.B,1.C,1.D,1
00
.E) / Section IX
(1,2,3)
H0 is
Reject
ed.
Sect
ion
XI,
Sect
ion
III,
IV,
V,
VI,
VII,
VIII
, IX.
Section
XI(1.A,1.B,1.C,1.
D,1.E ) / Section
III(1,2,3,4),
Section
IV(1,2,3,4),
Section
V(1,2,3,4,5,6),
Section VI(1,2),
Section VII(1,2,3),
Section
VIII(1,2,3),
Section IX(1,2,3)
H0 is
Reject
ed.
Regre
ssion
0.0
00
or of
young
workin
g
216
women
for
consum
er
electro
nics.
(ii) H0: all
the
coefficien
ts of the
study are
not
statisticall
y
significan
t
a. Online
Regre
ssion
purchase
behaviour
b.
Consumer
Buying
Behaviour
c.
Complex
Buying
Behaviour
d. Habitual
Buying
Behaviour
e. Variety
Seeking
Buying
Behaviour
f.
Dissonanc
e Buying
Behaviour
g.
Impulsive
Buying
Behaviour
2.
(i) H0
:Model is
not
Sect
ion
XI,
Sect
ion
Section
XI(1.A,1.B,1.C,1.
D,1.E ) / Section
III(1,2,3,4),
Section
Regre
ssion
0.0
52
H0
Accep
ted
0.0
4
H0
Reject
ed
0.0
00
H0
Reject
ed
0.2
04
H0
Accep
ted
0.0
06
0.0
14
H0
Reject
ed
H0
Reject
ed
0.7
71
H0
Accep
ted
0.0
0
H0 is
Reject
ed.
217
statisticall
y fit
III,
IV,
V,
VI,
VII,
VIII
, IX.
IV(1,2,3,4),
Section
V(1,2,3,4,5,6),
Section VI(1,2),
Section VII(1,2,3),
Section
VIII(1,2,3),
Section IX(1,2,3)
(ii) H0:
all
the
coefficien
ts of the
study are
not
statisticall
y
significan
t
Regre
ssion
a. Online
purchase
behaviour
b.
Consumer
Buying
Behaviour
c.
Complex
Buying
Behaviour
d. Habitual
Buying
Behaviour
e. Variety
Seeking
Buying
Behaviour
f.
Dissonanc
e Buying
Behaviour
g.
Impulsive
Buying
Behaviour
3.
(i) H0 :
Model is
not
statisticall
Sect
ion
XI,
Sect
ion
III,
Section
XI(1.A,1.B,1.C,1.
D,1.E ) / Section
III(1,2,3,4),
Section
IV(1,2,3,4),
Regre
ssion
0.9
13
H0
Accep
ted
0.0
01
H0
rejecte
d
0.1
60
H0
Accep
ted
0.4
56
H0
Accep
ted
0.0
00
H0
rejecte
d
0.5
22
Ho
Accep
ted
0.8
53
Ho
Accep
ted
0.0
0
3.
(i) H0 :
Model
is not
statisti
218
y fit
IV,
V,
VI,
VII,
VIII
, IX.
Section
V(1,2,3,4,5,6),
Section VI(1,2),
Section VII(1,2,3),
Section
VIII(1,2,3),
Section IX(1,2,3)
(ii) H0:
all
the
coefficien
ts of the
study are
not
statisticall
y
significan
t
cally
fit
Regre
ssion
a. Online
purchase
behaviour
b.
Consumer
Buying
Behaviour
c.
Complex
Buying
Behaviour
d. Habitual
Buying
Behaviour
e. Variety
Seeking
Buying
Behaviour
f.
Dissonanc
e Buying
Behaviour
g.
Impulsive
Buying
Behaviour
4.
To
study
the
effectiv
Nil
Sect
ion
X
Section
X(1.A,1B,1C,1D
,1E, 2,3 & 4A,4B)
Rank
Order
and
Frequ
ency
distrib
0.5
77
Ho
Accep
ted
0.0
04
Ho
Reject
ed
0.0
25
Ho
Reject
ed
0.0
43
H0
Reject
ed
0.0
01
Ho
Reject
ed
0.0
17
Ho
Reject
ed
0.3
91
Ho
Accep
ted
--
Faceb
ook is
the
most
effecti
ve in
219
eness of
ution
all 3
cities.
It is
follow
ed by
Twitte
r in
some
cities
and
Linke
dIn in
other
cities.
Anova 0.3
38
H0 is
Accep
ted.
Social
Media
tools
like
face
book,
twitter,
Linked
In on
the
consum
er
behavi
our.
5.
To
study
the
impact
of
social
media
adverti
sing on
workin
g
women
belongi
1. H0 :
Impact of
social
media
advertisin
g and the
qualificati
on of
young
working
women in
Mumbai
are
independe
nt of each
other.
Sect
ion
XI,
Sect
ion
I
Section XI (1,2,3)
/ Section I
(Education)
ng to
differe
nt
demogr
aphic
220
factors
such as
qualific
ation,
annual
income,
occupa
tion
and
place.
2. H0 :
Impact of
social
media
advertisin
g and the
qualificati
on of
young
working
women in
Nashik
are
independe
nt of each
other.
3. H0 :
Impact of
social
media
advertisin
g and the
qualificati
on of
young
working
women in
Surat are
independe
nt of each
Sect
ion
XI,
Sect
ion
I
Section XI (1,2,3)
/ Section I
(Education)
Anova 0.0
01
H0 is
Reject
ed.
Sect
ion
XI,
Sect
ion
I
Section XI (1,2,3)
/ Section I
(Education)
Anova 0.0
00
H0 is
Reject
ed.
221
other.
4. Impact
of social
media
advertisin
g and the
Annual
Income of
young
working
women in
Mumbai
are
independe
nt of each
other
5. Impact
of social
media
advertisin
g and the
Annual
Income of
young
working
women in
Nashik
are
independe
nt of each
other
6. Impact
of social
media
advertisin
g and the
Annual
Income of
young
working
women in
Surat are
independe
Sect
ion
XI,
Sect
ion
I
Section XI (1,2,3) Anova 0.7
/ Section I (Annual
97
income)
H0 is
Accep
ted.
Sect
ion
XI,
Sect
ion
I
Section XI (1,2,3) Anova 0.0
/ Section I (Annual
29
income)
H0 is
Reject
ed.
Sect
ion
XI,
Sect
ion
I
Section XI (1,2,3) Anova 0.0
/ Section I (Annual
00
income)
H0 is
Reject
ed.
222
nt of each
other
7. Impact
of social
media
advertisin
g and the
Occupatio
n of
young
working
women in
Mumbai
are
independe
nt of each
other
8. Impact
of social
media
advertisin
g and the
Occupatio
n of
young
working
women in
Nashik
are
independe
nt of each
other
9. Impact
of social
media
advertisin
g and the
Occupatio
n of
young
working
women in
Surat are
Sect
ion
XI,
Sect
ion
I
Section XI (1,2,3)
/ Section I
(Occupation)
Anova 0.1
28
H0 is
Accep
ted.
Sect
ion
XI,
Sect
ion
I
Section XI (1,2,3)
/ Section I
(Occupation)
Anova 0.0
49
H0 is
Reject
ed.
Sect
ion
XI,
Sect
ion
I
Section XI (1,2,3)
/ Section I
(Occupation)
Anova 0.0
87
H0 is
Accep
ted.
223
independe
nt of each
other
7.3 Summery of Hypothesis :
7.7 Tabular representation showing Hypothesis (Accepted/Rejected).
Sr.
No.
1.
2.
3.
4.
H0 Hypotheis Accepted
H0c: There is no association
between the factor i.e.
positive reactions/feelings
towards advertisements
displayed with Consumer
buying behaviour in Surat.
Sr.
No.
1.
H0b: There is no association 2.
between the appealing factor
of social media
advertisements with
Consumer buying behavior
in Nashik.
H0c: There is no association 3.
between the appealing factor
of social media
advertisements with
Consumer buying behavior
in Surat.
H0a: There is no association 4.
between the memorable
visuals and slogans factor
of social media
H0 Hypotheis Rejected
H0a: There is no association
between the factor i.e.
positive reactions/feelings
towards advertisements
displayed with Consumer
buying behaviour in
Mumbai.
H0b: There is no association
between the factor i.e.
positive reactions/feelings
towards advertisements
displayed with Consumer
buying behaviour in Nashik.
H0a: There is no association
between the appealing factor
of social media
advertisements with
Consumer buying behavior
in Mumbai.
H0c: There is no association
between the attractive factor
of social media
advertisements with
224
5.
6.
7.
8.
9.
10.
advertisements with
Consumer buying behavior
in Mumbai.
H0b: There is no association
between the memorable
visuals and slogans factor
of social media
advertisements with
Consumer buying behavior
in Nashik.
H0c: There is no association
between the memorable
visuals and slogans factor
of social media
advertisements with
Consumer buying behavior
in Surat.
H0a: There is no association
between the attractive factor
of social media
advertisements with
Consumer buying behavior
in Mumbai.
H0b: There is no association
between the attractive factor
of social media
advertisements with
Consumer buying behavior
in Nashik.
H0a: There is no association
between the trustworthiness
factor of social media
advertisements with
Consumer buying behavior
in Mumbai.
H0b: There is no association
between the trustworthiness
factor of social media
advertisements with
Consumer buying behavior
in Nashik.
Consumer buying behavior
in Surat.
5.
H0c: There is no association
between the trustworthiness
factor of social media
advertisements with
Consumer buying behavior
in Surat.
6.
H0b : There is no association
between the factor i.e.
positive reactions/feelings
towards advertisements
displayed with online
purchase behavior in Nashik.
7.
H0c : There is no association
between the factor i.e.
positive reactions/feelings
towards
advertisements
displayed
with
online
purchase behavior in Surat.
8.
H0b : There is no association
between the appealing factor
of the advertisements
displayed with online
purchase behavior in Nashik.
9.
H0c : There is no association
between the appealing factor
of
the
advertisements
displayed
with
online
purchase behavior in Surat.
10.
H0b : There is no association
between the memorable
visuals and slogans factor of
the advertisements with
online purchase behavior in
Nashik.
225
11.
H0a : There is no association 11.
between the factor i.e.
positive reactions/feelings
towards advertisements
displayed with online
purchase behavior in
Mumbai.
H0a : There is no association 12.
between the appealing factor
of the advertisements
displayed with online
purchase behavior in
Mumbai.
H0a : There is no association 13.
between the memorable
visuals and slogans factor of
the advertisements with
online purchase behavior in
Mumbai.
H0c : There is no association
between the memorable
visuals and slogans factor of
the advertisements with
online purchase behavior in
Surat.
14.
H0a : There is no association 14.
between the attractive factor
of the advertisements with
online purchase behavior in
Mumbai.
15.
H0a : There is no association 15.
between the trustworthiness
factor of the advertisements
with
online
purchase
behavior in Mumbai.
16.
H0b : There is no association
between the trustworthiness
factor of the advertisements
with online purchase
behavior in Nashik.
17.
H0c : There is no association 17.
between the trustworthiness
factor of the advertisements
H0a: There is no association
between the factor i.e.
positive reactions/feelings
towards advertisements
displayed with Complex
buying behaviour in
Mumbai.
H0b : There is no association
between the factor i.e.
positive reactions/feelings
towards advertisements
displayed with Complex
buying behaviour in Nashik.
H0c : There is no association
between the factor i.e.
positive reactions/feelings
towards advertisements
displayed with Complex
buying behaviour in Surat.
H0b : There is no association
between the appealing factor
i.e. of the advertisements
12.
13.
16.
H0b : There is no association
between the attractive factor
of the advertisements with
online purchase behavior in
Nashik.
H0c : There is no association
between the attractive factor
of the advertisements with
online purchase behavior in
Surat.
226
with
online purchase
behavior in Surat.
18.
19.
20.
21.
22.
23.
24.
displayed with Complex
buying behaviour in Nashik.
H0a: There is no association
between the appealing factor
of the advertisements
displayed with Complex
buying behaviour in
Mumbai.
H0b : There is no association
between the memorable
visuals and slogans factor of
the advertisements displayed
with Complex buying
behaviour in Nashik.
H0c : There is no association
between the memorable
visuals and slogans factor of
the advertisements
displayed with Complex
buying behaviour in Surat.
H0a: There is no association
between the attractiveness
factor of the advertisements
displayed with Complex
buying behaviour in
Mumbai.
H0b : There is no association
between the attractiveness
factor of the advertisements
displayed with Complex
buying behaviour in Nashik.
H0c : There is no association
between the trustworthiness
factor of the advertisements
displayed with Complex
buying behaviour in Surat.
18.
H0c : There is no association
between the appealing factor
of the advertisements
displayed with Complex
buying behaviour in Surat.
19.
H0a: There is no association
between the memorable
visuals and slogans factor of
the advertisements displayed
with Complex buying
behaviour in Mumbai.
H0c : There is no association
between the attractiveness
factor of the advertisements
displayed with Complex
buying behaviour in Surat.
H0a : All the factors of
Social Media Advertisement
and all the factors of
Habitual Buying Behaviour
24.
20.
21.
22.
23.
H0a: There is no association
between the trustworthiness
factor of the advertisements
displayed with Complex
buying behaviour in
Mumbai.
H0b : There is no association
between the trustworthiness
factor of the advertisements
displayed with Complex
buying behaviour in Nashik.
H0b : All the factors of Social
Media Advertisement and all
the factors of Habitual
Buying Behaviour in Nashik
are independent of each
other.
H0c : All the factors of Social
Media Advertisement and all
the factors of Habitual
Buying Behaviour in Surat
227
25.
26.
27.
28.
in Mumbai are independent
of each other.
H0b : All the factors of
Social Media Advertisement
and all the factors of
Dissonance Buying
Behaviour in Nashik are
independent of each other.
H0c : All the factors of
Social Media Advertisement
and all the factors of
Dissonance Buying
Behaviour in Surat are
independent of each other.
H0a : All the factors of
Social Media Advertisement
and all the factors of
Impulsive Buying
Behaviour in Mumbai are
independent of each other.
H0a: In Mumbai Online
purchase behaviour is not
statistically significant.
25.
26.
27.
28.
29.
H0a: In Mumbai Habitual
Buying Behaviour is not
statistically significant.
29.
30.
H0a: In Mumbai Impulsive
Buying Behaviour is not
statistically significant.
30.
31.
H0b: In Nashik Online
purchase behaviour is not
statistically significant.
H0b: In Nashik Complex
Buying Behaviour is not
31.
32.
32.
are independent of each
other.
H0a : All the factors of Social
Media Advertisement and all
the factors of Variety
Seeking Buying Behaviour
in Mumbai are independent
of each other.
H0b : All the factors of Social
Media Advertisement and all
the factors of Variety
Seeking Buying Behaviour
in Nashik are independent
of each other.
H0c : All the factors of Social
Media Advertisement and all
the factors of Variety
Seeking Buying Behaviour
in Surat are independent of
each other.
H0a : All the factors of Social
Media Advertisement and all
the factors of Dissonance
Buying Behaviour in
Mumbai are independent of
each other.
H0b : All the factors of Social
Media Advertisement and all
the factors of Impulsive
Buying Behaviour in Nashik
are independent of each
other.
H0c : All the factors of Social
Media Advertisement and all
the factors of Impulsive
Buying Behaviour in Surat
are independent of each
other.
H0a: In Mumbai Model is not
statistically fit.
H0a: In Mumbai Consumer
Buying Behaviour
228
statistically significant.
33.
34.
35.
36.
37.
38.
39.
40.
41.
H0b: In Nashik Habitual
Buying Behaviour is not
statistically significant.
H0b: In Nashik Dissonance
Buying Behaviour is not
statistically significant.
H0b: In Nashik Impulsive
Buying Behaviour is not
statistically significant.
H0c: In Surat Online purchase
behaviour is not statistically
significant.
H0c: In Surat Impulsive
Buying Behaviour is not
statistically significant.
H0a : Impact of social media
advertising and the
qualification of young
working women in Mumbai
are independent of each
other.
H0a :Impact of social media
advertising and the Annual
Income of young working
women in Mumbai are
independent of each other.
H0a :Impact of social media
advertising and the
Occupation of young
working women in Mumbai
are independent of each
other.
H0c :Impact of social media
advertising and the
Occupation of young
working women in Surat are
independent of each other.
32.
33.
is not statistically
significant.
H0a: In Mumbai Complex
Buying Behaviour is not
statistically significant.
H0a: In Mumbai Variety
Seeking Buying Behaviour is
34.
35.
36.
37.
not statistically significant.
H0a: In Mumbai Dissonance
Buying Behaviour is not
statistically significant.
H0b :In Nashik Model is not
statistically fit.
H0b: In Nashik Consumer
Buying Behaviour is not
statistically significant.
H0b: In Nashik Variety
Seeking Buying Behaviour is
not statistically significant.
38.
H0c : In Surat Model is not
statistically fit
39.
H0c: In Surat Consumer
Buying Behaviour is not
statistically significant.
40.
H0c: In Surat Complex Buying
Behaviour is not statistically
significant.
41.
H0c: In Surat Habitual Buying
Behaviour is not statistically
significant.
H0c: In Surat Variety Seeking
42.
229
Buying Behaviour is not
43.
44.
45.
46.
47.
48.
statistically significant.
H0c: In Surat Dissonance
Buying Behaviour is not
statistically significant.
H0b : Impact of social media
advertising and the
qualification of young
working women in Nashik
are independent of each
other.
H0c : Impact of social media
advertising and the
qualification of young
working women in Surat are
independent of each other.
H0b :Impact of social media
advertising and the Annual
Income of young working
women in Nashik are
independent of each other
H0c :Impact of social media
advertising and the Annual
Income of young working
women in Surat are
independent of each other.
H0b :Impact of social media
advertising and the
Occupation of young
working women in Nashik
are independent of each
other.
230
Chapter 8
Conclusion
The growth of Social Media signifies the technological development of cities.
Women play an important role in taking the buying decision and they constitute
50% of the population. According to the IAMAI report the access rate of women
accessing the social media is more as compared to that of men and it is
increasing day by day. Therefore women have a prominent role to play as far as
the consumer electronics market is concerned.
The detailed research has lead to certain conclusions which are being discussed
in this chapter.
Association between Positive reactions or feelings towards social media
advertisements with consumer buying behaviour :
It has been concluded from the study that there is a strong positive association
between the factor of social media advertising i.e. positive reactions/feelings
with the consumer buying behaviour in Mumbai and Nashik. So if there is any
increase in the positive reactions/feelings it will positively affect the consumer
buying behaviour. However there is no association between the factor of social
media advertising i.e. positive reactions/feelings with the consumer buying
behaviour in Surat.
Association between appealing factor of social media advertisements with
consumer buying behaviour :
It has been concluded from the study that there is a strong positive association
231
between the appealing factor of social media advertising with the consumer
buying behaviour in Mumbai. However there is no association between the
appealing factor of social media advertising with the consumer buying behaviour
in Nashik and Surat.
Association between memorable visuals and slogans factor of social media
advertisements with consumer buying behaviour :
It has been concluded from the study that the memorable visuals and slogans
factor of social media advertising and consumer buying behaviour are
independent of each other and there is no association between the memorable
visuals and slogans factor of social media advertising with the consumer buying
behaviour in Mumbai, Nashik and Surat.
Association between attractive factor of social media advertisements with
consumer buying behaviour :
It has been concluded from the study that the attractiveness factor of social
media advertising and consumer buying behaviour are dependent of each other
and there is a strong association between the attractiveness factor of social media
advertising with the consumer buying behaviour in Surat. However there is no
association between the attractiveness factor of social media advertising with the
consumer buying behaviour in Mumbai and Nashik and they are independent of
each other.
Association between trustworthiness factor of social media advertisements
with consumer buying behaviour :
It has been concluded from the study that the trustworthiness factor of social
232
media advertising and consumer buying behaviour are dependent of each other
and there is a strong association between the trustworthiness factor of social
media advertising with the consumer buying behaviour in Surat. However there
is no association between the trustworthiness factor of social media advertising
and the consumer buying behaviour in Mumbai and Nashik and they are
independent of each other.
Association between Positive reactions or feelings towards social media
advertisements with online purchase behaviour :
It has been revealed from the study that there is an association between the factor
of social media advertising i.e. positive reactions/feelings with the online
purchase behaviour in Nashik and Surat. So if there is any change in the positive
reactions/feelings it will lead to change in the online purchase behaviour.
However there is no association between the factor of social media advertising
i.e. positive reactions/feelings with the online purchase behaviour in Mumbai.
Association between appealing factor of social media advertising and online
purchase behaviour :
It has been revealed from the study that there is an association between the
appealing factor of social media advertising and the online purchase behaviour in
Nashik and Surat. So if there is any change in the appealing factor of social
media advertising it will lead to change in the online purchase behaviour.
However there is no association between the appealing factor of social media
advertising and the online purchase behaviour in Mumbai.
Association between memorable visuals and slogans factor of social media
233
advertising and online purchase behaviour :
It has been revealed from the study that there is an association between the
memorable visuals and slogans factor of social media advertising and the online
purchase behaviour in Nashik and Surat. So if there is any change in the
memorable visuals and slogans factor of social media advertising it will lead to
change in the online purchase behaviour. However there is no association
between the memorable visuals and slogans factor of social media advertising
and the online purchase behaviour in Mumbai.
Association between attractiveness factor of social media advertising and
online purchase behaviour :
It has been revealed from the study that there is an association between the
attractiveness factor of social media advertising and the online purchase
behaviour in Nashik and Surat. So if there is any change in the attractiveness
factor of social media advertising it will lead to change in the online purchase
behaviour. However there is no association between the attractiveness factor of
social media advertising and the online purchase behaviour in Mumbai.
Association between trustworthiness factor of social media advertising and
online purchase behaviour :
It has been revealed from the study that there is no association between the
trustworthiness factor of social media advertising and the online purchase
behaviour in Mumbai, Nasik and Surat. The trustworthiness factor of social
media advertising and the online purchase behaviour are independent of each
other.
234
Association between Positive reactions or feelings towards social media
advertisements with complex buying behaviour :
It has been revealed from the study that there is a strong relationship between the
factor of social media advertising i.e. positive reactions/feelings with online
consumer behaviour in Mumbai, Nashik and Surat. So if there is any change in
the positive reactions/feelings factor of social media advertising, it will lead to
change in the complex buying behaviour.
Association between appealing factor of social media advertisements with
complex buying behaviour :
It has been revealed from the study that there is a strong relationship between the
appealing factor of social media advertising with complex buying behaviour in
Nashik and Surat. So if there is any change in the appealing factor of social
media advertising, it will lead to change in the complex buying behaviour.
However there is no relationship between the appealing factor of social media
advertising and complex buying behaviour in Mumbai and they are independent
of each other.
Association between memorable visuals and slogans factor of social media
advertisements with complex buying behaviour :
It has been revealed from the study that there is a strong relationship between the
memorable visuals and slogans factor of social media advertising with complex
buying behaviour in Mumbai. So if there is any change in the memorable visuals
and slogans factor of social media advertising, it will lead to change in the
complex buying behaviour. However there is no relationship between the
235
memorable visuals and slogans factor of social media advertising and complex
buying behaviour in Nashik and Surat.
Association between attractiveness factor of social media advertisements
with complex buying behaviour :
It has been revealed from the study that there is a strong relationship between the
attractiveness factor of social media advertising with complex buying behaviour
in Surat. So if there is any change in the attractiveness factor of social media
advertising, it will lead to change in the complex buying behaviour in Surat.
However there is no relationship between the attractiveness factor of social
media advertising and complex buying behaviour in Mumbai and Nashik and
they are independent of each other.
Association between trustworthiness factor of social media advertisements
with complex buying behaviour :
It has been revealed from the study that there is a strong relationship between the
trustworthiness factor of social media advertising with complex buying
behaviour in Mumbai and Nashik. So if there is any change in the
trustworthiness factor of social media advertising, it will lead to change in the
complex buying behaviour in Mumbai and Nashik. However there is no
relationship between the trustworthiness factor of social media advertising and
complex buying behaviour in Surat and they are independent of each other.
Relationship between all the factors of Habitual Buying Behaviour with all
the factor of Social Media Advertisement in different cities :
From the study it has been concluded that all the factors of Social Media
236
Advertisement and all the factors of Habitual Buying Behaviour of young
working women for consumer electronics in Mumbai are independent of each
other. However in Nashik and Surat, all the factors of Social Media
Advertisement and all the factors of Habitual Buying Behaviour of young
working women for consumer electronics are dependent of each other.
Relationship between all the factors of Variety Seeking Buying Behaviour
with all the factor of Social Media Advertisement in different cities :
It has been concluded from the study that all the factors of Social Media
Advertisement and all the factors of Variety Seeking Buying Behaviour of
young working women for consumer electronics in Mumbai, Nashik and Surat
are dependent of each other.
Relationship between all the factors of Dissonance Buying Behaviour with
all the factor of Social Media Advertisement in different cities :
It has been concluded from the study that all the factors of Social Media
Advertisement and all the factors of Dissonance Buying Behaviour of young
working women for consumer electronics in Mumbai are dependent of each
other. However all the factors of Social Media Advertisement and all the factors
of Dissonance Buying Behaviour of young working women for consumer
electronics in Nashik and Surat are independent of each other.
Relationship between all the factors of Impulsive Buying Behaviour with all
the factor of Social Media Advertisement in different cities :
It has been concluded from the study that all the factors of Social Media
Advertisement and all the factors of Impulsive buying behaviour of young
237
working women for consumer electronics in Nashik and Surat are dependent of
each other. However all the factors of Social Media Advertisement and all the
factors of Impulsive buying behaviour of young working women for consumer
electronics in Mumbai are independent of each other.
Impact of Social Media advertising on different factors of buying behaviour
of young working women for consumer electronics in Mumbai :
From the study it has been concluded that in Mumbai, Social media advertising
has a significant impact on the following factors of buying behaviour Consumer Buying Behaviour, Complex buying behaviour, Variety-seeking
buying behaviour, Dissonance buying behaviour.
Impact of Social Media advertising on different factors of buying behaviour
of young working women for consumer electronics in Nashik :
It has been revealed from the study that in Nashik, Consumer Buying Behaviour
and Variety - Seeking buying behaviour are the factors of buying behaviour
which are significantly impacted by Social media advertising.
Impact of Social Media advertising on different factors of buying behaviour
of young working women for consumer electronics in Surat :
It has been revealed from the study that in Surat, Consumer Buying Behaviour,
Complex buying behaviour, Habitual buying behaviour, Dissonance and Variety
- Seeking buying behaviour are the factors of buying behaviour which are
significantly impacted by Social media advertising.
Effectiveness of social media tools like Face book, Twitter , LinkedIn on the
238
consumer Behaviour in Mumbai :
Audience:
From the research study it has been concluded that in Mumbai the young
working women prefer most Face book, as the Social networking sites that have
a large number of groups (networks) available for any demographics you are
looking for; for instance group of teenagers, group of kids, youth, group of new
moms, brides, sports fans, technology enthusiasts, entrepreneurs etc. Face-book
is followed by LinkedIn and Twitter is the least preferred site.
Targeting :
From the study it has been concluded that Face book is the most preferred Social
networking site that targets the advertisements to specific group of audience
according to the young working women in Mumbai. Face book is followed by
LinkedIn and the least preferred site for targeting the advertisements to specific
group of audience is Twitter in Mumbai.
More followers due to acquaintances :
From the study it has been observed that in Mumbai, Face-book has more
followers due to acquaintances, followed by Twitter and LinkedIn has the least
number of followers due to acquaintances.
More Unknown followers :
From the study it has been observed that in Mumbai, Face-book has more
unknown followers, followed by Twitter and LinkedIn has least number of
unknown followers.
239
Effectiveness of social media tools like Face book, Twitter , LinkedIn on the
consumer Behaviour in Nashik :
Audience:
From the study it is concluded that in Nashik, the young working women prefer
most Face book, followed by Twitter and least preferred is LinkedIn as the
Social networking sites that have a large number of groups (networks) available
for any demographics; for instance group of teenagers, group of kids, youth,
group of new moms, brides, sports fans, technology enthusiasts, entrepreneurs
etc.
Targeting :
From the study it has been observed that in Nashik, the young working women
prefer most Face book, followed by LinkedIn and least preferred is twitter as the
social networking sites targeting the advertisements to specific group of
audience.
More followers due to acquaintances :
From the study it has been observed that in Nashik a maximum number of young
working women agreed that Face book has more number of followers due to
acquaintances, followed by Twitter and LinkedIn has the minimum number of
followers due to acquaintances.
More Unknown followers :
From the research it has been revealed that in Nashik, a maximum number of
young working women said that face book has more unknown followers,
240
followed by Twitter and LinkedIn has minimum unknown followers.
Effectiveness of social media tools like Face book, Twitter , LinkedIn on the
consumer Behaviour in Surat :
Audience :
From the study it has been observed that in Surat the young working women
prefer Face book most, followed by Twitter and the least preferred is LinkedIn,
as the Social networking sites that have a large number of groups (networks)
available for any demographics; for instance group of teenagers, group of kids,
youth, group of new moms, brides, sports fans, technology enthusiasts,
entrepreneurs etc.
Targeting :
From the study it has been concluded that in Surat, the young working women
prefer most Face book, followed by Twitter and least preferred is LinkedIn as
the Social networking sites targeting the advertisements to specific group of
audience.
More followers due to acquaintances :
From the study it has been observed that in Surat, maximum number of young
working women said that Face book has more followers due to acquaintances,
followed by Twitter and LinkedIn has least number of followers due to
acquaintances.
More Unknown followers :
From the study it has been observed that in Surat, a maximum number of
241
respondents said that LinkedIn has more unknown followers, followed by Face
book and Twitter has minimum number of unknown followers.
Relationship between impact of social media advertising of young working
women with their qualification in different cities :
From the study it has been concluded that in Mumbai qualification of the young
working women does not have any effect on the impact of Social media
advertising. However qualification of the young working women has a direct
effect on the impact of social media advertising in Nashik and Surat. Social
media advertising has more impact on non graduate young working women
followed by post graduate and graduates in Nashik. While Social media
advertising has more impact on non graduate young working women followed
by graduates and post graduate in Surat.
Relationship between impact of social media advertising of young working
women with their Annual Income in different cities :
From the study it has been observed that annual income of young working
women does not have any relationship with the impact of social media
advertising in Mumbai. However in Nashik and Surat the annual income of
young working women has a substantial relationship with the impact of social
media advertising. From the findings of the study it has been observed that the
impact of social media advertising is more observed on the young working
women having annual income upto 3 lakhs followed by young working women
in the income groups of 3.1- 5, 5.1 – 10 and above 10 lakhs in Nashik. In surat
the impact of social media advertising is found to be more on young working
242
women having annual income upto 3 lakhs followed by young working women
in the income groups of 3.1- 5, above 10 lakhs and minimum impact is
observed on the young working women having annual income in the group of
5.1 – 10 lakhs.
Relationship between impact of social media advertising and the
Occupation of young working women in different cities :
From the study it has been concluded that Occupation of young working women
does not have any effect on the impact of social media advertising in Mumbai
and Surat. However in Nashik, occupation of the young working women does
affect the impact of social media advertising. The impact of social media
advertising is more observed on business class women, followed by women
doing service and finally the self-employed women.
243
Chapter 9
Recommendations and Suggestions
9.1 Recommendations and Suggestions :
The percentage of young working women accessing Social Media in Surat and
Mumbai is more as compared to Nashik. In Nashik, a certain amount of
unawareness, lack of trust and reluctance is observed among the young working
women as far as using Social Media is concerned.
Also if the impact of social media advertising on consumer buying behaviour is
taken into consideration, it has been observed from the study that there is
significant impact found on the consumer buying behaviour in cities Mumbai
and Surat, however there is no impact found on the consumer buying behaviour
of the young working women in Nashik. Consequently it can be suggested that
the consumer electronics segment has a large scope of penetrating in smaller
cities like Nashik, where large market is still untapped. This gap should be
bridged and the awareness of Social Media should be increased in smaller cities,
so that organizations can directly reach more and more consumers and can
interact with them. Also young working women in smaller cities can be
benefitted with more product knowledge through Social Media so that they can
take an informed buying decision.
The Social networking sites like LinkedIn and Twitter can improve their
advertising efficiency by enhancing features like targeting the advertisements to
the right group of audience, introducing more groups for any demographics like
244
group of engineers, manufacturers, entrepreneurs, doctors, youth, house wives
etc., more user friendliness so that more and more audience are attracted towards
them for socialising as well as accessing product information.
The study has revealed that the impact of social media advertising is more on
undergraduates, business class and young working women having annual income
around three lakhs. Therefore there is a need for the consumer electronics
companies to find out the reasons for not accessing social media, among the
young working women belonging to other educational, economic and
occupational background and spreading awareness among them about the Social
Media tools and to reach out to them through social media in order to tap more
consumers and increase the business. So consumer electronics segment should
take social media to smaller cities and spread awareness about social media in
smaller cities so that their social media promotions can target the consumers
from smaller cities which are not currently active users of SNS and tap this less
explored market.
9.2 Future Scope of Study :
1. The study can be extended to other product segments instead of
consumer electronics which has been taken for this study.
2. The study can be conducted on various other social networking sites.
3. Similar study can be conducted in other cities of India.
4. Additionally the study can be extended to other groups of women e.g.
college students, house wives etc.
245
Annexure - I
Bibliography
1. Andreas M. Kaplan and Michael Haenlein (2010), “Users of the
world, unite! The challenges and opportunites of Social Media”,
Business Horizons, Kelley School of Business, Vol.53, 59-68.
2. Áine Dunne, Margaret-Anne Lawlor, Jennifer Rowley (2010),
“Young people’s
use of online social networking sites-a uses and
gratifications perspective”.
3. Anil Bhat (May 2012), “Blog Popularity And Activity On Social
Media : An Exploratory Research”, Indian Journal of Marketing, Pg
10-18.
4. Ambrose Jagongo, Catherine Kinyua (2013), “The Social Media and
Entrepreneurship Growth”, International Journal of Humanities and
Social Science, 3(10), 213-227.
5. Afendi Hamat, Mohamed Amin Embi, Haslinda Abu Hassan (2012),
“The Use of Social Networking Sites among Malaysian university
students”, International Education Studies, Canadian Centre of
Science and Education, p 56, Vol. 5, Issue no. 3.
6. Andreas Kaplan and Michael Heinlein (May-June 2011), Two hearts
in three quarter time : How to waltz the social media/viral marketing
dance, Business Horizons, Elsevier Inc., pp. 253-263, Vol. 54, Issue
246
3.
7. Andreas Kaplan and Michael Heinlein (Nov. - Dec.2009), “The
fairyland of Second Life : Virtual social worlds and how to use
them”, Business Horizons, Elsevier Inc., pp 563-572, Volume 52,
Issue 6.
8. Boris Bartikowski, Gianfranco Walsh (4 February2014), “ Attitude
contagion in consumer opinion platforms : posters and lurkers”,
Electron Markets, Institute of Information Management, University
of St. Gallen.
9. Botha, E., Farshid, M., Pitt, L. (2011), “How Sociable? An
exploratory study of university brand visibility in social media”,
South African Journal of Business Management, SA ePublications,
43-51, Vol. 42, Issue 2.
10. Efthymios Constantinides, Carlota Lorenzo Romero and Miguel A.
Gomez Boria (2009), “Social Media : A New Frontier for Retailers?”,
European Retail Research, Gabler Verlag, pp 1-28, Vol. 22.
11. Erkan Akar & Birol Topcu (March 2011) ,“An examination of the
factors influencing consumer’s attitudes towards social media
marketing”, Journal of Internet Commerce, Routledge Informa Ltd,
pp. 35-67, Volume 10, Issue 1.
12. Florian Probst(2011),“ Predicting User’s future level of
communication activity in online social networks : A first step
towards more advertising effectiveness”, AMCIS 2011 Proceedings,
247
http://aisel.aisnet.org/amcis2011_submissions/39
13. Garima Gupta (January-June 2013), “Assessing the Influence of Social
Media on Consumer’s Purchase Intentions”, Asia-Pacific Marketing
Review, Asia Pacific Institute of Management, pp.31-39, Vol.II , Issue No.
1.
14. Guang Tian, Luis Borges (2012),“The effectiveness of social
marketing mix strategy : Towards an Anthropological Approach”,
International Journal of Business Anthropology, North American
Business Press, pp. 102-113, Vol. 3, Iss. 1.
15. Irene, falsePollach (Oct-Dec 2008), “Media Richness in Online
Consumer Interactions : An Exploratory Study of Consumer-Opinion
Web Sites”, Information Resources Management Journal, 49-65,
Vol.21, Issue No.4 .
16. Jugal Kishor, Prof. V. K. Singh (August 2014), “An empirical study
on shopping tendency through social networking sites (SNSs)”,
International Journal of Advanced Research in Management and
Social Sciences, GreenField Advanced Research Publishing House,
Page 49-62, Vol. 3, Issue no. 8.
17. Jiyoung Cha (2009), “Shopping on Social networking websites ;
Attitudes toward real versus virtual items”, Journal of Interactive
Advertising, pp. 77-93, Vol. 10, Issue 1.
18. Jae Eun Chung, Namkee Park, Hua Wang, Janet Fulk, Margaret
McLaughlin, “Age differences in perceptions of online community
248
participation among non-users :An extension of the Technology
Acceptance Model”, Computers in Human Behavior, Elsevier Inc.,
pp. 1674 - 1684, Vol. 26, Issue 6.
19. Jacinta Hawkins, Sandy Bulmer and Lynne Eagle (2011), “Evidence
of IMC in social marketing”, Journal on Social Marketing, Emerald
Group Publishing Limited, pp. 228-239, Vol. 1, Issue no. 3.
20. Kevin J. Trainor, James (Mick) Andzulis, Adam Rapp, Raj Agnihotri
(June 2014) “Social media technology usage and customer
relationship performance : A capabilities-based examination of social
CRM”, Journal of Business Research, Elsevier Inc., pp.1201-1208,
Vol. 67, Issue no. 6.
21. Logan, Kelty; Bright, Laura F; Gangadharbatla, Harsha (2012),
“Facebook versus television: advertising value perceptions among
females”, Journal of Research in Interactive Marketing, 164-179,
Vol. 6, Issue No. 3.
22. Mariana Baca and Henry Holtzman (2008),“Television meets
Facebook : Social Networks through Consumer Electronics”, Social
interactive television, MIT Media Laboratory.
23. Michel Laroche, Mohammad Reza Habibi, Marie-Odile Richard
(Feb. 2013) ,“To be or not to be in social media : How brand loyalty
is affected by social media?”, International Journal of Information
Management, Elsevier B. V., pp. 76-82, Vol. 33, Issue no. 1.
24. O’Brien, Clodagh (2011), “The emergence of the social media
empowered consumer”, Irish Marketing Review, Mercury Publishers
249
Ltd., 32-40, 21, 1/2.
25. P.Sri Jothi, M. Neelamalar and R. Skakthi Prasad (March 2011),
“Analysis of social networking sites: A study on effective
communication strategy in developing brand communication”,
Journal of Media and Communication Studies, online
http://www.academicjournals.org/jmcs, pp. 234-242, Vol. 3 (7).
26. Philip J. Kitchen & Don E. Schultz (2009),“IMC: New horizon/false
dawn for a marketplace in turmoil”, Journal of Marketing
Communications, pp. 197-204, Vol. 15, Issue no. 2-3.
27. Richard D. Waters & Kevin D. Lo (2012), “Exploring the impact of
Culture in Social Media Sphere : A content analysis of non-profit
organization’s use of facebook”, Journal of Intercultural
Communication Research, Routledge Informa Ltd, pp.297-319,
Volume 41, Issue 3.
28. Rajiv Kaushik (March 2012),“Impact of Social Media on Marketing”,
International Journal of Computational Engineering and
Management, www. Ijcem.org, pp. 91-95, Vol. 15, Issue no. 2.
29. Ramulu Bhukya (2012), “Presence of Indian Big IT Brands on Social
Media: an Empirical Study”, Open Access Scientific Reports, Vol.1,
Issue no. 10.
30. Shahir Bhatt and Amola Bhatt (2012), “Factors influencing Online
Shopping : An Empirical Study in Ahmedabad”, The IUP journal of
Marketing Management, Vol. XI, Issue No.4.
250
31. Simona Vinerean, Iuliana Cetina, Luigi Dumitrescu & Mihai
Tichindelean (June,2013), “The Effects of Social Media Marketing
on Online Consumer Behaviour”, International Journal of Business
and Management, Published by Canadian Centre of Science and
Education,Vol. 8, Issue No.14.
32. Smith, Nicola (Nov 26,2009), “Consumer Electronics Vertical Focus
: The heights of invention”, New Media Age, Centaur
Communications Ltd., 17-19.
33. Sunil Karve, Shilpa C. Shinde (March 2013), “Effectiveness of Social
Networking Sites (SNS)”, IBMRD’s Journal of Management and
Research, Informatics Publishing Limited, Vol. 2, Issue no. 1.
34. S. Vivin. Richard, Ms. Sri. Jothi (Aug. 2012), “ A study on online
marketing strategies used by E-Entrepreneurs in India”, International
Journal of Marketing and Technology, International journals of
Multidisciplinary Research Academy, Page 111-129, Vol.2, Issue no.
8.
35. Shu-Chuan Chu, Yoojung Kim(2011), “Determinants of consumer
engagement in electronic word-of-mouth (eWOM) in social
networking sites”, International Journal of Advertising, World
Advertising Research Center Limited, Pg. 47-75, 30(1).
36. Sarah, falseDiffley; Kearns, James; Bennett, Williams; Kawalek,
Peter (2011), “Consumer Behaviour in Social Networking Sites:
Implications for Marketers”, Irish Journal of Management, Irish
251
Academy of Management, 47-65, 30, 2.
37. Stephanie Hays, Stephen John Page & Dimitrios Buhalis (2012), “
Social Media as a destination marketing tool: its use by national
tourism organisations”, Current issues in tourism, Routledge Informa
Ltd, pp.211-239, Vol. 16, Iss. 3.
38. Shu-Chuan Chu, Sara Kamal & Yoojung Kim (May 2013), “
Understanding consumer’s responses toward social media advertising
and purchase intention towards luxury products”, Journal of Global
Fashion Marketing, Routledge Informa Ltd, pp.158-174, Vol. 4, Issue
no. 3.
39. Ronald T. Rust and Richard W. Oliver (June 2013), “The Death of
Advertising”, Journal of Advertising, Vol. 23, Issue no. 4.
40. Tom Smith (2010), “The social media revolution”, International
Journal of Market Research, Vol. 51, Issue 4.
41. Tan, Wei Jia; Kwek, Choon Ling; Li, Zhongwei (March 2013), “The
Antecedents of Effectiveness Interactive Advertising in the Social
Media”, International Business Research,88-99.
42. T. S. Venkateswaran, B. Sowmya, R. Arun (July-Aug. 2012),
“Effective use of Social Websites towards business among
academicians and students in Namakkal District”, Online
International Interdisciplinary Research Journal, Online Research
Book Publication, Page 14-21, Vol. 2, Issue no. 4.
252
43. Tim Finin, Anupam Joshi, Pranam Kolari, Akshay Java, Anubhav
Kale, and Amit Karandikar (2008),“The information ecology of
social media and online communities”, AI Magazine, Association for
the Advancement of Artificial Intelligence, Vol. 29, Issue no. 3.
44. Wright, Elizabeth; Khanfar, Nile M (Nov. 2010), “The lasting effects
of Social Media Trends on Advertising”, Journal of Business &
Economics Research ,73-80.
45. W. Glynn Mangold and David J. Faulds (2009), “Social Media : The
new hybrid element of the promotion mix”
46. Yin, Sara (May 2008), “How Social Media and PR Connect”,
Media[Hong Kong], 20-21.
47. Ramulu Bhukya (2012), “Presence of Indian Big IT Brands on Social
Media: an Empirical Study”, Open Access Scientific Reports, Vol.1,
Issue no. 10.
48. Jalaja Ramanunni (31 March 2013), “Be a Thought Leader with
Social Media”, Dataquest, CyberMedia Publication, pp.98-99.
1.1 Webliography :
1. http://www.businessinsider.com/how-facebook-was-founded,
dated 28/10/2014
2. http://inventors.about.com, dated 28/10/2014.
3. www. Statista.com, dated 28/10/2014.
253
4. www.facebook.com/notes, dated 28/10/2014.
5. www.statisticbrain.com dated 28/11/2014.
6. http://muchtech.org, dated 28/11/2014.
7. www.techcrunch.com dated 28/11/2014
8. http://twitter140characters.blogspot.in
9. http://press.linkedin.com/about dated 20/10/2014.
10.https://sites.google.com/a/pressatgoogle.com/youtube5year/home/shor
t-story-of-youtube dated 5/12/2014.
11. https://www.youtube.com/yt/press/statistics.html dated 6/12/14
12. http://rss.softwaregarden.com/aboutrss.html dated 06/12/2014.
13. https://community.linkedin.com, dated 07/12/2014.
14. www.businessdictionary.com, date 26/12/2014.
15. www. Socialbakers.com.
16.
https://econsultancy.com/blog/10364-the-apple-approach-to-socialmedia.
17. www.socialtimes.com.
18.http://www.analytics-magazine.org/january-february-2011/80-hpsworld-class-e-marketing-business Sep 8, 2011.
19.http://www.socialsamosa.com/2012/08/understanding-lg-indiassocial-media-strategy/
20.http://www.informationweek.com/enterprise/hcls-homegrown-socialnetwork-connects-60000-employees/ dated 3/12/14.
21. http://www.hcltech.com/media-entertainment/social-media-services
254
22.http://www.alixpartners.com/en/Publications/……/A-Tale-of-TwoTiers.aspx dated 08/12/2014.
23.http://www.slideshare.net/IBEFIndia/electronics-august-2013
Dated
10/12/ 2014.
24.http://www.iamwire.com/2012/04/consumer-durable-market-reach-rs52000-crores-assocham/4389, Dated 10/12/ 2014.
255
Annexure - II
Questionnaire
Dear participants,
This is a research survey relating to consumer electronics devices (like Music
players, Television set, Video recorder, DVD players, Digital cameras, Personal
computers, Telephones, Mobile phones, Video games consoles, camcorders).
This questionnaire is meant for internet savvy working women in the age
group of 18-35. All information acquired from this questionnaire will be kept
confidential and will only be used for academic purpose. Thanks for your time
and appreciate your assistance.
Section I: Demographic Information
Kindly encircle the appropriate option
Education:
Annual Income:
1.Non-graduates
2.Graduates
3. PG
1. Up to Rs. 3 lakhs
2. 3.1 – 5 lakhs
3. 5.1 – 10 lakhs
4. Above 10
lakhs-4
Occupation:
Place:
1.Service
1. Mumbai
2. Business
2. Surat
3. Self-employed Professionals
3. .Nashik
Section II: Usage of Social Media
1. How often do you access internet either on a computer or a mobile phone
or on other devices like iPad?
1. Almost Everyday
Once a week
5. Rarely
2.
2. 4-5 days/week
3. 2-3 days a week
4.
6. Never
Do you use social networking sites?
1. Yes 2. No
3. Which of these Social Networking Sites do you use?
1.Face-book 2. Twitter
3. LinkedIn
4.Others(specify)_______________
256
4.
How often do you use Social networking sites like face-book, twitter,
LinkedIn?
1.Almost Everyday
5.Rarely 6.Never
2. 4-5 days/week
3. 2-3 days a week
4.Once a week
5.
If you don’t use social networking sites, Why don’t you use social
networking sites?
1.Not interested
4.Prefer face to
2.Security concerns
face interaction
interaction.
3.Non-availability of enough time
5.Lack of computer skills
6.Prefer to use the phone for
6. Roughly how much time do you spend each time you access these social
networking sites Face-book/Twitter/LinkedIn? (tick in the appropriate cell)
Site
15 min
30 min
1 hour
2
hours
More
than
hours
2
FaceBook
Linked-in
Twitter
Any
Other
-----------7. Compared to last year, have you increased, decreased or spent about the same
amount of time using the social networking site.
1.Increased
2.Decreased
3.Nearly the same
8. When you think about the time that you are spending currently on the social
networking sites for product information search, do you feel that it is about
right, too much or not enough.
1.Not enough
2.Just right
3.Too much
9. Looking at the next twelve months, compared to the last year for product
information search do you think you will be increasing, decreasing or spending
the same amount of time using social networking sites?
1.Increasing
2.Decreasing
3.About the same time.
10. Do you share your opinion about a particular product or service with your
family or friends by writing reviews or blogs?
257
1.Yes
2.No
11. Do you share your feedback about a product or service with the
organization /Company?
1.Yes
2.No
12. Do you visit company website and provide a particular rating for a
particular product or service.
1.Yes
2.No
13. How many times have you provided online rating in one year?
1.None/Don’t rate
5.Over 50 times
2.Up to 10 times
3.11-20 times
4.21-50 times
14. Do you send the company link of your favorite brand to your family and
friends?
1.Yes
2.No
Section III: Consumer buying behavior
1.
Have you purchased any consumer electronic items (like Music players,
Television set, Video recorder, DVD players, Digital cameras, Personal
computers, Telephones, Mobile phones, Video games consoles, camcorders)
through social media?
1.Yes
2.No
2.
If yes, what was the reason behind your purchase of the electronic item
through social media?
1. Read online review or blog about that particular product which you are
interested in.
2. Viewed the advertisement of the product over the social networking site
promotions.
3. None of the above. Any Other ___________________________ (specify)
3.
Do you provide online ratings? If yes to which electronic products do
you provide online rating?
1.Music players.
2.Television set.
3.Video recorder.
4.DVD players.
258
5.Digital cameras.
6.Personal computers/Laptops.
7.Telephone Instruments.
8.Mobile phones.
9.Video games consoles.
10.Camcorders
4.
Do you read blogs or online reviews about a product or service before
making buying decision?
1.Yes
2.No
Section IV : Online Purchase Behaviour
1.
To what extent you were yourself involved in the buying decision?
1.Completely
extent
2.To a great extent
3. To Moderate extent
4. To Less
2.
Do you think there is any difference between the products of different
brands?
1.
Yes, Significant difference
3.
Do you think the price of the branded product is:
1.High
2.Appropriate
2.Some difference
3. No difference
3. Low
4.
Do you think taking the buying decision, about a particular product
whose advertisement you have viewed on any social networking sites, to be
time consuming?
1.Very Time consuming
consuming
2.Somewhat Time consuming
3.Less time
Section V : Complex buying behavior
1.
Before actual buying, what type of product information search was
conducted on social media?
1.Extensive search
2.Moderate search
3.Minimal search
4.No search
2.
How frequently do you pay attention to the advertisements of consumer
electronic products on social networking sites?
1.Always
2. Mostly
3.Sometimes
4. Occasionally
5. Never
259
3.
After viewing the advertisement on any social networking site, how
much time and efforts do you spend on researching for the product information
on the network before actual online purchase?
1.Very much
2. Good deal
3.Some
4.Little
5.None
4.
How many online electronic stores do you visit on an average before
making a buying decision?
1.One to three 2.three to five 3.five to seven 4.more than seven
5.
What attributes do you consider while taking the buying decision of
consumer electronic product through a social networking site? (encircle
whichever are applicable)
1.Physical appearance
2.Availability of a variety of functions
3.Price
4.Quality
5.Popularity
6.Association with a particular brand
7.None of the above
6.
How often do you compare different electronic products available in
retail store by physically visiting the stores in the market before making a final
online purchase?
1.Always
2.Mostly
3.Sometimes
4.Ocassionally
5.Never
Section VI: Habitual buying behaviour
1.
Do you agree that you buy the product because you buy it regularly?
1.Strongly Agree
2.Agree
3.Disagree
4.Strongly Disagree
2.
Do you agree that you buy the product because you think that the product
is best fit for you?
1.Strongly Agree
2.Agree
3.Disagree
4.Strongly Disagree
Section VII : Variety-Seeking buying Behavior
1.
Do you agree that you bought the product because you wanted to try out
a different variety of product, belonging to a different brand?
1.Strongly Agree
2.Agree
3.Disagree
4.Strongly Disagree
260
2.
Do you agree that you like to buy a new variety of product belonging to
a new brand; each time you make a purchase-decision after viewing an
advertisement on social networking site?
1.Strongly Agree
2.Agree
3.Disagree
4.Strongly Disagree
3.
Do you agree that the different brands of the same product serve, one and
the same purpose ?
1.Strongly Agree
2.Agree
3.Disagree
4.Strongly Disagree
Section VIII: Dissonance buying behaviour
1.
Do you agree that taking a buying decision of an expensive electronic
product is difficult and needs a lot of thinking?
1.Strongly Agree
2.Agree
3.Disagree
4.Strongly Disagree
2.
Do you agree that taking a buying decision of an expensive electronic
product is time consuming?
1.Strongly Agree
2.Agree
3.Disagree
4.Strongly Disagree
3.
After the actual purchase do you agree that you have the feeling of
anxiety that whether your purchase decision is correct?
1.Strongly Agree
2.Agree
3.Disagree
4.Strongly Disagree
Section IX: Impulsive buying behaviour
1.
Do you agree that you had no plans of buying any consumer electronic
products when you logged on a social networking site?
1.Strongly Agree
2.Agree
3.Disagree
4.Strongly Disagree
2.
Do you agree that the advertisement of the product on the social
networking site provokes your purchase intentions?
1.Strongly Agree
2.Agree
3.Disagree
4.Strongly Disagree
3.
Do you agree that at times you buy a product just because you found the
discount scheme displayed in the advertisement on the social networking site is
interesting and not available in the retail stores?
1.Strongly Agree
2.Agree
3.Disagree
4.Strongly Disagree
Section X: Effectiveness of Social Media Advertising
Reach:
261
1.
Tick anyone in the appropriate cell:
Site
A. Which site do
you like the most?
B. Which site is
the most useful?
C. Which site do
you prefer to use?
D. Which site is
the most userfriendly?
E. Which site
strikes you the
most?
Face-book
Twitter
LinkedIn
Audience:
2.
Give appropriate rating on a scale of 1 (lowest) to 10 (highest) to
the Social networking sites that have a large number of groups
(networks) available for any demographics you are looking for; for
instance group of teenagers, group of kids, youth, group of new moms,
brides, sports fans, technology enthusiasts, entrepreneurs etc.
Ratings
Site
1
2 3 4 5 6 7 8 9 10
Facebook
Twitter
LinkedIn
Targeting:
3.
Give appropriate rating on a scale of 1 (lowest) to 10 (highest) to
the social networking sites according to the way they are targeting the
advertisements to specific group of audience.
Ratings
Site
1
2 3 4 5 6 7 8 9 10
Facebook
Twitter
LinkedIn
262
Affinity:
1.
Tick the appropriate cell given below:
Site
A. Which social networking site
according to you has more
followers due to acquaintances
(i.e. friends and relatives)?
B. Which social networking site
according to you has more
unknown followers?
Facebook
Twitter
LinkedIn
Section XI: Impact of Social Media Advertising
1.
Answer YES or NO in the appropriate cells given below:
Facebook Twitter
A.
On
which
social
networking site do you have Yes/No
Yes/No
positive
reactions/feelings
towards advertisements displayed
on it?
B.
On which social network Yes/No
Yes/No
sites the advertisements displayed
appeal you?
C.
On
which
social Yes/No
Yes/No
networking sites the visuals and
slogans of the advertisements
displayed are memorable?
D.
On which social network Yes/No
Yes/No
site do you find the product
advertisement
displayed
attractive?
E.
On which social network Yes/No
Yes/No
sites
do
you
trust
the
advertisements displayed?
LinkedIn
Yes/No
Yes/No
Yes/No
Yes/No
Yes/No
2. In the time spent on Social networking site, how many times have you
seen an advertisement for consumer electronics?
1. None
times.
2. 1-2 time
3. 3-4 time
4. 4-5 time
5. More than 5
3.Were you satisfied with the actual product which you purchased after
watching the advertisement on one of the social networking sites?
1.Highly satisfied
2. Satisfied
3. Neither
4. Dissatisfied
5. Highly dissatisfied.
263
Annexure - III
Descriptive Analysis
(a) Frequency distribution of the young working women -
Table 10.1.1. Education
Valid
NON
GRADUATE
GRADUATES
POST
GRADUATE
Total
Frequency
Percent
16.5
Valid
Percent
16.5
Cumulative
Percent
16.5
210
707
355
55.6
27.9
55.6
27.9
72.1
100.0
1272
100.0
100.0
Out of 1272 respondents, 210 women are non graduate , 707 are graduates and
355 are post graduates. And of 100% respondents 16.5 % women are non
graduate 55.6% are graduates and 27.9% are post graduates.
Table 10.1.2. Annual income
Valid
UPTO RS. 3
LAKHS
3.1- 5 LAKHS
5.1-10 LAKHS
ABOVE
10
LAKHS
Total
Frequency
Percent
43.4
Valid
Percent
43.4
Cumulative
Percent
43.4
552
454
223
43
35.7
17.5
3.4
35.7
17.5
3.4
79.1
96.6
100.0
1272
100.0
100.0
Out of 1272 respondents, 552 women are having annual income Up to Rs. 3
lakhs, 454 women upto 3.1-5 lakhs, 223 women upto 5.1-10 lakhs and 43
women upto above 10 lakhs. And out of 100% respondents 43.4% women are
earning annual income upto 3 lakhs, 35.7% women upto 3.1-5 lakhs, 17.5%
women upto 5.1-10 lakhs and 3.4% women upto above 10 lakhs.
264
Table 10.1.3. Occupation
Frequency Percent
Valid SERVICE
BUISNESS
SELFEMPLOYED
PROFESSION
Total
907
226
139
71.3
17.8
10.9
Valid
Percent
71.3
17.8
10.9
1272
100.0
100.0
Cumulative
Percent
71.3
89.1
100.0
Out of 1272 respondents, 907 women are doing service, 226 women do business
and 139 women are self-employed. And out of 100% respondents 71.3% women
are doing service, 17.8% women do business and 10.9% women are selfemployed professionals.
Table 10.1.4. Place
Frequency Percent
Valid
MUMBAI
SURAT
NASHIK
Total
516
397
359
1272
40.6
31.2
28.2
100.0
Valid
Percent
40.6
31.2
28.2
100.0
Cumulative
Percent
40.6
71.8
100.0
Out of 1272 respondents, 516 women are residing in Mumbai, 397 women are
from Surat and 359 women are from Nashik. And out of 100% respondents
40.6% women are residing in Mumbai, 31.2% women are from Surat and 28.2%
women are from Nashik.
(b) Frequency distribution of usage of social media by young working women 10.2.1. USAGE OF SOCIAL MEDIA - HOW OFTEN DO YOU ACCESS
INTERNET EITHER ON A COMPUTER OR A MOBILE OR ON
OTHER DEVICES LIKE IPAD
265
Table 10.2.1. Frequency of accessing internet
Frequency Percent
Valid ALMOST
EVERYDAY
4-5
DAYS/WEEK
2-3 DAYS A
WEEK
ONCE A WEEK
RARELY
NEVER
Total
911
71.6
Valid
Percent
71.6
Cumulative
Percent
71.6
222
17.5
17.5
89.1
60
4.7
4.7
93.8
26
30
23
1272
2.0
2.4
1.8
100.0
2.0
2.4
1.8
100.0
95.8
98.2
100.0
Out of 1272 respondents, 911 women access internet almost everyday, 222
women access internet 4-5 days / week, 60 women access internet 2-3 days /
week, 26 women access internet once a week, 30 women access internet rarely in
a week and 23 women do not access internet at all. And out of 100% respondents
71.6% women access internet almost everyday, 17.5% women access internet 45 days / week, 4.7% women access internet 2-3 days / week, 2.0% women access
internet once a week, 2.4% women access internet rarely in a week and 1.8%
women do not access internet at all.
10.2.2. USAGE OF SOCIAL MEDIA - DO YOU USE SOCIAL
NETWORKING SITES
Table 10.2.2. Table showing number of women using social networking
sites.
Frequency Percent Valid
Percent
Valid YES 1137
89.4
89.4
NO
135
10.6
10.6
Total 1272
100.0
100.0
Cumulative
Percent
89.4
100.0
Out of 1272 respondents, 1137 women use social networking sites, 135 women
do not use social networking sites at all. And out of 100% respondents 89.4%
266
women use social networking sites and 10.6% women do not use social
networking sites at all.
10.2.2.1. USAGE OF SOCIAL MEDIA - WHICH OF THESE SOCIAL
NETWORKING SITES DO YOU USE- "Facebook"
Table 10.2.2.1. Table showing number of women who use facebook.
Frequency Percent
Valid Yes
No
3.00
4.00
Total
1019
251
1
1
1272
80.1
19.7
.1
.1
100.0
Valid
Percent
80.1
19.7
.1
.1
100.0
Cumulative
Percent
80.1
99.8
99.9
100.0
Out of 1272 respondents, 1019 women use social networking site Facebook and
251 women do not use social networking site Facebook. And out of 100%
respondents 80.1% women use social networking sites Facebook and 19.7%
women do not use social networking sites Facebook.
10.2.2.2. USAGE OF SOCIAL MEDIA - WHICH OF THESE SOCIAL
NETWORKING SITES DO YOU USE - "Twitter"
Table 10.2.2.2. Table showing number of women who use Twitter.
Frequency Percent
Valid Yes
No
6.00
Total
318
953
1
1272
25.0
74.9
.1
100.0
Valid
Percent
25.0
74.9
.1
100.0
Cumulative
Percent
25.0
99.9
100.0
Out of 1272 respondents, 318 women use social networking site Twitter and 953
women do not use Twitter. And out of 100% respondents 25.0% women use
social networking sites Twitter and 74.9% women do not use social networking
sites Twitter.
10.2.2.3. USAGE OF SOCIAL MEDIA - WHICH OF THESE SOCIAL
267
NETWORKING SITES DO YOU USE - "LinkedIn"
Table 10.2.2.3. Table showing number of women who use LinkedIn.
Frequency Percent
Valid Yes
No
3.00
4.00
Total
241
1029
1
1
1272
18.9
80.9
.1
.1
100.0
Valid
Percent
18.9
80.9
.1
.1
100.0
Cumulative
Percent
18.9
99.8
99.9
100.0
Out of 1272 respondents, 241 women use social networking site LinkedIn and
1029 women do not use LinkedIn. And out of 100% respondents 18.9% women
use social networking sites LinkedIn and 99.8% women do not use social
networking site LinkedIn.
10.2.2.4. USAGE OF SOCIAL MEDIA - WHICH OF THESE SOCIAL
NETWORKING SITES DO YOU USE - "Others"
Table 10.2.2.4. Table showing number of women who use other SNSs.
Frequency Percent
Valid Yes 133
No
1139
Total 1272
10.5
89.5
100.0
Valid
Percent
10.5
89.5
100.0
Cumulative
Percent
10.5
100.0
Out of 1272 respondents, 133 women use other social networking sites and
1139 women do not use other social networking sites. And out of 100%
respondents 10.5% women use other social networking sites and 89.5% women
do not use other social networking sites.
10.2.5. USAGE OF SOCIAL MEDIA - HOW OFTEN DO YOU USE
SOCIAL NETWORKING SITES LIKE FACE-BOOK, TWITTER,
LINKEDIN
Table 10.2.5. Table showing the frequency of using SNS within a week
among working women.
268
Frequency Percent
Valid ALMOST
EVERYDAY
4-5
DAYS/
WEEK
2-3 DAYS A
WEEK
ONCE A WEEK
RARELY
NEVER
Total
707
55.6
Valid
Percent
55.6
Cumulative
Percent
55.6
292
23.0
23.0
78.5
99
7.8
7.8
86.3
65
46
63
1272
5.1
3.6
5.0
100.0
5.1
3.6
5.0
100.0
91.4
95.0
100.0
Out of 1272 respondents, 707 women use social networking sites like Facebook, Twitter and LinkedIn almost everyday,292 women use SNS 4-5
days/week, 99 women use SNS 2-3 days/week, 65 women use SNS once a week,
46 women use SNS rarely and 63 women never use SNS in a week. And out of
100% respondents 55.6% women use social networking sites like Face-book,
Twitter and LinkedIn almost everyday, 23.0% women use SNS 4-5 days/week,
7.8% women use SNS 2-3 days/week, 5.1% women use SNS once a week, 3.6%
women use SNS rarely and 5.0% women never use SNS in a week.
10.2.5.1. USAGE OF SOCIAL MEDIA - IF YOU DON'T USE SOCIAL
NETWORKING
SITES,
WHY
DON’T
YOU
USE
SOCIAL
NETWORKING SITES - Not Interested
Table 10.2.5.1. showing the number of women not using SNS because they
are not interested.
Frequency Percent
Valid
Cumulative
Percent
Percent
Valid Yes 74
5.8
5.8
5.8
No
1198
94.2
94.2
100.0
Total 1272
100.0
100.0
Out of 1272 respondents, 74 women don’t use social networking sites because
they are not interested and 1198 women use SNS. And out of 100% respondents
269
5.8% women don’t use social networking sites because they are not interested
and 94.2% women use SNS.
10.2.5.2. USAGE OF SOCIAL MEDIA - IF YOU DON'T USE SOCIAL
NETWORKING
SITES,
WHY
DON’T
YOU
USE
SOCIAL
NETWORKING SITES - Security Concerns
Table 10.2.5.2. showing the number of women not using SNS because of
security concerns.
Frequency Percent
Valid Yes 156
No
1116
Total 1272
12.3
87.7
100.0
Valid
Percent
12.3
87.7
100.0
Cumulative
Percent
12.3
100.0
Out of 1272 respondents, 156 women don’t use social networking sites because
they have security concerns and 1116 women use SNS. And out of 100%
respondents 12.3% women don’t use social networking sites because of security
concerns and 87.7% women use SNS.
10.2.5.3. USAGE OF SOCIAL MEDIA - IF YOU DON'T USE SOCIAL
NETWORKING
SITES,
WHY
DON’T
YOU
USE
SOCIAL
NETWORKING SITES - Non Availability of Enough time
Table 10.2.5.3. showing the number of women not using SNS because of Non
Availability of Enough time.
Frequency Percent
Valid Yes 135
No
1137
Total 1272
10.6
89.4
100.0
Valid
Percent
10.6
89.4
100.0
Cumulative
Percent
10.6
100.0
Out of 1272 respondents, 135 women don’t use social networking sites because
of non-availability of enough time and 1137 women use SNSs. And out of 100%
respondents, 10.6% women don’t use social networking sites because of non270
availability of enough time and 89.4% women use SNS.
10.2.5.4. USAGE OF SOCIAL MEDIA - IF YOU DON'T USE SOCIAL
NETWORKING
SITES,
WHY
DON'T
YOU
USE
SOCIAL
NETWORKING SITES - Prefer face to face interactions
Table 10.2.5.4. Table showing the number of women not using SNS because
they prefer face to face interactions
Frequency Percent
Valid
Cumulative
Percent
Percent
Valid Yes 54
4.2
4.2
4.2
No
1218
95.8
95.8
100.0
Total 1272
100.0
100.0
Out of 1272 respondents, 54 women don’t use social networking sites because
they prefer face to face interaction and 1218 women use SNS. And out of 100%
respondents, 4.2% women don’t use social networking sites because they prefer
face to face interaction and 95.8% women use SNS.
10.2.5.5. USAGE OF SOCIAL MEDIA - IF YOU DON'T USE SOCIAL
NETWORKING
SITES,
WHY
DON'T
YOU
USE
SOCIAL
NETWORKING SITES - Lack of computer skills
Table 10.2.5.5. showing the number of women not using SNS because of
Lack of computer skills.
Frequency Percent
Valid
Cumulative
Percent
Percent
Valid Yes 41
3.2
3.2
3.2
No
1231
96.8
96.8
100.0
Total 1272
100.0
100.0
Out of 1272 respondents, 41 women don’t use social networking sites because of
lack of computer skills and 1231 women use SNS. And out of 100%
respondents, 3.2% women don’t use social networking sites because of lack of
computer skills and 96.8% women use SNS.
271
10.2.5.6. USAGE OF SOCIAL MEDIA - IF YOU DON'T USE SOCIAL
NETWORKING
SITES,
WHY
DON'T
YOU
USE
SOCIAL
NETWORKING SITES - Prefer to use phone for interaction
Table 10.2.5.6. showing the number of women not using SNS because they
Prefer to use phone for interaction.
Frequency Percent
Valid Yes
No
6.00
Total
83
1188
1
1272
6.5
93.4
.1
100.0
Valid
Percent
6.5
93.4
.1
100.0
Cumulative
Percent
6.5
99.9
100.0
Out of 1272 respondents, 83 women don’t use social networking sites because
they prefer to use phone for interaction and 1188 women use SNS. And out of
100% respondents, 6.5% women don’t use social networking sites because they
prefer to use phone for interaction and 93.4% women use SNS.
10.2.6.1. ROUGHLY HOW MUCH TIME DO YOU SPEND EACH TIME
YOU ACCESS THESE SOCIAL NETWORKING SITES - FACE BOOK
Table 10.2.6.1. showing the time spent by working women for using
Facebook each time they access SNS.
Frequency Percent
Valid 15 MIN.
391
30 MIN.
277
HOUR
314
2HOURS
208
MORE THAN 2 82
HOURS
Total
1272
30.7
21.8
24.7
16.4
6.4
Valid
Percent
30.7
21.8
24.7
16.4
6.4
100.0
100.0
Cumulative
Percent
30.7
52.5
77.2
93.6
100.0
Out of 1272 respondents, 391 women access Face-book for 15 min. each time
they access these SNS’s, 277 women use face-book for 30 min., 314 women use
272
face-book for an hour, 208 women use face-book for 2 hours and 82 use facebook for more than 2 hours. And out of 100% respondents, 30.7% women access
Face-book for 15 min. each time they access these SNS’s, 21.8% women use
face-book for 30 min., 24.7% women use face-book for an hour, 16.4% women
use face-book for 2 hours and 6.4% use face-book for more than 2 hours each
time they access these SNS’s.
10.2.6.2. ROUGHLY HOW MUCH TIME DO YOU SPEND EACH TIME
YOU ACCESS THESE SOCIAL NETWORKING SITES - LINKED-IN
Table 10.2.6.2. showing the time spent by working women for using
LinkedIn each time they access SNS.
Frequency Percent
Valid
Percent
27.3
36.6
18.1
10.3
7.8
Cumulative
Percent
27.3
63.8
81.9
92.2
100.0
Valid 15 MIN.
347
27.3
30 MIN.
465
36.6
HOUR
230
18.1
2HOURS
131
10.3
MORE THAN 2 99
7.8
HOURS
Total
1272
100.0
100.0
Out of 1272 respondents, 347 women access LinkedIn for 15 min. each time
they access these SNS’s, 465 women use Linked-In for 30 min., 230 women use
Linked-In for an hour, 131 women use Linked-In for 2 hours and 99 use
LinkedIn for more than 2 hours. And out of 100% respondents, 27.3% women
access LinkedIn for 15 min. each time they access these SNS’s, 36.6% women
use LinkedIn for 30 min., 18.1% women use LinkedIn for an hour, 10.3%
women use LinkedIn for 2 hours and 7.8% use LinkedIn for more than 2 hours
each time they access these SNS’s.
10.2.6.3. ROUGHLY HOW MUCH TIME DO YOU SPEND EACH TIME
YOU ACCESS THESE SOCIAL NETWORKING SITES - TWITTER
273
Table 10.2.6.3. showing the time spent by working women for using Twitter
each time they access SNS.
Frequency Percent
Valid 15 MIN.
315
30 MIN.
375
HOUR
341
2HOURS
168
MORE THAN 2 73
HOURS
Total
1272
24.8
29.5
26.8
13.2
5.7
Valid
Percent
24.8
29.5
26.8
13.2
5.7
100.0
100.0
Cumulative
Percent
24.8
54.2
81.1
94.3
100.0
Out of 1272 respondents, 315 women access Twitter for 15 min. each time they
access these SNS’s, 375 women use Twitter for 30 min., 341 women use
Twitter for an hour, 168 women use Twitter for 2 hours and 73 use Twitter for
more than 2 hours. And out of 100% respondents, 24.8% women access Twitter
for 15 min. each time they access these SNS’s, 29.5% women use Twitter for
30 min., 26.8% women use Twitter for an hour, 13.2% women use Twitter for
2 hours and 5.7% use Twitter for more than 2 hours each time they access these
SNS’s.
10.2.6.4. ROUGHLY HOW MUCH TIME DO YOU SPEND EACH TIME
YOU ACCESS THESE SOCIAL NETWORKING SITES - ANY OTHER
Table 10.2.6.4. showing the time spent by working women for using other
SNS(other than facebook, twitter & LinkedIn) each time they access SNS.
Frequency Percent
Valid 15 MIN.
171
30 MIN.
357
HOUR
446
2 HOURS
222
MORE THAN 2 76
HOURS
Total
1272
13.4
28.1
35.1
17.5
6.0
Valid
Percent
13.4
28.1
35.1
17.5
6.0
100.0
100.0
Cumulative
Percent
13.4
41.5
76.6
94.0
100.0
274
Out of 1272 respondents, 171 women access other SNS for 15 min. each time
they access these SNS’s, 357 women use other SNS for 30 min., 446 women use
other SNS for an hour, 222 women use other SNS for 2 hours and 76 use other
SNS for more than 2 hours. And out of 100% respondents, 13.4% women
access other SNS for 15 min. each time they access these SNS’s, 28.1% women
use other SNS for 30 min.,35.1% women use other SNS for an hour, 17.5%
women use other SNS for 2 hours and 6.0% use other SNS for more than 2
hours each time they access these SNS’s.
10.2.7. Compared to last year, have you increased, decreased or spent about
the same amount of time using the social networking site
Table 10.2.7. showing the number of women who have increased, decreased
or spent about the same amount of time using the social networking site
compared to last year.
Frequency Percent
Valid Increased
530
Decreased
453
Nearly
the 289
same
Total
1272
41.7
35.6
22.7
Valid
Percent
41.7
35.6
22.7
100.0
100.0
Cumulative
Percent
41.7
77.3
100.0
Out of 1272 respondents, 530 women have increased the time spent using the
SNS compared to last year, 453 women have decreased, 289 women spend the
same amount of time using the SNSs. And out of 100% respondents, 41.7%
women have increased the time spent using the SNSs, 35.6% women have
decreased and 22.7% women spend the same amount of time using the SNS
compared to last year.
275
10.2.8. When you think about the time that you are spending currently on
the social networking sites for product information search, do you feel that
it is about right, too much or not enough
Table 10.2.8. showing the number of women who think the time that they
are spending currently on the social networking sites for product
information search, is about right, too much or not enough.
Frequency Percent
Valid not
enough
just right
too much
Total
295
23.2
Valid
Percent
23.2
755
222
1272
59.4
17.5
100.0
59.4
17.5
100.0
Cumulative
Percent
23.2
82.5
100.0
Out of 1272 respondents, 295 women think the time that they spend currently
on SNS for product information search is not enough, 755 women think that it is
just right and 222 women think that it is too much. And out of 100%
respondents, 23.2% women think that the time they spend on SNS for product
information search is not enough, 59.4% women think it is just right and 17.5%
women think that it is too much.
10.2.9. Looking at the next twelve months, compared to the last year for
product information search do you think you will be increasing, decreasing
or spending the same amount of time using social networking sites?
Table 10.2.9. showing the number of women who in the next twelve months
will be increasing, decreasing or spending the same amount of time using
social networking sites for product information search compared to the last
year.
276
Frequency Percent
Valid Increased
544
Decreased
384
about the same 344
time
Total
1272
42.8
30.2
27.0
Valid
Percent
42.8
30.2
27.0
100.0
100.0
Cumulative
Percent
42.8
73.0
100.0
Out of 1272 respondents, 544 women think that they will be increasing the
amount of time that they spend on SNS for product information search in the
next twelve months compared to last year, 384 women think they will be
decreasing the amount of time spent on SNS and 344 women think that they will
be spending the same amount time. And out of 100% respondents, 42.8%
women think that there will be an increase in the time they spend on SNS for
product information search in the next twelve months compared to the last year,
30.2% women think there will a decrease in the amount of time spent and 27.0%
women think that it is about the same.
10.2.10. Do you share your opinion about a particular product or service
with your family or friends by writing reviews or blogs?
Table 10.2.10. showing the number of women who share their opinion about
a particular product or service with your family or friends by writing
reviews or blogs.
Frequency Percent
Valid Yes 687
No
585
Total 1272
54.0
46.0
100.0
Valid
Percent
54.0
46.0
100.0
Cumulative
Percent
54.0
100.0
Out of 1272 respondents, 687 women share their opinion about a particular
product or service with their family or friends by writing reviews or blogs and
585 women do not share their opinion. And out of 100% respondents, 54.0%
women share their opinion with family and friends by writing blogs or reviews
277
and 46.0% women do not share their opinion about a particular product or
service with their family or friends by writing reviews or blogs.
10.2.11. Do you share your feedback about a product or service with the
organization /Company?
Table 10.2.11. showing the number of women who share their feedback
about a particular product or service with the organization/company.
Frequency Percent
Valid Yes 668
No
604
Total 1272
52.5
47.5
100.0
Valid
Percent
52.5
47.5
100.0
Cumulative
Percent
52.5
100.0
Out of 1272 respondents, 668 women share their feedback about a particular
product or service with the organization/company and 604 women do not share
their feedback. And out of 100% respondents, 52.5% women share their
feedback with organization/company and 47.5% women do not share their
feedback about a particular product or service with the organization/company.
10.2.12. Do you visit company website and provide a particular rating for a
particular product or service.
Table 10.2.12. showing the number of women who visit company website
and provide a particular rating for a particular product or service.
Frequency Percent
Valid Yes
No
12.00
Total
706
565
1
1272
55.5
44.4
.1
100.0
Valid
Percent
55.5
44.4
.1
100.0
Cumulative
Percent
55.5
99.9
100.0
Out of 1272 respondents, 706 women provide a particular rating for a particular
product or service by visiting company website and 565 women do not provide a
particular rating for a particular product or service. And out of 100%
278
respondents, 55.5% women provide a particular rating for a particular product or
service by visiting company website
and 44.4% women do not provide a
particular rating for a particular product or service by visiting company website.
10.2.13. How many times have you provided online rating in one year?
Table 10.2.13. showing the number of times women have you provided
online rating in one year for a particular product or service.
Frequency Percent
Valid none/dont
rate
upto
10
times
11-20 times
21-50 times
over
50
times
Total
712
56.0
Valid
Percent
56.0
Cumulative
Percent
56.0
454
35.7
35.7
91.7
64
21
21
5.0
1.7
1.7
5.0
1.7
1.7
96.7
98.3
100.0
1272
100.0
100.0
Out of 1272 respondents, 712 said they do not provide online rating, 454 said
they provide online rating upto 10 times, 64 said they provide online rating 1120 times, 21 said they provide online rating 21-50 times and 21 said they provide
online rating over 50 times. And out of 100% respondents 56.0% women said
they do not provide online rating, 35.7% said they provide online rating upto 10
times, 5.0% said they provide it 11-20 times, 1.7% said they provide it 21-50
times and 1.7% said they provide it over 50 times in one year.
10.2.14. Do you send the company link of your favourite brand to your
family and friends?
10.2.14. Table showing whether working women send the company link of
their favourite brand to their family and friends.
279
Frequency
Valid Yes
No
Total
729
543
1272
Percent
57.3
42.7
100.0
Valid
Cumulative
Percent
Percent
57.3
57.3
42.7
100.0
100.0
Out of 1272 respondents, 729 said they send the company link of favourite brand
to their family and friends and 543 said they do not send the company link of
favourite brand to their family and friends. And out of 100% respondents, 57.3%
said they send the company link and 42.7% said they do not send the company
link of favourite brand to their family and friends.
(c) Frequency distribution of Consumer buying behaviour 10.3.1. Have you purchased any consumer electronic items (like Music
players, Television set, Video recorder, DVD players, Digital cameras,
Personal computers, Telephones, Mobile phones, Video games consoles,
camcorders) through social media?
Table 10.3.1. showing the number of women who have purchased consumer
electronic items through social media.
Frequency Percent
Valid
Cumulative
Percent
Percent
Valid Yes 665
52.3
52.3
52.3
No
605
47.6
47.6
99.8
3.00 2
.2
.2
100.0
Total 1272
100.0
100.0
Out of 1272 respondents, 665 said they have purchased the consumer electronic
items through social media and 605 said they have not purchased the consumer
electronic items through social media. And out of 100% respondents 52.3 %
women said they have purchased consumer electronics items through social
media and 47.6% women said they have not purchased the consumer electronics
items through social media.
280
10.3.2. If yes, what was the reason behind your purchase of the electronic
item through social media?
Table 10.3.2. Table showing the list of reasons due to which women
purchased consumer electronic items through social media.
Frequency Percent
Valid Read online review
or blog about that
particular product
whi
Viewed
the
advertisement of the
product over the
social netw
None of the above.
Any Other
Total
368
28.9
Valid
Percent
28.9
Cumulative
Percent
28.9
797
62.7
62.7
91.6
107
8.4
8.4
100.0
1272
100.0
100.0
Out of 1272 respondents, 368 women said they read online review or blog about
that particular product, 797 women said they viewed the advertisement of the
product over the social network and 107 women said there was some other
reason than the one mentioned behind the purchase of electronic item through
social media. And out of 100% respondents, 28.9 % women said they read
online review or blog about that particular product, 62.7% women said they
viewed the advertisement of the product over the social network and 8.4%
women said there was some other reason than the one mentioned behind the
purchase of electronic item through social media.
10.3.3.1. Do you provide online ratings? If yes to which electronic products
do you provide online rating? - music players.
Table 10.3.3.1. showing the number of women providing online rating to
281
music players.
Frequency Percent
Valid Yes 157
No
1115
Total 1272
12.3
87.7
100.0
Valid
Percent
12.3
87.7
100.0
Cumulative
Percent
12.3
100.0
Out of 1272 respondents, 157 women said they provided online rating for music
players and 1115 said they did not provide online rating for music players. And
out of 100% respondents, 12.3% women said they provided online rating for
music players and 87.7% said they did not provide online rating for music
players.
10.3.3.2. Do you provide online ratings? If yes to which electronic products
do you provide online rating? - Television Set.
Table 10.3.3.2. showing the number of women providing online rating to
Television set.
Frequency Percent
Valid Yes 161
No
1111
Total 1272
12.7
87.3
100.0
Valid
Percent
12.7
87.3
100.0
Cumulative
Percent
12.7
100.0
Out of 1272 respondents, 161 women provided online rating for television and
1111 women did not provide online rating for television. Out of 100%
respondents, 12.7% women provided online rating for television and 87.3%
women did not provide online rating for television.
10.3.3.3. Do you provide online ratings? If yes to which electronic products
do you provide online rating? - Video Recorder.
Table 10.3.3.3. showing the number of women providing online rating to
Video Recorder.
282
Frequency Percent
Valid Yes 99
No
1173
Total 1272
7.8
92.2
100.0
Valid
Percent
7.8
92.2
100.0
Cumulative
Percent
7.8
100.0
Out of 1272 respondents, 99 women provided online rating for video recorder
and 1173 women did not provide online rating for video recorder. Out of 100%
respondents, 7.8% women provided online rating for video recorder and 92.2%
women did not provide online rating for video recorder.
10.3.3.4. Do you provide online ratings? If yes to which electronic products
do you provide online rating? - DVD Players.
Table 10.3.3.4. showing the number of women providing online rating to
DVD Players.
Frequency Percent
Valid Yes 82
No
1190
Total 1272
6.4
93.6
100.0
Valid
Percent
6.4
93.6
100.0
Cumulative
Percent
6.4
100.0
Out of 1272 respondents, 82 women provided online rating for DVD Players and
1190 women did not provide online rating for DVD Players. Out of 100%
respondents, 6.4% women provided online rating for DVD Players and 93.6%
women did not provide online rating for DVD Players.
10.3.3.5. Do you provide online ratings? If yes to which electronic products
do you provide online rating? - Digital Cameras
Table 10.3.3.5. showing the number of women providing online rating to
Digital Cameras.
Frequency Percent
Valid Yes 235
No
1037
Total 1272
18.5
81.5
100.0
Valid
Percent
18.5
81.5
100.0
Cumulative
Percent
18.5
100.0
283
Out of 1272 respondents, 235 women provided online rating for Digital Cameras
and 1037 women did not provide online rating for Digital Cameras. Out of
100% respondents, 18.5% women provided online rating for Digital Cameras
and 81.5% women did not provide online rating for Digital Cameras.
10.3.3.6. Do you provide online ratings? If yes to which electronic products
do you provide online rating? - Personal computers/Laptops.
Table 10.3.3.6. showing the number of women providing online rating to
Personal computers/Laptops.
Frequency Percent
Valid Yes 314
No
958
Total 1272
24.7
75.3
100.0
Valid
Percent
24.7
75.3
100.0
Cumulative
Percent
24.7
100.0
Out of 1272 respondents, 314 women provided online rating for Personal
computers/Laptops and 958 women did not provide online rating for Personal
computers/Laptops. Out of 100% respondents, 24.7% women provided online
rating for Personal computers/Laptops and 75.3% women did not provide online
rating for Personal computers/Laptops.
10.3.3.7. Do you provide online ratings? If yes to which electronic products
do you provide online rating? - Telephone Instruments.
Table 10.3.3.7. showing the number of women providing online rating to
Telephone Instruments.
Frequency Percent
Valid Yes 183
No
1089
Total 1272
14.4
85.6
100.0
Valid
Percent
14.4
85.6
100.0
Cumulative
Percent
14.4
100.0
Out of 1272 respondents, 183 women provided online rating for Telephone
284
Instruments and 1089 women did not provide online rating for
Telephone
Instruments. Out of 100% respondents, 14.4% women provided online rating for
Telephone Instruments and 85.6% women did not provide online rating for
Telephone Instruments.
10.3.3.8. Do you provide online ratings? If yes to which electronic products
do you provide online rating? - Mobile Phones.
Table 10.3.3.8. showing the number of women providing online rating to
Mobile Phones.
Frequency Percent
Valid Yes 517
No
755
Total 1272
40.6
59.4
100.0
Valid
Percent
40.6
59.4
100.0
Cumulative
Percent
40.6
100.0
Out of 1272 respondents, 517 women provided online rating for Mobile Phones
and 755 women did not provide online rating for Mobile Phones. Out of 100%
respondents, 40.6% women provided online rating for Mobile Phones and
59.4% women did not provide online rating for Mobile Phones.
10.3.3.9. Do you provide online ratings? If yes to which electronic products
do you provide online rating? - Video Games Console.
Table 10.3.3.9. showing the number of women providing online rating to
Video Games Console
Frequency Percent
Valid yes
34
no
1238
Total 1272
2.7
97.3
100.0
Valid
Percent
2.7
97.3
100.0
Cumulative
Percent
2.7
100.0
Out of 1272 respondents, 34 women provided online rating for Video Games
285
Console and 1238 women did not provide online rating for Video Games
Console. Out of 100% respondents, 2.7% women provided online rating for
Video Games Console and 97.3% women did not provide online rating for
Video Games Console.
10.3.3.10. Do you provide online ratings? If yes to which electronic products
do you provide online rating? - Camcorders.
Table 10.3.3.10. showing the number of women providing online rating to
Camcorders
Frequency Percent
Valid Yes 70
No
1202
Total 1272
5.5
94.5
100.0
Valid
Percent
5.5
94.5
100.0
Cumulative
Percent
5.5
100.0
Out of 1272 respondents, 70 women provided online rating for Camcorders and
1202 women did not provide online rating for Camcorders. Out of 100%
respondents, 5.5% women provided online rating for Camcorders and 94.5%
women did not provide online rating for Camcorders.
10.3.4. Do you read blogs or online reviews about a product or service
before making buying decision?
Table 10.3.4. showing the number of women who read blogs or online
reviews about a product or service before making buying decision
Frequency Percent
Valid
Percent
Valid Yes 878
69.0
69.0
No
394
31.0
31.0
Total 1272
100.0
100.0
Out of 1272 respondents, 878 women read blogs
Cumulative
Percent
69.0
100.0
or online reviews about a
product or service before making buying decision and 394 women did not read
286
blogs or online reviews about a product or service before making buying
decision. Out of 100% respondents, 69.0% women read blogs or online reviews
about a product or service before making buying decision and 31.0% women did
not read blogs or online reviews about a product or service before making
buying decision.
(d) Frequency distribution of Online Purchase Behaviour..
10.4.1. To what extent you were yourself involved in the buying decision?
Table 10.4.1. showing the number of women who are personally involved in
making a buying decision.
Frequency Percent
Valid Completely
To a great extent
To
moderate
extent
To less extent
Total
574
444
117
45.1
34.9
9.2
Valid
Percent
45.1
34.9
9.2
137
1272
10.8
100.0
10.8
100.0
Cumulative
Percent
45.1
80.0
89.2
100.0
Out of 1272 respondents, 574 women said they were completely involved in the
buying decision, 444 women said they were involved to a great extent in the
buying decision, 117 women said they were involved to a moderate extent in the
buying decision and 137 women said they were involved to a less extent in the
buying decision. Out of 100% respondents, 45.1% women said they were
completely involved in the buying decision, 34.9% women said they were
involved to a great extent in the buying decision, 9.2% women said they were
involved to a moderate extent in the buying decision and 10.8% women said
they were involved to a less extent in the buying decision.
10.4.2. Do you think there is any difference between the products of
287
different brands?
Table 10.4.2. showing the number of women who think there is any
difference between the products of different brands.
Frequency Percent
Valid yes,
significant
difference
some differences
No difference
Total
621
48.8
Valid
Percent
48.8
592
59
1272
46.5
4.6
100.0
46.5
4.6
100.0
Cumulative
Percent
48.8
95.4
100.0
Out of 1272 respondents, 621 women said there is significant difference between
the products of different brands, 592 women said there are some difference and
59 women said there is no difference between the products of different brands.
Out of 100% respondents, 48.8% women said there is significant difference
between the products of different brands, 46.5% women said there are some
difference and 4.6% women said there is no difference between the products of
different brands.
10.4.3. Do you think the price of the branded product is high, appropriate
or low?
Table 10.4.3. showing the women’s opinion towards the price of the branded
product.
Valid
High
Appropriate
Low
Total
Frequency
Percent
Valid Percent
617
536
119
1272
48.5
42.1
9.4
100.0
48.5
42.1
9.4
100.0
Cumulative
Percent
48.5
90.6
100.0
Out of 1272 respondents, 617 women said the price of the branded product is
high, 536 women said the price of the branded product is appropriate and 119
women said the price of the branded product is low. Out of 100% respondents,
48.5% women said the price of the branded product is high, 42.1% women said
288
the price of the branded product is appropriate and 9.4% women said the price
of the branded product is low.
10.4.4. Do you think taking the buying decision, about a particular product
whose advertisement you have viewed on any social networking sites, to be
time consuming?
Table 10.4.4. showing women’s perception regarding the time consumed in
taking the buying decision, about a particular product whose advertisement
they have viewed on any social networking sites.
Frequency Percent
Valid very
consuming
somewhat
consuming
Less
consuming
4.00
Total
time 365
28.7
Valid
Percent
28.7
time 651
51.2
51.2
79.9
time 255
20.0
20.0
99.9
.1
100.0
.1
100.0
100.0
1
1272
Cumulative
Percent
28.7
Out of 1272 respondents, 365 women said taking the buying decision is very
time consuming, 651 women said it is somewhat time consuming and 255
women said it is less time consuming. Out of 100% respondents, 28.7% women
said taking the buying decision is very time consuming, 51.2% women said it is
somewhat time consuming and 20.0% women said it is less time consuming.
(e) Frequency distribution of Complex Buying Behaviour.
10.5.1. Before actual buying, what type of product information search was
conducted on social media?
Table 10.5.1. showing the type of product information search conducted on
social media by women before actual buying.
289
Frequency Percent
Valid Extensive
search
Moderate
search
Minimal
Serach
No search
21.00
Total
324
25.5
Valid
Percent
25.5
Cumulative
Percent
25.5
797
62.7
62.7
88.1
122
9.6
9.6
97.7
28
1
1272
2.2
.1
100.0
2.2
.1
100.0
99.9
100.0
Out of 1272 respondents, 324 women said they conducted extensive product
information search on social media before actual buying, 797 women said they
conducted moderate search, 122 said they conducted minimal search and 28 said
they did not conduct any product information search on social media before
actual buying. Out of 1272 respondents, 25.5% women said they conducted
extensive product information search on social media before actual buying,
62.7% women said they conducted moderate search, 9.6% women said they
conducted minimal search and 2.2% women said they did not conduct any
product information search on social media before actual buying.
10.5.2. How frequently do you pay attention to the advertisements of
consumer electronic products on social networking sites?
Table 10.5.2. showing how frequently the women pay attention to the
advertisements of consumer electronic products on social networking sites
Valid Always
Mostly
Sometimes
Occasionally
Never
Total
Frequency
Percent
234
352
510
110
66
1272
18.4
27.7
40.1
8.6
5.2
100.0
Valid
Percent
18.4
27.7
40.1
8.6
5.2
100.0
Cumulative
Percent
18.4
46.1
86.2
94.8
100.0
Out of 1272 respondents, 234 women said they always paid attention to the
290
advertisements of consumer electronic products on social networking sites, 352
women said they mostly paid attention, 510 women said they sometimes paid
attention, 110 said they occasionally paid attention and 66 women said they
never paid attention to the advertisements of consumer electronic products on
social networking sites. Out of 100% respondents, 18.4% women said they
always paid attention to the advertisements of consumer electronic products on
social networking sites, 27.7% women said they mostly paid attention, 40.1%
women said they sometimes paid attention, 8.6% said they occasionally paid
attention and 5.2% women said they never paid attention to the advertisements
of consumer electronic products on social networking sites.
10.5.3. After viewing the advertisement on any social networking site, how
much time and efforts do you spend on researching for the product
information on the network before actual online purchase?
Table 10.5.3. showing the amount of time and efforts the women spend on
researching for the product information on the network before actual online
purchase.
Frequency Percent
Valid very
much
Good
Deal
Some
Little
None
Total
239
18.8
Valid
Percent
18.8
Cumulative
Percent
18.8
611
48.0
48.0
66.8
251
101
70
1272
19.7
7.9
5.5
100.0
19.7
7.9
5.5
100.0
86.6
94.5
100.0
Out of 1272 respondents, 239 women said they spend high amount of time and
efforts on researching for the product information on the network before actual
291
online purchase, 611 women said they spend good deal of time and efforts, 251
said they spend some time and efforts, 101 said they spend little time and efforts
and 70 said they do not spend any time and effort on researching for the product
information on the network before actual online purchase after viewing the
advertisement on social networking site. Out of 100% respondents, 18.8%
women said they spend high amount of time and efforts on researching for the
product information on the network before actual online purchase, 48.0%
women said they spend good deal of time and efforts, 19.7% said they spend
some time and efforts, 7.9% said they spend little time and efforts and 5.5% said
they do not spend any time and effort on researching for the product information
on the network before actual online purchase after viewing the advertisement on
social networking site.
10.5.4. How many online electronic stores do you visit on an average before
making a buying decision?
Table 10.5.4. showing the number of online electronic stores visited on an
average by women before making the buying decision.
Frequency Percent
Valid One to three
Three to five
Five to seven
More
than
seven
Total
579
483
143
67
45.5
38.0
11.2
5.3
Valid
Percent
45.5
38.0
11.2
5.3
1272
100.0
100.0
Cumulative
Percent
45.5
83.5
94.7
100.0
Out of 1272 respondents, 579 women said they visit 1-3 online electronic stores
on an average before making a buying decision, 483 said they visit 3-5, 143 said
they visited 5-7, 67said they visited more than 7 online electronic stores on an
292
average before making a buying decision. Out of 100% respondents, 45.5%
women said they visit 1-3 online electronic stores on an average before making
a buying decision, 38.0% said they visit 3-5 online electronic stores, 11.2% said
they visited 5-7 online electronic stores, 5.3% said they visited more than 7
online electronic stores on an average before making a buying decision.
10.5.5.a. What attributes do you consider while taking the buying decision
of consumer electronic product through a social networking site? - (Physical
Appearance)
Table 10.5.5.a. showing the number of women who consider the Physical
Appearance of the product while taking the buying decision of consumer
electronic product through a social networking site .
Frequency Percent
Valid Yes 303
No
969
Total 1272
23.8
76.2
100.0
Valid
Percent
23.8
76.2
100.0
Cumulative
Percent
23.8
100.0
Out of 1272 respondents, 303 women consider Physical appearance of consumer
electronic product and 969 women don’t consider physical appearance while
taking the buying decision of consumer electronic product through a social
networking site. Out of 100% respondents, 23.8% women consider Physical
appearance of consumer electronic product and 76.2% women don’t consider
physical appearance while taking the buying decision of consumer electronic
product through a social networking site.
10.5.5.b. What attributes do you consider while taking the buying decision
of consumer electronic product through a social networking site? 293
(Availability of a variety of function)
Table 10.5.5.b. showing the number of women who consider the feature of
Availability of a variety of functions in the product while taking the buying
decision of consumer electronic product through a social networking site .
Frequency Percent
Valid Yes 303
No
969
Total 1272
23.8
76.2
100.0
Valid
Percent
23.8
76.2
100.0
Cumulative
Percent
23.8
100.0
Out of 1272 respondents, 303 women consider availability of a variety of
functions in a consumer electronic product and 969 women don’t consider
availability of a variety of functions while taking the buying decision of
consumer electronic product through a social networking site. Out of 100%
respondents, 23.8% women consider availability of a variety of functions in a
consumer electronic product and 76.2% women don’t consider availability of a
variety of functions while taking the buying decision of consumer electronic
product through a social networking site.
10.5.5.c. What attributes do you consider while taking the buying decision
of consumer electronic product through a social networking site?- Price.
Table 10.5.5.c. showing the number of women who consider the price of the
product while taking the buying decision of consumer electronic product
through a social networking site .
Frequency Percent
Valid Yes 585
No
687
Total 1272
46.0
54.0
100.0
Valid
Percent
46.0
54.0
100.0
Cumulative
Percent
46.0
100.0
Out of 1272 respondents, 585 women consider price of a consumer electronic
294
product and 687 women don’t consider price while taking the buying decision of
consumer electronic product through a social networking site. Out of 100%
respondents, 46.0% women consider price of a consumer electronic product and
54.0% women don’t consider price while taking the buying decision of
consumer electronic product through a social networking site.
10.5.5.d. What attributes do you consider while taking the buying decision
of consumer electronic product through a social networking site? -Quality.
Table 10.5.5.d. showing the number of women who consider the quality of
the product while taking the buying decision of consumer electronic product
through a social networking site .
Frequency Percent
Valid Yes 717
No
555
Total 1272
56.4
43.6
100.0
Valid
Percent
56.4
43.6
100.0
Cumulative
Percent
56.4
100.0
Out of 1272 respondents, 717 women consider quality of a consumer electronic
product and 555 women don’t consider quality while taking the buying decision
of consumer electronic product through a social networking site. Out of 100%
respondents, 56.4% women consider quality of a consumer electronic product
and 43.6% women don’t consider quality while taking the buying decision of
consumer electronic product through a social networking site.
10.5.5.e. What attributes do you consider while taking the buying decision
of consumer electronic product through a social networking site? Popularity.
Table 10.5.5.e. showing the number of women who consider the popularity
of the product while taking the buying decision of consumer electronic
product through a social networking site .
295
Frequency Percent
Valid Yes 208
No
1064
Total 1272
16.4
83.6
100.0
Valid
Percent
16.4
83.6
100.0
Cumulative
Percent
16.4
100.0
Out of 1272 respondents, 208 women consider popularity of a consumer
electronic product and 555 women don’t consider popularity while taking the
buying decision of consumer electronic product through a social networking site.
Out of 100% respondents, 16.4% women consider popularity of a consumer
electronic product and 83.6% women don’t consider popularity while taking the
buying decision for consumer electronic product through a social networking
site.
10.5.5.f. What attributes do you consider while taking the buying decision of
consumer electronic product through a social networking site?-Association
with a particular brand.
Table 10.5.5.f. showing the number of women who consider the Association
with a particular brand for the product while taking the buying decision of
consumer electronic product through a social networking site .
Frequency Percent
Valid Yes
No
4.00
Total
108
1163
1
1272
8.5
91.4
.1
100.0
Valid
Percent
8.5
91.4
.1
100.0
Cumulative
Percent
8.5
99.9
100.0
Out of 1272 respondents, 108 women consider association with a particular
brand of a consumer electronic product and 1163 women don’t consider
association with a particular brand while taking the buying decision of consumer
electronic product through a social networking site. Out of 100% respondents,
296
8.5% women consider association with a particular brand of a consumer
electronic product and 91.4% women don’t consider association with a particular
brand while taking the buying decision of consumer electronic product through a
social networking site.
10.5.5.g. What attributes do you consider while taking the buying decision
of consumer electronic product through a social networking site? -None of
the above.
Table 10.5.5.g. showing the number of women who consider none of the
mentioned features of the product while taking the buying decision of
consumer electronic product through a social networking site .
Frequency Percent
Valid Yes
No
7.00
Total
34
1237
1
1272
2.7
97.2
.1
100.0
Valid
Percent
2.7
97.2
.1
100.0
Cumulative
Percent
2.7
99.9
100.0
Out of 1272 respondents, 34 women consider none from the attributes mentioned
therein and 1237 women don’t consider none from the attributes mentioned
therein, while taking the buying decision of consumer electronic product through
a social networking site. Out of 100% respondents, 2.7% women consider none
from the attributes mentioned therein and 97.2% women don’t consider none
from the attributes mentioned therein, while taking the buying decision of
consumer electronic product through a social networking site.
10.5.6. How often do you compare different electronic products available in
retail store by physically visiting the stores in the market before making a
297
final online purchase?
Table
10.5.6. showing the number of women who compare different
electronic products available in retail store by physically visiting the stores
in the market before making a final online purchase.
Frequency Percent
Valid Always
Mostly
Sometimes
Occasionally
Never
Total
425
469
238
88
52
1272
33.4
36.9
18.7
6.9
4.1
100.0
Valid
Percent
33.4
36.9
18.7
6.9
4.1
100.0
Cumulative
Percent
33.4
70.3
89.0
95.9
100.0
Out of 1272 respondents, 425 women said they always compare different
electronic products available in retail store by physically visiting the stores in the
market before making a final online purchase, 469 women said they mostly
compare different electronic products available in retail store, 238 women said
they sometimes compare different electronic products available in retail store, 88
said they occasionally compare and 52 said they never compare different
electronic products available in retail store by physically visiting the stores in the
market before making a final online purchase. Out of 100% respondents, 33.4%
women said they always compare different electronic products available in retail
store by physically visiting the stores in the market before making a final online
purchase, 36.9% women said they mostly compare different electronic products
available in retail store, 18.7% women said they sometimes compare different
electronic products available in retail store, 6.9% said they occasionally compare
and 4.1% said they never compare different electronic products available in
retail store by physically visiting the stores in the market before making a final
online purchase.
298
(f) Frequency distribution of Habitual Buying Behaviour.
10.6.1. Do you agree that you buy the product because you buy it regularly?
Table 10.6.1. showing the number of women who buy the product because
they buy it regularly
Frequency Percent
Valid Strongly Agree
Agree
Disagree
Strongly
Disagree
Total
294
604
307
67
23.1
47.5
24.1
5.3
Valid
Percent
23.1
47.5
24.1
5.3
1272
100.0
100.0
Cumulative
Percent
23.1
70.6
94.7
100.0
Out of 1272 respondents, 294 women strongly agree that they buy the product
because they buy it regularly, 604 women agree, 307 women disagree and 67
women strongly disagree that they buy the product because they buy it regularly.
Out of 100% respondents, 23.1% women strongly agree that they buy the
product because they buy it regularly, 47.5% women agree, 24.1% women
disagree and 5.3% women strongly disagree that they buy the product because
they buy it regularly.
10.6.2. Do you agree that you buy the product because you think that the
product is best fit for you?
Table 10.6.2. showing the number of women who buy the product because
they think that the product is best fit for them.
Frequency Percent
Valid Strongly Agree
Agree
Disagree
Strongly
Disagree
Total
218
864
154
36
17.1
67.9
12.1
2.8
Valid
Percent
17.1
67.9
12.1
2.8
1272
100.0
100.0
Cumulative
Percent
17.1
85.1
97.2
100.0
299
Out of 1272 respondents, 218 women strongly agree that they buy the product
because they think that the product is best fit for them, 864 women agree, 154
women disagree and 36 women strongly disagree that they buy the product
because they think that the product is best fit for them. Out of 100% respondents,
17.1% women strongly agree that they buy the product because they think that
the product is best fit for them, 67.9% women agree, 12.1% women disagree and
2.8% women strongly disagree that they buy the product because they think that
the product is best fit for them.
(g) Frequency distribution of variety seeking buying behaviour of working
women.
10.7.1. Do you agree that you bought the product because you wanted to try
out a different variety of product, belonging to a different brand?
Table 10.7.1. showing the number of women who buy the product because
they wanted to try out a different variety of product, belonging to a
different brand .
Frequency Percent
Valid Strongly Agree
Agree
Disagree
Strongly
Disagree
Total
191
670
355
56
15.0
52.7
27.9
4.4
Valid
Percent
15.0
52.7
27.9
4.4
1272
100.0
100.0
Cumulative
Percent
15.0
67.7
95.6
100.0
Out of 1272 respondents, 191 women strongly agree that they bought the
product because they wanted to try out a different variety of product belonging
to a different brand, 670 women agree, 355 women disagree and 56 women
strongly disagree that they bought the product because they wanted to try out a
300
different variety of product belonging to a different brand. Out of 100%
respondents, 15.0% women strongly agree that they bought the product because
they wanted to try out a different variety of product belonging to a different
brand, 52.7% women agree, 27.9% women disagree and 4.4% women strongly
disagree that they bought the product because they wanted to try out a different
variety of product belonging to a different brand.
10.7.2. Do you agree that you like to buy a new variety of product belonging
to a new brand; each time you make a purchase-decision after viewing an
advertisement on social networking site?
Table 10.7.2. showing the number of women who like to buy a new variety
of product belonging to a new brand; each time they make a purchasedecision after viewing an advertisement on social networking site
Frequency Percent
Valid Strongly Agree
Agree
Disagree
Strongly
Disagree
Total
177
662
382
51
13.9
52.0
30.0
4.0
Valid
Percent
13.9
52.0
30.0
4.0
1272
100.0
100.0
Cumulative
Percent
13.9
66.0
96.0
100.0
Out of 1272 respondents, 177 women strongly agree that they like to buy a new
variety of product belonging to a new brand; each time they make a purchasedecision after viewing an advertisement on social networking site, 662 women
agree, 382 women disagree and 51 women strongly disagree that they like to
buy a new variety of product belonging to a new brand; each time they make a
purchase-decision after viewing an advertisement on social networking site. Out
of 100% respondents, 13.9% women strongly agree that they like to buy a new
301
variety of product belonging to a new brand; each time they make a purchasedecision after viewing an advertisement on social networking site, 52.0% women
agree, 30% women disagree and 4% women strongly disagree that they like to
buy a new variety of product belonging to a new brand; each time they make a
purchase-decision after viewing an advertisement on social networking site.
10.7.3. Do you agree that the different brands of the same product serve,
one and the same purpose ?
Table 10.7.3. showing the number of women who agree that the different
brands of the same product serve, one and the same purpose
Frequency Percent
Valid Strongly Agree
Agree
Disagree
Strongly
Disagree
Total
216
734
277
45
17.0
57.7
21.8
3.5
Valid
Percent
17.0
57.7
21.8
3.5
1272
100.0
100.0
Cumulative
Percent
17.0
74.7
96.5
100.0
Out of 1272 respondents, 216 women strongly agree that the different brands of
the same product serve, one and the same purpose, 734 women agree, 277
women disagree and 45 women strongly disagree that the different brands of the
same product serve, one and the same purpose. Out of 100% respondents, 17.0%
women strongly agree that the different brands of the same product serve, one
and the same purpose, 57.7% women agree, 21.8% women disagree and 3.5%
women strongly disagree that the different brands of the same product serve, one
and the same purpose.
(h) Frequency distribution of Dissonance buying behaviour of working women.
10.8.1. Do you agree that taking a buying decision of an expensive electronic
302
product is difficult and needs a lot of thinking?
Table 10.8.1. showing the number of women who agree that taking a buying
decision of an expensive electronic product is difficult and needs a lot of
thinking.
Frequency Percent
Valid Strongly Agree
Agree
Disagree
Strongly
Disagree
Total
371
712
136
53
29.2
56.0
10.7
4.2
Valid
Percent
29.2
56.0
10.7
4.2
1272
100.0
100.0
Cumulative
Percent
29.2
85.1
95.8
100.0
Out of 1272 respondents, 371 women strongly agree that taking a buying
decision of an expensive electronic product is difficult and needs a lot of
thinking, 712 women agree, 136 women disagree and
53 women strongly
disagree that taking a buying decision of an expensive electronic product is
difficult and needs a lot of thinking. Out of 100% respondents, 29.2% women
strongly agree that taking a buying decision of an expensive electronic product is
difficult and needs a lot of thinking, 56.0% women agree, 10.7% women
disagree and 4.2% women strongly disagree that taking a buying decision of an
expensive electronic product is difficult and needs a lot of thinking.
10.8.2. Do you agree that taking a buying decision of an expensive electronic
product is time consuming?
303
Table
10.8.2. showing the number of women who agree that taking a
buying decision of an expensive electronic product is time consuming.
Frequency Percent
Valid Strongly Agree
Agree
Disagree
Strongly
Disagree
Total
279
714
209
70
21.9
56.1
16.4
5.5
Valid
Percent
21.9
56.1
16.4
5.5
1272
100.0
100.0
Cumulative
Percent
21.9
78.1
94.5
100.0
Out of 1272 respondents, 279 women strongly agree that taking a buying
decision of an expensive electronic product is time consuming, 714 women
agree, 209 women disagree and 70 women strongly disagree that taking a
buying decision of an expensive electronic product is time consuming. Out of
100% respondents, 21.9% women strongly agree that taking a buying decision of
an expensive electronic product is time consuming, 56.1% women agree, 16.4%
women disagree and 5.5% women strongly disagree that taking a buying
decision of an expensive electronic product is time consuming.
10.8.3. After the actual purchase do you agree that you have the feeling of
anxiety that whether your purchase decision is correct?
Table 10.8.3. showing the number of women who agree that they have the
feeling of anxiety that whether their purchase decision is correct.
Frequency Percent
Valid Strongly Agree
Agree
Disagree
Strongly
Disagree
Total
243
735
210
84
19.1
57.8
16.5
6.6
Valid
Percent
19.1
57.8
16.5
6.6
1272
100.0
100.0
Cumulative
Percent
19.1
76.9
93.4
100.0
Out of 1272 respondents, 243 women strongly agree that after the actual
304
purchase they have the feeling of anxiety of their purchase decision being correct
or wrong, 735 women agree, 210 women disagree and 84 women strongly
disagree that after the actual purchase they have the feeling of anxiety of their
purchase decision being correct or wrong. Out of 100 respondents, 19.1%
women strongly agree that after the actual purchase they have the feeling of
anxiety of their purchase decision being correct or wrong, 57.8% women agree,
16.5% women disagree and 6.6% women strongly disagree that after the actual
purchase they have the feeling of anxiety of their purchase decision being correct
or wrong.
(i) Frequency distribution of Impulsive buying behaviour of working women.
10.9.1. Do you agree that you had no plans of buying any consumer
electronic products when you logged on a social networking site?
Table 10.9.1. showing the number of women who agree that they had no
plans of buying any consumer electronic products when they logged on a
social networking site.
Frequency Percent
Valid Strongly Agree
Agree
Disagree
Strongly
Disagree
Total
241
721
262
48
18.9
56.7
20.6
3.8
Valid
Percent
18.9
56.7
20.6
3.8
1272
100.0
100.0
Cumulative
Percent
18.9
75.6
96.2
100.0
Out of 1272 respondents, 241 women strongly agree that they had no plans of
buying any consumer electronic products when they
logged on a social
networking site, 721 women agree, 262 women disagree and
48
women
305
strongly disagree that they had no plans of buying any consumer electronic
products when they
logged on a social networking site. Out of 100%
respondents, 18.9% women strongly agree that they had no plans of buying any
consumer electronic products when they logged on a social networking site,
56.7% women agree, 20.6% women disagree and
3.8%
women strongly
disagree that they had no plans of buying any consumer electronic products
when they logged on a social networking site.
10.9.2. Do you agree that the advertisement of the product on the social
networking site provokes your purchase intentions?
Table 10.9.2. showing the number of women who agree that the
advertisement of the product on the social networking site provokes their
purchase intentions.
Frequency Percent
Valid Strongly Agree
Agree
Disagree
Strongly
Disagree
22.00
Total
345
671
227
28
27.1
52.8
17.8
2.2
Valid
Percent
27.1
52.8
17.8
2.2
1
1272
.1
100.0
.1
100.0
Cumulative
Percent
27.1
79.9
97.7
99.9
100.0
Out of 1272 respondents, 345 women strongly agree that the advertisement of
the product on the social networking site provokes their purchase intentions, 671
women agree, 227 women disagree and 28 women strongly disagree that the
advertisement of the product on the social networking site provokes their
purchase intentions. Out of 100% respondents, 27.1% women strongly agree that
the advertisement of the product on the social networking site provokes their
purchase intentions, 52.8% women agree, 17.8% women disagree and 2.2%
306
women strongly disagree that the advertisement of the product on the social
networking site provokes their purchase intentions.
10.9.3. Do you agree that at times you buy a product just because you found
the discount scheme displayed in the advertisement on the social networking
site is interesting and not available in the retail stores?
Table 10.9.3. showing the number of women who agree that at times they
buy a product just because they find the discount scheme displayed in the
advertisement on the social networking site interesting and not available in
the retail stores.
Frequency Percent
Valid Strongly Agree
Agree
Disagree
Strongly
Disagree
Total
254
747
215
56
20.0
58.7
16.9
4.4
Valid
Percent
20.0
58.7
16.9
4.4
1272
100.0
100.0
Cumulative
Percent
20.0
78.7
95.6
100.0
Out of 1272 respondents, 254 women strongly agree that at times they buy a
product just because they find the discount scheme displayed in the
advertisement on the social networking site interesting and not available in the
retail stores, 747 women agree, 215 women disagree and 56 women strongly
disagree that at times they buy a product just because they find the discount
scheme displayed in the advertisement on the social networking site interesting
and not available in the retail stores. Out of 100% respondents, 20.0% women
strongly agree that at times they buy a product just because they find the
discount scheme displayed in the advertisement on the social networking site
interesting and not available in the retail stores, 58.7% women agree, 16.9%
307
women disagree and 4.4% women strongly disagree that at times they buy a
product just because they find the discount scheme displayed in the
advertisement on the social networking site interesting and not available in the
retail stores.
(j) Frequency distribution of effectiveness of Social networking tools like
Facebook, Twitter and LinkedIn.
10.10.1.A. Which site do you like the most?
Table 10.10.1.A showing the number of women liking anyone SNS from
Facebook, Twitter and LinkedIn the most.
Frequency Percent
Valid Facebook
Twitter
Linkedin
Total
1065
135
72
1272
83.7
10.6
5.7
100.0
Valid
Percent
83.7
10.6
5.7
100.0
Cumulative
Percent
83.7
94.3
100.0
Out of 1272 respondents, 1065 women like Facebook, 135 like Twitter and 72
like LinkedIn the most. Out of 100% respondents, 83.7% like Facebook, 10.6%
like Twitter and only 5.7% like LinkedIn.
10.10.1.B. Which site is the most useful?
Table 10.10.1.B. showing the number of women according to whom the
most useful SNS is anyone from Facebook, Twitter and LinkedIn.
Frequency Percent
Valid Facebook
Twitter
Linkedin
Total
810
270
192
1272
63.7
21.2
15.1
100.0
Valid
Percent
63.7
21.2
15.1
100.0
Cumulative
Percent
63.7
84.9
100.0
Out of 1272 respondents, 810 women think that Facebook is the most useful,
270 think Twitter and 192 think LinkedIn is the most useful site. Out of 100%
308
respondents, 63.7% women think that Facebook is the most useful, 21.2% think
Twitter and 15.1% think LinkedIn is the most useful site.
10.10.1.C. Which site do you prefer to use?
Table 10.10.1.C. showing the number of women according to whom the
most preferable SNS
to use is anyone from Facebook, Twitter and
LinkedIn.
Frequency Percent
Valid Facebook
Twitter
Linkedin
11.00
Total
899
200
172
1
1272
70.7
15.7
13.5
.1
100.0
Valid
Percent
70.7
15.7
13.5
.1
100.0
Out of 1272 respondents, 899 women prefer using
Cumulative
Percent
70.7
86.4
99.9
100.0
Facebook, 200
prefer
Twitter and 172 prefer using LinkedIn. Out of 100% respondents, 70.7%
women prefer using Facebook, 15.7% prefer Twitter and 13.5% prefer using
LinkedIn.
10.10.1.D. Which site is the most user-friendly
Table 10.10.1.D. showing the number of women according to whom the
most user-friendly SNS is anyone from Facebook, Twitter and LinkedIn.
Frequency Percent
Valid Facebook
Twitter
Linkedin
4.00
Total
931
216
124
1
1272
73.2
17.0
9.7
.1
100.0
Valid
Percent
73.2
17.0
9.7
.1
100.0
Cumulative
Percent
73.2
90.2
99.9
100.0
Out of 1272 respondents, 931 women find Facebook as the most user friendly
site, 216 find Twitter most user friendly and 124 find LinkedIn to be the most
user friendly. Out of 100% respondents, 73.2% women find Facebook as the
309
most user friendly site, 17% find Twitter most user friendly and 9.7% find
LinkedIn to be the most user friendly sites.
10.10.1.E. Which site strikes you the most?
Table 10.10.1.E. showing the number of women according to whom the
most striking SNS is anyone from Facebook, Twitter and LinkedIn.
Frequency Percent
Valid Facebook
Twitter
Linkedin
Total
911
157
204
1272
71.6
12.3
16.0
100.0
Valid
Percent
71.6
12.3
16.0
100.0
Cumulative
Percent
71.6
84.0
100.0
Out of 1272 respondents, 911 women find Facebook as the most striking site,
157 find Twitter most striking and 204 find LinkedIn to be the most striking.
Out of 100% respondents, 71.6% women find Facebook as the most striking
site, 12.3% find Twitter most striking and 16.0% find LinkedIn to be the most
striking site.
10.10.2.A. Give appropriate rating on a scale of 1 (lowest) to 10 (highest) to
the Social networking sites that have a large number of groups (networks)
available for any demographics you are looking for; for instance group of
teenagers, group of kids, youth - (Facebook).
Table 10.10.2.A. showing the Facebook ratings for having a large number
of groups (networks) available for any demographics you are looking for;
for instance group of teenagers, group of kids etc provided by working
women.
310
Frequency Percent
Valid
1
2
3
4
5
6
7
8
9
10
Total
51
32
35
46
67
75
190
235
110
431
1272
4.0
2.5
2.8
3.6
5.3
5.9
14.9
18.5
8.6
33.9
100.0
Valid
Percent
4.0
2.5
2.8
3.6
5.3
5.9
14.9
18.5
8.6
33.9
100.0
Cumulative
Percent
4.0
6.5
9.3
12.9
18.2
24.1
39.0
57.5
66.1
100.0
Out of 1272 respondents, minimum 51 and maximum 431 women think that
Facebook has a large number of groups available for any demographics like for
instance group of teenagers, group of professionals etc. Out of 100%
respondents, minimum 4.0% and maximum 33.9% of women think that
Facebook has a large number of groups available for any demographics like for
instance group of teenagers, group of professionals etc.
10.10.2.B. Give appropriate rating on a scale of 1 (lowest) to 10 (highest) to
the Social networking sites that have a large number of groups (networks)
available for any demographics you are looking for; for instance group of
teenagers, group of kids, youth. (Twitter)
Table 10.10.2.B. showing the Twitter ratings for having a large number of
groups (networks) available for any demographics you are looking for; for
instance group of teenagers, group of kids etc provided by working women.
Frequency Percent
Valid 1
2
3
4
44
27
69
107
3.5
2.1
5.4
8.4
Valid
Percent
3.5
2.1
5.4
8.4
Cumulative
Percent
3.5
5.6
11.0
19.4
311
5
6
7
8
9
10
Total
227
232
216
122
126
102
1272
17.8
18.2
17.0
9.6
9.9
8.0
100.0
17.8
18.2
17.0
9.6
9.9
8.0
100.0
37.3
55.5
72.5
82.1
92.0
100.0
Out of 1272 respondents, minimum 44 and maximum 102 women think that
Twitter has a large number of groups available for any demographics like for
instance group of teenagers, group of professionals etc. Out of 100%
respondents, minimum 3.5% and maximum 8.0% of women think that Facebook
has a large number of groups available for any demographics like for instance
group of teenagers, group of professionals etc.
10.10.2.C. Give appropriate rating on a scale of 1 (lowest) to 10 (highest) to
the Social networking sites that have a large number of groups (networks)
available for any demographics you are looking for; for instance group of
teenagers, group of kids, youth.(LinkedIn)
Table 10.10.2.C. showing the LinkedIn ratings for having a large number
of groups (networks) available for any demographics you are looking for;
for instance group of teenagers, group of kids etc. provided by working
women.
Frequency Percent
Valid 1
2
3
4
5
6
7
85
169
78
117
124
150
108
6.7
13.3
6.1
9.2
9.7
11.8
8.5
Valid
Percent
6.7
13.3
6.1
9.2
9.7
11.8
8.5
Cumulative
Percent
6.7
20.0
26.1
35.3
45.0
56.8
65.3
312
8
9
10
Total
211
94
136
1272
16.6
7.4
10.7
100.0
16.6
7.4
10.7
100.0
81.9
89.3
100.0
Out of 1272 respondents, minimum 85 and maximum 136 women think that
LinkedIn has a large number of groups available for any demographics like for
instance group of teenagers, group of professionals etc. Out of 100%
respondents, minimum 6.7% and maximum 10.7% of women think that
LinkedIn has a large number of groups available for any demographics like for
instance group of teenagers, group of professionals etc.
10.10.3.A. Give appropriate rating on a scale of 1 (lowest) to 10 (highest) to
the social networking sites according to the way they are targeting the
advertisements to specific group of audience.(Facebook)
Table 10.10.3.A. showing the Facebook ratings according to the way they
are targeting the advertisements to specific group of audience provided by
working women.
Frequency Percent
Valid 1
2
3
4
5
6
7
8
9
10
Total
63
32
42
67
87
72
165
221
157
366
1272
5.0
2.5
3.3
5.3
6.8
5.7
13.0
17.4
12.3
28.8
100.0
Valid
Percent
5.0
2.5
3.3
5.3
6.8
5.7
13.0
17.4
12.3
28.8
100.0
Cumulative
Percent
5.0
7.5
10.8
16.0
22.9
28.5
41.5
58.9
71.2
100.0
Out of 1272 respondents, minimum 63 and maximum 366 women think that
Facebook is the best site for targeting the advertisements to specific group of
audience. Out of 100% respondents, minimum 5.0% and maximum 28.8%
313
women think that Facebook is the best site for targeting the advertisements to
specific group of audience.
10.10.3.B. Give appropriate rating on a scale of 1 (lowest) to 10 (highest) to
the social networking sites according to the way they are targeting the
advertisements to specific group of audience.(Twitter)
Table 10.10.3.B. showing the Twitter ratings according to the way they are
targeting the advertisements to specific group of audience, provided by
working women.
Frequency Percent
Valid 1
2
3
4
5
6
7
8
9
10
Total
52
98
89
152
193
219
138
137
84
110
1272
4.1
7.7
7.0
11.9
15.2
17.2
10.8
10.8
6.6
8.6
100.0
Valid
Percent
4.1
7.7
7.0
11.9
15.2
17.2
10.8
10.8
6.6
8.6
100.0
Cumulative
Percent
4.1
11.8
18.8
30.7
45.9
63.1
74.0
84.7
91.4
100.0
Out of 1272 respondents, minimum 52 and maximum 110 women think that
Twitter is the best site for targeting the advertisements to specific group of
audience. Out of 100% respondents, minimum 4.1% and maximum 8.6% women
think that Twitter is the best site for targeting the advertisements to specific
group of audience.
10.10.3.C. Give appropriate rating on a scale of 1 (lowest) to 10 (highest) to
the social networking sites according to the way they are targeting the
advertisements to specific group of audience.(LinkedIn)
314
Table 10.10.3.C. showing the LinkedIn ratings according to the way they
are targeting the advertisements to specific group of audience, provided by
working women.
Frequency Percent
Valid 1
2
3
4
5
6
7
8
9
10
Total
136
222
83
81
97
135
137
125
115
141
1272
10.7
17.5
6.5
6.4
7.6
10.6
10.8
9.8
9.0
11.1
100.0
Valid
Percent
10.7
17.5
6.5
6.4
7.6
10.6
10.8
9.8
9.0
11.1
100.0
Cumulative
Percent
10.7
28.1
34.7
41.0
48.7
59.3
70.0
79.9
88.9
100.0
Out of 1272 respondents, minimum 136 and maximum 141 women think that
LinkedIn is the best site for targeting the advertisements to specific group of
audience. Out of 100% respondents, minimum 10.7% and maximum 11.1%
women think that LinkedIn is the best site for targeting the advertisements to
specific group of audience.
10.10.4.A. Which social networking site according to you has more followers
due to acquaintances (i.e. friends and relatives)?
Table 10.10.4.A. showing the number of women who select the site having
more followers due to acquaintances (i.e. friends and relatives) .
Frequency Percent
Valid Facebook
Twitter
Linkedin
Total
1067
126
79
1272
83.9
9.9
6.2
100.0
Valid
Percent
83.9
9.9
6.2
100.0
Cumulative
Percent
83.9
93.8
100.0
Out of 1272 respondents, 1067 women said that facebook has more followers
315
due to acquaintances (i.e. friends and relatives), 126 women said that Twitter has
more followers and 79 said that LinkedIn has more followers due to
acquaintances (i.e. friends and relatives). Out of 100% respondents, 83.9%
women said that facebook has more followers due to acquaintances (i.e. friends
and relatives), 9.9% women said that Twitter has more followers and 6.2% said
that LinkedIn has more followers due to acquaintances (i.e. friends and
relatives).
10.10.4.B. Which social networking site according to you has more unknown
followers?
Table 10.10.4.B. showing the number of women who select the site having
more unknown .
Frequency Percent
Valid Facebook
Twitter
Linkedin
Total
595
323
354
1272
46.8
25.4
27.8
100.0
Valid
Percent
46.8
25.4
27.8
100.0
Cumulative
Percent
46.8
72.2
100.0
Out of 1272 respondents, 595 women said that Facebook has more unknown
followers, 323 said Twitter has more unknown followers and 354 women said
LinkedIn has more number of unknown followers. Out of 100% respondents,
46.8% women said that Facebook has more unknown followers, 25.4% said
Twitter has more unknown followers and 27.8% women said LinkedIn has more
number of unknown followers.
(k) Frequency distribution of impact of social media advertising.
10.11.1.A.1. On Facebook do you have positive reactions/feelings towards
advertisements displayed on it?
316
Table 10.11.1.A.1. showing the number of women who have positive
reactions/feelings towards advertisements displayed on Facebook.
Frequency Percent
Valid Yes
No
12.00
Total
1030
241
1
1272
Valid
Percent
81.0
18.9
.1
100.0
81.0
18.9
.1
100.0
Out of 1272 respondents, 1030
Cumulative
Percent
81.0
99.9
100.0
women said that
they have positive
reactions/feelings towards advertisements displayed on Facebook and
241
women said they don’t have positive reactions/feelings towards advertisements
displayed on Facebook. Out of 100% respondents, 81.0% women said that they
have positive reactions/feelings towards advertisements displayed on Facebook
and 18.9% women said they don’t have positive reactions/feelings towards
advertisements displayed on Facebook.
10.11.1.A.2. On Twitter do you have positive reactions/feelings towards
advertisements displayed on it?
Table
10.11.1.A.2. showing the number of women who have positive
reactions/feelings towards advertisements displayed on Twitter.
Frequency Percent
Valid Yes 268
No
1004
Total 1272
21.1
78.9
100.0
Out of 1272 respondents, 268
Valid
Percent
21.1
78.9
100.0
Cumulative
Percent
21.1
100.0
women said that
they have positive
reactions/feelings towards advertisements displayed on Twitter and
1004
women said they don’t have positive reactions/feelings towards advertisements
displayed on Twitter. Out of 100% respondents, 21.1% women said that they
317
have positive reactions/feelings towards advertisements displayed on Twitter and
78.9%
women said they don’t have positive reactions/feelings towards
advertisements displayed on Twitter.
10.11.1.A.3. On LinkedIn do you have positive reactions/feelings towards
advertisements displayed on it?
Table
10.11.1.A.3. showing the number of women who have positive
reactions/feelings towards advertisements displayed on LinkedIn.
Frequency Percent
Valid Yes 241
No
1031
Total 1272
Valid
Percent
18.9
81.1
100.0
18.9
81.1
100.0
Out of 1272 respondents, 241
Cumulative
Percent
18.9
100.0
women said that
they have positive
reactions/feelings towards advertisements displayed on LinkedIn and 1031
women said they don’t have positive reactions/feelings towards advertisements
displayed on LinkedIn. Out of 100% respondents, 18.9% women said that they
have positive reactions/feelings towards advertisements displayed on LinkedIn
and 81.1% women said they don’t have positive reactions/feelings towards
advertisements displayed on LinkedIn.
10.11.1.B.1. On Facebook, the advertisements displayed appeal you?
Table 10.11.1.B.1. showing the number of women who think advertisements
displayed on Facebook are appealing.
Frequency Percent
Valid Yes 874
No
398
Total 1272
68.7
31.3
100.0
Valid
Percent
68.7
31.3
100.0
Cumulative
Percent
68.7
100.0
318
Out of 1272 respondents, 874 women said that the advertisements displayed on
Facebook appeals them and 398 said the advertisements on Facebook don’t
appeal them. Out of 100% respondents, 68.7%
women said that the
advertisements displayed on Facebook appeals them and 31.3% said the
advertisements on Facebook don’t appeal them.
10.11.1.B.2. On Twitter the advertisements displayed appeal you?
Table 10.11.1.B.2. showing the number of women who think advertisements
displayed on Twitter are appealing.
Frequency Percent
Valid Yes 246
No
1026
Total 1272
19.3
80.7
100.0
Valid
Percent
19.3
80.7
100.0
Cumulative
Percent
19.3
100.0
Out of 1272 respondents, 246 women said the advertisements displayed on
Twitter appeal them and 1026 women said the advertisements displayed on
Twitter don’t appeal them. Out of 100% respondents, 19.3% women said the
advertisements displayed on Twitter appeal them and 80.7% women said the
advertisements displayed on Twitter don’t appeal them.
10.11.1.B.3. On LinkedIn the advertisements displayed appeal you?
Table 10.11.1.B.3. showing the number of women who think advertisements
displayed on LinkedIn are appealing.
Frequency Percent
Valid Yes 216
No
1056
Total 1272
17.0
83.0
100.0
Valid
Percent
17.0
83.0
100.0
Cumulative
Percent
17.0
100.0
Out of 1272 respondents, 216 women said the advertisements displayed on
LinkedIn appeal them and 1056 women said the advertisements displayed on
319
LinkedIn don’t appeal them. Out of 100% respondents,17.0% women said the
advertisements displayed on LinkedIn appeal them and 83.0% women said the
advertisements displayed on LinkedIn don’t appeal them.
10.11.1.C.1. On Facebook the visuals and slogans of the advertisements
displayed are memorable?
Table 10.11.1.C.1. showing the number of women who find the visuals and
slogans of the advertisements displayed on Facebook memorable.
Frequency Percent
Valid Yes
No
11.00
Total
934
337
1
1272
73.4
26.5
.1
100.0
Out of 1272 respondents, 934
Valid
Percent
73.4
26.5
.1
100.0
Cumulative
Percent
73.4
99.9
100.0
women said the visuals and slogans of the
advertisements displayed on Facebook are memorable and 337 women said the
visuals and slogans of the advertisements displayed on Facebook are not
memorable. Out of 100% respondents,73.4%
women said the visuals and
slogans of the advertisements displayed on Facebook are memorable and 26.5 %
women said that the visuals and slogans of the advertisements displayed on
Facebook are not memorable.
10.11.1.C.2. On Twitter the visuals and slogans of the advertisements
displayed are memorable?
Table 10.11.1.C.2. showing the number of women who find the visuals and
slogans of the advertisements displayed on Twitter memorable.
Frequency Percent
Valid Yes 221
No
1051
Total 1272
17.4
82.6
100.0
Valid
Percent
17.4
82.6
100.0
Cumulative
Percent
17.4
100.0
320
Out of 1272 respondents, 221 women said the visuals and slogans of the
advertisements displayed on Twitter are memorable and 1051 women said the
visuals and slogans of the advertisements displayed on Twitter are not
memorable. Out of 100% respondents, 17.4% women said the visuals and
slogans of the advertisements displayed on Twitter are memorable and 82.6%
women said the visuals and slogans of the advertisements displayed on Twitter
are not memorable.
10.11.1.C.3. On LinkedIn the visuals and slogans of the advertisements
displayed are memorable?
Table 10.11.1.C.3. showing the number of women who find the visuals and
slogans of the advertisements displayed on LinkedIn memorable.
Frequency Percent
Valid Yes 227
No
1045
Total 1272
17.8
82.2
100.0
Valid
Percent
17.8
82.2
100.0
Cumulative
Percent
17.8
100.0
Out of 1272 respondents, 227 women said the visuals and slogans of the
advertisements displayed on LinkedIn are memorable and 1045 women said the
visuals and slogans of the advertisements displayed on LinkedIn are not
memorable. Out of 100% respondents, 17.8% women said the visuals and
slogans of the advertisements displayed on LinkedIn are memorable and 82.2%
women said the visuals and slogans of the advertisements displayed on LinkedIn
are not memorable.
10.11.1.D.1. On Facebook do you find the product advertisement displayed
attractive?
321
Table 10.11.1.D.1. showing the number of women who find the product
advertisement displayed on Facebook memorable.
Frequency Percent
Valid Yes 837
No
435
Total 1272
65.8
34.2
100.0
Valid
Percent
65.8
34.2
100.0
Cumulative
Percent
65.8
100.0
Out of 1272 respondents, 837 women said the product advertisement displayed
on Facebook was attractive and 435 said the product advertisement displayed on
Facebook was not attractive. Out of 100% respondents, 65.8% women said the
product advertisement displayed on Facebook was attractive and 34.2% said the
product advertisement displayed on Facebook was not attractive.
10.11.1.D.2. On Twitter do you find the product advertisement displayed
attractive?
Table 10.11.1.D.2. showing the number of women who find the product
advertisement displayed on Twitter memorable.
Frequency Percent
Valid Yes 269
No
1003
Total 1272
21.1
78.9
100.0
Valid
Percent
21.1
78.9
100.0
Cumulative
Percent
21.1
100.0
Out of 1272 respondents, 269 women said the product advertisement displayed
on Twitter was attractive and 1003 said the product advertisement displayed on
Twitter was not attractive. Out of 100% respondents, 21.1% women said the
product advertisement displayed on Twitter was attractive and 78.9% said the
product advertisement displayed on Twitter was not attractive.
10.11.1.D.3. On LinkedIn do you find the product advertisement displayed
attractive?
322
Table 10.11.1.D.3. showing the number of women who find the product
advertisement displayed on LinkedIn memorable.
Frequency Percent
Valid Yes 191
No
1081
Total 1272
15.0
85.0
100.0
Valid
Percent
15.0
85.0
100.0
Cumulative
Percent
15.0
100.0
Out of 1272 respondents, 191 women said the product advertisement displayed
on LinkedIn was attractive and 1081 said the product advertisement displayed on
LinkedIn was not attractive. Out of 100% respondents, 15.0% women said the
product advertisement displayed on LinkedIn was attractive and 85.0% said the
product advertisement displayed on LinkedIn was not attractive.
10.11.1.E.1. On Facebook do you trust the advertisements displayed?
Table 10.11.1.E.1. showing the number of women who trust the product
advertisement displayed on Facebook.
Frequency Percent
Valid Yes 793
No
479
Total 1272
62.3
37.7
100.0
Valid
Percent
62.3
37.7
100.0
Cumulative
Percent
62.3
100.0
Out of 1272 respondents, 793 women said they trust the product advertisement
displayed on Facebook and 479 said they did not trust the product advertisement
displayed on Facebook. Out of 100% respondents, 62.3% women said they trust
the product advertisement displayed on Facebook and 37.7% said they did not
trust the product advertisement displayed on Facebook.
10.11.1.E.2. On Twitter do you trust the advertisements displayed?
323
Table 10.11.1.E.2. showing the number of women who trust the product
advertisement displayed on Twitter.
Frequency Percent
Valid Yes 209
No
1063
Total 1272
16.4
83.6
100.0
Valid
Percent
16.4
83.6
100.0
Cumulative
Percent
16.4
100.0
Out of 1272 respondents, 209 women said they trust the product advertisement
displayed on Twitter and 1063 said they did not trust the product advertisement
displayed on Twitter. Out of 100% respondents, 16.4% women said they trust
the product advertisement displayed on Twitter and 83.6% said they did not trust
the product advertisement displayed on Twitter.
10.11.1.E.3. On LinkedIn do you trust the advertisements displayed?
Table 10.11.1.E.3. showing the number of women who trust the product
advertisement displayed on LinkedIn.
Frequency Percent
Valid Yes 219
No
1053
Total 1272
17.2
82.8
100.0
Valid
Percent
17.2
82.8
100.0
Cumulative
Percent
17.2
100.0
Out of 1272 respondents, 219 women said they trust the product advertisement
displayed on LinkedIn and 1053 said they did not trust the product advertisement
displayed on LinkedIn. Out of 100% respondents, 17.2% women said they trust
the product advertisement displayed on LinkedIn and 82.8% women said they
did not trust the product advertisement displayed on LinkedIn.
10.11.2. In the time spent on Social networking site, how many times have
you seen an advertisement for consumer electronics?
324
Table 10.11.2. showing the number of times the working women have seen
an advertisement for consumer electronics on SNS in the time they spend on
Social networking site .
Frequency Percent
Valid None
114
1-2 times
396
3-4 times
457
4-5 times
226
More than 5 78
times
52.00
1
Total
1272
Out of 1272 respondents, 114
9.0
31.1
35.9
17.8
6.1
Valid
Percent
9.0
31.1
35.9
17.8
6.1
Cumulative
Percent
9.0
40.1
76.0
93.8
99.9
.1
100.0
.1
100.0
100.0
women said
they have never seen an
advertisement for consumer electronics on Social networking site when they
spent time on social networking site, 396 women said they have seen the
advertisement for consumer electronics 1-2 times, 457 women said they have the
ad for consumer electronics 3-4times, 226 women said they have seen the ad 4-5
times and 78 women said they have seen the ad more than 5 times. Out of 100%
respondents, 9.0% women said they have never seen an advertisement for
consumer electronics on Social networking site when they spent time on social
networking site, 31.1% women said they have seen the advertisement for
consumer electronics 1-2 times, 35.9% women said they have the ad for
consumer electronics 3-4 times, 17.8% women said they have seen the ad 4-5
times and 6.1% women said they have seen the ad more than 5 times.
10.11.3. Were you satisfied with the actual product which you purchased
after watching the advertisement on any of the social networking sites?
Table 10.11.3. showing the number of working women who were satisfied
with the actual product which they purchased after watching the
325
advertisement on any of the social networking sites.
Frequency Percent
Valid Highly Satisfied
Satisfied
Neither
Dissatisfied
Highly
Dissatisfied
Total
203
792
194
66
17
16.0
62.3
15.3
5.2
1.3
Valid
Percent
16.0
62.3
15.3
5.2
1.3
1272
100.0
100.0
Cumulative
Percent
16.0
78.2
93.5
98.7
100.0
Out of 1272 respondents, 203 women said they were highly satisfied with the
actual product which they purchased after watching the advertisement on the
social networking sites, 792 women said they were satisfied with the product,
194 women said they were neither satisfied nor dissatisfied, 66 women said they
were dissatisfied and 17 said they were highly dissatisfied with the actual
product which they purchased after watching the advertisement on social
networking sites. Out of 100% respondents, 16.0% women said they were
highly satisfied with the actual product which they purchased after watching the
advertisement on the social networking sites, 62.3% women said they were
satisfied with the product, 15.3% women said they were neither satisfied nor
dissatisfied, 5.2% women said they were dissatisfied and 1.3% said they were
highly dissatisfied with the actual product which they purchased after watching
the advertisement on social networking sites.
Annexure - IV
Inferential Analysis
Objective 1 – To identify the Social Media Usage by young working women
in different cities.USAGE OF SOCIAL MEDIA - HOW OFTEN DO YOU ACCESS INTERNET
326
EITHER ON A COMPUTER OR A MOBILE OR ON OTHER DEVICES
LIKE IPAD?, IN DIFFERENT CITIES Table 10.12.1. Showing the frequency with which the young working
women access internet in Mumbai, Nashik and Surat.
USAGE OF SOCIAL MEDIA - HOW OFTEN
DO YOUR ACCESS INTERNET EITHER ON
A COMPUTER OR A MOBILE OR ON
Tot
OTHER DEVICES LIKE IPAD
al
2-3
ALMO
Pl
MU
Co
ac
MBA
unt
e
I
DA
ON
YS
CE
ST
4-5
A
A
NE
EVER
DAYS/
WE
WE
RAR
VE
YDAY
WEEK
EK
EK
ELY
R
385
79
27
13
8
4
2.1
1.0
30.3%
6.2%
.6%
.3%
516
%
of
Tot
40.6
%
%
%
17
9
11
6
.7%
.9%
.5%
al
SUR
Co
AT
unt
277
77
21.8%
6.1%
397
%
of
1.3
Tot
31.2
%
%
al
NAS
Co
HK
unt
%
249
66
16
4
11
13
359
19.6%
5.2%
1.3
.3%
.9%
1.0
28.2
327
of
%
%
%
Tot
al
Total
Co
127
911
222
60
26
30
23
unt
2
%
of
4.7
71.6%
2.0
17.5%
Tot
1.8
100.
%
0%
2.4%
%
%
al
From the above table, it is observed that,
Mumbai
It was found that out of total 516 respondents, 385 said that they access
internet on a computer or a mobile or on other devices like iPad almost every
day and only 4 said that they never access internet.
Surat
It was found that out of total 397 respondents, 277 said that they access
internet on a computer or a mobile or on other devices like iPad almost every
day and only 13 said that they never access internet.
Nashik
It was found that out of total 359 respondents, 249 said that they access
internet on a computer or a mobile or on other devices like iPad almost every
day and only 4 said that they never access internet.
328
2) USAGE OF SOCIAL MEDIA - DO YOU USE SOCIAL NETWORKING
SITES ?IN DIFFERENT CITIES Kindly refer the Data Analysis and Findings chapter, Inferential analysis
number 1. Table no. 7.1.1.
3.USAGE OF SOCIAL MEDIA - WHICH OF THESE SOCIAL
NETWORKING SITES DO YOU USE- "Face book" –
Kindly refer the Data Analysis and Findings chapter, Inferential analysis
number 2. Table no. 7.1.2.
4) USAGE OF SOCIAL MEDIA - WHICH OF THESE SOCIAL
NETWORKING SITES DO YOU USE - "Twitter" –
Table 10.12.4. Showing the number of young working women accessing
or using “Twitter” in Mumbai, Nashik and Surat.
USAGE
MEDIA
THESE
OF
-
SOCIAL
WHICH
OF
SOCIAL
NETWORKING SITES DO
PLACE
MUMBAI
SURAT
NASHIK
Total
YOU USE - "Twitter"
Total
Yes
No
Yes
Count
160
356
516
% of Total
12.6%
28.0%
40.6%
Count
73
324
397
% of Total
5.7%
25.5%
31.2%
Count
86
273
359
% of Total
6.8%
21.5%
28.2%
Count
319
953
1272
% of Total
25.1%
74.9%
100.0%
329
Mumbai
It was found that out of total 516 respondents, 160 agreed that they used
Twitter and 356 disagreed.
Surat
It was found that out of total 397 respondents, 73 agreed that they used
Twitter and 324 disagreed.
Nashik
It was found that out of total 359 respondents, 86 agreed that they used
Twitter and 273 disagreed.
5)USAGE OF SOCIAL MEDIA - WHICH OF THESE SOCIAL
NETWORKING SITES DO YOU USE - "LinkedIn" Table 10.12.5. Showing the number of young working women accessing
or using “LinkedIn” in Mumbai, Nashik and Surat.
USAGE OF SOCIAL MEDIA WHICH OF THESE SOCIAL
NETWORKING
PLACE
MUMBAI
SURAT
NASHIK
Total
SITES
DO
YOU USE - "LinkedIn"
Total
Yes
No
Yes
Count
132
384
516
% of Total
10.4%
30.2%
40.6%
Count
34
363
397
% of Total
2.7%
28.5%
31.2%
Count
76
283
359
% of Total
6.0%
22.2%
28.2%
Count
242
1030
1272
330
% of Total
19.0%
81.0%
100.0%
Mumbai
It was found that out of total 516 respondents, 132 agreed that they used
LinkedIn and 384 disagreed.
Surat
It was found that out of total 397 respondents, 34 agreed that they used
LinkedIn and 363 disagreed.
Nashik
It was found that out of total 359 respondents, 76 agreed that they used
LinkedIn and 283 disagreed.
6)USAGE OF SOCIAL MEDIA - WHICH OF THESE SOCIAL
NETWORKING SITES DO YOU USE - "Others" –
Table 10.12.6 Showing the number of young working women accessing
or using “Other SNSs” in Mumbai, Nashik and Surat.
USAGE OF SOCIAL MEDIA - WHICH OF
THESE SOCIAL NETWORKING SITES DO
YOU USE - "Others"
PLACE
MUMBAI
SURAT
NASHIK
Total
Total
Yes
No
Yes
Count
29
487
516
% of Total
2.3%
38.3%
40.6%
Count
20
377
397
% of Total
1.6%
29.6%
31.2%
Count
84
275
359
% of Total
6.6%
21.6%
28.2%
Count
133
1139
1272
331
% of Total
10.5%
89.5%
100.0%
Mumbai
It was found that out of total 516 respondents, 29 agreed that they used other
social networking sites for social networking and 487 disagreed.
Surat
It was found that out of total 397 respondents, 20 agreed that they used other
social networking sites for social networking and 377 disagreed.
Nashik
It was found that out of total 359 respondents, 84 agreed that they used Other
social networking sites for social networking and 275 disagreed.
7) USAGE OF SOCIAL MEDIA - HOW OFTEN DO YOU USE SOCIAL
NETWORKING SITES LIKE FACE-BOOK, TWITTER, LINKEDIN
Kindly refer the Data Analysis and Findings chapter, Inferential analysis
number 3. Table no. 7.1.3.
8) USAGE OF SOCIAL MEDIA - IF YOU DON'T USE SOCIAL
NETWORKING
SITES,
WHY
DON’T
YOU
USE
SOCIAL
NETWORKING SITES - Not Interested Table 10.12.8. Showing the number of young working women not
accessing SNS for the reason lack of interest in Mumbai, Nashik and
Surat.
332
USAGE
OF
SOCIAL
MEDIA - IF YOU DON'T
USE
SOCIAL
NETWORKING
SITES,
WHY DON'S YOU USE
SOCIAL
NETWORKING
SITES - Not Interested
PLACE
MUMBAI Count
%
Total
Yes
No
31
485
516
2.4%
38.1%
40.6%
32
365
397
2.5%
28.7%
31.2%
11
348
359
.9%
27.4%
28.2%
74
1198
1272
5.8%
94.2%
100.0%
of
Total
SURAT
Count
%
of
Total
NASHIK
Count
%
of
Total
Total
Count
%
of
Total
Mumbai
It was found that out of total 516 respondents, 31 agreed that they were not
interested in using social networking sites and 485 disagreed.
Surat
It was found that out of total 397 respondents, 32 agreed that they were not
333
interested in using social networking sites and 365 disagreed.
Nashik
It was found that out of total 359 respondents, 11 agreed that they were not
interested in using social networking sites and 348 disagreed.
9) USAGE OF SOCIAL MEDIA - IF YOU DON'T USE SOCIAL
NETWORKING
SITES,
WHY
DON'S
YOU
USE
SOCIAL
NETWORKING SITES - Security Concerns Table 10.12.9. Showing the number of young working women not
accessing SNS for the reason of security concerns in Mumbai, Nashik
and Surat.
USAGE OF SOCIAL MEDIA - IF YOU DON'T
USE SOCIAL NETWORKING SITES, WHY
DON'S YOU USE SOCIAL NETWORKING
SITES - Security Concerns
PLACE
MUMBAI
SURAT
NASHIK
Total
Total
Yes
No
Count
71
445
516
% of Total
5.6%
35.0%
40.6%
Count
60
337
397
% of Total
4.7%
26.5%
31.2%
Count
25
334
359
% of Total
2.0%
26.3%
28.2%
Count
156
1116
1272
% of Total
12.3%
87.7%
100.0%
Mumbai
It was found that out of total 516 respondents, 71 agreed that they did not use
social networking sites due to security concerns and 445 disagreed.
334
Surat
It was found that out of total 397 respondents, 60 agreed that they did not use
social networking sites due to security concerns and 337 disagreed.
Nashik
It was found that out of total 359 respondents, 25 agreed that they did not use
social networking sites due to security concerns and 334 disagreed.
10) USAGE OF SOCIAL MEDIA - IF YOU DON'T USE SOCIAL
NETWORKING
SITES,
WHY
DON'T
YOU
USE
SOCIAL
NETWORKING SITES - Non Availability of Enough time -
Table 10.12.10. Showing the number of young working women not
accessing SNS for the reason of Non Availability of Enough time in
Mumbai, Nashik and Surat.
335
USAGE OF SOCIAL MEDIA - IF YOU DON'T
USE SOCIAL NETWORKING SITES, WHY
DON'S YOU USE SOCIAL NETWORKING
SITES - Non Availability of Enough time
PLACE
MUMBAI
SURAT
NASHIK
Total
Total
Yes
No
Count
59
457
516
% of Total
4.6%
35.9%
40.6%
Count
31
366
397
% of Total
2.4%
28.8%
31.2%
Count
45
314
359
% of Total
3.5%
24.7%
28.2%
Count
135
1137
1272
% of Total
10.6%
89.4%
100.0%
Mumbai
It was found that out of total 516 respondents, 59 agreed that they did not use
social networking sites due to non availability of enough time and 457
disagreed.
Surat
It was found that out of total 397 respondents, 31 agreed that they did not use
social networking sites due to non availability of enough time and 366
disagreed.
Nashik
It was found that out of total 359 respondents, 45 agreed that they did not use
social networking sites due to non availability of enough time and 314
disagreed.
336
11) USAGE OF SOCIAL MEDIA - IF YOU DON'T USE SOCIAL
NETWORKING
SITES,
WHY
DON'T
YOU
USE
SOCIAL
NETWORKING SITES - Prefer face to face interactions -
Table 10.12.11. Showing the number of young working women not
accessing SNS for the reason of more Prefering face to face interactions
in Mumbai, Nashik and Surat.
USAGE OF SOCIAL MEDIA - IF YOU
DON'T
USE
SOCIAL
NETWORKING
SITES, WHY DON'S YOU USE SOCIAL
NETWORKING SITES - Prefer face to face
interactions
PLACE
MUMBAI
SURAT
NASHIK
Total
Total
Yes
No
Count
33
483
516
% of Total
2.6%
38.0%
40.6%
Count
6
391
397
% of Total
.5%
30.7%
31.2%
Count
15
344
359
% of Total
1.2%
27.0%
28.2%
Count
54
1218
1272
% of Total
4.2%
95.8%
100.0%
Mumbai
It was found that out of total 516 respondents, 33 agreed that they did not use
social networking sites because they preferred face to face interactions and
483 disagreed.
Surat
337
It was found that out of total 397 respondents, 6 agreed that they did not use
social networking sites because they preferred face to face interactions and
391 disagreed.
Nashik
It was found that out of total 359 respondents, 15 agreed that they did not use
social networking sites because they preferred face to face interactions and
344 disagreed.
12) USAGE OF SOCIAL MEDIA - IF YOU DON'T USE SOCIAL
NETWORKING
SITES,
WHY
DON'T
YOU
USE
SOCIAL
NETWORKING SITES - Lack of computer skills –
Table 10.12.12.
Showing the number of young working women not
accessing SNS for the reason of Lack of computer skills in Mumbai,
Nashik and Surat.
USAGE OF SOCIAL MEDIA - IF YOU DON'T
USE SOCIAL NETWORKING SITES, WHY
DON'S YOU USE SOCIAL NETWORKING
SITES - Lack of computer skills
PLACE
MUMBAI
SURAT
NASHIK
Total
Total
Yes
No
Count
24
492
516
% of Total
1.9%
38.7%
40.6%
Count
9
388
397
% of Total
.7%
30.5%
31.2%
Count
8
351
359
% of Total
.6%
27.6%
28.2%
Count
41
1231
1272
338
% of Total
3.2%
96.8%
100.0%
Mumbai
It was found that out of total 516 respondents, 24 agreed that they did not use
social networking sites because of lack of computer skills and 492 disagreed.
Surat
It was found that out of total 397 respondents, 9 agreed that they did not use
social networking sites because of lack of computer skills and 388 disagreed.
Nashik
It was found that out of total 359 respondents, 8 agreed that they did not use
social networking sites because of lack of computer skills and 351 disagreed.
13) USAGE OF SOCIAL MEDIA - IF YOU DON'T USE SOCIAL
NETWORKING
SITES,
WHY
DON'T
YOU
USE
SOCIAL
NETWORKING SITES - Prefer to use phone for interaction -
Table 10.12.13. Showing the number of young working women not
accessing SNS for the reason of Prefering to use phone for interaction in
Mumbai, Nashik and Surat.
339
USAGE OF SOCIAL MEDIA IF YOU DON'T USE SOCIAL
NETWORKING SITES, WHY
DON'S YOU USE SOCIAL
NETWORKING SITES - Prefer
PLACE
MUMBAI
SURAT
NASHIK
Total
to use phone for interaction
Total
Yes
No
Yes
Count
34
482
516
% of Total
2.7%
37.9%
40.6%
Count
21
376
397
% of Total
1.7%
29.6%
31.2%
Count
29
330
359
% of Total
2.3%
25.9%
28.2%
Count
84
1188
1272
% of Total
6.6%
93.4%
100.0%
Mumbai
It was found that out of total 516 respondents, 34 agreed that they did not use
social networking sites because they prefer phone for interaction and 482
disagreed.
Surat
It was found that out of total 397 respondents, 21 agreed that they did not use
social networking sites because they prefer phone for interaction and 376
disagreed.
340
Nashik
It was found that out of total 359 respondents, 29 agreed that they did not use
social networking sites because they prefer phone for interaction and 330
disagreed.
14) ROUGHLY HOW MUCH TIME DO YOU SPEND EACH TIME YOU
ACCESS THESE SOCIAL NETWORKING SITES - FACE BOOK –
Kindly refer the Data Analysis and Findings chapter, Inferential analysis
number 4. Table no. 7.1.4.
15) ROUGHLY HOW MUCH TIME DO YOU SPEND EACH TIME YOU
ACCESS THESE SOCIAL NETWORKING SITES - LINKED-IN -
Table 10.12.15. Showing the time the young working women spend each
time they access LinkedIn in Mumbai, Nashik and Surat.
ROUGHLY HOW MUCH TIME DO YOU SPEND
EACH TIME YOU ACCESS THESE SOCIAL
NETWORKING SITES - LINKED-IN
Total
MORE
30
PLACE
MUMBAI
SURAT
NASHIK
Total
THAN 2
15 MIN.
MIN.
HOUR
HOURS
HOURS
Count
119
172
123
69
33
516
% of Total
9.4%
13.5%
9.7%
5.4%
2.6%
40.6%
Count
112
166
48
24
47
397
% of Total
8.8%
13.1%
3.8%
1.9%
3.7%
31.2%
Count
116
127
59
38
19
359
% of Total
9.1%
10.0%
4.6%
3.0%
1.5%
28.2%
Count
347
465
230
131
99
1272
341
% of Total
27.3%
36.6%
18.1%
10.3%
7.8%
100.0%
Mumbai
It was found that out of total 516 respondents, 119 spent 15 min of their time
on the social networking site LinkedIn and 33 spent more than 2 hours.
Surat
It was found that out of total 397 respondents, 112 spent 15 min of their time
on the social networking site LinkedIn and 47 spent more than 2 hours.
Nashik
It was found that out of total 359 respondents, 116 spent 15 min of their time
on the social networking site LinkedIn and 19 spent more than 2 hours.
16) ROUGHLY HOW MUCH TIME DO YOU SPEND EACH TIME YOU
ACCESS THESE SOCIAL NETWORKING SITES - TWITTER Table 10.12.16. Showing the time the young working women spend each
time they access Twitter in Mumbai, Nashik and Surat.
ROUGHLY HOW MUCH TIME DO YOU SPEND EACH
TIME YOU ACCESS THESE SOCIAL NETWORKING
SITES - TWITTER
Total
MORE
THAN
PLACE
MUMBAI
SURAT
NASHIK
2
15 MIN.
30 MIN.
HOUR
HOURS
HOURS
Count
133
130
144
80
29
516
% of Total
10.5%
10.2%
11.3%
6.3%
2.3%
40.6%
Count
81
137
106
43
30
397
% of Total
6.4%
10.8%
8.3%
3.4%
2.4%
31.2%
Count
101
108
91
45
14
359
% of Total
7.9%
8.5%
7.2%
3.5%
1.1%
28.2%
342
Total
Count
315
375
341
168
73
1272
% of Total
24.8%
29.5%
26.8%
13.2%
5.7%
100.0%
Mumbai
It was found that out of total 516 respondents, 133 spent 15 min of their time
on the social networking site Twitter and 29 spent more than 2 hours.
Surat
It was found that out of total 397 respondents, 81 spent 15 min of their time
on the social networking site Twitter and 30 spent more than 2 hours.
Nashik
It was found that out of total 359 respondents, 101 spent 15 min of their time
on the social networking site Twitter and 14 spent more than 2 hours.
17) ROUGHLY HOW MUCH TIME DO YOU SPEND EACH TIME YOU
ACCESS THESE SOCIAL NETWORKING SITES - ANY OTHER Table 10.12.17. Showing the time the young working women spend each
time they access Other SNS in Mumbai, Nashik and Surat.
ROUGHLY HOW MUCH TIME DO YOU SPEND EACH
TIME YOU ACCESS THESE SOCIAL NETWORKING
Total
SITES - ANY OTHER
MORE
THAN 2
PLACE
MUMBAI
SURAT
NASHIK
15 MIN.
30 MIN.
HOUR
HOURS
HOURS
Count
69
134
184
94
35
516
% of Total
5.4%
10.5%
14.5%
7.4%
2.8%
40.6%
Count
72
124
139
45
17
397
% of Total
5.7%
9.7%
10.9%
3.5%
1.3%
31.2%
Count
30
99
123
83
24
359
% of Total
2.4%
7.8%
9.7%
6.5%
1.9%
28.2%
343
Total
Count
171
357
446
222
76
1272
% of Total
13.4%
28.1%
35.1%
17.5%
6.0%
100.0%
Mumbai
It was found that out of total 516 respondents, 69 spent 15 min of their time
on any other social networking site and 35 spent more than 2 hours.
Surat
It was found that out of total 397 respondents, 72 spent 15 min of their time
on any other social networking site and 17 spent more than 2 hours.
Nashik
It was found that out of total 359 respondents, 30 spent 15 min of their time
on any other social networking site and 24 spent more than 2 hours.
18) Compared to last year, have you increased, decreased or spent about
the same amount of time using the social networking site –
Table 10.12.18. Showing whether the young working women has
increased or decreased the time they spend using SNS in Mumbai,
Nashik and Surat.
Compared to last year, have you increased, decreased
or spent about the same amount of time using the
social networking site
PLACE
MUMBAI
Total
Increased
Decreased
Nearly the same
Count
251
170
95
516
% of Total
19.7%
13.4%
7.5%
40.6%
344
SURAT
NASHIK
Total
Count
157
127
113
397
% of Total
12.3%
10.0%
8.9%
31.2%
Count
122
156
81
359
% of Total
9.6%
12.3%
6.4%
28.2%
Count
530
453
289
1272
% of Total
41.7%
35.6%
22.7%
100.0%
Mumbai
It was found that out of total 516 respondents, 251 increased and 95 spent
nearly the same amount of time using the social networking site.
Surat
It was found that out of total 397 respondents, 157 increased and 113 spent
nearly the same amount of time using the social networking site.
Nashik
It was found that out of total 359 respondents, 122 increased and 81 spent
nearly the same amount of time using the social networking site.
19) When you think about the time that you are spending currently on the
social networking sites for product information search, do you feel that
it is about right, too much or not enough -
345
Table 10.12.19. Showing whether the amount of time spent by young
working women for product information search is right, too much or
not enough the time they spent using SNS in Mumbai, Nashik and
Surat.
When you think about the time that
you are spending currently on the
social networking sites for product
information search, do you feel that
it is about right, too much or not
enough
Total
too
PLACE
MUMBAI
Count
%
not enough
just right
much
117
315
84
516
9.2%
24.8%
6.6%
40.6%
96
231
70
397
7.5%
18.2%
5.5%
31.2%
82
209
68
359
6.4%
16.4%
5.3%
28.2%
295
755
222
1272
23.2%
59.4%
17.5%
100.0%
of
Total
SURAT
Count
%
of
Total
NASHIK
Count
%
of
Total
Total
Count
%
of
Total
346
Mumbai
It was found that out of total 516 respondents, 315 said that they spent just
right time on social networking site and 84 said that they spent too much.
Surat
It was found that out of total 397 respondents, 231 said that they spent just
right time on social networking site and 70 said that they spent too much.
Nashik
It was found that out of total 359 respondents, 209 said that they spent just
right time on social networking site and 68 said that they spent too much.
20) Looking at the next twelve months, compared to the last year for
product
information search do you think you will be increasing,
decreasing or spending the same amount of time using social
networking sites?Table 10.12.20. Showing whether the young working women will be
increasing, decreasing or spending the same amount of time using
social networking sites for product information search as compared to
the last year in Mumbai, Nashik and Surat.
Looking at the next twelve months,
compared to the last year for product
information search do you think you
will be increasing, decreasing or
spending the same amount of time
using social networking sites?
Total
347
about
PLACE
MUMBAI
SURAT
NASHIK
Total
the
Increased
Decreased
same time
Count
240
146
130
516
% of Total
18.9%
11.5%
10.2%
40.6%
Count
182
108
107
397
% of Total
14.3%
8.5%
8.4%
31.2%
Count
122
130
107
359
% of Total
9.6%
10.2%
8.4%
28.2%
Count
544
384
344
1272
30.2%
27.0%
100.0%
% of Total 42.8%
Mumbai
It was found that out of total 516 respondents, 240 said that they would
increase time on social networking site and 130 said that they would spend
about same time.
Surat
It was found that out of total 397 respondents, 182 said that they would
increase time on social networking site and 107 said that they would spend
about same time.
Nashik
It was found that out of total 359 respondents, 122 said that they would
increase time on social networking site and 107 said that they would spend
about same time.
21) Do you share your opinion about a particular product or service with
your family or friends by writing reviews or blogs?
348
Kindly refer the Data Analysis and Findings chapter, Inferential analysis
number 5. Table no. 7.1.5.
22) Do you share your feedback about a product or service with the
organization /Company?
Table 10.12.22. Showing whether the young working women share their
feedback about a product or service with the organization /Company in
Mumbai, Nashik and Surat.
Do you share your feedback
about a product or service
with
the
organization
/Company?
PLACE
MUMBAI
SURAT
NASHIK
Total
Total
yes
no
Count
267
249
516
% of Total
21.0%
19.6%
40.6%
Count
259
138
397
% of Total
20.4%
10.8%
31.2%
Count
142
217
359
% of Total
11.2%
17.1%
28.2%
Count
668
604
1272
% of Total
52.5%
47.5%
100.0%
349
Mumbai
It was found that out of total 516 respondents, 267 agreed that they shared
their feedback about a product or service with the organization /Company and
249 disagreed.
Surat
It was found that out of total 397 respondents, 259 agreed that they shared
their feedback about a product or service with the organization /Company and
138 disagreed.
Nashik
It was found that out of total 359 respondents, 142 agreed that they shared
their feedback about a product or service with the organization /Company and
217 disagreed.
23) Do you visit company website and provide a particular rating for a
particular product or service –
Table 10.12.23. Showing whether the young working women visit
company website and provide a particular rating for a particular
product or service in Mumbai, Nashik and Surat.
Do you visit company
website and provide a
particular rating for a
particular
service.
product
or
Total
350
PLACE
MUMBAI
SURAT
NASHIK
Total
Yes
no
Yes
Count
265
251
516
% of Total
20.8%
19.7%
40.6%
Count
285
112
397
% of Total
22.4%
8.8%
31.2%
Count
157
202
359
% of Total
12.3%
15.9%
28.2%
Count
707
565
1272
% of Total
55.6%
44.4%
100.0%
Mumbai
It was found that out of total 516 respondents, 265 agreed that they visit
company website and provide a particular rating for a particular product or
service and 251 disagreed.
Surat
It was found that out of total 397 respondents, 285 agreed that they visit
company website and provide a particular rating for a particular product or
service and 112 disagreed.
Nashik
It was found that out of total 359 respondents, 157 agreed that they visit
company website and provide a particular rating for a particular product or
service and 202 disagreed.
24) How many times have you provided online rating in one year? –
Kindly refer the Data Analysis and Findings chapter, Inferential analysis
351
number 6. Table no. 7.6.
25) Do you send the company link of your favourite brand to your family
and friends? –
Table 10.12.25. Showing whether the young working women send the
company link of their favourite brand to their family and friends in
Mumbai, Nashik and Surat.
Do you send the company link of your
favourite brand to your family and
friends?
PLACE
MUMBAI
Count
%
Total
Yes
No
281
235
516
22.1%
18.5%
40.6%
289
108
397
22.7%
8.5%
31.2%
159
200
359
12.5%
15.7%
28.2%
729
543
1272
57.3%
42.7%
100.0%
of
Total
SURAT
-
Count
%
of
Total
NASHIK
Count
%
of
Total
Total
Count
%
of
Total
Mumbai
It was found that out of total 516 respondents, 281 agreed that they send
352
the company link of their favourite brand to their family and friends and
235 disagreed.
Surat
It was found that out of total 397 respondents, 289 agreed that they send
the company link of their favourite brand to their family and friends and
108 disagreed.
Nashik
It was found that out of total 359 respondents, 159 agreed that they send
the company link of their favourite brand to their family and friends and
200 disagreed.
Objective 2 – To study the Different types of buying behaviour with
respect to Social Media Advertising in different cities –
(I) To study the customer buying behaviour with respect to Social Media
Advertising in different cities –
b) Relationship between consumer buying behaviour with the factor of
Social Media Advertisement i.e. “On social media do you have positive
reactions/feelings towards advertisements displayed on it” in different
cities –
(iv)In Mumbai - Kindly refer the Data Analysis and Findings chapter,
Objective 2, Inferential analysis number I, a, i. Table no. 7.2.1.1.m.a. and
7.2.1.1.m.b.
(v) In Nashik - Kindly refer the Data Analysis and Findings chapter,
353
Objective 2, Inferential analysis number I, a, ii. Table no. 7.2.1.1.n.a. and
7.2.1.1.n.b.
(vi) In Surat - Kindly refer the Data Analysis and Findings chapter,
Objective 2, Inferential analysis number I, a, iii. Table no. 7.2.1.1.s.a.
and 7.2.1.1.s.b.
c) Relationship between consumer buying behaviour with the factor of
Social Media Advertisement i.e. “Social network site the advertisements
displayed appeal you” in different cities –
(i)
In Mumbai –
H0a : There is no association between the factor i.e. “Social network site
the advertisements displayed appeal you” with Consumer buying
behavior of young working women for consumer electronics in
Mumbai.
H1a : There is association between the factor i.e. “Social network site
the advertisements displayed appeal you” with Consumer buying
behaviour of young working women for consumer electronics in
Mumbai
Chi-Square Tests
Table 10.13.1.2.m.a.
Relationship between consumer buying
behaviour with the appealing factor of social media advertisement
towards advertisements displayed on SNS in Mumbai.
354
Asymp.
Value
Df
sided)
Pearson Chi-Square
39.429(a)
6
.000
Likelihood Ratio
34.623
6
.000
22.184
1
.000
Sig.
(2-
Linear-by-Linear
Association
N of Valid Cases
516
From the above table, it is observed that at 5 % level of significance p <
α (0.05), so the null hypothesis is rejected and alternative is accepted.
Therefore, we can conclude that there is association between the factor
i.e. “Social network site the advertisements displayed appeal you” with
Consumer buying behaviour of young working women for consumer
electronics in Mumbai. This means the factor i.e. “Social network site
the advertisements displayed appeal you” Consumer buying behaviour
of young working women in Mumbai for consumer electronics are
dependent of each other. Further to check how much association exists
between them we will use the Contingency Coefficient Statistics.
Symmetric Measures
Table 10.13.1.2.m.b. Table of Symmetric Measures to determine how
much relationship exists in between consumer buying behaviour and
the appealing factor of social media advertising towards advertisements
displayed on SNS in Mumbai.
355
Approx.
Nominal by Nominal
Value
Sig.
.766
.000
Contingency
Coefficient
N of Valid Cases
516
From the above table, it is observed that there is a very strong positive
opinion that advertisement displayed on social media appeals young working
women in Mumbai to a great extent for buying electronics products and it
affects consumer buying behaviour by 76.6 %.
(ii) In Nashik –
H0b : There is no association between the factor .i.e. “Social network site the
advertisements displayed appeal you” with Consumer buying behaviour of
young working women for consumer electronics in Nashik.
H1b : There is association between the factor i.e. “Social network site the
advertisements displayed appeal you” with Consumer buying behaviour of
young working women for consumer electronics in Nashik.
Chi-Square Tests
Table 10.13.1.2.n.a. Relationship between consumer buying behaviour
with the appealing factor of social media advertisement towards
advertisements displayed on SNS in Nashik.
356
Asymp. Sig. (2Value
Df
sided)
Pearson Chi-Square
45.171(a)
3
.099
Likelihood Ratio
52.591
3
.000
Linear-by-Linear Association
12.181
1
.000
N of Valid Cases
359
From the above table, it is observed that at 5 % level of significance p > α
(0.05), so the null hypothesis is accepted and alternative is rejected.
Therefore, we conclude that there is no association between the factor i.e.
“Social network site the advertisements displayed appeal you” with
Consumer buying behaviour of young working women for consumer
electronics in Nashik. This means the factor i.e. “Social network site the
advertisements displayed appeal you” with Consumer buying behaviour of
young working women in Nashik for consumer electronics are independent of
each other. So, we can conclude that advertisement displayed on social media
sites does not appeal young working women in Nashik while buying
electronics products, which will not affect consumer buying behaviour.
(ii) In Surat –
H0c : There is no association between the factor i.e. “Social network site the
advertisements displayed appeal you” with Consumer buying behaviour of
young working women for consumer electronics in Surat.
H1c : There is association between the factor i.e. “Social network site the
advertisements displayed appeal you” with Consumer buying behaviour of
357
young working women for consumer electronics in Surat
Chi-Square Tests
Table 10.13.1.2.s.a.
Relationship between consumer buying
behaviour with the appealing factor of social media advertisement
towards advertisements displayed on SNS in Surat.
Asymp.
Value
Df
sided)
Pearson Chi-Square
66.440(a)
3
.777
Likelihood Ratio
62.391
3
.066
22.836
1
.044
Sig.
(2-
Linear-by-Linear
Association
N of Valid Cases
397
From the above table, it is observed that, at 5 % level of significance p > α
(0.05), so the null hypothesis is accepted and alternative is rejected, so, we
conclude that there is no association between the factor i.e. “Social network
site the advertisements displayed appeal you” with Consumer buying
behaviour of young working women for consumer electronics in Surat. This
means the factor i.e. “Social network site the advertisements displayed appeal
you” with Consumer buying behaviour of young working women in Surat for
consumer electronics are independent of each other. So, we can conclude that
advertisement displayed on social media sites does not appeal young working
women in Surat while buying electronics products and it will not affect
consumer buying behaviour.
358
d) Relationship between consumer buying behaviour with the factor of
Social Media Advertisement i.e. “on social networking sites the visuals
and slogans of the advertisements displayed are memorable” in different
cities –
(i) In Mumbai –
H 0a : There is no association between the factor i.e. “on social
networking sites the visuals and slogans of the advertisements displayed
are memorable” with Consumer buying behaviour of young working
women for consumer electronics in Mumbai.
H1a : There is association between the factor i.e. “on social networking
sites the visuals and slogans of the advertisements displayed are
memorable” with Consumer buying behaviour of young working women
for consumer electronics in Mumbai
Chi-Square Tests
Table
10.13.1.3.m.a.
Relationship
between
consumer
buying
behaviour with the factor of memorable visuals and slogans of the
advertisements displayed on SNS in Mumbai.
Value
Df
Asymp. Sig. (2-sided)
Pearson Chi-Square
36.721(a)
8
.566
Likelihood Ratio
38.522
8
.044
359
Linear-by-Linear
14.979
1
.444
Association
N of Valid Cases
516
From the above table, it is observed that at 5 % level of significance p > α
(0.05), so the null hypothesis accepted and alternative is rejected,
therefore, we conclude that there is no association between the factor i.e.
“on Social networking sites the visuals and slogans of the advertisements
displayed are memorable” with Consumer buying behaviour of young
working women for consumer electronics in Mumbai. This means the
factor i.e. on social networking sites the visuals and slogans of the
advertisements displayed are memorable” with Consumer buying
behaviour of young working women in Surat for consumer electronics are
independent of each other. So, we can conclude that the visuals and
slogans of the advertisement displayed on social media sites are not
memorable for young working women in Mumbai while buying
electronics products and it will not affect consumer buying behaviour.
(ii) In Nashik –
H0b : There is no association between the factor i.e. “on social networking
sites the visuals and slogans of the advertisements displayed are
memorable” with Consumer buying behaviour of young working women
for consumer electronics in Nashik
H1b : There is association between the factor i.e. “on social networking
360
sites the visuals and slogans of the advertisements displayed are
memorable” with Consumer buying behaviour of young working women
for consumer electronics in Nashik
Chi-Square Tests
Table 10.13.1.3.n.a. Relationship between consumer buying behaviour
with the
factor of memorable visuals and slogans of the
advertisements displayed on SNS in Nashik.
Asymp. Sig. (2Value
Df
sided)
Pearson Chi-Square
46.595(a)
3
.768
Likelihood Ratio
49.746
3
.088
10.821
1
.041
Linear-by-Linear
Association
N of Valid Cases
359
From the above table, it is observed that at 5 % level of significance p > α
(0.05), so the null hypothesis is accepted and alternative is rejected, so, we
conclude that there is no association between the factor i.e. “on Social
networking sites the visuals and slogans of the advertisements displayed
are memorable” with Consumer buying behaviour of young working
women for consumer electronics in Nashik. This means the factor i.e. on
social networking sites the visuals and slogans of the advertisements
displayed are memorable” and Consumer buying behaviour of young
working women in Nashik for consumer electronics are independent of
each other. So, we can conclude that the visuals and slogans of the
361
advertisement displayed on social media sites are not memorable for
young working women in Nashik while buying electronics products and it
will not affect consumer buying behaviour.
(iii) In Surat –
H0c : There is no association between the factor i.e. “on social networking
sites the visuals and slogans of the advertisements displayed are
memorable” with Consumer buying behaviour of young working women
for consumer electronics in surat.
H1c : There is association between the factor i.e. “on social networking
sites the visuals and slogans of the advertisements displayed are
memorable” with Consumer buying behaviour of young working women
for consumer electronics in Surat
Chi-Square Tests
Table 10.13.1.3.s.a. Relationship between consumer buying behaviour
with the
factor of memorable visuals and slogans of the
advertisements displayed on SNS in Surat.
Asymp. Sig. (2Value
Df
sided)
Pearson Chi-Square
70.152(a)
3
.566
Likelihood Ratio
66.970
3
.066
Linear-by-Linear Association
18.026
1
.055
N of Valid Cases
397
From the above table, it is observed that at 5 % level of significance p > α
(0.05), so the null hypothesis is accepted and alternative is rejected, so, we
362
conclude that there is no association between the factor i.e. “on Social
networking sites the visuals and slogans of the advertisements displayed
are memorable” with Consumer buying behaviour of young working
women for consumer electronics in Surat. This means the factor i.e. on
social networking sites the visuals and slogans of the advertisements
displayed are memorable” with Consumer buying behaviour of young
working women in Surat for consumer electronics are independent of each
other. So, we can conclude that the visuals and slogans of the
advertisement displayed on social media sites are not memorable for
young working women in Surat while buying electronics products and it
will not affect consumer buying behaviour.
e) Relationship between consumer buying behaviour with the factor of
Social Media Advertisement i.e. “On which social network sites young
working women find the product advertisement displayed attractive” in
different cities(i) In Mumbai –
H 0a : There is no association between the factor i.e. “On which social
network sites young working women find the product advertisement
displayed attractive” with Consumer buying behaviour of young working
women for consumer electronics in Mumbai.
H1a : There is association between the factor i.e. “On which social
network sites young working women find the product advertisement
displayed attractive” with Consumer buying behaviour of young working
women for consumer electronics in Mumbai.
363
Chi-Square Tests
Table 10.13.1.4.m.a.
Relationship between consumer buying
behaviour with the attractive factor of the advertisements displayed
on SNS in Mumbai.
Asymp.
Value
Df
sided)
Pearson Chi-Square
39.452(a)
6
.811
Likelihood Ratio
36.467
6
.066
25.847
1
.455
Sig.
(2-
Linear-by-Linear
Association
N of Valid Cases
516
From the above table, it is observed that at 5 % level of significance p > α
(0.05), so the null hypothesis is accepted and alternative is rejected, so, we
conclude that there is no association between the factor i.e. “On which
social network sites young working women find the product advertisement
displayed attractive” with Consumer buying behaviour of young working
women for consumer electronics in Mumbai. This means the factor i.e.
“On which social network sites young working women find the product
advertisement displayed attractive” and Consumer buying behaviour of
young working women in Mumbai for consumer electronics are
independent of each other. So, we can conclude that in Mumbai young
working women feel that on social networking sites product advertisement
displayed is not attractive and it will not affect their consumer buying
364
behaviour.
(ii) In Nashik H0b : There is no association between the factor i.e. “On which social
network sites young working women find the product advertisement
displayed attractive” with Consumer buying behaviour of young working
women for consumer electronics in Nashik
H1b : There is association between the factor i.e. “On which social
network sites young working women find the product advertisement
displayed attractive” with Consumer buying behaviour of young working
women for consumer electronics in Nashik
Chi-Square Tests
Table
10.13.1.4.n.a.
Relationship
between
consumer
buying
behaviour with the attractive factor of the advertisements displayed
on SNS in Nashik.
Asymp.
Value
Df
sided)
Pearson Chi-Square
39.452(a)
6
.766
Likelihood Ratio
36.467
6
.055
25.847
1
.255
Sig.
(2-
Linear-by-Linear
Association
N of Valid Cases
516
From the above table, it is observed that at 5 % level of significance p > α
(0.05), so the null hypothesis accepted and alternative is rejected, so, we
365
conclude that there is no association between the factor i.e. “On which
social network sites young working women find the product
advertisement displayed attractive” with Consumer buying behaviour of
young working women for consumer electronics in Nashik. This means
the factor i.e. “On which social network sites young working women find
the product advertisement displayed attractive” and Consumer buying
behaviour of young working women in Nashik for consumer electronics
are independent of each other for. So, we can conclude that in Nashik
young working women feel that on social networking sites product
advertisement displayed is not attractive and it will not affect their
consumer buying behaviour.
(iii) In Surat H0c : There is no association between the factor i.e. “On which social
network sites young working women find the product advertisement
displayed attractive” with Consumer buying behaviour of young working
women for consumer electronics in surat
H1c : There is association between the factor i.e. “On which social
network sites young working women find the product advertisement
displayed attractive” with Consumer buying behaviour of young working
women for consumer electronics in Surat.
Chi-Square Tests
Table 10.13.1.4.s.a. Relationship between consumer buying behaviour with
the attractive factor of the advertisements displayed on SNS in Surat.
366
Value
Df
Asymp. Sig. (2-sided)
Pearson Chi-Square
56.820(a)
3
.000
Likelihood Ratio
53.552
3
.000
Linear-by-Linear Association
29.525
1
.000
N of Valid Cases
397
From the above table, it is observed that at 5 % level of significance p < α
(0.05), so the null hypothesis is rejected and alternative is accepted,
therefore, we conclude that there is association between the factor i.e.
“On which social network sites young working women find the product
advertisement displayed attractive” with Consumer buying behaviour of
young working women for consumer electronics in Surat. This means the
factor i.e. “On which social network sites young working women find the
product advertisement displayed attractive” with Consumer buying
behaviour of young working women in Surat for consumer electronics
are dependent of each other. Further to check how much association
exists we will use the Contingency Coefficient Statistics.
Symmetric Measures
Table 10.13.1.4.s.b. Table of Symmetric Measures to determine how
much relationship exists in between consumer buying behaviour and
the attractive factor of social media advertisements displayed on SNS
in Surat.
Nominal
Nominal
N of Valid Cases
Value
Approx. Sig.
.759
.000
by Contingency
Coefficient
359
367
From the above table, it is observed that young working women in Surat
are having very strong feeling that on social networking sites the product
advertised and displayed are very attractive which will affect consumer
buying behaviour by 75.9 %.
f) Relationship between consumer buying behaviour with the factor of
Social Media Advertisement i.e. “the young working women are having
trust on the advertisements displayed on social networking sites” in
different cities –
(i) In Mumbai –
H 0a : There is no association between the factor i.e. “the young working
women are having trust on the advertisements displayed on social
networking sites” with Consumer buying behaviour of young working
women for consumer electronics in Mumbai.
H1a : There is association between the factor i.e. “the young working
women are having trust on the advertisements displayed on social
networking sites” with Consumer buying behaviour of young working
women for consumer electronics in Mumbai
Chi-Square Tests
Table 10.13.1.5.m.a. Relationship between consumer buying behaviour
with the trustworthiness factor of the advertisements displayed on SNS
in Mumbai.
Asymp.
Value
Df
Sig.
(2-
sided)
368
Pearson Chi-Square
26.061(a)
6
.089
Likelihood Ratio
26.905
6
.055
Linear-by-Linear Association
20.660
1
.333
N of Valid Cases
516
From the above table, it is observed that at 5 % level of significance p > α
(0.05), so the null hypothesis is accepted and alternative is rejected,
therefore, we conclude that there is no association between the factor i.e.
“the young working women are having trust on the advertisements
displayed on social networking sites” with Consumer buying behaviour of
young working women for consumer electronics in Mumbai. This means
the factor i.e. “the young working women are having trust on the
advertisements displayed on social networking sites” with Consumer
buying behaviour of young working women in Mumbai for consumer
electronics are independent of each other. So, we can conclude that in
Mumbai young working women feel that the product advertisement
displayed on social networking sites are not trustworthy and it will not
affect their consumer buying behaviour.
(ii) In Nashik –
H0b : There is no association between the factor i.e. “the young working
women are having trust on the advertisements displayed on social
networking sites” with Consumer buying behaviour of young working
women for consumer electronics in Nashik
H1b : There is association between the factor i.e. “the young working
369
women are having trust on the advertisements displayed on social
networking sites” with Consumer buying behaviour of young working
women for consumer electronics in Nashik
Chi-Square Tests
Table 10.13.1.5.n.a.
Relationship between consumer buying behaviour
with the trustworthiness factor of the advertisements displayed on SNS in
Nashik.
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
27.177(a)
3
.444
Likelihood Ratio
34.252
3
.535
.046
1
.830
Linear-by-Linear
Association
N of Valid Cases
359
From the above table, it is observed that at 5 % level of significance p > α
(0.05), so the null hypothesis is accepted and alternative is rejected. Therefore
we conclude that there is no association between the factor i.e. “the young
working women are having trust on the advertisements displayed on social
networking sites” with Consumer buying behaviour of young working women
for consumer electronics in Nashik. This means the factor i.e. “the young
working women are having trust on the advertisements displayed on social
networking sites” with Consumer buying behaviour of young working women
in Nashik for consumer electronics are independent of each other. So, we can
conclude that in Nashik young working women feel that on social networking
370
sites the product advertisement displayed are not trustworthy and they will
not affect their consumer buying behaviour.
(i) In Surat –
H0c : There is no association between the factor i.e. “the young working
women are having trust on the advertisements displayed on social networking
sites” with Consumer buying behaviour of young working women for
consumer electronics in Surat.
H1c : There is association between the factor i.e. “the young working women
are having trust on the advertisements displayed on social networking sites”
with Consumer buying behaviour of young working women for consumer
electronics in Surat.
Chi-Square Tests
Table 10.13.1.5.s.a. Relationship between consumer buying behaviour
with the trustworthiness factor of the advertisements displayed on SNS
in Surat.
Value
Df
Asymp. Sig. (2-sided)
Pearson Chi-Square
68.279(a)
3
.000
Likelihood Ratio
63.629
3
.000
Linear-by-Linear Association
17.665
1
.000
N of Valid Cases
397
From the above table, it is observed that at 5 % level of significance p < α
(0.05), so the null hypothesis is rejected and alternative is accepted, therefore
371
we conclude that there is association between the factor i.e. “the young
working women are having trust on the advertisements displayed on social
networking sites” with Consumer buying behaviour of young working
women for consumer electronics in Surat. This means the factor i.e. “the
young working women are having trust on the advertisements displayed on
social networking sites” with Consumer buying behaviour of young working
women in Surat for consumer electronics are dependent of each other.
Further to check how much association exist between them, we will use the
Contingency Coefficient Statistics.
Symmetric Measures
Table 10.13.1.5.s.b. of Symmetric Measures to determine how much
relationship exists in between consumer buying behaviour and the
attractive factor of social media advertisements displayed on SNS in
Surat.
Approx.
Nominal by Nominal
Value
Sig.
.812
.000
Contingency
Coefficient
N of Valid Cases
397
From the above table, it is observed that young working women in Surat are
having very strong positive opinion that advertisement displayed on social
media are trustworthy for buying electronics products and they will affect
372
the consumer buying behaviour by 81.2 %.
(II)To study the online purchase behaviour with respect to Social Media
Advertising in different cities b) Relationship between online purchase behaviour with the factor of Social
Media Advertisement i.e. “On social media do you have positive
reactions/feelings towards advertisements displayed on it” in different cities
–
(iv) In Mumbai - Kindly refer the Data Analysis and Findings chapter,
Objective 2, Inferential analysis number II, a, i. Table no. 7.2.2.1.m.a. and
7.2.2.1.m.b.
(v) In Nashik - Kindly refer the Data Analysis and Findings chapter, Objective
2, Inferential analysis number II, a, ii. Table no. 7.2.2.1.n.a. and 7.2.2.1.n.b.
(vi) In Surat - Kindly refer the Data Analysis and Findings chapter, Objective 2,
Inferential analysis number II, a, iii. Table no. 7.2.2.1.s.a. and 7.2.2.1.s.b.
c) Relationship between online purchase behavior with the factor of Social
Media Advertisement i.e. “Social network site the advertisements displayed
appeal you” in different cities –
(i) In Mumbai -
H0a : There is no association between the factor i.e. “On social network sites
the advertisements displayed appeal you” with online purchase behavior of
young working women for consumer electronics in Mumbai.
373
H1a : There is association between the factor i.e. “On social network sites the
advertisements displayed appeal you” with online purchase behavior of young
working women for consumer electronics in Mumbai
Chi-Square Tests
Table 10.13.2.2.m.a. Relationship between online purchase behaviour
with the appealing factor of social media advertisements displayed on
SNS in Mumbai.
Asymp. Sig.
Value
Df
(2-sided)
Pearson Chi-Square
31.469(a)
27
.252
Likelihood Ratio
32.253
27
.223
5.285
1
.022
Linear-by-Linear
Association
374
N of Valid Cases
516
From the above table, it is observed that at 5 % level of significance p > α
(0.05), so the null hypothesis is accepted and alternative is rejected, so, we
conclude that there is no association between the factor i.e. “On social
network sites the advertisements displayed appeal you” with online purchase
behaviour of young working women for consumer electronics in Mumbai.
This means the factor i.e. “On social network sites the advertisements
displayed appeal you” with online purchase behaviour of young working
women in Mumbai for consumer electronics are independent of each other.
So, we can conclude that the social network sites the advertisements are not
appealing the young working women in Mumbai. So, it will not affect the
online purchase behaviour of young working women in Mumbai.
(ii) In Nashik -
H0b :There is no association between the factor i.e. “On social network sites the
advertisements displayed appeal you” with online purchase behavior of young
working women for consumer electronics in Nashik.
H1b :There is association between the factor i.e. “On social network sites the
advertisements displayed appeal you” with online purchase behaviour of young
working women for consumer electronics in Nashik
Chi-Square Tests
Table 10.13.2.2.n.a. Relationship between online purchase behaviour with
the appealing factor of social media advertisements displayed on SNS in
Nashik.
375
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
147.224(a) 24
.000
Likelihood Ratio
136.279
24
.000
Linear-by-Linear Association
13.059
1
.000
N of Valid Cases
359
From the above table, it is observed that at 5 % level of significance p < α
(0.05), so the null hypothesis is rejected and alternative is accepted, so, we
conclude that there is association between the factor i.e. “On social network
sites the advertisements displayed appeal you” with online purchase
behaviour of young working women for consumer electronics in Nashik.
This means the factor i.e. “On social network sites the advertisements
displayed appeal you” with online purchase behaviour of young working
women in Nashik for consumer electronics are dependent of each other.
Further to check how much association is existing between them we will use
the Contingency Coefficient Statistics.
Symmetric Measures
Table 10.13.2.2.n.b. Table of Symmetric Measures to determine how
much relationship exists in between online purchase behaviour and
appealing factor of social media advertisements displayed on SNS in
Nashik.
Approx.
Value
Sig.
376
Nominal by Nominal
Contingency
.639
.000
Coefficient
N of Valid Cases
359
From the above table, it is observed that they are having very strong positive
opinion that advertisement displayed on social media appeals young working
women in Nashik for buying electronics products, which will affect their
online purchase behaviour by 64.2 %.
(i) In Surat H0c :There is no association between the factor i.e. “On social network sites
the advertisements displayed appeal you” with online purchase behaviour of
young working women for consumer electronics in Surat.
H1c :There is association between the factor i.e. “On social network sites the
advertisements displayed appeal you” with online purchase behaviour of
young working women for consumer electronics in Surat.
Chi-Square Tests
Table 10.13.2.2.s.a.
Relationship between online purchase behaviour
with the appealing factor of social media advertisements displayed on
SNS in Surat.
Asymp. Sig. (2Value
Df
sided)
377
Pearson Chi-Square
167.224(a) 24
.000
Likelihood Ratio
112.279
24
.000
23.059
1
.000
Linear-by-Linear
Association
N of Valid Cases
397
From the above table, it is observed that at 5 % level of significance p < α
(0.05), so the null hypothesis is rejected and alternative is accepted, so, we
conclude that there is association between the factor i.e. “On social network
sites the advertisements displayed appeal you” with online purchase
behaviour of young working women for consumer electronics in Surat. This
means the factor i.e. “On social network sites the advertisements displayed
appeal you” with online purchase behaviour of young working women in
Surat for consumer electronics are dependent of each other. Further to check
how much association exists we will use the Contingency Coefficient
Statistics.
Symmetric Measures
Table 10.13.2.2.s.b. Table of Symmetric Measures to determine how
much relationship exists in between online purchase behaviour and
378
appealing factor of social media advertisements displayed on SNS in
Surat.
Approx.
Nominal by Nominal
Value
Sig.
.672
.000
Contingency
Coefficient
N of Valid Cases
397
From the above table, it is observed that they are having very strong positive
opinion that advertisement displayed on social media appeals young working
women in Surat for buying electronics products, which will affect online
purchase behaviour by 67.2 %.
d) Relationship between online purchase behaviour with the factor of Social
Media Advertisement i.e. “on social networking sites the visuals and slogans
of the advertisements displayed are memorable” in different cities –
(i) In Mumbai –
H0a :There is no association between the factor i.e. “on social networking sites
the visuals and slogans of the advertisements displayed are memorable” with
online purchase behaviour of young working women for consumer electronics
in Mumbai
H1a :There is association between the factor i.e. “on social networking sites
the visuals and slogans of the advertisements displayed are memorable” with
online purchase behaviour of young working women for consumer electronics
379
in Mumbai
Chi-Square Tests
Table 10.13.2.3.m.a. Relationship between online purchase behaviour
with the factor of social media advertising i.e memorable visuals and
slogans of the advertisements displayed on SNS in Mumbai.
Asymp.
Pearson
Value
Df
sided)
28.851(a)
36
.796
29.508
36
.769
2.927
1
.087
Sig.
(2-
Chi-
Square
Likelihood Ratio
Linear-by-Linear
Association
N of Valid Cases
516
From the above table, it is observed that at 5 % level of significance p > α
(0.05), so the null hypothesis is accepted and alternative is rejected, so, we
conclude that there is no association between the factor i.e. “on Social
networking sites the visuals and slogans of the advertisements displayed are
memorable” with online purchase behaviour of young working women for
consumer electronics in Mumbai. This means the factor i.e. on social
networking sites the visuals and slogans of the advertisements displayed are
memorable” with online purchase behaviour of young working women in
Surat for consumer electronics are independent of each other. So, we can
conclude that the visuals and slogans of the advertisement displayed on social
380
media sites are not memorable for young working women in Mumbai while
buying electronics products and it does not affect the online purchase
behaviour.
(ii) In Nashik –
H0b :There is no association between the factor i.e. “on social networking sites
the visuals and slogans of the advertisements displayed are memorable” with
online purchase behaviour of young working women for consumer electronics
in Nashik
H1b :There is association between the factor i.e. “on social networking sites
the visuals and slogans of the advertisements displayed are memorable” with
online purchase behaviour of young working women for consumer electronics
in Nashik
Chi-Square Tests
Table 10.13.2.3.n.a. Relationship between online purchase behaviour with
the factor of social media advertising i.e memorable visuals and slogans of
the advertisements displayed on SNS in Nashik.
Asymp.
Value
Df
sided)
Pearson Chi-Square
163.837(a)
24
.000
Likelihood Ratio
143.705
24
.000
4.735
1
.030
Sig.
(2-
Linear-by-Linear
Association
N of Valid Cases
359
381
From the above table, it is observed that at 5 % level of significance p < α
(0.05), so the null hypothesis is rejected and alternative is accepted, so, we
conclude that there is association between the factor i.e. “on Social
networking sites the visuals and slogans of the advertisements displayed are
memorable” with online purchase behaviour of young working women for
consumer electronics in Nashik. This means the factor i.e. “on social
networking sites the visuals and slogans of the advertisements displayed are
memorable” with online purchase behaviour of young working women in
Nashik for consumer electronics are dependent of each other. Further to
check how much association is existing the Contingency Coefficient
Statistics is used.
Symmetric Measures
Table 10.13.2.3.n.b. Table of Symmetric Measures to determine how
much relationship exists in between online purchase behaviour with the
factor of social media advertising i.e memorable visuals and slogans of
the advertisements displayed on SNS in Nashik.
Approx.
Value
Sig.
.607
.000
Nominal by Nominal Contingency
Coefficient
N of Valid Cases
359
From the above table, it is observed that they are having very strong positive
382
opinion that on
social networking sites the visuals and slogans of the
advertisements displayed are memorable in Nashik for buying electronics
products and it affected the online purchase behaviour by 60.7 %.
(iii) In Surat –
H0c :There is no association between the factor i.e. “on social networking sites
the visuals and slogans of the advertisements displayed are memorable” with
online purchase behaviour of young working women for consumer electronics
in Surat
H1c :There is association between the factor i.e. “on social networking sites
the visuals and slogans of the advertisements displayed are memorable” with
online purchase behaviour of young working women for consumer electronics
in Surat
Chi-Square Tests
Table 10.13.2.3.s.a.
Relationship between online purchase behaviour
with the factor of social media advertising i.e memorable visuals and
slogans of the advertisements displayed on SNS in Surat.
Asymp.
Value
Df
sided)
Pearson Chi-Square
163.837(a)
24
.000
Likelihood Ratio
143.705
24
.000
4.735
1
.030
Sig.
(2-
Linear-by-Linear
Association
N of Valid Cases
359
383
From the above table, it is observed that at 5 % level of significance p < α
(0.05), so the null hypothesis is rejected and alternative is accepted, so, we
conclude that there is association between the factor i.e. “Social networking
sites the visuals and slogans of the advertisements displayed are memorable”
with online purchase behaviour of young working women for consumer
electronics in Surat. This means the factor i.e. “on social networking sites the
visuals and slogans of the advertisements displayed are memorable” with
online purchase behaviour of young working women in Surat for consumer
electronics are dependent of each other. Further to check how much
association exists the Contingency Coefficient Statistics is used.
Symmetric Measures
Table 10.13.2.3.s.b. Table of Symmetric Measures to determine how
much relationship exists between online purchase behaviour with the
factor of social media advertising i.e memorable visuals and slogans of
the advertisements displayed on SNS in Surat.
Approx.
Value
Sig.
.649
.000
Nominal by Nominal Contingency
Coefficient
N of Valid Cases
397
From the above table, it is observed that they are having very strong positive
384
opinion that on social networking sites the visuals and slogans of the
advertisements displayed are memorable in Surat for buying electronics
products and it affected the online purchase behaviour by 64.9 %.
e) Relationship between online purchase behaviour with the factor of Social
Media Advertisement i.e. “On which social network sites young working
women find the product advertisement displayed attractive” in different cities
–
(i) In Mumbai –
H 0a : There is no association between the factor i.e. “On which social network
sites young working women find the product advertisement displayed
attractive” with online purchase behaviour of young working women for
consumer electronics in Mumbai.
H1a : There is association between the factor i.e. “On which social network
sites young working women find the product advertisement displayed
attractive” with online purchase behaviour of young working women for
consumer electronics in Mumbai
Chi-Square Tests
Table 10.13.2.4.m.a. Relationship between online purchase behaviour
with the attractive factor of social media advertising in Mumbai.
Asymp. Sig. (2Value
df
sided)
Pearson Chi-Square
25.900(a)
27
.524
Likelihood Ratio
26.993
27
.464
385
Linear-by-Linear
.016
1
.901
Association
N of Valid Cases
516
From the above table, it is observed that at 5 % level of significance p > α
(0.05), so the null hypothesis is accepted and alternative is rejected, so, we
conclude that there is no association between the factor i.e. “On which social
network sites young working women find the product advertisement
displayed attractive” with online purchase behaviour of young working
women for consumer electronics in Mumbai. This means the factor i.e. “On
which social network sites young working women find the product
advertisement displayed attractive” with online purchase behaviour of young
working women in Mumbai for consumer electronics are independent of each
other. So, we can conclude that in Mumbai young working women feel that
on social networking sites product advertisement displayed is not attractive
and it does not affect their online purchase behaviour.
(ii) In Nashik –
H0b :There is no association between the factor i.e. “On which social network
sites young working women find the product advertisement displayed
attractive” with online purchase behaviour of young working women for
consumer electronics in Nashik.
H1b :There is association between the factor i.e. “On which social network
sites young working women find the product advertisement displayed
386
attractive” with online purchase behaviour of young working women for
consumer electronics in Nashik
Chi-Square Tests
Table 10.13.2.4.n.a. Relationship between online purchase behaviour
with the attractive factor of social media advertising in Nashik.
Asymp. Sig. (2Value
Df
sided)
Pearson Chi-Square 163.152(a) 24
.000
Likelihood Ratio
146.183
24
.000
2.473
1
.116
Linear-by-Linear
Association
N of Valid Cases
359
From the above table, it is observed that at 5 % level of significance p < α
(0.05), so the null hypothesis is rejected and alternative is accepted, so, we
conclude that there is association between the factor i.e. “On which social
network sites young working women find the product advertisement
displayed attractive” with online purchase behaviour of young working
women for consumer electronics in Nashik. This means the factor i.e. “On
which social network sites young working women find the product
advertisement displayed attractive” with online purchase behaviour of young
working women in Nashik for consumer electronics are dependent of each
other. Further to check how much association is existing between them the
Contingency Coefficient Statistics is used.
387
Symmetric Measures
Table 10.13.2.4.n.b. Table of Symmetric Measures to determine the
relationship between online purchase behaviour with the attractive factor
of social media advertising in Nashik.
Approx.
Value
Sig.
.601
.000
Nominal by Nominal Contingency
Coefficient
N of Valid Cases
359
From the above table, it is observed that young working women’s are having
very strong positive opinion that advertisement displayed on social
networking sites are very attractive, and it affected online purchase behaviour
by 60.1 %.
(i) In Surat –
H0c :There is no association between the factor i.e. “On which social network
sites young working women find the product advertisement displayed
attractive” with online purchase behaviour of young working women for
consumer electronics in Surat.
H1c :There is association between the factor i.e. “On which social network
sites young working women find the product advertisement displayed
attractive” with online purchase behaviour of young working women for
388
consumer electronics in Surat.
Chi-Square Tests
Table 10.13.2.4.s.a. Relationship between online purchase behaviour
with the attractive factor of social media advertising in Surat.
Asymp. Sig. (2Value
Df
sided)
Pearson Chi-Square
134.224(a) 24
.000
Likelihood Ratio
111.279
24
.000
13.059
1
.000
Linear-by-Linear
Association
N of Valid Cases
397
From the above table, it is observed that at 5 % level of significance p < α
(0.05), so the null hypothesis is rejected and alternative is accepted, so, we
conclude that there is association between the factor i.e. “On which social
network sites young working women find the product advertisement
displayed attractive” with online purchase behaviour of young working
women for consumer electronics in Surat. This means the factor i.e. “On
which social network sites young working women find the product
advertisement displayed attractive” with online purchase behaviour of young
working women in Surat for consumer electronics are dependent of each
other. Further to check how much association exists the Contingency
Coefficient Statistics is used.
Symmetric Measures
389
Table 10.13.2.4.s.b. Table of Symmetric Measures to determine the
relationship between online purchase behaviour with the attractive
factor of social media advertising in Surat.
Approx.
Value
Sig.
.612
.000
Nominal by Nominal Contingency
Coefficient
N of Valid Cases
397
From the above table, it is observed that young working women from Surat
are having very strong positive opinion that advertisement displayed on social
networking sites are very attractive and it affected online purchase behaviour
by 61.2 %.
f) Relationship between online purchase behaviour with the factor of Social
Media Advertisement i.e. “the young working women are having trust on the
advertisements displayed on social networking sites” in different cities –
(i) In Mumbai –
H 0a : There is no association between the factor i.e. “the young working
women are having trust on the advertisements displayed on social networking
sites” with online purchase behaviour of young working women for consumer
electronics in Mumbai.
H1a : There is association between the factor i.e. “the young working women
390
are having trust on the advertisements displayed on social networking sites”
with online purchase behaviour of young working women for consumer
electronics in Mumbai.
Chi-Square Tests
Table 10.13.2.5.m.a. Relationship between online purchase behaviour
with the trust factor of social media advertising in Mumbai.
Asymp.
Pearson
Value
df
sided)
34.725(a)
27
.146
37.779
27
.081
1.182
1
.277
Sig.
(2-
Chi-
Square
Likelihood Ratio
Linear-by-Linear
Association
N of Valid Cases
516
From the above table, it is observed at 5 % level of significance p > α (0.05),
so the null hypothesis accepted and alternative is rejected, so, we conclude
that there is no association between the factor i.e. “the young working women
are having trust on the advertisements displayed on social networking sites”
with online purchase behaviour of young working women for consumer
electronics in Mumbai. This means the factor i.e. “the young working women
are having trust on the advertisements displayed on social networking sites”
with online purchase behaviour of young working women in Mumbai for
consumer electronics because they are independent of each other for. So, we
391
can conclude that in Mumbai young working women feel that on social
networking sites are not trustworthy for the product advertisement display,
which will not affect their online purchase behaviour.
(ii) In Nashik –
H0b :There is no association between the factor i.e. “the young working
women are having trust on the advertisements displayed on social networking
sites” with online purchase behaviour of young working women for consumer
electronics in Nashik.
H1b :There is association between the factor i.e. “the young working women
are having trust on the advertisements displayed on social networking sites”
with online purchase behaviour of young working women for consumer
electronics in Nashik.
Chi-Square Tests
Table 10.13.2.5.n.a. Relationship between online purchase behaviour
with the trust factor of social media advertising in Nashik.
392
Pearson
Value
Df
Asymp. Sig. (2-sided)
94.289(a)
24
.117
96.810
24
.098
5.699
1
.017
Chi-
Square
Likelihood
Ratio
Linear-byLinear
Association
N
of
Valid
397
Cases
From the above table, it is observed at 5 % level of significance p > α (0.05),
so the null hypothesis accepted and alternative is rejected, so, we conclude
that there is no association between the factor i.e. “the young working women
are having trust on the advertisements displayed on social networking sites”
with online purchase behaviour of young working women for consumer
electronics in Nashik. This means the factor i.e. “the young working women
are having trust on the advertisements displayed on social networking sites”
with online purchase behaviour of young working women in Nashik for
consumer electronics because they are independent of each other for. So, we
can conclude that in Nashik young working women feel that on social
networking sites are not trustworthy for the product advertisement display,
which will not affect their online purchase behaviour.
(iii) In Surat –
393
H0c :There is no association between the factor i.e. “the young working
women are having trust on the advertisements displayed on social networking
sites” with online purchase behaviour of young working women for consumer
electronics in Surat
H1c :There is association between the factor i.e. “the young working women
are having trust on the advertisements displayed on social networking sites”
with online purchase behaviour of young working women for consumer
electronics in Surat.
Chi-Square Tests
Table 10.13.2.5.s.a. Relationship between online purchase behaviour with
the trust factor of social media advertising in Surat.
Asymp. Sig. (2Value
Pearson
Df
sided)
Chi169.688(a) 24
.665
180.242
24
.000
.498
1
.481
Square
Likelihood
Ratio
Linear-byLinear
Association
N
of
Valid
359
Cases
394
From the above table, it is observed at 5 % level of significance p > α (0.05),
so the null hypothesis accepted and alternative is rejected, so, we conclude
that there is no association between the factor i.e. “the young working women
are having trust on the advertisements displayed on social networking sites”
with online purchase behaviour of young working women for consumer
electronics in Surat. This means the factor i.e. “the young working women are
having trust on the advertisements displayed on social networking sites” with
online purchase behaviour of young working women in Surat for consumer
electronics because they are independent of each other for. So, we can
conclude that in Surat young working women feel that on social networking
sites are not trustworthy for the product advertisement display, which will not
affect their online purchase behaviour.
(IV) Relationship between complex buying behaviour with the factor of Social
Media Advertisement i.e. “On social media do you have positive
reactions/feelings towards advertisements displayed on it” in different cities
–
(i) In Mumbai - Kindly refer the Data Analysis and Findings chapter, Objective
2, Inferential analysis number III, a, i. Table no. 7.2.3.1.m.a. and 7.2.3.1.m.b.
(ii) In Nashik - Kindly refer the Data Analysis and Findings chapter, Objective 2,
Inferential analysis number III, a, ii. Table no. 7.2.3.1.n.a. and 7.2.3.1.n.b.
(iii) In Surat - Kindly refer the Data Analysis and Findings chapter, Objective 2,
Inferential analysis number III, a, iii. Table no. 7.2.3.1.s.a. and 7.2.3.1.s.b.
b) Relationship between complex buying behaviour with the factor of Social
Media Advertisement i.e. “Social network site the advertisements displayed
395
appeal you” in different cities –
(i) In Mumbai –
H 0a : There is no association between the factor i.e. “Social network site the
advertisements displayed appeal you” with Complex buying behaviour of
young working women for consumer electronics in Mumbai.
H1a : There is association between the factor i.e. “Social network site the
advertisements displayed appeal you” with Complex buying behaviour of
young working women for consumer electronics in Mumbai
Chi-Square Tests
Table 10.13.3.2.m.a. Relationship between complex buying behaviour
with the appealing factor of social media advertising in Mumbai.
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square 73.963(a)
36
.213
Likelihood Ratio
71.617
36
.112
33.921
1
.023
Linear-by-Linear
Association
N of Valid Cases
516
From the above table, it is observed that at 5 % level of significance p > α
(0.05), so the null hypothesis is accepted and alternative is rejected, so, we
conclude that there is no association between the factor i.e. “Social network
site the advertisements displayed appeal you” with Complex buying
behaviour of young working women for consumer electronics in Mumbai.
396
This means the factor i.e. “Social network site the advertisements displayed
appeal you” with Complex buying behaviour of young working women in
Mumbai for consumer electronics are independent of each other. So, we can
conclude that the advertisements displayed on social media sites does not
appeal young working women in Mumbai while buying electronics products
which will not affect Complex buying behaviour.
(ii)In Nashik –
H0b : There is no association between the factor i.e. “Social network site the
advertisements displayed appeal you” with Complex buying behaviour of
young working women for consumer electronics in Nashik
H1b : There is association between the factor i.e. “Social network site the
advertisements displayed appeal you” with Complex buying behaviour of
young working women for consumer electronics in Nashik
Chi-Square Tests
Table 10.13.3.2.n.a. Relationship between complex buying behaviour
with the appealing factor of social media advertising in Nashik.
Value
Df
Asymp. Sig. (2-sided)
Pearson Chi-Square
246.727(a)
36
.000
Likelihood Ratio
205.114
36
.000
16.108
1
.000
Linear-by-Linear
Association
N of Valid Cases
359
From the above table, it is observed that at 5 % level of significance p < α
397
(0.05), so the null hypothesis is rejected and alternative is accepted, so, we
conclude that there is association between the factor i.e. “Social network site
the advertisements displayed appeal you” with Complex buying behaviour of
young working women for consumer electronics in Nashik. This means the
factor i.e. “Social network site the advertisements displayed appeal you”
with Complex buying behaviour of young working women in Nashik for
consumer electronics are independent of each other. Further to check how
much association is existing between them, we will use the Phi & Cramer’s
V Statistics.
Symmetric Measures
Table 10.13.3.2.n.b. Table of Symmetric Measures to determine
how
much relationship exist between complex buying behaviour and the
appealing factor of social media advertising in Nashik.
Nominal
Value
Approx. Sig.
.829
.000
by Phi &
Nominal
Cramer's V
N of Valid Cases
359
From the above table, it is observed that young working women in Nashik are
having a very strong positive feeling that the advertisements displayed on
social media are appealing for buying electronics products, which will affect
Complex buying behaviour 82.9 %.
(ii) In Surat –
398
H0c : There is no association between the factor i.e. “Social network site the
advertisements displayed appeal you” with Consumer buying behaviour of
young working women for Complex electronics in Surat.
H1c : There is association between the factor i.e. “Social network site the
advertisements displayed appeal you” with Consumer buying behaviour of
young working women for Complex electronics in Surat
Chi-Square Tests
Table 10.13.3.2.s.a. Relationship between complex buying behaviour with
the appealing factor of social media advertising in Surat.
Pearson
Value
Df
Asymp. Sig. (2-sided)
132.529(a)
36
.000
130.254
36
.000
29.244
1
.000
Chi-
Square
Likelihood Ratio
Linear-by-Linear
Association
N of Valid Cases
397
From the above table, it is observed that at 5 % level of significance p < α
(0.05), so the null hypothesis is rejected and alternative is accepted, so, we
conclude that there is association between the factor i.e. “Social network site
the advertisements displayed appeal you” with Complex buying behaviour of
young working women for consumer electronics in Surat. This means the
factor i.e. “Social network site the advertisements displayed appeal you” with
399
Complex buying behaviour of young working women in Surat for consumer
electronics are independent of each other.
Further to check how much association exists, the Phi & Cramer’s V
Statistics is used.
Symmetric Measures
Table 10.13.3.2.s.b. Table of Symmetric Measures to determine how much
relationship exists between complex buying behaviour and the appealing
factor of social media advertising in Surat.
Nominal
Value
Approx. Sig.
.778
.000
by Phi &
Nominal
N of Valid Cases
Cramer's V
397
From the above table, it is observed that the advertisements displayed on
social network sites appeal young working women in Surat for buying
electronics products, which will affect Complex buying behaviour by 77.8 %.
c) Relationship between Complex buying behaviour with the factor of Social
Media Advertisement i.e. “social networking sites the visuals and slogans of
the advertisements displayed are memorable” in different cities –
i) In Mumbai –
H 0a : There is no association between the factor i.e. “social networking sites
the visuals and slogans of the advertisements displayed are memorable” with
Complex buying behaviour of young working women for consumer
400
electronics in Mumbai
H1a : There is association between the factor i.e. “social networking sites the
visuals and slogans of the advertisements displayed are memorable” with
Complex buying behaviour of young working women for consumer
electronics in Mumbai
Chi-Square Tests
Table 10.13.3.3.m.a. Relationship between complex buying behaviour
with the memorable visuals and slogans factor of social media advertising
in Mumbai.
Asymp.
Value
Df
sided)
Pearson Chi-Square
95.886(a)
48
.000
Likelihood Ratio
93.643
48
.000
31.763
1
.000
Sig.
(2-
Linear-by-Linear
Association
N of Valid Cases
516
From the above table, it is observed that at 5 % level of significance p < α
(0.05), so the null hypothesis is rejected and alternative is accepted, so, we
conclude that there is association between the factor i.e. “Social networking
sites the visuals and slogans of the advertisements displayed are memorable”
with Complex buying behaviour of young working women for consumer
electronics in Mumbai. This means the factor i.e. “social networking sites the
visuals and slogans of the advertisements displayed are memorable” with
Complex buying behaviour of young working women in Surat for consumer
401
electronics are dependent of each other. Further to check how much
association is existing, the Phi & Cramer’s V Statistics is used.
Symmetric Measures
Table 10.13.3.3.m.b. Table of symmetric measures to determine how
much relationship exists between complex buying behaviour and the
memorable visuals and slogans factor of social media advertising in
Mumbai.
Nominal
Value
Approx. Sig.
.731
.000
by Phi &
Nominal
Cramer's V
N of Valid Cases
516
From the above table, it is observed that the visuals and slogans of the
advertisements displayed are memorable according to the opinion of the
young working women in Mumbai, which will affect Complex buying
behaviour by 73.1 %.
(iii) In Nashik –
H0b : There is no association between the factor i.e. “social networking sites
the visuals and slogans of the advertisements displayed are memorable” with
Complex buying behaviour of young working women for consumer
electronics in Nashik
H1b : There is association between the factor i.e. “social networking sites the
visuals and slogans of the advertisements displayed are memorable” with
Complex buying behaviour of young working women for consumer
402
electronics in Nashik
Chi-Square Tests
Table 10.13.3.3.n.a. Relationship between complex buying behaviour
with the memorable visuals and slogans factor of social media advertising
in Nashik.
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
46.595(a)
3
.097
Likelihood Ratio
49.746
3
.081
Linear-by-Linear Association
10.821
1
.034
N of Valid Cases
359
From the above table, it is observed that at 5 % level of significance p > α
(0.05), so the null hypothesis is accepted and alternative is rejected, so, we
conclude that there is no association between the factor i.e. “Social
networking sites the visuals and slogans of the advertisements displayed are
memorable” with Complex buying behaviour of young working women for
consumer electronics in Nashik. This means the factor i.e. social networking
sites the visuals and slogans of the advertisements displayed are memorable”
with Complex buying behaviour of young working women in Nashik for
consumer electronics are independent of each other. So, we can conclude that
the visuals and slogans of the advertisement displayed on social media sites
are not memorable for young working women in Nashik while buying
electronics products which will not affect Complex buying behaviour.
(iv) In Surat –
403
H0c : There is no association between the factor i.e. “social networking sites
the visuals and slogans of the advertisements displayed are memorable” with
Complex buying behaviour of young working women for consumer
electronics in surat
H1c : There is association between the factor i.e. “social networking sites the
visuals and slogans of the advertisements displayed are memorable” with
Complex buying behaviour of young working women for consumer
electronics in Surat
Chi-Square Tests
Table 10.13.3.3.s.a. Relationship between complex buying behaviour with
the memorable visuals and slogans factor of social media advertising in
Surat.
Asymp. Sig. (2Value
Df
sided)
Pearson Chi-Square
70.152(a)
3
.098
Likelihood Ratio
66.970
3
.055
Linear-by-Linear Association
18.026
1
.044
N of Valid Cases
397
From the above table, it is observed that at 5 % level of significance p > α
(0.05), so the null hypothesis is accepted and alternative is rejected, so, we
conclude that there is no association between the factor i.e. “Social
networking sites the visuals and slogans of the advertisements displayed are
404
memorable” with Complex buying behaviour of young working women for
consumer electronics in Surat. This means the factor i.e. social networking
sites the visuals and slogans of the advertisements displayed are memorable”
with Complex buying behaviour of young working women in Surat for
consumer electronics are independent of each other. So, we can conclude that
the visuals and slogans of the advertisement displayed on social media sites
are not memorable for young working women in Surat while buying
electronics products which will not affect Complex buying behaviour.
d) Relationship between Complex buying behaviour with the factor of Social
Media Advertisement i.e. “On which social network sites young working
women find the product advertisement displayed attractive” in different cities
–
(ii) In Mumbai –
H 0a : There is no association between the factor i.e. “On which social network
sites young working women find the product advertisement displayed
attractive” with Complex buying behaviour of young working women for
consumer electronics in Mumbai.
H1a : There is association between the factor i.e. “On which social network
sites young working women find the product advertisement displayed
attractive” with Complex buying behaviour of young working women for
consumer electronics in Mumbai
Chi-Square Tests
Table 10.13.3.4.m.a. Relationship between complex buying behaviour
405
with the attractive factor of social media advertising in Mumbai.
Asymp.
Value
df
sided)
Pearson Chi-Square
19.452(a)
6
.788
Likelihood Ratio
26.467
6
.121
35.847
1
.344
Sig.
(2-
Linear-by-Linear
Association
N of Valid Cases
516
From the above table, it is observed that at 5 % level of significance p > α
(0.05), so the null hypothesis is accepted and alternative is rejected, so, we
conclude that there is no association between the factor i.e. “On which social
network sites young working women find the product advertisement
displayed attractive” with Complex buying behaviour of young working
women for consumer electronics in Mumbai. This means the factor i.e. “On
which social network sites young working women find the product
advertisement displayed attractive” with Complex buying behaviour of young
working women in Mumbai for consumer electronics are independent of each
other. So, we can conclude that in Mumbai young working women feel that
on social networking sites product advertisement displayed is not attractive
which will not affect their Complex buying behaviour.
(iii) In Nashik H0b : There is no association between the factor i.e. “On which social network
sites young working women find the product advertisement displayed
406
attractive” with Complex buying behaviour of young working women for
consumer electronics in Nashik
H1b : There is association between the factor i.e. “On which social network
sites young working women find the product advertisement displayed
attractive” with Complex buying behaviour of young working women for
consumer electronics in Nashik
Chi-Square Tests
Table 10.13.3.4.n.a. Relationship between complex buying behaviour
with the attractive factor of social media advertising in Nashik.
Asymp.
Value
Df
sided)
Pearson Chi-Square
45.112(a)
6
.122
Likelihood Ratio
56.444
6
.233
32.22
1
.222
Sig.
(2-
Linear-by-Linear
Association
N of Valid Cases
516
From the above table, it is observed that at 5 % level of significance p > α
(0.05), so the null hypothesis is accepted and alternative is rejected, so, we
conclude that there is no association between the factor i.e. “On which social
network sites young working women find the product advertisement
displayed attractive” with Complex buying behaviour of young working
women for consumer electronics in Nashik. This means the factor i.e. “On
which social network sites young working women find the product
407
advertisement displayed attractive” with Complex buying behaviour of young
working women in Nashik for consumer electronics are independent of each
other. So, we can conclude that in Nashik young working women feel that on
social networking sites product advertisement displayed is not attractive
which will not affect their Complex buying behaviour.
(iv) In Surat H0c : There is no association between the factor i.e. “On which social network
sites young working women find the product advertisement displayed
attractive” with Complex buying behaviour of young working women for
consumer electronics in surat
H1c : There is association between the factor i.e. “On which social network
sites young working women find the product advertisement displayed
attractive” with Complex buying behaviour of young working women for
consumer electronics in Surat
Chi-Square Tests
Table 10.13.3.4.s.a. Relationship between complex buying behaviour with
the attractive factor of social media advertising in Surat.
Asymp. Sig. (2Value
Df
sided)
Pearson Chi-Square
23.78(a)
3
.000
Likelihood Ratio
33.552
3
.000
Linear-by-Linear Association 19.525
1
.000
N of Valid Cases
397
408
From the above table, it is observed that at 5% level of significance p < α
(0.05), so the null hypothesis is rejected and alternative is accepted, so, we
conclude that there is association between the factor i.e. “On which social
network sites young working women find the product advertisement
displayed attractive” with Complex buying behaviour of young working
women for consumer electronics in Surat. This means the factor i.e. “On
which social network sites young working women find the product
advertisement displayed attractive” with Complex buying behaviour of
young working women in Surat for consumer electronics are dependent of
each other. Further to check how much association is existing the
Contingency Coefficient Statistics is used.
Symmetric Measures
Table 10.13.3.4.s.b. Table of symmetric measures to determine how much
relationship exists between complex buying behaviour and the attractive
factor of social media advertising in Surat.
Nominal
Nominal
N of Valid Cases
Value
Approx. Sig.
.759
.000
by Contingency
Coefficient
359
From the above table, it is observed that young working women in Surat are
having very strong feeling that social network sites the product advertised and
displayed are very attractive which will affect Complex buying behaviour by
75.9 %.
409
g) Relationship between complex buying behaviour with the factor of Social
Media Advertisement i.e. “On which social networking sites young women trust
the advertisement displayed” in different cities –
(vii) In Mumbai -
H 0a : There is no association between the factor i.e. “On which social
networking sites young women trust the advertisement displayed” with Complex
buying behaviour of young working women for consumer electronics in
Mumbai.
H1a : There is association between the factor i.e. “On which social networking
sites young women trust the advertisement displayed” with Complex buying
behaviour of young working women for consumer electronics in Mumbai
Chi-Square Tests
Table 10.13.3.5.m.a. Relationship between complex buying behaviour
with the trust factor of social media advertising in Mumbai.
Asymp.
Value
Df
sided)
Pearson Chi-Square
42.121(a)
8
.000
Likelihood Ratio
56.77
8
.000
18.778
1
.000
Sig.
(2-
Linear-by-Linear
Association
N of Valid Cases
516
From the above table, it is observed at 5 % level of significance p < α (0.05),
so the null hypothesis rejected and alternative is accepted, so, we conclude
410
that there is association between the factor i.e. “On which social networking
sites young women trust the advertisement displayed” with Complex buying
behaviour of young working women for consumer electronics in Mumbai.
This means the factor i.e. “On which social networking sites young women trust
the advertisement displayed” with Complex buying behaviour of young
working women in Mumbai for consumer electronics because they are dependent of each other for. Further to check how much association is exist
we will use the Contingency Coefficient Statistics.
Symmetric Measures
Table 10.13.3.5.m.b. Table of symmetric measures to determine how
much relationship exists between complex buying behaviour and the
trust factor of social media advertising in Mumbai.
Approx.
Value
Sig.
.860
.000
Nominal by Nominal Contingency
Coefficient
N of Valid Cases
516
From the above table, it is observed that they are having very strong trust
towards advertisements displayed on social media for buying electronics products,
which will affect complex buying behaviour 86.0 %.
(viii)
In Nashik -
411
H0b : There is no association between the factor i.e. “On which social
networking sites young women trust the advertisement displayed” with Complex
buying behaviour of young working women for consumer electronics in
Nashik.
H1b : There is association between the factor i.e. “On which social networking
sites young women trust the advertisement displayed” with Complex buying
behaviour of young working women for consumer electronics in Nashik
Chi-Square Tests
Table 10.13.3.5.n.a. Relationship between complex buying behaviour
with the trust factor of social media advertising in Nashik.
Value
Df
Asymp. Sig. (2-sided)
Pearson Chi-Square
34.666(a)
3
.000
Likelihood Ratio
56.77
3
.000
20.122
1
.000
Linear-by-Linear
Association
N of Valid Cases
359
From the above table, it is observed at 5 % level of significance p < α (0.05),
so the null hypothesis rejected and alternative is accepted, so, we conclude
that there is association between the factor i.e. “On which social networking
sites young women trust the advertisement displayed” with Complex buying
behaviour of young working women for consumer electronics in Nashik. This
means the factor i.e. “On which social networking sites young women trust the
advertisement displayed” with Complex buying behaviour of young working
women in Nashik for consumer electronics because they are dependent of
412
each other for. Further to check how much association is exist we will use the
Contingency Coefficient Statistics.
Symmetric Measures
Table 10.13.3.5.n.b. Table of symmetric measures to determine how much
relationship between complex buying behaviour and the trust factor of
social media advertising in Nashik.
Nominal
Nominal
N of Valid Cases
Value
Approx. Sig.
.742
.000
by Contingency
Coefficient
359
From the above table, it is observed that there are having very strong trust
towards advertisements displayed on social media for buying electronics products,
which will affect complex buying behaviour 74.2 % in Nashik City.
(ix) In Surat -
H0c : There is no association between the factor i.e. “On which social
networking sites young women trust the advertisement displayed” with Complex
buying behaviour of young working women for cconsumer electronics in
Surat.
H1c : There is association between the factor i.e. “On which social networking
sites young women trust the advertisement displayed” with Complex buying
behaviour of young working women for consumer electronics in Surat
413
Chi-Square Tests
Table 10.13.3.5.s.a. Relationship between complex buying behaviour with
the trust factor of social media advertising in Surat.
Value
Df
Asymp. Sig. (2-sided)
Pearson Chi-Square
34.333(a)
3
.045
Likelihood Ratio
35.777
3
.000
12.344
1
.005
Linear-by-Linear
Association
N of Valid Cases
397
From the above table, it is observed at 5 % level of significance p > α (0.05),
so the null hypothesis accepted and alternative is rejected, so, we conclude
that there is no association between the factor i.e. “On which social networking
sites young women trust the advertisement displayed” with Complex buying
behaviour of young working women for consumer electronics in Surat. This
means the factor i.e. “On which social networking sites young women trust the
advertisement displayed” with Complex buying behaviour of young working
women in Surat for consumer electronics because they are independent of
each other for. So we can say that they do not have trust towards advertisements
displayed on social media with buying behaviour of young working women in
Surat for consumer electronics which will not affect complex buying
behaviour.
414
V) Relationship between all the factors of Habitual Buying Behaviour with all
the factor of Social Media Advertisement in different cities –
(iv) In Mumbai – Kindly refer the Data Analysis and Findings chapter, Objective
2, Inferential analysis number IV, i. Table no. 7.2.4.1.m.
(v) In Nashik – Kindly refer the Data Analysis and Findings chapter, Objective
2, Inferential analysis number IV, ii. Table no. 7.2.4.1.n.
(vi) In Surat – Kindly refer the Data Analysis and Findings chapter, Objective
2, Inferential analysis number IV, iii. Table no. 7.2.4.1.s.
(V)Relationship between all the factors of Variety Seeking Buying Behaviour
with all
the factor of Social Media Advertisement in different cities –
(i) In Mumbai –
H 0a : All the factors of Social Media Advertisement and all the factors of
Variety Seeking Buying Behaviour of young working women for consumer
electronics in Mumbai are independent of each other.
H1a : All the factors of Social Media Advertisement and all the factors of
Variety Seeking Buying Behaviour of young working women for consumer
electronics in Mumbai are dependent of each other.
ANOVA
Table 10.13.5.m. showing relationship between all the factors of variety
seeking buying behaviour and all the factors of social media advertising
in Mumbai.
Sum
of Df
Mean
F
Sig.
415
Squares
Square
Between
3.626
9
.403
Within Groups
29.838
506
.059
Total
33.464
515
6.832
.000
Groups
From the above table, it is observed that p < α (0.05), so the null hypothesis is
rejected and alternative is accepted, so we can conclude that all the factors of
Social Media Advertisement and all the factors of Variety Seeking Buying
Behaviour of young working women for consumer electronics in Mumbai are
dependent of each other. So, we can say that social media advertisement have
an impact on Variety Seeking Buying Behaviour of the young working
women in Mumbai.
(ii) In Nashik –
H0b : All the factors of Social Media Advertisement and all the factors of
Variety Seeking Buying Behaviour of young working women for consumer
electronics in Nashik are independent of each other
H1b : All the factors of Social Media Advertisement and all the factors of
Variety Seeking Buying Behaviour of young working women for consumer
electronics in Nashik are dependent of each other
ANOVA
Table 10.13.5.n. showing relationship between all the factors of variety
seeking buying behaviour and all the factors of social media advertising
416
in Nashik.
Sum
of
Squares
Df
Mean Square
F
Sig.
45.528
17
2.678
13.109
.000
Within Groups 69.663
341
.204
Total
358
Between
Groups
115.191
From the above table, it is observed that p < α (0.05), so the null hypothesis is
rejected and alternative is accepted, so we can conclude that all the factors of
Social Media Advertisement and all the factors of Variety Seeking Buying
Behaviour of young working women for consumer electronics in Nashik are
dependent of each other. So, we can say that social media advertisements are
having impact on Variety Seeking Buying Behaviour of the young working
women in Nashik.
(iii) In Surat –
H0c : All the factors of Social Media Advertisement and all the factors of
Variety Seeking Buying Behaviour of young working women for consumer
electronics in Surat are independent of each other.
H1c : All the factors of Social Media Advertisement and all the factors of
Variety Seeking Buying Behaviour of young working women for consumer
electronics in urat are dependent of each other.
ANOVA
417
Table 10.13.5.s. showing relationship between all the factors of variety
seeking buying behaviour and all the factors of social media advertising
in Surat.
Sum
of
Mean
Squares
Df
Square
F
Sig.
Between Groups
2.418
9
.269
12.095
.000
Within Groups
8.596
387
.022
Total
11.014
396
From the above table, it is observed that p < α (0.05), so the null hypothesis is
rejected and alternative is accepted, so we can conclude that all the factors of
Social Media Advertisement and all the factors of Variety Seeking Buying
Behaviour of young working women for consumer electronics in Surat are
dependent of each other. So, we can say that social media advertisements are
having impact on Variety Seeking Buying Behaviour of the young working
women in Surat.
(VI)Relationship between all the factors of Dissonance Buying Behaviour with all
the factor of Social Media Advertisement in different cities –
(i) In Mumbai –
H 0a : All the factors of Social Media Advertisement and all the factors of
Dissonance Buying Behaviour of young working women for consumer
electronics in Mumbai are independent of each other.
418
H1a : All the factors of Social Media Advertisement and all the factors of
Dissonance Buying Behaviour of young working women for consumer
electronics in Mumbai are dependent of each other.
ANOVA
Table 10.13.6.m. showing relationship between all the factors of
Dissonance buying behaviour and
all the
factors
of social media
advertising in Mumbai.
Sum
of
Mean
Squares
Df
Square
F
Sig.
1.914
9
.213
3.411
.000
Within Groups 31.550
506
.062
Total
515
Between
Groups
33.464
From the above table, it is observed that p < α (0.05), so the null hypothesis is
rejected and alternative is accepted, so we can conclude that all the factors of
Social Media Advertisement and all the factors of Dissonance Buying
Behaviour of young working women for consumer electronics in Mumbai are
419
dependent of each other. So, we can say that social media advertisement have
an impact on Dissonance Buying Behaviour of the young working women in
Mumbai.
(ii) In Nashik –
H0b : All the factors of Social Media Advertisement and all the factors of
Dissonance Buying Behaviour of young working women for consumer
electronics in Nashik are independent of each other
H1b : All the factors of Social Media Advertisement and all the factors of
Dissonance Buying Behaviour of young working women for consumer
electronics in Nashik are dependent of each other
ANOVA
Table 10.13.6.n. relationship between all the factors of Dissonance
buying behaviour and all the factors of social media advertising in
Nashik.
Sum
of
Mean
Squares
Df
Square
F
Sig.
50.819
17
2.989
9.486
.778
Within Groups 107.458
341
.315
Total
358
Between
Groups
158.277
From the above table, it is observed that p > α (0.05), so the null hypothesis is
accepted and alternative is rejected, so we can conclude that all the factors of
420
Social Media Advertisement and all the factors of Dissonance Buying
Behaviour of young working women for consumer electronics in Nashik are
independent of each other. So, we can say that social media advertisements
are not having impact on Dissonance Buying Behaviour of the young working
women in Nashik.
(iii) In Surat –
H0c : All the factors of Social Media Advertisement and all the factors of
Dissonance Buying Behaviour of young working women for consumer
electronics in Surat are independent of each other.
H1c : All the factors of Social Media Advertisement and all the factors of
Dissonance Buying Behaviour of young working women for consumer
electronics in Surat are dependent of each other.
ANOVA
Table 10.13.6.s. showing relationship between all the factors of
Dissonance buying behaviour and
all the
factors
of social media
advertising in Surat.
Sum of Squares
df
Mean Square
F
Sig.
2.418
9
.269
12.095 .234
8.596
387
.022
11.014
396
Between
Groups
Within
Groups
Total
421
From the above table, it is observed that p > α (0.05), so the null hypothesis is
accepted and alternative is rejected, so we can conclude that all the factors of
Social Media Advertisement and all the factors of Dissonance Buying
Behaviour of young working women for consumer electronics in Surat are
independent of each other. So, we can say that social media advertisements
are not having impact on Dissonance Buying Behaviour of the young working
women in Surat.
(VII)Relationship between all the factors of Impulsive Buying Behaviour with all
the factor of Social Media Advertisement in different cities –
(iv) In Mumbai –
H 0a : All the factors of Social Media Advertisement and all the factors of
Impulsive Buying Behaviour of young working women for consumer
electronics in Mumbai are independent of each other.
H1a : All the factors of Social Media Advertisement and all the factors of
Impulsive Buying Behaviour of young working women for consumer
electronics in Mumbai are dependent of each other.
ANOVA
Table 10.13.7.m. showing relationship between all the factors of
Impulsive buying behaviour and
all the
factors
of social media
advertising in Mumbai.
Between Groups
Sum of Squares
Df
Mean Square
F
Sig.
.979
10
.098
1.522
.128
422
Within Groups
32.484
505 .064
Total
33.464
515
From the above table, it is observed that p > α (0.05), so the null hypothesis is
accepted and alternative is rejected, so we can conclude that all the factors of
Social Media Advertisement and all the factors of Impulsive
Buying
Behaviour of young working women for consumer electronics in Mumbai are
independent of each other. So, we can say that social media advertisement
does not have an impact on Impulsive Buying Behaviour of the young
working women in Mumbai.
(ii) In Nashik –
H0b : All the factors of Social Media Advertisement and all the factors of
Impulsive Buying Behaviour of young working women for consumer
electronics in Nashik are independent of each other
H1b : All the factors of Social Media Advertisement and all the factors of
Impulsive Buying Behaviour of young working women for consumer
electronics in Nashik are dependent of each other
ANOVA
Table 10.13.7.n. showing relationship between all the factors of
Impulsive buying behaviour and
all the
factors
of social media
advertising in Nashik.
Sum
of Df
Mean
F
Sig.
423
Squares
Square
Between Groups
19.612
17
1.154
Within Groups
88.021
341
.258
Total
107.634
358
4.469
.000
From the above table, it is observed that p < α (0.05), so the null hypothesis is
rejected and the alternative is accepted, so we can conclude that all the factors
of Social Media Advertisement and all the factors of Impulsive Buying
Behaviour of young working women for consumer electronics in Nashik are
dependent of each other. So, we can say that social media advertisements are
having impact on Impulsive Buying Behaviour of the young working women
in Nashik.
(iii) In Surat –
H0c : All the factors of Social Media Advertisement and all the factors of
Impulsive Buying Behaviour of young working women for consumer
electronics in Surat are independent of each other.
H1c : All the factors of Social Media Advertisement and all the factors of
Impulsive Buying Behaviour of young working women for consumer
electronics in Surat are dependent of each other.
424
ANOVA
Table 10.13.7.s. showing relationship between all the factors of
Impulsive buying behaviour and
all the
factors
of social media
advertising in Surat.
Sum
of
Mean
Squares
Df
Square
F
Sig.
.790
8
.099
3.750
.000
10.224
388
.026
11.014
396
Between
Groups
Within
Groups
Total
From the above table, it is observed that p < α (0.05), so the null hypothesis is
rejected and alternative is accepted, so we can conclude that all the factors of
Social Media Advertisement and all the factors of Impulsive
Buying
Behaviour of young working women for consumer electronics in Surat are
dependent of each other. So, we can say that social media advertisements are
having an impact on Impulsive Buying Behaviour of the young working
women in Surat.
Objective 3 – To Study the impact of social media advertising on the
buying behaviour of young working women for consumer electronics in
all the cities –
b) Impact of Social Media advertising on different factors of buying behaviour
425
of young working women for consumer electronics in Mumbai – Kindly refer
the Data Analysis and Findings chapter, Objective 3, Inferential analysis
number (a), Table no. 7.3.1.m.a., 7.3.1.m.b. and 7.3.1.m.c.
c) Impact of Social Media advertising on different factors of buying behaviour
of young working women for consumer electronics in Nashik –
In the model, the dependent variable Y is Social Media Advertising whereas
independent variables X1 , X 2 , ......, X n are all buying Behaviours of young
working women i.e. Online purchase Behaviour, Consumer Buying
Behaviour, Complex Buying Behaviour, Habitual Buying Behaviour,
Variety-Seeking Buying Behaviour, Dissonance Buying Behaviour and
Impulsive Buying Behaviour. The estimated regression model is as follows:
Y (Social Media Advertising) = 1.251+ (-0.003) Online Purchase Behaviour
+ (0.056) Consumer Buying Behaviour + (0.021) Complex Buying
Behaviour + (-0.016) Habitual Buying Behaviour + (0.150) Variety-Seeking
Buying Behaviour+ (0.012) Dissonance Buying Behaviour + (- 0.004)
Impulsive Buying Behaviour.
The results indicate that all the independent variables namely consumer
buying behaviour, complex buying behaviour, variety-seeking buying
behaviour and dissonance buying behaviour have a positive impact on the
Social Media Advertising. The independent variables namely Online
Purchase Behaviour, Habitual Buying Behaviour and impulsive buying
behaviour have a negative impact on Social Media Advertising.
Model Summary
426
Table 10.14.n.a. Table of Model Summary for Nashik
Adjusted
R Std. Error of
Model
R
R Square
Square
the Estimate
1
.800 (a)
.643
.345
.2345
From the above it is observed, The R2 value for the model is 0.643 which
indicates that 64.3 % of the variations in the Social Media Advertising are
explained by Online Purchase Behaviour, Consumer Buying Behaviour,
Complex Buying Behaviour, Habitual Buying Behaviour, Variety-Seeking
Buying Behaviour, Dissonance Buying Behaviour and Impulsive Buying
Behaviour. The significance of R2 is tested with the help of F statistic,
which is shown in below table,
2
H 0a : R is not statistically significant
2
H1a : R is statistically significant
ANOVA(b)
Table 10.14.n.b. Table of Anova to determine the level of significance
Sum of
Mean
Squares df
Square
F
Regression
3.508
6
.585
15.695 .000(a)
Residual
13.114
352
.037
Total
16.622
358
Model
1
Sig.
From the above table it is observed that the , the p < (0.05) , so we reject null
hypothesis and alternative hypothesis is accepted, so we conclude that that at
427
5% level of significance R2 is statistically significant .
The significance of the individual coefficients can be tested using t-statistic,
H 0a : There is no significant impact of social media advertising on young
working women for various buying behaviour in Nashik
H1a : There is significant impact of social media advertising on young
working women for various buying behaviour in Nashik
Coefficients (a)
Table 10.14.n.c. Table for significance of Coefficients
Model
Unstandardized
Standardized
Coefficients
Coefficients
T
Sig.
Std.
1
(Constant)
B
Std. Error
1.251
.061
-.003
.027
0.056
Beta
B
Error
20.432
.000
-.006
-.109
.913
.099
0.67
.234
0.001
.021
.015
.085
1.410
.160
.016
.021
.046
.747
.456
.150
.021
.395
7.063
.000
Online Purchase
Behaviour
Consumer
Buying
Behaviour
Complex
Buying
Behaviour
Habitual Buying
Behaviour
Variety Seeking
Buying
Behaviour
428
Dissonance
Buying
.012
.018
.036
.641
.522
-.004
.021
-.010
-.185
.853
Behaviour
Impulsive
Buying
Behaviour
From the above table at 5 % level of significance p > α (0.05), so the null
hypothesis is accepted and alternative is rejected, so the coefficients of Online
Purchase behaviour , Complex buying behaviour , Habitual buying behaviour
, Dissonance and Impulsive buying behaviour is not statistically significant.
But the coefficients of Consumer Buying Behaviour and Variety - Seeking
buying behaviour is statistically significant. Therefore, we conclude that
Consumer and Variety - Seeking buying behaviour has a significant impact
on influencing social media advertising amongst young working women in
Nashik.
d) Impact of Social Media advertising on different factors buying behaviour of
young working women for consumer electronics in Surat –
In the model, the dependent variable Y is Social Media Advertising whereas
independent variables X1 , X 2 , ......, X n are all buying Behaviours of young
working women i.e. Online purchase Behaviour, Consumer Buying
Behaviour, Complex Buying Behaviour, Habitual Buying Behaviour,
Variety-Seeking Buying Behaviour, Dissonance Buying Behaviour and
Impulsive Buying Behaviour. The estimated regression model is as follows:
Y (Social Media Advertising) = 1.249 + (.013) Online Purchase Behaviour
429
+ (0.066) Consumer Buying Behaviour + (0.026) Complex Buying
Behaviour + (0.031) Habitual Buying Behaviour + (0.058) Variety-Seeking
Buying Behaviour + (0.048) Dissonance Buying Behaviour + (0.016)
Impulsive Buying Behaviour.
The results indicate that all the independent variables namely consumer
Buying Behaviour , online purchase behaviour, complex buying behaviour,
variety-seeking buying behaviour, Habitual Buying Behaviour, impulsive
buying behaviour and dissonance buying behaviour have a positive impact
on the Social Media Advertising.
Model Summary
Table 10.14.s.a. Table of Model Summary for Surat
Adjusted
Std. Error of the
Model
R
R Square
R Square
Estimate
1
.956(a)
.913
.234
.0081
From the above it is observed, The R2 value for the model is 0.913 which
indicates that 91.3 % of the variations in the Social Media Advertising are
explained by Online purchase Behaviour, Consumer Buying Behaviour,
Complex Buying Behaviour, Habitual Buying Behaviour, Variety-Seeking
Buying Behaviour, Dissonance Buying Behaviour and Impulsive Buying
Behaviour. The significance of R2 is tested with the help of F statistic, which
is shown in below table,
2
H 0a : R is not statistically significant
2
H1a : R is statistically significant
430
ANOVA(b)
Table 10.14.s.b. Table of Anova to determine the level of significance of
R2
Sum of
Model
1
Squares
Mean
Df
Square
F
Sig.
Regression 1.997
6
.333
14.681
.000(a)
Residual
8.844
390
.023
Total
10.841
396
From the above table it is observed that the , the p < (0.05) , so we reject null
hypothesis and alternative hypothesis is accepted, so we conclude that that at
5% level of significance R2 is statistically significant .
The significance of the individual coefficients can be tested using t-statistic,
H 0a : There is no significant impact of social media advertising on young
working women for various buying behaviour in Surat
H1a : There is significant impact of social media advertising on young
working women for various buying behaviour in Surat
Coefficients (a)
Table 10.14.s.c. for significance of Coefficients
Model
Unstandardized
Standardized
Coefficients
Coefficients
T
Sig.
Std.
1
(Constant)
Online
B
Std. Error
1.249
.051
.013
.024
Beta
B
Error
24.486
.000
.558
.577
Buying
.029
Behaviour
431
Consumer
Buying
.034
.056
.044
.455
.004
.026
.011
.128
2.248
.025
.031
.015
.105
2.031
.043
.058
.017
.188
3.465
.001
.040
.017
.130
2.407
.017
.016
.018
.047
.859
.391
Behaviour
Complex
Buying
Behaviour
Habitual Buying
Behaviour
Variety Seeking
Buying
Behaviour
Dissonance
Buying
Behaviour
Impulsive
Buying
Behaviour
From the above table at 5 % level of significance p > α (0.05), so the null
hypothesis accepted and alternative is rejected, so the coefficients of Online
purchase Behaviour and Impulsive buying behaviour is not statistically
significant. But the coefficients of Consumer Buying Behaviour, Complex
buying behaviour, Habitual buying behaviour, Dissonance and Variety Seeking buying behaviour is statistically significant. Therefore, we conclude
that Consumer Buying Behaviour, Complex buying behaviour, Habitual
buying behaviour, Dissonance and Variety - Seeking buying behaviour has a
significant impact on influencing social media advertising amongst young
working women in Surat.
432
Objective 4 – To Study the effectiveness of social media tools like Face
book, Twitter , LinkedIn on the consumer Behaviour in different cities –
b) Effectiveness of social media tools like Face book, Twitter , LinkedIn on
the consumer Behavior in Mumbai –
(iii) Rank Order – Audience – Kindly refer the Data Analysis and Findings
chapter, Objective 4, Inferential analysis number a, i. Table no. 7.4.m.1.
(iv) Rank Order – Targeting – Kindly refer the Data Analysis and Findings
chapter, Objective 4, Inferential analysis number a, ii. Table no. 7.4.m.2.
(v) Social Networking Site having more followers due to acquaintances (i.e.
friends and relatives) - Kindly refer the Data Analysis and Findings chapter,
Objective 4, Inferential analysis number a, iii. Table no. 7.4.m.3.
(vi) Social Networking Site having more unknown followers - Kindly refer the
Data Analysis and Findings chapter, Objective 4, Inferential analysis number
a, iv. Table no. 7.4.m.4.
b. Effectiveness of social media tools like Face book, Twitter , LinkedIn on
the consumer Behaviour in Nashik –
(i) Rank Order – Audience –
Table 10.15.1.n. Showing effectiveness of SNSs in terms of more
Audience groups in Nashik.
433
Rank
Face book
Twitter
LinkedIn
1
2
3
4
5
6
7
8
9
10
Total
17
6
15
17
20
25
73
85
19
82
2574
15
5
21
30
54
65
65
34
33
37
2262
28
19 24
40
35
58
25
68
26
36
2134
From the above table it was found that as an audience young working women
in Nashik prefer most Face book and least preferred is LinkedIn as the Social
networking sites that have a large number of groups (networks) available for
any demographics you are looking for; for instance group of teenagers, group
of kids, youth, group of new moms, brides, sports fans, technology
enthusiasts, entrepreneurs etc.
(ii) Rank Order – Targeting –
Table 10.15.2.n. Showing effectiveness of SNSs in terms of targeting
consumers in Nashik.
Tota
Rank
1
2
3
4
5
6
7
8
9
10
250
Face
book
l
20
9
15
25
27
18
60
74
43
68
5
199
Twitter
9
41
29
45
45
69
40
36
23
22
2
208
LinkedIn
29
43
24
26
30
43
42
40
46
36
7
From the above table it was found that as an audience young working women
434
in Nashik prefer most Face book and least preferred is twitter as the Social
networking sites targeting the advertisements to specific group of audience.
(iii) Social Networking Site having more followers due to acquaintances (i.e.
friends and relatives) Table 10.15.3.n.
Showing effectiveness of SNSs in terms of more
followers due to acquaintances in Nashik.
Cumulative
Valid
Frequency
Percent
Valid Percent
Percent
Face book
282
78.6
78.6
78.6
Twitter
47
13.1
13.1
91.6
LinkedIn
30
8.4
8.4
100.0
Total
359
100.0
100.0
Out of the total 359 valid respondents, a maximum of 78.6 % agreed that
Face book has more followers due to acquaintances and the minimum of 8.4
% respondents said that LinkedIn has more followers due to acquaintances.
(iv) Social Networking Site having more unknown followers Table 10.15.4.n. Showing effectiveness of SNSs in terms of more
unknown followers in Nashik.
Cumulative
Valid
Frequency
Percent
Valid Percent
Percent
Face book
216
60.2
60.2
60.2
Twitter
79
22.0
22.0
82.2
LinkedIn
64
17.8
17.8
100.0
435
Total
359
100.0
100.0
Out of the total 359 respondents, a maximum percentage of 60.2 % said that
face book has more unknown followers and minimum of 17.8 % said that
LinkedIn has more unknown followers.
c) Effectiveness of social media tools like Face book, Twitter , LinkedIn on
the consumer Behaviour in Surat –
(i) Rank Order – Audience –
Table 10.15.1.s. Showing effectiveness of SNSs in terms of more Audience
groups in Surat.
Rank
Face book
Twitter
LinkedIn
1
2
3
4
5
6
7
8
9
10
Total
3
7
5
9
22
17
36
44
25
229
3399
9
3
20
32
99
92
49
26
39
28
2432
28
120
29
30
26
38
20
51
16
39
1915
From the above table it was found that as a audience young working women
in Surat prefer most Face book and least preferred is prefer LinkedIn as the
Social networking sites that have a large number of groups (networks)
available for any demographics you are looking for; for instance group of
teenagers, group of kids, youth, group of new moms, brides, sports fans,
technology enthusiasts, entrepreneurs etc.
(ii) Rank Order – Targeting –
436
Table 10.15.2.s. Showing effectiveness of SNSs in terms of targeting
consumers in Surat.
Rank
Face book
Twitter
LinkedIn
Total
1
2
3
4
5
6
7
8
9
10
8
7
14
18
26
17
33
49
32
193
3209
18
16
23
43
90
87
35
26
25
34
2281
51
123
28
17
25
33
25
26
21
48
1284
From the above table it was found that as an audience the young working
women prefer most Face book and least preferred is LinkedIn as the Social
networking sites targeting the advertisements to specific group of audience.
(iii) Social Networking Site having more followers due to acquaintances (i.e.
friends and relatives) Table 10.15.3.s. Showing effectiveness of SNSs in terms of more followers
due to acquaintances in Surat.
Valid
Frequency
Percent
Valid Percent
Cumulative Percent
Face book
355
89.4
89.4
89.4
Twitter
29
7.3
7.3
96.7
LinkedIn
13
3.3
3.3
100.0
Total
397
100.0
100.0
Out of the total 397 valid respondents, a maximum of 89.4 % agreed that
Face book has more followers due to acquaintances and the minimum of 3.3
% respondents said that LinkedIn has more followers due to acquaintances.
(iv) Social Networking Site having more unknown followers -
437
Table 10.15.4.s. Showing effectiveness of SNSs in terms of more unknown
followers in Surat.
Frequency
Percent
Valid Percent
Cumulative Percent
121
30.5
30.5
30.5
Twitter
104
26.2
26.2
56.7
LinkedIn
172
43.3
43.3
100.0
Total
397
100.0
100.0
Valid Face book
Out of the total 397 respondents, a maximum percentage of 43.3 % said that
LinkedIn has more unknown followers and minimum of 26.2 % said that
Twitter has unknown followers.
Objective 5- To study the impact of social media advertising on people
belonging to different demographic factors such as qualification, annual
income, occupation and place –
(IV) Relationship between impact of social media advertising of young working
women with their qualification in different cities –
(c) In Mumbai – Kindly refer the Data Analysis and Findings chapter, Objective
5, Inferential analysis number I, a. Table no. 7.5.I.m.
(d) In Nashik –
H0b : Impact of social media advertising and the qualification of young
working women in Nashik are independent of each other.
438
H1b : Impact of social media advertising and the qualification of young
working women in Nashik are dependent of each other.
ANOVA
Table 10.16.1.n.a. Relationship between qualification of young working
women and impact of social media advertising in Nashik.
Sum of Squares
Df
Mean Square
F
Sig.
Between Groups .617
2
.309
6.865
.001
Within Groups
16.005
356
.045
Total
16.622
358
From the above table, it is observed that p < α (0.05), so the null hypothesis is
rejected and alternative is accepted, so we can conclude that impact of social
media advertising and the qualification of young working women in Nashik
are dependent of each other. So, we can say that social media advertisement
has an impact on qualification of the young working women in Nashik. So,
which qualification group has more or less impact we can refer descriptive
statistics table which is given below:
Descriptive
Table 10.16.1.n.b. To determine qualification group has more or less
impact of social media advertisement in Nashik.
439
N
Mean
Std. Deviation
NON GRADUATE
42
1.7889
.25711
GRADUATES
184
1.6699
.21337
POST GRADUATE
133
1.6521
.19390
Total
359
1.6773
.21548
From the above table, we observed that non graduate young working women
are having more impact of social media advertisement followed by post
graduate and graduates.
b) In Surat –
H0c : Impact of social media advertising and the qualification of young
working women in Surat are independent of each other
H1c : Impact of social media advertising and the qualification of young
working women in Surat are dependent of each other
ANOVA
Table 10.16.1.s.a. Relationship between qualification of young working
440
women and impact of social media advertising in Surat.
Sum of Squares
Df
Mean Square
F
Sig.
Between Groups
.702
2
.351
13.643 .000
Within Groups
10.139
394
.026
Total
10.841
396
From the above table, it is observed that p < α (0.05), so the null hypothesis is
rejected and alternative is accepted, so we can conclude that impact of social
media advertising and the qualification of young working women in Surat are
dependent of each other. So, we can say that social media advertisement has
an impact on qualification of the young working women in Surat. So, which
qualification group has more or less impact we can refer descriptive statistics
table which is given below:
Descriptive
Table 10.16.1.s.b. To determine how much relationship exists between
qualification of young working women and impact of social media
advertising in Surat.
N
Mean
Std. Deviation
NON GRADUATE
86
1.6767
.10895
GRADUATES
241
1.6437
.16724
POST GRADUATE
70
1.5476
.18715
Total
397
1.6339
.16546
From the above table, we observed that non graduate young working women
are having more impact on social media advertisement followed by graduates
441
and post graduates.
(V) Relationship between impact of social media advertising of young working
women with their Annual Income in different cities –
(a) In Mumbai –
H 0a : Impact of social media advertising and the Annual Income of young
working women in Mumbai are independent of each other
H1a : Impact of social media advertising and the Annual Income of young
working women in Mumbai are dependent of each other
ANOVA
Table 10.16.2.m.a. Relationship between annual income of young
working women and impact of social media advertising in Mumbai.
Sum of Squares
Df
Mean Square
F
Sig.
Between Groups
.040
3
.013
.339
.797
Within Groups
20.089
512
.039
Total
20.129
515
From the above table, it is observed that p > α (0.05), so the null hypothesis is
accepted and alternative is rejected, so we can conclude that impact of social
media advertising and the Annual Income of young working women in
Mumbai are independent of each other. So, we can say that social media
advertisement does not have any impact on Annual Income of the young
working women in Mumbai.
442
(b) In Nashik –
H0b : Impact of social media advertising and the Annual Income of young
working women in Nashik are independent of each other
H1b : Impact of social media advertising and the Annual Income of young
working women in Nashik are dependent of each other
ANOVA
Table 10.16.2.n.a. Relationship between annual income of young working
women and impact of social media advertising in Nashik.
Sum
of
Mean
Squares
Df
Square
F
Sig.
Between Groups
.417
3
.139
3.044
.029
Within Groups
16.205
355
.046
Total
16.622
358
From the above table, it is observed that p < α (0.05), so the null hypothesis is
rejected and alternative is accepted, so we can conclude that impact of social
media advertising and the Annual Income of young working women in
Nashik are dependent of each other. So, we can say that social media
advertisement has an impact on Annual Income of the young working women
in Nashik. So, which Annual Income group has more or less impact we can
refer descriptive statistics table which is given below:
443
Descriptive
Table 10.16.2.n.b. To determine how much relationship exists between
annual income of young working women and impact of social media
advertising in Nashik.
N
Mean
Std. Deviation
UPTO RS. 3 LAKHS
169
1.7018
.25790
3.1- 5 LAKHS
132
1.6763
.17457
5.1-10 LAKHS
52
1.6167
.14272
ABOVE 10 LAKHS
6
1.5333
.00000
Total
359
1.6773
.21548
From the above table, we observed that income group i.e. upto Rs. 3 lakhs
earning of young working women are having more impact of social media
advertisement followed by other income groups i.e. 3.1- 5, 5.1 – 10 and above
10 lakhs.
(e) In Surat – Kindly refer the Data Analysis and Findings chapter, Objective 5,
Inferential analysis number II, c. Table no. 7.5.II.s.a. and 7.5.II.s.b.
(VI) Relationship between impact of social media advertising of young working
women with their Occupation in different cities –
a) In Mumbai –
444
H 0a : Impact of social media advertising and the Occupation of young
working women in Mumbai are independent of each other
H1a : Impact of social media advertising and the Occupation of young
working women in Mumbai are dependent of each other
ANOVA
Table 10.16.3.m.a. Relationship between occupation of young working
women and impact of social media advertising in Mumbai.
Sum
of
Mean
Squares
Df
Square
F
Sig.
Between Groups
.161
2
.080
2.065
.128
Within Groups
19.968
513
.039
Total
20.129
515
From the above table, it is observed that p > α (0.05), so the null hypothesis is
accepted and alternative is rejected, so we can conclude that impact of social
media advertising and the Occupation of young working women in Mumbai
are independent of each other. So, we can say that social media advertisement
does not have any impact on Occupation of the young working women in
Mumbai.
(b) In Nashik – Kindly refer the Data Analysis and Findings chapter, Objective
5, Inferential analysis number III, b. Table no. 7.5.III.n.a. and 7.5.III.n.b.
445
(c) In Surat –
H0c : Impact of social media advertising and the Occupation of young
working women in Surat are independent of each other
H1c : Impact of social media advertising and the Occupation of young
working women in Surat are dependent of each other
ANOVA
Table 10.16.3.s.a. Relationship between occupation of young working
women and impact of social media advertising in Surat.
Sum
of
Mean
Squares
Df
Square
F
Sig.
.133
2
.067
2.455
.087
10.708
394
.027
10.841
396
Between
Groups
Within
Groups
Total
From the above table, it is observed that p > α (0.05), so the null hypothesis
accepted and alternative is rejected, so we can conclude that impact of social
media advertising and the Occupation of young working women in Surat are
independent of each other. So, we can say that social media advertisement
does not have any impact on Occupation of the young working women in
Surat.
446
447
448