Embed Size (px)
Citation preview
FACTORS AFFECTING CUSTOMER LOYALTY IN
TELECOM SECTOR
Thesis submitted to Jiwaji University in partial fulfilment of the
requirements for the award of the degree of
Ph.D. in Management
2014
Under Supervision of
Prof. Yogesh Upadhyay
Vice Chancellor
ITM University, Gwalior
By
Anil Kumar Singh
School of Studies in Management
Jiwaji University, Gwalior (M.P)
APPENDIX -3
i
Declaration by the Candidate
(Para 12 –B)
I declare that the thesis entitled Factors Affecting Customer Loyalty in Telecom
Sector is my own work, conducted under the supervision of Prof. Yogesh Upadhyay,
Vice Chancellor, ITM University, Gwalior, approved by Research Degree Committee
(RDC). I have put in more than 200 days of attendance with the supervisor at the centre,
School of Studies in Management, Jiwaji University, Gwalior, (M.P), India.
I further declare that to the best of my knowledge, the thesis does not contain any
part of any work which has been submitted for award of any degree, either in this
university or in other university/deemed university, without proper citation.
Prof. Yogesh Upadhyay
Supervisor
Anil Kumar Singh
Research Scholar
Signature of Head U.T.D/Principal
APPENDIX -4
ii
CERTIFICATE
(Para 12 – C)
This is to certify that the work entitled “Factors Affecting Customer Loyalty in
Telecom Sector” is a piece of research work done by Mr. Anil Kumar Singh, under my
guidance and supervision, for the degree of Doctor of Philosophy of Jiwaji University,
Gwalior (M.P), India. The candidate has put in the attendance of more than 200 days
with me.
To the best of my knowledge and belief, this thesis:
1. embodies the work of himself;
2. has been duly completed;
3. fulfils latest requirements of the ordinance relating to Ph.D degree of the
university; and
4. is up to the standard, both in respect of content and language being referred to the
examiner.
Date:
Prof. Yogesh Upadhyay
Supervisor
FORWARDED
Signature of Head U.T.D/Principal
ACKNOWLEDGEMENT
It is all due to Almighty’s blessings that I was inspired to take this research work,
sustain my motivation through the vicissitudes of this work and finally could conclude
this work.
As I look back and remember the moment when I got enrolled for Ph.D., I can
recall that it was only the scholarly aura of Prof. Yogesh Upadhyay,Vice Chancellor,
ITM University, Gwalior, which stimulated me to pursue research and finally pulled me
into it. I am immensely thankful to him for allowing me to be his research scholar and for
bearing with me specially during under productive phases of this work. Every session
with him was rewarding and propelled me closer to my goal. I am highly indebted to him
as I owe every page of this work to him.
I am immensely grateful to Dr.S.K.Singh, Professor, SOS in Commerce, Jiwaji
University, Gwalior for the concern shown towards my work whenever I came across
him.
I am thankful to Prof. Suvijana Awasthi, Dean and Head of the Department, SOS
in Management, Jiwaji University, Gwalior for her kind support during my research
work.
I acknowledge my gratitude to my employer Dr. Arun Tyagi, Director, IPS Group
of Colleges and Mr.P.K.Ghosh, Chief Administrative Officer, IPS Group of Colleges
who extended their kind co-operation, and allowed me flexibility in fulfilling my duties.
Without their support and encouragement it would not have been possible for me to
complete this work.
I am grateful to my father Sh. Man Singh, Retd. Principal, whose hard working
and uncompromising lifestyle has always remained a source of inspiration for me. In past
one year his age has been failing him to talk and listen with ease but even in this state he
has been constantly enquiring the progress of my research work like a concerned father
who would not let his son deviate from his goal.
I am grateful to my loving mother Smt. Sharda Devi for being kind and
affectionate towards me in all circumstances. Her confidence in my abilities always
inspired me.
I acknowledge my gratitude towards my elder brothers Dr.P.K.Singh,
Dr.A.K.Singh, Er.P.K.Singh who despite being busy professionals were always
forthcoming in guiding and helping me in hours of crisis and also to my bhabhies
Mrs.Rita, Mrs. Kamlesh and Mrs. Chaman for all their support. I am also thankful to my
elder sisters Smt. Madhu and Smt. Sadhana who never forgot to pray for me though
having their own family responsibilities.
My very special thanks are due to my wife Dr. Sarla, a communication professor
with Technical Education, M.P. Govt. and our recently born kids. In fact my twin babies
and most of my thesis work have acquired shape and grew in mass simultaneously. When
my “Beti” and “Beta” prepared themselves to say their first Hello to this world, I was
busy shaping conclusion of my research work. Besides being helpful and cooperative in
general, my wife showed tremendous patience and understanding in past few months. In
her special circumstances, she must have needed my attention and help in several ways,
some I could sense and attend but many must have skipped my poor sensibility, she bore
everything silently and ensured that I don’t feel guilty of being less than a caring
husband.
I must show my gratitude towards my father in law Sh. R.P.Verma whose
presence specially in last few months created a cushion impact and allowed me to focus
on my work. I am also thankful to my sisters in law Mrs.Kiran, Mrs.Asha and brother in
law Mr.Ashutosh for keeping good connectivity with my wife and making things less
challenging for me.
Last but not the least I am thankful to my former Head of the Deptt. Dr. George
Thomas, for his friendly and practical suggestions which kept me going. I am also
thankful to Mr.Jitender Bhalla, Sr. Manager, Tata Tele Services for his support. I am also
thankful to my friends Dr. Vivek Agrawal, Mr.Anil Parihar for providing the much
needed supporting and motivating moments and a platform to discuss my research work.
ANIK KUMAR SINGH
“FACTORS AFFECTING CUSTOMER LOYALTY IN
TELECOM SECTOR”
CONTENTS CHAPTER -1 INTRODUCTION 1-26
1.1 Purpose of the study 01
1.2 Reasons to focus on mobile sector 01
1.3 Basic Framework of the study 03
1.4 Indian Telecom Industry 04
1.5 Invention of telephone and Indian scenario 05
1.6 History of Telecommunication in India 05
1.7 Telecom and Employment in India 06
1.8 Telecom Industry: Global Perspective 06
1.9 Cellular Technology 08
1.9.1 GSM and CDMA 08
1.9.2 Advantages of GSM 08
1.9.3 Advantages of CDMA 08
1.9.4 Disadvantages of GSM 09
1.9.5 Disadvantages of CDMA 09
1.9.6 Ratio of GSM and CDMA in India 09
1.10 Challenges in Telecommunication 10
1.11 Drivers behind the growth of telecom sector 11
1.12 Contribution of Telecom Sector in GDP 11
1.13 Telecom circles in India 12
1.14 Mobile service providers in India 12
1.15 Market share of different service providers in India 13
1.16 National Telecom Policy 15
1.17 Telecom Bodies 15
1.17.1 Department of Telecommunication 15
1.17.2 Telecom Regulatory Authority of India (TRAI) 16
1.17.3 The Telecom Commission 16
1.18 Facts about Indian Telecom Industry 16
1.19Present Research 20
1.20 The Research Model 20
1.21 Objectives of the study 21
1.22 Research Questions 22
1.23 Need for the Study 23
1.24 Importance of the study 23
1.25 Structural Contents of the Thesis 24
1.25.1 Chapter One 24
1.25.2 Chapter Two 24
1.25.3 Chapter Three 25
1.25.4 Chapter Four 25
1.25.5 Chapter Five 26
CHAPTER -2 REVIEW OF LITERATURE 27-52
2.1 Service Quality 27
2.1.1 Concept of Service Quality 27
2.1.2 Characteristics of Services 27
2.1.3 Dimensions of Service Quality 29
2.1.4 Measurement of Service Quality 31
2.1.5 Noteworthy contributions in service Quality 33
2.2 Customer Perceived Value 36
2.2.1 Concept of customer perceived value 36
2.2.2 Dimensionality of customer perceived value 36
2.2.3 Noteworthy contributions in customer perceived value 36
2.3 Customer Satisfaction 38
2.3.1 Concept of customer satisfaction 38
2.3.2 Importance of Customer Satisfaction 39
2.3.3 Measurement of customer satisfaction 40
2.3.4 Factors influencing customer’s level of satisfaction 42
2.3.5 Service Quality: the key influence in customer satisfaction 43
2.4 Customer Loyalty 46
2.4.1 Concept of Customer Loyalty 46
2.4.2 Customer Loyalty and Customer Retention 47
2.5 Switching cost 50
2.5.1 Concept of Switching cost 50
2.5.2 Dimensions of switching cost 51
2.5.3 Noteworthy contribution 51
2.6 Inertia 52
2.6.1 Concept of Inertia 52
CHAPTER -3 RESEARCH METHODOLOGY 53-65
3.1 Research 53
3.2 Research Methodology 53
3.3 Research Design 53
3.4 Sample Design 54
3.4.1 Sampling Techniques 54
3.4.2 Sample Size 54
3.4.3 The Sample 55
3.5 Data Collection 55
3.5.1 Data Collection design 55
3.5.2 Data collection Procedure 55
3.6 Area of study: Gwalior 56
3.7 Research Instrument Design 56
3.8 Measurement Scales 57
3.8.1 Service Quality 57
3.8.2 Customer Perceived value 57
3.8.3 Customer Satisfaction 58
3.8.4 Customer Loyalty 58
3.8.5 Switching cost 58
3.8.6 Inertia 58
3.9 Proposed hypotheses 58
3.10. Statistical Tools and Techniques 60
3.10.1 Descriptive Statistics 60
3.10.2 Inferential Statistics 61
3.11 Statistical Tools used 61
3.11.1 t-Test 61
3.11.2 ANOVA (Analysis of Variance) 62
3.11.3 Factor Analysis 63
3.11.4 Correlation 63
3.11.5 Regression 65
3.11.6 Hierarchical Regression 65
CHAPTER -4. DATA ANALYSIS 67-153
Definition 67
Research Objectives 67
Research Model 67
Section 1: Descriptive Statistics of Demographic Variables 69
4.1.1Age of Respondents 70
4.1.2 Gender of Respondents 72
4.1.3 Marital status of Respondents 73
4.1.4 Educational Qualification of Respondent 74
4.1.5 Occupational pattern of respondents 76
4.1.6 Income of Respondents 77
4.1.7 Service provider of respondents 79
4.1.8 Type of connection of respondents 80
Section 2: Descriptive Statistics of Research Constructs 82
4.2.1 Service Quality 82
4.2.1.1 Descriptive Statistics 82
4.2.1.2 Reliability Analysis 82
4.2.2 Customer Perceived Value 83
4.2.2.1 Descriptive Statistics 83
4.2.2.2 Reliability Analysis 83
4.2.3 Customer satisfaction 83
4.2.3.1 Descriptive Statistics 84
4.2.3.2 Reliability Analysis 84
4.2.4 Customer Loyalty 84
4.2.4.1 Descriptive Statistics 84
4.2.4.2 Reliability Analysis 85
4.2.5 Switching cost 85
4.2.5.1 Descriptive Statistics 85
4.2.5.2 Reliability Analysis 85
4.2.6 Inertia 86
4.2.6.1Descriptive Statistics 86
4.2.6.2 Reliability Analysis 86
Section 3: Factors Affecting Service Quality 87
4.3 Service Quality 87
4.3.1 Factor Analysis 87
4.3.2 Communalities 88
4.3.3 Total Variance Explained 89
4.3.4 Rotated Component matrix 91
4.3.5 Factors of Service Quality 95
Section.4: Customer Satisfaction among Respondents of
different demography 97
4.4.1. Customer Satisfaction and Age 97
4.4.2. Customer Satisfaction and Gender 100
4.4.3 Customer Satisfaction and Marital status 101
4.4.4 Customer Satisfaction and Educational Qualification 103
4.4.5 Customer Satisfaction and Occupational Pattern 104
4.4.6 Customer Satisfaction and Income Pattern 105
4.4.7 Customer Satisfaction and Service Provider 106
4.4.8 Customer Satisfaction and Type of connection 109
Section 5: Customer Loyalty among Respondents of different demography 111
4.5.1. Customer Loyalty and Age 111
4.5.2. Customer Loyalty and Gender 114
4.5.3 Customer Loyalty and Marital status 116
4.5.4 Customer Loyalty and Educational Qualification 118
4.5.5 Customer Loyalty and Occupational Pattern 120
4.5.6 Customer Loyalty and Income Pattern 123
4.5.7 Customer Loyalty and Service Provider 123
4.5.8 Customer Loyalty and Type of connection 126
Section 6: Factors affecting customer satisfaction: 128
4.6.1 Service Quality and Customer Satisfaction 129
4.6.2 Customer Perceived Value and Customer Satisfaction 129
4.6.3 Correlation Analysis: Customer Satisfaction, Service
Quality and Customer Perceived Value 129
4.6.4 Regression Analysis - Customer Satisfaction: Service
Quality and Customer Perceived Value 131
4.6.5 Regression Equation: Customer Satisfaction-Service Quality and CPV 135
4.6.6 Rank Analysis: Customer Satisfaction: Service Quality Dimensions & CPV 138
Section 7: Factors affecting Customer Loyalty 139
4.7.1 Factors affecting Customer Loyalty: Direct Impact 140
4.7.2 Factors affecting Customer Loyalty: Moderating Impact 140
4.7.3. Moderating Impact & Multiollinearity 140
4.7.4 Regression Models 141
4.7.4.1 Model 1 141
4.7.4.2 Model 2 144
4.4.4.3 Model 3 147
4.7.5 Direct impact & moderating impact: Hypotheses Testing 150
4.76 Direct impact & moderating impact: Regression Equation 151
4.7.7Comparison of Model1, Model 2 and Model 3 153
CHAPTER -5: FINDINGS AND CONCLUSIONS 155-164
5.1 Findings and conclusions related to demographic aspects of customers 155
5.2Findings related to levels of Customer satisfaction among
respondents of different demographic background 157
5.3Findings related to levels of Customer satisfaction among
respondents of different demographic background 158
5.4 Findings related to Factors affecting Customer Satisfaction 159
5. 5 Findings related to Factors affecting customer Loyalty 159
5.6 Conclusion 162
5.7 Limitations 163
5.8 Suggestions for Future Research 164
References 165- 181
LIST OF TABLES
1.1:Mobile service Providers in India 03
3.1:Details of Gwalior District 56
4.1.1: Age of Respondent 71
4.1.2: Gender of Respondent 72
4.1.3: Marital status of Respondent 73
4.1.4: Educational Qualification of Respondent 75
4.1.5: Occupational pattern of Respondent 76
4.1.6: Income of Respondent 78
4.1.7: Service provider of Respondent 79
4.1.8: Type of connection 80
4.2.1: Descriptive Statistics: Service Quality 82
4.2.2: Reliability Statistics: Service Quality 82
4.2.3: Descriptive Statistics: Customer Perceived Value 83
4.2.4: Reliability Statistics: Customer Perceived Value 83
4.2.5: Descriptive Statistics: Customer Satisfaction 84
4.2.6: Reliability Statistics: Customer Satisfaction 84
4.2.7: Descriptive Statistics: Customer Loyalty 85
4.2.8: Reliability Statistics : Customer Loyalty 85
4.2.9: Descriptive Statistics: Switching Cost 85
4.2.10: Reliability Statistics: Switching Cost 86
4.2.11: Descriptive Statistics: Inertia 86
4.2.12: Reliability Statistics: Inertia 86
4.3.1: KMO and Bartlett's Test: Service Quality 87
4.3.2: Communalities: Service Quality 88
4.3.3: Total Variance Explained: Service Quality 90
4.3.4: Rotated Component Matrix: Service Quality 91
4.4.1: ANOVA: Customer satisfaction & Age 98
4.4.2: Post hoc test – Scheffe method: Customer satisfaction & Age 98
4.4.3: Independent Samples Test : Customer satisfaction & Gender 100
4.4.4: Group Statistics: Customer Satisfaction-Gender 100
4.4.5: Independent Samples Test : Customer satisfaction & Marital Status 102
4.4.6: Group Statistics: Customer Satisfaction - Marital status 102
4.4.7: ANOVA: Customer satisfaction & Educational Qualification 103
4.4.8: Customer Satisfaction-ANOVA: Occupational pattern 104
4.4.9: ANOVA: Customer satisfaction & Income 105
4.4.10: ANOVA: Customer satisfaction & Service Provider 106
4.4.11: Post hoc test – Scheffe method: Customer satisfaction & Service 107
4.4.12: Independent Samples t- Test: Customer satisfaction & Type of connection 110
4.5.1 :ANOVA: Customer Loyalty & Age 112
4.5.2: Post hoc test – Scheffe method: Customer Loyalty & Age 113
4.5.3: Independent Samples t- Test: Customer Loyalty & Gender 115
4.5.4: Group Statistics: Customer Loyalty-Gender 115
4.5.5: Independent Samples t- Test: Customer Loyalty & Marital status 117
4.5.6: Group Statistics: Customer Loyalty- Marital Status 117
4.5.7:ANOVA: Customer Loyalty & Educational Qualification 118
4.5.8:Post hoc test – Scheffe method: Customer Loyalty & Educational Qualification 119
4.5.9:ANOVA: Customer Loyalty & Occupational Pattern 121
4.5.10:Post hoc test – Scheffe method: Customer Loyalty & Occupational Pattern 121
4.5.11:ANOVA: Customer Loyalty & Income 123
4.5.12:ANOVA: Customer Loyalty & Service Provider 124
4.5.13:Post hoc test – Scheffe method: Customer Loyalty & Service Provider 124
4.5.14: Independent Samples t-Test: Customer Loyalty & Type of connection 127
4.5.15: Group Statistics :Customer Loyalty-Type of connection 127
4.6.1:Correlations : Customer Satisfaction – Service Quality Dimensions – CPV 130
4.6.2:Customer Satisfaction:Service Quality Dimensions & CPV-Regression Analysis 133
4.6.3:Customer Satisfaction: Service Quality Dimensions & CPV-Regression ANOVA 134
4.6.4:Customer Satisfaction: Service Quality Dimensions & CPV-Regression Coefficients 135
4.6.5:Rank Analysis Customer Satisfaction: Service Quality Dimensions & CPV 138
4.7.1:Regression Analysis: Customer Loyalty-Customer Satisfaction, Switching cost, Inertia 142
4.7.2:Regression ANOVA: Customer Loyalty-Customer Satisfaction, Switching cost, Inertia: 142
4.7.3:Regression Coefficients: Customer Loyalty-Customer Satisfaction, Switching cost,Inertia: 142
4.7.4:Regresion Analysis :Customer Loyalty: Moderating impact of Switching cost 144
4.7.5:Regression ANOVA:Customer Loyalty-Moderating impact of Switching cost 145
4.7.6:Coefficients: Regression Model 2 145
4.7.7: Correlations: Regression Model 3 147
4.7.8:Regresion Analysis: Customer Loyalty-Moderating impact of Switching cost & Inertia 148
4.7.9: Regression ANOVA:Customer Loyalty-Moderating impact of Switching cost & Inertia 148
4.7.10:Regression Coefficients :Customer Loyalty-Moderating impact of Switching cost &Inertia149
4.7.11:Comparasion of Model 1,2 &3 : R, R² & adjusted R 154
LIST OF FIGURES
1.1 Number of Landline subscribers 02
1.2 Market share of GSM mobiles & CDMA mobiles-Dec.2013 10
1.3 Market Share of Different Service Providers-Dec 2013 14
1.4 Teledensity in India: 2009-2013 17
1.5 Average Revenue Per Month: GSM& CDMA 18
1.6 Minutes of Usage per Month: GSM & CDMA 19
1.7 Teledensity in MP 20
2.1 Customer Loyalty model 49
3.1 Figure 64
4:Research Model 68
4.1.1:Age of Respondent 72
4.1.2 : Gender of Respondent 73
4.1.3 :Marital status of the Respondent 74
4.1.4 :Educational Qualification of respondents 75
4.1.5 :Occupational Pattern of respondents 77
4.1.6 :Income of respondents 78
4.1.7 : Service Provider of respondents 80
4.1.8 :Type of connection (Pre-paid or Post paid 81
4.3.1:Scree Plot: Service Quality 91
4.4.1:Mean of Customer Satisfaction: Age of customer 99
4.4.2: Mean of Customer Satisfaction: Gender of customer 101
4.4.3:Mean of Customer Satisfaction: Marital status of customer 102
4.4.5:Mean of Customer Satisfaction: Educational Qualification of customer 103
4.4.6:Mean of Customer Satisfaction: Occupation of customer 104
4.4.7:Mean of Customer Satisfaction: Occupation of customer 105
4.4.8:Mean of Customer Satisfaction: Service provider of customer 119
4.4.9:Mean of Customer Satisfaction: Type of connection 110
4.5.1:Mean of Customer Loyalty: Age of Respondent 114
4.5.2:Mean of Customer Loyalty: Gender of Respondent 116
4.5.3:Mean of Customer Loyalty: Marital status of Respondent 117
4.5.4:Mean of Customer Loyalty: Educational Qualification of Respondent 120
4.5.5:Mean of Customer Loyalty: Employment of Respondent 122
4.5.6:Mean of Customer Loyalty: Service Provider of Respondent 126
4.5.7:Mean of Customer Loyalty: Type of connection 127
4.6.1:Research Model 128
4.6.2:Variance Explained by 7 models 132
4.6.3:Customer Satisfaction-Impact of Service Quality and CPV 137
4.7.1:Research Model 139
4.7.2:Direct Impact on Customer Loyalty: Customer Satisfaction, Switching cost & Inertia 143
4.7.3:Direct & Moderating Impact on Customer Loyalty: Customer Satisfaction,
Switching cost & Inertia 146
4.7.4:Customer Loyalty: Direct Impact & Moderating Impact 150
Appendices:
Table No.3.1: Service quality 182
Table No.3.2: Customer Perceived Value 184
Table No.3.3: Customer satisfaction 184
Table No.3.4: Customer Loyalty 185
Table No.3.5:Switching Cost 185
Table No.3.6: Inertia 186
Table 5.1: Findings: Hypotheses (H1a to H1h) 186
Table 5.2: Hypotheses (H2a to H2h) 187
Table 5.3: Hypotheses (H3a to H3g and H4) 188
Table 5.4: Hypotheses (H5to H9) 188
Questionnaire 189
CHAPTER I
INTRODUCTION
1
1.1 Purpose of the study:
The need to communicate and stay connected is central to meaningful human
existence. Telephonic communication system in any country serves a great role in
facilitating the people of that country to interact mentally without coming in contact face
to face.
Rising per capita income and reduced call tariffs have made telecommunication a
major and affordable mode of communication in India, thus pushing up the subscriber
base to almost 915 million out of which 886 million are mobile phone users and 28
million are landline phone users as per TRAI report April 2014.
Though Telecom Sector comprises both landline and mobile phone users, this
study aims to find out the factors which affect customer loyalty in mobile sector of
Telecom industry.
1.2 Reasons to focus on mobile sector:
Mobile phone users constitute as high as 97% of total telephone users.
Presently, the increase in teledensity is mainly driven by the increase in mobile
phone users.
Mobile sector is registering continuous positive growth while landline sector is
registering negative growth since 2006.
There is cut throat competition among companies to increase or retain their
market share in mobile sector which in turn poses a challenge and threat before
mobile companies to retain existing customers and enhance Customer Loyalty. In
case of landline sector the change of service provider is not so frequent and there
is not so intense competition to lure the customers of other companies.
It is assumed that factors which affect customer loyalty in mobile sector will also
explain Customer Loyalty in landline sector though to a different degree.
2
All telecom service providers are not providing landline telephone service due to
declining demands, hence attempt to study landline sector will narrow the scope
and diminish the relevance of this study.
Figure 1.1
Number of Landline Subscribers: 2009-2013
Source: TRAI reports
The above mentioned line diagram depicts the trend in landline subscribers in past
five years. It shows ever decreasing pattern of landline subscribers. This provides further
support to the approach taken by this research work to focus mainly on Mobile services.
As shown in figure 1.1 number of landline subscribers dropped from 37.06
million in December 2009 to 28.79 million in December 2013.
37
.06
33
.09
32
.69
30
.79
28
.79
Dec.09 Dec.10 Dec.11 Dec.12 Dec.13
Total Landline Subscriber
Series 1
3
1.3 Basic Framework of the study:
With entry of more and more service providers in mobile sector, the challenge to
increase customer base and retain them has pushed the mobile sector into a state of
hyper-competition. Mobile companies are leaving no stone unturned to enhance customer
loyalty.
Today’s Cellular Industry in India is characterized by cut-throat competition,
South-bound Tariff rates, a highly volatile and demanding customer base with shifting
loyalties and fast changing technological environment
The concept of Customer Loyalty, which is focal point of this research work, is
abstract one and methodology to develop and consolidate it amongst one’s customer base
remains an elusive task that makes it a matter of research for both academicians and
practicing managers from the industry.
There are many factors affecting customer loyalty. Present study is an attempt in
the direction of defining Customer Loyalty and identifying major factors affecting
customer loyalty in Telecom (mobile) sector.
This study considers Customer Loyalty in mobile sector mainly as a function of
Service Quality and Perceived Customer Value. The study attempts to ascertain that
Service Quality and Perceived Customer Value enhance Customer Satisfaction which in
turn lays the foundation of Customer Loyalty for the company.
This study also focuses on the impact of Switching Cost and Inertia on Customer
Loyalty as well as their moderating influence on Customer Satisfaction-Customer
Loyalty link.
Before we move on to define the above mentioned constructs of this research
work and explore and explain various relationships between these constructs it would be
appropriate to understand the eco system of telecom industry in India.
4
1.4 Indian Telecom Industry:
India has emerged as the fastest growing telecom industry in the world. Presently
it has over 900 million telecom subscribers out of which 19.68 million were added in last
one year only. Today India stands out as the second largest country in terms of number of
telephone connections. The teledensity too has increased to 74 % (Urban-144.95% and
Rural 42.67%). The growth of Indian Telecom sector has made it a cynosure of the world
and it has attracted foreign investments from different parts of the world. The growth of
Telecom Sector has also fuelled the inclusive growth agenda. Consequently every third
person is connected in rural India and from wage earners to shopkeepers and from
farmers to fishermen everybody has been able to enhance productivity because of mobile
connectivity.
As rural India constitutes almost 70 percent population of India, a significant
percentage of overall demand of goods and services now come from rural population.
Keeping in mind the large rural base, Govt. of India has introduced specific measures for
rural upliftment like Bharat Nirman Yojana, National Rural Employment Guarantee
Scheme and others. To promote growth of telecom industry in rural India Universal
Service Obligation Fund was setup to ensure penetration of telecom infrastructure and
services.
In her address in June 2009, President Pratibha Patil announced that the
government’s targets will include a rural teledensity of 40 percent in the next five years
and the expansion of broadband coverage to every panchayat in three years. She also
mentioned that the scheme for Common Service Centers or e-kiosks will be repositioned
so as to be a network of panchayat-level Bharat Nirman Common Service Centers
providing government services to rural citizens.
As the world succumbed to the economic slowdown, Indian economy managed to
perform relatively better than rest of the world because of its strong fundamentals in
agriculture, manufacturing and service sector.
5
1.5 Invention of Telephone and Indian scenario:
In 1876, Alexander Graham Bell invented telephone and got patent for the same,
and next year in 1877 the Bell Telephone Company came into existence. In less than five
years British firms brought the first POTS (Plain Old Telephone Service) to India in
1881. The firm was granted license to operate till 1944 by the British government. In
1947, when India got independence, the firm had set up 321 telephone exchanges, mostly
in five Indian cities, 86,000 working lines and 338 long-distance public-call offices. The
telephone density (teledensity) was 0.025 (Mody, 1995).
The Indian Post Office and the Indian Telegraph Department existed and operated
as two separate entities until 1914, when they were merged together under a single
Director-General.
1.6 History of Telecommunication in India:
Lord Dalhousie was appointed the governor - general of India by the East India
Company in 1848. His mission was simple: to unify India, a land of numerous kingdoms,
and control it. Under his enthusiastic support, the first telegraph lines in India were laid in
1851 by the British government. These were mostly installed near Calcutta, which was
then the headquarters of the British government in India. The British rulers were
primarily interested in telecommunications as a law-and-order maintenance tool
Headrick, (1998).
The Indian government decided that its telephone and telegraph systems would be
a government monopoly administered by its own civil service Menon, (1999). Just after
independence, all foreign telecommunication companies were nationalized to create the
Posts and Telegraphs Department (P & T), the central government completely controlled
the telecommunications, a legacy of British colonial rule which had enacted the Indian
Telegraph Act of 1885 and gave the central government complete authority over
telegraph technology. P & T also provided employment to a huge segment of Indians,
thus becoming a vehicle for employment and welfare. In post independence India
6
telephones were considered more of a luxury rather than an essential service. New
telephone lines were laid only for big cities and metropolitan centers.
Bella Mody (1995) found that unlike other sectors like energy, manufacturing,
nuclear technologies which were considered critical for national development and
security, telecommunications did not have any champions.
Souter (1999), suggested that the economic development of rural areas depends
on availability of good telecom infrastructure and related services but such infrastructure
and services cannot develop in rural areas unless the rural population has enough
disposable income to purchase such services.
In an important study Jain & Sridhar, (2003), concluded that the level of
investment required for good rural telecommunication infrastructure cannot come just
from government and infusion of capital from the private sector is very important. But
development of sound infrastructure was not possible under such a highly regulatory
condition so even after 50 years of independence, teledensity remained 1.92 percent in
the year 1998, as much of India did not have telephone network.
1.7 Telecom and Employment:
According to the Nasscom-McKinsey Report (McKinsey, 2005) the IT-ITeS
sector have helped in creating approximately 3 million job opportunities through indirect
and induced employment, in different sectors such as telecommunications, power,
construction, facility management, transportation, catering and other services.
1.8 Telecom Industry: Global Perspective
In the later part of 20th
century, developed countries of the world started realizing
the importance of Information and Communication Technology (ICT) to develop
efficient telecommunication network for their respective economies. To pursue the aim
effectively, most of the countries started to liberalize their existing stringent policies and
regulations.
7
In September 2000, 189 countries met at UN General Assembly and a Millennium
Declaration was made to reaffirm their commitment to improve the conditions of
downtrodden in the world through intense poverty removal programs. Participating
countries also resolved that “In co-operation with the private sector efforts would be
made to provide the benefits of new technologies, especially information and
communication.” Indicators were also fixed, to evaluate the progress, as follows:
Number of telephone lines and mobile subscribers per 100 of population.
Number of personal computers per 100 of population.
Number of internet users per 100 of population.
Even before the above declaration came into existence, many developing
countries had started liberalizing their internal policies to enable efficient development of
telecom network. By 1995 many under developed and developing countries had started
liberalizing their domestic licensing policy and import policies to attract and facilitate
inflow of foreign investment particularly in telecom field which led to development of
telecom infrastructure and penetration of mobile services.
All the efforts started bearing fruits and teledensity reached to 27.4 in 2002 in
East Asia which included China but the growth was less impressive in South Asia
including India where teledensity reached 4.5 in 2002. The largest telecom market in the
world, China, also attributes its success to rural China which contributes almost 50% of
its new subscriber base. As China focused on rural markets it avoided drop in ARPU by
creating a demand for high margin value added services such as SMS, ring tones etc.With
more than 30 million broadband users Japan is the third largest country in the world after
US and China.
Till 2006, telecom sector world over had registered phenomenal growth as well as
rapid progress in policy and technology development which resulted in intense
competition. This competition helped in reducing the digital divide between developed
and developing countries.
8
1.9 Cellular Technology:
There are mainly two types of mobile technology which are prevalent throughout
the world.
1.9.1 GSM and CDMA:
GSM i.e. Global System for Mobile Communication and CDMA i.e. Code
Division Multiple Access ,both technologies are available but India primarily uses GSM
technology at 900 MHz and 1800 MHz band.
1.9.2 Advantages of GSM:
Following advantages are associated with GSM technology:
GSM has more stabilized network.
There is little distortion and deterioration of signals inside building.
Availability of SIM i.e. subscriber identification module permits users to change
network and their handsets as per their choice.
Prevalence of huge GSM networks throughout the world makes international
roaming easily possible.
Huge subscriber base globally allows handset makers, carriers and end users a
global market to sell and buy from.
1.9.3 Advantages of CDMA: Following advantages are associated with CDMA
technology:
CDMA can accommodate more users per MHz of bandwidth.
CDMA is more power efficient and covers less area so cell size is larger.
CDMA produces a reasonable call with lower signal level where chances of call
dropping is minimized.
CDMA has variable rate voice coders which reduce the rate of transmission when
speaker is not talking, this permits channel to be packed more efficiently.
9
1.9.4 Disadvantages of GSM:
Following disadvantages are associated with GSM technology:
In GSM technology, the intellectual property is concentrated among very few
players of the industry that creates entry barriers and limits competition.
In GSM technology maximum cell site range is limited to 35 km and it is fixed.
1.9.5 Disadvantages of CDMA:
Following disadvantages are associated with CDMA technology:
CDMA doesn’t perform well in hilly areas as CDMA towers interfere with
themselves as they are normally installed on much shorter towers.
As CDMA technology is less prevalent in the world so CDMA phones generally
fail to roam internationally.
Manufacturers of CDMA handsets are normally reluctant to invest heavily as
CDMA devices have small market and less demand.
1.9.6 Ratio of GSM and CDMA in India:
Ratio of GSM and CDMA mobiles phones in India is heavily tilted in favor of
GSM mobile phones. This can be seen from Figure 1.2 that in India market share of GSM
is 98.98% while CDMA Market share is restricted to 7.02%.
Most of the service providers in Indian Telecom Industry are providing GSM
based service. There are Mukesh Ambani owned Reliance and Tata which provide
CDMA based services. Anil Ambani owned wing of Reliance is providing GSM based
service under the name of Smart.
10
Figure 1.2
Market Share of GSM mobiles & CDMA mobiles-Dec.2013
1.10 Challenges in Telecommunication:
As the market dynamics of Indian Telecom Sector is very volatile, the mobile
service providers are facing constant challenge to increase their customer base as well as
to retain the present subscriber base. Retaining present subscriber has become
challenging because of predominantly pre-paid and high churn market as customers are
not very thoughtful while they change their service provider. Similarly, attracting and
acquiring new customer has become challenging as all service providers are ready to
offer irresistible plans to woo their prospective customers.
There is another dimension which has made the environment even tougher and
that is ever decreasing ARPU (Average Revenue Per User) which has resulted in
distortion of revenue stream. Minutes of Usage data also shows either constant or
decreasing trends in past 5 years which compel service provider to find innovative ways
to encourage customers to use their mobiles for more number of minutes per month.
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
GSM & CDMA -Dec 2013
GSM, 98.98%
CDMA, 7.02%Mar
ket
Shar
e
11
In such circumstances mobile service providers want customer satisfaction to be
the strategic priority of the firm as there are many researches which have proved that
customer satisfaction has direct bearing on customer retention,Smith and Wright (2004);
Ittner and Larcker (1998). Cronin et al (2000) concluded that those customers who
experienced higher customer satisfaction with their service provider are more likely to
stay with existing service provider. Another study by Reichheld (2003) also proved that
satisfied customers are also likely to say positive words about their service provider and
recommend its services to others.
Therefore the purpose of this study is to enable the mobile service providers to
have a right understanding about the relationship among service quality, customer
satisfaction, and customer loyalty and also to study the factors like switching cost and
inertia which moderate the relationship between customer satisfaction and customer
loyalty. Cellular or mobile segment has been the key contributor to record growth in
telephone subscriptions with its wide range of offers of services. It has led the growth
wave of telecom sector in the country.
More than 95 per cent of wireless connections are prepaid. In India GSM mobile
system is pre-dominant. There is a clear distinction between the Global System for
Mobile Communications (GSM) and Code Division Multiple Access (CDMA)
technologies.
1.11 Drivers behind the growth of telecom sector:
Factors which are responsible for such a steep rise in subscriber base are constant
reduction in call tariff rates in past 15 years. In 1999 the call charges were Rs 6.70 per
minute and they have come down to 1paisa per second.
1.12 Contribution of Telecom Sector in GDP:
The contribution of telecom sector in India’s GDP has also reached to 1.6% in
2006 from 1.5% in 2000. This was partly due to huge foreign direct investment in
telecom sector of India by different countries. The growth in subscriber base also resulted
12
in overall growth of revenue but ARPU declined as many tariff plans with low charges
and life time free plans came into existence.
1.13 Telecom Circles in India:
Cellular Operators Association of India (COAI) divides the cellular market of
India into four circles.
• Four Metros: Delhi, Mumbai, Chennai and Kolkata
• A Circle: Maharashtra, Gujarat, Andhra Pradesh, Karnataka, Tamil Nadu
• B Circle: Kerala, Punjab, Haryana, U.P.(West), U.P.(East), Rajasthan, M.P.,
West- Bengal, Andaman and Nicobar.
• C Circle: Himachal Pradesh, Bihar, Orissa, Assam, North Eastern States,
Jammu and Kashmir.
At the end of the year 2013 market share of different mobile service providers were as
follows:
1.14 Mobile Service Providers in India:
Table 1.1 shows the name as well as theirArea of operations of different service
providers in India.
From the table 1.1 shown in next page it is evident that Bharti(Airtel), Aircel
group, Reliance Communication, Vodafone, Tata Teleservices, Idea and BSNL are
licensed to provide mobile services through out the country.
Reliance Telecom , Sistema Shyam Telelink, MTNL,Loop, Telewings, Quadrant
and Videocon are all local players and their presence is restricted to either one or few
circles.
13
Table 1.1
Mobile service Providers in India
Sl. No. Service Provider Area of Operation
1 Bharti All India
2 Aircel Group All India
3 Reliance Communications Ltd All India (except Assam & NE)
4 Reliance Telecom Ltd Kolkata, MP, WB, HP, Bihar, OR, Assam
& NE
5 Vodafone All India
6 Tata Teleservices All India except Assam, NE & J&K
7 Idea/Spice All India
8 Sistema Shyam Telelink Delhi, Kolkata, Gujrat, Karnataka, T.N.
including Chennai, Kerala, UP(W),
Rajasthan & W.B.
9 BSNL All India (except Delhi & Mumbai)
10 MTNL Delhi & Mumbai
11 Loop Mobile (India) Ltd Mumbai
12 Quadrant Punjab
13 Telewings Communications
Services Pvt. Ltd.
Maharashtra , Guj., AP, UP(W), UP(E),
Bihar
14 Videocon Telecommunications
Ltd
Gujrat, Haryana, UP(W), UP(E), MP,
Bihar
1.15 Market share of different service provider in India:
Figure 1.3 shows market share of different service providers in India.It is evident
from picture that Bharti( Airtel) hold maximum market share and is followed by
Vodafone, Idea and then Reliance. Reliance includes both CDMA and GSM based
mobile service provided by Mukesh Ambani as well as Anil Ambani group.
14
Figure 1.3
Market Share of Different Service Providers- Dec 2013
0.00% 5.00% 10.00% 15.00% 20.00% 25.00%
MTNL
BSNL
Videocon
Telewings
Sistema
Quadrant
Loop Mobile
Aircel
Tata
Reliance
IDEA
Vodafone
Bharti
Service Providers
Service Providers
15
1.16 National Telecom Policy 1999:
The policy had following aims:
To ensure availability of affordable and effective telecom services is most
essential for proper realization of country’s social and economic goals.
To promote a balanced development and universal access to all in uncovered
areas and provision of high level services to meet needs of the country’s economy.
To promote development of telecom facilities in hilly, tribal and remote areas of
the country.
To promote development of modern and efficient telecom infrastructure to allow
convergence of IT, Media and consumer electronics to propel India’s claim as IT super
power.
1.17 Telecom Bodies:
As telecommunication has become basic framework of country which maintains
coordination between different parts of the country. Several telecom bodies established at
different times make the smooth functioning of this sector possible. They are briefly
discussed below:
1.17.1 Department of Telecommunications:
Established in 1985, as a result of bifurcation of Department of Posts and
Telecommunication into Department of Posts and Department of Telecommunications, it
remained the only telephone service provider in India till 1986 as well as a body for
policy making in telecom field. Though it was a profitable body but it depended on Govt.
of India for its expansion and funding. It enjoyed the central position in
telecommunication field till Telecom Regulatory Authority of India (TRAI) came into
existence.
16
1.17.2 Telecom Regulatory Authority of India (TRAI):
TRAI was established as an independent regulatory body to supervise growth of
telecom sector in India. It was founded through an act of parliament and the main
function of this body is to finalize tariff structure and to settle disputes between telecom
players. The major policy document developed after TRAI was National Telecom Policy
1994, which was the outcome of ongoing process of liberalization.
1.17.3 The Telecom Commission:
It was setup in 1989 and was given financial and administrative power to deal
with various aspects of telecommunication. The Telecom Commission and DOT are
jointly responsible for policy formation, licensing, spectrum management, research and
development, standardization and validation of telecom related equipment. The Telecom
Commission took multipronged approach which has not only transformed the structure of
this sector but has also motivated other players to contribute their best.
1.18 Facts about Indian Telecom Industry
Teledensity:
Teledensity in any area can be defined as number of telephone users per 100
population. In our country Total Teledensity stands at 74.02 as on 31st December 2013.
This can be further be broken down into Urban teledensity and Rural teledensity. We
have Urban teledensity of 144.95 and Rural teledensity of 42.67.
17
Figure 1.4
Teledensity in India: 2009 - 2013
Monthly ARPU:
It stands for monthly Average Revenue per User. As we have both GSM and
CDMA telephony in our country, the Figure 1.5 shows details of ARPU for both GSM
and CDMA users in Rupees per month. The Figure shows the changing pattern of ARPU
in past five years .i.e. 2009 to 2013.
110.96
147.88
167.85
149.9 144.95
47.88
66.1676.86 73.34 74.02
21.1631.18
37.48 39.85 42.67
Dec. 09 Dec. 10 Dec. 11 Dec.12 Dec. 13
Teledensity
Urban Teledensity Total Teledensity Rural Teledensity
18
Figure 1.5
Average Revenue Per Month : GSM & CDMA
144
105
96 98
112
82
6873
80 80
Dec.09 Dec.10 Dec.11 Dec.12 Dec.13
Monthly ARPU
GSM CDMA
19
Minutes of Usage: Figure 1.6 shows trends in minutes of usage per month for both GSM
and CDMA mobile users in past 5 years.
Figure: 1.6
Minutes of Usage per Month: GSM & CDMA
It is evident from above data that number of minutes of usage has always been higher for
GSM phones.
Teledensity in Madhya Pradesh:
Our area of research is Gwalior district of state of Madhya Pradesh so it is
important to mention teledensity of Madhya Pradesh. Figure 1.7 shows total density as
well as urban and rural teledensity in Madhya Pradesh.
Dec.09 Dec.10 Dec.11 Dec.12 Dec.13
411
360 332359 379
318
270226 230 230
GSM CDMA
20
Figure: 1.7
1.19 Present Research:
This research is about finding out the factors which cause customers to show
Customer Loyalty and also the factors which affect Customer Loyalty. In this research
Customer Loyalty is studied as mainly an outcome of Customer Satisfaction which in
turn is caused by Service Quality and Customer Perceived Value.
In present study other factors studied and found to impact customer Loyalty are
Switching Cost and Inertia. The uniqueness of this research lies in its attempt to decode
the moderating impact of Switching Cost and Inertia on Customer Satisfaction-Customer
Loyalty link besides studying their direct impact on Customer Loyalty.
1.20 The Research Model: The model in Figure 1.8 shows all the constructs in present
study and their relationship with each other
Rural Telednsity
Urban …
Total Density
Madhya Pradesh
32.8
116.9
55.52
Teledensity in MP
Rural Telednsity Urban Teledensity Total Density
21
Figure: 1.8
Research Model
1.21 Objectives of the Study
The study aims to identify the various factors affecting Customer Satisfaction and
their role in developing Customer Loyalty.
The study intends to develop a model explaining relationship between Service
Quality, Customer Perceived Value, Customer Satisfaction, Switching Cost,
Inertia and Customer Loyalty.
The Study also focuses on Switching cost and Inertia as moderators of Customer
Satisfaction and Customer Loyalty link.
22
To fulfill these objectives the study has progressed in the following manner:
Service Quality is defined and various factors affecting Service Quality
are identified.
Perceived Customer Value is defined and measured.
Customer Satisfaction is defined and studied as mediating variable
between Service Quality and Customer Perceived Value on the one hand
and Customer Loyalty on the other.
Switching Costs and Inertia are defined and their direct impact on
Customer Loyalty is studied.
Switching Costs and Inertia are also studied as moderating variables
modifying the impact of Customer Satisfaction on Customer Loyalty.
1.22 Research Questions
Following research questions were framed to meet the objectives of the study:
1. What are the Key factors affecting service quality?
2. Is there a difference in level of customer satisfaction among respondents coming
from different demographic background?
3. Is there a difference in perception of Customer Loyalty among respondents
coming from different demographic background?
4. Is there an association between various Service Quality dimensions and Customer
Satisfaction?
5. Is there an association between Customer Perceived Value and Customer
Satisfaction?
6. Is there an association between Customer Satisfaction and Customer Loyalty?
7. Is there an association between Switching Cost and Customer Loyalty?
8. Is there an association between Inertia and Customer Loyalty?
23
9. If there exists a positive link between Customer Satisfaction – Customer Loyalty?
Is this link moderated by Switching Cost and Inertia?
1.23 Need for the study
The existing literature is replete with studies on service quality and customer
satisfaction. There is also no dearth of work to explain customer loyalty in connection
with above mentioned constructs. There are some studies to understand service quality
dimensions and customer loyalty in context of telecommunication but the model
developed in this study stands out as a unique proposition as none of the earlier works
explained the role of Switching Cost and Inertia on Customer Loyalty as well as their
moderating role on Customer Satisfaction-Customer Loyalty link.
There are studies which have established the Customer satisfaction – Customer
loyalty link as a linear relationship. There has not been a single study of Indian telecom
sector which properly addresses the issue of Moderating role of Switching Cost and
Inertia on Customer Satisfaction-Customer Loyalty link.
The present study is an attempt to fill the knowledge gap which has existed in
Customer Satisfaction and Customer Loyalty link and to explain to that this relationship
is not linear and it is moderated by the presence of Switching Cost and Inertia.
1.24 Importance of the study
Importance of this Research work can be summed up in following words:
It expands the literature of Customer Loyalty in mobile sector and also adds to it a
new dimension by studying moderating impacts of Switching Cost and Inertia on
Customer Loyalty.
The Output of this research will benefit both academicians as well as practising
managers in telecom industry to understand and tackle the intricate relationships of
Service Quality, Customer Satisfaction and Customer Loyalty in highly volatile market of
telecommunication.
24
The findings of this research will also enable the managers in telecom industry to
consolidate their customer base and to enhance their revenue by devising appropriate
retention strategy which finds appropriate application of switching cost.
1.25 Structural contents of the thesis
This thesis is divided into 5 chapters.
1.25.1 Chapter One:
This chapter emphasizes the importance of telecommunication and introduces the
present research study in brief, then it moves on to give reasons behind focusing on
mobile services instead of landline services. This chapter traces the invention of
telephone, and refers to different telecommunication technologies their advantages and
disadvantages.
Later this chapter discusses the history of telecom industry and its present status
in Indian as well as global contexts. This chapter also identifies drivers of growth behind
this sector as well as contribution of this sector in India’s GDP. Present teledensity,
monthly average revenue per user, ratio of GSM and CDMA phones, number of telecom
circles, major service providers in India as well as in Gwalior are also mentioned in this
chapter.
Finally, Objectives of this research study are enumerated which are followed by
problem statements, need for the study, importance of the study and finally brief
summary of contents of different chapters.
1.25.2 Chapter Two:
The second chapter of this research work contains elaborate review of literature
related to different constructs used in this study. The chapter opens with concept of
service quality and enumerates characteristics of Service Quality as identified by
different researchers from time to time. This is followed by a discussion about
dimensionality of Service Quality and tools for measurement of Service Quality. Next,
25
the concept as well as noteworthy contributions about the construct of Customer
Perceived Value are discussed.
Then the focus of this chapter shifts to concept of Customer Satisfaction,
measurement of Customer Satisfaction and noteworthy contribution in this field. Studies
about relationship between service quality and customer satisfaction also find place in
this chapter. Then Customer Loyalty is defined and various studies related to it are
discussed.
Finally, Switching Cost and Inertia are described and their role as moderator is
explained.
1.25.3 Chapter Three:
This chapter discusses the Research Methodology, Research Design, Sampling
Design used in this work to achieve its research objectives. The chapter also mentions
data collection method and measurement scales along with the questions used in each
scale and sources from where they have been taken.
This chapter also discusses the hypotheses developed to achieve research
objectives as well as the tools applied to test these hypotheses.
The chapter also gives brief account of t-test, ANOVA, Factor Analysis,
Correlation and Regression along with the situations where these tools find application in
this work.
1.25.4 Chapter Four:
This chapter is about Data Analysis and it is divided into seven sections.
First section deals with various demographic aspects of respondents and tells
about Age, Gender, Marital status, Educational Qualification, Occupational pattern ,
Service provider, type of connection of respondents.
26
Second section deals with different aspects of Constructs under study. It mentions
Descriptive statistics, Reliability statistics and findings of factor analysis about various
constructs in the study.
Third section deals with the Factors affecting service quality,it also ate.mpts to
identify various dimensions of service quality
Fourth section is about differences in customer satisfaction levels of customers
coming from different demographic background.
Fifth section is about differences in customer loyalty levels of customers coming
from different demographic background.
Sixth section is about establishing an association between customer satisfactions
on the one hand and service quality and customer perceived value on the other.
Seventh section is an elaborate study of factors affecting customer loyalty.
1.25.5 Chapter Five:
This chapter is about Findings and conclusions of the research study which is
followed by discussion about limitations of the study and also few words about scope of
further research.
CHAPTER II
REVIEW OF LITERATURE
27
2.1 Service Quality
2.1.1Concept of Service Quality:
Service Quality is combination of two words, Service and Quality. As per
Hasenfield (1974) service can be defined as actions of an individual or organization that
maintain and improve well being or functioning of people. Quality focuses on standard or
specification that a generating organization promises.
Parasuraman, Zeithaml & Berry (1988) defined service quality as the customer‟s
overall judgment of the excellence of the service or the difference between one‟s
expectation and the actual service performed.
The American Society for Quality Control defined Quality as "the totality of
features and characteristics of a product or service that bear on its ability to satisfy the
stated or implied needs". Parasuraman et al., (1994) considered quality as a gap between
what customers feel should be offered and what is actually provided.
According to Thomas, Dan R. E., (1978), service differentiation is necessary for
the growth and development of service businesses.
2.1.2 Characteristics of Services:
Although service industries are quite heterogeneous in nature, there are some
common service characteristics which are found in every service industry. Kandampully,
( 2002) described services as intangible in the sense that they cannot be seen, felt, tasted,
or touched. He explained four unique characteristics that differentiate service from a
product. These four characteristics are:
1 Intangibility
2 Heterogeneity
3 Inseparability
4 Perishability
Intangibility: Intangibility is the primary characteristic that differentiates a service
from a product. Services are not tangible. A good is an object, a device, a thing; whereas
a service is a deed, a performance, an effort (Berry, 1984). When we buy a service, there
28
is generally nothing tangible about it. He says that services are consumed but not
possessed. The services offered in general are a combination of tangible and intangible
elements. It is whether the essence of what is being bought is tangible, or intangible, that
determines its classification as a physical good or a service.
Heterogeneity: In comparison to goods service are generally less standardized and
uniform. Services are not homogeneous. Service industries have human component
involved in performing some services than others. They can be specified as people or
equipment based. Equipment-based services vary depending on whether they are
automated or monitored by skilled or unskilled operators. People-based services also may
differ depending on whether they are provided by unskilled or professional workers.
Inseparability: Inseparability refers to the notion that a service is both
simultaneously produced and consumed at the same time. Kandampully (2002) points
out that goods are normally produced first and then consumed. Whereas, a service is
generally sold, and then produced and consumed simultaneously, at the same time.
Lovelock and Gummesson (2004) suggest that a group of separable services exist that do
not involve the customer directly such as transporting freight and laundering clothes.
Participation of customers in the production process, or delivery process, the interaction
between the service provider, the service environment and the customer, are also some of
the characteristic of services.
Perishability: Services cannot be stored, hence services are highly perishable, and
e.g. empty tables in a restaurant can be seen as a revenue opportunity lost forever. Time
cannot be held over for future sale, thus, services cannot be inventoried. The Perishability
of services is not a problem when demand is steady, because it is easy to staff the services
in advance, when demand fluctuates, service companies have difficult problems.
Berry, Parasuraman and Zeithaml (1985) were pioneers in service quality
research. They carried their research in four different service areas namely banking, stock
broking, credit card companies, and household appliances. They came up with ten factors
to describe service quality namely 1 Dependability, 2 Willingness, 3 Competence, 4
29
Availability, 5 Courtesy, 6 Communication, 7 Trustworthiness, 8 Assurance, 9 Empathy
and 10 Tangibility.
In a later study, the authors reduced the ten factors to five claiming that these
were valid in general terms Parasuraman et al., (1985):
1 Tangibility;
2 Dependability;
3. Willingness, readiness;
4. Assurance;
5 Empathy, insight.
2.1.3 Dimensions of Service Quality:
Being an elaborate concept, service quality is supposed to have many dimensions.
Different researchers studied the construct in various industries and came up with their
own models of service quality.
Martinez & Martinez (2010) concluded that in past 30 years there has been
considerable interest and debate both among academicians as well as practitioner to
define and measure service quality.
Lehtinen and Lehtinen (1982) studied service quality and found it to be three
dimensional 1. Physical quality 2. Interactive quality 3. Corporate (image) quality. They
also found that a comparative analysis revealed that corporate quality tended to be more
stable over time in comparison to two other quality dimensions.
Later on in a separate study Lehtinen (1983) described service quality in terms of
“process quality” and “output quality”. Process quality is judged by a customer when
service is being delivered whereas output quality is judged by a customer after a service
has been delivered.
Rust and Oliver (1994). proposed a three dimensional model in which the overall
perception of service quality is dependent on a customer‟s evaluation of three dimensions
of the service encounter:
30
(1) The customer-employee interaction i.e. functional or process quality
(2) The service environment
(3) The outcome i.e. technical quality
Berry et al. (1994) on the basis of his ten years study of service quality in America
concluded that service quality possess many facets. The ten lessons learned from their
study are as follows:
(1) Listening - Businesses must listen to their customers.
(2) Reliability - Businesses must deliver the promised services dependably and
accurately.
(3) Basic Service - Customers are interested in the basics, fundamentals, and
performance; not in promises. They are not expecting “fanciness,” and they are not
unreasonable in their expectation.
(4) Service Design - Customers want a system or systems that give good and reliable
customer service.
(5) Recovery - Businesses must be quick at handling services, efficiently, and fairly.
(6) Surprising Customers - Businesses should be in position to surprise customers with
their uncommon swiftness, grace, courtesy, competence, commitment, and
understanding.
(7) Fair Play - Customers expect that the companies must treat them fairly and become
resentful and mistrustful when they perceive things otherwise.
(8) Teamwork - Various systems within a company should work as an overall team in
providing quality service to customers.
(9) Employee Research - Businesses should collect information from employees about
the level of service quality provided to them and, things that hinder the provision of
good service quality and also potential problems in providing good service quality.
(10) Servant Leadership - Top management must lead by serving those who provide
direct service to customers and by providing what is needed for good quality service.
31
Garvin(1998) suggested a multi dimensional model for service quality and
emphasized that customer survey can reveal that which dimensions are important for a
particular industry. He suggested following dimensions:
1.Performance 2. Features 3. Reliability.4.Conformance 5.Durability 6 Serviceability 7
Aesthetics.
Bitner et al (1996) defined satisfaction as the customers‟ evaluation of a product
or service in terms of whether that product or service has met their needs and
expectations.
2.1.4 Measurement of Service Quality:
It has always remained an elusive task to measure service quality. There have
been several approaches to measure it, out of which two measurement scales namely
SERVQUAL and SERVPERF are worth discussing.
In SERVQUAL, service quality is derived by Comparing customer expectations
with customer perceptions of actual service performance. The difference between
perceptions and expectations is described as service quality gap (Q = P-E), also known as
GAP 5 Parasuraman et al., (1985; 1988). A wide gap would reflect poor service quality
and it implies that the service provider needs to bring improvement on the service offered
to its customers.
The SERVPERF scale is comprised of 22 perception related items and it excludes
any reference to expectations. According to Cronin and Taylor (1992), their unweighted
performance-based SERVPERF scale was a superior method of measuring service
quality. This scale‟s reliability ranges from .884 to .964, depending on industry type, and
it exhibits both convergent and discriminant validity.
The main difference between these two scales is that SERVQUAL operationalised
service quality by comparing the perceptions of the service received with expectations,
while SERVPERF maintained only the perceptions of service quality.
32
Service quality is more difficult to measure as compared to measurement of quality of
goods Parasuraman, Berry, and Zeithaml, (1985).They suggested the Gap model.
GAP 1: Gap between consumer expectation and management perception:
GAP 2 : Gap between management perception and service quality specification:
GAP 3: Gap between service quality specification and service delivery:
GAP 4 : Gap between service delivery and external communication GAP
GAP 5: Gap between expected service and experienced service:
According to Lovelock (1994), in addition to the five gaps, two more gaps have
been identified, which exists during design and delivery of service offering. The modified
gaps as mentioned by Lovelock can be described as follows:
1. The Knowledge gap
2. The standards gap
3. The delivery gap
4. The internal communications gap
5. The perceptions gap
6. The interpretation gap
7. The service
Bakakus and Boller (1992) found that although SERVQUAL had been in use for
the assessment of service quality in different types of service industries, there were many
limitations as well as criticism about SERVQUAL. Part of the criticism concerns the 5
dimension configuration of the scale, part about the appropriateness of operationalization
of service quality as the expectations-performances gap score, and the scale‟s
applicability in different type of service industry.
Cronin and Taylor (1992) developed a model and criticized Parasurman et al.
(1985) that their gap theory of service quality was not supported by much empirical or
theoretical evidence, and they developed a “performance-based” service quality
measurement scale called SERVPERF.
33
Kerlin (2000) used the SERVQUAL survey instrument to evaluate student
satisfaction in service quality. Student expectations and perceptions of service quality in
registration, financial aid, counseling, and career center and library services were probed.
Outcome showed that students attach less importance to the tangible aspects of service
quality, such as appearance of facilities and brochures and more importance to aspects
that provide reliable services and demonstrate attention to their personal needs.
2.1.5 Noteworthy contributions in service quality:
Ladhari(2009) studied service quality and found it to be top priority of present
day business organisations as it give them not only competitive advantage but also play a
crucial role in sustaining growth.
Howat et al (2008) & Chen (2008) found that interest of marketers and
academicians in service quality is due to its favorable impact on customer satisfaction
and customer loyalty. Jonson (2008) stated that there is clear relationship between
improving service quality & higher profit.
Seth et al (2005) studied the role of service quality in company performance and
attracting new customers.
Swoboda et al (2007) while studying service quality in retail sector found that
most important factor affecting customer choice of store for grocery purchase is service
quality.
Watson (1999) described the relationship among product quality, service quality,
image of the firm and customer satisfaction in a commodity industry. Product quality and
some items of service quality and image of the firm were found to have significant
influence on customer‟s satisfaction. In the area of service quality it was found that
people intensive areas were significant, while process related service areas were not
significant. More items in the area of image, or reputation of the firm were found to be
significant predictors than in the area of service quality.
34
Caruana (2002) studied the concept of service loyalty and distinguished it from
customer satisfaction and developed a model that links service quality to service loyalty
through customer satisfaction. Results established that customer satisfaction plays a
mediating role in the effect of service quality on service loyalty.
Kang and James (2004) empirically studied the European perspective of service
quality i.e. Gronroos‟ model which depicted service quality to be consisting of three
dimensions: technical, functional an image and that image functions as a filter in service
quality perception. The results of his study about a mobile service sample revealed that
Gronroos‟ model represents service quality in a better way than the American perspective
with its limited attention on the dimension of functional quality.
Edvardsson (2005) emphasized that service quality was perceived and determined
by the customer solely on the basis of co-production, delivery and consumption
experiences. He suggested that favourable and unfavourable customer experiences are
more important in forming service quality perceptions. He further described that there
were two types of service quality clues: 1.Clues of experience related to functionality and
2.Clues of experience related to emotions. Positive and negative emotions seem to be
more and more important in forming service quality perceptions, and negative emotions
had a stronger effect on perceived service quality than positive emotions.
Kang (2006) practically examined the conceptualization of service quality (both
technical and functional). He concluded that a two-component model gives better fit than
a model concentrating on solely functional quality such as SERVQUAL.
Berry (1986) studied service quality in retail sector and found it to be a basic
Strategy which results into differential advantage over competitors.
Berry, Leonard L., Parasuraman, A., (1992) suggested that the service revolution
should occur at two different levels: 1 a fundamental change in attitude towards service
quality and increase in aspirations for service quality, and 2 promotion of the culture and
adoption tools that make quality improvement a habit.
35
Parasuraman, A, Zeithmal, Valarie A, Berry, Leonard L,(1994) found that more
and more use of SERVQUAL has been followed by an increasing debate about the need
for SERVQUAL's expectations component, the interpretation and operationalization of
expectations, the psychometric validity of SERVQUAL's difference-score formulation
and the number of dimensions expressed by SERVQUAL's items. They also noticed that
managers can arrive at better assessment of service quality by comparing perceptions
against expectations than by interpreting perceptions only.
Gobbott Mark (2000) studied several psychological studies and showed that non-
verbal behaviour by the service provider affects service evaluation, because the quality of
interaction between the customer and the service provider influences the customers‟
perception of service quality.
Macro Antonio Robledo (2001) explained that burden of running a successful
organizations with top quality services make the measurement of service quality and its
subsequent management an important objective. Their study compared four different
methods for measuring service quality within an airline setting. Six instruments were
used to measure the service of three international airline companies. The dimensionality
of quality in airlines was explored and three factors appear as determinants: tangibility,
reliability, and customer care.
Clare Chow-Chua, Raj Komaran (2002), developed a simple methodology for
managing service quality that takes into account of what customers expect to receive and
what the service provider can offer. They suggested a four-step procedure that explains
the derivation of the customer-service provider matrix (CSM). Customer feedback and
data on an international coffee outlet are employed to demonstrate the application of the
CSM as a visual tool. Two versions of the CSM matrix are developed; one with raw data
and another with an illustrative weighting procedure. Service providers can prioritize and
re-allocate resources to increase the levels of the attributes of service quality that would
be valued by customers.
Parasuraman (2002) observed in his article that companies which are into
delivering services should broaden their assessment of productivity from the conventional
36
company oriented perspective to a dual company customer perspective. This
comprehensive approach can help reconcile conflicts – the leverage synergies – between
improving service quality and boosting service productivity. They also suggested a
conceptual frame work for understanding the inter-linkages among service quality and
the various components of the company-customer perspective of productivity and discuss
the implications of the frame work for service executives and researchers.
Douglas M Stewart, (2003) developed a framework based on the three T's of task,
treatment, and tangibles as a means of organizing the application of the diverse and
growing body of service quality literature to encounter design.
Nimit Chowdhary, Monika Prakash (2007) tried to investigate whether
generalization of service quality dimensions is possible. Service providers are often not
sure of the amount of tangibilization necessary and the right mix of other service quality
dimensions- reliability, assurance, empathy, responsiveness and the role of price added
by the researcher.
According to Alok Goel and Seema Erum (2010), customer satisfaction and
service quality measurement practices in call centers of India have emerged as a leading
player in the global business process outsourcing industry. Their findings indicate that it
is imperative for call center managers to develop systematic and comprehensive
measurement of perceived service quality in order to provide superior call center
experience to their customers
2.2 Customer Perceived Value
2.2.1Concept of Customer Perceived Value:
Zeithaml (1988) defined Customer Perceived Value as “the consumer‟s overall
assessment of the utility of a product based on perceptions of what is received and what is
given.” She considered this assessment as a comparison of a product or service‟s „get‟
and „give‟ components.
37
The most popular definition of customer value is the ratio or trade-off between
quality and price, Monroe, (1990), which is a value-for-money conceptualization.
2.2.2 Dimensionality of Customer Perceived Value:
Petrick J.F (2002) states that Perceived Value of service comprises five
dimensions: quality, emotional response, monetary price, behavioral price and reputation.
In another study in 2004, he found that marketers could benefit from multidimensional
concept of perceived value by comparing the relative importance of each dimension and
identifying the dimensions that perform well or poor in to give specific direction for
improving value.
2.2.3 Noteworthy Contributions:
Porter (1990) discussed the concept of value as „superior value to the buyer in
terms of product quality, special features, or after-sale service.
P. Kotler, and L. Keller (2006) concluded that Customer perceived value is the
differences between the prospective customer‟s evaluation of all the benefits and all the
costs of an offering and the perceived alternatives.
Oliver and DeSarbo (1988) studied explained Customer Perceived Value by
taking idea from Equity Theory and described it as ratio of consumer‟s outcome/input to
service provider‟s outcome/input. It is seen as comparison of relative rewards and
sacrifices related to an offer.
Holbrook (1994) studied customer value and emphasized its importance by
declaring it the fundamental basis for all the marketing activity.
Sirdeshmukh, Singh, and Sabol (2002) while drawing comparison between
customer value and customer loyalty, described customer value as super ordinate goal
and customer loyalty as subordinate goal, he further elaborated that according to goal and
action identity theory super ordinate goal regulates the subordinate goal. Behavioral
intentions of loyalty are directed towards a particular service provider as long as
customer receives superior value.
38
Bolton & Drew, (1991) defined customer perceived value as a major determinant
of customer loyalty in telephone industry.
2.3Customer satisfaction
2.3.1 Concept of Customer satisfaction:
Oliver (1997) defined Customer satisfaction as Consumer‟s fulfillment response.
It is consumer‟s judgment that a particular product or service is providing pleasurable
level of fulfillment, though, it could be under or over fulfillment. Over fulfillment
provide pleasure by delivering additional unexpected pleasure and underfulfilment
provides pleasure by providing greater pleasure than one anticipated.
Westbrook and Oliver (1991) described customer satisfaction is a post choice
evaluative judgment concerning a specific purchase selection. Oliver (1987) examined
whether satisfaction was an emotion and concluded that satisfaction is a summary
attribute phenomenon coexisting with other consumption emotion
Rust and Zahorik (1993) Studied Customer Satisfaction in retail banking sector
and developed a mathematical model to determine which customer satisfaction
component has greatest impact and how much money should be spent to maximize
customer satisfaction.
Measuring Customer satisfaction has a critical role in bringing service
improvement. It allows an agency to understand what its customer‟s value, how values
vary between different types of customers, and where the agency can take action to
improve service delivery.
Lawler Edward (1995) explained that companies are successful which possess
quality service in the top of their vision list. These companies measure customer
satisfaction and identify the most common reasons behind customer dissatisfaction and
then they attempt to eliminate them.
39
2.3.2 Importance of customer satisfaction:
Abdel Moniem Ahmed and Mohamed Zairi(2002), conducted an analysis on
„Customer Satisfaction „and found it to be fundamental to the well being of individual
consumers, to the profits of firms and to the stability of Economic and political structures.
The authors developed a methodology for self-assessment about customer satisfaction at
seven levels. The authors found that there are three groups of customers which are often
neglected in the existing customer satisfaction programmes they are
1.Internal customers,
2.Channel members, and
3. Buying center members in business-to-business markets.
They stated that an effective customer satisfaction program must include 1.Management
commitment and support, 2.Employees involvement and training, 3.Information
gathering from stakeholders, 4.Customer contact and personnel data, 5.Warranty cards
and service records, 6. Face-to-face evaluation,7. Responses sorting, 8.Wants formulating
and satisfaction and 9.Action plan. They found that, many companies seeking business
excellence are assessing themselves against these nine criteria of the model and thus they
first understand fully their today‟s position and use this benchmark to pursue continuous
improvement. Thus, a comprehensive self-assessment on a regular basis, in a systematic
way and constant reviews of organization‟s activities and yields the best results.
Edward C. Malthouse (2003) studied the relationship between overall satisfaction
of service and satisfaction of the service for organizations with multiple units. The
customers explain their satisfaction with a product or service in terms of specific aspects
such as the product features, price, customer service, or a combination of all these
features. This study explained how particular type of customer satisfaction impacts
overall satisfaction, using regression analysis. Different subunits within an organization
show different relationship between specific aspects of satisfaction and overall
satisfaction. Such variation could be relevant for marketing decisions and the
organization needs different strategies for different subunits. Hierarchical Linear Models
(HLM) was used to evaluate how strongly a specific type of satisfaction is related to
40
overall satisfaction and whether the strength of these relationships changes across
subunits. The empirical results of this study shows that some specific type of satisfaction
may be a strong predictor of overall satisfaction and for same specific type of satisfaction
have no relationship to overall satisfaction.
The expectancy disconfirmation theory proposes that consumers make
satisfaction judgments by evaluating actual product/service. Four psychological theories
were identified by Anderson that can be used to explain the impact of expectancy or
satisfaction:1 Assimilation Theory 2. Contrast Theory 3.Generalised Negativity Theory 4.
Assimilation-Contrast Theory
Deyong (1994) came up with a methodology to identify conceptual links between
customer satisfaction dimensions and process performance metrics. Their methodologies
indicated a link between the customer satisfaction dimensions and process performance
metrics.
Brown & Swartz (1989) found that when a service is given, the personal
relationship that gets established between employees and customers will be extremely
important in determining the service quality perception. In turn, the perception of the
quality offered by the organization on the part of the employee has an impact on the real
quality offered.
Parkington & Schneider (1979), have shown that when employees have a
different service orientation from the orientation adopted by the management, the
employees from low level of satisfaction and feel strong intention to leave their jobs and
have high levels of frustration and the sensation that customers have a poor opinion of the
service quality provided by the firm.
2.3.3Measurement of Customer Satisfaction:
Vavra, T.G. (1997). found that at the heart of the satisfaction process lie the
comparison of what was expected with the product or service‟s performance and what
was perceived by the customer – this process has been described as the „confirmation /
41
disconfirmation‟ process. If perceived performance is only slightly less than expected
performance, assimilation will follow and perceived performance will be adjusted
upward to equal expectations. If perceived performance lags expectations substantially,
opposite will follow and the shortfall in the perceived performance will be exaggerated.
Boulding et al (1993) studied another dimension of customer satisfaction, which
deals with the difference between transaction specific and cumulative customer
satisfaction. Customer satisfaction is viewed as outcome of post-purchase evaluative
judgment of a specific purchase occasion according to transaction-specific perspective.
Cumulative customer satisfaction is an overall evaluation based on the total purchase and
consumption experience with goods or service over a period of time. Cumulative
satisfaction is a more fundamental and basic indicator of the firm‟s past, present and
future performance and its cumulative satisfaction that encourages firm to invest in
customer satisfaction.
Bitner et al (1996) described customer satisfaction as customers‟ evaluation of a
product or service on the basis of whether that product or service has fulfilled their needs
and expectations.
Giese & Cote (2000) did an elaborate survey and defined customer satisfaction as
a response (emotional or cognitive), the response is about a particular focus
(expectations, product, consumption experience, etc) and the response occurs at a
particular time (after consumption, after choice, based on accumulated experience, etc)
A study by stated that consumer/customer satisfaction is determined by the relationship
between the customer‟s expectations and perceived performance derived from the use of
a product or service.
Oliver (1999) defined, "Satisfaction as pleasurable fulfillment. Satisfaction is the
customer‟s sense that consumption provides outcomes against a standard of pleasure
versus displeasure. It is judgment that a product or service feature, or the product or
service itself, provides a pleasurable level of consumption related fulfillment."
42
Tse and Wilton (1998) indicated customer satisfaction as the customer‟s response
to the evaluation of the perceived difference between prior expectations and the actual
performance of the product as perceived after its consumption.
In a study Luo and Homburg (2007) found significant evidence that
customer/customer satisfaction is an important driver of firm‟s profitability. They also
explained that customer satisfaction does free word-of-mouth advertising and reduces
marketing costs.
Gustafsson et al (2005) carried the research in a large Swedish
telecommunications company to find out the effect of customer satisfaction on retention
and concluded that customer satisfaction has a positive effect on retention.
2.3.4 Factors influencing Customer’s Level of Satisfaction
Fornell et al (1996) developed the American Customer Satisfaction Index (ACSI),
a customer-based measurement system for evaluating the performance of firms,
industries, economic sectors and national economies. ACSI measures the quality of the
goods and services as experienced by the customers. Their findings proved that customer
satisfaction is determined by customization. Customer expectations and quality drives
customer satisfaction that value or price.
Atkinson (1988) found out that cleanliness, security, value for money and
courtesy of staff determine customer satisfaction.
Pothas et al (2001) discovered an untraditional way of monitoring customer satisfaction
based upon expression of customer perceptions from customer‟s point of view rather than
from the viewpoint of investigator.
Turel and Serenko (2004) in a study to validate American Customer Satisfaction
Model in mobile telecommunication sector and found a positive association between
perceived customer expectations, perceived quality, value and satisfaction and a negative
association between satisfaction and customer complaints.
43
Rust and Zahorik (1993) studied customer satisfaction and linked it to individual
loyalty, aggregate retention rate, market share and profits earned.
2.3.5 Service Quality: the Key Influence in Customer Satisfaction
According to Berry et al (1997) found that service quality has become an
important differentiator and also the most powerful weapon against competitor, which all
the service organizations want to possess.
In a study Zeithaml, Berry and Parasuraman (1996) show that companies which
offer superior service register higher than normal growth in market Share. Zeithmal et al
(1996) developed a conceptual framework for the behavioral and financial consequences
of service quality. Superior (inferior) service quality is related to favorable (unfavorable)
behavior intentions.
Taylor and Baker (1994) in a study found that service quality and customer
satisfaction are recognized as key factors in the formation of customers purchase
intentions in service industry. It appears that customer decision-making which comprises
the interaction of satisfaction and service quality explains customer purchase intentions to
better extent.
Bolton (1998) proved that customer satisfaction is directly related to the tenure of
the relationship. The strength of the relationship between tenure and satisfaction levels
depends on the length of customer‟s prior experience with the organization.
Anderson et al. (1994) found that as quality and expectations increase, this
positively effects customer satisfaction in the long run, but increased expectations may
result in negative impact in the short run. Expectations have a positive effect on customer
satisfaction in the long run because they take into account the accumulated memory of
the market concerning all past quality information and experience.
Anderson and Sullivan (1993) did an elaborate study about the antecedents and
consequences of customer/customer satisfaction based on representative survey of 22,300
44
customers of a variety of products and service s in Sweden during 1989-90. They came
up with following conclusions:
Firms, which provide high-quality products, have a more satisfied customer base and
will have higher chances of retention of their customers.
To effectively manage customer satisfaction the firms should try to control the impact
of negative disconfirmation through proper complaint handling and effective
customer service.
The firm‟s future profitability is directly linked to satisfying customers in the present.
Hallowell (1996) studied the relationship between customer satisfaction and
customer loyalty and customer loyalty and profitability using multiple measures of
satisfaction, loyalty and profitability and found that attainable increase in satisfaction
leads to dramatic increase in profitability
Cronin Brady and Hult (2000) conceptualized the effects of quality, satisfaction
and value on consumer‟s‟ behavioral intentions and concluded that indirect effects of
service quality and value enhance their impact on behavioral intentions.
Anderson et al 1994 found Customer Satisfaction to be fundamental indicator of firms‟
performance as it has behavioral as well as financial consequences for the firm.
Reichheld ans Sasser(1990) found that greater customer satisfaction reduces costs
of future transactions.
Anderson 1996 found that greater is the customer satisfaction lesser is the price elasticity
Aaker and Jacobson (1994) investigated that whether movement in firm‟s stock
price which shows firm‟s value is associated with perceived quality measures. They
found a positive relationship between the two and suggested that managers should convey
the brand‟s quality image to the market so that stock market will rely less on short term
measures of business performance ensuring long term viability of the firm.
45
Cronin and Taylor (1992) investigated the measurement issue of service quality as
well as relationship between service quality, consumer satisfaction and purchase
intention. Their findings suggested that performance based measurement of service
quality may be an improved means of measuring service quality construct. They also
found that service quality is an antecedent of customer satisfaction which affects
purchase intention. They also found that in comparison to customer satisfaction service
quality has less effect on purchase intention. Lawler Edward (1995) found those
companies to be successful which have service quality as their top most priority.
Customer Satisfaction has become one of the most important construct for
marketing scholars McQuitty et al.(2000); Morgan et al. (1996) and also a precious goal
for marketing managers as found by Erevelles and Leavitt (1992).
It is evident from the above definitions different scholars have taken different approach to
define the construct of customer satisfaction.
Woo& Fock 1999 did extensive literature review on customer satisfaction and
concluded that there are four aspects.
1. Process of evaluation of customer satisfaction is personal and subjective and the
outcome of this process is perceived judgment emerging out of comparison of prior
expectation and actual performance.
2. Process of evaluation of customer satisfaction is related to a particular attribute of
product or service and to the whole product or service.
3. “pleasurable fulfillment” of Oliver does not necessary relate to tangible alone.
4. There would be an optimal point for customer satisfaction and Dissatisfaction can be
result of underfulfilment and overfulfilment.”
Capraro, Broniarczyk, and Srivastava (2003) observe that “today, most firm‟s
programs to control customer defections center heavily on the management of customer
satisfaction.
Kim et al., (2004) found that the service quality positively affected customer
satisfaction. He elaborated that call quality is the most important issue that influences
46
customer satisfaction for mobile services. Customer satisfaction and switching barriers
has positive impact on customer loyalty.
Palkar (2004) studied the factors which determine the customers‟ satisfaction and
customer loyalty in mobile service market. He found that important determinants of these
two are quality of service, price structure and value added services offered by the
provider.
Moreover, satisfied customers have a higher propensity to stay with their existing
service provider than the less satisfied ones (Cronin et al., 2000) and are more likely to
recommend the service provider to others, leading to improved bottom line for the
company Reichheld (2003, 2006).
2.4 Customer Loyalty
2.4.1 Concept of Customer Loyalty
Customer Loyalty may be defined as a favorable attitude towards a particular
brand resulting in a regular purchase of that brand over time, suggesting that loyalty is
present when favourable attitudes are manifested in repeat purchase behaviour (Keller,
1993).
Engel et al., (1982) explained brand loyalty as the preferential, attitudinal, and
behavioral response of consumers towards one or more brands in a product category
shown over a period of time.
There have been several studies to uncover the antecedents of customer loyalty.
Lee, Lee,& Feick, (2001) argued that effective means of generating customer loyalty is to
delight customers while Parasuraman & Grewal, (2000). Considered customer loyalty as
a result of superior value delivered to customers, they developed a pyramid model to
explain quality –value-loyalty chain.
47
2.4.2 Customer Loyalty and Customer Retention:
Several studies in the past did not try to discriminate between terms customer
retention and customer loyalty, both terms were used interchangeably to describe the
same phenomenon( Zenithal et al.,1996;Reichheld and Sasser,1990)
This study has also taken the same approach and term Customer Loyalty is used
to describe customer retention.
There are many studies which consider switching cost as an important moderating
variable which influence customer loyalty through customer satisfaction (Fornell, 1992;
Lee et al., 2001; Oliver, 1999)
Customer Loyalty has always remained a cherished goal for all marketing
companies Reichheld & Schefter, 2000).
Dawes and Swailes (1999) proved that high customer loyalty is crucial to
successful customer retention, and firms who compete on the basis of loyalty will win
over the battle of competition.
The concept of customer loyalty construct has developed gradually over the years.
In the earlier times, the emphasis of loyalty was on brand loyalty with respect to tangible
goods Tucker, (1964); Day, (1969). Cunningham (1956) explained brand loyalty as the
proportion of purchases of a household dedicated to the brand it purchases most often.
Dick and Basu, (1994)studied Brand loyalty in marketing context and concluded
that it is consumer‟s commitment to repurchase or otherwise continue using the brand
and can be demonstrated by repeated buying of a good or service or other positive
behaviors‟ such as word of mouth advocacy
Day (1964) concluded that there is more to brand loyalty than just regular
purchasing of same brand . Jacoby (1971) presented a conceptualization of brand loyalty
that includes both a behavioural and an attitudinal constituent. Jacoby and Chestnut
(1978) in a later study defied brand loyalty by incorporating both behavioral and
attitudinal constituents. Brand loyalty is the extent of the faithfulness shown by the
48
consumers‟ for a particular brand, expressed through their repeat purchases, despite of the
marketing pressure generated by the competitors.
Brand loyalty is non random behavioural response expressed over time by some
decision making unit with regard to one or more brands out of a set of brands and is a
function of psychological processes Jacoby and Chestnut, (1978).
Gremler and Brown (1996) revealed that past studies on customer loyalty focused
largely on goods related brand loyalty and research on customer loyalty in context of
service firms remained limited. The findings about loyalty related with goods cannot be
generalized to service related loyalty because of following reasons:
Service related customer loyalty depends more on interpersonal relationships
between firms and consumers as compared to loyalty of goods Berry,(1983).
Person to person interaction is an essential element in marketing of services
Suprenant and Solomon, (1987).
Impact of perceived risk is larger in case of services, as customer loyalty may act as a
barrier to customer switching behaviour Zeithaml,(1981).
Intangible attributes like reliability, and confidence may play a vital role in
building or maintaining loyalty in the context of services Dick and Basu, (1994).
In a later study Gremler and Brown (1996) extended the concept of loyalty to
services (intangible goods) and defined the service loyalty as the degree to which a
customer exhibits repeat buying behaviour from a service organization, possesses a
positive attitudinal temperament towards the organization, and considers only this
organization when a need for this service exist.
A discussion on the customer loyalty concept and the definition of customer
loyalty is presented. Every organization is rushing after the loyal customers as they
provide enormous benefits offered to the organization. This special category of customers
are the source for constant stream of profit, reduce marketing and operating costs,
49
increase referral, and was immune to competitors‟ promotion efforts Reicheld and Sasser,
(1990)
Geropott et al., (2001) analysed the relationship between customer satisfaction
and loyalty in cellular mobile service market in Germany. They found that the three
constructs, namely, customer satisfaction, customer loyalty and customer retention are
different. Customer satisfaction derives customer loyalty, which in turn has an impact on
customer retention.
Customer loyalty means that the customer may come under environmental effect
or marketing technique, which induce their possibly latent transformation behaviour, but
they wouldn‟t change their repeat purchase intention with preference commodity or
service Oliver, Rust and Varki (1997).
Figure 2.1
Customer Loyalty Model Rundle-Thiele, (2005)
Rundle -Thiele, (2005) described customer Loyalty as Attitudinal Loyalty, Behavioral
Loyalty and Composite loyalty which represents combination of both.
Selvarasu et al., (2006)42 identified that the important factors in influencing the
customer satisfaction in GSM mobile service market are basic services, net work
50
performance, value added services, recharging comfortability, customer care support and
internet support.
2.5 SWITCHING COST
2.5.1 Concept of Switching Cost:
Switching costs can be defined as the costs which are borne by the consumers for
ending their relationship with present service provider and starting a fresh relationship
with the new service provider.
Dick and Basu(1994) defined Switching Cost as a cost of changing services and it
is comprised of Time Cost, Monetary cost and Psychological cost.
Studies have shown that good service quality binds the customers with present
service provider and poor service quality stimulates them to move on to new service
providers. Keaveney(2001); Jones and Sasser (1995).
Morgan and Hunt, (1994) found that changing a service provider becomes
expensive and customers feel dependent on present service provider due to switching
cost.
Jones et al (2000) found that in the presence of low switching cost, less satisfied
customers prefer to leave the service provider but in the presence of high switching cost
even highly dissatisfied customers may continue their relationship with the service
provider.
Lee et al (2001) studied the role of switching cost in Mobile sector of France and
concluded that it is due to switching cost that some seemingly loyal customers are
actually dissatisfied but do not defect because of high switching cost. Thus, the level of
switching costs moderates the link between satisfaction-loyalty link.
Ranweera and Prabhu (2003) studied cost effective ways of customer retention.
They studied combined effects of customer satisfaction, trust and switching cost on
customer retention. They found that switching costs have both a significant positive
51
effect on customer retention as well as moderating effect on the relationship between
satisfaction and retention. He further concluded that service provider may be able to
retain even dissatisfied customers who perceive switching costs to be on higher side.
2.5.2 Dimensions of Switching Cost:
Jackson (1985) studied switching cost in context of telecommunication and found
it to three dimensional consisting of (1)Psychological, (2) Physical and (3)Economic
costs.
Jones et al (2002) conceptualized the switching costs in two different industries
namely Banks and Hairstylists and found it to have six dimensions. (1) Lost performance
costs; (2) Uncertainty costs; (3) Pre-switching search and evaluation costs; (4) Post-
switching behavioral and cognitive costs; (5) Setup costs; and (6) Sunk costs.
2.5.3 Noteworthy contributions:
Zeithaml, Berry and Parasuraman,(1996) argued that it always cheaper to retain
existing customers than to acquire new ones.
Benkenstein and Stuhlreier, (2004) studied switching behaviour in banking sector and
found it to be the outcome of poor service quality.
Gerrard and Cunnininggham,( 2004) argued that switching behavior of customers
is the outcome of high prices.
Bowen and Chen, (2001) argued that switching behavior of customers is the
outcome of decency of customer satisfaction
Chada and Kapoor (2009) studied switching cost in comparison of service quality,
customer satisfaction and found a positive association between the switching cost, service
quality, customer satisfaction and customer loyalty. The customer satisfaction was found
to be the best predictor of customer loyalty. The improvement in network quality, pricing
value added services and switching costs contribute to increased loyalty and customer
retention.
52
2.6 INERTIA
2.6.1 Concept of Inertia:
Inertia is defined as the regular purchase of a product or service by a customer without
being thoughtful and analytical. As per White and Yanamandram ( 2004) Inertia is the
repeat purchase of the same brand passively without much thought.
Huang and Yu(1999) considered inertia as a „non conscious form of human
emotions‟ and „passive service patronage without true loyalty.‟
CHAPTER-III
RESEARCH METHODOLOGY
53
3.1 Research:
Research is a systematic investigation carried out for the purpose of finding
solutions to a problem and deriving general principles. It is a generally accepted fact that
Research is a scientific method .George Lundberg (1948) defined scientific method as
one consisting of systematic observation, classification and interpretation of data.
Zina O’Leary (2005) defined research as a creative and strategic thinking process
that involves constantly assessing ,reassessing and making decisions about the best
possible means for obtaining trustworthy information, carrying out appropriate analysis
and tracing credible solution.
This chapter starts with defining research and research methodology and proceeds
on to explain research design, sample design and area of study. Subsequently, the chapter
explains how the data was collected and the research instruments used in this study. To
achieve high degree of precision all the steps were carried out in most meticulous way to
maintain reliability and validity.
3.2 Research Methodology:
Research Methodology is defined as a systematic way of solving a research
problem, it tells about methods to be followed during the research process starting from
investigation to conclusion. The methodology used to find out and analyze factors
affecting customer loyalty among mobile phone users in Gwalior are presented in this
chapter. The chapter also throws light on research design, data collection, development of
construct, development of investigative questions, data sampling and tools for data
analysis.
3.3 Research Design:
It outlines and lays the condition for collection and analysis of data. It is about
what, when, where, how much data should be collected and it also mentions the method
of data collection and data analysis.
54
One of the most common research designs that is frequently used by the
researchers contain is exploratory, descriptive and causal studies. In the present study,
exploratory and descriptive study is used as a purpose of the study to analyze the data.
3.4 Sample Design
A sample is collected from the population of Gwalior which is target population
of the research .About sampling adequacy Zicmund opined that if sample is adequate it
will have all the characteristics of population. Sample is used to draw inferences and
make generalization about the target population.
3.4.1 Sampling Technique
Saunders, Lewis & Thornhill (2007) defined sampling techniques as methods
used select sample from the population by reducing it to manageable size. In present
study Simple Random Sampling and as well as Judgmental Sampling were used to collect
data from
3.4.2 Sample size
Rowe,Burns & Bush (2010) advocated that sample size has an influence on how
the sample findings accurately represent the population. There has been diverse opinion
about appropriate sample size for a particular research study. Few opinions are listed
below:
Tabachnick and Fidell (2007) advocated that for factor analysis a sample size of
300 is adequate for and for regression analysis a sample size of N>= 50 + 8*M is
adequate where M is the number of independent variables.
Hair (2006) advised that a sample size of more than 100 is good for factor
analysis and as a general rule the sample should be 5 times the number of variable in
study.
55
3.4.3 The Sample
As the focus of this study is to identify factors which affect customer loyalty in
mobile phone users, so the population for the research work consists of individuals who
have the experience of using mobile services in Gwalior. For present study data was
collected from 530 respondents. This size of sample is more than enough to come to good
conclusions as per studies mentioned above.
3.5 Data Collection:
After identifying the target population data collection followed in following way.
3.5.1 Data Collection Design:
For data collection keeping in mind the research objectives, universe is defined as
"all the customers who are having a Cellular phone and are residing in any part of
Gwalior city". Only those individuals who were having mobile connection at the time of
data collection were included in the study. Responses were collected from 530 customers
using mobile services of different service providers.
3.5.2 Data Collection Procedures
Data was collected from mobile users of different companies using structured
questionnaire with closed-ended questions as a response measurement tool. The survey was
conducted in Gwalior city by researcher himself.
The first part of the structured questionnaire collected information from customer
about their demographic aspects. Second part of the questionnaire collected information
about customers’ perceptions about service quality attributes, customer perceived value,
customer satisfaction, customer loyalty, switching cost and inertia.
56
3.6 Area of Study: Gwalior
Gwalior is a historical city of Madhya Pradesh. It is located at a distance of 320
km from National capital Delhi. Until 1948, Gwalior was the capital of the princely state
of Gwalior. From 1948 to 1956 it was the summer capital of the Madhya Bharat State.
When Madhya Bharat became part of Madhya Pradesh it became a district of Madhya
Pradesh.
Important details of Gwalior district are mentioned in table3.1
Table 3.1
Details of Gwalior District
Area 5.214 Square K.M.
Population 1,901,981 (2011)
Population Density 390/km2
Literacy 77.93 Percent
Sex Ratio 862
Gwalior city is divided in 3 prominent parts namely Lashkar, Gwalior and Morar.
Between Lashkar and Morar a new area has emerged whch is partly residential and partly
commercial. This new area is known as City Centre and it houses all important offices
like Collectorate and SP office as well as Gwalior bench of MP High Court. Industrially
Gwalior is seat of factories producing cotton, yarn, paint, ceramics, chemicals and leather
products.
3.7 Research Instrument Design:
57
For the current study a structured questionnaire was developed and responses
from customers were measured on 5- point Likert type scale. The questions in the scale
were carefully chosen after thorough and extensive literature review. List of all the
questions used for measuring a particular construct is given in appendix along with the
source of question.
In the context of current study, structured questionnaire with closed-ended
questions will be used and responses will be measured on 5–point Likert type scale. The
survey instruments are revised versions of the originally developed scales.
3.8 Measurement Scale:
All the questions were measured on a scale of 1 to 5. It is explained below that what
1,2,3,4 and 5 stands for:
1. Strongly Disagree
2. Disagree
3. Neither Agree nor Disagree
4. Agree
5. Strongly Agree
MEASUREMENT SCALES
3.8.1 Service Quality
Scale to measure Service Quality was developed by including 30 items from
various research works after extensive literature review. The list of questions used in the
scale is in appendix in table 3.1.
3.8.2Customer Perceived Value
Customer Perceived Value was measured with the help of 3 item scale. The
source of the questions is mentioned in appendix in table 3.2
58
3.8.3 Customer Satisfaction
To measure customer satisfaction a scale was developed with 5 questions. All the
questions included in the scale were used by earlier researchers. The source of questions
is mentioned appendix in the table 3.3
3.8.4 Customer loyalty
Customer Loyalty is one of the most important and central construct in this
research work. To measure it already validated questions were picked to develop a 5 item
measurement scale .Questions and their source are mentioned in appendix table 3.4.
3.8.5 Switching Cost
Switching cost was measured with the help of 4 item scale. The source of the
questions is mentioned in in appendix in table 3.5
3.8.6 Inertia
A three item scale was developed to measure the construct of Inertia. All the
questions in the scale were selected after extensive review of literature. The questions as
well as the source of the questions are mentioned in appendix in table 3.6
3.9 Proposed Hypotheses
There were mainly two types of hypotheses developed to achieve the objectives of
this research work.
1. Hypotheses to test the significant difference in responses of customers towards
Customer Satisfaction and Customer Loyalty on the basis of their demographic
characteristics like Age, Marital status etc. The hypotheses in this category are
listed below:
59
H1a: There is no significant difference in the customer satisfaction levels of customers of
different age groups
H1b: There is no significant difference in the customer satisfaction levels of male and
female customers
H1c: There is no significant difference in the customer satisfaction levels of married and
unmarried customers
H1d: There is no significant difference in the customer satisfaction levels of customers
of different educational qualifications.
H1e: There is no significant difference in the customer satisfaction levels of customers of
different occupations
H1f: There is no significant difference in the customer satisfaction levels of customers of
different income groups.
H1g: There is no significant difference in customer satisfaction levels of customers of
different service providers.
H1h: There is no significant difference in the customer satisfaction levels of pre-paid and
post paid customers.
H2a: There is no significant difference in the customer loyalty levels of customers of
different age groups
H2b: There is no significant difference in the customer loyalty levels of male and female
customers.
H2c: There is no significant difference in the customer loyalty levels of married and
unmarried customers.
H2d: There is no significant difference in the customer loyalty levels of customers of
different educational qualification.
60
H2e: There is no significant difference in the customer loyalty levels of customers of
different occupations.
H2f: There is no significant difference in the customer loyalty levels of customers of
different income groups.
H2g: There is no significant difference in the customer loyalty levels of customers of
different service providers
H2h: There is no significant difference in the customer loyalty levels of pre-paid and
post-paid customers.
2. Hypothesis which were developed to test the relationship between different
constructs in the study. Following hypotheses fall in this category:
H3: Customer Satisfaction is not impacted by Service quality
H4: Customer Satisfaction is not impacted by Customer Perceived Value
H5: Customer Loyalty is not impacted by Customer Satisfaction.
H6: Customer Loyalty is not impacted by Switching Cost.
H7: Customer Loyalty is not impacted by Inertia
H8: Customer Satisfaction-Customer Loyalty link is not moderated by Switching Cost.
H9: Customer Satisfaction-Customer Loyalty link is not moderated by Inertia.
3.10 Statistical Tools and Techniques:
SPSS version 20 was used to derive descriptive statistics and to analyse the data
with the application of different tools and tests. The details are mentioned below:
3.10.1 Descriptive Statistics:
61
Frequency; Mean; Standard deviation; Percentages for demographical and psycho
graphical variables were calculated by using SPSS 20.
3.10.2 Inferential Statistics:
Exploratory Factor Analysis, Correlation Analysis and Regression analysis were
used through SPSS 20
3.11 Statistical Tools Used:
Appropriate statistical tools like Correlation analysis, Regression
Analysi,Independent samples t-test, One-way ANOVA, Factor Analysis were applied
with the help of the Statistical Package for Social Sciences (SPSS), which resulted in
observations, interpretations and findings.
3.11.1: t-Test
t-test was used for comparing means of two groups. t- Test was applied in this
study to compare the mean scores between two groups in following places:
Comparison of mean score of customer satisfaction between male customers
and female customers
Comparison of mean score of customer satisfaction between married
customers and unmarried customers
Comparison of mean score of customer satisfaction between Pre paid
customers and Post paid customers
Comparison of mean score of customer loyalty between male customers and
female customers
Comparison of mean score of customer loyalty between married customers
and unmarried customers
62
Comparison of mean score of customer loyalty between Pre paid customers
and Post paid customers
3.11.2: ANOVA (Analysis of Variance):
It enables us to separate the total variation of our data into compartments which
may be attributed to various “sources” or “causes” of variation. This technique consists of
classifying and cross classifying statistical results and testing whether the means of
specified classification differ significantly.
ANOVA as a technique was developed by R A Fischer so the test used in this
technique is known as F test. As the F-Test is based on the ratio of two variances it is also
known as “Variance Ratio”
F = Between group variance / Within group variance
In this study ANOVA is applied to compare means of more than two groups in
following places:
Comparison of “mean score” of customer satisfaction among customers of
different age groups.
Comparison of “mean score” of customer satisfaction among customers with
different educational qualifications.
Comparison of “mean score” of customer satisfaction among customers with
different types of employment.
Comparison of “mean score” of customer satisfaction among customers with
different income levels.
Comparison of “mean score” of customer satisfaction among customers using
mobile services of different service providers.
Comparison of “mean score” of customer loyalty among customers of
different age groups.
63
Comparison of “mean score” of customer loyalty among customers with
different educational qualifications.
Comparison of “mean score” of customer loyalty among customers with
different types of employment.
Comparison of “mean score” of customer loyalty among customers with
different income levels.
Comparison of “mean score” of customer loyalty among customers using
mobile services of different service providers
3.11.3: Factor Analysis:
Factor analysis is one of the most commonly used inter dependency technique, it
is used when the relevant set of variable show a systematic interdependence and the
objective is to find out the latent factors that create a commonality.
The term was used for the first time by Thurstone in 1931.Factor analysis finds
application mainly to reduce the number of variables and to identify structure in the
relationships between variables that is to classify variables. Therefore, factor analysis is
applied as a data reduction or structure detection method.
Application of Factor Analysis in this study: In this study Factor analysis was
applied to understand the construct structure of service quality and to find out different
dimensions of service quality.
3.11.4: Correlation:
It is a statistical device used for analyzing the co variation of two or more
variables. It helps us in determining the degree of relationship between two or more
variables without telling us cause and effect relationship. It means even a high degree of
correlation does not necessarily mean that a relationship of cause and effect exists
between the variables.
Types of correlation: Correlation can be classified in different ways but following 3
emerge as major categories and subtypes:
64
1 Positive or Negative correlation
2 Simple, Partial and Multiple correlations
3 Linear or Non linear.
In our present study application of positive, negative, simple and linear correlation is
Common
Figure3.1
Positive Linear Correlation Negative Linear Correlation
Measurement of Correlation: There are several mathematical methods to measure
correlation; most common and acceptable method is Karl Pearson method. In this method
Pearson coefficient of correlation is denoted by the symbol “r” and formula to calculate it
is :
𝒓 =𝑵𝜮𝑿𝒀 − 𝜮𝑿𝜮𝒀
𝑵𝜮𝑿𝟐 − (𝜮𝑿)𝟐 𝑵𝜮𝒀𝟐 − (𝜮𝒀)𝟐
N is the number of paired data and X and Y are the two variables between which
the correlation needs to be calculated.
65
The value obtained by this formula is always between +1 and -1.In this study
value of Karl Pearson coefficient is computed by SPSS 20 software.
Application of correlation in this study: In this research work correlation is
applied before regression wherever co variation between two or more than two variable is
studied. Its application can be found in following places:
1. Correlation was checked for all the seven dimensions of service quality, customer
perceived value and customer satisfaction.
2. Correlation was tested among scores of customer loyalty ,customer satisfaction,
Switching cost and Inertia
3.11.5: Regression:
According to Morris Hamburg Regression analysis refers to the method by which
estimates are made of the values of a variable from knowledge of the values of one or
more other variables and to the measurement of the errors involved in this estimation
process.
From the above definition it is clear that regression analysis is a statistical device with the
help of which unknown values of one variable are estimated on the basis known values of
the other variables. The variables which are used to predict the variable of interest are
called independent variables or explanatory variables. The variable whose value is
estimated is called as dependent variable or explained variable.
Application of regression in present study:
1. Regression was carried out for customer satisfaction as dependent variable and all
the seven dimensions of service quality and customer perceived value as
independent variables.
2. Regression was carried out for customer loyalty as dependent variable and
customer satisfaction, switching cost, inertia and as independent variables.
66
3.11.6: Hierarchical Regression:
It is type of regression analysis that involves entering predictors in the regression
model in a preconceived order of entry, determined on the basis of theory, instead of
entering all predictors simultaneously.
Application of hierarchical regression in present study:
This type of regression was applied to test the impact of moderating variables 1 (Cust.Sat
X switching cost) and moderating variables 2 (Cust.Sat. X Inertia) as independent
variables on customer loyalty as dependent variable.
CHAPTER-IV
DATA ANALYSIS
67
Definition
Once data was collected, data processing and data analysis followed. Gromme
1998 defined Data Processing as activities and technologies which prepare collected data
for next stage i.e. data analysis and it includes data checking, data entry, data coding and
data editing.
Gromme1998 defined Data Analysis as combination of activities and
technologies like weighting, tabulation and response analysis that are used to draw
conclusion from the collected data.
It would be relevant to re mention the research objectives as well as the research model:
Research Objectives:
The study aims to identify the various factors affecting Customer satisfaction and
their role in developing customer loyalty.
The study intends to develop a model explaining relationship between Service
Quality, Customer Perceived Value, Customer Satisfaction, Switching Cost,
Inertia and Customer Loyalty.
The Study also focuses on Switching cost and Inertia as moderators of Customer
Satisfaction - Customer Loyalty link.
Research Model:
The research model shown below in figure 4 is bifurcated in two parts.
Part I explains the relationship between Customer Satisfaction, Service Quality
and Customer Perceived Value. Here customer satisfaction is dependent variable and
service quality and customer perceived value are independent variables. This part shows
hypothesis H3 and H4.
Part II of the model explains relationship between customer satisfaction, customer
loyalty, switching cost and inertia. Here customer loyalty is dependent variable and
68
customer satisfaction, switching cost and inertia are independent variables. This part of
the model also shows moderating impact of switching cost and inertia on customer
satisfaction – customer loyalty link. This part shows hypotheses H5,H6, H7,H8 and H9.
Figure 4
Research Model
69
This chapter is divided into 7 sections; Section 1 and 2 deal with Descriptive
Statistics of demographic variables and different research constructs respectively. Section
3 to 5 deal with different research questions framed to achieve research objective.
First section comprises of descriptive statistics of various demographic variables.
Second section deals with descriptive statistics and reliability analysis of all
research constructs.
Third section is about key factors affecting Service Quality.
Fourth section deals with differences in the customer satisfaction levels of
respondents from different demographic background.
Fifth section is about differences in the customer loyalty levels of respondents
from different demographic background.
Sixth section traces an association between customer satisfactions on the one hand
and various service quality dimensions and customer perceived value on the other.
Seventh section studies - Is there a direct impact of customer satisfaction, switching
cost and inertia on customer loyalty and is the customer satisfaction-customer
loyalty link moderated by switching cost and inertia?
SECTION 1: Descriptive Statistics of Demographic Variables
Demographic factors are known to influence score for every research construct
because individuals who come from different background are bound to have differences
in their psychological constitution which gets reflected in their choice.
Questionnaire for this research work consisted of items on customer‟s profile, as various
demographic and other factors were likely to influence the customer response towards
following variables under study:
70
1. “Service quality” offered by the company
2. “Customer Perceived Value” perceived by the customers
3. “Customer satisfaction” felt or derived by the customers
4. “Customer loyalty” developed among the customers
5. “Switching cost” imposed by the service providers.
6. “Inertia” shown by the customers
This section deals with the Demographic characteristics of respondents. It
includes data related with following aspects of respondents:
1. “Age” of respondents
2. “Gender” of respondents
3. “Marital Status” of respondents
4. “Educational Qualification” of respondents
5. “Occupational Pattern” of respondents
6. “Income Level” of respondents
7. “Mobile Service Provider” of respondents
8. “Type of Connection” (Pre-paid or Post paid)
4.1.1. Age of Respondent:
Age of customers play an important role in the determining expectation and
perception of customers regarding various aspects of service quality offered by the
mobile service providers. It also affects the level of customer satisfaction and customer
loyalty. Hence, age is included as one of the profile variables in the present study. The
data was collected from customers whose age ranged from 15 years to 80 years, as use of
cell phone is quite common among the customers of every age.
71
For the sake of convenience and to derive meaningful conclusions, the customers
were grouped into 5 subcategories on the basis of their age.
Group 1 comprised of customers whose age was below 20 years.
Group 2 comprised of customers whose age was between 21-35 years.
Group 3 comprised of customers whose age was between 36-45 years.
Group 4 comprised of customers whose age was between 46-60 years.
Group 5 comprised of customers whose age was between 61-80 years.
Collection of data from different subcategories is mentioned below.
TABLE No. 4.1.1
Age of Respondent
Group Age Frequency Percent Valid Percent Cumulative Percent
1.00 Below 20 50 9.4 9.4 9.4
2.00 21-35 149 28.1 28.1 37.5
3.00 36-45 139 26.2 26.2 63.8
4.00 46-60 119 22.5 22.5 86.2
5.00 61-80 73 13.8 13.8 100.0
Total 530 100.0 100.0
Table 4.1.1 explains that the important age groups are 2, 3 and 4 and they together
constitute almost 75% of respondents and include customers between 21 years to 60
years. Almost 10% of customers belong to less than 20 years and around 12% belong to
more than 60 years.
72
Figure 4.1.1
Age of Respondent
4.1.2. Gender of Respondent:
Gender of the customer is likely to influence their response towards various
constructs under study. Data was collected from both males and females and their
proportion is mentioned in the table 4.1.2.
TABLE No. 4.1.2
Gender of Respondent
Group Gender Frequency Percent Cumulative Percent
1.00 Male 307 57.9 57.9
2.00 Female 223 42.1 100.0
Total 530 100.0
The Male customers constitute almost 60 % of the total customers and females constitute
40%. The data suggests that prominent gender among the customers in this study is
„male‟. The data is presented graphically through the pie diagram in Figure 4.1.2.
Below 20 21-35 36-45 46-60 61-80
Series1 50 149 139 119 73
0
20
40
60
80
100
120
140
160
Fre
qu
en
cy
Age of Respondent
73
Figure 4.1.2
Gender of Respondent
4.1.3. Marital status of Respondent:
To study the impact of marital status on customers‟ response towards different
constructs under study data was collected from both married and unmarried customers.
The findings are summarized and shown in the table numbers 4.1.3
TABLE No. 4.1.3
Marital status of Respondent
Group Marital status Frequency Percent Cumulative Percent
1.00 Married 388 73.2 73.2
2.00 Unmarried 142 26.8 100.0
Total 530 100.0
From the table 4.1.3 it is clear that majority of the respondents are married and they
constitute almost 75% of total respondents.
Male58%
Female42%
Gender of Respondent
74
The Bar diagram shown in figure 4.1.3 represents the customers belonging to married and
unmarried category .
Figure 4.1.3
Marital status of Respondent
4.1.4. Educational Qualification of Respondent:
Education of customers reflect their knowledge, level of understanding and their
analytical potentials and they all affect their perception and response towards different
constructs used in this study. As mobile is very common among customers, data was
collected from diverse field of customers whose educational qualification ranged from
not educated at all to the possession of highest degrees like PhD and other professional
qualifications. With their qualification their level of expectation and perception may also
differ from others. The distribution of number of customers with different education
levels are shown below:
For proper analysis customers were grouped into 5 sub categories on the basis of
their educational qualification.
Group 1 comprised of customers whose education was below Higher Secondary.
Group 2 comprised of customers with Higher Secondary level of education.
388
142
0
100
200
300
400
500
Married Unmarried
Marital Status of Respondent
Frequency
75
Group 3 comprised of customers who were Graduates.
Group 4 had customers with Post Graduation as their qualification
Group 5 comprised of customers who had taken some professional degree and consisted
of doctors, engineers, CAs and Ph.D.s.
TABLE No. 4.1.4
Educational Qualification of Respondent
Group Educational Qualification Frequency Percent Cumulative Percent
1.00 Below Hr. Secondary 92 17.4 17.4
2.00 Hr. Secondary 186 35.1 52.5
3.00 Graduation 138 26.0 78.5
4.00 Post Graduation 69 13.0 91.5
5.00 Doctor/Engineer/CA/Ph.D. 45 8.5 100.0
Total 530 100.0
The table 4.1.4 shows that 17.4 % of the respondents have Pre-Hr.Secondary level
of education. Almost 60% respondents are either Hr.Sec. or Graduate. 13 % respondents
are Post Graduates and 8.5% respondents have Ph.D or some professional degree.
Figure 4.1.4
Educational Qualification of Respondent
92
186138
69
45
Educational Qualification of Respondent
Below Hr. Secondary
Hr.Secondary
Graduation
Post Graduation
Doctor/Engineer/CA/Ph.D.
76
4.1.5 Occupational Pattern of Respondent:
The reason behind inclusion of occupational pattern of customers in this study is
that it influences the customers‟ attitude towards different constructs which are being
explored through this study. As mobile service has now become affordable to literally
each and every person, so the data for the study was also collected from customers
employed in diverse field. On the basis of type of employment or occupation all
customers were divided into 5 groups and composition of different groups is mentioned
below:
Group 1 customers who were occupied in their own businesses.
Group 2 customers who were employed in Govt.Services.
Group 3 customers who were employed in Pvt.Sector
Group 4 consisted of students.
Group 5 consisted of customers who could not be categorized in any of the above
category and were marked as others.
The data from respondents are categorized and tabulated in table 4.1.5.
From the data below we can see that maximum %age of respondents i.e. 35.1% are
employed in private sector 20.2% are govt.employees, 14.2 % carry on their own
business and a considerable segment i.e. 22.1% are students.
TABLE No. 4.1.5
Occupational Pattern of Respondent
Group Employment Pattern Frequency Percent Cumulative
Percent
1.00 Business 75 14.2 14.2
2.00 Govt.Sector Employee 107 20.2 34.3
3.00 Pvt.Sector Employee 186 35.1 69.4
4.00 Students 117 22.1 91.5
5.00 Others 45 8.5 100.0
Total 530 100.0
The above data is also shown below in figure 4.1.5 through a Bar diagram :
77
Figure 4.1.5
Occupational Pattern of Respondent
4.1.6. Income of Respondent:
Income of the customers has a great influence on the way customers develop their
opinions about service quality of the mobile service and this affects their level of
satisfaction which may ultimately affect their loyalty towards the service provider. This
explains the reason behind inclusion of question related to Income of customers in this
study. Customers were put into 5 subcategories on the basis of their income; the groups
formed are defined below:
Group 1 customers with monthly income less than Rs 10,000/-
Group 2 customers with monthly income between Rs 10,000/- to Rs25, 000/-
Group 3 customers with monthly income between Rs 25,000/- to Rs 50,000/-
Group 4 customers with monthly income between Rs 50,000/- to Rs 75,000/-
Group 5 customers with monthly income of more than Rs 75,000/-
0
50
100
150
200
Business Govt.Sector Employee
Pvt.Sector Employee
Students Others
75
107
186
117
45
Occupation of Respondent
Frequency
78
The Table 4.1.6 depicts the proportion of different subcategories in the sample.
TABLE No. 4.1.6
Income of Respondent
From the data shown by table 4.1.6, it is evident that maximum number of respondents
i.e. 27.5% have their income below Rs 10,000/-. Group 3 and 4 which together constitute
almost 50% of respondents have their income from Rs 10,000/- to Rs 50,000/-.
The above data is also shown through a Bar diagram in figure 4.1.6
Figure 4.1.6
Income of Respondent
146135 128
7348
020406080
100120140160
Income of Respondent
Frequency
Group Income Frequency Percent Cumulative Percent
1.00 Below Rs 10,000 146 27.5 27.5
2.00 Rs 10.000-25,000 135 25.5 53.0
3.00 Rs 25,000-50,000 128 24.2 77.2
4.00 Rs 50,000-75,000 73 13.8 90.9
5.00 Above Rs 75,000 48 9.1 100.0
Total 530 100.0
79
4.1.7 Service Provider of Respondent:
It is common knowledge that not all the service providers are equally caring and
sensitive towards their customers. Accordingly, they differ from each other on almost all
scores related to service quality, customer satisfaction etc.
As Gwalior city, our area of research study, belongs to Madhya Pradesh our data
also reflect all the major telecom players in Gwalior. Table 4.1.7 shows different number
of customers using services of different service providers.
TABLE No. 4.1.7
Service Provider of Respondent
Group Name of Service Provider Frequency Percent Cumulative Percent
1.00 AIRTEL 128 24.2 24.2
2.00 IDEA 146 27.5 51.7
3.00 RELIANCE 72 13.6 65.3
4.00 BSNL 58 10.9 76.2
5.00 VODAFONE 45 8.5 84.7
6.00 TATA DOCOMO 58 10.9 95.7
7.00 VIDEOCON 23 4.3 100.0
Total Total 530 100.0
From the data shown in table 4.1.7 it can be concluded that maximum number of
customers i.e. 27.5% are using services of Idea. Bharti follows Idea in market share with
24.2% customers using Airtel services. Reliance is third with 13.6% market share.Tata
and state player BSNL have market share 10.9% each. Vodafone and Videocon has
market share of 8.5% and 4.3% each.
80
Figure 4.1.7 reflects the above statistics in graphical patter.
Figure 4.1.7
Service Provider of Respondent
4.1.8. Type of Connection of Respondent:
As customers have the option of using either Pre-paid or Post paid SIMs,
collected data was also grouped on the basis of type of connection. Data revealed that
majority of respondents i.e. as high as 79.6% had Pre paid connection, this data also
conform to national trends in Pre paid and Post paid.
TABLE No. 4.1.8
Type of connection of Respondent
Group Type of
connection Frequency Percent Valid Percent
Cumulative
Percent
1 PRE PAID 422 79.6 79.6 79.6
2 POST PAID 108 20.4 20.4 100.0
Total 530 100.0 100.0
128
146
7258
4558
23
0
20
40
60
80
100
120
140
160
Service provider of Customer
Frequency
81
Figure 4.1.8
Type of connection of Respondent
Pre Paid Post Paid
Series1 422 108
0
50
100
150
200
250
300
350
400
450
Axi
s Ti
tle
Type of Connection
82
SECTION-2: Descriptive Statistics of Research Constructs
4.2.1 Service quality:
It is one of the most important construct of this study. To ensure validity of the
items used to measure service quality, all the items were picked from different research
works after extensive literature review.
A detailed questionnaire containing 30 questions was developed to measure
service quality. Descriptive statistics and Reliability analysis of the scale is presented
below.
4.2.1.1 Descriptive Statistics:
The mean value all questions were computed as 3.56 with standard deviation of
.66.The table 4.2.1 shows the value of skewness and kurtosis along with mean and
standard deviation.
TABLE No.4.2.1
Descriptive Statistics: Service Quality
4.2.1.2 Reliability Analysis:
SPSS based reliability test was carried out on the thirty items scale to check the
reliability. Table generated by SPSS shows the Cronbach‟s alpha value of .836,which
indicates higher value in comparison to .7 which is considered benchmark value as
suggested by Nunally (1978).Table 4.2.2 shows the output generated by SPSS.
TABLE No 4.2.2
Reliability Statistics: Service Quality
Number of Items Cronbach's Alpha
30 .836
Number of Items Mean Std.Deviation Skewness Kurtosis
30 3.56 .66 -.998 .073
83
4.2 Customer Perceived Value
A three item scale was used to measure customer perceived value among
customers. All the questions in the scale were developed after extensive review of
literature.
4.2.2.1 Descriptive Statistics:
The scale has a mean score of 3.586 and standard deviation of .866.The value of
skewness and kurtosis were -.481 and -.643.These values are tabulated in table 4.2.3
TABLE No. 4.2.3
Descriptive Statistics: Customer Perceived Value
Number of Items Mean Std.Deviation Skewness Kurtosis
3 3.586 .866 -.481 -.643
4.2.2.2 Reliability Analysis:
SPSS based reliability test was carried out on the five item scale to check the
reliability. Table generated by SPSS shows the Cronbach‟s alpha value of .810,which
indicates moderately high value in comparison to .7 the value suggested by
Nunally(1978) as reference value.
TABLE No 4.2.4
Reliability Statistics: Customer Perceived Value
Cronbach's Alpha N of Items
.810 5
4.2.3 Customer Satisfaction
A five item scale was used to measure customer satisfaction among customers. All
the questions in the scale were developed after extensive review of literature.
84
4.2.3.1 Descriptive Statistics:
The scale has mean score of 3.709 and standard deviation of .829. The value of
skewness and kurtosis were -.827 and -.273.These values are tabulated in table 4.2.5
TABLE No. 4.2.5
Descriptive Statistics: Customer Satisfaction
Number of Items Mean Std.Deviation Skewness Kurtosis
5 3.7091 .82958 -.827 -.273
4.2.3.2 Reliability Analysis:
SPSS based reliability test was carried out on the five item scale to check the
reliability. Table generated by SPSS shows the Cronbach‟s alpha value of .798,which
indicates moderately high value in comparison to .7 suggested by Nunally (1978) as
reference value.
TABLE No. 4.2.6
Reliability Statistics: Customer Satisfaction
Cronbach's Alpha N of Items
.798 5
4.2.4 Customer loyalty
This is most important construct of this research work and all other constructs
revolve around this. After extensive literature review 5 questions were picked to measure
customer loyalty
4.2.4.1 Descriptive Statistics:
Mean score of customer loyalty was 3.703 with standard deviation of
.817.Skewness and kurtosis for this scale was -.650 and -.614 respectively. The values are
shown in table 4.2.7.
85
TABLE No. 4.2.7
Descriptive Statistics: Customer Loyalty
Number of Items Mean Std.Deviation Skewness Kurtosis
5 3.703 .8175 -.650 -.614
4.2.4.2 Reliability Analysis:
SPSS based reliability test was carried out on the five item scale to check the
reliability. Table generated by SPSS shows the Cronbach‟s alpha value of .836,which
indicates moderately high value in comparison to .7 suggested by Nunally(1978)
TABLE No. 4.2.8
Reliability Statistics : Customer Loyalty
Cronbach's Alpha N of Items
.836 5
4.2.5 Switching Cost:
A four item scale was used to measure switching cost among customers. All the
questions in the scale were developed after extensive review of literature.
4.2.5.1 Descriptive Statistics:
This scale has mean value of 3.98 and standard deviation of .622.Values for
skewness and kurtosis were -.591 and -.063 respectively.
TABLE No. 4.2.9
Descriptive Statistics: Switching Cost
Number of Items Mean Std.Deviation Skewness Kurtosis
4 3.9835 .62239 -.591 -.063
4.2.5.2 Reliability Analysis:
SPSS based reliability test was carried out on the five item scale to check the
reliability. Table 4.2.10 generated by SPSS shows the Cronbach‟s alpha value of
86
.634,which indicates moderately high value in comparison to .7 suggested by
Nunally(1978) as reference value.
TABLE No. 4.2.10
Reliability Statistics: Switching Cost
Cronbach's Alpha N of Items
.634 4
4.2.6 Inertia:
A three item scale was used to measure inertia among customers. All the
questions in the scale were developed after extensive review of literature.
4.2.6.1 Descriptive Statistics:
Scale for Inertia has mean value of 3.65 with .84 standard deviation. For this
scale value of skewness and kurtosis were calculated to be -.363 and -.784 respectively.
TABLE No. 4.2.11
Descriptive Statistics: Inertia
Number of Items Mean Std.Deviation Skewness Kurtosis
3 3.6509 .84330 -.363 -.784
4.2.6.2 Reliability Analysis:
SPSS based reliability test was carried out on the five item scale to check the
reliability. Table 4.2.12 generated by SPSS shows the Cronbach‟s alpha value of
.757,which indicates moderately high value in comparison to .7 as suggested by
Nunally(1978
TABLE No. 4.2.12
Reliability Statistics: Inertia
Cronbach's Alpha N of Items
.757 3
87
SECTION 3: Key factors affecting service quality
4.3 Service quality:
To identify key factors which affect service quality extensive literature review
was done which resulted in 30 variables known to influence service quality. A factor
analysis of these 30 variables resulted into seven dimensional structure for service quality
in present study.
4.3.1 Factor Analysis:
Factor Analysis with varimax rotation was applied to find out different
dimensions of service quality. The purpose behind using a factor analysis was to
minimise the number of variables of service quality without compromising on the amount
of information in the analyses (Steward, 1981).
Before applying Factor Analysis the data was tested to check appropriateness for
factor analysis. This was done by using KMO (Kaiser-Meyer-Olkin) test for measuring
sampling adequacy and Bartlett‟s test for sphericity.
The table 4.3.1 shows that KMO test gave value of .886 which is far above the minimum
desired value of .5.
Kaiser and Rice(1974) in a study suggested appropriate value of KMO as more
than .6.
Bartlett‟s test which was used to check multivariate normality and whether
correlation matrix was an identity matrix; this resulted into a significant value (p value
less than .05) which further showed appropriateness of data for using factor analysis.
George and Mallery, (2000) found that a significant value (p-value less than .05)
indicates that the data do not produce an identity matrix and differ significantly from
identity
TABLE No. 4.3.1
KMO and Bartlett's Test: Service Quality
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .886
Bartlett's Test of Sphericity
Approx. Chi-Square 12539.443
Df 435
Sig. .000
88
4.3.2 Communalities:
The table 4.3.2 shows the initial communalities as well as communalities after
extraction of all 30 items. The table shows that communalities ranged from .622 to .828
which is far above the minimum value of .5 suggested by Stewart (1981)
TABLE No. 4.3.2
Communalities: Service Quality
Initial Extraction
My service provider gives me services reliably ,consistently
and dependably 1.000 .708
My service provider is trustworthy and its employees are
honest and believable. 1.000 .745
My service provider keeps its promises. 1.000 .708
My service provider‟s employees are easily approachable. 1.000 .824
My service provider‟s employees are courteous, polite and
respectful. 1.000 .749
My service provider‟s employees listen to customers and
are willing to help them. 1.000 .734
My service provider‟s employees are pleasant, friendly and
caring. 1.000 .828
My service provider‟s employees are neat and clean in their
office. 1.000 .740
My service provider‟s employees are efficient and caring 1.000 .801
My service provider‟s billing is accurate and easy to
understand. 1.000 .810
My service provider has reputation and good image. 1.000 .730
My service provider is innovative and forward looking. 1.000 .792
The advertisements and promotional campaigns of my
service provider are effective. 1.000 .796
My service provider has sufficient presence in different
geographical areas through own offices or dealers,
franchises.
1.000 .622
My service provider has Physical facilities at their office
which are visually appealing . 1.000 .827
89
It is easy and convenient to take up a new mobile
connection as well as get recharges and top-ups from my
service provider.
1.000 .671
My service provider has up-to-date network and low
congestion problem even during peak traffic. 1.000 .733
My service provider has good call quality in terms of voice
clarity and minimal call drop problem. 1.000 .681
My service provider has wide coverage area. 1.000 .638
My service provider makes efforts to understand the
specific needs of customers 1.000 .748
My service provider gives individual and personal attention
to the customers 1.000 .662
My service provider maintains all record accurately. 1.000 .714
My service provider accurate and timely information 1.000 .671
Services given my service provider are prompt i.e. low
waiting time and quick response. 1.000 .810
My service provider is sympathetic and reassuring
whenever there is a problem. 1.000 .779
Working hours of my service provider are convenient for
customers. 1.000 .679
Services given by my service provider are competitive. 1.000 .778
Pricing of services by my service provider are reasonable
and competitive. 1.000 .743
My service provider gives good range of pricing plans to
choose from 1.000 .753
Value Added Services (SMS, Ringtones etc.) given by my
service provider are comprehensive and competitive. 1.000 .732
4.3.3 Total variance explained:
Table 4.3.3 shows the output generated by SPSS on extraction with Principal
Component Analysis method; it shows total variance explained by all the factors. The
seven factor solution accounted for 74.018 percent of the variance. Total variance
explained (74.018 percent) by these seven components exceeds the 60 percent threshold
criterion commonly used in social science researches (Hair et al., 1995).
90
TABLE No. 4.3.3
Total Variance Explained: Service Quality
Component
Initial Eigenvalues Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total % of
Variance Cumulative
% Total
% of
Variance Cumulative
% Total
% of
Variance Cumulative
%
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
12.249
2.306
2.050
1.801
1.343
1.320
1.137
.865
.796
.704
.615
.492
.490
.428
.408
.353
.329
.310
.266
.241
40.831
7.685
6.833
6.003
4.476
4.400
3.789
2.884
2.655
2.347
2.048
1.640
1.632
1.428
1.361
1.175
1.098
1.033
.888
.804
40.831
48.517
55.350
61.353
65.829
70.229
74.018
76.901
79.556
81.903
83.952
85.592
87.224
88.652
90.014
91.189
92.287
93.320
94.208
95.013
12.249
2.306
2.050
1.801
1.343
1.320
1.137
40.831
7.685
6.833
6.003
4.476
4.400
3.789
40.831
48.517
55.350
61.353
65.829
70.229
74.018
4.705
4.071
3.521
3.328
3.011
1.787
1.784
15.682
13.569
11.735
11.093
10.035
5.955
5.948
15.682
29.251
40.986
52.080
62.115
68.070
74.018
91
21
22
23
24
25
26
27
28
29
30
.209
.195
.188
.174
.164
.147
.132
.112
.096
.080
.695
.649
.626
.580
.547
.491
.441
.374
.320
.266
95.708
96.356
96.982
97.562
98.109
98.600
99.041
99.414
99.734
100.000
Extraction Method: Principal Component Analysis.
The Scree plot shown in figure 4.3.1 also supports 7 dimension structure service quality.
Figure 4.3.1
Scree Plot: Service Quality
Inclusion of an item in a factor depended on its factor loading for that particular
factor which shows its correlation with that factor. This denotes strength of relationship
92
of the item with the latent construct and predicts convergent and discriminant validity of
the scales.(Hair et al.,2006)
4.3.4 Rotated Component Matrix:
Table 4.3.4 shows the Rotated Component Matrix for Service Quality generated
by SPSS 20.In this table all the items which loaded together for a particular factor are
grouped at one place to make interpretation easier.
TABLE No. 4.3.4
Rotated Component Matrix: Service Quality
FACTORS QUESTIONS 1 2 3 4 5 6 7
FACTOR 1
EMPLOYEE
PERFORMANC
E
4. My service
provider‟s employees
are easily
approachable.
.687 .460 .096 .316 .025 .158 .072
9.My service
provider‟s employees
are efficient and caring .702 .142 .205 .289 .239 .304 .103
10.My service
provider‟s billing is
accurate and easy to
understand.
.792 .098 -.162 .283 .215 .140 .025
11.My service
provider has
reputation and good
image .
.764 .258 .132 -.016 -.036 .005 .245
20.My service
provider makes efforts
to understand the
specific needs of
customers
.661 -.094 .164 .439 .277 .080 -.028
1.My service provider
gives me services
reliably ,consistently
and dependably
.413 .566 .196 .057 .199 -.251 .271
FACTOR 2
RELIABILITY
2.My service provider
is trustworthy and its
employees are honest
and believable.
.514 .600 .119 .304 -.099 .050 -.047
3.My service provider
keeps its promises. .286 .619 .197 .335 .279 .107 -.047
93
7.My service
provider‟s employees
are pleasant, friendly
and caring.
.454 .574 .271 .274 .237 .285 .077
16.It is easy and
convenient to take up
a new mobile
connection as well as
get recharges and top-
ups from my service
provider.
.039 .764 .142 -.054 -.001 .110 .223
17.My service
provider has up-to-
date network and low
congestion problem
even during peak
traffic.
.067 .701 .133 .055 .217 .398 -.102
24.Services given my
service provider are
prompt i.e. low
waiting time and quick
response.
.154 .563 .157 .546 .352 .104 -.004
5.My service
provider‟s employees
are courteous, polite
and respectful.
.278 .437 .462 .290 .328 .087 .260
FACTOR 3
ASSURANCE
26.Working hours of
my service provider
are convenient for
customers.
.032 .158 .784 .050 .127 -.139 .025
27Services given by
my service provider
are competitive.
.516 .124 .661 -.001 .209 .048 .118
28.Pricing of services
by my service
provider are
reasonable and
competitive.
.426 .079 .613 .030 .404 .086 -.073
29.My service
provider gives good
range of pricing plans
to choose from
-.020 .158 .760 .274 -.122 .236 .072
94
30.Value Added
Services (SMS,
Ringtones etc.) given
by my service
provider are
comprehensive and
competitive.
-.033 .313 .582 .415 .119 .296 .130
6.My service
provider‟s employees
listen to customers and
are willing to help
them.
.391 .243 .055 .623 .122 .120 .315
FACTOR 4
RESPONSIVE
NESS
21.My service
provider gives
individual and
personal attention to
the customers
.330 -.007 .083 .682 .197 .175 -.111
22.My service
provider maintains all
record accurately.
.111 .076 .278 .658 .019 -.145 .407
25.My service
provider is
sympathetic and
reassuring whenever
there is a problem.
.407 .341 .228 .555 .349 -.005 -.125
23.My service
provider accurate and
timely information
.388 .205 .196 .377 .508 .145 .136
FACTOR 5
COMPETITIVE
NESS
12.My service
provider is innovative
and forward looking .
.058 .180 .342 .256 .746 -.029 .122
13.The advertisements
and promotional
campaigns of my
service provider are
effective.
.173 .098 .015 .107 .843 -.019 .185
19.My service
provider has wide
coverage area.
.172 .271 -.233 .286 .353 .422 .311
FACTOR 6
NETWORK
QUALITY
18.My service
provider has good call
quality in terms of
voice clarity and
minimal call drop
problem.
.343 .295 .272 .004 .330 .523 -.141
95
14.My service provider
has sufficient presence
in different
geographical areas
through own offices or
dealers, franchises.
.149
.140
.062
.070
-.105
.738
.114
8.My service
provider‟s employees
are neat and clean in
their office.
.194 -.015 .269 .162 .151 .054 .761
FACTOR 7
TANGIBILITY
15. My service
provider has Physical
facilities at their office
which are visually
appealing .
-.015 .383 -.304 -.097 .384 .211 .621
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
4.3.5 Factors of service quality:
On the basis of table 4.3.3 which shows the variance explained and table 4.3.4 which
shows Rotated Component Matrix for service quality following conclusion can be drawn:
1. Factor 1 is linear combination of 5 questions 4, 9, 10, 11 and 20 with Eigen value
of 12.24 it explains 40.83% of variance.
2. Factor 2 is linear combination of 7 questions 1, 2,3,7,16,17 and 24, with Eigen
value of 2.30 it explains 7.68% of variance.
3. Factor 3 is linear combination of 6 questions 5, 26,27,28,29 and 30 with Eigen
value of 2.05 it explains 6.83% of variance.
4. Factor 4 is linear combination of 4 questions 6, 21, 22 and 25, with Eigen value of
1.80 it explains 6.0% of variance.
5. Factor 5 is linear combination of 3 questions 23,12 and 13, with Eigen value of
1.34 it explains 4.47% of variance.
96
6. Factor 6 is linear combination of 3 questions 14, 18 and 19, with Eigen value of
1.32 it explains 4.40% of variance.
7. Factor 7 is linear combination of 2 questions 8 and 15, with Eigen value of 1.13 it
explains 3.78% of variance.
Once the factors were extracted the next task was to name these factors.
1. Factor 1 is named as EMPLOYEE PERFORMANCE as all the 5 questions in
this factor directly or indirectly measure the performance of employees of service
provider.
2. Factor 2 is named as RELIABILITY as all 7 questions which loaded together to
form this factor have reliability of service provider as their focal point.
3. Factor 3 is names as ASSURANCE as all the 6 questions which constituted this
factor were meant to check levels of assurance felt by the customers.
4. Factor 4 is named as RESPONSIVENESS as all the 4 questions which loaded
together to form this factor were meant to assess responsiveness of service
provider‟s employees towards their customers.
5. Factor 5 is named as COMPETITIVENESS as all the questions in this factor
assess the competitiveness of service provider.
6. Factor 6 is named a NETWORK QUALITY as all the questions in this factor
were meant to assess network and call quality.
7. Factor 7 is named as TANGIBILITY as both the questions which formed this
factor were asked to test physical evidence shown by the service provider.
97
Section 4: Customer Satisfaction among Respondents of different
demography
In this section one of the major variables in the research namely Customer
Satisfaction was studied in comparison to all the demographic variables namely 1.Age 2
Gender 3 Marital Status 4 Educational Qualification 5 Employment pattern 6 Income and
7 Name of the Service provider and 8 Type of connection i.e. pre-paid or post-paid.
Comparison was done by using t-test, ANOVA and post hoc tests wherever
applicable.
H1a: There is no significant difference in the customer satisfaction levels of customers of
different age groups.
H1b: There is no significant difference in the customer satisfaction levels of male and
female customers.
H1c: There is no significant difference in the customer satisfaction levels of married and
unmarried customers.
H1d: There is no significant difference in the customer satisfaction levels of customers
of different educational qualifications.
H1e: There is no significant difference in the customer satisfaction level of customers of
different occupations.
H1f: There is no significant difference in the customer satisfaction levels of customers of
different income groups.
H1g: There is no significant difference in customer satisfaction levels of customers of
different service providers.
H1h: There is no significant difference in the customer satisfaction levels of pre-paid and
post paid customers.
98
4.4.1 Customer Satisfaction and Age:
H1a: There is no significant difference in the customer satisfaction levels of
customers of different age groups.
One way ANOVA is applied to test this hypothesis. The output table generated by SPSS
is shown in table 4.4.1. The table shows the F value of 3.864 which is significant at 5%
level of significance as the p-value is .004 which is less than .05.It means that above
stated Null Hypothesis cannot be supported and it can be concluded that there is
significant difference in customer satisfaction levels of customers of different age groups.
TABLE No. 4.4.1
ANOVA: Customer satisfaction & Age
Sum of
Squares df
Mean
Square F Sig. NS/S
Between
Groups 10.272 4 2.568 3.864 .004 S
Within Groups 348.923 525 .665
Total 359.195 529
NS-Not Significant, S- Significant
Post hoc test – Customer satisfaction and age: To find out which age group differs
significantly from others on the basis of their mean score of customer satisfaction, Post
Hoc test was applied through Scheffe method as numbers of customers are different in
different age groups. Findings are compared in table 4.4.2
TABLE No. 4.4.2
Post hoc test scheffe method : Customer satisfaction & Age
Dependent Variable: Customer satisfaction
Independent Variables: Different Age Groups
(I) age of customer (J) age of
customer
Mean Difference
(I-J) Std. Error Sig. NS/S
Below 20
21-35 -.03977 .13324 .999 NS
36-45 -.10350 .13444 .964 NS
46-60 .00079 .13739 1.000 NS
61-85 -.43153 .14966 .082 NS
21-35 Below 20 .03977 .13324 .999 NS
99
36-45 -.06372 .09613 .979 NS
46-60 .04056 .10023 .997 NS
61-85 -.39176* .11647 .024 S
36-45
Below 20 .10350 .13444 .964 NS
21-35 .06372 .09613 .979 NS
46-60 .10429 .10182 .902 NS
61-85 -.32804 .11784 .103 NS
46-60
Below 20 -.00079 .13739 1.000 NS
21-35 -.04056 .10023 .997 NS
36-45 -.10429 .10182 .902 NS
61-85 -.43232* .12120 .013 S
61-85
Below 20 .43153 .14966 .082 S
21-35 .39176* .11647 .024 S
36-45 .32804 .11784 .103 NS
46-60 .43232* .12120 .013 S
*. The mean difference is significant at the 0.05 level.
NS-Not Significant, S- Significant
The result of the Scheffe‟s test shows that customers in Age group 5 (61-85years) differs
from customers in Age groups 2 (21-35years) and 4 (46-60 years)
Figure 4.4.1 shows that customers in Group 5 (61 to 85 years) differ significantly from
customers in group 2(21-35) and 4(46-60) in terms of their mean scores for customer
satisfactio
Figure 4.4.1
Mean of Customer Satisfaction: Age of customer
100
4.4.2 Customer Satisfaction and Gender:
H1b: There is no significant difference in the customer satisfaction levels of male
and female customers.
To test the above Hypothesis , t-test was carried out and its output is shown in the
table 4.4.3.It shows insignificant Levene‟s test as p value is .139 which is more than .05
so we can assume equal variance in both the groups and the corresponding value of t
statistic is -2.331 which is significant at 5% level of significance as p value is .020 which
is less than .05, this guides us to conclude that there is a significant difference between
customer satisfaction levels of male and female customers.
TABLE No. 4.4.3
Independent Samples t-Test : Customer satisfaction & Gender
Levene's Test for
Equality of
Variances
t-test for Equality of Means
F Sig. t df Sig. (2-
tailed) NS/S
Equal variances assumed 2.503 .114 -2.331 528 .020
S Equal variances not
assumed -2.360 498.328 .019
NS-Not Significant, S- Significant
TABLE No. 4.4.4
Group Statistics: Customer Satisfaction-Gender
Group Gender of
customer N Mean Std. Deviation Std. Error Mean
1 MALE 307 3.6528 .84665 .04832
2 FEMALE 223 3.8211 .78324 .05245
From the group statistic, shown in table 4.4.4, it is evident that mean score for
customer satisfaction is more for females than for male customers so conclusion can be
101
drawn that females are more satisfied with their service providers in comparison to male
customers. The slope in the line diagram shown below in figure 4.4.2, also reflect the
above findings
Figure 4.4.2
Mean of Customer Satisfaction: Gender of customer
4.4.3 Customer Satisfaction and Marital status:
H1c: There is no significant difference in the customer satisfaction levels of married
and unmarried customers.
o test the above hypothesis, t-test was carried out and its output is shown in the
table 4.4.5 It shows insignificant Levene‟s test as p value is .185 which is more than .05
so we can assume equal variance and the corresponding value of t statistic is .505 which
is insignificant at 5% level of significance as p value is .613 which is more than .05,
which guides us to conclude that there is a no significant difference between customer
satisfaction levels of married and unmarried customers.
From the above discussion it is clear that marital status is not a factor to influence
customer satisfaction level and this can be concluded that married as well as unmarried
customers are satisfied with their service providers to the same extent.
102
TABLE No. 4.4.5
Independent Samples Test : Customer satisfaction & Marital Status
Levene's Test for
Equality of Variances t-test for Equality of Means
F Sig. t df Sig. (2-
tailed) NS/S
Equal variances
assumed 1.764 .185 .505 528 .613
NS Equal variances not
assumed .527 272.569 .599
NS-Not Significant, S- Significant
TABLE No. 4.4.6
Group Statistics: Customer Satisfaction - Marital status
Group
marital status of
customer N Mean
Std.
Deviation Std. Error Mean
1 MARRIED 388 3.7345 .84336 .04282
2 UNMARRIED 142 3.6937 .77076 .06468
The line diagram shown below in figure 4.4.3, has negligible slope which also confirm
the above findings.
Figure 4.4.3
Mean of Customer Satisfaction: Marital status of customer
103
4.4.4 Customer Satisfaction and Educational Qualification:
H1d: There is no significant difference in the customer satisfaction levels of
customers of different educational qualifications.
The above hypothesis is tested by using one way ANOVA. The output table
generated by SPSS is shown above. The table 4.4.7 shows the F value of 1.720 which is
not significant at 5% level of significance as the p-value is .144 which is more than .05.It
means that above stated Null Hypothesis can be supported and it can be concluded that
there is no significant difference in customer satisfaction levels of customers with
different educational qualification.
TABLE No. 4.4.7
ANOVA: Customer satisfaction & Educational Qualification
Sum of
Squares df
Mean
Square F Sig. NS/S
Between
Groups 4.648 4 1.162 1.720 .144 NS
Within Groups 354.548 525 .675
Total 359.195 529
NS-Not Significant, S- Significant
Figure 4.4.5
Mean of Customer Satisfaction: Educational Qualification of customer
104
4.4.5 Customer Satisfaction and Occupational pattern:
H1e: There is no significant difference in the customer satisfaction level of
customers of different occupations.
One way ANOVA is applied to test this hypothesis. The output table generated by
SPSS is shown below. The table 4.4.8 shows the F value of 1.945 which is not significant
at 5% level of significance as the p-value is .102 which is more than .05.It means that
above stated Null Hypothesis can not be supported and it can be concluded that there is
no significant difference in customer satisfaction levels of customers of different
occupations.
TABLE No. 4.4.8
Customer Satisfaction-ANOVA: Occupational pattern
Sum of
Squares df
Mean
Square F Sig. NS/S
Between
Groups 5.317 4 1.329 1.945 .102 NS
Within Groups 358.739 525 .683
Total 364.057 529
NS-Not Significant, S- Significant
Figure 4.4.6
Mean of Customer Satisfaction: Occupation of customer
105
4.4.6 Customer Satisfaction and Income pattern:
H1f: There is no significant difference in the customer satisfaction levels of
customers of different income groups.
One way ANOVA is applied to test this hypothesis. The output table generated by
SPSS is shown below. The table 4.4.9 shows the F value of 0.300 which is not significant
at 5% level of significance as the p-value is 0.878 which is more than 0.05.It means that
above stated Null Hypothesis can be supported and it can be concluded that there is no
significant difference in customer satisfaction levels of customers of different income
groups
TABLE No. 4.4.9
ANOVA: Customer satisfaction & Income
Sum of
Squares df
Mean
Square F Sig. NS/S
Between Groups 0.820 4 0.205 0.300 0.878 NS.
Within Groups 358.375 525 0.683
Total 359.195 529
NS-Not Significant, S- Significant
Figure 4.4.7
Mean of Customer Satisfaction: Occupation of customer
106
4.4.7 Customer Satisfaction and Service Provider:
H1g: There is no significant difference in customer satisfaction levels of customers
of different service providers.
One way ANOVA is applied to test this hypothesis. The output table generated by
SPSS is shown below. The table 4.4.10 shows the F value of 8.218 which is significant at
5% level of significance as the p-value is .000 which is less than .05.It means that above
stated Null Hypothesis cannot be supported and it can be concluded that there is
significant difference in customer satisfaction levels of customers using mobile service of
different service providers.
TABLE No. 4.4.10
ANOVA: Customer satisfaction & Service Provider
Sum of
Squares df
Mean
Square F Sig. NS/S
Between Groups 30.947 6 5.158 8.218 .000 S
Within Groups 328.248 523 .628
Total 359.195 529
Post hoc test - Customer satisfaction and service provider:
To find out which age group differs significantly from others on the basis of their
customer satisfaction, Post Hoc test was applied through Scheffe Method as numbers of
customers are different in different groups on the basis of service providers. Table 4.4.11
shows findings of post hoc test.
107
TABLE No. 4.4.11
Post hoc test- Scheffe method : Customer satisfaction & Service Providers
Dependent Variable: Customer Satisfaction
Independent Variables: Service Providers
(I) service provider of
customer
(J) service
provider of
customer
Mean
Differenc
e (I-J)
Std.
Error Sig. NS/S
Airtel
Idea .09785 .09593 .984 NS
Reliance .47700* .11671 .011 S
BSNL .59577* .12540 .001 S
Vodafone .61700* .13730 .003 S
Tata Docomo .48198* .12540 .023 S
Videocon .31226 .17942 .805 NS
Idea
Airtel -.09785 .09593 .984 NS
Reliance .37915 .11409 .089 NS
BSNL .49792* .12296 .013 S
Vodafone .51915* .13508 .023 S
Tata Docomo .38413 .12296 .138 NS
Videocon .21441 .17773 .962 NS
Relinace
Airtel -.47700* .11671 .011 S
Idea -.37915 .11409 .089 NS
BSNL .11877 .13978 .994 NS
Vodafone .14000 .15055 .990 NS
Tata Docomo .00498 .13978 1.000 NS
Videocon -.16473 .18975 .993 NS
BSNL
Airtel -.59577* .12540 .001 S
Idea -.49792* .12296 .013 S
Reliance -.11877 .13978 .994 NS
Vodafone .02123 .15738 1.000 NS
108
Tata Docomo -.11379 .14711 .996 NS
Videocon -.28351 .19522 .909 NS
Vodafone
Airtel -.61700* .13730 .003 S
Idea -.51915* .13508 .023 S
Reliance -.14000 .15055 .990 NS
BSNL -.02123 .15738 1.000 NS
Tata Docomo -.13502 .15738 .994 NS
Videocon -.30473 .20306 .895 NS
Tata Docomo
Airtel -.48198* .12540 .023 S
Idea -.38413 .12296 .138 NS
Reliance -.00498 .13978 1.000 NS
BSNL .11379 .14711 .996 NS
Vodafone .13502 .15738 .994 NS
Videocon -.16972 .19522 .993 NS
Videocon
Airtel -.31226 .17942 .805 NS
Idea -.21441 .17773 .962 NS
Reliance .16473 .18975 .993 NS
BSNL .28351 .19522 .909 NS
Vodafone .30473 .20306 .895 NS
Tata Docomo .16972 .19522 .993 NS
*. The mean difference is significant at the 0.05 level.
Figure 4.4.8also support the above mentioned results.
109
Figure 4.4.8
Mean of Customer Satisfaction: Service provider of customer
1-Airtel, 2-Idea,3-Reliance,4-BSNL,5-Vodafone,6Tata Docomo,7-Videocon
4.4.8 Customer Satisfaction and Type of connection:
H1h: There is no significant difference in the customer satisfaction levels of pre-paid
and post paid customers.
To test the above Hypothesis, t-test was carried out and its output is shown in the
table 4.4.12 It shows insignificant Levene‟s test as p value is .829 which is more than .05
so we can assume equal variance and the corresponding value of t statistic is .307 which
is insignificant at 5% level of significance as p value is .759 which is more than .05, this
guides us to conclude that there is a no significant difference between customer
satisfaction levels of pre-paid and post-paid customers.
110
TABLE No. 4.4.12
Independent Samples t- Test: Customer satisfaction & Type of connection
Levene's Test for
Equality of Variances t-test for Equality of Means
F Sig. t df Sig. (2-
tailed) NS/S
Equal variances assumed .047 .829 .307 528 .759 NS
Equal variances not assumed .311 169.162 .756
NS-Not Significant, S- Significant
Almost horizontal line in the line diagram shown below confirms the above findings.
Figure 4.4.9
Mean of Customer Satisfaction: Type of connection
111
SECTION 5: Customer Loyalty among Respondents of different
demography
In this section scores of customer loyalty were compared across various
demographic variables. Following hypotheses were tested in this section.
H2a: There is no significant difference in the customer loyalty levels of customers of
different age groups
H2b: There is no significant difference in the customer loyalty levels of male and female
customers.
H2c: There is no significant difference in the customer loyalty levels of married and
unmarried customers.
H2d: There is no significant difference in the customer loyalty levels of customers of
different educational qualification.
H2e: There is no significant difference in the customer loyalty levels of customers of
different occupations.
H2f: There is no significant difference in the customer loyalty levels of customers of
different income groups.
H2g: There is no significant difference in the customer loyalty levels of customers of
different service providers.
H2h: There is no significant difference in the customer loyalty levels of pre-paid and
post-paid customers.
112
4.5.1 Customer Loyalty and Age:
H2a: There is no significant difference in the customer loyalty levels of customers of
different age groups.
TABLE No. 4.5.1
ANOVA: Customer Loyalty & Age
Sum of
Squares df
Mean
Square F Sig. NS/S
Between Groups 19.711 4 4.928 7.750 .000 S
Within Groups 333.824 525 .636
Total 353.535 529
NS-Not Significant, S –Significant
ANOVA was applied to test this Hypothesis. The output table generated by SPSS
is shown above. The table 4.5.1 shows the F value of 4.928 which is significant at 5%
level of significance as the p-value is .000 which is less than .05.It means that above
stated Null Hypothesis can be rejected and it can be concluded that there is significant
difference in customer loyalty level of customers of different age groups.
113
TABLE No. 4.5.2
Post hoc test-Scheffe method: Customer Loyalty & Age
Dependent Variable: Customer Loyalty
Independent Variables: Age Groups
(I) age of customer (J) age of customer
Mean
Difference
(I-J)
Std.
Error Sig. NS/S
Below 20
21-35 -.15369 .13033 .846 NS
36-45 -.15612 .13150 .842 NS
46-60 -.11681 .13439 .944 NS
61-85 -.67260* .14638 .000 S
21-35
Below 20 .15369 .13033 .846 NS
36-45 -.00242 .09403 1.000 NS
46-60 .03688 .09803 .998 NS
61-85 -.51891* .11392 .000 S
36-45
Below 20 .15612 .13150 .842 NS
21-35 .00242 .09403 1.000 NS
46-60 .03931 .09959 .997 NS
61-85 -.51649* .11526 .001 S
46-60
Below 20 .11681 .13439 .944 NS
21-35 -.03688 .09803 .998 NS
36-45 -.03931 .09959 .997 NS
61-85 -.55580* .11855 .000 S
61-85
Below 20 .67260* .14638 .000 S
21-35 .51891* .11392 .000 S
36-45 .51649* .11526 .001 S
46-60 .55580* .11855 .000 S
NS-Not Significant, S –Significant
114
To find out which age group differs from others post hoc test was applied through
Scheffe method as numbers of customers are different in different age groups.
Table 4.5.2 shows results of the Scheffe‟s test which shows that Age group 5 (61-
85years) differs from all the Age groups i.e.1 (below 20), 2 (21-35), 3 (36-45) and 4 (46-
60 years)
The Figure 4.5.1 shows that Group 5 customers who are 61 to 85 years old differ
significantly from group 1, 2, 3 and 4 in terms of mean score for Customer Loyalty
Figure 4.5.1
Mean of Customer Loyalty: Age of Respondent
4.5.2 Customer Loyalty and Gender:
H2b: There is no significant difference in the customer loyalty levels of male and
female customers.
To test the above Hypothesis t-test was carried out and its output is shown in the
table 4.5.3.It shows insignificant Levene‟s test as the F value is .065 and corresponding p
115
value is .799 which is more than .05,so equal variance can be assumed and the
corresponding value of t statistic is -2.908 which is significant at 5% level of significance
as p value is .004 which is less than .05, this guides us to conclude that there is a
significant difference between customer loyalty levels of male and female customers.
From the group statistics shown in table 4.5.3, it can be seen that mean score for
customer loyalty is greater for female customers so it can be concluded that female
customers are more loyal towards their service provider.
TABLE No. 4.5.3
Independent Samples t- Test: Customer Loyalty & Gender
Levene's Test
for Equality
of Variances
t-test for Equality of Means
F Sig. t df Sig. (2-
tailed) NS/S
CUST_LOYAL
Equal variances
assumed .065 .799 -2.908 528 .004 S
Equal variances not
assumed -2.903
475.
781 .004
NS-Not Significant, S –Significant
TABLE No. 4.5.4
Group Statistics: Customer Loyalty-Gender
Group gender of
customer N Mean
Std.
Deviation Std. Error Mean
1 MALE 307 3.6156 .80846 .04614
2 FEMALE 223 3.8233 .81638 .05467
Sloping line shown in figure 4.5.2 in the line diagram below also confirms the above
finding.
116
Figure 4.5.2
Mean of Customer Loyalty: Gender of Respondent
4.5.3 Customer Loyalty and Marital status:
H2c: There is no significant difference in the customer loyalty levels of married and
unmarried customers.
To test the above Hypothesis t-test was carried out and its output is shown in the
table 4.5.5 ,which shows insignificant Levene‟s test as the F value is .091 and
corresponding p value is .763 which is more than .05 so equal variance can be assumed
and the corresponding value of t statistic is -.548 which is insignificant at 5% level of
significance as p value is .584 which is more than .05, this guides us to conclude that
there is no significant difference between customer loyalty levels of married and
unmarried customers.
117
TABLE No. 4.5.5
Independent Samples t- Test: Customer Loyalty & Marital status
Levene's Test
for Equality of
Variances
t-test for Equality of Means
F Sig. t df Sig. (2-
tailed) NS/S
Equal variances assumed .091 .763 -.548 528 .584
NS Equal variances not
assumed -.556 257.530 .579
NS-Not Significant, S –Significant
TABLE No. 4.5.6
Group Statistics: Customer Loyalty- Marital Status
Group marital status of
customer N Mean
Std.
Deviation
Std. Error
Mean
1 MARRIED 388 3.6912 .82432 .04185
2 UNMARRIED 142 3.7352 .80055 .06718
Almost horizontal line in the line diagram shown in figure 4.5.3 also confirms the above
finding.
Figure 4.5.3
Mean of Customer Loyalty: Marital status of Respondent
118
4.5.4 Customer Loyalty and Educational Qualification:
H2d: There is no significant difference in the customer loyalty levels of customers of
different educational qualification.
ANOVA was applied to test this Hypothesis. The output table generated by SPSS
is shown below. The table 4.5.7 shows the F value of 2.853 which is significant at 5%
level of significance as the p-value is .023 which is less than .05.It means that above
stated Null Hypothesis can be rejected and it can be concluded that there is significant
difference in customer loyalty levels of customers of different qualification levels.
To find out which group differs from others, Post Hoc test was applied through Scheffe
Method as numbers of customers are different in different age groups
TABLE No. 4.5.7
ANOVA: Customer Loyalty & Educational Qualification
Sum of
Squares df
Mean
Square F Sig. NS/S
Between
Groups 7.521 4 1.880 2.853 .023 S
Within Groups 346.014 525 .659
Total 353.535 529
NS-Not Significant, S –Significant
119
TABLE No. 4.5.8
Post hoc test – Scheffe method: Customer Loyalty & Educational Qualification
Dependent Variable: Customer Loyalty
Independent Variables: Educational Qualification
(I) educational
qualification of customer
(J) educational
qualification of customer
Mean
Differen
ce (I-J)
Std.
Error Sig. NS/S
PRE HR.SEC
HR.SEC -.01954 .10348 1.000 NS
GRADUATION .04638 .10927 .996 NS
POST GRADUATION .06232 .12929 .994 NS
DOCTOR/ENGINEER/CA
/ Ph.D. -.39739 .14768 .125 NS
HR.SEC
PRE HR.SEC .01954 .10348 1.000 NS
GRADUATION .06592 .09121 .971 NS
POST GRADUATION .08186 .11443 .972 NS
DOCTOR/ENGINEER/CA
/ Ph.D. -.37785 .13487 .099 NS
GRADUATION
PRE HR.SEC -.04638 .10927 .996 NS
HR.SEC -.06592 .09121 .971 NS
POST GRADUATION .01594 .11970 1.000 NS
DOCTOR/ENGINEER/CA
/ Ph.D. -.44377
* .13936 .039 S
POST GRADUATION
PRE HR.SEC -.06232 .12929 .994 NS
HR.SEC -.08186 .11443 .972 NS
GRADUATION -.01594 .11970 1.000 NS
DOCTOR /ENGINEER
/CA/
Ph.D.
-.45971 .15556 .070 NS
DOCTOR/ENGINEER/
CA/Ph.D.
PRE HR.SEC .39739 .14768 .125 NS
HR.SEC .37785 .13487 .099 NS
GRADUATION .44377* .13936 .039 S
POST GRADUATION .45971 .15556 .070 NS
NS-Not Significant, S –Significant
The result of the Scheffe‟s test shows that Qualification group 3 (Graduates)
differs from Qualification groups 5 (Doctor/Engineer).
120
The Figure 4.5.4 shows that Group 3 customers who are Graduates differ
significantly from group 5(Doctor/Engineer) in terms of mean score for Customer
Loyalty
Figure 4.5.4
Mean of Customer Loyalty: Educational Qualification of Respondent
4.5.5 Customer Loyalty and Occupational pattern:
H2e: There is no significant difference in the customer loyalty levels of customers of
different occupations.
ANOVA was applied to test this Hypothesis. The output table generated by SPSS
is shown above. The table 4.5.9 shows the F value of 2.932 which is significant at 5%
level of significance as the p-value is .020 which is less than .05.It means that above
stated Null Hypothesis can be rejected and it can be concluded that there is significant
difference in customer loyalty level of customers of different employment pattern.
121
TABLE No. 4.5.9
ANOVA: Customer Loyalty & Occupational Pattern
Sum of
Squares df
Mean
Square F Sig. NS/S
Between Groups 7.725 4 1.931 2.932 .020 S
Within Groups 345.810 525 .659
Total 353.535 529
NS-Not Significant, S –Significant
TABLE No. 4.5.10
Post hoc test – Scheffe method: Customer Loyalty & Occupational Pattern
Dependent Variable: Customer Loyalty
Independent Variables: Employment Pattern
(I) employment status
of customer
(J) employment status
of customer
Mean
Differen
ce (I-J)
Std.
Error Sig. NS/S
BUSINESS
GOVT.SECTOR
EMPLOYEE .32613 .12222 .041 S
PVT.SECTOR
EMPLOYEE .31295 .11101 .035 S
STUDENTS .11945 .12005 .911 NS
OTHERS .27022 .15304 .039 S
GOVT.SECTOR
EMPLOYEE
BUSINESS -.32613 .12222 .041 S
PVT.SECTOR
EMPLOYEE -.01318 .09847 1.000 NS
STUDENTS -.20668 .10856 .460 NS
OTHERS -.05591 .14420 .997 NS
PVT.SECTOR
EMPLOYEE
BUSINESS -.31295 .11101 .035 S
GOVT.SECTOR
EMPLOYEE .01318 .09847 1.000 NS
STUDENTS -.19349 .09577 .396 NS
122
OTHERS -.04272 .13483 .999 NS
STUDENTS
BUSINESS -.11945 .12005 .911 NS
GOVT.SECTOR
EMPLOYEE .20668 .10856 .460 NS
PVT.SECTOR
EMPLOYEE .19349 .09577 .396 NS
OTHERS .15077 .14236 .891 NS
OTHERS
BUSINESS -.27022 .15304 .039 S
GOVT.SECTOR
EMPLOYEE .05591 .14420 .997 NS
PVT.SECTOR
EMPLOYEE .04272 .13483 .999 NS
STUDENTS -.15077 .14236 .891 NS
Figure 4.5.5
Mean of Customer Loyalty: Employment of Respondent
123
4.4.6 Customer Loyalty and Income:
H2f: There is no significant difference in the customer loyalty levels of customers of
different income groups.
The Hypothesis is tested by using one way ANOVA. The output table generated
by SPSS is shown below. The table 4.5.11 shows the F value of .416 which is not
significant at 5% level of significance as the p-value is .648 which is more than .05.It
means that above stated Null hypothesis can not be rejected and this can be concluded
that there is no significant difference between customer loyalty levels of customers of
different income groups.
TABLE No. 4.5.11
ANOVA: Customer Loyalty & Income
Sum of
Squares df
Mean
Square F Sig. NS/S
Between Groups 1.663 4 .416 .620 .648 NS
Within Groups 351.873 525 .670
Total 353.535 529
NS-Not Significant, S –Significant
4.5.7 CUSTOMER LOYALTY AND SERVICE PROVIDER:
H2g: There is no significant difference in the customer loyalty levels of customers of
different service providers.
One way ANOVA is applied to test this hypothesis. The output table generated by
SPSS is shown below. The table 4.5.12 shows the F value of 9.707 which is significant at
5% level of significance as the p-value is .000 which is less than .05.It means that above
stated Null Hypothesis cannot be supported and it can be concluded that there is
significant difference in customer satisfaction levels of customers using mobile service of
different service providers.
124
TABLE No. 4.5.12
ANOVA: Customer Loyalty & Service Provider
Sum of
Squares df
Mean
Square F Sig. NS/S
Between Groups 35.424 6 5.904 9.707 .000 S
Within Groups 318.112 523 .608
Total 353.535 529
Post hoc test – customer loyalty and service providers:
To find out which age group differs significantly from others on the basis of their
customer satisfaction, post hoc test was applied through Scheffe method as numbers of
customers are different in different groups on the basis of service providers
TABLE No. 4.5.13
Post hoc test – Scheffe method: Customer Loyalty & Service Provider
Dependent Variable: Customer Loyalty
Independent Variables: Service Providers
(I) service provider of
customer
(J) service
provider of
customer
Mean
Differen
ce (I-J)
Std.
Error Sig. NS/S
Airtel
Idea -.02277 .09443 1.000 NS
Reliance .48472* .11489 .007 S
BSNL .54784* .12345 .004 S
Vodaphone .55972* .13516 .010 S
Tata Docomo .40302 .12345 .102 NS
Videocon .53750 .17663 .162 NS
Idea
Airtel .02277 .09443 1.000 NS
Reliance .50750* .11231 .003 S
BSNL .57062* .12105 .001 S
Vodaphone .58250* .13298 .004 S
Tata Docomo .42579 .12105 .056 NS
Videocon .56027 .17496 .117 NS
Reliance
Airtel -.48472* .11489 .007 S
Idea -.50750* .11231 .003 S
BSNL .06312 .13760 1.000 NS
125
Vodaphone .07500 .14820 1.000 NS
Tata Docomo -.08170 .13760 .999 NS
Videocon .05278 .18680 1.000 NS
BSNL
Airtel -.54784* .12345 .004 S
Idea -.57062* .12105 .001 S
Reliance -.06312 .13760 1.000 NS
Vodaphone .01188 .15493 1.000 NS
Tata Docomo -.14483 .14482 .986 NS
Videocon -.01034 .19218 1.000 NS
Vodaphone
Airtel -.55972* .13516 .010 S
Idea -.58250* .13298 .004 S
Reliance -.07500 .14820 1.000 NS
BSNL -.01188 .15493 1.000 NS
Tata Docomo -.15670 .15493 .985 NS
Videocon -.02222 .19990 1.000 NS
Tata Docomo
Airtel -.40302 .12345 .102 NS
Idea -.42579 .12105 .056 NS
Reliance .08170 .13760 .999 NS
BSNL .14483 .14482 .986 NS
Vodaphone .15670 .15493 .985 NS
Videocon .13448 .19218 .998 NS
Videocon
Airtel -.53750 .17663 .162 NS
Idea -.56027 .17496 .117 NS
Reliance -.05278 .18680 1.000 NS
BSNL .01034 .19218 1.000 NS
Vodaphone .02222 .19990 1.000 NS
Tata Docomo -.13448 .19218 .998 NS
*. The mean difference is significant at the 0.05 level.
126
Figure 4.5.6
Mean of Customer Loyalty: Service Provider of Respondent
4.5.8 Customer Loyalty and Type of connection:
H2h: There is no significant difference in the customer loyalty levels of pre-paid and
post-paid customers.
To test the above Hypothesis t-test was carried out and its output is shown in the
table 4.5.14 It shows insignificant Levene‟s test as “p-value” is .531 which is more than
.05 so we can assume equal variance and the corresponding value of t statistic is .333
which is insignificant at 5% level of significance as “p-value” is .739 which is more than
.05, this guides us to conclude that there is no significant difference between customer
loyalty levels of pre - paid and post-paid customers.Hypothesis can be supported and it
can be concluded that there is no significant difference in customer satisfaction level of
customers of different qualification.
127
TABLE No. 4.5.14
Independent Samples t-Test: Customer Loyalty & Type of connection
Levene's Test for
Equality of Variances t-test for Equality of Means
F Sig. t df Sig. (2-tailed) NS/S
Equal variances assumed .393 .531 .333 528 .739 NS
Equal variances not assumed .344 173.533 .731
NS-Not Significant, S –Significant
TABLE No. 4.5.15
Group Statistics :Customer Loyalty-Type of connection
Group Type of connection N Mean Std.
Deviation Std. Error Mean
1.00 Pre Paid 422 3.7090 .82725 .04027
2.00 Post Paid 108 3.6796 .78153 .07520
The horizontal line shown in the table 4.5.7also confirms the same finding.
Figure 4.5.7
Mean of Customer Loyalty: Type of connection
128
SECTION 6: Factors affecting customer satisfaction
This section studies Customer satisfaction as an outcome of Service quality and
Customer Perceived Value. Association between customer satisfaction on the one hand
and various service quality dimensions and customer perceived value on the other is
established with help of hypotheses H3 and H4. As service quality is a composite
construct having seven dimensions, hypothesis H3 is divided in seven hypotheses i.e.
H3a to H3g.
In this section hypothesized relationships shown in part I of the Research model are
tested. The research model is shown below.
Figure 4.6.1
Research Model
129
4.6.1 Service Quality and Customer Satisfaction:
Hypothesis H3 explains this relationship. Hypothesis H3 has following seven
components as service quality is found to have 7 dimensions.
H3a: Customer Satisfaction is not impacted by Employee Performance.
H3b: Customer Satisfaction is not impacted by Reliability
H3c: Customer Satisfaction is not impacted by Assurance.
H3d: Customer Satisfaction is not impacted by Responsiveness.
H3e: Customer Satisfaction is not impacted by Competitiveness.
H3f: Customer Satisfaction is not impacted by Network Quality
H3g: Customer Satisfaction is not impacted by Tangibility.
4.6.2 Customer Perceived Value and Customer Satisfaction:
Hypothesis H4 explains this relationship
H4: Customer Satisfaction is not impacted by Customer Perceived Value.
All the above hypotheses related with service quality and customer perceived value were
tested through Correlation and Regression Analysis.
4.6.3 Correlation Analysis: Customer Satisfaction, Service Quality and Customer
Perceived Value
First of all a correlation analysis was done to test whether all the seven
dimensions of service quality and single dimensional Customer Perceived Value are
having significant correlation with customer satisfaction. The Table 4.6.1 shows the
correlation table generated by SPSS.
130
The table 4.6.1 shows that all the seven dimensions of service quality and Customer
Perceived value correlate significantly with customer satisfaction and the value of
correlation ranged from .308 to .817. The table also shows that all the correlation values
are significant as p-values are less than .05.
TABLE No. 4.6.1
Correlations : Customer Satisfaction – Service Quality Dimensions - CPV
CUST
_SAT SQ1 SQ2 SQ3 SQ4 SQ5 SQ6 SQ7 CPV
Pearson
Correlatio
n
CUST_SAT 1.000 .671 .817 .613 .591 .601 .589 .308 .735
SQ1 .671 1.000 .676 .512 .690 .544 .544 .322 .710
SQ2 .817 .676 1.000 .645 .653 .568 .606 .383 .821
SQ3 .613 .512 .645 1.000 .572 .545 .424 .244 .665
SQ4 .591 .690 .653 .572 1.000 .600 .473 .291 .656
SQ5 .601 .544 .568 .545 .600 1.000 .459 .478 .507
SQ6 .589 .544 .606 .424 .473 .459 1.000 .371 .572
SQ7 .308 .322 .383 .244 .291 .478 .371 1.000 .317
CPV .735 .710 .821 .665 .656 .507 .572 .317 1.00
0
Sig. (1-
tailed)
CUST_SAT . .000 .000 .000 .000 .000 .000 .000 .000
SQ1 .000 . .000 .000 .000 .000 .000 .000 .000
SQ2 .000 .000 . .000 .000 .000 .000 .000 .000
SQ3 .000 .000 .000 . .000 .000 .000 .000 .000
SQ4 .000 .000 .000 .000 . .000 .000 .000 .000
SQ4 .000 .000 .000 .000 .000 . .000 .000 .000
SQ5 .000 .000 .000 .000 .000 .000 . .000 .000
SQ7 .000 .000 .000 .000 .000 .000 .000 . .000
CPV .000 .000 .000 .000 .000 .000 .000 .000 .
SQ1-SQ7 are short form for all the seven dimensions of service quality as follows:
SQ1- Employee Performance, SQ2-Reliability, SQ3 -Assurance, SQ4- Responsiveness
SQ5- Competitiveness, SQ6- Network Quality SQ7-Tangibility
131
4.6.4 Regression Analysis - Customer Satisfaction: Service Quality and Customer
Perceived Value
Regression was carried out with STEPWISE method, so output table generated 7
models with different values of R and R square. Value of R square shows the variance
explained in dependent variable i.e. Customer Satisfaction by all the 8 independent
variables i.e. seven service quality dimensions and Customer Perceived Value. R square
values for different models ranged from .668 to .729, it means that values for explained
variance in customer satisfaction ranged from 66.8% to 72.9%. in different models.
In other words it can be stated that different regression models are able to explain
variance in customer satisfaction to different extent and it is summarized as follows:
Model 1: It has only “Reliability” as independent variable and “Customer satisfaction” as
dependent variable and it is able to explain 66.8% variance in customer satisfaction.
Model 2: It has “Reliability” and “Competitiveness” as independent variables and
“Customer satisfaction” as dependent variable and it is able to explain 69.5% variance in
customer satisfaction.
Model 3: It has “Reliability”, “Competitiveness” and “Customer Perceived Value” as
independent variables and “Customer satisfaction” as dependent variable and it is able to
explain 71.1% variance in customer satisfaction.
Model 4: It has “Reliability”, “Competitiveness”, “Customer Perceived Value” and
“Employee Performance” as independent variables and “Customer satisfaction” as
dependent variable and it is able to explain 71.8% variance in customer satisfaction.
Model 5: It has “Reliability”, “Competitiveness”, “Customer Perceived Value”,
“Employee Performance” and “Tangibility” as independent variables and “Customer
satisfaction” as dependent variable and it is able to explain 72.1% variance in customer
satisfaction.
Model 6: It has “Reliability”, “Competitiveness”, “Customer Perceived Value”,
“Employee Performance”, “Tangibility” and “Network Quality” as independent variables
132
and “Customer satisfaction” as dependent variable and it is able to explain 72.6%
variance in customer satisfaction.
Model 7: It has “Reliability”, “Competitiveness”, “Customer Perceived Value”,
“Employee Performance”, “Tangibility”, “ Network Quality” and “ Responsiveness” as
independent variables and “Customer satisfaction” as dependent variable and it is able to
explain 72.9% variance in customer satisfaction.
Figure 4.6.2 : Variance Explained by 7 models
The above graph also shows the gradual increase in value of R² that leads to
enhanced predictive ability of successive models starting from Mode 1 to Model 7.
Just below the model summary table another table generated by SPSS is shown which is
ANOVA table. The table 4.6.2 shows F values and corresponding p values for different
models. From this table it can be concluded that all the regression models are significant
as p -values for all seven models are less than .05.
Here, seventh model is picked for further analysis because it is able to explain
72.9% variance in customer satisfaction.
66.80%
69.50%71.10%
71.80% 72.10% 72.60%
72.90%
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Variance Explained by 7 Models in Customer Satisfaction
Seri…
133
TABLE No. 4.6.2
Customer Satisfaction:Service Quality Dimensions & CPV-Regression Analysis
Model R R
Square
Adjust
ed R
Square
Std.
Error of
the
Estimate
Change Statistics
R
Square
Change
F Change df1 df2
Sig. F
Chang
e
1 .817a .668 .667 .47559 .668 1060.076 1 528 .000
2 .834b .695 .694 .45577 .028 47.916 1 527 .000
3 .843c .711 .709 .44457 .015 27.880 1 526 .000
4 .847d .718 .716 .43941 .007 13.425 1 525 .000
5 .849e .721 .719 .43704 .004 6.705 1 524 .010
6 .852f .726 .723 .43376 .005 8.967 1 523 .003
7 .854g .729 .725 .43213 .003 4.954 1 522 .026
a. Predictors: (Constant), SQ2
b. Predictors: (Constant), SQ2, SQ5
c. Predictors: (Constant), SQ2, SQ5, CPV
d. Predictors: (Constant), SQ2, SQ5, CPV, SQ1
e. Predictors: (Constant), SQ2, SQ5, CPV, SQ1, SQ7
f. Predictors: (Constant), SQ2, SQ5, CPV, SQ1, SQ7, SQ6
g. Predictors: (Constant), SQ2, SQ5, CPV, SQ1, SQ7, SQ6, SQ4
134
TABLE No. 4.6.3
Customer Satisfaction: Service Quality Dimensions & CPV-Regression ANOVA
Model Sum of Squares df Mean
Square F Sig.
1
Regression 239.771 1 239.771 1060.076 .000b
Residual 119.424 528 .226
Total 359.195 529
2
Regression 249.724 2 124.862 601.092 .000c
Residual 109.471 527 .208
Total 359.195 529
3
Regression 255.234 3 85.078 430.461 .000d
Residual 103.961 526 .198
Total 359.195 529
4
Regression 257.826 4 64.457 333.828 .000e
Residual 101.369 525 .193
Total 359.195 529
5
Regression 259.107 5 51.821 271.305 .000f
Residual 100.088 524 .191
Total 359.195 529
6
Regression 260.794 6 43.466 231.020 .000g
Residual 98.401 523 .188
Total 359.195 529
7
Regression 261.719 7 37.388 200.221 .000h
Residual 97.476 522 .187
Total 359.195 529
a. Dependent Variable: CUST_SAT
b. Predictors: (Constant), SQ2
c. Predictors: (Constant), SQ2,SQ5
d. Predictors: (Constant), SQ2,SQ5, CPV
e. Predictors: (Constant), SQ2,SQ5, FIRST_Q
f. Predictors: (Constant), SQ2,SQ5, CPV, SQ1, SQ6
g. Predictors: (Constant), SQ2,SQ5, CPV, SQ1, SQ7, SQ6
h. Predictors: (Constant), SQ2, SQ5, CPV, SQ1, SQ7, SQ6, SQ4
135
TABLE No. 4.6.4
Customer Satisfaction: Service Quality Dimensions & CPV-Regression Coefficients
Model
Unstandardiz
ed
Coefficients
Standa
rdized
Coeffic
ients t Sig.
Correlations Collinearity
Statistics
B
Std.
Erro
r
Beta Zero-
order Partial Part
Tolera
nce VIF
7
(Constant) .288 .140 2.052 .041
RELIABILITY .528 .045 .513 11.793 .000 .817 .459 .269 .275 3.641
COMPETITIVENESS .197 .031 .200 6.259 .000 .601 .264 .143 .508 1.968
CPV .147 .042 .152 3.537 .000 .750 .153 .081 .280 3.572
EMPL.PERFOR. .125 .032 .141 3.876 .000 .671 .167 .088 .391 2.560
TANGIBILITY -.093 .029 -.085 -3.196 .001 .308 -.139 -.073 .728 1.374
NETWORK QUAL .129 .043 .090 2.978 .003 .589 .129 .068 .569 1.758
RESPONSIVENESS -.084 .038 -.079 -2.226 .026 .591 -.097 -.051 .410 2.439
a. Dependent Variable: CUST_SAT
The above table 4.6.4 is based on model 7 and shows regression coefficients for
all the independent variables i.e different dimensions of service quality and Customer
Perceived Value. It shows “t” values of 6 dimensions of service quality and their
respective significance levels. From the table it can be inferred that 6 dimensions of
service quality are significant in explaining variance in customer satisfaction as p values
for all the dimensions are less than .05.
Thus, we can reject all the above Null hypotheses except H3c and conclude that
Customer satisfaction is impacted by 6 dimensions of service quality as well as Customer
perceived value (CPV)
4.6.5 Regression Equation: Customer Satisfaction-Service Quality and CPV
Customer Satisfaction = .288+.528(Reliability) +.197(Competitiveness)+.147(customer
perceived value)+ .129(Network Quality)+.125(Employee performance) +(-.093)(
Tangibility)+(- .084)( Responsiveness)
136
The above equation has taken unstandardized β coefficient to explain the Customer
Satisfaction and can be further decoded in following way:
.288 is constant which shows that even in the absence of all the variables impacting
customer satisfaction there will be at least .288 unit customer satisfactions.
1 unit increase in the value of reliability will bring .528 unit positive change in the value
of customer satisfaction.
1 unit increase in the value of competitiveness will bring .197 unit positive change in the
value of customer satisfaction.
1 unit increase in the value of customer perceived value will bring .147 unit positive
change in the value of customer satisfaction.
1 unit increase in the value of network quality will bring .129 unit positive change in the
value of customer satisfaction.
1 unit increase in the value of employee performance will bring .125 unit positive change
in the value of customer satisfaction.
1 unit increase in the value of tangibility will bring .093 unit negative change or decrease
in the value of customer satisfaction.
1 unit increase in the value of responsiveness will bring .084 unit negative change or
decrease in the value of customer satisfaction.
Customer Satisfaction = .288+.528(Reliability)+.197(Competitiveness)+.147(customer
perceived value)+.125(Employee performance)+.129(Network Quality )+(-.093)
(Tangibility)+(- .084)(Responsiveness)
137
Figure 4.6.3
Customer Satisfaction-Impact of Service Quality and CPV
138
4.6.6 Rank Analysis: Customer Satisfaction: Service Quality Dimensions & CPV
From the above model we can conclude that six dimensions of service quality and
customer perceived value positively affect customer satisfaction and their impact on
customer satisfaction is ranked in table 4.6.5
TABLE No. 4.6.5
Rank Analysis
Customer Satisfaction: Service Quality Dimensions & CPV
Service dimension Beta value Rank
RELIABILITYS SQ2 .528 I
COMPETITIVENESS SQ5 .197 II
CPV .147 III
NETWORK QUALITY SQ6 .129 IV
EMLPOYEE PERFORMANCE SQ1 .087 V
RESPONSIVENESS SQ2 -.084 VI
TANGIBILITY SQ7 -.093 VII
So all the hypotheses from H3a to H3g except H3c are rejected and hypothesis H4 is also
rejected.
139
SECTION 7: Factors affecting customer Loyalty
This section deals with direct impact of customer satisfaction, switching cost and
inertia on customer loyalty and moderating impact of switching cost and inertia on
customer satisfaction-customer loyalty link .
In this section hypothesized relationships shown in part II of the Research model
are tested. The research model is shown below.
Figure: 4.7.1
Research Model
140
4.7.1 Factors affecting Customer Loyalty: Direct Impact
Following hypotheses were tested to evaluate direct impact of customer
satisfaction, switching cost, inertia on customer loyalty.
H5: Customer Loyalty is not impacted by Customer satisfaction
H6: Customer Loyalty is not impacted by Switching Cost.
H7: Customer Loyalty is not impacted by Inertia.
The above 3 hypothesis measure the direct impacts or main effects of Customer
Satisfaction, Switching Cost and Inertia on Customer Loyalty.
4.7.2 Factors affecting Customer Loyalty: Moderating Impact:
Following two hypotheses were tested to evaluate moderating impact of switching
cost and inertia on customer satisfaction-customer loyalty link.
H8: Customer Satisfaction –Customer Loyalty link is not moderated by Switching Cost.
H9: Customer Satisfaction –Customer Loyalty link is not moderated by Inertia.
To test hypotheses involving moderating impact, the method recommended by
Aiken and West (1991) was adopted. In this method regression analysis was carried out
in hierarchical manner to measure interactions or moderating impacts of Switching cost
and Inertia on CS-CL link over and above the simple impacts of Customer satisfaction,
Switching Cost and Inertia on Customer Loyalty. This method was also followed by
McClellan in 2001.
4.7.3. Moderating Impact & Multiollinearity:
In study of Moderating impact or Interaction effects, a major problem of
multicollinearity arises when independent variables are multiplied to create Moderating
variable or Interaction term, this spoils the study. To overcome this problem all the
variables in this regression were centered.
141
To create “centered term” for any variable its mean value was deducted from its
individual value, this was done through SPSS only. Now these centered variables were
multiplied to create Moderating variable.
VIF ( Variance Inflation Factor) of all the 3 regression models show that multicollinearity
was very much in control as all VIF values ranged between 1.345 and 2.409 which were
far below the acceptable limit of 10 as suggested by Ranveera and Neely(2003)
4.7.4 Regression Models:
To test all the above given hypotheses 3 regression models were developed. The
three models differed from each other in number of independent variables selected to
explain variance in dependent variable i.e. customer loyalty.
All the 3 models gave different values of R, R² and adjusted R². The model that gave
maximum value of R² was picked to test all the above mentioned 5 hypothesis and
explain direct as well as moderating impact of all independent variables on dependent
variable i.e. customer loyalty.
MODEL -1
4.7.4.1 Model 1:
This includes Customer Loyalty as dependent variable and Customer satisfaction,
Switching cost and Inertia as independent variables.
This model turned out to be significant as p-value was less than .05 and 61.8% variance
was explained by this model. This model was developed as base model with which next
model will be compared on the basis of change in the value of R Square to conclude
about moderating impact of switching cost on customer satisfaction- customer Loyalty
link.
142
TABLE No. 4.7.1
Regression Analysis: Customer Loyalty-Customer Satisfaction, Switching cost,
Inertia
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .786a .618 .616 .51290
Dependent Variable : Customer Loyalty
Independent Variables: Customer Satisfaction, Switching Cost, Inertia
TABLE No. 4.7.2
Regression ANOVA: Customer Loyalty-Customer Satisfaction, Switching cost,
Inertia:
Model Sum of
Squares df
Mean
Square F Sig.
1
Regression 222.541 3 74.180 279.374 .000b
Residual 139.665 526 .266
Total 362.207 529
Dependent Variable : Customer Loyalty
Independent Variables: Customer Satisfaction, Switching Cost, Inertia
TABLE No. 4.7.3
Regression Coefficients: Customer Loyalty-Customer Satisfaction, Switching cost,
Inertia:
Model
Unstandardized
Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
1
(Constant) .488 .158 3.092 .002
Cust.Satis. .495 .032 15.628 .000
Switching -.020 .042 -.471 .638
Inertia .400 .034 .493 11.748 .000
The model is shown in Figure 4.7.2, it gave R² value of .618 and ANOVA table
generated by SPSS showed that it is significant as p value is less than .05.
143
Figure 4.7.2
Direct Impact on Customer Loyalty: Customer Satisfaction, Switching cost &
Inertia
First Model
Customer Loyalty =β0+β1+β2+β3
S-Significant
NS-Not significant
144
MODEL-2
Direct impact of Customer Satisfaction, Switching cost and Inertia and Moderating
impact of Switching Cost on Customer Loyalty
4.7.4.2 Model 2:
This includes Customer Loyalty as dependent variable and as independent
variables all the variables of model 1 were retained in their centered form and a new
variable, derived from multiplication of Customer Satisfaction_centered and Switching
cost_centered , was also introduced to test its moderating impact.
Regression model generated by SPSS showed that R² value increased to .649
showing remarkable improvement over model 1.
It proves that the variable “Customer Satisfaction_centered X Switching Cost_
centered” has improved model‟s ability to explain variance and it has moderating impact
on Customer Satisfaction- Customer Loyalty link.
This model 2 was developed so that it can be compared to Model 3 in terms of
change in value of R square so that moderating impact of inertia can be ascertained on
customer satisfaction-customer loyalty link.
ANOVA table 4.75 shows that model is significant and all the variables have
significant p values as per the coefficient table generated by SPSS.
All the regression related table generated by SPSS is shown below:
TABLE No. 4.7.4
Regresion Analysis :Customer Loyalty: Moderating impact of Switching cost
Model R R Square Adjusted
R Square
Std. Error
of the
Estimate
Change Statistics
R Square
Change F Change df1 df2 Sig. F Change
1 .806a .649 .646 .49219 .649 242.540 4 525 .000
Dependent Variable: Cust.Loyalty_centred
Independent Variables: Inertia _centered, MOD_1, Switching_ centered, Cust.Satis._centered
145
TABLE No. 4.7.5
Regression ANOVA:Customer Loyalty-Moderating impact of Switching cost
Model Sum of
Squares df
Mean
Square F Sig.
1
Regression 235.024 4 58.756 242.540 .000b
Residual 127.183 525 .242
Total 362.207 529
Dependent Variable: Cust.Loyalty_centred
Independent Variables: Inertia _centered, MOD_1, Switching_ centered,
Cust.Satis._centered
TABLE No. 4.7.6
Coefficients: Regression Model 2
Unstandardized
Coefficients
Standa
rdized
Coeffi
cients t Sig.
Correlations Collinearity
Statistics
B Std.
Error Beta
Zero-
order Partial Part
Toler
ance VIF
1
(Constant) 3.642 .023 158.159 .000
MOD_1(CSXSC) .284 .057 .149 4.981 .000 .111 .212 .129 .743 1.345
Cust.sat_centred .496 .035 .494 13.975 .000 .701 .521 .361 .536 1.866
Switching_centred -.141 .044 -.106 -3.247 .001 .348 -.140 -.084 .623 1.605
Inertia_centred .444 .038 .452 11.552 .000 .715 .450 .299 .437 2.289
Dependent Variable: Cust.Loyalty_centered
Independent Variables:Inertia_centered,MOD_1,Switching_centeredCust.Satis._centered
MOD_1- Cust.Satis._cenetered X Switching cost_centered
Following relationship is based on model 2
146
Figure 4.7.3
Direct & Moderating Impact on Customer Loyalty: Customer Satisfaction,
Switching cost & Inertia
S-Significant
NS-Not significant
Customer Loyalty =β0+β1+β2+β3+ β4
Customer loyalty = 3.642+.496(CS)+.444(I)+.284(CSXSC)+-.141(SC)
All 4 variables show significant p-value.
147
MODEL-3
Direct impact of Customer Satisfaction, Switching cost and Inertia and Moderating
impact of Switching Cost & Inertia on Customer Loyalty:
4.7.4.3 Model 3:
This includes Customer Loyalty as dependent variable and as independent
variables all the variables of 2nd
Model with their centered values were retained and one
more variable, derived from multiplying Customer Satisfaction_centered and
Inertia_centerd was introduced.
SPSS tables generated for model 3 are mentioned below:
TABLE No. 4.7.7
Correlations: Regression Model 3
Cust.Lo
y.
MOD_
1
MOD
_2
Cust.sat
_centred
Switchin
g_centred
Inertia_cen
tered
Pearson
Correlation
Cust. Loyalty 1.000 .111 -.406 .701 .348 .715
MOD_1 .111 1.000 .485 -.201 .324 .212
MOD_2 -.406 .485 1.000 -.551 .169 -.165
Cust.sat_centred .701 -.201 -.551 1.000 .293 .593
Switching_centred .348 .324 .169 .293 1.000 .578
Inertia_cenetered .715 .212 -.165 .593 .578 1.000
Sig. (1-tailed)
Cust. Loyalty . .005 .000 .000 .000 .000
MOD_1 .005 . .000 .000 .000 .000
MOD_2 .000 .000 . .000 .000 .000
Cust.sat_centred .000 .000 .000 . .000 .000
Switching_centred .000 .000 .000 .000 . .000
Inertia_cenetered .000 .000 .000 .000 .000 .
MOD_1- Cust.Satis._cenetered X Switching cost_centered
MOD_2- Cust.Satis._cenetered X Inertia_centered
148
TABLE No. 4.7.8
Regresion Analysis: Customer Loyalty-Moderating impact of Switching cost &
Inertia
Model R R
Square
Adjuste
d R
Square
Std. Error
of the
Estimate
Change Statistics
R
Square
Change
F
Change df1 df2
Sig. F
Change
1 .821 .675 .672 .47426 .675 217.279 5 524 .000
Dependent Variables: Customer Loyalty_Centered
Independent Variables: (Constant), inertia_cenetered , MOD_2, MOD_1
switching_centred, cust.sat_centred
TABLE No. 4.7.9
Regression ANOVA: Customer Loyalty-Moderating impact of Switching cost &
Inertia
Model Sum of
Squares df
Mean
Square F Sig.
1
Regression 244.350 5 48.870 217.279 .000b
Residual 117.857 524 .225
Total 362.207 529
Dependent Variables: Customer Loyalty_Centered
Independent Variables: (Constant), Inertia_cenetered , MOD_2, MOD_1
Switching_centred, Customer Satis_centred
149
TABLE No. 4.7.10
Regression Coefficients :Customer Loyalty-Moderating impact of Switching cost &
Inertia
Model
Unstandardized
Coefficients
Standa
rdized
Coeffi
cients t Sig.
Correlations Collinearity
Statistics
B Std.
Error Beta
Zero-
order Partial Part
Toler
ance VIF
1
(Constant) 3.726 .026 144.67
4 .000
MOD_1 .417 .059 .219 7.104 .000 .111 .296 .177 .651 1.535
MOD_2 -.253 .039 -.226 -6.439 .000 -.406 -.271 -
.160 .505 1.982
csat_centred .377 .039 .376 9.709 .000 .701 .390 .242 .415 2.409
switching_centred -.062 .044 -.047 -1.424 .155 .348 -.062 -
.035 .574 1.742
inertia_cenetered .427 .037 .436 11.529 .000 .715 .450 .287 .435 2.300
Dependent Variables: Customer Loyalty_Centered
Independent Variables: (Constant), inertia_cenetered , MOD_2, MOD_1 switching_centred,
csat_centred
Following relationship is based on Model 3:
150
Figure 4.7.4
Customer Loyalty: Direct Impact & Moderating Impact
S-Significant
NS-Not significant
Customer Loyalty =β0+β1+β2+β3+ β4+ β5
Customer Loyalty = 3.726+.427(I)+.417(CSXSC)+.377(CS)+(-.217)(I)
4.7.5 Direct impact & Moderating impact: Hypotheses Testing
Both types of hypotheses i.e. Direct impact as well as Moderating impact are
tested using the Third Model as it shows maximum value of R² which is .675, it means
this model 3 explains 67.5% variance in customer loyalty with the help of all the
151
variables; this is higher variance than explained by previous 2 models. Hence, this model
is retained for further analysis and hypothesis testing.
4.7.6 Direct impact & Moderating impact: Regression Equation
Customer Loyalty = 3.726+.427(I) +.417(CSXSC) +.377(CS) + (-.217) (CSXI)
I= Inertia
CS X SC = Cust. Sat X Switching cost
CS= Customer Satisfaction
CS X I =Cust. Sat. X Inertia
The above equation is based on Model 3 and inferences for different hypothesis can be
drawn from it.
All the hypotheses i.e. H5, H6, H7, H8 and H9 are tested using this model.
H5: Customer Loyalty is not impacted by Customer satisfaction
Customer Satisfaction shows significant positive impact on Customer Loyalty and
1 unit increase in customer satisfaction leads to 0.377 unit positive change in customer
loyalty, hence above Null hypothesis is rejected and this can be concluded that higher is
the customer satisfaction higher is customer loyalty.
H6: Customer Loyalty is not impacted by Switching Cost.
Switching cost shows unstandardised beta value of -0.062 with p-value of 0.155
which is more than 0.05, hence this Null hypothesis is retained and conclusion can be
drawn that Customer Loyalty is not impacted by switching cost. It means that no direct
impact of switching cost is observed on customer loyalty.
Above finding seems quite logical and obvious, as switching costs are imposed by
the service provider to compel their customers to stay with them. So in the absence of
152
zero value of other variables like customer satisfaction, inertia and any other moderating
variables, switching cost cannot prevent customers from leaving their service provider.
H7: Customer Loyalty is not impacted by Inertia.
Inertia shows unstandardised beta value of 0.427 which is significant at 5% level
of significance as p-value is less than 0.05. It means 1 unit increase in inertia will lead to
0.427 unit positive change in customer loyalty in the absence of other variables. It proves
direct positive impact of inertia on Customer Loyalty. Hence above stated Null
hypothesis is rejected and it can be concluded that higher is the inertia higher is customer
loyalty.
The finding is quite logical and obvious as inertia prevents the customer from
acting rationally because of natural tendency to remain in same state or to preserve status
quo until something very extra ordinary forces him to move and act. So higher is the
inertia higher is the customer loyalty sounds logical.
H8: Customer Satisfaction –Customer Loyalty link is not moderated by Switching
Cost.
The moderating impact of (CSXSC) is analyzed keeping in mind the positive beta
value of 0.417. It shows positive impact of this moderating variable on Customer
Satisfaction-Customer Loyalty link. Hence the Null hypothesis is rejected and
moderating impact of CSXSC is established.
Here, it is interesting to note that Switching Cost was not capable of influencing
Customer Loyalty on its own but along with Customer Satisfaction it plays a significant
positive role. It means for a fixed level of Customer Satisfaction higher is the
Switching Cost higher would be Customer Loyalty.
The positive value of this moderating variable implies that it strengthens the
relationship between Customer Satisfaction and Customer Loyalty.
We can further emphasize, that when Customer Satisfaction is higher Customer
Loyalty is bound to be higher because of direct positive relationship between CS and CL.
153
In the presence of Switching cost, this relationship would be strengthened and impact of
customer satisfaction would be even more pronounced on customer loyalty in the
presence of higher switching cost.
It can be interpreted logically because customers will have reasons to stay with
their service provider firstly because of Higher CS and secondly because higher
Switching cost. This could be best retention approach.
H9: Customer Satisfaction –Customer Loyalty link is not moderated by Inertia.
The unstandardised beta value for Moderating variable (CS X I) is -.217 which is
significant at 5% level of significance as p-value is less than 0 .05.It shows that inertia
weakens the impact of customer satisfaction on customer loyalty. Above Null hypothesis
can not be supported and this can be concluded that inertia moderates the relationship
between customer satisfaction and customer loyalty by diminishing the link
The above conclusion can be further analyzed that in the presence of high inertia
even dissatisfied customers may not leave their present service provider.
4.7.7 Comparison of Model 1, Model 2 and Model 3:
In table 4.7.11 different values of all the 3 models are summarized. The table
compares the model on the basis of independent variables used, value of R, R² and
adjusted R². The comparison reveals that Model 3 is clearly superior as it explains
maximum variance and also gives enough evidence for direct as well as moderating
impacts of customer satisfaction, switching cost and inertia.
154
TABLE No. 4.7.11
Comparasion of Model 1,2 &3 : R, R² & adjusted R²
Independent Variables VIF R R² Adjusted
R²
Model
1
Customer Satisfaction 1.509
.786 .618 .616 Switching Cost 1.473
Inertia 2.028
Model
2
Customer Satisfaction_centered 1.866
.806 .649 .646
Switching Cost_centerd 1.605
Inertia_centered 2.289
Customer Satisfaction_centered X
Switching Cost_centerd 1.345
Model
3
Customer Satisfaction_centered 2.409
.821 .675 .752
Switching Cost_centerd 1.742
Inertia_centered 2.300
Customer
Satisfaction_centered X Switching
Cost_centerd
1.535
Customer Satisfaction_center X
Inertia_centered 1.982
CHAPTER-V
FINDINGS AND CONCLUSION
155
This chapter summarizes findings of research study arrived at different stages of
the research process. Before discussing the main findings in response to the research
objectives, a short account of findings in context of demographic details of customers are
presented.
This chapter also discusses the contributions made by this research work in
filling the knowledge gap and widening the horizon of marketing constructs which were
part of this study.
At the end of the chapter few words about limitations and also about suggestions
for future research are also mentioned.
5.1 Findings and conclusions related to demographic aspects of customers
Age: As data was collected with the intention of including every age group in the
research study, mobile users of every age were contacted to collect data. Out of all the
530 valid respondents maximum mobile users 28.1% were having their age between 21
years to 35 years. This can be concluded that most prominent age group was group 2 (21-
35 yrs). Another finding worth mentioning is that almost 75% respondents had their age
between 21- 60 years.
Gender: As data was collected from both male and female mobile phone users,
out of 530 valid respondents prominent gender is male as they constitute 59.7% of total
respondents. Keeping in mind the developing state of our country in general and Gwalior
city in particular, females are not equally active, dynamic and employed in comparison to
their male counterparts. This explains their less than proportionate representation in this
study.
Marital status: From the data collected for study it was found that as high as
73.2% of the respondents were married. This goes well with the fact that out of 530
respondents as high as 90% were above 21 years of age so it is all logical to have 73.2%
married respondents.
156
Educational Qualification: As mobile phone use is no longer confined to elite
class, collected data also reflected the same reality. As high as 35.1% respondents were
only Hr.Secondary passed and this educational group emerged out as most prominent
group on the basis of educational qualification. 17.4% respondents had Pre-Hr.Seconday
level education and 26% respondents were graduates.
Occupational Pattern: All the 530 valid respondents were categorized in 5
groups on the basis of their occupational pattern. 35.1% respondents were employed with
Pvt. Sector and this turned out to most prominent group on the basis of employment
pattern. Other important groups in the study were students which constituted 22.1% and
Govt. employees which constituted 20.1% of total respondents.
Income: As use of mobile phone is no longer restricted to elite class, our data also
reflected this. The prominent group on the basis of income is below Rs10,000/- as it
constituted 27.5% of the respondents but slightly less prominent is the group with
income Rs 10,000/- to Rs 25,000/- as it constituted 25.5% of the respondents. From the
data it is evident that now mobile phone is affordable to every segment of the society and
that is the reason behind increasing teledensity and increasing tariff war among
telecommunication companies.
Service Provider: Data for the present research come from mobile phone users of
seven different service providers. Maximum numbers of mobile users are customers of
Idea as they constitute 25.7% of total respondents, next to Idea is Airtel as 23.6%
respondents are customers of Airtel. It is evident that these 2 companies together have
almost 50% market share in mobile industry in Gwalior region.
Type of connection: Out of 530 respondents almost 80% mobile phone users
have pre-paid connection. This finding is in conformance with the national trend as
almost 80% customers in mobile sector are keeping pre-paid SIMs. This finding also gets
support from the fact that nowadays a big segment of mobile users is not only poorly
educated but also poorly paid so keeping a permanent burden in terms of post paid
connection is highly undesirable. This segment uses mobile services by getting it
frequently recharged by recharge coupons of small amounts.
157
5.2 Findings related to levels of Customer Satisfaction among respondents of
different demographic background:
Significant difference in customer satisfaction levels were found among the
respondents of different age group. Age group 5 which had respondents with age 61- 85
years differs significantly from age group2 (21-35years) and 4(46-60 years).The reason
behind this difference could be that people in age group 61-85 years are less aware about
latest changes, they are less tech savvy and their use of mobile is basically for voice
calling, all this makes them more satisfied in comparison to other age groups.
Female respondents were found more satisfied in comparison to male
respondents. The reason could be lack of knowledge among females about different type
of value added services and price offers given by different service providers. Their
assessment of their service provider may be voice centric and all this make them
comparatively more satisfied.
Respondents using services of different service providers differ significantly
regarding their level of customer satisfaction. Airtel users were more satisfied in
comparison to users of Reliance, BSNL, Vodafone and Tata Docomo. Similarly, users of
Idea were more satisfied in comparison to BSNL and Vodafone. This difference could be
due to respondents’ varied experiences with different service providers or it could be due
to the fact that users of Airtel and to slightly lesser degree users of Idea were getting all
they expected and this made them more satisfied in comparison to others.
No significant difference in customer satisfaction levels was found between
married and unmarried respondents, among respondents with different qualifications,
among respondents of different occupations, among respondents with different income
levels and between respondents with pre-paid and post paid connections.
Hypotheses H1a, H1b and H1g, were rejected and hypotheses H1c, H1d, H1e,
H1f and H1h could not be rejected
The above findings are tabulated in Table 5.1in appendix.
158
5.3 Findings related to levels of Customer Loyalty among respondents of different
demographic background:
Significant difference in customer loyalty levels was found among the
respondents of different age groups. Respondents of age group 5 with their age between
61-85 years were found to be more loyal in comparison to age groups 1, 2, 3 and 4 whose
age varied from below 20 to 60 years. The reason behind this higher level of loyalty
could be higher satisfaction towards service providers and also reluctance and inertia to
change their service provider due to their higher age which makes them to keep present
service provider if it is able to provide even moderate level of satisfaction.
Female respondents were more loyal in comparison to male respondents. The
reason could be higher satisfaction, lack of awareness about different moves by different
service providers and comparatively higher level of inertia due to their home bound life.
Respondents with professional degrees were found to be more loyal towards their
service providers in comparison to graduates. The reason could be that busy schedule of
professional are making them less prone to evaluate different offers by different
companies and this keeps them more loyal in comparison to graduates.
Respondents of business background are more loyal towards their service
provider in comparison to respondents from govt. and private sector. Their busy schedule
may be keeping them more loyal as changing service provider requires efforts.
Respondents using Airtel and Idea were found to be more loyal in comparison to
respondents using Reliance, BSNL and Vodafone. The reason could be that they feel
more satisfied because of better service quality or they may be loyal because of higher
switching cost imposed by their service provider that render them less likely to change
their service provider.
No significant difference in customer loyalty levels was found between married
and unmarried respondents, among respondents with different income levels and between
respondents with pre-paid and post paid connections
159
Hypotheses H2a, H2b, H2d, H2e and H2g were rejected and hypotheses H2c,
H2f, H2h could not be rejected.
The above findings are also reflected in results of hypothesis testing shown in table 5.2.
in appendix.
5.4 Findings related to Factors affecting Customer Satisfaction:
Factor analysis of Service Quality resulted into 7 dimensional structure of service
quality. The relationship between service quality dimensions & customer perceived value
on the one hand and customer satisfaction on the other hand was tested through
regression analysis.
Findings showed that 6 dimensions of service quality namely Employee
Performance, Reliability, Responsiveness, Competitiveness, Network Quality,
Tangibility and single dimensional Customer Perceived Value have significant impact on
customer satisfaction.
Hence, hypotheses H3a, H3b, H3d, H3e, H3f, H3g and hypotheses H4 were
rejected. Hypothesis H3c could not be rejected as service quality i.e. Assurance did not
have significant impact on customer satisfaction.
The same findings are reflected in the results of hypothesis testing shown in table 5.3. of
appendix.
5.5 Findings related to Factors affecting Customer Loyalty:
Main effects of customer satisfaction, switching cost, inertia on customer loyalty
were tested through regression analysis and moderating effects of switching cost and
inertia were tested through hierarchical regression analysis. Findings and conclusions are
as follows:
160
Customer Loyalty and Customer Satisfaction:
Customer loyalty is positively impacted by customer satisfaction, it was proved
through positive correlation and then a significant positive value of unstandardized beta,
generated by regression model, supported the relationship.
Customer Loyalty and Switching Cost:
Customer Loyalty is not impacted by Switching Cost as regression model gave
marginally negative but absolutely insignificant value of unstandardised beta coefficient.
Above finding seems quite logical and obvious, as switching costs are imposed by the
service provider to compel their customers to stay with them. So in the absence of zero
value of other variables like Customer Satisfaction, Inertia and any other moderating
variables, Switching Cost cannot prevent customers from leaving their service provider.
Customer Loyalty and Inertia:
Customer loyalty is positively impacted by Inertia as regression model gave
positive and significant value of unstandardized beta.
The finding is quite logical and obvious as inertia prevents the customer from acting
rationally because it promotes natural tendency to remain in same state or to preserve
status quo until something very extra ordinary forces customers to move out and act. So
higher the Inertia, higher is the Customer Loyalty, sounds logical.
Moderating impact of Switching Cost:
Customer Satisfaction – Customer Loyalty link is positively moderated by
Switching Cost as hierarchical regression model gave positive and significant beta value.
This finding can be further elaborated that in the presence of switching cost, impact of
customer satisfaction increases on customer loyalty.
It is worth paying attention that Switching Cost is not able to impact Customer Loyalty
on its own but when it is clubbed with Customer Satisfaction it increases the impact of
Customer Satisfaction on Customer Loyalty.
161
We can further emphasize, that when Customer Satisfaction is higher, Customer
Loyalty is bound to be higher because of direct positive relationship between Customer
Satisfaction and Customer Loyalty. In the presence of Switching Cost, this relationship
would be strengthened and impact of Customer Satisfaction would be even more
pronounced on customer loyalty in the presence of higher Switching Cost.
The above situation can be logically interpreted because customers will have two
reasons to stay with their service provider; firstly higher Customer Satisfaction will
encourage them to continue to stay with same service provider and secondly higher
Switching cost will discourage them to leave their service provider because of petty
issues. This could be best retention approach.
From above discussion this can be concluded that higher switching cost may be a
good marketing strategy only in the presence of higher customer satisfaction but not in
case of low customer satisfaction.
Moderating impact of Inertia:
Customer Satisfaction – Customer Loyalty link is negatively moderated by Inertia
as regression model gave negative but significant value of unstandardised beta
coefficient. Negative value of beta implies that in the presence of Inertia impact of
Customer Satisfaction on Customer Loyalty diminishes.
The above conclusion can be further elaborated as, in the presence of high Inertia
even dissatisfied customers may not leave their present service provider thus ensuring
Customer Loyalty.
Hypotheses H5, H7, H8 and H9 were rejected and hypothesis H6 could not be rejected.
All the above findings are reflected in the results of hypotheses testing also shown in
table 5.4 of appendix.
162
5.6 Conclusions:
The present study concludes that major factors that affect customer satisfaction
are Service Quality and Customer Perceived Value. The conclusion is in alignment with
previous researches by Anderson et al. (1994), Taylor and Baker (1994) ,Zeithaml, Berry
and Parasuraman (1996),Watson (1999), Turel and Serenko (2004), Howat et al (2008) &
Chen (2008).
In this study, Service Quality turned out to be seven dimensional, out of which
six dimensions were found to have significant and positive impact on Customer
Satisfaction. These dimensions are “Employee Performance,” “Reliability,”
“Responsiveness,” “Network Quality” and “Tangibility”. Multidimensionality of service
quality has been proved in earlier researches also, Lehtinen and Lehtinen (1982),
Lehtinen (1983), Rust and Oliver (1994), Berry et al. (1994).
This can be concluded that service providers should pay due attention to provide
customers good Service Quality that encompasses all the above mentioned areas to
enhance Customer Satisfaction. Service providers should also ensure that their customers
feel that they are getting best value of their efforts and money by getting services from
them as “Customer Perceived Value” also enhances Customer Satisfaction.
From present study it found that greater Customer Satisfaction leads to higher
Customer Loyalty. This conclusion is in harmony with the outcome of previous
researches, Cronin and Taylor(1992), Anderson and Sullivan (1993), Hallowell (1996),
Cronin Brady and Hult (2000).
Present study also concludes that higher Inertia also leads to higher Customer
Loyalty. This conclusion is symmetrical with conclusions of previous researches,
Solomon(1994). But direct impact of Switching Cost was not found significant on
Customer Loyalty in present study thus defying the conclusion arrived at by, Bansal and
Taylor, (1999) Jones et al., (2000), Lee et al., (2000) and Ranaweera and Prabhu,(2003).
163
Moderating impact of Switching Cost and Inertia both were found to be
significant, switching cost have positive inertia have negative impact on customer
loyalty. These conclusions are in line with previous conclusions of Jones et al. (2000)
Anderson and Srinivasan (2003) respectively.
From above findings it can be concluded that service providers should keep
Customer Satisfaction high and if it is clubbed with high Switching Cost it will lead to
best marketing strategy as presence of Switching Cost will further enhance impact of
Customer Satisfaction on Customer Loyalty.
Higher Inertia also leads to higher Customer Loyalty and high Inertia also
dimishes impact of Customer Satisfaction on Customer Loyalty.
As higher Inertia among customers prove beneficial to service provider this
information will help service providers in proper assessment of their customers’
psychological constitution and devising means to reap benefits out of it, further analysis
of it is beyond the scope of this study.
5.7 Limitations of the Study:
Although, present research work contributes a lot from theoretical as well as
practitioners’ point of view, a modest acceptance about the limitations of this research
work would be appropriate.
This research work tried to encompass all the major factors affecting Customer
Loyalty but there may be other possible variables affecting Service Quality, Customer
Satisfaction and consequently Customer Loyalty.
In this research work Customer Loyalty is found to be unidimensioanl and
Customer Loyalty and Customer Retention are treated as one construct which is widely
accepted and applied practice but there are studies which consider them as two separate
constructs.
This study was conducted in Gwalior city with a sample of 530 and most of the
respondents came from urban background, so generalization of its findings and
164
conclusion for rural mobile market and for rest of the country should be made with
caution.
5.8 Suggestion for Future research:
Moderating impacts of other variables like price and indifference on customer
satisfaction and customer loyalty link can be undertaken in Indian context.
Impact of Value Added services on Customer satisfaction and consequently on
customer loyalty can be studied.
How the concept of Mobile Number Portability has impacted customer
satisfaction is a new unstudied area.
REFERENCES
165
REFERENCES
Aaker, D. A., & Jacobson, R. (1994). The financial information content of perceived
quality. Journal of marketing research, 191-201.
Ahmed,A.M.(2002).Customer Satisfaction: The Driving Force for Winning Business
Excellence Award ” Bradford University , Working Paper No .02/06.
Anderson, R. E., & Srinivasan, S. S. (2003). E‐satisfaction and e‐loyalty: A
contingency framework. Psychology & marketing, 20(2), 123-138.
Anderson, E. W. & Sullivan, M.(1993). The antecedents and consequences of
customer satisfaction for Firms, Marketing Science, 12, pp. 125-143.
Anderson, E. W., Fomell,C. & Lehmann, D. (1994). Customer satisfaction, market
share, and profitability, Joumal of Marketing, 58 (3), July, pp. 53-66.
Assael, H. (1998). Managing brand equity: consumer behavior and market action,
South-Western, Cincinnati, OH.
Asubonteng, P., McCleary, K. & Swan, J. E. (1996).SERVQUAL revisited: A
critical review of service quality.The Jounal of Services Marketing, 10 (6), pp.
62-81.
Atkinson, A. (1988). Answering the eternal question: What does the Customer Want?
The Cornell Hotel and Restaurant Administration Quarterly, 29(2): pp.12-14.
Aydin, S. & Ozer, G.(2005). National customer satisfaction indices: An
implementation in the Turkish mobile telephone market, Marketing
Intelligence and Planning, 23 (5), pp. 486 – 504.
Babakus, E. & Boller, G.W. (1992). An empirical assessment of the SERVQUAL
scale.Journal of Business Research, Vol. 24 No. 3, May, pp. 253-68.
166
Bansal, H. S., & Taylor, S. F. (1999). The service provider switching model (spsm) a
model of consumer switching behavior in the services industry. Journal of
Service Research, 2(2), 200-218.
Barlow,R.G .( 1992 ). Relationship marketing – the ultimate in customer services .
Retail Control 60 (3) : 29 – 37.
Barnes , J . G . ( 1994 ). The issue of establishing relationships with customers in
service companies: When are relationships feasible and what form should they
take? Working Paper, Memorial University of Newfoundland.
Barnes, J. G. (1994, October). The issue of establishing relationships with customers
in service companies: When are relationships feasible and what form should
they take. In Third Annual “Frontiers in Services” Conference, Owen
Graduate School of Management, Vanderbilt University, Nashville,
Tennessee, October (pp. 6-8).
Barnes, S. J. (2003). Enterprise mobility: concept and examples. International
Journal of Mobile Communications, 1(4), 341-359.
Berry, L.L., Parasuraman, A.( 1992).Prescription for a service quality revolution in
America.Organizational Dynamics, Vol.13, Issue:4/5, pp. 5-15
Bitner , M . J . ( 1995 ).Building service relationships: It’s all about promises .
Journal of the Academy of Marketing Science 23 (Fall) : 246 – 251 .
Bitner, M. J., William, T., Faranda, A. R. H., & Valarie, A. Z. (1996). Quality, and
Productivity in Service Experiences: Customers’ Roles. Advancing Service
Quality: A Global Perspective, 289-298.
Bolton, R. N., & Drew, J. H. (1991). A multistage model of customers' assessments
of service quality and value. Journal of consumer research, 375-384.
167
Bolton, R. N., & Lemon, K. N. (1999). A dynamic model of customers' usage of
services: usage as an antecedent and consequence of satisfaction. Journal of
marketing research, 171-186.
Boulding, W., Kalra, A., Staelin, R. & Zeithaml, V.A. (1993). A dynamic process
model of service quality: From expectations to behavioural intentions, Journal
of Marketing Research, Vol. 30, pp. 7-27.
Bowen, J. T., & Chen, S. L. (2001). The relationship between customer loyalty and
customer satisfaction. International journal of contemporary hospitality
management, 13(5), 213-217.
Brown, S.W., Swartz, T.A. (1989). A gap analysis of professional service quality,
Journal of Marketing, Vol.53 No.4, pp.92-8.
Capraro, Anthony J., Susan Broniarczyk, and Rajendra K. Srivastava. "Factors
influencing the likelihood of customer defection: the role of consumer
knowledge." Journal of the Academy of Marketing Science 31.2 (2003): 164-
175.
Caruana, A. (2002). Service loyalty: The effects of service quality and the mediating
role of customer satisfaction. European Journal of Marketing, Vol. 36 No.
7/8, pp. 811-29.
Chada, S.K & Kapoor,D. (2009). Effect of switching cost, service quality and
customer satisfaction on customer loyalty of cellular service providers in
Indian market.The ICFAI University Journal of Marketing Management, 8(1),
pp. 23-37
Chen, L., Nath, R., (2004). A framework for mobile business applications.
International Journal of Mobile Communications 2 (4), 368-381.
Chettayar, K. (2002).Using customer information effectively. Financial Executive.
Vol. 18, No. 3, pp. 42– 43.
168
Chin, W. W. (1998). The partial least squares approach to structural equation
modeling. Modern methods for business research, 295(2), 295-336.
Chin, W. W. (2001). PLS-Graph user’s guide. CT Bauer College of Business,
University of Houston, USA.
Chintagunta, P.K. & Honore, B.E. (1996). Investigating the effects of marketing
variables and unobserved heterogeneity in a multinomial probit model.
International Journal of Research in Marketing, Vol. 13, pp. 1-15
Chowdhary,N., Prakash, M.(2007).Prioritizing service quality dimensions. Managing
Service Quality Vol17, Issue:5, pp.493-509.
Churchill, G. A. & Suprenant C. (1982).An Investigation into the determinants of
customer satisfaction, Joumal of Marketing Research, 19 (4), pp. 491-504.
Chow-Chua,C.& Komaran,R. (2002).Managing service quality by combining voice
of the service provider and voice of their customers. Managing Service
Quality, Volume: 12, Issue:2, pp.77-86.
Cravens, D. W., Holland, C. W., Lamb Jr, C. W., & Moncrief III, W. C. (1988).
Marketing's role in product and service quality. Industrial Marketing
Management, 17(4), 285-304.
Cronin, J.J. & Taylor, S.A. (1992).Measuring service quality: A re-examination and
extension. Journal of Marketing, No. 56, July, pp. 55-68.
Cronin, J. J., and S. A. Taylor (1994). SERVPERF versus SERVQUAL: Reconciling
performance based and perceptions - minus - expectations measurement of
service quality, Joumal of Marketing, 58(1), pp. 125-131.
Cronin, Jr. J. J., Brady, M. K. &. Hult, G. T. M. (2000).Assessing the effects of
quality, value and customer satisfaction on consumer behavioral intentions in
service environments. Journal of Retailing, 76 (2), pp. 193-218.
169
Dahholkar, P. A., Shepherd C. D. & Thorpe I. D. (2000).A Comprehensive
framework for service quality: An investigation of critical conceptual and
measurement issues through a longitudinal study. Joumal of Retailing, 76 (2),
pp. 139-173.
Danaher, P. J. & Gallagher R. W. (1997).Modeling customer satisfaction in telecom
New Zealand.European Journal of Marketing, 31 (2), pp. 122-133.
Dawes, J., & Swailes, S. (1999). Retention sans frontieres: issues for financial service
retailers. International Journal of Bank Marketing, 17(1), 36-43.
Day, GS (1969) A two-dimensional concept of brand loyalty. Journal of Advertising
Research, pp., 29-35.
Dick, A.S. & Basu, K. (1994).Customer loyalty: Toward an integrated conceptual
framework. Journal of the Academy of Marketing Science, 22:2, 99-113.
Deyong, C. F. (1994).A methodology for linking customer satisfaction dimensions
with process performance metrics. Unpublished Thesis for the degree of PhD,
Oklahoma State University.
Dodd, J. (2002).Loyalty and commitment – Not such a subtle difference. New
Zealand Direct Marketing Association Inc., http://www.dma.co.nz
Ehrenberg, A. S. C., Goodhardt, J. G. & Barwise, T. P. (1990).Double jeopardy
revisited. Journal of Marketing, Vol. 54, No. 3, pp. 82–91.
Engel, J. F., & Roger, D. (1995). Blackwell (1982), Consumer Behavior. New York:
Holt, Rinehart and Winston.
Erevelles, S., & Leavitt, C.(1992).A comparison of current models of consumer
satisfaction/dissatisfaction.Journal of Consumer Satisfaction, Dissatisfaction
& Complaining Behavior, 5, pp. 104-114.
170
Evangelia, B.(2009). Measurement and Analysis for Marketing.Journal of Targeting,
(2009) 17, 27 – 37
Edvardsson, B. (2005).Service quality: beyond cognitive assessment. Managing
Service Quality, Vol. 15 No. 2, pp. 127-131.
Fornell, C. (1992).A national customer satisfaction barometer: The Swedish
experience, Journal of Marketing, 56:Jan., pp.6-21.
Friday, S. & Cotts, D.G. (1995). Quality facility management: A marketing and
customer service approach, New York: John Wiley.
Garvin, D. A. (1988).Managing Quality – The Strategic and Competitive Edge, The
Free Press, New York. 132
Geoge,L.(1948).Social Research,Longman Greens Publication,N
Gerrard, P., & Cunningham, J. B. (2004). Consumer switching behavior in the Asian
banking market. Journal of Services Marketing, 18(3), 215-223.
Gerpott, T.J., Rams, W. and Shindler, A. (2001).Customer retention, loyalty and
satisfaction in the German mobile cellular telecommunication market’,
Telecommunication Policy, 25:4, 249-269.
Gitomer, J.H. (1998). Customer satisfaction is worthless, customer loyalty is
priceless, Marietta, GA: Bard Press.
Giese, J. L., & Cote, J. A. (2000). Defining consumer satisfaction. Academy of
marketing science review, 1(1), 1-22.
Glass, R. M. (2002). What’s the value of a wireless business. Wireless Business &
Technology, 2(4), 34-35.
Gobbott, M., & Gillian, H., (2000).An empirical investigation of the impact of non-
verbal communication on service evaluation. European Journal of Marketing,
Volume:34, Issue:3/4, pp.384-398
171
Goel,A.& Erum,S.(2010). Service quality measurement and customer satisfaction in
Indian BPO industry. International Business, Vol. 6, Issue 6, p. 33.
Goodwin, R. and Ball, B. (2003) What marketing wants the CEO to know’,
Marketing Management, Vol. 12, No. 5, pp. 18–24
Gremler, D. D., & Brown, S. W. (1996). Service loyalty: its nature, importance, and
implications.
Gronroos, C., & Shostack, G. L. (1983). Strategic management and marketing in the
service sector (p. 25). Cambridge, MA: Marketing Science Institute.
Gustafsson, A., Johnson, M. D., & Roos, I. (2005). The effects of customer
satisfaction, relationship commitment dimensions, and triggers on customer
retention. Journal of marketing, 69(4), 210-218.
Hair, J. F., Tatham, R. L., Anderson, R. E., & Black, W. (2006). Multivariate data
analysis (Vol. 6). Upper Saddle River, NJ: Pearson Prentice Hall.
Hallowell, R . (1996). The relationships of customer satisfaction, customer loyalty,
and profitability: An empirical study. International Journal of Service
Industry Management 7(4):27– 42.
Hasenfeld, Y. & Richard A. (1974).Human Service Organizations : A Book of
Readings, The University of Michigan Press.
Holbrook, M. B. (1994). The nature of customer value: an axiology of services in the
consumption experience. Service quality: New directions in theory and
practice, 21, 21-71.
Howat, G., Crilley, G. & McGrath, R. (2008), A focused service quality, benefits,
overall satisfaction and loyalty model for public aquatic centres. Managing
Leisure, Vol. 13, pp. 139-161.
172
Huang, M. H., & Yu, S. (1999). Are consumers inherently or situationally brand
loyal? A set intercorrelation account for conscious brand loyalty and
nonconscious inertia. Psychology & Marketing, 16(6), 523-544.
Hunt H.K.(1977).Conceptualization and Measurement of Consumer Satisfaction and
Dissatisfaction ,Cambridge MA: .Marketing Science Institute
Iacobucci, D., & Calder, B. J. (Eds.). (2003). Kellogg on integrated marketing. John
Wiley & Sons.
Ittner, C.D. & Larcker, D.F. (1996).Measuring the impact of quality initiatives on
firm financial performance, Advances in the Management of Organisational
Quality, Vol. 1, JAI Press, Greenwich, CT, pp. 1-37
Ittner, C. & Larcker, D. (1998). Are nonfinancial measures leading indicators of
financial performance? An analysis of customer satisfaction. Journal of
Accounting Research, 36 (1), pp. 1-35.
Jacoby, J. & Chestnut, R. (1978). Brand loyalty: Measurement and management. John
Wiley & Sons, New York.
Johnson, W. C. & Sirikit, A. (2002).Service quality in the Thai telecommunication
industry: A tool for achieving a sustainable competitive
advantage.Management Decision, 40 (7), pp. 693-701.
Jones ,T.O . & Sasser, E.W. ( 1995 ). Why satisfied customers defect . Harvard
Business Review , Reprint 95606, November – December, pp. 1 – 13 .
Kaiser, H. F., & Rice, J. (1974). Little Jiffy, Mark IV. Educational and psychological
measurement.
Kandampully, J. (2002). Innovation as the core competency of a service organisation:
the role of technology, knowledge and networks. European Journal of
Innovation Management, 5(1), 18-26.
173
Kang, G. D., & James, J. (2004). Service quality dimensions: an examination of
Gronroos’s service quality model. Managing Service Quality, 14(4), 266-277.
Kang, G.D. (2006).The hierarchical structure of service quality: Integration of
technical and functional quality.Managing Service Quality, Vol. 16 No. 1, pp.
37-50
Keaveney, S. M., & Parthasarathy, M. (2001). Customer switching behavior in online
services: an exploratory study of the role of selected attitudinal, behavioral,
and demographic factors. Journal of the Academy of Marketing Science,
29(4), 374-390.
Keller, K. L. (1993). Conceptualizing, measuring, and managing customer-based
brand equity. The Journal of Marketing, 1-22.
Keller, K. L., & Kotler, P. (2006). Administração de marketing. São Paulo.
Kerlin, C. A. (2000).Measuring student satisfaction with the service processes of
selected student educational support services at Everett Community College
(Washington).Unpublished Thesis for the degree of EdD, Oregon State
University.
Kim, M. K., Park, M. C., & Jeong, D. H. (2004). The effects of customer satisfaction
and switching barrier on customer loyalty in Korean mobile
telecommunication services. Telecommunications policy, 28(2), 145-159.
Kotler P.& Keller L., Marketing Management, 12th ed., Upper Saddler River, NJ:
Prentice Hall: 2006
Ladhari, R. (2008).Alternative measures of service quality: a review. Managing
Service Quality, Vol. 18 No. 1, pp. 65-86.
Ladhari, R. (2009).Service quality, emotional satisfaction, and behavioural intentions.
Managing Service Quality, Vol. 19 No.3, pp. 308-331.
174
Lai, F., Hutchinson J., Li D. & Bai C. (2007). An empirical assessment and
application of SERVQUAL in Mainland China's mobile communications
industry. International Journal of Quality and Reliability Management, 24 (3),
pp. 244-262.
Lawler, Edward E, “ Believing What can’t be seen: Protective Life’s Vision of
Quality Service ” - Loma’s FOCUS Customer Service Survey,Journal
Resource, Jossey-Bass Publishers, November, 1995.
Lee, J. Lee, and Feick, L. (2001), “The impact of switching costs on the customer
satisfaction – loyalty link: Mobile phone service in France”, Journal of
services marketing, 15(1), pp. 35-48.
Lehtinen, U. and J. R. Lehtinen (1982), "Service Quality: A Study of Quality
Dimensions," unpublished working paper, Helsinki, Finland: Service
Management Institute.
Levesque,T., & McDougall,G. H.G. (1996). Determinants of customer satisfaction in
retail banking. International Journal of Bank Marketing, 14, pp.12–20.
Lovelock, C., & Gummesson, E. (2004). Whither services marketing? In search of a
new paradigm and fresh perspectives. Journal of service research, 7(1), 20-
41.
Luo, X., & Homburg, C. (2007). Neglected outcomes of customer satisfaction.
Journal of Marketing, 71(2), 133-149.
Robledo,M.A.(2001) Measuring and managing service quality: integrating customer
expectations. Managing Service Quality, 2001, Volume:11, Issue:1, pp.22-31
Rundle-Thiele, S. (2005). Exploring loyal qualities: assessing survey-based loyalty
measures. Journal of Services Marketing, 19(7), 492-500.
175
Malthouse, E. C., Calder, B. J., & Eadie, W. P. (2003). Conceptualizing and
measuring magazine reader experiences. Media Management Center,
Northwestern University.
Martinez, J. & Martinez, L. (2010), “Some insights on conceptualizing and measuring
service quality”, Journal of Retailing and Consumer Services, Vol. 17, pp. 29-
42.
McQuitty, S., A. Finn and J. B. Wiley (2000),"Systematically Varying Consumer
Satisfaction and its Implications for Product Choice," 10, pp. 1-16.
Mei, A.W.O., Dean, A.M. and White, C.J. (1999), “Analysing service quality in the
hospitality industry”, Managing Service Quality, Vol. 9 No. 2, pp. 136-143.
Mody,B.(1995). State consolidation through liberalization of telecommuniactions
services in India, Journal of Communication Vol.45,No. 4,Dec.,pp104-124
Monroe, Kent B. (1990). Pricing: making profitable decisions. (2nd ed.). New York:
McGraw-Hill Book Company.
Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship
marketing. the journal of marketing, 20-38.
O'Leary, Z. (2005). Researching real-world problems: A guide to methods of inquiry.
Sage.
Oliver, R. (1980). A cognitive model of the antecedents and consequences of
satisfaction decisions.Journal of Marketing Research, 17(November), pp.
460-469.
Oliver, R. L. (1997). Satisfaction: A behavioral perspective on the customer. New
York.
176
Oliver,R.L., & Swan, E. (1989). Equity and disconfirmation perceptions as influences
on merchant and product satisfaction. Journal of Consumer Research, Vol. 16,
pp, 372-83
Oliver, Richard L., Roland T. Rust, and Sajeev Varki. "Customer delight:
foundations, findings, and managerial insight." Journal of Retailing 73.3
(1997): 311-336.
Oliver, R.L. (1999).Whence customer loyalty? Journal of Marketing, 63, Special
Issue, pp.33-44.
Palkar A. (2004). Determinants of customer satisfaction for cellular service providers.
Udyaog Pragati,28(1), pp.19-26.
Parkington, J.& Schneider, B.(1979).Some correlates of experienced job stress: A
boundary role study. Academy of Management Journal, Vol.22, No.2, pp.270-
81
Parasuraman, A., Zeithaml, V.A. & Berry, L.L. (1985).A conceptual model of service
quality and its implication for future research. Journal of Marketing, No. 49,
April, pp. 41-50.
Parasuraman, A., V. A. Zeithaml & Berry L. L. (1988).SERVQUAL: A Multiple
item scale for measuring consumer perceptions of service quality. Journal of
Retailing, 64 (1), pp. 12 - 40.
Parasuraman, A., Zeithmal, Valarie A., Berry, L. L. (1994).Reassessment of
expectations as a comparison standard in measuring service quality:
Implications for further research. Journal of Marketing, Vol.58, Issue: 1, pp.
111-125.
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1994). Alternative scales for
measuring service quality: a comparative assessment based on psychometric
and diagnostic criteria. Journal of retailing, 70(3), 201-230.
177
Parasuraman,A.(2002).Service quality and productivity: a synergistic
perspective.Managing Service Quality,Volume:12, Issue:1, pp.6-9.
Peterson, R. A. (1995). Relationship marketing and the consumer. Journal of the
Academy of Marketing Science 23 (Fall),pp. 278 – 281 .
Petrick J.F.(2002).Development of a multi- dimensional scale for measuring the
perceived value of service. Journal of Travel Research, Vol. 34, No. 2, p. 119
134,
Petrick J.F.(2004). First timers’ and repeaters’ perceived value , Journal of Travel
Research, Vol. 43, pp. 29-38.
Porter, M. E. (1996). Competitive advantage, agglomeration economies, and regional
policy. International regional science review, 19(1-2), 85-90.
Pothas, A. M., De Wet, A. G., & De Wet, J. M. (2001). Customer satisfaction:
Keeping tabs on the issues that matter. Total Quality Management, 12(1), 83-
94.
Rana Weera, C & Neely, A. (2003),Some moderating effects on the Service Quality–
Customer Retention Link. International Journal of Operations and
Production Management, 23(2), pp. 230-248.
Reichheld, F. & Sasser, W.E. Jr. (1990). Zero defections: Quality comes to services.
Harvard Business Review, 68, September/October, 105-11.
Reichheld, F. F. (1996). Learning from customer defections. Harvard business
review, 74(2), 56.
Reichheld, F. F. & Schefter, P. (2000). E-loyalty. Harvard business review, 78(4),
105-113.
Reichheld, F. F. (2003). The one number you need to grow. Harvard business review,
81(12), 46-55.
178
Reichheld, F. F. (2006). The ultimate question: Driving good profits and true growth.
Harvard Business Press.
Rosenblatt, P. C. (1977). Needed research on commitment in marriage. Close
relationships: Perspectives on the meaning of intimacy, 73-86.
Rowe, S., Alexander, N., Almeida, N. G., Black, R., Burns, R., Bush, L., ... &
Weaver, C. (2011). Translating the Dietary Guidelines for Americans 2010 to
bring about real behavior change. Journal of the American Dietetic
Association, 111(1), 28-39.
Rust, R. & Zahorik, A. J.(1993).Customer satisfaction, customer retention and market
share, Journal of Retailing, Summer Vol. 69, Issue 2, p 193
Rust, R. T., & Oliver, R. L. (1994). Service quality: new dimensions in theory and
practice.London:Sage ,pp.1-19.
Ruyter, K., Wetzels, M. & Bloemer, J. (1998).On the relationship between perceived
service quality, service loyalty and switching costs”, International Journal of
Service Industry Management, Vol. 9 No. 5, pp. 436 - 453.
Saunders, M. L., & Thornhill, P. (2004). A.(2007), Research Methods for Business
Students. Financial Times/Prentice Hall, Harlow.
Seth, N., Deshmukh, S. G. & Vrat, P. (2005).Service quality models: a review’,
International Journal of Quality & Reliability Management, Vol. 22 No. 9,
pp. 913-949.
Selvarasu, A. Gomathi Shankar, K. & Loganathan, (2006). GSM mobile service in
telecom sector: An ontology of quality of service”, The ICFAI Journal of
Service Marketing, 4(4), pp. 36-44.
Sirdeshmukh, D., Singh, J., & Sabol, B. (2002). Consumer trust, value, and loyalty in
relational exchanges. Journal of marketing, 66(1), 15-37.
179
Smith, R. E. & Wright, W. F. (2004).Determinants of customer loyalty and financial
performance. Journal of Management Accounting Research, 16 (Special
Issue), pp. 183-205.
Souter, D. (1999). The role of information and communication technologies in
democratic government. info, 1(5), 405-417.
Stewart, D. W. (1981). The application and misapplication of factor analysis in
marketing research. Journal of Marketing Research, 51-62.
Stewart,D.M.(2003).Piecing together service quality: A framework for robust
services.Production and Operations Management, Vol 12, Issue:2, pp.246-
265.
Sureshchander, G.S., Rajendran, C. & Kamalanaban, T.J. (2001) .Customer
perceptions of service quality – A critique. Total Quality Management, Vol.
12 No.1, pp. 111-124.
Surprenant, C. F., & Solomon, M. R. (1987). Predictability and personalization in the
service encounter. the Journal of Marketing, 86-96.
Swoboda, B., Haelsig, F., Morschett, D. & Schramm-Klein, H. (2007).An inter-sector
analysis of the relevance of service in building a strong retail brand.
Managing Service Quality, Vol. 17 No. 4, pp. 428-448.
Tabachnick,B.G.,&Fidell,L.S.(2007).Experimental designs using ANOVA.
Thomson/Brooks/Cole.
Taylor, S.A., & Baker, T.L. (1994).An assessment of the relationship between service
quality and customer satisfaction in the formation of consumer’s purchase
intentions’, Journal of Retailing, 70:2, 163-178.
Teas, R.K., (1993). Consumer expectations and the measurement of perceived service
quality.Journal of Professional Services Marketing, Vol, H, No. 2, pc. 33-54.
180
Thomas,R.E.(1978).Strategy is different in service business. Harvard Business
Review, Vol. 56, Issue:4, pp. 158-165.
Tse, D. K. & Wilton, P. W. (1988).Models of customer satisfaction formation: An
extension.Journal of Marketing Research, 25 (May), pp. 204-212.
Turel, O. & Serenko,A.(2006).Satisfaction with mobile services in Canada: An
empirical investigation .Telecommunications Policy, 30, pp. 314-331.
Varshney, U., Malloy, A., Ahluwalia, P., & Jain, R., (2004). Wireless in the
enterprise: requirements, solutions, and research directions. International
Journal of Mobile Communications 2 (4), 354-367.
Wang, Y. & Lo, H. P. (2002).Service quality, customer satisfaction and behavior
intentions: Evidence from China's telecommunication industry.Information
Systems Frontiers, 4 (6), pp. 50 - 60.
Watson, A. P. (1999).Customer satisfaction, product quality, service quality and
image: Findings in a commodity industry” Unpublished Thesis for the degree
of DBA, Nova Southeastern University.
Westbrook, R. A., & Oliver, R. L. (1991). The dimensionality of consumption
emotion patterns and consumer satisfaction. Journal of consumer research,
84-91.
White, L., & Yanamandram, V. (2004). Why customers stay: reasons and
consequences of inertia in financial services. Managing Service Quality,
14(2/3), 183-194.
Winston, W. (1988). The Multiplier effect on economic value of customer service.s,
Vol. 3 No. ¾, pp. 5-8.
Woo, K. S., & Fock, H. K. (1999). Customer satisfaction in the Hong Kong mobile
phone industry. Service Industries Journal, 19(3), 162-174.
181
Yi, Y. (1990). A critical review of consumer satisfaction. Review of marketing,
Chicago: American Marketing .Association, pp 68-234(1), 68-123.
Yoo, D. K.& Park, J. A. (2007).Perceived service quality: Analyzing relationships
among employees, customers, and financial performance. International
Journal of Quality & Reliability Management, 24 (9), pp. 908-926.
Zeithaml , V . A . , Berry , L . L . and Parasuraman , A . (1996) The behavioral
consequences of service quality . Journal of Marketing 60 (2) : 31 – 47 .
BOOKS
Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting
interactions. Newbury Park, CA: Sage.
George, D., & Mallery, P. (2000). SPSS for Windows: A simple guide and reference.
Boston, MA: Allyn & Bacon.
Gromme, N. (1998). Data processing and analysis. McDonald, C. & Vangeleder,
P.(1998): ESOMAR Handbook of Market and Opinion Research, 389-415
Hamburg, M. (1970). Statistical analysis for decision making.
Headrick, D. (1998). The tentacles of progress: Technology transfer in the age of
imperialism, 1880-1940. New York: Oxford University Press.
Vavra, T.G. (1995), Aftermarketing: How to Keep Customers for Life through
Relationship Marketing, McGraw-Hill, New York, USA.
REPORTS:
TRAI Reports:
Nasscom-McKinsey Report (McKinsey, 2005)
Performance Indicators Report, October-December 2009
Performance Indicators Report, October-December 2010
Performance Indicators Report, October-December 2011
Performance Indicators Report, October-December 2012
Performance Indicators Report, October-December 2013
APPENDICES
182
TABLE No. 3.1
SERVICE QUALITY
1.
My service provider gives me services reliably, consistently
and dependably
Caruana, 2002
2 My service provider is trustworthy and its employees are
honest and believable.
Caruana, 2002
3 My service provider keeps its promises.
Caruana, 2002
4 My service provider’s employees are easily approachable.
Cronin, Brady and
Hult, 2000
5 My service provider’s employees are courteous, polite and
respectful.
Cronin, Brady and
Hult, 2000
6 My service provider’s employees listen to customers and are
willing to help them.
Cronin, Brady and
Hult, 2000
7 My service provider’s employees are pleasant, friendly and
caring.
Caruana, 2002
8 My service provider’s employees are neat and clean in their
office.
Caruana, 2002
9 My service provider’s employees are efficient and caring.
Danaher, P. J. and
R. W. Gallagher
(1997),
10 My service provider’s billing is accurate and easy to
understand.
Levesque and
McDougall, 1996
11 My service provider has reputation and good image.
Aydin and Ozer,
2005
12 My service provider is innovative and forward looking.
Aydin and Ozer,
2005
13 The advertisements and promotional campaigns of my service
provider are effective.
Aydin and Ozer,
2005
14 My service provider has sufficient presence in different
geographical areas through own offices or dealers, franchises.
Aydin and Ozer,
2005
15 My service provider has Physical facilities at their office which
are visually appealing.
Caruana, 2002
183
16 It is easy and convenient to take up a new mobile connection as
well as get recharges and top-ups from my service provider.
Lai et al. 2007
17 My service provider has up-to-date network and low congestion
problem even during peak traffic.
Olorunniwo and
Hsu, 2006
18 My service provider has good call quality in terms of voice
clarity and minimal call drop problem.
Kim et al. 2004
19 My service provider has wide coverage area.
Aydin and Ozer,
2005
20 My service provider makes efforts to understand the specific
needs of customers
Caruana, 2002
21 My service provider gives individual and personal attention to
the customers.
Caruana, 2002;
Johnson and
Sirikit, 2002
22 My service provider maintains all records accurately.
Wang and Lo
2002; Lai et al.
2007; Johnson and
Sirikit, 2002
23 My service provider gives me accurate and timely information.
Ndubisi and Wah,
2005
24 Services given by my service provider are prompt i.e. low
waiting time and quick response.
Olorunniwo and
Hsu, 2006
25 My service provider is sympathetic and reassuring whenever
there is a problem.
Lai et al. 2007
26 Working hours of my service provider are convenient for
customers.
Wang and Lo,
2002
27 Services given by my service provider are competitive.
Athanassopoulos
and Iliakopoulos,
2003
28 Pricing of services by my service provider are reasonable and
competitive.
Host and
Andersen, 2004
29 My service provider gives good range of pricing plans to
choose from.
Kim et al. 2004
30 Value Added Services (SMS, Ringtones etc.) given by my
service provider are comprehensive and competitive.
Aydin and Ozer,
2005
184
TABLE No. 3.2
CUSTOMER PERCEIVED VALUE
1 Compared to other companies my service provider charges me
fairly.
Levesque &
McDougall(1996)
2 Compared to what I get for what I pay my service provider
gives me good value.
Levesque &
McDougall(1996)
3 Compared to other companies my service providers gives me
more value added and free services.
Levesque &
McDougall(1996)
TABLE No. 3.3
CUSTOMER SATISFACTION
1 Overall I am happy with my mobile service provider. Hellier et al.
(2003
2 Services given by my mobile service provider are close to my
expectation.
Hellier et al.
(2003
3 My decision to use the services of my mobile service provider is
wise one.
Hellier et al.
(2003
4 My present mobile service provider can be compared to an ideal
service provider.
Hellier et al.
(2003
5 I would positively recommend the services of my mobile service
provider to others.
Hellier et al.
(2003
185
TABLE No. 3.4
CUSTOMER LOYALTY
1 I intend to remain with my present service provider for next 6
months.
Morgan and Hunt
(1994)
2 I intend to remain with my present service provider for next one
year.
Morgan and Hunt
(1994)
3 I intend to remain with my present service provider for next two
year.
Morgan and Hunt
(1994)
4 I would recommend the services of my service provider to my
friends and relatives.
Morgan and Hunt
(1994)
5 If I were to choose mobile service provider once again I will
choose my present service provider once again.
Morgan and Hunt
(1994)
TABLE No. 3.5
SWITCHING COST
1 It is risky to change my service provider as new service
provider may not give good services.
Jones et al. (2000)
2 I would be frustrated if I end my relationship with current
service provider.
Jones et al. (2000)
3 Costs in terms of time, money and efforts to change my service
provider are very high.
Jones et al. (2000)
4 It would be a hassle/burden to change my present service
provider.
Jones et al. (2000)
186
TABLE No. 3.6
INERTIA
1 Unless some great advantage is given by some other
company, I will not bother to change my present service
provider.
Huang and Yu (1999)
and Anderson and
Srinivasan (2003)
2 Unless I become highly dissatisfied, I will not bother to
change my present service provider.
Huang and Yu (1999)
and Anderson and
Srinivasan (2003)
3 Unless I am highly dissatisfied with my present service
provider changing service provider will be inconvenient
for me.
Huang and Yu (1999)
and Anderson and
Srinivasan (2003)
TABLE No. 5.1
Findings: Hypotheses Testing (H1a to H1h)
Customer satisfaction & Demographic variables
HYPOTHESIS REJECTED/NOT
REJECTED
1
H1a: There is no significant difference in the
customer satisfaction levels of customers of
different age groups.
REJECTED
2
H1b: There is no significant difference in the
customer satisfaction levels of male and female
customers
REJECTED
3
H1c: There is no significant difference in the
customer satisfaction levels of married and
unmarried customers
NOT REJECTED
4
H1d: There is no significant difference in the
customer satisfaction levels of customers of
different educational qualifications.
NOT REJECTED
5
H1e: There is no significant difference in the
customer satisfaction level of customers of
different occupations.
NOT REJECTED
6 H1f: There is no significant difference in the
customer satisfaction levels of customers of NOT REJECTED
187
different income groups.
7
H1g: There is no significant difference in customer
satisfaction levels of customers of different service
providers.
REJECTED
8
H1h: There is no significant difference in the
customer satisfaction levels of pre-paid and post
paid customers.
NOT REJECTED
TABLE No. 5.2
Findings: Hypotheses Testing (H2a to H2h)
Customer loyalty & Demographic variables
HYPOTHESIS REJECTED/NOT
REJECTED
1
H2a: There is no significant difference in the
customer loyalty levels of customers of different
age groups
REJECTED
2
H2b: There is no significant difference in the
customer loyalty levels of male and female
customers.
REJECTED
3
H2c: There is no significant difference in the
customer loyalty levels of married and unmarried
customers.
NOT REJECTED
4
H2d: There is no significant difference in the
customer loyalty levels of customers of different
educational qualification.
REJECTED
5
H2e: There is no significant difference in the
customer loyalty levels of customers of different
occupations.
REJECTED
6
H2f: There is no significant difference in the
customer loyalty level of customers of different
income groups.
NOT REJECTED
7
H2g: There is no significant difference in the
customer loyalty levels of customers of different
service providers.
REJECTED
8
H2h: There is no significant difference in the
customer loyalty levels of pre-paid and post-paid
customers.
NOT REJECTED
188
TABLE No. 5.3
Findings: Hypotheses Testing (H3a to H3g &H4)
Customer satisfaction & Service Quality Dimensions & CPV
HYPOTHESIS REJECTED/NOT
REJECTED
1 H3a: Customer Satisfaction is not impacted by
Employee Performance. REJECTED
2 H3b: Customer Satisfaction is not impacted by
Reliability REJECTED
3 H3c: Customer Satisfaction is not impacted by
Assurance. NOT REJECTED
4 H3d: Customer Satisfaction is not impacted by
Responsiveness. REJECTED
5 H3e: Customer Satisfaction is not impacted by
Competitiveness. REJECTED
6 H3f: Customer Satisfaction is not impacted by
Network Quality. REJECTED
7 H3g: Customer Satisfaction is not impacted by
Tangibility. REJECTED
8 H4: Customer Satisfaction is not impacted by
Customer Perceived Value. REJECTED
TABLE No. 5.4
Findings: Hypotheses Testing (H5 to H9)
Customer loyalty, Customer satisfaction, Switching cost and Inertia
HYPOTHESIS REJECTED/NOT
REJECTED
1 H5: Customer Loyalty is not impacted by
Customer satisfaction REJECTED
2 H6: Customer Loyalty is not impacted by
Switching Cost. NOT REJECTED
3 H7: Customer Loyalty is not impacted by Inertia. REJECTED
4 H8: Customer Satisfaction –Customer Loyalty
link is not moderated by Switching Cost. REJECTED
5 H9: Customer Satisfaction –Customer Loyalty
link is not moderated by Inertia. REJECTED
189
Questionnaire
Dear Respondents,
I am pursuing a research project concerning factors affecting customer loyalty in telecom
industry, as part of the requirement to complete my PhD thesis work.
I seek your assistance in this regard by kindly requesting that you take a few minutes to
complete the enclosed questionnaire.
PART I
Demographic Profile
NAME :……………………………………………………………………………….
Please Mark ( √ ) your responses to the following :
1. Age : 1. below 20 [ ] 2. 21 - 35 [ ] 3. 36 - 45 [ ] 4. 46 – 60 [ ] 5. 61 – 80[ ]
2. Gender : 1 Male [ ] 2. Female [ ]
3. Marital Status : 1. Married [ ] 2. Unmarried [ ]
4. Educational Qualification 1. Pre- Hr. Secondary[ ] 2. Hr. Secondary [ ] 3. Graduate
[ ] 4 Post Graduate[ ] 5.Doctor/Engineer/CA/Ph.D.[ ]
5. Occupation : 1. Business [ ] 2. Govt.Employee [ ] 3. Pvt.Sector Employee [ ]
4. Student [ ] 5.Others [ ]
6. Monthly Household Income (in Rs.): 1. below 10,000 [ ]
2. 10,000 - 25,000 [ ]
3. 25,000 - 50,000 [ ]
4. 50,000 - 75,000 [ ]
5. above 75,000 [ ]
190
7. Name of Service Provider : ……………………………………………….
8 .Type of connection : 1. Pre Paid [ ] 2. Post Paid[ ]
ADDRESS: -------------------------------------------------------------------------------------------------
--------------------------------------------------------------------------------------------------------
Contact No. --------------------------------------------------------------------------------------------
PART II
On a scale of 1 to 5, 1 stands for “Strongly Disagree” and 5 stands for “Strongly Agree”.
Please circle the appropriate rating as per your experience with your service provider.
CUSTOMER SATISFACTION
1 Overall I am happy with my mobile service provider. 1 2 3 4
5
2 Services given by my mobile service provider are close to my
expectation.
1 2 3 4
5
3 My decision to use the services of my mobile service provider is
wise one.
1 2 3 4
5
4 My present mobile service provider can be compared to an ideal
service provider.
1 2 3 4
5
5 I would positively recommend the services of my mobile service
provider to others.
1 2 3 4
5
SWITCHING COST
1 It is risky to change my service provider as new service provider
may not give good services.
1 2 3 4
5
2 I would be frustrated if I end my relationship with current service
provider.
1 2 3 4
5
3 Costs in terms of time, money and efforts to change my service
provider are very high.
1 2 3 4
5
4 It would be a hassle/burden to change my present service
provider.
1 2 3 4
5
191
SERVICE QUALITY
1 My service provider gives me services reliably ,consistently and
dependably
1 2 3 4
5
2 My service provider is trustworthy and its employees are honest
and believable.
1 2 3 4
5
3 My service provider keeps its promises. 1 2 3 4
5
4 My service provider’s employees are easily approachable.
1 2 3 4
5
5 My service provider’s employees are courteous, polite and
respectful.
1 2 3 4
5
6 My service provider’s employees listen to customers and are
willing to help them.
1 2 3 4
5
7 My service provider’s employees are pleasant, friendly and
caring.
1 2 3 4
5
8 My service provider’s employees are neat and clean in their
office.
1 2 3 4
5
9 My service provider’s employees are efficient and caring.
1 2 3 4
5
10 My service provider’s billing is accurate and easy to understand.
1 2 3 4
5
11 My service provider has reputation and good image .
1 2 3 4
5
12 My service provider is innovative and forward looking .
1 2 3 4
5
13 The advertisements and promotional campaigns of my service
provider are effective.
1 2 3 4
5
14 My service provider has Physical facilities at their office which
are visually appealing .
1 2 3 4
5
15 It is easy and convenient to take up a new mobile connection as
well as get recharges and top-ups from my service provider.
1 2 3 4
5
16 My service provider has up-to-date network and low congestion
problem even during peak traffic.
1 2 3 4
5
17 My service provider has good call quality in terms of voice
clarity and minimal call drop problem.
1 2 3 4
5
18 My service provider has wide coverage area.
1 2 3 4
5
19 My service provider makes efforts to understand the specific
needs of customers
1 2 3 4
5
20 My service provider gives individual and personal attention to 1 2 3 4
192
the customers 5
21 My service provider performs any service right first time
1 2 3 4
5
22 My service provider maintains all record accurately.
1 2 3 4
5
23 My service provider accurate and timely information
1 2 3 4
5
24 Services given my service provider are prompt i.e. low waiting
time and quick response.
1 2 3 4
5
25 My service provider is sympathetic and reassuring whenever
there is a problem.
1 2 3 4
5
26 Working hours of my service provider are convenient for
customers.
1 2 3 4
5
27 Services given by my service provider are competitive.
1 2 3 4
5
28 Pricing of services by my service provider are reasonable and
competitive.
1 2 3 4
5
29 My service provider gives good range of pricing plans to choose
from.
1 2 3 4
5
30 Value Added Services (SMS, Ringtones etc.) given by my
service provider are comprehensive and competitive.
1 2 3 4
5
INERTIA
1 Unless some great advantage is given by some other company, I
will not bother to change my present service provider.
1 2 3 4
5
2 Unless I become highly dissatisfied, I will not bother to change
my present service provider.
1 2 3 4
5
3 Unless I am highly dissatisfied with my present service provider
changing service provider will be inconvenient for me.
1 2 3 4
5
CUSTOMER LOYALTY
1 I intend to remain with my present service provider for next 6
months.
1 2 3 4
5
2 I intend to remain with my present service provider for next one
year.
1 2 3 4
5
3 I intend to remain with my present service provider for next two
year.
1 2 3 4
5
193
4 I would recommend the services of my service provider to my
friends and relatives.
1 2 3 4
5
5 If I were to choose mobile service provider once again I will
choose my present service provider once again.
1 2 3 4
5
CUSTOMER PERCEIVED VALUE
1 Compared to other companies my service provider charges me
fairly.
1 2 3 4
5
2 Comparing what I get for what I pay my service provider gives
me good value.
1 2 3 4
5
3 Comparing to other companies my service provider gives me
more value added and free services.
1 2 3 4
5
