thesis telecom mobile sector preetkanwal
TRANSCRIPT
AN EMPIRICAL STUDY OF TRUST AND COMMITMENT IN CELLULAR USERS OF SELECTED TELECOM
SERVICE PROVIDERS
A THESIS Submitted to the
FACULTY OF BUSINESS MANAGEMENT & COMMERCE PANJAB UNIVERSITY, CHANDIGARH
for the degree of
DOCTOR OF PHILOSOPHY
2007
PREET KANWAL
UNIVERSITY BUSINESS SCHOOL PANJAB UNIVERSITY, CHANDIGARH
ACKNOWLEDGEMENTS
I consider it pleasure and privilege to pay regards and thanks to Dr. Suresh
Kumar Chadha, Professor, University Business School, Panjab University,
Chandigarh who supervised this work with critical insight and intellectual
acumen. Under his guidance, it always looked within my reach. In fact, without his
critical suggestions, encouragement and co-operation, this thesis would have not
touched its zenith.
I thank Prof. S.C. Vaidya, Chairman, University Business School, Panjab
University, Chandigarh and other members of the teaching faculty of this
department for their help and co-operation.
I am also thankful to Dr. Satish Kapoor, Dean, Faculty of Business
Management and Commerce for his support.
My thanks are also due to Sh. S.S. Bedi, Librarian in providing references
on the subject. As also to other members of non-teaching staff of the department for
their help.
I am also grateful to Dr. S.K. Sharma, Chairman, Department of Statistics,
who lent me his expertise at the time of analysis of data.
I would also acknowledge the help provided by subscribers, dealers and
telecom service providers in providing precious data and suggestions.
I express my gratitude to my parent in-laws and parents who motivated me
to carry my research to its culmination. This work couldn’t have been completed
without their blessings and encouragement.
I owe very special thanks to my husband, Narinder, for his unstinted
support and inspiration. I am thankful to my little kids, Hargun and Guragam, for
understanding the hard work involved in the research and bearing with me.
I am also thankful to M/s Kaizen Graphics for typing the thesis.
Above all, I am grateful to Almighty, without Whose grace I would not have
succeeded.
Preet Kanwal
TABLE OF CONTENTS Acknowledgements List of Tables List of Figures List of Abbreviations CHAPTER – 1: INTRODUCTION 1.1 INTRODUCTION 1 1.2 GROWTH OF INDIAN TELECOM SECTOR: AN OVERVIEW 3 1.3 TRUST AND COMMITMENT AND TELECOM SECTOR 11 1.4 RATIONALE OF TRUST AND COMMITMENT 19 1.5 RESEARCH OBJECTIVES 20 1.6 TRUST AND COMMITMENT MODEL OF THE RESEARCH STUDY 20
1.6.1 Shared Value 21 1.6.2 Communication 22 1.6.3 Opportunistic Behaviour 23 1.6.4 The Consequence of Trust –Relationship Commitment 24
1.7 PROPOSED MODEL AND HYPOTHESES 25 1.8 NEED AND SIGNIFICANCE OF STUDY 28 1.9 RESEARCH METHODOLOGY 30
1.9.1 Research Problem 30 1.9.2 Scope of Study 30 1.9.3 Selection of Services 30 1.9.4 Questionnaires 30 1.9.5 Sample Design and Sample Size 30 1.9.6 Methods of Data Collection 31 1.9.7 Limitations of the Study 32 1.9.8 Analysis of Data 32
1.10 CHAPTER PLAN 38 CHAPTER – 2: REVIEW OF LITERATURE 2.1 BRIEF OVERVIEW 46 2.2 STUDIES RELATED TO GROWTH AND DEVELOPMENTS IN 46
INDIAN TELECOM SECTOR 2.2.1 Studies Related to Technology Upgradation in Telecom Sector 52 2.2.2 Studies on Investment Policy of Telecom Sector 54 2.2.3 Studies Relating to Competition in Indian Telecom Service Sector 55
2.3 TRUST AND COMMITMENT IN SERVICES SECTOR 56 2.4 IDENTIFICATION OF RESEARCH GAPS IN LITERATURE 74
2.4.1 Research gaps related to Indian Telecom Sector 74 2.4.2 Research Gaps Related to Trust and Commitment 75
CHAPTER – 3: GROWTH OF INDIAN TELECOM SECTOR 3.1 INTRODUCTION 76 3.2 GLOBAL TELECOM INDUSTRY: AN OVERVIEW 76 3.3 TRENDS IN INDIAN TELECOM INDUSTRY 78 3.4 TRANSITION OF INDIAN TELECOM INDUSTRY 80
3.4.1 Objectives of the National Telecom Policy 82 3.4.2 Telecommunication Services 83 3.4.3 Revenue Analysis 84 3.4.4 Teledensity 85 3.4.5 Value Added Services 86 3.4.6 Village Telephones 87 3.4.7 Thrust on broadband, high-speed Internet 87 3.4.8 Growth in Telecom manufacturing 88 3.4.9 Broadcasting sector 88
3.5 PRIVATIZATION AND DISINVESTMENTS OF BASIC SERVICES 88 3.6 FOREIGN DIRECT INVESTMENT (FDI) 91 3.7 FUTURE GROWTH OPPORTUNITIES OF INDIAN TELECOM SECTOR 93 3.8 GROWTH OF TELECOM SECTOR IN PUNJAB 97 3.9 LEADING PLAYERS OF INDIAN TELECOM INDUSTRY 98
3.9.1 Airtel 101 3.9.2 Tata Teleservices Limited 102 3.9.3 Hutchison Essar Limited 105 3.9.4 Reliance Infocomm Limited 106
3.10 CONCLUSION 107 CHAPTER – 4: A CONCEPTUAL FRAMEWORK OF TRUST
AND COMMITMENT 4.1 INTRODUCTION 108
4.1.1 Concept of Trust and Commitment 111 4.2 DISCRETE VS. RELATIONAL EXCHANGE 114 4.3 THE RELATIONSHIP DEVELOPMENT PROCESS 116 4.4 TRUST AND COMMITMENT AS KEY MEDIATING VARIABLES 119 4.5 CATEGORIES OF TRUST 120 4.6 TRUST AND PROFITABILITY 122 CHAPTER – 5: RESEARCH FINDINGS OF THE STUDY 5.1 BRIEF OVERVIEW 124 5.2 RELIABILITY FOR DATA COLLECTED FROM SUBSCRIBERS 124 5.3 RELATIONSHIP BETWEEN KEY-MEDIATING VARIABLES OF PREPAID 125
AND POSTPAID SUBSCRIBERS OF SELECTED TELECOM SERVICE OPERATORS
5.4 THE EMPIRICAL TEST 133 5.4.1 Reliability Analysis - Scale (Alpha) for Key Mediating Variable - 133
Shared Value 5.4.1.1 Results for Shared Value between Service Provider 133
and Prepaid and Postpaid Subscribers 5.4.1.2 Results of Prepaid and Postpaid Subscribers and City-Wise 136
Results for Shared Value 5.4.1.3 Results of Shared Value and its Dimensions for City-Wise and 140
Telecom Service Operator-Wise 5.4.1.4 Results of Telecom Service Provider-Wise, City-Wise and 144
Service-Wise Results of Shared Value 5.4.3 Reliability Analysis - Scale (Alpha) for Key Mediating Variable 147
Communication
5.4.3.1 Results for Communication between Service Provider 147 and Prepaid and Postpaid Subscribers
5.4.3.2 Results of Prepaid and Postpaid Subscribers and 150 City-Wise Results for Communication
5.4.3.3 Findings of a Comparison of City-Wise and Telecom Service 154 Provider-Wise Results of Communication
5.4.3.4 Results of Telecom Service Provider-Wise, City-Wise 159 and Service-Wise Results of Communication
5.4.5 Reliability Analysis – Scale (Alpha) for Key Mediating Variable 162 Opportunistic Behavior 5.4.5.1 Results for Opportunistic Behaviour between Telecom 163
Service Providers and Prepaid and Postpaid Subscribers 5.4.5.2 Results for Opportunistic Behaviour between Selected 166
Cities and Prepaid and Postpaid Subscribers 5.4.5.3 Results for Opportunistic Behaviour between Selected 168
Cities and Telecom Service Providers (TSP) 5.4.5.4 Results for Opportunistic Behaviour between Prepaid 173
and Postpaid Subscribers, Selected Cities and Telecom Service Providers (TSP)
5.4.6 Reliability Analysis - Scale (Alpha) for Key Mediating Variable-Trust 177 5.4.6.1 Results for Opportunistic Behaviour between Prepaid and 178
Postpaid Subscribers and Telecom Service Providers (TSPs) 5.4.6.2 Results for Trust between Selected Cities and Prepaid 180
and Postpaid Subscribers 5.4.6.3 Results for Opportunistic Behaviour between Selected 183
Cities and Telecom Service Providers (TSP) 5.4.6.4 Results for Trust between Selected Cities, Prepaid and 187
Postpaid Subscribers and Telecom Service Providers (TSPs) 5.4.7 Reliability Analysis - Scale (Alpha) for Key Mediating Variable 190
Relationship Commitment 5.4.7.1 Results for Relationship Commitment between Selected 191
Cities and Prepaid and Post Paid Subscribers 5.4.7.2 Results for relationship commitment between Prepaid 193
and Postpaid subscribers Selected Cities 5.4.7.3 Results for Relationship Commitment between Selected 197
Cities and Telecom Service Providers (TSP) 5.4.7.4 Results for Relationship Commitment between Selected 200
Cities, Prepaid & Postpaid Subscribers and Telecom Service Providers (TSP)
5.4.7.5 Results of β Values of Reliance 204 5.4.7.6 Results of β Values of Airtel 206 5.4.7.7 Results of β Values of Hutch 208 5.4.7.8 Results of β Values of Tata Indicom 210 5.4.7.9 Results of β Values of Prepaid Subscribers 212 5.4.7.10 Results of β Values of Postpaid Subscribers 214 5.4.7.11 Results of β Values of Combined Prepaid and Postpaid 216
Subscribers 5.5 SOCIO DEMOGRAPHIC PROFILE OF SUBSCRIBERS 234 5.6 RESEARCH FINDINGS OF DATA COLLECTED FROM DEALERS 240 5.7 RESEARCH FINDINGS OF TELECOM SERVICE PROVIDERS 247 5.8 IMPACT OF TRUST AND COMMITMENT IN TELECOM SECTOR 255
CHAPTER – 6: SUMMARY, SUGGESTIONS AND RECOMMENDATIONS 6.1 INTRODUCTION 257 6.2 NEED AND SIGNIFICANCE OF STUDY 257 6.3 RESEARCH OBJECTIVES OF THE STUDY 259 6.4 RESEARCH METHODOLOGY 259 6.5 SUMMARY OF THE FINDINGS 260
6.5.1 Growth of Indian Telecom Sector 260 6.5.2 Trust and Commitment as Key Mediating Variable 263 6.5.3 Impact of Trust and Commitment in Indian Telecom Sector 264
6.6 DISCUSSIONS OF THE FINDINGS 265 6.7 MANAGERIAL IMPLICATIONS 267 6.8 LIMITATIONS OF THE STUDY 269 6.9 DIRECTIONS FOR FUTURE RESEARCH 269 6.10 CONCLUSION 270 BIBLIOGRAPHY 271-294 APPENDICES i-xiv ANNEXURE – I: LIST OF SERVICE PROVIDERS ANNEXURE – II: QUESTIONNAIRE FOR SUBSCRIBER ANNEXURE – III: QUESTIONNAIRE FOR DEALERS ANNEXURE – IV: QUESTIONNAIRE FOR TELECOM SERVICE PROVIDER
LIST OF FIGURES
Figure No.
Title Page No.
1.1 Major Developments in Indian Telecom Industry during 1986-2006 5 1.2 Trend Exhibited by Subscriber Base, Traffic and Average Revenue
Per User (ARPU) during (2000 – 2006) 7
1.3 Growth for GSM vs. CDMA Subscribers for the Period of 1999-2006
10
1.4 Operator-wise Market Share of GSM Mobile as on 30.9.2006 10 1.5 Operator-Wise Market Share of CDMA Mobile as on 30.9.2006 11 1.6 Trust and Commitment Model of the Research Study 26 3.1 Regulatory Bodies of Indian Telecom Industry 80 3.2 A comparison of Private and Public Sector Undertaking Subscriber
base for the period (1998-2006) 89
4.1 Discrete Forms of Relationship Marketing 114 4.2 The Hypothesized Realm of Buyer Seller Relationship 116 4.3 Stages in buyer and seller relationship development 117 4.4 Benefits of Loyal Customers 120 5.1 Results of Reliance 204
5.2 Results of Airtel 206
5.3 Results of Huch 208
5.4 Results of Tata Indicom 210
5.5 Results of Prepaid Subscribers 212
5.6 Results of Postpaid Subscribers 214
5.7 Results of Combined Prepaid and Postpaid Subscribers 216
5.8 Purposes of Mobile Usage 220 5.9 Different Mobile Services between Prepaid and Postpaid Subscribers 222
5.10 Considerations Prior to Purchase Decision for a Mobile Service Provider
225
5.11 Evaluation of Service Providers’ Touch Points 227 5.12 Evaluation of Customer Care services for Selected Mobile Service
Provider 229
5.13 Evaluation of Service Quality 232 5.14 Satisfaction Level of Network Quality 232 5.15 Age-Wise Composition of Prepaid and Postpaid Subscribers 234 5.16 Occupation-Wise Composition of Prepaid and Postpaid Subscribers 235 5.17 Gender-Wise Composition of Prepaid and Postpaid Subscribers 236 5.18 Income-Wise Composition of Prepaid and Postpaid Subscribers 237
Figure No.
Title Page No.
5.19 Experience-Wise Composition of Prepaid and Postpaid Subscribers 238 5.20 Time-Spent on Mobile-Usage of Subscribers Amongst Selected Cities 239 5.21 Dealership of Telecom Operators 241 5.22 Evaluation of Company’s Directions with to Dealers 241 5.23 Dealers’ Participation in Marketing Decisions 242 5.24 Customer Feedback and After Sale Service 243 5.25 Effectiveness of Product Strategy 245 5.26 Effectiveness of Price Strategy 245 5.27 Compensation and Seller Support 246 5.28 Telecom Service Providers’ Collaboration 247 5.29 Telecom Service Provider and Call Line Identification Services 248 5.30 Methods of marketing of products 254
LIST OF TABLES
Table No.
Title Page No.
1.1 Mobile Subscribers (GSM and CDMA) as %of Basic Subscribers in India – March, 2000 to March, 2006
6
1.2 Performance of Mobile Service Providers from 2000-2006 7 1.3 List of Cellular (GSM & CDMA) Service Providers in Operation as on
31st March, 2006 9
1.4 Categories of Trust 15 1.5 List of Variables and Measures 25 1.6 Prepaid vs. Postpaid Subscribers 31 1.7 City Wise Prepaid and Postpaid Subscribers 32 1.8 Summary Statistics and Reliability Estimates
for the Model Constructs 33
1.9 ANOVA Table 34 2.1 Drivers of the New Telephony 75 3.1 Cellular Tariffs across South – Eastern Countries 78 3.2 Subscriber Base of Fixed and Wireless Services as on Feb,2007 78 3.3 Indian Telecom Services at a Glance (FY 2006-07) 84 3.4 Average Revenue Per User (ARPU) (Rupee / per month during the
quarter) 85
3.5 Service-wise Actual Inflow of FDI upto March 2006 (in millions) 92 3.6 Potential of Indian Telecom Industry 96 3.7 Subscriber Base of GSM Service Providers for the period 2004-2006 99 3.8 Subscriber Base of CDMA Operators for the period 2004-06 99 3.9 Coverage of Leading Mobile Service Providers 101 5.1 Evaluation of Relationship between Key Mediating Variables of
Reliance Prepaid Subscribers 125
5.2 Evaluation of Relationship between Key Mediating Variables of Airtel Prepaid Subscribers
126
5.3 Evaluation of Relationship between Key Mediating Variables of Hutch Prepaid Subscribers
127
5.4 Evaluation of Relationship between Key Mediating Variables of Tata Indicom Prepaid Subscribers
128
5.5 Evaluation of Relationship between Key Mediating Variables of Reliance Postpaid Subscribers
129
5.6 Evaluation of Relationship between Key Mediating Variables of Airtel Postpaid Subscribers
130
5.7 Evaluation of Relationship between Key Mediating Variables of Hutch Postpaid Subscribers
131
5.8 Evaluation of Relationship between Key Mediating Variables of Tata Indicom Postpaid Subscribers
132
5.9 Two way ANOVA for Service Provider and Prepaid and Postpaid Subscribers
134
Table No.
Title Page No.
5.10 Comparison of Shared Value and its Dimensions for Reliance Subscribers
134
5.11 Comparison of Shared Value and its Dimensions for Airtel Subscribers 135 5.12 Comparison of Shared Value and its Dimensions for Hutch Subscribers 135 5.13 Comparison of Shared Value and its Dimensions for Tata Indicom
Subscribers 136
5.14 Two Way ANOVA for Selected Cities and Prepaid and Postpaid Subscribers
137
5.15 Multiple Comparisons (Selected Cities) 138 5.16 Comparison of Prepaid and Postpaid Subscribers for Shared Value and
its Dimensions in Ludhiana City 138
5.17 Comparison of Prepaid and Postpaid Subscribers for Shared Value and its Dimensions in Amritsar City
139
5.18 Comparison of Prepaid and Postpaid Subscribers for Shared Value and its Dimensions in Chandigarh City
139
5.19 Two Way ANOVA for Selected Cities and Telecom Service Providers 141 5.20 Multiple Comparisons (Selected Cities) 141 5.21 One-Way ANOVA - Ludhiana and Telecom Service Provider 142 5.22 Multiple Comparison for Shared Value between
Telecom Service Providers for Ludhiana City 142
5.23 One-Way ANOVA - Amritsar and Telecom Service Providers 142 5.24 Multiple Comparison for Shared Value between Telecom Service
Providers for Amritsar City143
5.25 One-Way ANOVA – Patiala and Telecom Service Providers 143 5.26 Multiple Comparisons for Shared Value between Telecom Service
Providers for Patiala City 143
5.27 One-Way ANOVA – Chandigarh and Telecom Service Providers 144 5.28 Multiple Comparison for Shared Value between Telecom Service
Providers for Chandigarh City 144
5.29 Multiple Factor ANOVA for Prepaid and Postpaid Subscribers, Selected Telecom Provider and Selected Cities
146
5.30 Comparison of Shared Value its Dimensions for Amritsar Hutch 146 5.31 Comparison of Shared Value its Dimensions for Amritsar Tata Indicom 147 5.32 Two-Way for ANOVA Service Provider and Prepaid and Postpaid
Subscribers 148
5.33 Comparison of Communication and its Dimensions for Reliance 149 5.34 Comparison of Communication and its Dimensions of Hutch 149 5.35 Comparison of Communication and its Dimensions for Tata Indicom 150 5.36 Two way ANOVA Prepaid and Postpaid Subscribers and Selected
Cities 151
5.37 Multiple Comparisons (Selected Cities) 152 5.38 Comparison of Communication and its Dimensions for Ludhiana City 152 5.39 Comparison of Communication and its Dimensions for Amritsar City 153 5.40 Comparison of Communication and its Dimensions for Chandigarh City 153
Table No.
Title Page No.
5.41 Two Way ANOVA for Selected Telecom Provider and Selected Cities
155
5.42 Multiple Comparisons (Selected Cities) 155 5.43 One-Way ANOVA for Ludhiana and Selected Telecom Service
Providers 156
5.44 Multiple Comparisons for Communication between Telecom Service Providers for Ludhiana City
156
5.45 One-way ANOVA for Amritsar and Selected Telecom Operators 157 5.46 Multiple Comparisons for Communication between Telecom Service
Providers for Amritsar City157
5.47 One-Way ANOVA: Patiala and Selected Telecom Operators 157 5.48 Multiple Comparisons for Communication between Telecom Service
Providers for Patiala City 158
5.49 One Way ANOVA: Chandigarh and Selected Telecom Operators 158 5.50 Multiple Comparisons for Communication between Telecom Service
Providers for Chandigarh City 159
5.51 Multi-factor ANOVA for Selected Telecom Provider, Prepaid and Postpaid and Selected Cities
160
5.52 Comparison of Communication and its Dimensions for Reliance Ludhiana
161
5.53 Comparison of Communication and its Dimensions for Airtel Ludhiana 161 5.54 Comparison of Communication and its Dimensions for Hutch Amritsar 161 5.55 Comparison of Communication and its Dimensions for Tata Indicom
Amritsar 162
5.56 Comparison of Communication and its Dimensions for Reliance Patiala 162 5.57 Comparison of Communication and its Dimensions for Airtel
Chandigarh 162
5.58 Two-way ANOVA for Prepaid and postpaid subscribers and Selected Telecom Provider
163
5.59 Comparison of Opportunistic Behaviour and its Dimensions for Reliance 164 5.60 Comparison of Opportunistic Behaviour and its Dimensions for Airtel 164 5.61 Comparison of Opportunistic Behaviour and its Dimensions for Hutch 165 5.62 Comparison of Opportunistic Behaviour and its Dimensions for Tata
Indicom 165
5.63 Two Way ANOVA for Prepaid and Postpaid Subscribers and Selected Cities
166
5.64 Multiple Comparisons (Selected Cities) 167 5.65 Comparison of Opportunistic Behaviour and its Dimensions for Amritsar
City 167
5.66 Comparison of Opportunistic Behaviour and its Dimensions for Chandigarh City
168
5.67 Two-way ANOVA for Telecom Service Providers and Selected Cities
16
5.68 One-way ANOVA for Opportunistic Behaviour Ludhiana and Selected Telecom Operators
170
Table No.
Title Page No.
5.69 Multiple Comparison for Opportunistic Behaviour between Telecom Service Providers for Ludhiana City
170
5.70 One-way ANOVA for Amritsar and Selected Telecom Operators 170 5.71 Multiple Comparison for Opportunistic Behaviour between
Telecom Service Providers for Amritsar City 171
5.72 One Way ANOVA for Patiala and Selected Telecom Operators 171 5.73 Multiple Comparison for Opportunistic Behaviour between
Telecom Service Providers for Patiala City 172
5.74 One-way ANOVA for Chandigarh and Selected Telecom Operators 172 5.75 Multiple Comparison for Opportunistic Behaviour between
Telecom Service Providers for Chandigarh City 173
5.76 Multiple Factor ANOVA for Telecom Service Providers, Selected Cities and Prepaid and Postpaid Services
174
5.77 Multiple Comparisons (Selected Cities) 175 5.78 Comparison of Opportunistic Behaviour and its Dimensions for Airtel
Ludhiana 175
5.79 Comparison of Opportunistic Behaviour and its Dimensions for Hutch Ludhiana
176
5.80 Comparison of Opportunistic Behaviour and its Dimensions for Hutch Amritsar
176
5.81 Comparison of Opportunistic Behaviour and its Dimensions for Tata Indicom Amritsar
176
5.82 Comparison of Opportunistic Behaviour and its Dimensions for Reliance Patiala
177
5.83 Comparison of Opportunistic Behaviour and its Dimensions for Hutch Patiala
177
5.84 Two Way ANOVA for Selected Service Provider and Prepaid and Postpaid Subscribers
178
5.85 Comparison of Trust and its Dimensions for Reliance 179 5.86 Comparison of Trust and its Dimensions for Hutch 179 5.87 Comparison of Trust and its Dimensions for Tata Indicom 180 5.88 Two-way ANOVA for Trust in Selected cities and Prepaid and Postpaid
Subscribers 181
5.89 Multiple Comparisons (Selected Cities) 181 5.90 Comparison of Trust and its Dimensions for Amritsar City 182 5.91 Comparison of Trust and its Dimensions for Patiala City 182 5.92 Comparison of Trust and its Dimensions for Chandigarh City 183 5.93 Two Way ANOVA for Selected Cities and Selected Telecom Service
Providers 184
5.94 One Way ANOVA for Ludhiana and Selected Telecom Operators 185 5.95 Multiple Comparison for Trust between Telecom Service Providers for
Ludhiana City 185
5.96 One Way ANOVA for Amritsar and Selected Telecom 0perators 185 5.97 Multiple Comparison for Trust between Telecom Service Providers for
Amritsar City 185
Table No.
Title Page No.
5.98 One-way ANOVA for Patiala and Selected Telecom 0perators 186 5.99 Multiple Comparison for Trust between Telecom Service Providers for
Patiala City 186
5.100 One Way ANOVA for Chandigarh and Selected Telecom 0perators 187 5.101 Multiple Comparison for Trust between Telecom Service Providers for
Chandigarh City 187
5.102 Multiple Factor ANOVA for Selected Cities, Selected Service Providers and Prepaid and Postpaid Services
189
5.103 Comparison of Trust and its Dimensions for Reliance Ludhiana 189 5.104 Comparison of Trust and its Dimensions for Airtel Amritsar 190 5.105 Comparison of Trust and its Dimensions for Hutch Amritsar 190 5.106 Two Way ANOVA Prepaid and Postpaid Subscribers and Selected
Cities 191
5.107 Comparison of Relationship Commitment and its Dimensions for Reliance
192
5.108 Comparison of Relationship Commitment and its Dimensions for Airtel 192 5.109 Comparison of Relationship Commitment and its Dimensions for Hutch 193 5.110 Two Way ANOVA for Prepaid and Postpaid Subscribers and Selected
Cities 194
5.111 Multiple Comparisons (Selected Cities) 195 5.112 Comparison of Relationship Commitment and its Dimensions for
Amritsar City 195
5.113 Comparison of Relationship Commitment and its Dimensions for Patiala City
196
5.114 Comparison of Relationship Commitment and its Dimensions for Chandigarh City
196
5.115 Two Way ANOVA for Selected Telecom Service Providers and Selected Cities
198
5.116 One Way ANOVA: Ludhiana and Selected Telecom 0perators 199 5.117 Multiple Comparison for Relationship Commitment between
Telecom Service Providers for Ludhiana City 199
5.118 One Way ANOVA: Patiala and Selected Telecom 0perators 199 5.119 Multiple Comparison for Shared Value between Telecom Service
Providers for Patiala City199
5.120 Multifactor ANOVA for Prepaid and Postpaid Subscribers, TSPs and Selected Cities
201
5.121 Comparison of Relationship Commitment and its Dimensions for Ludhiana Reliance
202
5.122 Comparison of Relationship Commitment and its Dimensions for Amritsar Airtel
202
5.123 Comparison of relationship commitment and its dimensions for Amritsar Hutch
202
5.124 Comparison of Relationship Commitment and its Dimensions for Amritsar Tata Indicom
203
5.125 Effectiveness of Source of Information between Prepaid and Postpaid Subscribers
218
Table No.
Title Page No.
5.126 Subscription of Other Services of Selected Telecom Operators 218 5.127 Subscription between Prepaid and Postpaid Subscribers 219 5.128 Evaluation of Various Purposes of Mobile Usage 219 5.129 Evaluation of Different Mobile Services between Prepaid and Postpaid
Subscribers 221
5.130 Evaluation of Executive’s Knowledge About the Product 223 5.131 Considerations Prior to Purchase Decision for a Mobile Service
Provider 223
5.132 Evaluation of Awareness of Company’s Offices 226 5.133 Response About Service Providers’ Touch Points 227 5.134 Evaluation of Customer Care Services 228 5.135 Efficient Solution of Query Handling 229 5.136 Effectiveness of After-Sales Services 230 5.137 Evaluation of Service Quality 230 5.138 Evaluation of Satisfaction Level of Network Quality 231 5.139 Evaluation of Correlation between Pre-Purchase and Post-Purchase
Behaviour 233
5.140 Age-Wise Composition of Subscribers 234 5.141 Occupation-Wise Composition of Subscribers 235 5.142 Gender-Wise Composition of Subscribers 236 5.143 Education-Wise Composition of Subscribers Amongst Selected Cities 237 5.144 Income-Wise Composition of Subscribers Amongst
Selected Cities 237
5.145 Experience-Wise Composition of Subscribers Amongst Selected Cities 238 5.146 Time-Spent on Mobile-Usage of Subscribers Amongst Selected Cities 239 5.147 Place-Wise Mobile-Usage of Subscribers Amongst Selected Cities 240 5.148 Dealership of Telecom Operators 240 5.149 Evaluation of Company’s Directions with to Dealers 242 5.150 Evaluation of Dealers 243 5.151 Evaluation of Company’s Product Attributes 243 5.152 Evaluation of Promotional Strategy 246 5.153 Functional Domain of Products and Services 248 5.154 Export of Services 249 5.155 Localisation of Products and Services 249 5.156 Promotional Activities 249 5.157 Effectiveness of Marketing Channels and Distribution Networks 250 5.158 Evaluation of Pre and After-Sale Services 250 5.159 Availability of After-Sale Service Station 251 5.160 Fixation of After-Sale Charges 251
Table No.
Title Page No.
5.161 Customer Care and After-Sale Services 252 5.162 Maintenance of Customer Database 252 5.163 Customer Feedback After Sale Service 252 5.164 Feedback Channels of Telecom Service Providers 253 5.165 Usage of Indian Technology vis-à-vis Foreign Technology 253 5.166 Company’s Policy with regard to Training Programme 254 5.167 Areas of Marketing Strategy 255
ABBREVIATIONS
Abbreviation Expression GNP Gross National Product GDP Gross Domestic Product RBI Reserve Bank of India ASCON Confederation of Industry Associated Councils VAS Value Added Services TRAI Telecom Regulatory Authority Of India NLD National Long Distance ILD International Long Distance ARPU Average Revenue Per User BSNL Bharat Sanchar Nigam Limited Hutch Hutchinson Essar Limited RIM Reliance India Mobile GSM Global System for Mobile CDMA Code Division Multiple Access NTP National Telecom Policy CPP Calling Party Pays UAL Unified Access Licensing ADC Access Deficit Charges FDI Foreign Direct Investment LISREL Linear Structural Relationships GMPCS Global Communication by Satellite OECD Organization for Economic Cooperation and Development DEL Direct Exchange Lines NCAER National Council of Applied Economic Research 3G Third Generation DoT Department of Telecommunications SMS Short Message Services PSUs Public Sector Undertakings CRM Customer Relationship Management TQM Total Quality Management JIT Just-in-time MRP Material Resource Planning GAM Global Account Management ICT Information and Communications Technology USO Universal Service Obligation MoC Ministry of Communications VSNL Videsh Sanchar Nigam Limtied DTO Department of Telecom Operations DTS Department of Telecom Services NET X-25 based Packer Switched Data Network GPSS Gateway Packet Switched Data Services GEDIS Gateway Electronic Data Interchange Service GEMS-400 Gateway E-Mail and Store & Forward FAX Service CPS Concert Packet Service VPT Village Public Telephones
1
CHAPTER – 1
INTRODUCTION
1.1 INTRODUCTION
In the 21st century, the new economy is becoming increasingly customer
centric. In the current marketplace, considerable attention has been paid to the
concept of relationships between service providers and their customers (Barnes,
1997; Gwinner et al., 1998; Reynolds and Arnold, 2000), and this concept has
been enthusiastically embraced by academics and practitioners (Beatty et al.,
1996; Berry, 1995; Reynolds and Arnold, 2000). Both operationally and
theoretically, the word relationship is poorly defined (Bagozzi, 1995). A
relationship may be seen to exist in an operational context, where the
relationship is created through a series of episodes, so that in the buying of a
service at least two encounters are required before a relationship exists
(Liljander and Strandvik, 1995; Storbacka et al., 1994). This position is further
developed by Barnes (1997), who suggests that before a relationship may be
said to exist, both parties must mutually perceive that the relationship exists and
the relationship must be characterized by a special status. Relationships are,
therefore, a series of transactions which build an awareness of a shared
relationship through trust and commitment, among several other factors (Morgan
and Hunt, 1994).
On the surface, there is considerable merit in the idea of a service
company building relationships with its customers. It is in an attempt to increase
the level of trust and commitment that customers feel towards the company
(Sheaves and Barnes, 1996). The higher levels of trust and commitment, in turn,
are associated with higher levels of customer retention and, inevitably,
organisational profitability. However, there is little consideration given to what
actually constitutes a relationship, and even less to how it is practised in
business and other organisations (Barnes, 1997; Sheaves and Barnes, 1996). It
is plausible that a certain interaction may be perceived by some people as a
relationship, while others may perceive the same interaction to be merely an
2
interaction, devoid of the elements that would make up a relationship (Bendapudi
and Berry, 1997). Therefore, the concept of a relationship is highly subjective,
and given the lack of a clear definition of a relationship, it may be useful to
explore the various dimensions of a relationship and address their impact on the
overall quality of a relationship.
A service has been defined as, "any act or performance that one party
can offer to another that is essentially intangible, and does not result in the
ownership of anything..." (Kotler, 2004) Unlike physical products, service
products cannot be seen, tasted, felt, heard, or smelled before they are bought
(Parasuraman et al; 1985; Lovelock, 1981). Since services are intangible,
consumers are often faced with not knowing what to expect of a service until
they have consumed it, and hence perceive services as risky (Murray and
Schlacter, 1990). Further, research has demonstrated that the need for trust
arises in any situation characterized by a high degree of risk, uncertainty, and/or
a lack of knowledge or information on the part of the interaction participants
(Mayer et al., 1995). Thus, customers have an inherent need to trust in their
service provider to deliver the desired service outcome.
Services sector is the fastest growing segment as compared to other
sectors of the Indian economy. A major stimulus in this shift is the movement to
information age spurred by invention of computer and advancements in
telecommunications. As countries continue to shift from agricultural base to
services oreientation, the demand for services further holds huge potential.
Additional factors contributing to the growth of service sector are higher per
capita income, increased time pressure, advances in product technology (Kurtz,
2002), spiralling competition, rise of individualism (Seth & Seth, 2005)
technological advances, globalisation, (Balchandaran, 2004), competition,
greater life expectancy and cost effectiveness drives (Rampal and Gupta, 2002)
and growth of service chains and networks and service quality movement Thus,
tremendous growth of services sector implies the role of marketing in terms of
vast opportunities and implications, marketing opportunities arising from new
3
technology, in franchising from fewer regulations and professional restrictions, in
servicing physical goods and international markets (Lovelock, 1999).
1.2 GROWTH OF INDIAN TELECOM SECTOR: AN OVERVIEW
The service sector growth worldwide has been phenomenal which is
reflected in its increased contribution to Gross Domestic Product (GDP) as well
as employment generation mechanism. Liberalisation, Privatisation and
Globalisation have brought unprecedented changes in the economic, trade, and
industrial scenarios. India is fast moving from a protected economy to an open
market economy and becoming integrated with the world economy. The change
environment has exposed various organizations including the service sector to
the challenges of competition, service quality, cost, and the competitive
environment .It will help organizations to modernize. Some of those unable to
cope with the changes may have to face the consequences of survival of the
fittest.
The Reserve Bank of India (RBI) Annual Report for the year 2005-06
states that services have emerged as the fastest growing sector and has
imparted much of the effect to the overall growth of the economy, particularly in
the times of adverse agricultural shocks and industrial slow-down. Share of
services sector has risen from 41.3 percent in 1995-96 of services (including
construction) the real gross domestic product to 61.2 percent in 2005-06. A
major part of the share in service sector has taken place in trade,
communication, banking and insurance segments.
According to a survey conducted by the Confederation of Industry
Associated Councils (ASCON) in 2006, the three service sectors --- cellular
phone, housing finance and IT services showed the highest growth rates
recently. These services may change from time to time, but what is remarkable
about their growth rates is that many are growing at more than 20 percent a
year, which is far higher than the overall growth in the economy as a whole.
Thus, among these sectors, telecom sector is likely to be the growth engine for
the Indian economy.
4
India, like many other countries of the world, has adopted a gradual approach to telecom sector reform through selective privatisation and managed competition in different segments of the telecom market. To begin with, India introduced private competition in value-added services in 1992 followed by opening up of cellular and basic services for local area to private competition. Private competition was also introduced in National Long Distance (NLD) and International Long Distance (ILD) telephony at the start of the current decade.
The Indian mobile services industry is moving in full swing, be it investment, subscriber base, technology or Value Added Services (VAS). Also the industry is coming up with innovative ways to lower their cost of operations. Apart from this, cut-throat competition in terms of technology as well as among the service providers has pushed the industry to innovate which has benefited the ultimate consumer.
Gartner (2006), in its recently global forecast for mobile phones predicted that mobile phones sales will exceed 1 billion in 2009. Asia/Pacific accounts for most of the sales. China and India alone will account for nearly 200 million units in 2007, with the Indian market surpassing China in 2009 to reach 139 million units. The revenues of Indian cellular markets will reach $ 24 billion by year 2009 recording Compounded Annual Growth Rate (CAGR) of 35.6%. Mobile Value Added Services (VAS) market in India is estimated at $0.55 billion (2005) and is expected to grow more than fifteen fold to greater than $9 billion by 2010 (Lehman Brothers, 2006).
India has gone through a period of adjustment over the past 8 years. The Telecom Regulatory Authority of India (TRAI) was constituted in 1997. It acts as an independent regulator in this sector. Several progressive measures have been taken over the past few years. At the time of launch of GSM cellular service in the country, there were a number of impediments in the form of high handset costs, exorbitant tariffs, high initial entry/activation charges, Mobile Party Pays (MPP) regime etc. With the passage of time, these initial barriers have almost disappeared as on date.
The most important of them that are considered to be landmarks in Indian Telecom history are given in Figure 1.1.
5
Figure 1.1: Major Developments in Indian Telecom Industry during 1986-2006
EstablishedMTNL & VSNL
Awarded 8 licences for
cellular services in 4 metros
Announced New Telecom
Policy (NTP’99)
Transition torevenue sharing
arrangement
Enhanced and separated role ofTRAE regulatory
& appellate Tribunal
Guidelines foropening up fixed-
line operations to unlimited
competition; limited mobility permitted
Privatizationof VSNL
1 9 8 6 M a y 9 4 J a n 9 5 M a r 9 6 M a r 9 9 J u l y 9 9 O c t 9 9 D e c 9 9 A u g 0 0 J a n 0 1 J u l 0 1 F e b 0 2 A p r i l 0 3 J u n e 0 4 A p r i l 0 5 S e p t . 0 6
AnnouncedTelecom policyfor deregulation
of sector
Awarded33 cellular
and 6 fixed linelicenses for
individual circles
Separated DoT as:a) DTO - Service provider
b) DoT - Policy maker
Long distancepolicy
announced.IncorporatedBSNL, DTOcoprporation
process initiated
Fourth cellular licenses awarded
pursuant to abidding process
Policy forinternational
long distance announced
Introduction ofCPP regime
Lowering of access deficit
Introduction ofrevenue share
regime
Increase inFDI ceiling
Source: Merrill Lynch, December, 2006.
6
In the pre-reform period, growth in telecom services was primarily driven
by public sector monopoly, showing very marginal growth, as the incremental
tele-density between 1948 and 1998, a 50-year period, was only
1.92%.Telecommunication development in the initial stage of the reforms
process beginning with National Telecom Policy (NTP) in I994, which provided
for migration from fixed license fee to revenue share regime. Cost-Oriented
Telecom Tariffs were also introduced by TRAI in 1999. From 2003 onwards, as a
result of certain pragmatic decisions by the Government and the Regulator, viz.,
introduction of Calling Party Pays (CPP) regime, Unified Access licensing
regime, lowering of access deficit coupled with introduction of revenue share
regime triggered further growth. The incremental growth in the ratio of total
mobile subscribers as a proportion of the basic subscribers has been indicated in
table below:
Table 1.1: Mobile Subscribers (GSM and CDMA) as %of Basic Subscribers in India – March, 2000 to March, 2006
March, 2000
March, 2001
March, 2002
March, 2003
March, 2004
March, 2005
March, 2006
7.07% 10.94% 16.73% 30.36% 78.64% 113.05% 179.28%
Source: www.trai.gov.in as on 22nd Oct, 2006
It is very clear from table 1.1 that the number of mobile subscribers as a
proportion of basic service subscribers continued to increase, which reflects the
global trend. The ratio of mobile subscribers as a proportion of the basic
subscribers has shown tremendous growth of 179.28% for the year 2005-2006
as compare to 7.07% for 2000-01. Further, the performance of mobile service
providers can be studied on the basis of parameters of subscriber base,
teledensity and traffic.
Table 1.2: Performance of Mobile Service Providers from 2000-2006
Year Indicator
2000 2001 2002 2003 2004 2005 2006
Population (in million) 1016 1032 1048 1069 1088 1108 1130
Subscriber base (in million) 1.90 3.58 6.54 13.0 33.69 52.22 90.14
Tele-density subscriber 100 0.18 0.34 0.62 1.27 3.09 9.08 14.05
Traffic (min of use/sub/month) 197 223 218 225 302 367 443
Source: AUSPI.in as on 15th Nov, 2006
7
From table1.2, following observations can be drawn:
i) Rate of growth in mobile subscriber base has been substantially higher
than growth in population, indicating a rapid proliferation of mobile
telephone and adoption by non-users/first-time users.
ii) Tele-density has also gone up which is reflection of above discussion.
iii) From 2000 to 2006, traffic or usage of mobiles phones increased, but the
increase was not uniform during the period. Till 2003, mobile usage went
up gradually but year 2004 onwards saw an era of speedy increase in
mobile traffic. It was probably because of the reasons that in initial days of
mobile telephony, call rates were high and the number of services offered
by cellular operators was limited. However 2003 onwards, because of the
slashed prices and add-on-services resulted in sudden increased traffic.
Figure 1.2: Trend Exhibited by Subscriber Base, Traffic and Average Revenue Per User (ARPU) during (2000 – 2006)
Source: www.trai.gov.in as on 15th Nov, 2006
Figure 1.2 exhibits trend of subscriber base, traffic and ARPU for the
period from 2000-2006. The revenue generated by a cellular company is the
resultant of two factors – number of subscribers and the usage rate (traffic).
Although traffic has been showing upward trend but the rate at which the
revenue has been increasing is far less than the rate of growth in subscriber
base and usage. The trend indicates that from 2000-2006, Average Revenue
0
100
200
300
400
500
2000 2001 2002 2003 2004 2005 2006
Year
Subscriber base (inmillion)Tele-density subscriber100Traffic (min ofuse/sub/month)
8
Per User (ARPU) was consistently decreasing which means the revenue
generated through individual connection had been decreasing. This reflects the
decreasing profitability of incremental connections. The reason behind this
paradox is drastic reduction in rental and call rates over the last five years. In
order to woo the customers, companies slashed prices. Today, rental tariffs have
hit the lowest in the world at 40 paise for a local call and Rs.1.99 for an STD call.
Six years ago, mobile users had to pay Rs.16.40 for local calls and Rs.40 per
minute for an STD call. The pre-paid card, which was once sold for Rs.450-500
now costs just 10 paise now in most markets.
Service Providers are Categorised as per service into two parts --- Basic
Service Providers and Value Added Service Providers. Basic service providers
are those who provide mainly voice communication. The subscriber’s connection
to the telecom network is called a Direct Exchange Line (DEL) and people use it
for talking. Basic services can be differentiated as per call destination into
domestic and international. Domestic calls, both local and long-distance are
routed through cables and wireless links. International calls are routed overseas,
mainly through satellite links. The international telecom service providers of
various nations liaison with one another to ensure smooth operations and
efficient call transfer. Major global telecom service providers, mainly private
operators from the developed nations, determine international call tariffs.
Revenue sharing agreements exist between various international carriers. Value
added service providers are those which provide services, such as cellular
telephony, paging, e-mail and VSAT network, which provide the subscriber
greater ease of communication and enhance the utility of basic services network.
After the liberalization of Indian telecom sector in 1994, the Indian cellular
market witnessed a surge in cellular services. In cellular service there are two
main competing network technologies: Global System for Mobile
Communications (GSM) and Code Division Multiple Access (CDMA). With the
advent of cellular phones doing double and triple duty as streaming video
devices, pod cast receivers and email devices, speed is important to those who
use the phone for more than making calls. CDMA has been traditionally faster
than GSM, though both technologies continue to rapidly leapfrog along this path.
In GSM phones, SIM cards are used. The removable SIM card allows phones to
9
be instantly activated, interchanged, swapped out and upgraded, all without
carrier intervention. The SIM itself is tied to the network, rather than the actual
phone and the phones that are card-enabled can be used with any GSM carrier.
CDMA operators require proprietary handsets that are linked to one operator
only and are not card-enabled. To upgrade a CDMA phone, the operator must
deactivate the old phone then activate the new one and the old phone becomes
useless.
By 2004, there were a total of 12 players in the market with the five major
players being Bharti-Tele-ventures Limited (Bharti), Bharat Sanchar Nigam
Limited (BSNL), Hutchinson-Essar Limited (Hutch), Idea, Cellular Limited (Idea),
and Reliance India Mobile (RIM). All the players except RIM offered services
based on the Global System for Mobile (GSM) technology whereas RIM
provided services based on code division Multiple Access (CDMA) technology.
At the end of March, 2006, following operators were providing mobile service
(GSM & CDMA) in the service areas mentioned in Table 1.3 below:
Table 1.3: List of Cellular (GSM & CDMA) Service Providers in Operation as on 31st March, 2006
S. No. Service Provider No. of Circles
1. BSNL 21
2. Bharti 23
3. Reliance Communications 23
4. Tata Teleservices 20
5. Hutch 16
6. IDEA 8
7. BPL 1
8. Aircel 7
9. MTNL 2
10. Spice Communications 2
11. HFCL 1
12. Shyam Telelink 1
13. Escorts Telecommunications 3
Source: www.dot.com as on 20th March 2007
10
Figure 1.3: Growth for GSM vs. CDMA Subscribers for the Period of 1999-2006
Figure 1.3 signifies the comparison of the growth trend for GSM and
CDMA subscribers for the period of 1999-2006. On cumulative basis, India has
grown by 73.5% taking the overall tally to 90.8 million subscribers in year 2006
as compare to 1.2 million subscribers in March, 1999. Of this, GSM contributes
76% whereas CDMA contributes 24%. The industry has added 38.5 million
mobile lines i.e. around 3.2 million lines every month and around 90,000
connections every month. As every month, 4-5 million lines are being added,
growth continues even in the year 2006.
Figure 1.4: Operator-wise Market Share of GSM Mobile as on 30.9.2006
Source: www.trai.gov.in as on 30th Sep 2006
11
Figure 1.4 explains Operator-Wise Market Share of GSM mobile services.
The GSM subscribers’ base has reached 91.01 million in the quarter ending
September 2006 as against 78.49 million at the end of the previous quarter
registering a growth rate of 15.94%.Bharti with 27.06-million subscriber base
remains the largest GSM mobile operator holding 29.73% share. It is followed by
BSNL, Hutch and Idea with subscriber base of 20.93 million, 20.36 million and
10.36 million, respectively. The subscriber base of all the GSM operators has
increased except that of BPL which is operating in Mumbai. Private operators
have 74% subscribers whereas Public sector Operators (BSNL & MTNL) have
26% subscribers in the GSM segment.
Figure 1.5: Operator-Wise Market Share of CDMA Mobile as on 30.9.2006
Source: www.trai.gov.in as on 30th Sep, 2006
The figure 1.5 explains the market share of CDMA operator on Sep, 2006.
Reliance Communications Ltd continued to have the highest subscriber base
with 60% market share followed by Tata Teleservices with 32% market share,
while other players in CDMA Technology have the remaining 8%.
1.3 TRUST AND COMMITMENT AND TELECOM SECTOR
Telecommunication markets have changed dramatically in recent
years. Customers in many countries who used to have only one service
provider now have a wide variety to choose from. The fight to attract and
keep customers has resulted in the development of relationship marketing
strategies. The telecom companies are developing a mix of relationship-
12
marketing tools to establish and build profitable customer relationship.
With the concept of relationship marketing, we focus on the need for
companies to be market oriented by building up the ability to manage
networks, relationships and interactions (Gronroos, 1983; Gummesson,
1987). In other words, the main thrust has been on expanding the
relationship with existing customers. It has been fully accepted in
marketing literature that long-term customers are more profitable than
short-term customers (Reichheld and Teal, 1996; Johnson, 1998).
The evolution of the competition forces firms to cope with an
increasing difficulty in the management of technological options and
market relations. In telecommunication industry, technologies are in
continual development; market relations are frequently threatened by new
or more aggressive competitors. In this situation, the behaviour of
entrepreneurs and managers is turned in search of new models to
manage market relations, suitable for operating with success in face of
continual change and a high level of uncertainty.
The marketing literature has shown that trust between firms, and
between firms and consumers, is a crucial factor in the move from discrete
market transactions to continuous exchange relationships. Most firms
have reacted to this dynamics by trying to develop long-term cooperative
relationships with other firms (Valdani 1997; Lanza 1998) and, above all,
with the clients, final and intermediate (Busacca 1994; Castaldo 1994;
Costabile 1999). Such relationships are based on mutual trust, and are
intended to control the variety and the variability (Vicari 1991; Rullani,
1992; Busacca, 1997). Trust established between firms, and between
firms and consumers, is one of the fundamental resources that firms can
make use of in order to control complexity. The continuous growth of trust
makes it possible, according to several authors, to pursue the objective of
the firm’s value generation and diffusion (Guatri, 1991; Vicari, 1991; Guatri
and Massari, 1992; Busacca, 1994; Costabile 1996b and 1998; Vicari,
Busacca and Bertoli, 1999).
13
As a first approach, trust whether in someone or something, can be
defined as an attitude, characterised by the belief in the counterparty’s
reliability, for example, supplier or client. More specifically, according to
some authors, this is the belief (Castaldo, 1995) that the behaviour of the
counter party is predictable in terms of its direction and intensity, which means that future actions of the counterparty will conform to obligations
assumed, implicity or explicity. In general, this perception of reliability comes from experience, and more particularly, from a sequence of
satisfactory interactions, that is a series of evaluative processes from
which a systematic confirmation of expectations emerges (Costabile,
1996).
The trust concept has been the object of particular attention in
research about markets relations in which two specific parties are
involved: one which gives trust (trustor) and one to which trust is given
(trustee). These relationships can take place at different levels: between individuals, between firms, or between firms and individuals (Baccarani,
1995); there has been no lack of efforts to differentiate trust towards the
individuals from that towards the organizations (Doney and Cannon, 1997; Zaheer, McEvily and Perrone, 1998), but most of these studies do not
hypothesis any distinction on this point. Besides, they assume that there is
a symmetry in the trust relationships. Trust is perceived, then, as a phenomenon that is characterised by reciprocity, even if this symmetry is
not always confirmed (Ganesan, 1994).
It has been observed from marketing literature that trust is crucial
factor in the shift from discrete market transactions to continuous
exchange relationships (Dwyer, Schurr and Oh, 1987). Trust has thus a
central role in the development of relationship marketing, which refers to
all activities intended to establishing, developing and maintaining
exchange relationships with clients (Morgan and Hunt, 1994).
Some recent studies have also considered the trust concept in the
area of consumer marketing, that is in the relationships between firms and
14
final consumers. This is particularly the subject matter of studies aimed to
analyze the constructs involved in the customer buying behavior, such as
satisfaction, brand image and customer loyalty (Valdani and Busacca,
1992; Busacca, 1994; Vicari, 1995; Berry, 1995; Gruen, 1995; Gurviez,
1995; Busacca and Castaldo, 1996; Fletcher and Peters, 1997; Costabile, 1999).
On the grounds of exhaustive review done by Castaldo (1995), it is possible to affirm that the conceptualization of trust concept in the
marketing literature has been at the beginning uni-dimensional. In
particular, the meaning recognized to trust by some authors, both in the area of sales management (Swan and Nolan, 1985; Swan, Trawick and
Silva, 1985; Hawkes, Strong and Winick, 1996) and in the area of channel
management (Schurr and Ozanne, 1985; Dwyer and Oh, 1987), reflects
the first of the elements proposed in social psychology, that is to say it
refers to expectations of the counterparty’s behaviour and, more specifically, to the certainty that the counterparty will keep his promises
(Rotter, 1967). Trust has thus been defined only with reference to the
dimension of “reliability”, considered as probability, more of less high, that the expectations of performance – typically from a firm – are followed by
actual performance lined up with the expectations.
Subsequently, other definitions of trust, as those provided by Anderson e Weitz (1989) and Anderson and Narus (1990) in the studies
on distribution channels and by Crossby, Evans and Cowles (1990) in the
study of the relationships between salespeople and consumers in the services sector, have emphasized some new dimensions (Castaldo,
1995). The conceptualisation of the construct and its dimensions is
certainly one of the most controversial issues in the studies about the
topic. Indeed, most authors have offered a conceptualization of the trust
in terms of multidimensionality, but there is no agreement about number and the nature of these dimensions.
15
In the opinion of some authors, trust would depend on the
perception that there is not opportunism from the counterparty, which
would be, in its turn, reinforced by the certainty that a behaviour is intented
to achieve a joint objective (goal congruence). This is the view, even if
there are some other changes, of Andaleeb (1992) who, referring to
channel relationships, has affirmed that trust is influenced by the party’s
perception about the motivations governing the other party’s actions. The
stronger the belief that these motivations are debatable, or even
opportunist, the more difficult it is to trust. The same author says that the
formation of trust can be adequately explained only by taking into account
another dimension: exchange partner ability, or rather the perception of
that competence.
To come to the point, Andaleeb (1992), who is quoted subsequently
by Fletcher and Peters (1997), suggests to conceptualize the trust of a
party “A” in a party “B” by reference to two basic elements: A’s perceptions
of B’s motivations (or intentions); and/or A’s perceptions of B’s ability to
produce the desired outcomes. On the basis of these considerations,
Andaleeb (1992) identifies a scheme for categorizing the different levels of
trust that can be found in market relationships, by using two dimensions
combined in different ways to form different types of trust (Table 1.4).
Table 1.4: Categories of Trust
ABILITY
High Low
Posi
tive
Bonding trust Hopeful trust
MO
TIVE
S
Neg
ativ
e
Unstable trust Distrust
Source: Andaleeb (1992),”The trust concept: research issues for channel distribution”, Research in Marketing, vol. 11, pp.1-34
16
This matrix, subsequently considered also by Busacca and Castaldo
(1996), allows both to conceptualize the multidimensional nature of trust
and to hypothesize different configurations of it. Indeed, it would not exist
only one type of trust, but different types, which can be identified by
combining the perceptions about motivations and ability of the
counterparty – typically the supplier.
Also Ganesan (1994), to whom more recently Doney and Cannon
(1997) also refer, proposes two dimensions of trust, which are very similar
to those identified by Andaleeb (1992):
a) credibility, which depends on the buyer’s belief that the supplier has
the required expertise to carry out his role effectively and reliability;
b) benevolence, based on the buyer’s belief that the supplier acts on
the basis of intentions that are beneficial to the buyer himself.
In the same point of view, finally, Castaldo (1995) and Busacca and
Castaldo (1996) propose a conceptualization tri-dimensional of the
construct, taking account both the dimension originally identified in the
studies of social psychology, that is the predictability of the behaviour, and
the dimensions identified by Andaleeb (1992): the perceptions about ability
and the perceptions about motivations of the firm.
Among the authors who have proposed a multidimensional
definition of trust, Moorman, Zaltman and Deshpande (1992), in a study of
the relationships between providers and users of market research, identify
cognitive and behavioural dimensions of trust. The cognitive dimension
would consist of the belief in the partner’s reliability (trustworthiness), or
credibility, which comes from his motivation and knowledge – therefore
taking form in the same way as proposed by Andaleeb (1992) and
Ganesan (1994). The behavioural dimension, instead, would concern the
behavioural intention, that is the concrete act of placing trust in the
partner, and this implies vulnerability and uncertainty in the trustor, the
person doing trust.
17
Morgan and Hunt (1994) do not agree with last definition. Indeed,
even though they identify two different dimensions of the construct, yet
they hold that trust also exists in the presence of the cognitive component
alone. That is, it would be enough that the buying firm believes in the
honesty and reliability of the supplier firm. The behavioural intention
incorporated in the honesty and reliability of the supplier firm. The
behavioural intention incorporated in the willingness (“willingness to act”)
identified by Moorman, Zaltman and Deshpande (1992) would be implicit
in the concept of trust itself. Morgan and Hunt (1994), on this point, argue
that, although it might be appropriate to have items that incorporate
“declared willingness” into a measure of trust, willingness is redundant to
its definition: willingness to rely should be seen as a result (or,
alternatively, as a potential indicator) of trust, and not as one of its
constitutive dimensions.
The most recent marketing studies are characterised by a further
development, still as regards to the discussion about multi-dimensionality
of the construct. More specifically, recent works on the conceptualisation
of the trust have been dominated by two trends (Fenneteau and Guibert,
1997): the first one suggests a distinction between the cognitive and
affective dimensions of trust; the second one, conversely, leads to the
conceptual differentiation of the concepts of perceived trustworthiness,
that is the degree to which a subject perceives his partner as trustworthy,
trust and trusting behaviour, which is behavioural manifestation of trust.
In the light of these distinctions, trust can be, therefore, defined as
the willingness of one party to be vulnerable to the actions of the other
party, on the basis of the expectation that the other one will carry out a
particular action for the trustor, irrespective of the ability to control that
party.
Whereas, definition work on the construct of commitment began in
the sociology and psychology disciplines. In the sociological literature, the
18
concept of commitment is used to analyse both individual and
organizational behaviour (Becker, 1960). Sociologists use commitment as
a descriptive concept to mark out forms of action characteristic of
particular kinds of people or their groups. They also use it as an
independent variable to account for certain kinds of behaviour of
individuals and groups, as well as in analyses of a wide variety of
phenomena:
• Power;
• Religion;
• Occupational Recruitment;
• Bureaucratic Behaviour; and
• Political Behavior (Becker, 1960).
On the other hand, psychologists defined commitment in terms of
decisions or cognitions that fix or bind an individual to a behavioural
disposition (Kiesler, 1971).
Conceptualisations of commitment as a relationship, in the context
of a marriage or work, have interpreted the construct within a social-
psychological framework, whereby the construct was conceptually
characterized by intent to remain, along with certain personal and
environmental factors that underpin intent (Mowday et al., 1982). In this
sense, commitment was inferred not only from the employee’s beliefs and
options (a series of cognitions) but also by their level of intent to act in a
particular way (Pritchard et al., 1999). In other disciplines, commitment
has been characterized as a multidimensional phenomenon, composed of
several cognitive features (Kiesler, 1971). Nevertheless, in consumer
research, the complex nature of the construct has seldom been
considered. For example, Kelley and Davis (1994) examined customer
commitment as a general trait, adapting Mowday et al.’s (1979) measure
of organizational commitment. Likewise, Morgan and Hunt’s (1994) study
in relationship marketing, adopted the same scale.
19
1.4 RATIONALE OF TRUST AND COMMITMENT
It is important to keep and build strong bond of trust and
commitment with customers for the following five reasons.
1. There are higher marketing costs associated with generating
interest in new customers as opposed to already informed existing
customers.
The marketing costs involved in the creation of interest in an
uniformed new customer far outweigh those involved in maintaining
the relationship necessary to continue exchanges between buyer
and seller (Barnes and Glynn, 1992).
It has been estimated that the cost of attracting new customers can
be as high as six times that of retaining existing customers
(Desatnick, 1987; Sellers, 1989; Congram, 1991).
2. The close and long-term relationships with customers imply
continuing exchange opportunities with existing customers at a
lower marketing cost per customer (Gronroos, 1990).Across a wide
range of businesses, the pattern is the same: longer a company
keeps a customer, the more money it tends to make (Reichheld and
Sasser, 1990).
3. Viewing customer exchanges as a revenue stream, as opposed to a
compendium of isolated transactions, enables cross-selling of
related services over time and premium pricing for the customer’s
confidence in the business (Reichheld and Sasser, 1990; Congram,
1991).
4. Strong customer relationships with a high degree of familiarity and
communications on both sides can generate more practical new
product ideas from customers and contact personnel (Kiess-Moser
and Barnes, 1992).
20
5. Good relationships with customers can result in good work-of-mouth
from successful exchanges and minimal bad work-of-mouth in the
event of unsuccessful exchanges.
1.5 RESEARCH OBJECTIVES
The study has the following objectives:-
1. To study the rapid growth of Indian telecom sector.
2. To explore the impact of key dimensions of trust and commitment for
mobile services in the telecom sector.
3. To study the relationship between trust and commitment in both pre-paid
and post-paid services in selected telecom companies.
4. To analyse the level of trust and commitment in selected telecom service
providers across Punjab and Chandigarh.
5. To suggest managerial implications of trust and commitment to Indian
Telecom Sector.
1.6 TRUST AND COMMITMENT MODEL OF THE RESEARCH STUDY
Trust has been defined in various ways in literature. Trust is “willingness
to rely on an exchange partner in whom one has confidence” (Moorman et al.
(1993). Morgan and Hunt (1994) felt trust exists “when one party has confidence
in an exchange partner’s reliability and integrity”. According to Deutsch (1960),
trust consists of two components: confidence in ability and intention. There are
three dimensions of trust: perceived risk, technology orientation and reputation.
Perceived risk is a key dimension of trust in this research. The issue of
trust arises because economic transactions involve risk (Humphrey and Schmitz,
1998). More experienced more customers have more information, and therefore
they perceive the risk to be less and thus have more trust in transactions (Ba,
2001).
The customers’ orientation towards the technology of electronic
communication is frequently a proxy for their trust. Customers use various
performance measures such as network and download speed, navigability,
reliability, connectivity and availability to evaluate electronic transactions (Lee
and Turban, 2001). When customers are transmitting personal financial data
21
over the electronic network, there are risks that unauthorized parties could
intercept this information (Clay and Strauss, 2000).
Reputation is another dimension of trust. Reputation is defined as “overall
quality of character as seen or judged by people in general”. Reputation arises
from the strength of a particular brand name, endorsement from trusted third
parties, and previous interactions on and/or off-line (Egger, 2000). While
assessing the reputation, customers also assess the innovative abilities of the
service provider, which is based on the customers’ expectations of the skills and
competencies that it possesses in electronic transactions (Lee and Turban,
2001).
This research formulates following hypothesis:
H1: Trust has significant influence on customer commitment.
H1a: Perceived risk has significant influence on trust.
H1b: Technology Orientation has significant influence on trust.
H1c: Reputation has significant influence on trust.
The antecedents of trust
In this research, three main antecedents to trust have been identified:
1. Shared Value;
2. Communication;
3. Opportunistic behaviour.
1.6.1 Shared Value
Shared value is the extent to which partners have beliefs in common
about what behaviours, goals and policies are important or unimportant,
appropriate or inappropriate, and right or wrong (Morgan and Hunt, 1994). In this
research, shared value has been treated as a multi-dimensional construct. In the
telecommunication context, shared value symbolizes the extent to which the
company and the customers share common beliefs on critical values like ethics,
security, and privacy.
Ethical values determine the chances of companies giving incomplete
product information or divulging confidential personal information about
22
customers and selling customer information to other parties. Ethics and honesty
in a broad sense, which are aspects of good business morality, build trust
(Huemer, 1998). Mechanisms such as code of ethics and institutional
governance that establish and enforce rules and regulations can build trust by
addressing security and privacy concerns (Benassi, 1999).
A number of surveys have found consistently high levels of customer
concern about privacy (Ackerman et al., 1999; Swaminathan et al., 1999). The
main privacy concern of customers is violation and lack of confidentiality, which
is the misuse and lack of control of personal information subsequent to the
transaction (Novak et al., 1999). Due to the fall in cost of data transmission and
emerging technologies, it is now easier to collect personal information from
customers and share it with third parties (Clay and Strauss, 2000). There is thus
a risk of loss of confidentiality, which is a significant factor in building trust
(Culman and Armstrong, 1999).
Security is another factor that affects customer trust. This reduces the
customers’ level of trust, discouraging them from engaging in information
sharing. However, one study has shown that recent developments in electronic
exchange systems have caused an average customer to be less concerned
about the security of electronic exchanges or privacy issues (Swaminathan et al.,
1999). Security was identified as one of the least important factors to distinguish
a compelling online environment (Novak et al., 2000).
This research hypothesizes that in when there is a higher perception of
shared value, such perceptions will lead to increased trust.
H2: Shared value has a significant influence on customer trust and
commitment.
H2a: Privacy has significant influence on shared value.
H2b: Security has significant influence on shared value.
H2c: Ethics has significant influence on shared value.
1.6.2 Communication
Communication can be defined as “the formal as well as informal sharing
of meaningful and timely information” (Anderson and Narus, 1990).
23
Communication, especially timely communication (Moorman et al., 1993), fosters
trust by assisting in resolving disputes and ambiguities, and aligning perceptions
and expectations (Etgar, 1979). Anderson and Narus (1990) and Morgan and
Hunt (1994) have used past communication as an antecedent of trust.
In this research, communication is considered as a multidimensional
construct. The variables that constitute communication are openness, speed of
response, and quality of information.
Trust is negotiated through openness in communication and is specific to
the individual customers involved and their relationship. Research conducted by
Gefen and Straub (2001) found that man-machine communication, or at least the
belief that the electronic system has characteristics of social presence, is critical
to building customer trust. Methods to increase a social presence include real-
time interaction, fast response, and personalization of messages (Gefen and
Straub, 2001).
This research hypothesizes that the communication between the service
provider and the customer is positively related to trust.
H3: Communication has a significant influence on customer trust and
commitment .
H3a: Openness has a significant influence on communication.
H3b: Speed of response has a significant influence on communication.
H3c: Quality of Information has a significant influence on communication
1.6.3 Opportunistic Behaviour
Opportunistic behaviour has its roots in the transaction cost literature, and
is defined as (Williamson, 1975). In this research, opportunistic behaviour has
been conceptualized as regulatory control and information asymmetry.
The integrity of the telecom service providers and adherence to expected
roles and obligations depend on the extent of regulatory control, which is a major
determinant of customer trust (Lee and Turban, 2001). Customers assess the
level of confidence in regulatory control mechanism at the time of usage of
mobile phones. Due to the higher risk of opportunistic behaviour by company’s
owing to nascent and poorly-developed rules and regulations, customers
24
frequently have low levels of trust (Clay and Strauss, 2000). Identities can be
forged (Ba, 2001) and electronic documents can be falsified (Bailey and Bakos,
1997). The lack of adequate regulatory control also leads to the customers’
perception that their personal information may be used without their knowledge
during or after navigation (Novak et al., 1999; Li et al., 2001; Ackerman et al.,
1999). Therefore, the customers’ level of trust would be partly based on whether
they believe that the service provider would fulfill its obligations. Klang (2001)
pointed out that customers tend to assess the company’s interests and then
make a judgement about its integrity.
Information asymmetry is another important factor that affects customers’
trust. There is information asymmetry on the completeness of product
information, as complete information about the quality of the product is difficult or
impossible to obtain in a virtual environment (Klang, 2001; Ba, 2001). In
electronic transactions, customers cannot view the cues (Lee and Turban, 2001).
Under conditions of incomplete information on the quality, customers frequently
lack the trust to engage in transactions (Ba, 2001).
This research hypothesizes that in telecom service industry, when cellular
users believe that the company is engaging or can engage in opportunistic
behaviour, or vice versa, such perceptions will lead to reduced trust.
H4: Opportunistic behaviour has significant influence on customer trust and
commitment.
H4a: Regulatory control has significant influence on opportunistic
behaviour.
H4b: Information asymmetry has significant influence on opportunistic
behaviour.
1.6.4 The Consequence of Trust –Relationship Commitment
Moorman et al. (1992) defined relationship commitment as “an enduring
desire to maintain a valued relationship”. According to Morgan and Hunt (1994),
a critical complement of trust in exchange relationships is commitment. Trust
influences relationship commitment (Achrol, 1991; Moorman et al., 1992; and
Morgan and Hunt, 1994). The dimensions of commitment are the degree of
25
association, length of association and sense of belonging. Thus, this research
hypothesize that as trust increases, commitment also increases.
H5: Relationship Commitment has significant influence on customer
commitment and trust.
H5a: Degree and length of association has significant influence on
commitment.
H5b: Sense of belongingness has significant influence on commitment.
The hypothesized causal relationships and linkages are depicted in
Table 1.5.
Table 1.5: List of Variables and Measures
Construct Variable Measures used
Variable 1 Privacy
Variable 2 Security
Shared Value
Variable 3 Ethics
Variable 4 Openness
Variable 5 Speed of response
Communication
Variable 6 Quality of Information
Variable 7 Regulatory Control Opportunistic behaviour
Variable 8 Information asymmetry
Variable 9 Perceived Risk
Variable 10 Technology Orientation
Trust
Variable 11 Reputation
Variable 12 Degree and Length of association Relationship Commitment
Variable 13 Sense of belonging
1.7 PROPOSED MODEL AND HYPOTHESES
The overall research issues and the model tested are given in Figure 1.6.
26
Figure 1.6: Trust and Commitment Model of the Research Study
Privacy
Security
Ethics
Openness
Speed
Quality of Information
Shared Value
Communication Trust
Degree & length of association
Reputation Technology Orientation Perceived Risk
Relation Commitment
Sense of Belongingness
H1
H2
H3
H4
Information Asymmetry
Regulatory Control
Opportunistic Behaviour
H5
27
This research formulates following hypotheses:
H1: Trust has significant influence on customer commitment.
H1a: Perceived risk has significant influence on trust.
H1b: Technology orientation has significant influence on trust.
H1c: Reputation has significant influence on trust.
H2: Shared value has a significant influence on customer trust and
commitment.
H2a: Privacy has significant influence on shared value.
H2b: Security has significant influence on shared value.
H2c: Ethics has significant influence on shared value.
H3: Communication has a significant influence on customer trust and
commitment.
H3a: Openness has a significant influence on communication.
H3b: Speed of response has a significant influence on communication.
H3c: Quality of information has a significant influence on communication
H4: Opportunistic behaviour has significant influence on customer trust and
commitment.
H4a: Regulatory control has significant influence on opportunistic
behaviour.
H4b: Information asymmetry has significant influence on opportunistic
behaviour.
H5: Relationship commitment has significant influence on customer
commitment and trust.
H5a: Degree and length of association has significant influence on
commitment.
H5b: Sense of belongingness has significant influence on commitment.
H6: All the key mediating variables have significant positive influence on
overall customer trust and commitment.
H6a: Shared value has positive significant influence on customer trust
and commitment.
28
H6b: Shared value has positive significant influence on customer trust
and commitment.
H6c: Communication has positive significant influence on customer trust
and commitment.
H6d: Opportunistic behaviour has a negative significant influence on
customer trust and commitment.
H6e: Trust has positive significant influence on customer trust and
commitment.
H7: Level of customer trust and commitment among various telecom service
providers.
H8: There is no significant relationship between trust & commitment and
demographic variables.
H9: Trust and commitment have significant impact on both pre-paid and post-
paid subscribers.
1.8 NEED AND SIGNIFICANCE OF STUDY
It is well perceived fact that customer trust and commitment have become
the important factors of business success. However many conclusions have
been drawn with regard to trust and commitment. But there are very few studies
related with trust and commitment in telecom sector with special reference to
India. The relevance of Trust and Commitment Theory for mobile users of Indian
Telecom Sector has yet to be established.
This research is grounded in the well-known commitment-trust theory of
relationship marketing, originally proposed by Morgan and Hunt (1994). Morgan
and Hunt showed that relationship marketing as the act of establishing,
developing, and maintaining successful relational exchanges. It constitutes a
major shift in marketing theory and practice.
According to the theory (Morgan and Hunt, 1994), trust is central to
successful relationship marketing, because it encourages marketers to:
• Work at preserving relationship investments by co-operating with
exchange partners;
• Resist attractive short-term alternatives in favour of the expected long-
term benefits; and
29
• View potentially high-risk options as being prudent because of the belief
that their partners will not act opportunistically.
Trust, according to Speakman (1988) is so important to relational
exchange that it is “the cornerstone of the strategic partnership” between the
seller and the buyer.
This research attempts to test an adaptation of the commitment-trust
theory of relationship marketing in the telecommunication context. Although the
main variables were mostly borrowed from the commitment-trust framework
(Morgan and Hunt, 1994), the dimensions and the items were adapted
significantly to the context of cellular users of telecom sector. Therefore, one of
the primary contributions of this research is to test the applicability and
extendibility of the commitment-trust theory to the domain of telecom service
providers
In Indian Telecom Sector mobile telephony is called as “sun-rise industry.
It is one of the growing industries in the country rapid growing with rate of
subscriber base teledensity and traffic. Indian telecom sector holds huge
potential for growth because of following reasons.
• Liberalisation and privatization has brought around 12 majors in both
GSM and CDMA sectors. With intense competition, companies try to woo
and retain customers for longer period of time. For which, trust and
commitment are key variables to maximize the average revenue per user.
• Impact of technology
• Impact of FDI flows
• Trust and commitment model given by Morgan and Hunt comprises
various key dimensions-shared value, communication, and opportunistic
behaviour. How these variables affect trust and commitment among
mobile users of Indian telecom sectors has further to be studied.
• Companies are spending heavily on acquisition and retention of both pre
paid and post-paid subscribers. To what extent, different telecom services
providers are able to build trust with both pre-paid and post-paid require
further analysis.
30
1.9 RESEARCH METHODOLOGY
1.9.1 Defining the Research Problem
Defining the research problem is an important step in a research. The
research problem of this proposal is to study “An Empirical Study of Trust and
commitment in Cellular Users of Selected Telecom Providers”.
1.9.2 Scope of Study
The study covers Chandigarh, Patiala, Ludhiana and Amritsar. Basis of
the selection of these cities is:
• More Tele-density subscriber base.
• Close proximity to the Circle Switches based at Chandigarh and Amritsar.
• Socio-demographic features of these cities.
1.9.3 Selection of Services
While selecting services for the study, a number of factors are taken into
account. Both pre-paid and post-paid services are selected for this purpose. The
preliminary study showed that the ratio between prepaid and postpaid subscriber
base is 80: 20. Since, Average Revenue per User is 4 times more in case of
postpaid users as compared to the prepaid subscribers. So, more weightage is
paid to the post-paid subscribers. Besides, the operators adopt various retention
tools in the post-paid category to retain customers.
1.9.4 Questionnaires
The questionnaire is prepared in English only. It is structured
questionnaire that include questions and scales on all concepts included in
hypothesis viz. Shared Value, Communication, Opportunistic Behaviour, Trust
and Relationship Commitment. Three different questionnaires are framed for
subscribers, dealers and telecom service providers.
1.9.5 Sample Design and Sample Size
Four major telecom service providers are selected i.e. Reliance
Infocomm, Airtel, Tata Telecom and Hutch. Through random sampling, sample
of 400 respondents is selected for administering the questionnaire. The data is
collected from 4 cities i.e. Chandigarh, Ludhiana, Amritsar, Patiala.
31
Sample is selected in two phases:
In Phase 1, number of subscribers has been selected in the ratio of 20:80
for Pre-Paid vis-à-vis Post-Paid subscribers for all the selected four operators.
Table 1.6: Prepaid vs. Postpaid Subscribers
Operator Prepaid Postpaid Total
Reliance Infocom
Airtel
Tata Telecom
Hutch
In Phase 2, sample has been selected city- wise.
Table 1.7: City Wise Prepaid and Postpaid Subscribers
City
Operator Ludhiana Amritsar Chandigarh Patiala
Prepaid 5 5 5 5 Reliance Infocom Postpaid 20 20 20 20
Prepaid 5 5 5 5 Airtel
Postpaid 20 20 20 20
Prepaid 5 5 5 5 Tata Telecom
Postpaid 20 20 20 20
Prepaid 5 5 5 5 Hutch
Postpaid 20 20 20 20
1.9.6 Methods of Data Collection
This study will be based on primary and secondary data. The main
sources of secondary data will be published reports of World
Telecommunications development, Department of Telecommunications, Indian
Telecom Policy, Year book of Statistics, Journals, Books and various websites of
the Mobile operators. The primary data will be collected through the
questionnaire and personal Interviews. 4 Major telecom operators are selected:
Reliance Infocomm, Airtel, Tata Telecom and Hutch. Through a systematic
random sampling, a sample 400 respondents will be selected for administering
questionnaire at the touch-points of these Telecom Operators in these select
32
towns. Besides this, a sample of 60 questionnaires would be collected from
employees and 40 from the dealers of selected operators.
1.9.7 Limitations of the Study
Since data will be collected at the touch-points of the different operators, so customers who do not visit these touch-points will not be part of the study.
The study has geographical limitations as it covers only 4 locations.
1.9.8 Analysis of Data
The data collected will be analysed using various statistical tools. The
basic framework of the model consisted of unobservable theoretical constructs,
which would not be measured directly. So, this study used a set of indicator
variables, which measured the unobservable constructs.
To tackle the problem of measuring the latent variables, usually two
strategies are followed. The first is selection of a single indicator variable for
each theoretical construct. However, in most cases, it is unrealistic to assume
that a single indictor variable will provide a reliable measure of the latent
variables. The second method is to assign pre- determined weights to different
indictor variables. However, both these methods are prone to error. Dillon and
Goldstein (1984), showed that when such measures are used in linear models
(e.g. variance analysis, regression and path analysis models), the coefficients
would have an unknown bias. Hence, this research will use linear structural
relationships (LISREL), an algorithm that is used widely for latent structure
analysis.
Tests for significance are less useful in small samples (less than 30) and
quite sensitive in large samples (exceeding 1,000 observations)(Hair et al.,
1995). Thus, both graphical and statistical tests will be carried out using SPSS
version 10.0 for Windows to assess the actual degree of departure from
normality the mean, standard deviation and reliability estimate of each model
construct are furnished in Table 1.8.
33
Table 1.8: Summary Statistics and Reliability Estimates for the Model Constructs
Mean Standard Deviation Cronbach Alpha
Shared Value
Communication
Opportunistic behaviour
Trust
Relationship Commitment
The normal distribution is important, continuous distribution because
a good number of random variables occurring in practice can be
approximated to it. If a random variable is affected by many independent
causes, and the effect of each cause is not overwhelmingly large
compared to other effects, then the random variable will closely follow a
normal distribution.
+∞<<∞−=⎟⎠⎞
⎜⎝⎛ −
−xexf
x 2
21
21)( σ
µ
πσ (3.1)
ANOVA is a statistical method for determining the existence of
differences among several population means.
While the aim of ANOVA is to detect differences among several
population means, the technique requires the analysis of different forms of
variance associated with the random samples under study – hence the
name analysis of variance.
The ANOVA Model
There are four telecom service providers and data pertaining to 4 cities.
The linear model used for ith (i=1,2,3,4) telecom service operators and jth
(j=1,2,3,4) cities.
The linear model used for ANOVA with two way classification without
interaction is
Yij = µ+α1+β1+eij (3.2)
With i∑ αi = 0 j∑ βj = 0 and eij N (0, σ2)
34
Here
α1 : effect if ith telecom service provider
β1 : effect if jth city
In equation (3.2), the total sum of squares (TSS) can be decomposed into
three components i.e. sum of squares due to telecom service providers, sum of
squares due to place (SSP) and sum of squares due to error (SSE). Therefore, it
can be written.
F=MSY/MSE ~Fα (4,4)
Where α is the given level of significance
Once we have determined that differences exist among various telecom
service providers, the next step is to carry out post-hoc test for pair-wise multiple
comparisons, which determine which pair of telecom service providers differ
significantly.
Suppose, to test the hypothesis
H0 = αi = αi, i=1 for pair of telecom service providers and i. If
TSS=SSTSP+SSP+SSE
Thus, two hypotheses can be tested
H01 : α1 = α2 = α3 = α4 for telecom service providers
H02 : β1 = β2 = β3 = β4 for cities
These hypotheses correspond to variation between various telecom
service operators and variation of different cities, respectively. In order to test
these hypotheses, an ANOVA table is formed.
Table 1.9: ANOVA Table
Source of variation
Degree of Freedom
Sum of Squares
Mean SS F-Value
Between telecom service provider
4 SSTSP SSTSP/4=MSTSP MSTSP/MSE~F(4,16)
Between cities 4 SSP SSP/4=MSP MSP/MSE~F(4,16)
Error 16 SSE SSE/16=MSE
Total 24 TSS
35
Thus for testing H01 i.e. there is no variation between telecom service
provider, reject H01, if
F=MSBG/MSE > Fα (4,4)
And
For testing there is no variation between cities, reject H02, if yi0 and yi’0 be
the means for these telecom service providers, then we reject H0 if
(yi0 – yi’0) is greater than Scheffe otherwise, accept H0
The ANOVA is tested for trust and commitment and its variables for
different combinations:
(a) Selected telecom service provider and prepaid and postpaid subscribers
and the interaction between both of them.
(b) Selected city and prepaid and postpaid subscribers and the interaction
between both of them.
(c) Selected telecom service provider and selected city and the interaction
between both of them.
(d) Prepaid and postpaid subscriber, selected telecom service provider and
selected city and the interaction between both of them.
Another statistical tool, t-test is used. When both populations are
normally distributed; population standard deviations σ1 and σ2 are
unknown, but the sample standard deviations S1 and S2 are known. The
equations for the test statistic t depend on two subcases:
Subcase 1: σ1 and σ2 are believed to be equal (although unknown).
In this subcase, we calculate t using the formula
)1/n(1/nS
)µ(µ)XX(t21
2P
02121
+
−−−= (3.3)
Where 2PS is the pooled variance of the two samples, which serves as the
estimate of the common population variance given by the formula
2nn
1)S(n1)S(nS21
222
2112
P −+−+−
= (3.4)
36
The degrees of freedom for t are (n1+n2-2).
Subcase 2: σ1 and σ2 are believed to be unequal (although
unknown). In this subcase, we calculate t using the formula
2
221
21
02121
/nS/nS)µ(µ-)XX(t
+
−−= (3.5)
The degrees of freedom for this t are given by
⎥⎦
⎤⎢⎣
⎡−+−
+=
1)/(n(S1)/(n)/n(S)/nS/n(Sdf
2221
21
21
22
221
21 (3.6)
)1/n(1/nσ/nσ/nσ)XVar()XVar()XXVar( 212
22
12
2121 +=+=+=−
We estimate σ2 by-
2)n(n
1)S(n1)S(nS21
222
2112
P −+−+−
= (3.7)
Which is a weighted average of the two sample variances. As a result, if
the null hypothesis is true, then the quantity.
21p
02121
1/n1/nS)µ(µ)XX(
+−−− (3.8)
must follow a t distribution with (n1+n1-2) degrees of freedom.
Subcase 2 does not neatly fall into a t distribution as it combines two
sample means from two populations with two different unknown variances.
It can be shown that when the null hypothesis is true, the quantity.
2
221
21
02121
/n/nS)µ(µ)XX(
S+−−− (3.9)
will approximately follow a t distribution with degrees of freedom given by
the complex equation (iv). The symbol [ ] used in this equation means
rounding down to the nearest integer.
Another test of hypothesis used is the chi square test. Chi square is
one of the most important and widely used non-parametric test in
37
statistics. Many authors (Siegel and Castellan, 1988; Gupta, 1998; Singh,
1998) have discussed and explained chi square in detail.
Gupta (1998) describes the quantity of χ2 as the magnitude of the
discrepancy between theory and observation. It is defined as
E
E)Σ(Oχ2
2 −= (3.10)
O = observed frequencies
E = expected frequencies
Expected frequencies for nay cell is calculated by the following
equation
N
CT x RTE =
E = Expected frequency
RT = The row total for the row containing the cell
CT = The column total for the column containing the cell
N = The total number of observations
While comparing the calculated value of χ2 with the table value the
degrees of freedom are to be determined. Degrees of freedom means the
number of classes to which the values can be assigned arbitrarily or at will
without violating the restrictions or limitations placed. The degrees of
freedom are denoted by the symbol v or d.f. and are obtained by v=n – k,
where k refers to the number of independent constraints. In a contingency
table, the degrees of freedom are obtained by (c-1) (r-1) where ‘c’ refers to
column and ‘r’ refers to rows.
Singh (1998) explains that some times it happens that with 1df, any
one of the expected cell frequencies becomes less than 5. In such a
situation a correction called Yates correction for continuity is applied.
Where frequencies are large, this correction makes no difference but
where frequencies are small, Yates correction is significant. According to
38
Siegel and Castellan (1998) applying the χ2 test to data where both r and c
equal 2, the following equation should be used.
D)(B C)(A D)(C B)(A
-N/2)|BC-AD(| N 22
++++=χ df=1 (3.11)
1.10 CHAPTER PLAN
Chapter 1: Introduction
This chapter covers the fastest growing services and brief overview of
growth of Indian telecom sector. Research objectives and significance of study are also covered. This chapter is concerned trust and commitment, research
methodology used for selection of telecom service providers, geographical
scope, selection of variables and analysis of the tools required tools for the
study.
Chapter 2: Review of Literature
This chapter covers studies related to telecom sector, studies related to
relationship marketing, trust and commitment. Review of telecom sector has been further classified into technology up-gradation, changing investment
policies and competition. In studies related to relationship marketing review of
literature has been studied from conceptual and practical aspects. In studies
related to trust, various aspects –trust, relationship commitment, shared value, customer skills, opportunistic behaviour, communication and switching costs are
reviewed
Chapter 3: Indian Telecom Industry: Growth and Prospectus
This chapter covers global telecom industry, Indian telecom industry,
new telecom policy and regulatory development. It explains also studies various
telecom providers and services provided by them.
Chapter 4: A Conceptual Framework of Trust and Commitment
This chapter is devoted to the concept of relationship marketing and
discrete and relational exchanges. It studies the relationship development
process and various categories of trust for the relationship development. It also covers the analytical study of selected telecom service providers and various
customer relationship programmes taken by them.
39
Chapter 5: Research Findings of the Study
This chapter covers the trust and commitment of telecom sector by
studying according to selected city, development of a model in prepaid and
postpaid services of selected telecom service providers.
Chapter-6: Summary, Suggestions and Recommendations
This chapter presents summary, conclusion and recommendations of the
study. How these recommendations are useful for managerial decision, limitation
of the study and identify the directions for future research are also discussed..
Bibliography
Appendices
• Tables
• Questionnaire
• List of Abbreviations
40
REFERENCES
• Achrol, R. (1991), “Evolution of the marketing organization: new forms for
turbulent environments”, Journal of Marketing, Vol.55, No.4, pp.77-93.
• Akhil Gupta, “Importance of Competition in Telecommunication”,
Nasscom Telecom Conference 2002, in partnership with The Hindustan
Times and Stanford University, New Delhi, Nov.2002.
• Akhil Gupta, “Telecom – Is the Future Finally Near?”, 2001 Asian venture
forum, India, Dec. 2001.
• Anderson, J.C. and Narus, J.A. (1984), “A model of the distributor’s
perspective of distributor-manufacturer working relationships”, Journal of
Marketing, Vol.48, Fall, pp.62-74.
• Annual report on Telecommunications 2003-04, Department of
Telecommunications, Government of India, www.dotindia.com.
• AT Kearney, “How Wireless changes the way we work”, E-Business, June
2005, pg. 23.
• Bagozzi, R.P. 1995), “Reflections on relationship marketing in consumer
markets”, Academy of Marketing Science, Vol.23, No.4, pp.227-7.
• Barnes, J.G. (1997), “Closeness, strength and satisfaction: examining the
nature of relationships between providers of financial services and their
retail customers”, Psychology and Marketing, Vol.14, No.8, pp.765-90.
• Beatty, S.E. and Kahle, L.R. (1988), “Alternative hierarchies of the attitude
– behaviour relationship: the impact of brand commitment and habit”,
Journal of the Academy of Marketing Science, Vol.16, No.2, pp.1-10.
• Becker, H.S. (1960), “Notes on the concept of commitment”, American
Journal of Sociology, Vol.66, No.1, pp.32-40.
• Bendapudi, N. and Berry, L.L. (1997), “Customers’ motivations for
maintaining relationships with service providers”, Journal of Retailing,
Vol.73, No.1, pp.15-37.
41
• Berry, L.L. (1995), “Relationship marketing of services: growing interests,
emerging perspectives”, Journal of the Academy of Marketing Science,
Vol.23, No.4, pp.236-45.
• Bob Thomson, “Successful CRM: Turning Customer Loyalty into
Profitability”, www. Rightnowtechnology.com Oct 2004.
• Braff Adam, Passmore William, J, and Simpson Michael, “Going the
distance with telecom customers”, The Mackinsey Quarterly, 2003, No. 4,
Pg. 83.
• Business & Economy, “Telecom Czar” 30th July 2005.
• Business & Economy, 26th Aug-8th Sept. 2005.
• Business & Economy, 27th Jan-9th Feb. ph. 66.
• Business & Economy, 4th Nov. - 17th Nov. 2005.
• Business & Economy, 9th Sept-22Sept. 2005, pg. 26.
• BW Marketing whitebook, 2005, pg. 54.
• Carlsson Jeanette and Arias Salvador, “Transforming Wireline Telecom”,
E-business, Feb. 2005, pg. 13.
• Carsten Fink, Aaditya Mattoo and Randeep Rathindran, “An Assessment
of Telecommunication Reform in Developing Countries”, World Bank
Research Working Paper No.2909, October 2002. www.worldbank.org.
• Crosby, L.A. Evans, K.R. and Cowles, D. (1990), “Relationship quality in
services selling: an interpersonal influence perspective”, Journal of
Marketing, Vol.54, July, pp.68-81.
• David, L. Kurtz (2002) Kenneth and Clow Services Marketing, John Willy
& Sons.
• Doney, P.A. and Cannon, J.P. (1997), “An examination of the nature of
trust in buyer seller relationships”, Journal of Marketing, Vol.61, April,
pp.35-51.
• Ford, D. (1990), Understanding Business Markets: Interaction,
Relationships, and Networks, Academic Press, London.
42
• Gummerson, E. (1987), “The new marketing developing long-term
interactive relationships”, Long-range Planning, Vol.20, No.4, pp.10-20.
• Gundlach, G.T., Achrol, R.S. and Mentzer, J.T. (1995), “The structure of
commitment in exchange”, Journal of Marketing, Vol.59, No.1, pp.78-93.
• Gwinner, K.P., Gremler, D.D. and Bitner, M.J. (1998), “Relational benefits
in services industries: the customers perspective”, Academy of Marketing
Science, Vol.26, No.2, pp.101-14.
• Hakansson, H. (1982), International Marketing and Purchasing of
Industrial Goods: An Interaction Approach, John Wiley and Sons Ltd.,
Chester.
• Heide, J.B. and John, G. (1992), “Do norms matter in marketing
relationships?”, Journal of Marketing, Vol.56, April, pp.32-45.
• Hocutt, M.A. (1998), “Relationship dissolution model: antecedents of
relationship commitment and the likelihood of dissolving a relationship”,
International Journal of Service Industry Management, Vol.9, No.2,
pp.189-200.
• Hrebiniak, L.G. (1974), “Effect of job level and participation on employee
attitudes and perceptions of influence”, Academy of Management Journal,
Vol.17, pp.649-62.
• “ICT and Millennium Development Goals”, World Telecom Development
Report 2006, www.itu.int.
• “Indian Telecommunication Statistics 2004”, Ministry of Communications,
Government of India.
• Johnson, M.D. (1998), Customer orientation and Market Action, Prentice
Hall, Englewood Cliffs, N.J.
• Kelley, S.W., Donnelly, J.H. and Skinner, S.J.J. (1990), “Customer
participation in service production and delivery”, Journal of Retailing,
Vol.22, No.1, pp.52-61.
• Kiesler, C.A. (1971), The Psychology of Commitment, Academic Press,
New York, NY.
43
• Kotler, P. (1997), Marketing Management: Analysis, Planning,
Implementation and Control, 9th ed., Prentice Hall, Englewood Cliff, N.J.
• Liljander, V. and Standvik, T. (1995), “The nature of customer relationship
in services”, in Swartz T.A., Bowen, D.A. and Brown S.W., Advances in
Services Marketing and Management, Jai Press, London, Vol.4, pp.141-
167.
• Lovelock, C.H. (1981), “Why management needs to be different for
services”, in Donnelly, J.H. and George, W.R. (Eds.), Marketing of
services, American Marketing Association, Chicago, IL, pp.5-9.
• Mayer, R.C. Davis, J.H. and Schoorman, F.D. (1995), “An integrative
model of organizational trust”, Academy of Management Review, Vol.20,
March, pp.709-34.
• Miettila, A. and Moller, K. (1990), “Interaction perspective into professional
business services: a conceptual analysis”, paper presented at the
Research Development on International Industrial Marketing and
Purchasing, Milan.
• Moorman, c., Zaltman, G. and Deshpande, R. (1992), “Relationships
between providers and users of market research: the dynamics of trust
within and between organizations”, Journal of Marketing Research,
Vol.29, August, pp.314-28.
• Morgan, R.M. and Hunt, S.D. (1994), “The commitment-trust theory of
relationship marketing, Journal of Marketing, Vol.58, July, pp.20-30.
• Mowday, R., Porter, L. and Steers, R. (1982), Organizational Linkages:
The Psychology of Commitment, Absenteeism, and Turnover, Academic
Press Inc., New York, NY.
• Murray, K.B. and Schlacter, J.L. (1990), “The impact of services versus
goods on consumers assessment of perceived risk and variability”,
Journal of the Academy of Marketing Science, Vol.18, January, pp.51-5.
• “New Telecom Policy – 1999”, www.trai.gov.in
• “Opportunity India – Telecom Industry”, CII Report, 2006.
44
• Pritchard, M.P., Havitz, M.E. and Howard, D.R. (1999), “Analyzing the
commitment-loyalty link in service contexts”, Academy of Marketing
Science, Vol. 27, No.3, pp.333-48.
• Rakesh Seth and Kirti Seth (2005), Creating Customer Delight: The How
and Why of CRM, Response Books.
• Rampal, M.K. and Gupta, S.L. (2002), “Services Marketing: Concept,
applications and cases”, Galgttal Publishing Co.
• Ratter, J.B. (1967), J.B. (1967), “A new scale for the measurement of
interpersonal trust”, Journal of Personality, Vol.35, No.4, pp.651-65.
• Reichheld, F. and Teal, T. (1996), The loyalty effect: The hidden force
behind growth, Profits and Lasting Value, Harvard Business School
Press, Boston.
• Revathi S. and Dr. Padma Vathy, “Preference in cellular service providers
in post liberalization era”, Indian Journal of Marketing, Feb. 2005, pp. 6-
10.
• Reynold, K.E. and Arnold, M.J. (2000), “Customer loyalty to the
salesperson and the store: examining relationship of customers in an
upscale retail context”, Journal of Personal Selling and Sales
Management, Vol.20, No.2, pp.89-98.
• Reynolds, K.E. and Arnold, M.J. (2000), “Customer loyalty to the
salesperson and the store: examining relationship of customers in an
upscale retail context”, Journal of Personal Selling and Sales
Management, Vol. 20, No.2, pp.89-98.
• S. Balachandran (2004), “Customer Driven Services Marketing, Second
Edition, Response Books.
• Sangani Priyanka, “Cell commix” Mythology comes to the mobile”,
Business Today, Feb. 2005, pg. 18.
• Schurr, P.H. and Ozanne, J.L. (1985), “Influence on exchange processes:
buyer’s preconceptions of a seller’s trustworthiness and bargaining
toughness”, Journal of Consumer Research, Vol. 11, No.4, pp.939-53.
45
• Sheaves, D.E. and Barnes, J.G. (1996), “The fundamentals of
relationships: an exploration of the concept to guide marketing
implementation”, in Swartz T.A., Bowen, D.E. and Brown, S. (Eds.),
Advances in Services Marketing and Management, Jai Press Inc.,
Greenwich, C.T., Vol.5, pp.215-45.
• Sheaves, D.E. and Barnes, J.G. (1996), “The fundamentals of
relationships: an exploration of the concept to guide marketing
implementation”, in Swartz, T.A., Bowen, D.E. and Brown, S. (Eds),
Advances in Services Marketing and Management, Jai Press Inc.,
Greenwich, CT, Vol.5, pp.215-45.
• Storbacka, K., Strandvik, T. and Gronroos, C. (1994), “Managing
customer relationships for profit: the dynamics of relationship quality,
International Journal of Service Industry Management, Vol.5, No.5, pp.21-
38.
• “Telecom Sector update”, Aug 2004, www.indianfoline.com.
• “Telecommunications: Accessibility & Service Performance,
Infrastructure”, CMIE March 2004.
• Wilson, D.T. (1995), “An integrated model of buyer-seller relationships”,
Journal of the Academy of Marketing Science, Vol.23, No.4, pp.335-45.
46
CHAPTER – 2
REVIEW OF LITERATURE
2.1 BRIEF OVERVIEW
The growth in demand for telecom services in India is not limited to basic
telephone services. India has witnessed rapid growth in cellular, radio paging,
value added services, internet and global communication by satellite (GMPCS)
services. The agents of change, as observed from international perspective,
have been broadly categorized into economic structure, competition policy and
technology. Economic reforms and liberalisation have driven telecom sector
through several transmission channels of which these three categories are of
major significance.
The effective research cannot be accomplished without critically studying
what already exists in the form of general literature and specific studies.
Therefore, it is considered as an important pre-requisite for actual planning and
execution of research project. This helps to formulate hypotheses and framework
for further investigation. In this research, the survey of literature has been
classified into two parts --- studies related to telecom sector and studies related
to trust and commitment.
2.2 STUDIES RELATED TO GROWTH AND DEVELOPMENTS IN INDIAN TELECOM SECTOR
According to Snyder (2006) Communications is a process that allows
information to pass between a sender and one or more receivers and. the
transfer of meaningful information or ideas from one location to a second
location. Communications is a human process; humans communicate by
sending information between themselves. Whereas, telecommunication is the
transmission of data or information over a distance. Tele is a Greek word
meaning at a distance, far off. Thus, it classifies smoke signals, semaphore
flags, lanterns and signal flares, telegraph systems, televisions, telephones,
written letters, and hand signals as capabilities that support telecommunications.
47
The problems with these communications forms include reliability, speed of
transmission, and comprehension purposes.
Muller (1990) in his a research focuses that the success of the mobile
commerce can be attributed to the personal nature of wireless devices. Adding
to this are its unique features of voice and data transmission and distinct
features like localisation, feasibility and convenience. The sustained growth of
the mobile commerce around the world has been more because of the transfer
of technology according to the needs of local geography.
National Telecom Policy (1999) projected a target 75 million telephone
lines by the year 2005 and 175 million telephone lines by 2010 has been set.
Indian telecom sector has already achieved 100 million lines. With over 100
million telephone connections and an annual turnover of Rs. 61,000 crores, our
present teledensity is around 9.1%. The growth of Indian telecom network has
been over 30% consistently during last 5 years.
According to Wellenius and Stern (2001), information is regarded today
as a fundamental factor of production, alongside capital and labor. The
information economy accounted for one-third to one-half of gross domestic
product (GDP) and of employment in Organization for Economic Cooperation
and Development (OECD) countries in the 1980s and is expected to reach 60
percent for the European Community in the year 2000. Information also accounts
for a substantial proportion of GDP in the newly industrialized economies and the
modern sectors of developing countries.
Videsh Sanchar Nigam Limited (VSNL) 16th Annual Report (2002) India like many other countries has adopted a gradual approach to telecom
sector reform through selective privatisation and managed competition in
different segments of the telecom sector. India introduced private competition in
value-added services in 1992 followed by opening up of cellular and basic
services for local area to competition. Competition was also introduced in
National Long Distance (NLD) and International Long Distance (ILD) at the start
of the current decade.
48
World Telecommunication Development Report (2002) explains that
network expression in India was accompanied by an increase in productivity of
telecom staff measured in terms of ratio of number of main lines in operation to
total number of staff.
Indian Telecommunication Statistics (2002) in its study showed the
long run trend in supply and demand of Direct Exchange Lines (DEL). Potential
demand for telecom services is much more than its supply. In eventful decade of
sectoral reforms, there has been significant growth in supply of DEL.
Economic Survey, Government of India (2002-2003) has mentioned
two very important goals of telecom sector as delivering low-cost telephony to
the largest number of individuals and delivering low cost high speed computer
networking to the largest number of firms. The number of phone lines per 100
persons of the population which is called teledensity, has improved rapidly from
43.6 in March 2001 to 4.9 in December 2002.
Adam Braff, Passmore and Simpson (2003) focus that telecom service
providers even in United States face a sea of troubles. The outlook for US
wireless carriers is challenging. They can no longer grow by acquiring new
customers, in fact, their new customers are likely to be migrated from other
carriers. Indeed, churning will account for as much as 80% of new customers in
2005. At the same time, the carrier’s Average Revenue Per User (ARPU) is
falling because customers have.
Dutt and Sundram (2004) studied that in order to boost communication
for business, new modes of communication are now being introduced in various
cities of the country. Cellular Mobile Phones, Radio Paging, E-mail, Voice-mail,
Video, Text and Video-Conferencing now operational in many cities, are a boon
to business and industry. Value-added hi-tech services, access to Internet and
Introduction of Integrated Service Digital Network are being introduced in various
places in the country.
A study by Jeanette Carlss on and Salvador Arias (2004) wireless
substitution is producing significant traffic migration from wireline to wireless and
helping to fuel fierce price competition, resulting in margin squeezes for both
49
wireline voice tariffs in organisation for Economic Co-operation and Development
Countries have fallen by an average of three percent per year between 1999 and
2003.
T.V. Ramachandran (2005) analysed performance of Indian Telecom
Industry which is based on volumes rather than margins. The Indian consumer is
extremely price sensitive. Various socio-demographic factors-- -- high GDP
growth, rising income levels, booming knowledge sector and growing
urbanization have contributed towards tremendous growth of this sector. The
instrument that will tie these things together and deliver the mobile revolution to
the masses will be 3 Generation (3G) services.
Rajan Bharti Mittal (2005) explains the paradigm shift in the way people
communicate. There are over 1.5 billion mobile phone users in the world today,
more than three times the number of PCOs. India today has the sixth largest
telecom network in the world up from 14th in 1995 , and second largest among
the emerging economies. It is also the world’s 12th biggest market with a large
pie of $ 6.4 billion. The telecom revolution is propelling the growth of India as an
economic powerhouse while bridging the developed and the developing
economics.
ASEAN India Synergy Sectors (2005) point out that high quality of
telecommunication infrastructure is the pillar of growth for information technology
(IT) and IT enabled services. Keeping this in view, the focus of telecom policy is
vision of world class telecommunication services at reasonable rates. Provision
of telecom services in rural areas would be another thrust area to attain the goal
of accelerated economic development and social change. Convergence of
services is a major new emerging area.
Aisha Khan and Ruchi Chaturvedi (2005) explain that as the
competition in telecom area intensified, service providers took new initiatives to
customers. Prominent among them were celebrity endorsements, loyalty
rewards, discount coupons, business solutions and talk time schemes. The most
important consumer segments in the cellular market were the youth segment
and business class segment. The youth segment at the inaugural session of
50
cellular summit, 2005, the Union Minister for Communications and Information
Technology, Dayanidhi Maran had proudly stated that Indian telecom had
reached the landmark of 100 million telecom subscribers of which 50% were
mobile phone users. Whereas in African countries like Togo and Cape Verde
have a coverage of 90% while India manages a merely mobile coverage of 20%.
In overview in Indian infrastructure Report (2005) explains India’s
rapidly expanding telecom sector is continuing to witness stiff competition. This
has resulted in lower tariffs and better quality of services. Various telecom
services-basic, mobile, internet, national long distance and international long
distance have seen tremendous growth in year 2005 and this growth trend
promises to continue electronics and home appliances businesses each of which
are expected to be $ 2.5 bn in revenues by that year. So, driving forces for
manufacturing of handsets by giants in India include-sheer size of India market,
its frentic growth rates and above all the fact that its conforms in global
standards.
Marine and Blanchard (2005) identifies the reasons for the unexpected
boom in mobile networks. According to them, cellphones, based on Global
System for Mobile Communication (GSM) standard require less investment as
compared to fixed lines. Besides this, a wireless infrastructure has more mobility,
sharing of usage, rapid profitability. Besides this, usage of prepaid cards is the
extent of 90% simplifies management of customer base. Moreover, it is suitable
to people’s way of life-rural, urban, and sub-urban subscribers.
G. Dhananjayan (2005) did a case study on mobile users of Punjab and
concluded that despite the presence of only four mobile operators in Punjab
(against six in most of other states), due to aggressive market expansion and
segmentation strategy followed by two key operators (Airtel and Spice), the total
mobile population crossed 3.2 million by October 2004 for a population of just
24.35 million, thus achieving a penetration level of a whopping 13%. Most states
which are industrially advanced had just 6% mobile penetration, clearly
illustrating the lead achieved by Punjab.
51
According to Business and Economy (2005) the catalyst for Indian
mobile operators in the future will undoubtedly be increased marketing and
advertisement expenditure, along with better deals for mobile phone users like
the previously mentioned full talk time Rs. 10 recharge card, will go a long way in
not only retaining customers but also acquiring the vast market of lowered
customers who are extremely sticky about value for money and have extremely
low loyalties and almost non-existent switching costs.
According to Oliver Stehmann (2005) the telecommunications industry is
characterized by rapid innovation in the service and the transmission market.
The legally protected public or private monopolist does not have the same
incentive to foster innovation that would exist in a competitive environment.
Thus, state intervention based on the natural monopoly argument neglects
dynamic aspects, which are crucial in the telecommunications sector.
Marketing Whitebook (2005) explains with support of detailed data that
bigger players are close to 20% of the market each. In CDMA market, it is
Reliance Infocom and Tata Teleservices are dominating the scene whereas
Airtel is lead in GSM operators. Between 2003 and 2004, the total subscriber
base of the private GSM operators doubled. It rose from 12.6 million subscribers
at the end of March 2003 to 26.1 million by the end of March 2004. And yet that
100% growth rate notwithstanding, total industry revenue for 2003-04 were
around Rs. 8308 crores. Compared to Rs. 6400 crores that industry grossed in
2002-2003, that is an increase of 30%.
According to The Economic Times (2005) Indian mobile phone market is
set to surge ahead since urban India has a teledensity of 30 whereas rural India
has a teledensity of 1.74. It indicates that the market is on ascent, with more
than 85000 villages yet is come under teleconnectivity.
According to a paper released by the Associated Chambers of commerce and Industry of India (2005), it is stated that 30% of the new mobile
subscribers added by the operators worldwide will come from India by 2009.
10% of the third generation (3G) subscribers will be from India by 2011, Indian
handset segment could be between US $ 13 billion and US $ 15 billion by 2016.
52
It offers a great opportunity for equipment vendors to make India a
manufacturing hub. Indian infrastructure capital expenditure on cellular
equipment will be between 10 to 20% of the investment that will be made by
international operators by 2015. The other proposals included setting up of
hardware manufacturing cluster parks, conforming to global standards and fiscal
incentives for telecom manufacturing among others.
Virat Bahri (2006) explains the viewpoint of Sam Pitroda the Chairman of
Worldtel that identifies opportunities for investments in telecommunications. He
analyses that there is an increasing role for telecom in e-governance in India.
According to him, technology can be leveraged to take India’s development to
next level.
2.2.1 Studies Related to Technology Upgradation in Telecom Sector
Uehara (1990); King (1990); Glynn (1992); Mutoh (1994) emphasized
that technological changes in the telecom and computers have radically changed
the business scenario. In turn, the new demands of business have spurred many
telecom based technological innovations. In order to exploit these innovations for
competing in global markets, business community has been putting pressures on
governments to revise the policy, regulation and structure of the telecom sector.
Several countries across the world have responded by restructuring the state
controlled telecom provider, increasing private participation and deregulating
service provisions.
Business Today (1992) pointed out that due to lack of technical and
financial resources especially foreign exchange, the DOT generally lagged
behind in its level of technology. India’s indigenisation program in the switching
segment carried out by C-DOT was successful in the introduction of rural
exchanges designed specially for Indian conditions characterized by dust, heat
and humidity.
According to Economic Commission for Europe (2000) this transition of
the telecommunication area is mainly technology driven. The borderline between
computers and electronics, on the one hand, and telecommunications, on the
other, is disappearing. This convergence of technologies has led to the
53
acceleration of the innovation process, which is constantly bringing forward new
products and services. Besides expanding the market potential, this innovation
process has also given rise to major changes in industry and the institutional
structure.
E Pedersen and Methlie (2002) studied the technology aspect and
explained a comparative view. According to them, a comparison of the slow
adoption of WAP services in Europe with the successful adoption of comparable
I-mode services in Japan and technologically simple SMS based services in
Scandinavian suggest that aggregate and technology based models are
insufficient to explain the mobile service. Thus, technological models of the
supply side need to be supplemented with the views and impact of perceptions
from the demand side of the mobile commerce end user.
World Telecommunication Development Report (2002) technologies of
mobile telecommunications and internet are going to set the contours of further
technological progress in the current decade. The most recently initiatives aims
at convergence of voice and data received from multiple sources both web
based and real time video streams in mobile handsets and calling cards have
virtual presence possible almost everywhere overcoming the barriers of
distance, topography and remoteness.
Prithipal Singh (2004) with the convergence of technologies, data
services are expected to grow exponentially in the years to come. Broadband is
likely to take a lead in the development of Indian Telecom Sector. Broadband is
growing market and offers immense possibilities for investment. In Broadband
policy, India has envisaged a target of 40 million Internet subscribers and 20
million broadband subscribers by 2010.
P.S. Saran (2004) the telecom technology in India has transformed from
manual and electro-mechanical systems to the digital systems. India has
stepped into new millennium by having 100% electronic switching system. The
technological changes have made way for new services and economics in the
provision of telecom services.
54
According to Mather (2005) the challenge, of course, is that a competitor
can show up in one of your established markets with new technology, better
people, a better network of companies for support and a better management
style and steal huge chunks of your business before you can respond. Staying at
the forefront of all these issues will be the only way to stay successful.
2.2.2 Studies on Investment Policy of Telecom Sector
Moto (1990) researched the need of separate policy, regulation and
operation which require changes in legislation - for example the restructuring the
Japanese Nippon Telegraph and Telephone Public Corporation and Kokusai
Denshin Dewwa was preceded by appropriate changes in legal framework.
Melody (1990) points out that the Indian Government had not addressed
the basic requirement necessary for reform and there was no pre-planned
sequence of structural changes which are basic determinants of reform.
Therefore, the government, investors and subscribes could expect only marginal
benefits from the reform process.
MTNL Report (1991) explains that international bodies had supplemented
government resources and funded expansion and technology upgradation
programmes.
Akwule (1992) researched that in comparison Kenya, which had almost
the same level of gross domestic investment as percentage of GDP from 1981-
89 raided the telecom investment as a share of GDP from 3.28% to 8.67 in 1978.
The effect of under investment in these sectors was compounded by the
diffusion of these scarce resources over a number of areas where no specific
area in telecom was developed.
Jain and Chhokar (1993) points out the limitations of capital and
manpower as key constraints. The Athreya’s Committee’s report may be viewed
as an initiation of a process of examining organizational options. Management
incentives which would allow these organizations to increase profitability and the
structural mechanisms which would allow then to raise capital from markets had
been sketchily outlined.
55
2.2.3 Studies Relating to Competition in Indian Telecom Service Sector
Melody (1990) points out various concerns for the telecom sector
covering competition as important one. Competition is considered more
important factor than ownership in introducing efficiency. Further the order in
which structural adjustments take place determine the effectiveness. Donaldson (1994), Jussawala (1992); Jain, (1995); Wellenius (1995), recognize that
developing countries feel the important role a responsive, business oriented, and
technologically advanced telecom sector plays in the growth of the economy.
Many developing countries accept the limitations of a monolith state monopoly in
responding to the twin challenges of spurring internal growth and competing in
global economy.
Shyamal Ghosh (2003) mentions that the most significant development
since 1999 has been the progressive reduction in tariffs which has been
facilitated by competition through multi operator environment. The most dramatic
reduction in tariff has been from very high Rs. 16 per minute to Rs. 2 per minute.
N.M. Shanthi (2005) throws light on the factors that contributed to the
growth of telecom sectors. The studies various initiatives takes by government in
lien of liberalisation, privatisation and de-monopolisation initiatives. The trend is
expected to continue in the segment as prices are falling as a result of
competition in the segments. The beneficiaries of the competition are the
consumers who are given a wide variety of services.
Kushan Mitra (2005) analyses various factors contributing to competition
to Indian Telecom Industry. Besides lowering of prices, increased efficiency,
greater innovation, highly tech industry better quality services are some of the
reasons which are boosting competition amongst various telecom service
providers.
Michael Meltzer (2005) explain that in electronic age, the need to
manage customer relationships for profit is a marketing dilemma that many
telecommunication companies face.
56
Arindham Mukherjee (March, 2006) takes out various case studies like
Vodafone, Maxis, Telekopm Malaysia, Tatatele etc. to study the rising interest of
foreigners for investment in Indian telecom industry. Various reasons of
stemming growth can be rising subscriber base, rising teledensity, rising handset
requirements, saturated telecom markets of other countries, stiff competition,
requirement of huge capital, high growth curve on telecom, changing regulatory
environment, conducive FDI limits in telecom sector.
According to Stephen Y. Walters (2003) the telecommunications industry
is being rocked by change fueled by the advent of the tremendous success of
the internet and its technologies.. For quite some time, there has been
competition in the telephony business. Long-distance rates have seen
continuous decreases for two decades as new carriers sought to capture greater
and greater market share. Local carriers have seen competition for
interconnecting the networks of large corporate customers and for providing
them access to long-distance services. So, competition and change are not new
issues in telecommunications. But the internet has forced an entirely new set of
changes on the phone business. There are new carriers, new business
scenarios, new technologies, and new ways of thinking about end users and the
services they seek.
2.3 TRUST AND COMMITMENT IN SERVICES SECTOR
As Navin (1995) points out, these terms have been used to reflect a
variety of themes and perspectives. Some of these themes offer a narrow
functional marketing perspective while others offer a perspective that is broad
and somewhat paradigmatic in approach and orientation. A narrow perspective
of customer relationship management is database marketing emphasizing the
promotional aspects of marketing linked to database efforts (Bickert, 1992).
Another narrow, yet relevant, viewpoint is to consider CRM only as
customer retention in which a variety of after marketing tactics is used for
customer bonding or staying sin touch after the sale is made (Vavra 1992). A
more popular approach with recent application of information technology is to
focus on individual or one-to-one relationship with customers that integrate
57
database knowledge with a long-term customer retention and growth strategy
(Peppers and Rogers, 1993). Thus, Shani and Chalasani (1992) define
relationship marketing as “an integrated effort to identify, maintain, and build up
a network with individual consumers and to continuously strengthen the network
for the mutual benefit of both sides, through interactive, individualized and value-
added contacts over a long period of time”. Jackson (1985) applies the
individual account concept in industrial markets to suggest CRM to mean,
“Marketing oriented toward strong lasting relationships with individual accounts”.
McKenna (1991) professes a more strategic view by putting the customer
first and shifting the role of marketing from manipulating the customer (telling
and selling) to genuine customer involvement (communicating and sharing the
knowledge). Berry (1995), in somewhat broader terms, also has a strategic
viewpoint about CRM. He stresses that attracting new customers should be
viewed only as an intermediate step in the marketing process. Developing closer
relationship with these customers and turning them into loyal ones are equally
important aspects of marketing. Thus, he proposed relationship marketing as
“attracting, maintaining, and – in multi-service organizations – enhancing
customer relationships”.
Berry’s notion of customer relationship management – resembles that of
other scholars studying services marketing, such as Gronroos (1990), Gummesson (1987), and Levitt (1981). Although each of them is espousing the
value of interactions in marketing and its consequent impact on customer
relationships, Gronroos and Gummesson take a broader perspective and
advocate that customer relationships ought to be the focus and dominant
paradigm of marketing. For example, Gronroos (1990) states: “Marketing is to
establish, maintain and enhance relationships with customers and other
partners, at a profit, so that the objectives of the parties involved are met. This is
achieved by a mutual exchange and fulfillment of promises”. The implication of
Gronroos’ definition is that customer relationships is the ‘raison de etre’ of the
firm and marketing should be devoted to building and enhancing such
relationships. Similarly, Morgan and Hunt (1994), draw upon the distinction
58
made between transactional exchanges and relational exchanges by Dwyer, Schurr, and Oh (1987), to suggest that relationship marketing “refers to all
marketing activities directed toward establishing, developing, and maintaining
successful relationships.”
The core theme of all CRM and relationship marketing perspectives is its
focus on cooperative and collaborative relationship between the firm and its
customers, and/or other marketing actors. Dwyer, Schurr, and Oh (1987) have
characterized such cooperative relationships as being interdependent and long-
term oriented rather than being concerned with short-term discrete transactions.
The long-term orientation is often emphasized because it is believed that
marketing actors will not engage in opportunistic behaviour if they have a long-
term orientation and that such relationships will be anchored on mutual gains
and cooperation (Ganesan, 1994).
Another important facet of CRM is “Customer selectivity”. As several
research studies have shown not all customers are equally profitable for an
individual company (Storbacka, 2000). The company therefore must be
selective in tailors its program and marketing efforts by segmenting and selecting
appropriate customers for individual marketing programs. In some cases, it could
even lead to “outsourcing of some customers” so that a company better utilize its
resources on those customers it can serve better and create mutual value.
However, the objective of a company is not to really prune its customer base but
to identify appropriate programs and methods that would be profitable and create
value for the firm and the customer.
As observed by Sheth and Parvatiyar (1995), developing customer
relationships has historical antecedents going back into the pre-industrial era.
Much of it was due to direct interaction between producers of agricultural
products and their consumers. Similarly artisans often developed customized
products for each customer. Such direct interaction led to relational bonding
between the producer and the consumer. It was only after industrial era’s mass
production society and the advent of middlemen that there were less frequent
interactions between producers and consumers leading to transactions oriented
59
marketing. The production and consumption functions got separated leading to
marketing functions being performed by the middlemen. And middlemen are in
general oriented towards economic aspects of buying since the largest cost is
often the cost of goods sold.
Berry and Parsuraman (1991); Bitner (1995); Crosby and Stephens (1987); Crosby, et al. (1990); Gronroos (1995) the de-intermediation process
and consequent prevalence of CRM is also due to the growth of the service
economy. Since services are typically produced and delivered at the same
institutions, it minimizes the role of the middlemen. A greater emotional bond
between the service provider and the service users also develops the need for
maintaining and enhancing the relationship. It is therefore not difficult to see that
CRM is important for scholars and practitioners of services marketing
According to Frazier, Speakman and O’Neal (1988) another force driving
the adoption of CRM has been the total quality movement. When companies
embraced Total Quality Management (TQM) philosophy to improve quality and
reduce costs, it became necessary to involve suppliers and customers in
implementing the program at all levels of the value chain. This needed close
working relationships with customers, suppliers, and other members of the
marketing infrastructure. Thus, several companies formed partnering
relationships with suppliers and customers to practice TQM. Other programs
such as Just-in-time (JIT) supply and Material Resource Planning (MRP) also
made the use of interdependent relationships between suppliers and customers.
According to (Shapiro and Posner, 1979) with the advent of the digital
technology and complex products, systems selling approach became common.
This approach emphasized the integration of parts, supplies, and the sale of
services along with the individual capital equipment. Customers liked the idea of
systems integration and sellers were able to sell augmented products and
services to goods, as well as services. At the same time some companies
started to insist upon new purchasing approaches such as national contracts and
master purchasing agreements, forcing major vendors to develop key account
management programs
60
Similarly, in the current era of hyper-competition, marketers are forced to
be more concerned with customer retention and loyalty (Dick and Basu, 1994); Reicheld, 1996). As several studies have indicated, retaining customers is less
expensive and perhaps a more sustainable competitive advantage than
acquiring new ones. Marketers are realizing that it costs less to retain customers
than to compete for new ones (Rosenberg and Czepiel, 1984).
On the supply side it pays more to develop closer relationships with a few
suppliers than to develop more vendors (Hayes et al., 1998; Spekman, 1988). In addition, several marketers are also concerned with keeping customers for
life, rather than making a ne0time sale (Cannie and Caplin, 1991). There is
greater opportunity for cross-selling and up-selling to a customer who is loyal
and committed to the firm and its offerings . Also, customer expectations have
rapidly changed over the last two decades. Fueled by new technology and
growing availability of advanced product features and services, customer
expectations are changing almost on a daily basis. Consumers are less willing to
make compromises or trade-off in product and service quality. In the world of
ever changing customer expectations, cooperative and collaborative relationship
with customers seem to be the most prudent way to keep track of their changing
expectations and appropriately influencing it (Sheth and Sisodia, 1995).
According to Yip and Madsen (1996) today, many large internationally
oriented companies are trying to become global by integrating their worldwide
operations. To achieve this they are seeking cooperative and collaborative
solutions for global operations from their vendors instead of merely engaging in
transactional activities with them. Such customers needs make it imperative for
marketers interested in the business of companies who are global to adopt CRM
programs, particularly global account management programs). Global Account
Management (GAM) is conceptually similar to national account management
programs except that they have to be global in scope and thus they are more
complex. Managing customer relationships around the world calls for external
and internal partnering activities, including partnering across a firm’s worldwide
organization.
61
According to David L. Kurtz (2003) the purpose of relationship marketing
is to build long-term connections between the company and its customers and to
develop brand and firm loyalty. Relationship marketing works well for services
where transactions tend to be continuous and switching costs for customers are
high. Firms operating in the customization and functional service quality sector
do well with relationship marketing programs. The long-term goal of relationship
marketing is to build brand loyalty. Personal interaction with service personnel is
critical in the development of the long-term relationship.
Trust is a cross disciplinary concept, incorporating ideas from economics,
marketing, sociology, psychology, organization behaviour, strategy, information
systems and decisions sciences.
2.3.1 Trust
Trust has been defined in various ways in literature. According to
Deutsch (1960) trust consists of two components, confidence in ability and
intention (Moorman et al. 1993). Trust is “Willingness is truly on an exchange
partner in whom one has confidence.” Morgan and Hunt (1994) felt trust exists
“when one party has confidence in an exchange partner’s reliability and integrity”
(Anderson and Narus, 1990). The outcome of trust therefore is the “firms belief
that another company will perform” actions that will result in positive outcomes
for the firm as well as not take unexpected actions that result in negative
outcomes”. Hart and Saunder, (1997) the importance of trust is based on the
potential use of technology to increase information sharing. Trust increases the
probability of a firm’s willingness to expand the amount of information sharing
and explore new mutually beneficial arrangements. As trust declines, people are
increasingly unwilling to take risks and demand greater protections against the
probability of betrayal. In telecom sector, security and reliability of information
refers to a positive trust that is shown in the consistency and assurance between
what a trading partner says and does. Therefore, trust among the trading
partners in telecom commerce reinforces the prospect of continuity in a
relationship and commitment.
62
Analyzing data, Bercerra and Gupta (1999) categorized both negative
consequences of lack of trust and key positive results from high trust
relationships. For instance, a manager’s time and time spent on dealing with low-
trust relationships are higher than those spent in dealing with high trust would
enjoy open communication and willingness to take risks. People in high trust
relationships are not afraid to share all information received. They also indicated
that the overall performance would be enhanced the problems of distrust were
reduced .
According to Dr. Hal Varian (2001) in part, one got to have incentives set
up in a way that there is something for everybody in the transaction. A lot of the
trust comes out of the repeated nature of the interactions, or the contractual
interaction, or the system you set up for compensation, or revenue sharing.
Those are the behind-the-scenes components that really cause the trust to be
created and work successfully.
Kasper-Fuehrer and Ashkanasy in the (2001) trust results from
experiencing fair behavior by the other party together with acceptance of the
other party’s rights and interests. An additional factor implicit in the definition of
trust is the role of ethical behavior. Any change in a person’s value system
causes a change in behavior and thus influences trust. Trust also indicates a
joint undertaking with a level of understanding of shared business practices
between the parties. Finally, trust implies that the participants contribute to, and
gain from, the final outcome; and this awareness of common interest and mutual
benefit results in a foundation of goodwill.
According to Jeffrey Schuman (2003) trust results from experiencing fair
behavior by the other party and acceptance of the other party’s rights and
interests. The creation of shared goals and strategy, especially in the initial stage
of a relationship, facilitates collaboration on the level of the individual and on the
level of the community as a whole. As such, a common business understanding
provides an essential condition for the development of trust within the
relationship. This understanding fosters mutual goal setting, a willingness to
share information, and the creation of interpersonal trust. Communicating the
63
importance of trustworthiness and the qualities it takes to create it throughout the
Collaborative Community underlies trust building.
Rayport and Bernard (2004) explain the importance of customer
interface as competitive advantage in the era of shorter product cycles and fast
commoditization of products. Failing to manage these interfaces to the firm’s
advantage will affect its profits. Lack of proper management of touch points can
cause a churn among the firm’s existing customers. A study of 700 mobile
service customers in Cochin, Kerala reveals that 70% of the customers switched
over to other mobile service because of the customers switched over to other
mobile services because of poor customer service and lack of proper complaint
handling.
According to Scott Davis (2005) companies are forced to differentiate
through innovative touch points and boost brand value at reasonable cost. Hero
Honda’s new scooter “pleasure” aimed at ladies is being sold through “just 4 her”
showrooms where the salesperson is lady thus ensuring much more
personalized service to the target customers.
According to Chaturvedi & Chaturvedi (2005) trust is conceptualized as
a reciprocal orientation and interpretive assumption that is shared, has the
relationship as the object and is symbolized through international action”. Trust is
related to a partner’s perception of the other partner’s abilities, knowledge,
expertise, motives and intentions. It covers the actions that partners will take in
the relationship. Quality of the Interaction is a variable that measures the degree
of the social interaction, which may range from being a close personal friend to a
distant business relationship. A study where the degree of structural bonding
was controlled, found that there was little difference in social bonding between
individuals who saw their partner as a business friend or a close personal friend.
There was a difference in the actions a person would take to support a business
friend than the action she/he would take to support individuals who were
perceived to be more distant and formal business acquaintances.
Harish B (2006) explains the importance of company to identify the
customer touch points and manage those touch points affectively. Companies
64
spend millions of dollars for acquiring or customer but after they behave miserly
when managing those critical touch points. For example, while customer
interactions at the point of sale is usually initiated by company, complaints and
queries are initiated by the customers. It is important to understand that
customer oriented interfaces are more personal in nature and have more impact
on customer satisfaction than company initiated interfaces. Hence, the voice-
integrated services, internet enabled services and customer query response
services should be treated with greater importance.
Raja, Sharma and Shashikala (2006) identify product quality, service
support, product distribution, service personnel, information services and
corporate brand equality as the underlying factors of customer satisfaction. The
article examines the customer satisfaction of mobile handset end users in India.
It is important that technological models of the supply side need to be
supplemented with the views and impact of perceptions from the demand ride of
mobile end users. It ranks the mobile handset users on the basis of various
factors and identifies homogenous subgroups among the end users.
Barlow Moller (2006) in his book “A Complaint Is A Gift” writes that
complaints are mostly viewed as a nuisance by most firms. While complaints are
to be considered as an opportunity to demonstrate the firm’s commitment to
customers, companies often take this event to ruffle customer’s feathers. A
complaint is a strategic tool to increase business and customer satisfaction. But
to do that firms have to get into strong cultural change process.
Beauyean, Davidson and Madge (2006) supports the Mckinsey
research in Belgium, Germany and Italy that positive deviants play an important
role for winning customer’s trust. These deviants occur when customer has a
problem or financial advice either good or bad. By contrast, humdrum
transactions generally don’t offer the same opportunity to create an emotional
bond with the customer. Many companies make the mistake of over-investing in
humdrum transactions but fail to differentiate themselves in the customer
experiences that really matter.
65
Moorman, Zaltman and Deshpande (1992), in a study of relationships
between providers and users of market research identify a cognitive and
behavioural dimensions of trust. The cognitive dimension would consist of the
belief in the partner’s reliability (trustworthiness), or credibility which comes from
motivation and knowledge. The behavioural intention, that is the concrete act of
placing trust in the partner and this implies vulnerability in the trustor, the person
doing the trust.
Morgan and Hunt (1994) identify the two different dimensions of the
commitment, they hold that trust also exists in the presence of cognitive
component alone. That is, it would be enough that the buying firm believes in the
honesty and reliability of the supplier firm. The behavioural intention incorporated
in the ‘willingness to act’ would be implicit in the concept of trust itself.
Willingness to rely should be seen as a result rather than one of its constitutive
dimensions.
Fennetean and Gilbert (1997) suggest a distinction between the
cognitive and affective dimensions of trust. They maintain that trust is based on
both knowledge and feelings or emotions that the trustor has in dealings with the
trustee. Second, conversely, leads to the conceptual differentiation of the
concepts of perceived trustworthiness, that is, the degree to which a subject
perceives his partner as trustworthy, trust and trusting behaviour, which is
behavioural manifestation of trust. According to the authors, the three factors
that leads a subject to consider a partner trustworthy are, ability, which relates to
the partner’s competence to supply what the trustor expects, integrity, which
relates to the fact that partner is guided by the principles acceptable to trustor,
benevolence, which relates to the intention of the trustee to do his best for the
trustor, putting to one side his egoistic profit motives.
According to Benni, Hjartar and Laartz (2003) study that mobile-
telecom companies could build within the mobile-services domain might, for
example, charge customers for on-line games (a reusable service, since it
wouldn’t have to be created anew for each game launched), send out big
volumes of automated text messages, and reformat video streams or music so
66
that one service could address a variety of mobile devices. Standardized
application services are just beginning to emerge in mobile telecommunications,
but their value could grow rapidly. Application server software – a similar layer of
reusable IT functions deployed in a multitude of enterprise applications (for
instance, customer-relationship-management systems) – created a $2 billion
market in only a few years.
K. Sridhar (2004) identifies trust as important part of the culture which
involves embedding way of the issues into procedures, policies and practices.
He emphasizes on the trust based strategies for the benefit of long term
relationship. Relationships based on high degree of personal contact often
benefit as they result into reduced perceived risk, contractual safeguards,
customer confidence, emphasize competence, and commitment of the customer.
Singh and Gupta (2004) explain the application of data mining tools on
telecommunication industry. The telecom industry is offering local and long-
distance telephone services to provide many comprehensive communication
services including data and voice. Data mining tools are used for identifying
behaviour patterns of behaviours of different groups of users, the traffic data,
usage of services etc. For the purpose data from applications such as billing,
marketing, sales, fraud management, performance analysis systems, network
switches and customer service across the company.
Das Naryandas (2005) studies the benefits of customer loyalty in B2C
markets vs B2B markets. No doubt, the benefits of customer loyalty are
enormous but the means by which companies create and sustain are not same
as in the consumer markets. In business markets, every customer needs a
customized product, quantity or price. In fact each segment effectively consists
of one customer as compare to consumer market which has large number of
buyers with similar wants, transactions are small in value, mass production,
consumer’s perceptions and companies focus on brands.
According to T. Vekat Ram Raj and G. Radha Krishna (2006) all
customers who have been associated with any company for reasonably a longer
period of time should undoubtedly given priority treatment. In the present era of
67
customer acquisition, corporate have been aggressively pursuing different
customer retention strategies and tools for retaining customers, companies in
India are realizing the fact that retaining an old customer is easier than acquiring
a new one. Hence, most companies have started in feel the need to reward
those customers who have been loyal to them.
Kumar, Venkatesan and Reinartz (2006) disclose that to retain valuable
customers companies should give what they require. Companies that take
advantage of new technology in the right way will doubly benefit. Predicting
customer behaviour is highly unpredictable that companies spend millions
inundating and alienating-customers. So it is very important for companies to
know what to sell, when and to whom.
Anderson, Narus and Rossum (2006) emphasize the importance of
customer value propositions. They present an approach to customer value
propositions that businesses can implement to communicate with resonating
focus, the superior value their offerings provide to target market segments and
customers. Customer value propositions can be a guiding beacon as well as the
cornerstone for superior business performance.
According to Kingstone (2006) the only way to embark on customer-
centric CRM is to understand the importance of customer intelligence by using
integrated analytical applications. The thirst among enterprises for greater
customer insight is hardly new. However, the growing amount of customer data
coupled with the maturation and integration of analytics into CRM applications
has made it a measurable reality. Customer-centric CRM ensures that
companies use insight for more targeted and personalized customer
communications. Again, the coupling of analytics and action is not a new
concept, but the ability to blend analytical and real-time front-office capabilities
without considerable integration or custom development is. It is also critical that
companies provide an easy-to-use foundation to ensure employees have the
right information at the right time for impactful decision-making.
According to author Richard A. Buckinghan (2004) in his book titled
“Customer Once Client Forever” explains that a company will not be hard
68
pressed to search for new clients if it already has a good base of existing clients.
This will help it not only save the precious time but also investment. In fact, you
could expect much more than that. The author’s own experience suggests that
sometimes you may discover that these very clients are more discerning ones,
and are less price-conscious. They may want a fair price, but more importantly,
they want quality-service. And sometimes, to your pleasant surprise they may
want a lifetime relationship also. The author discovered that as he improved his
own organization’s service to his clients not only did the number of prospective
clients calling him increase but the order size was also big. This made selling a
lot easier.
According to K. Sridhar (2004) there has been a growing interest to
cultivate customer relationships. Trust is important because financial institutions
have an implicit responsibility to manage their customers funds and also towards
the nature of financial advice supplied, otherwise referred to as fiduciary
responsibility. Some strategies for establishing and maintaining trust for the
benefit of longer-term relationships include: 1. Reducing the perceived risk. 2.
Offering contractual safeguards. 3. Building customer confidence. 4.
Emphasizing competence. 5. Communication. 6. Signal commitment to the
customer. 7. Resolving conflicts. 8. Make-trust part of the corporate culture.
Making trust part of the culture involves embedding many of the above issues
into the procedures, policies and practice of the financial institution and its
employees. Establishing and maintaining the trust of customers can be very
beneficial in cementing the bond between the customer and the company.
Conversely, breaking the trust of one’s customers can lead to an early end to the
relationship.
As per Hommer and Krause (2004) mobile-telephone companies
tend to consider prepaid service a poor cousin of monthly bill subscriptions, and
it is true that prepaid customers generate, on average, only 35 percent of the
revenue that monthly subscribers do. Some mobile operators even talk about
getting out of the prepaid business altogether. Research suggests, however, that
prepaid customers, when managed properly, can offer a healthy revenue stream
69
whether or not they eventually become monthly subscribers. It was found that
when a carrier implements an effective acquisition program, it could increase the
profitability of prepaid customers by 40 to 80 percent. To turn the prepaid
segment around, companies must choose their customers more carefully
manage the life cycle and clean.
According to Care Sewell (2004) in book titled “Customer For Life”
explains that in order to attract and retain the customers, first a company is
required to differentiate itself from its customers. As the author says special
always wins over general. In the process of doing that it is always better not to
talk of prices in the promotional ads. The best way to let people know how the
company differentiates from its competitors is not by words but by giving
examples of what the company has done in the past. As everyone knows that
word of mouth publicity is effective means of advertising than any other mode, it
is always better to allow outsiders like research organizations or media
periodically and allow them to know what exactly is happening in the
organization. This gives customers a feeling that what the company says is
indeed true as it is supported by these third parties though their reports.
Authors Seybold and Marshak (2005) in their book titled “The Customer Revolution” explain a customer scenario net is a customer and project-specific
set of interrelated tasks that can be managed via the internet to accomplish a
specific outcome. It’s a particularly dynamic form of e-market. The players
involved and the services offered will depend on the customer’s context. To keep
on track, today’s business must effectively turn information about its customers
into intelligence that can guide the company’s actions. No longer can this be
offline market research conducted leisurely in some back room. The pace of
business now requires that near-real time information be analyzed and
responded to quickly.
Shuman, Twombly and Rottenberg (2005) on their book “Everyone Is A Customer” explain with the balance of power shifting towards the customer,
companies are realizing that they must collaborate with their customers and
other businesses in designing, developing and delivering the basket of goods
70
and services to profitably satisfy the customers’ needs. While working in the
collaborative community, the ability to develop a relationship and understand the
needs of the customer is the key and customer in such cases refer to every party
which must receive some value from the collaboration. By developing the
business through an iterative process, companies can know more precisely how
to get and retain customers, develop the products and services that satisfy the
customers’ needs and finally deliver and serve the customers.
According to K. Suresh (2005) in his book titled “customers rule”
explain a typical customer purchase decision process starts with need
recognition and progresses through search for information, alternatives
evaluation, purchase, consumption and post-consumption evaluation. It is during
the search process that internet offers a big advantage for customers. Even if the
internet search may not result in e-sales, it might materialize into in-store sales.
For products with limited distribution or appeal, websites prove quite handy for
search. For online evaluation of alternatives, shop robots are quite useful. These
shopping robots generate comparisons of different online sellers along different
parameters such as price, delivery terms and payment particulars. Online
auction sites go a step further and even broker purchases based on the price
range quoted by a prospective buyer.
Author Nagendra V. Chowdhary (2005) the two most important elements
in establishing a customer-centric organization are an enterprise database and a
workforce that can both willingly share information and make a willing
commitment to customers, rather than to products or organizational fiefdoms. It
is a long, hard slog to become – and maintain – a customer-centric organization,
but the result is a much more profitable brand. Some of the examples of the
unique customer-centric service offerings made by companies are General
Motors Vauxhall Division: Managing the Customer Experience Across Channels
and Touch Points, Charles Schwab: Sustain and Manage a Customer-centric
Culture, Hewlett-Packard: Monitor Customers Experience Across Channels and
Touch Points, Dell Computers where customers can configure their own systems
71
according to their requirements are few examples of the companies giving
importance to the customer and developing a customer-centric culture.
Author Algolin (2005) in his book “Trust and Consequences” explain
trust is the most basic element of social contact – the great intangible at the
heart of truly long-term success. Trust is both a process and an outcome; it is at
the heart of dealing with every relationship. The trust acts like an invested
money. If the company has always managed the customers well and provided
them with consistent and trustworthy performance then these customers are
more likely to support the company in times of a crisis. The same holds true for
investors and other stakeholders.
According to Kingstone (2006) the only way to embark on customer-
centric CRM is to understand the importance of customer intelligence by using
integrated analytical applications. The thirst among enterprises for greater
customer insight is hardly new. However, the growing amount of customer data
coupled with the maturation and integration of analytics into CRM applications
has made it a measurable reality. Customer-centric CRM ensures that
companies use insight for more targeted and personalized customer
communications. Again, the coupling of analytics and action is not a new
concept, but the ability to blend analytical and real-time front-office capabilities
without considerable integration or custom development is. It is also critical that
companies provide an easy-to-use foundation to ensure employees have the
right information at the right time for impactful decision-making.
According to Ranjana Kaushal (2006) the industry had sometime back
given its consensus on the same and such sharing by players of infrastructure
(mostly towers) had already been happening in some areas of Delhi and
Maharashtra. The rationale for such sharing: this would ward off the expected
burden of Rs.25,000 crore that the 140,000 towers needed to cater to 200 million
cellular subscribers across the country by 2007.
2.3.2 Relationship Commitment
Porter et al, (1974) explains commitment as central because it not only
leads to such important out comes as decreased turnover. This study borrows
72
the concept of commitment from Morgan and Hunt (1994) who defined
commitment as “an exchange partner believing that an ongoing relationship with
another is so important as to warrant maximum efforts at maintaining it, that is,
the committed party believes the relationship endures indefinitely” and
commitment is central to all of the relational exchanges between the firm and its
various partners.
2.3.3 Shared Value
Shared value is the extent to which partners have beliefs in common
about behaviour, goals and policies are important or unimportant, appropriate or
inappropriate, and right or wrong (Morgan and Hunt, 1994). Shared value has
been treated as a multidimensional construct. In the cellular services of telecom
sector, share value symbolizes the extent is which the company and the
customers share common beliefs on critical values like ethics, security and
privacy.
2.3.4 Customer Skills
In telecom business, companies offer support and service together with
transaction facilities to its customer and reap its benefits. The development of
customer skills has a positive impact on the commitment which customer feels
towards the customer. It is important that there is delightful experience of the
customer during the usage of mobile phone. Consequently, more skills one
acquires, the less Important are functional barriers.
2.3.5 Opportunistic Behaviour
Opportunistic behaviour has its roots in the transaction cost literature, and
is (Williamson 1975) defined as “self interest seeking with guide”. This research
posits that the strong negative relationship exists between organizational
commitment and propensity to leave the organization. As per Mathew and Zajac (1990) the negative relationship also exists between commitment and propensity
to leave the telecom organization relationship.
As to the switching costs are actually very low in the cellular services, the
Switching costs to an ongoing relationship being viewed as important and
73
generating commitment to the relationship. Therefore, relationship commitment
results when customers perceive the costs of terminating the relationship are
high. An assumption in literature is that a terminated party will seek an
alternative relationship and have “switching costs”, which lead to dependence
(Jackson 1985; Heide and John, 1988) described that switching costs are
exacerbated by idiosyncratic investments, that is, investments are difficult to
switch to another relationship. Also Dwyer et al (1987) proposed that the buyer’s
anticipation of higher switching costs give rise to the buyer’s interest in
maintaining a quality.
• Resist attractive short-term alternatives in favour of the expected long
term benefited.
• View potentially high-risk options as being prudent because of the belief
that their partners will not act opportunistically.
According to (Keveaney, 1995; Porter, 1985) the varying effects are the
result of the nature of risk that accompanies the establishment of a "relationship"
with a particular service provider. Initial service encounters carry a certain
degree of risk due to the intangible nature of the service product and, the
customer not knowing what to expect in terms of the service outcome.
Uncertainty (and hence risk) is reduced as knowledge is gained with repeat
exposure to the service supplier over time. However, at the same time risk is
reduced due to increased familiarity, risk is also increased because of the
switching costs incurred as a result of long-term service relationships.
According to (Klemperer, 1987) switching costs are defined as the
customer's anticipated time and effort associated with changing service
providers. Switching costs give firms the power to retain their customers, while
potentially raising prices and generating monopoly profits The greater the power
of service suppliers, the greater the likelihood that they are able to dictate the
terms of the service relationship, and hence (from the customer's perspective)
the greater the risk associated with establishing long-term relationships.
74
2.3.6 Communication
Egtar (1979) emphasized communication fosters trust by assisting in
resolving disputes and expectations. Farrell and Rusbult (1981) Communication can be defined as “the formal as well as informal sharing of
meaningful and timely information.” Communication especially timely
communication have used past communication as an antecedent of trust higher
motivation. (Anderson and Narus, 1990) and increased organizational
citizenship behaviours (Williamson and Anderson, 1991), but it also results
from such things that can be influenced by the firm as recruiting and training by
the firm as recruiting and training practices.
2.4 IDENTIFICATION OF RESEARCH GAPS IN LITERATURE
In this research, an attempt is made to test an adaptation of the
commitment trust theory of relationship marketing in the usage of mobile
services in telecom context. Although the main variables were mostly borrowed
from the commitment trust framework (Morgan and Hunt 1994) the dimensions
and the items were adapted significantly to the context of cellular services of
telecom sector.
Following gaps are identified from the survey of literature that will help to
research problem.
2.4.1 Research gaps related to Indian Telecom Sector
Most of the research concentrated on the strong growth of Indian Telecom
Sectors. Further, research is required to study various regulatory, economic and
market forces on Indian Telecom sector and their impact o India mobile users.
Following questions can further the researched in changing environment:
75
Table 2.1: Drivers of the New Telephony Drivers Questions to Consider
Customers How many customers does the company have? How tightly bound are they? Are they customers of other competing groups? Will they adopt new features? Do they also require the old features? How much will they spend? Will new customers be needed?
Economic Climate Is the economy expanding or shrinking? Are customers able to afford new services? Will economic initiatives help or hinder the operation? Is investment capital readily available? Will this continue, and for how long?
Regulatory environment Are regulations protecting incumbents or encouraging competitors? If so, will this continue? Will regulators possibly introduce new barriers to business? Will costs be driven up to meet mandated requirements?
Assets Is the equipment inventory useful in the new business? Are upgrades needed? How extensive are the required investments? Is the necessary intellectual property in place and protected?
2.4.2 Research Gaps Related to Trust and Commitment
Most of the literature available emphasizes on the trust and commitment
as important aspects in long-term relational exchange between buyer and seller.
But following questions require further research.
• Do trust and commitment are important in relational exchange of mobile
users?
• Do trust and commitment are important in rapidly growing Indian Telecom
industry which is affected by international competition, technological
advances and deregulation of nationalized industries?
• How various socio- demographic factors further affect trust and
commitment?
• What are the managerial implications of trust and commitment theory to
the domain of Cellular users of Indian Telecom Sector?
76
CHAPTER – 3
GROWTH OF INDIAN TELECOM SECTOR
3.1 INTRODUCTION
Globalisation, liberalisation and privatization are the three most spoken
words in today’s world. These initiatives paved way for all-round reforms,
especially in developing economies, like India. These countries realized that
development of effective and efficient means of communications and information
technology is important to push them onto the path of development. The growth
of the telecom sector in India during post-liberalization has been phenomenal.
This research aims to throw light on the factors that contributed to growth in the
segment and presents an insight on the present status of the industry.
3.2 GLOBAL TELECOM INDUSTRY: AN OVERVIEW
With the awareness spreading around the world on the Information and
Communications Technology (ICT), in the later part of the 20th century,
countries, especially the developing ones, began to realize the importance of
an efficient telecommunication network for the development of the economy.
At the dawn of the 21st century, the developing countries started to make
full use of the technology revolution taking place around the world, with many
countries liberalizing the existing stringent policies and regulations. To improve
information and telecommunication technology, 189 countries of the UN met at
the Fifty-Fifty General Assembly on September 2000. A millennium Declaration
was made, according to which the countries reaffirmed their commitment to
improve the living conditions of poor and downtrodden in the world by adopting
intense poverty alleviation programmes. One of the targets of this declaration
was adherent to “In co-operation with the private sector make available the
benefits of new technologies, especially information and communication”. The
indicators that were to be used for monitoring the progress were:
• Telephone line and cellular subscribers, per 100 units of population.
• Personal computers in use for 100 units of population.
77
• Internet user per 100 units of population.
Even before the declaration, many developing countries had started
liberalizing their internal policies to enable efficiency as to affordability and reach
ability of telecommunication system. By 1995, most of the low income
developing countries of the world, made their economies global, by liberalizing
the domestic licensing and important policies on the whole, to facilitate inflow of
foreign capital into the infrastructure sector, especially in the telecommunication
sector. This resulted in a telecom revolution, with countries adopting
liberalization initiatives, experiencing a ‘never-before’ growth in the telephone
network, including the penetration levels. Developing countries today account
for 49% of the total telephone network in the world. While in East Asia (including
China) the total teledensity grew at a rapid pace to reach 27.4 in 2002, the
teledensity grew at a slower pace in South Asia (including India), to reach 4.5 in
2002. This is due to imperfections in government regulatory and licensing
policies in the ‘90s in most of the South Asian countries. While there was
imbalanced development in ICT among the developing countries in individual
growth in telecom, country-wise also showed a partial development, where the
development in other segments apart from cellular was snail-paced. This was
due to phenomenal growth in the cellular segment, whose major contribution
was towards urban telephony.
By the end of 2006, the telecommunication industry had experienced
continuous growth, as well as rapid progress in policy and technology
development, resulting in an increasingly competitive and networked world. It is
true and encouraging that overall, the digital divide has been reduced and
continues to shrink. ITU statistics show that over the last 10 years, the digital
divide between the developing and the developed countries has been narrowing
in terms of fixed telephone lines, mobile subscribers and Internet users. In
contrast to the slow fixed line growth, phenomenal growth rates in the mobile
sector particularly, have been able to reduce the gap that separates the
developed from the developing countries from 27 in 1996, to 4 in 2006. The fixed
line gap has been reduced from 11 to 4 during the same period.
78
Table 3.1: Cellular Tariffs across South – Eastern Countries
S.No. Countries Per Minute Cellular Tariffs (in US Cents)
1. India 2.5
2. China 3.5
3. Bangladesh 6.5
4. Nepal 6.5
5. Pakistan 8.4
6. Sri Lanka 11.0
7. Bhutan 11.4
8. Maldives 14.4
Source: www.itu.int as on 20th Feb 2006
Table 3.1 explains that the mobile tariffs in India have also become lowest
in the world. A new mobile connection can be activated with a monthly
commitment of US$ 5-6 only.
3.3 TRENDS IN INDIAN TELECOM INDUSTRY
India has become one of the fastest growing mobile markets in the world.
The mobile services were commercially launched in August 1995 in India. In the
initial 5-6 years the average monthly subscribers additions were around 0.05 to
0.1 million only and the total mobile subscribers base in December 2002 stood at
10.5 millions. However, after the number of proactive initiatives taken by
regulator and licensor, the monthly mobile subscriber additions increased to
around 2 million per month in the year 2003-04 and 2004-05.
Table 3.2: Subscriber Base of Fixed and Wireless Services as on Feb,2007
(Subscribers in Millions) 2005-06 2006-07 March
2005 Feb. 2006
Additions during
April05- Feb. 06
Mar. 2006
*March* 2006
(As per new
policy)
Dec. 2006
Jan. 2007
Feb. 2007
Addi- tions
During Jan.
Addi- tions
During Feb.
Additions during
April06- Feb. 07
Wireless 52.22# 84.89# 31.21 90.14 98.78 149.50 156.31 162.53 6.81 6.22 63.75 Fixed 46.19 49.45 2.95 50.18 41.54 40.43 40.40 40.39 -0.03 -0.01 -1.15 Total 98.41 134.34 34.16 140.32 140.32 189.93 196.71 202.92 6.78 6.21 62.60
# Excluding WLL-F Subs. * WLL-F subs. Included in Wireless Subs. w.e.f. March 2006
79
Table 3.2 explains the total number of telephone subscribers have
reached 202.74 million at the end of February 2007 as compared to 196.91
million in January 2007. The overall tele-density has increased to 18.26 in
February 2007 as compared to 17.45 in January 2007. In the wireless segment,
6.22 million lines have been provided in February 2007 while 6.81 million lines
were provided in January 2007. The total wireless subscriber (GSM, CDMA &
WLL(F)) base is 162.53 million now. Whereas in the wireline segment with the
minor reduction in subscriber base by 0.01 million lines in February 2007, the
total wireline subscribers are now 40.39 million. During the eleven months of
current financial year 62.60 million lines have been added as compared to 35.93
million lines provision in the same period during the financial year 2005-06.
On the lines of previous three years, the year 2005-06 also witnessed a
phenomenal growth in the subscriber base for mobile services, and also
increase in the subscriber base of Fixed including WLL (F) services as well as
Internet services, thus building on the growth trend in subscriber base
experienced since mid-1990s. The mobile Industry crossed the 90.14 million
subscriber mark at the end of the financial year in comparison to the subscriber
base of 52.22 million at the end of March, 2005. It added 37.92 million
subscribers in the financial year 2005-06 registering an annual growth rate of
about 72.62%. The subscriber base of Fixed including WLL (F) services also
grew from 46.19 million at the end of March, 2005 to 50.17 million at the end of
March, 2006, registering a growth rate of about 8.62%. The Internet subscriber
base in the country as of 31st March, 2006 stood at 6.93 million as compared to
5.55 million during the previous year, and registered an annual growth rate of
about 25%. The tele-density at the end of March, 2006 reached to the mark of
14% as compared to 9.08% at the end of previous year recording an increase of
4.92%. This annual growth in tele-density is unprecedented and this was largely
due to steep increase in mobile subscriber base and the various innovative tariff
plans launched by the mobile service providers. This growth in tele-density also
becomes very significant in view of the fact that overall increase in tele-density
during the 50 years period from 1948 to 1998 on a much smaller population base
was only 1.92%.
80
3.4 TRANSITION OF INDIAN TELECOM INDUSTRY
The history of the Indian Telecom sector goes way back to 1851, when
the first operational land lines were laid by the then British Government in
Calcutta. With independence, all foreign telecommunication companies were
nationalized to form Post, Telephone and Telegraph, a monopoly run by the
Government of India.
The Indian Telecom Sector, like most other infrastructure sectors is
controlled by the state. The Department of Telecommunications (DoT), reporting
to the Ministry of Communications (MoC) is the key body for policy issues and
regulation, apart from being a basic service provider to rest of country. By an act
of Parliament, the Telecom Regulatory Authority of India (TRAI) was formed to
be the regulatory agency. The key players in this sector are depicted in the
figure 3.1:
Figure 3.1: Regulatory Bodies of Indian Telecom Industry
Telecom
Commission
Telecom
Regulatory Authority of
India
Department of Telecommuni-
cation
Ministry of Communi-
cations
Regulating Bodies of
Indian Telecom Industry
81
Ministry of Communication: All the operations of this sector come under
the purview of MoC. It is responsible for all major policy changes, planning,
supervision, spectrum control, etc.
Department of Telecommunications: DoT was formed in 1985 when the
Department of Posts and Telecommunications was separated into Department of
Posts and Department of Telecommunications. Till 1986, it was the only telecom
service provider in India. It played a role beyond service provider by acting as a
policy maker, planner, developer as well as an implementing body. In spite of
being profitable, non-corporate entity status ensured that it did not have to pay
taxes. DoT depends on Government of India for its expansion plans and funding.
Its pivotal role in the Indian telecom sector has got diluted after formation of
TRAI- Telecom Regulatory Authority of India.
Telecom Regulatory Authority of India: TRAI, was founded to act as an
independent regulatory body supervising telecom development in India. This
became important, as DoT was a regulator and a player as well. Founded by an
Act of Parliament, the main functions of the body was to finalize toll rates and
settle disputes between players. An independent regulator is critical at the
present situation as the sector witnesses competition.
The operations of this sector are determined as under the Indian
Telegraph Act of 1885 – A document buried in the sands of time. The next major
policy document, which was produced, was the National Telecom Policy of 1994,
a consequence of the ongoing process of liberalization.
The Telecom Commission: The Telecom Commission was set up by the
government of India vide Notification dated April 11, 1989 with administrative
and financial powers of the government of India to deal with various aspects of
Telecommunications. The Telecom Commission and the DoT are responsible for
policy formulation, licensing, wireless spectrum management, administrative
monitoring of PSUs, research and development and standardization/validation of
equipment, etc. The multi-pronged strategies followed by the Telecom
Commission have not only transformed the very structure of this sector, but also
82
have motivated all the partners to contribute in accelerating the growth of the
sector.
The other entities in the sector under the control of MoC are the two
public sector telecom equipment manufacturers, namely Indian Telephone
Industries (ITI) and Hindustan Teleprinters Ltd. (HTL). Both these companies are
facing financial problems because of product obsolescence, poor management
and over staffing. Telecommunications Consultants India Ltd. (TCIL), another
PSU was founded in 1978 to undertake consultancy services in the field of
telecom.
3.4.1 Objectives of the National Telecom Policy
The objectives of the NTP 1999 are as under:
• Access to telecommunications is of utmost importance for achievement of
the country’s social and economic goals. Availability of affordable and
effective communication for the citizens is at the core of the vision and
goal of the telecom policy.
• Strive to provide a balance between the provision of universal service to
all uncovered areas, including the rural areas, and the provision of high-
level services capable of meeting the needs of the country’s economy.
• Encourage development of telecommunications facilities in remote, hilly
and tribal areas of the country.
• Create a modern and efficient telecommunications infrastructure taking
into account the convergence of IT, media, telecom and consumer
electronics and thereby propel India into coming an IT superpower.
• Convert PCOs, wherever justified, into Public Tele information centers
having multimedia capability like ISDN services, remote database access,
government and community information systems, etc.
• Transform in a time bound manner, the telecommunications sector to a
greater competitive environment in both urban and rural areas providing
equal opportunities and level playing field for all players.
83
• Strengthen research and development efforts in the country and provide
an impetus to build world-class manufacturing capabilities.
• Achieve efficiency and transparency in spectrum management.
• Protect defence and security interests of the country.
• Enable Indian telecom companies to become truly global players.
3.4.2 Telecommunication Services
Today tariff for telecommunication services in India is one of the lowest in
the world. The Indian consumer has immensely benefited from such lower tariffs
which has also been a major factor for explosive growth in the sector. Following
is the list of services offered by both GSM and CDMA operators:
• Telephone services
• NSD/ISD services
• Computerised trunk services
• Pay phones
• National & international leased lines circuits
• Telex
• Telegraph services (manual & automatic)
• X-25 based Packer Switched Data Network (NET)
• Gateway Packet Switched Data Services (GPSS)
• Gateway Electronic Data Interchange Service (GEDIS)
• Gateway E-Mail and Store & Forward FAX Service (GEMS-400)
• Concert Packet Service (CPS)
• Satellite based remote area business message network
• Electronic Mail
• Voice Mail
• Audio-text
• Radio paging
• Cellular mobile telephone
• Public mobile radio trunked service
84
• Video-tex
• Video conferencing
• V-SAT
• Internet
• ISDN
• INMARSAT mobile service
• INMARSAT data service
• Home country direct service
• Intelligent Network (IN) services
Table 3.3: Indian Telecom Services at a Glance (FY 2006-07)
Telecom Services FY 2005-06 (in crores)
FY 2006-07 (in crores)
Growth
Fixed Access 32,684 33,715 3
Cellular 22,787 35,879 57
National Long Distance 6,231 9,017 45
International Long Distance 3,830 7,251 89
Internet Access 1,592 1,619 2
VSAT 369 443 20
Radio Trunking 30 38 27
Total Services Revenue 67,523 87,962 30
Table 3.4 exhibits various Indian telecom services for the period 2005-
2006 and 2006-2007 and growth for this period. Major telecom services of Indian
economy cover-fixed access, cellular national long distance, international long
distance, internet access, VSAT, radio trunking. Amongst them, international
long distance services and cellular services have recorded 89 and 57% growth
respectively,
3.4.3 Revenue Analysis
According to the revenue reports submitted by the service providers,
revenue, net of “pass through” and service tax, from GSM services is about Rs.
5308 Crores in the quarter ending Sept-05 as against Rs. 4842 Crores in the
85
June-05 quarter, translating into a growth rate of 9.63%. Growth in average
subscriber base in the corresponding period has been 11.77%.
Table 3.4: Average Revenue Per User (ARPU) (Rupee / per month during the quarter)
Postpaid Prepaid Blended ARPU
Circle Jun’05 Sept’05 Jun’05 Sept’05 Jun’05 Sept’05
Circle A 605 599 286 283 375 364
Circle B 561 554 265 276 327 330
Circle C 605 576 321 310 410 386
Metro 836 803 290 292 446 438
All India 662 646 282 284 381 374
• The all India blended ARPU per month has gone down by about 1.9%
from Rs. 381 in the quarter ending June-05 to Rs. 374 in the quarter
ending Sept-05.
• The lowest blended ARPU per month is in Circle B (Rs. 330), which is
higher than the previous quarter’s corresponding figure (Rs. 327). The
highest blended ARPU is in Metro (Rs. 438).
• Monthly ARPU in postpaid segment has declined from Rs. 662 in June-05
to Rs. 646 in Sept-05. On the other hand, prepaid segment has shown a
marginal increase in ARPU per month from Rs. 282 in June-05 to Rs. 284
in Sept-05.
• Gap between postpaid and prepaid ARPU is getting smaller. Postpaid
ARPU has been 2.27 times that of prepaid ARPU in the quarter ending
Sept-05, as against 2.35 as existed in the previous quarter. Such gap is
lowest in Circle C (1.86%) and highest in Metros (2.75%).
3.4.4 Teledensity
Teledensity denotes the number of telephones per 100 units of
population. National teledensity refers to total teledensity of the country, which is
an average of the total urban and the total rural density. Teledensity is an
economic indicator of development. Higher teledensity, denotes developed and
86
accessible means of communication, which means the country is coherent with
the development goals. In India, deliberate attempts made by the government to
increase teledensity have been fruitful with private participation in both fixed and
mobile telephony.
While the urban teledensity was more than 20, the rural teledensity was
lesser than 2, contributing to a national teledensity of 8.02, for the year 2005-06.
This has increased from a mere 1.28 in 1995-96. The increase in urban
teledensity was primarily due to heavy cellular penetration since 2000-01. The
specific information as to the teledensity as pertaining to different circles for
year2005-06 is as follows:
• Six states have teledensity greater than 10% - Punjab, Kerala, Andaman
& Nicobar, Himachal Pradesh and Gujarat.
• Five states have teledensity greater than 7% - Andhra Pradesh, Haryana,
Karnataka, Maharashtra and Tamil Nadu.
• Eight states have teledensity lesser then 3% - Assam, Bihar, Chattisgarh,
Jharkhand, Uttar Pradesh, North East-II, Orissa, and West Bengal.
• Teledensity in the metros was – Delhi 42%, Mumbai 36%, Chennai 39%
and Kolkata 19%.
3.4.5 Value Added Services
Service providers are facing the severity of dipping voice ARPUs and high
subscriber churn rate. With new and cheaper schemes being introduced
everyday, mobile content and applications are the only way to keep a subscriber
glued to particular services. Innovation is the key here; the more innovative a
service, the more popular it gets and more revenue it brings in. Presently value
added services (VAS) accounts for 10-12% of the operator’s revenue, but in the
next 10 years the growth of mobile industry is expected to be driven by mobile
VASs as they will form 60% of the revenue of the mobile network operators.
The Indian mobile content market is estimated to be around Rs.1,000
crores. It registered a growth around 80% in 2006 and is expected to grow at the
same pace. According to researches, Indian content market, which
87
encompasses music, gaming videos etc, is expected to grow 10 times in the next
five years.
3.4.6 Village Telephones
More than 70% of the country’s population lived in village. The growth of
telephone network in rural India has not been quite encouraging post-
privatisation. While the urban area has the most developed form of
communication, nearly 20% of the rural area is left unlinked. The rural
teledensity was at a very low 1.55. This is due to very slow or no (in some areas)
cellular penetration. In the fixed line segment also, the only service provider for
rural areas was the public sector service provider. The private players were not
interested to invest in rural areas, due to earning sensitivity issues. More than
increasing the individual phones, the government is initially concentrating to
increase the number of public phones in villages, namely (Village Public
Telephones) VPTs.
As on March 31, 2006, 86% of the villages is connected through VPTs
and the number of Direct Exchange Lines (DEL) stood at 122.72 lakhs. Out of
the total, 98% of the VPTs is provided by BSNL, the government owned service
provider, whereas the private players have contributed to only 2% of the total
VPTs.
3.4.7 Thrust on broadband, high-speed Internet
• The Telecom Regulatory Authority of India (TRAI), has projected that
Internet subscriptions will increase from 4.1 million in 2003 to 6 million in
2005, to 18 million by 2007 and to 40 million by 2010.
• Broadband subscriptions will rise from 0.2 million in 2003 to 3 million in
2005, to 9 million in 2007 and to 20 million by 2010.
• Broadband will be a preferred carrier with the central government’s US $
600 million budget for e-governance in India, favouring the rising use of
online education, telemedicine networks and connectivity for rural
knowledge centers.
88
• Greater use of online service in commerce, industry and transportation
will create a boom for broadband. The regulatory for suggested a range of
measures for an open-sky policy for DTH, VSAT and uplinking using
satellites that should boost broadband.
3.4.8 Growth in Telecom manufacturing
• The Indian telecom equipment manufacturing segment has registered
threefold production growth from US $ 1.3 billion to US $5.26 billion
during the period 1996 to 2006.
• There will be an increased manufacturing of telecom equipment to meet
the projected telecom expansion.
3.4.9 Broadcasting sector
• In the last decade since satellite TV started, there has been a huge
growth in subscriber number. Cable/satellite homes are estimated to be
40 million. India has close to 100 million TV homes currently. Total
viewership at 400 million is among the highest in the world. The Radio
Committee preparing the draft rules for radio broadcast, is expected to
moot 26% FDI kin FM Radio, FM stations may be allowed to beam news
contents.
3.5 PRIVATIZATION AND DISINVESTMENTS OF BASIC SERVICES
Basic services were opened to private participation in1994 by dividing the
country into 21 telecom circles and allowing one private operator per circle to
compete with DoT. An important event in the history of Indian Telecom scenario
took place on the 19th of February 1996, when the Supreme Court of India
upheld the rights of the government of India to privatise the telecommunication
sector, and also ruled out the DoT decision to adopt duopoly, by allowing
unrestricted entry into the sector, the licensing to be based obviously on the
conditions laid down by the DoT.
The industry is definitely witnessing a shift in importance toward private
sector. Latest statistics show that the erstwhile telecom giants – the government
owned BSNL and MTNL are wading through rough weather, with a whopping 4
89
million surrenders in the fixed line segment for the year ending December 2006.
The firms, which were notorious for bureaucratic attitudes and lethargic
operations, are now pulling up their sleeves to fight the competition, before it is
too late.
The policy and regulatory regime established by the Government and the
regulator has led to speedy growth of subscriber base of the incumbent Public
Sector Undertakings as well as that of the private sector operators.
Figure 3.2: A comparison of Private and Public Sector Undertaking Subscriber base for the period (1998-2006)
0102030405060708090
1998 1999 2000 2001 2002 2003 2004 2005 2006
Year
Subs
crib
er B
ase
(in m
illio
n)
Private Subscriber Base PSUs Subscriber Base
Source: www.dot.com as on 25th Dec 2006
The figure 3.2 shows the growth in subscriber base of private operators
and public sector undertaking. Private operators have also shown remarkable
growth in a highly competitive environment. The overall growth in the subscriber
base of private operators during 1998-2006 was 78.36 million comprising of 7.92
million fixed subscribers and 70.44 million mobile subscribers. Private operators
have contributed very largely to post 1998 growth in mobile services due to the
obvious cost and fast deployment advantages. During the period 1998-2006, the
absolute growth in subscriber base of PSU operators was 43.28 million
comprising of 23.58 million fixed subscribers and 19.70 million mobile
subscribers.
90
With the entrance of private players in the market, the competition is
further stiffened, and the players are eying the market to make hay when the sun
shines. With the advent of competitive environment in the industry, the most
excited are the consumers, who are emerging the kings in the industry. They are
not only given alternatives to choose, but they also become the deciders of the
fate of players, who do not provide them the quality of service they expect. At
this juncture it is imperative to analyse the shift in pattern of consumer decision-
making and choice, and also the strategies adopted by the players to satisfy
them, in order to gain an edge in competition.
Disinvestments of PSU’s in the telecom sector has been undertaken in
February 2002, with the disinvestments of VSNL by bringing down the
government equity to 26% and the management of the company was transferred
to Tata Group, a strategic partner. The basic service was opened up for
competition. Thirty three basic service licenses (31 private and one each to
MTNL and BSNL) were issued as on December 31, 2001. Out of 31 licenses
issued to private sector, 25 have been signed in 2001-02 (April-December). Out
of the 21 circles, the circles neglected by the private players were, Assam, the
North Eastern region and Jammu & Kashmir. Disinvestments and privatization
have brought following benefits:
• Lower prices – It has been witnessed across most industries that
competition helped in lowering the prices.
• Increased efficiency – In deregulated industries or license based cost plus
regime there was no incentive to shed inefficient assets and reduce
overheads. The advent of competition forces an industry to eliminate
inefficient and unproductive assets.
• Greater innovation – The deregulation in telecom industry gave birth to
the greatest innovation period in the past 20 years. New technologies,
standards, data services, new devices, CRM solutions and creative
bundling have all been the result of competition in telecom industry.
91
• Telecom is high technology industry. Telecom equipment is highly
complex to design and needs the expertise of both telecom and
computing fields. With the advent of competition, tremendous innovation
in services was made possible by improving the quality of technology by
the players.
• Telecom industry is services industry, hence the good quality services to
the customer and the customer relationship management is the key and
competition has totally changed the definition of service in Indian telecom
industry. The quality of service has improved by leaps and bounds.
3.6 FOREIGN DIRECT INVESTMENT (FDI)
The transition-trend in the economy that had resulted out of liberalization,
privatization and demonopolisation placed huge capital requirements, especially
for private ventures, which could not be met from domestic sources alone.
Hence, foreign investors were invited to take part in contributing to the capital
requirements of the telecom companies.
As per the FDI policy for the Telecom Sector, investment up to 49% is
permitted in Basic, Cellular and other value added services, which is hiked to
74%; up to 74% is permitted in Internet, infrastructure and radio paging services
and up to 100% is permitted in manufacturing, Internet service, voice and
electronic mail, based on certain conditions for fulfillment as a part of licensing
and security requirements, laid down by the Department of Telecommunications,
Government of India. Several announcements were made relating to policy
change covering change of ADC from per minute charges to revenue share, and
mobile number portability. FDI ceiling increment has led to an increase in FDI in
mobile services whereas ADC has resulted in reduction of mobile tariffs in the
country. On the policy front, per minute ADC on domestic calls was changed to
revenue share regime. And the percentage charged is 1.5% of AGR (adjusted
gross revenue).
The Indian entrepreneurs in the Telecom sector, used the provision of FDI
to the fullest possible extent, and the FDI inflow into the sector was the second
highest for the period 2003-04, after the petroleum, oil refinery sector. The
92
distribution of FDI inflow into various service areas upto March 2006, with
percentages is shown in the table given below:
Table 3.5: Service-wise Actual Inflow of FDI upto March 2006 (in millions)
S.No. Service/Item FDI (Rs.) %
1. Basic Telephone Service 3937 3.96
2. Cellular Mobile Telephone Service 26646 26.78
3. Radio Paging Service 910 0.91
4. E-Mail Service 688 0.69
5. VSAT Service 281 0.28
6. Cable TV Network+Internet 1704 1.71
7. Satellite Telephone Service 481 0.48
8. Radio Trunking Service 71 0.07
9. Manufacturing & Consultancy 15784 15.86
10. Holding Companies 48420 48.66
11. Other Value Added Services 227 0.23
12. Automatic Route 361 0.36
Total 99509
Source: www.investindiatelecom.com as on 3rd April 2006
Table 3.5 explains that during the year 2006, 926 approvals for FDI were
given amounting to a huge sum of Rs.57,260.14 crore as total approvals, out of
which Rs.99509 crores actually flew in to the economy for the period.
With 74% FDI, India has seen huge investments from players like Vodafone
in Bharti Airtel, Temasek Holdings in Tata Teleservices, Orascom in HTIL, Maxis
in Aircel, and Telekom Malyasia Berhad in Spice. Vodafone Group secured
around 10% interest in Bharti Tele-Ventures for an investment of approximately
Rs.6,700 crore. Temasek Holdings (Temasek), through its wholly owned
subsidiary, Aranda Investments (Mauritius) has taken a 9.9% stake in Tata
Teleservices for an undisclosed amount. Maxis has invested 26% in the
enlarged share capital of the AircelGroup for $280 mn. And later, Maxis along
with an Indian partner is planning to acquire the remaining 74% for Aircel for
$800 mn. Maxis acquired additional 34% for $422 mn and rest 35% through SPV
93
for $378 mn. Telekom Malaysia Berhad has acquired 49% stake in Spice
Communications for $178.8 mn.
3.7 FUTURE GROWTH OPPORTUNITIES OF INDIAN TELECOM SECTOR
As per TRAI, two other associated aspects for market growth are
availability of spectrum and availability of resources for network rollout and
expansion. The government is currently looking into these two areas. The 79%
hike in FDI has been cleared by the government to ensure continuous flow of
investments to expand the reach of the mobile operators.
To realize full market potential and achieve the forecasts, telecom
operators have to work on a segmented approach and focus on the five key
strategies given below:
1. Mobile in the hand of every urban youth (age group 15 to 24 years).
2. Mobile in the hand of every executive/businessman/ skilled worker.
3. Mobile in every household with income above Rs. 6000.
4. Mobile penetration in every town/village, with a population of over 5,000.
5. Mobile Phones affordable and available wherever mobile services
available.
The real potential of these strategies based on segmentation, if adopted
in the next 15 months:
3.7.1 Mobile in the Hand of Every Urban Youth
India is a young country and 26.5% of the population belongs to the age
group of 15-24 years. Of the estimated total individuals of age above 12 years
(707 million), youths consist of 187 million in both urban and rural markets. Of
this segment, 30% stay in urban areas and ensuring a mobile connection in the
hand of each youth would mean 56 million mobile phones. This means that a
whopping number of people can be tapped by the mobile phone industry;
94
Mobile Market Potential Estimated: 56 million.
3.7.2 Mobile in the Hand of Every Executive
In India, over 6.9% are employed and working as executive, managers,
businessmen, skilled workers, shop owners and self-employed professionals.
This translates to 48.78 million individuals (707 mn x 6.9%), who can afford a
mobile phone in the country.
Mobile Market Potential Estimated: 48.78 million
3.7.3 Mobile in Every Household with Income above Rs. 6000
India has approximately.180 million households. Out of it, 5.9% have an
income of over Rs .6000 across the country This translates to 10.62,
households, who can afford a mobile connection, at Rs.500, if tapped effectively.
Mobile Market Potential Estimated: 10.62 million
3.7.4 Mobile Penetration in Every Town and Village with a Population of over 5000
India has over 8,000 towns with a population of over 5000 and offers the
potential to sell a minimum of 1,000 mobile connections within a couple of years
of launch. However, currently only 1,600 towns are covered under the mobile
network by all service providers put together. This is where great opportunity lies
untapped and the first entrant is bound to gain substantially. The potential
untapped is 6400x Avg.l000 connections = 6.4 million mobile connections in the
next 15 months.
Mobile Market potential Estimated in Towns: 6.4 million
In the same way; India has over 6,30,000 villages and out of it over
14,000 villages have a population of over 5000 and offer the potential to sell a
minimum of 500-1000 mobile connections within a couple of years of its launch.
However, currently very few villages are covered under the mobile network, as
the focus of service providers is to cover towns with high income first. With huge
penetration of consumer goods happening in the villages, the aspiration levels of
the people are soaring and a great opportunity lies untapped. However, very few
95
mobile operators have ventured to cover villages till now. Just a simple
calculation will reveal that an untapped potential of 14,000 x Avg.750 = 10.5
million mobile connections in the villages in the next 15 months.
Mobile Market Potential Estimated in Villages: 10.5 million
The Total Market Potential through Four Focus Segments: 132.3 million.
3.7.5 Mobile Phones to be Made Available and Affordable
As discussed earlier, the major impediment for growth in the industry is
affordability and availability of handsets. Both mobile operators and handset
vendors have to work together to address these issues and ensure that handsets
are available wherever mobile services are offered so that customers have ready
choice arid it is easy to acquire a connection.
The service providers have to work out aggressive pricing/handset
bundling options, which make mobile phones more affordable when compared to
the current's situation. Today; service providers are riding on the piggy back of
handset vendors, allowing them to dominate the situation. However, in countries,
like China, Indonesia, Malaysia, Thailand, where there is a high growth in the
mobile phone industry it is the service providers who tie up with all the handset
vendors and offer attractive pricing to customers, which is equally reciprocated
by the vendors. These combined efforts have really expanded the market.
In addition to making mobiles affordable, handset vendors have to come
up with second hand phone options at very low prices with guarantee, to make a
large number of entry level customers go mobile. The average Indian income is
still around Rs. 6000 per month and an average Indian cannot afford a mobile for
Rs. 4000. The "go mobile" price limit for these set of customers is Rs. 2000 and
handset vendors have to work out suitable strategies to bring out handsets in
this price range to support the service providers in expanding the market. Table
3.6 explains the potential of Indian Telecom Market.
96
Table 3.6: Potential of Indian Telecom Industry S.
No. Facts Data
in mn Assumptions
1. Total population of India as per 2001 census
1027
2. Total population of India without 0-6 year olds
870 157 mn are in the 0-6 year old
3. Total population by Dec. 2003 (without 0-6 year olds)
887
4. Total estimated population by Dec. 2006 (without 0-6)
905 At 1% growth per year
5. Estimated No. of households in India by March 2000
176.4 Source: NCAER
6. Total estimated no. of households by December 2006 a. Urban households b. Rural households c. Urban households with assets like
scooter, car, radio and TV, classified as the very rich and the consuming class
d. Rural households with assets like
scooter, car, radio and TV, classified as the very rich and the consuming class
194.0
60.2
133.9
47.0
37.8
10% growth over March 2000/NCAER growth 31% of total households 69% of total households 4 million – the very rich, 40.8 million – the consuming class, out of the total urban households, growing by 5%. 12 million – the very rich, 34.8 million – the consuming class, out of the total rural households, growing by 5%
7. Potential urban market for mobile 51.24 (Assuming 2 mobiles per households (HH) x 4.2 mn very rich +1 mobile per HH x 42.84 million consuming)
8. Potential rural market for mobile 37.32 (Assuming 2 mobiles per households x 1.26 million very rich + 1 mobile per HH x 34.8 million consuming class)
Total Mobile penetration by December 2006
9.8%
Source: The Marketing Whitebook – Businessworld
If pre-owned mobiles are offered in the same way by mobile operators
along with connections, there will be a huge expansion in the market. It is
expected that at least 50% of the existing mobile owners will return their old
handsets and take a new set for better utilization and these mobiles can help
bring in customers who expect "low entry cost" to go mobile. The mobile phone
97
industry should adopt this strategy so that the overall mobile market expands
exponentially.
3.8 GROWTH OF TELECOM SECTOR IN PUNJAB
Despite the presence of only four mobile operators in Punjab (against six
in most of the other states), due to aggressive market expansion and
segmentation strategy followed. The total mobile population has crossed 8.21
million by October 2006 for a population of just 34.35 million. Most states
industrially advanced states are at just 6% mobile penetration, clearly illustrating
the lead achieved by Punjab. Today; the mobile network covers every urban
town and most of the rural villages and all highways in Punjab, which has
enabled the state's economy to grow substantially. The aggressive "marketing of
the services by operators has ensured that most of the youth, executives and
families in Punjab own a mobile.
However, to replicate the market penetration in Punjab nationally and
grow exponentially to cross 100 million customers (it will be just 10% penetration
nationally against 13% achieved already by Punjab) by December 2006, the
service providers have to specifically target the four customer segments listed
above and arrive at aggressive marketing programs to reach out to them.
However, it is not going to be easy and needs support in several areas:
1. To ensure that every youth has a mobile, service providers have to offer
services like SMS/MMS at low cost/free and ensure that the total mobile
bill for the youth does not cross Rs.300-400 per month, which is the
maximum this segment of customers can afford from their pocket money;
2. In the same way, for executives/businessmen, to tap the full potential, it is
essential that services like Closed User Group, National Closed User
Groups, low STD/ISD rates, Fixed cost for Network calling etc., are
offered so that they can lap up the services and go mobile soon.
3. To ensure that every household has a mobile connection, it is essential
that the utility of mobile phones is increased through better STD and ISD
rates vis-à-vis landline, friends and family offers, special rates to landlines
etc., with easy/low deposit schemes to acquire these facilities.
98
4. To ensure that the penetration targeted in towns and villages is achieved,
service providers have to invest in network expansion and reach out on
priority; to exploit the untapped potential in these markets.
5. To expand the network to a large number of towns and villages by all the
operators, network sharing should be allowed by BSNL and the
government should allow 74% FDI in mobile companies for easy access
to funds.
6. Lastly; both service providers and handset vendors have to combine their
strengths and address the issue of market expansion on priority and
launch aggressive programs to make mobile phones affordable and
available to all.
The need of the hour is a new revolution in mobile telephony and it is
imperative that service providers work towards the same and make it a reality.
3.9 LEADING PLAYERS OF INDIAN TELECOM INDUSTRY
Competition in telecom services driven by regulatory initiatives and
technological advancements continued to push the prices down. The trend was
more visible in mobile and long distance services. The competitive pressures
also made the service providers to be more innovative in their tariff offerings.
Products like “2 year prepaid coupons” and “Life Time Validity” schemes
launched after 2003 made mobile services more affordable and also led to large-
scale subscriber acquisitions. The National Long Distance (NLD) tariff has
further declined subsequent to the implementation of new ADC/IUC regime
effective from 1st March 2006.
As competition in the telecom sector intensified, service providers took
new initiatives to woo customers. Prominent among them were celebrity
endorsements, loyalty rewards, discount coupons, business solution and talk
time schemes. Table 3.7 explains that by 2004, Bharti Tele-Venture emerged as
an unprecedented leader commanding the largest marketing share in GSM
service providers
99
Table 3.7: Subscriber Base of GSM Service Providers for the period 2004-2006
Sr. No. Service Provider 2004 2005 2006
1. Bharti Group 6.504 9.826 19.58
2. BSNL 5.254 8.461 17.16
3. Hutchinson Group 5.148 7.179 15.36
4. Idea Group 2.733 4.696 7.37
5. BPL Group 1.883 1.030 1.34
Source: www.trai.gov.in as on 20th Dec,2006
The Cellular GSM services reached the 69.19 million subscriber mark at
the end of the financial year 2005-06, as compared to 41.07 million during the
previous year. It added around 28.12 million subscribers during the year, with a
remarkable growth of around 68.47%. Reliance and Bharti have acquired
licenses in all 23 service areas for offering mobile services. Bharti and Reliance
are the only private operators which providing services in All India, i.e., 23
service areas.
In Cellular GSM services, in terms of subscriber base and market share,
Bharti with 28.30 million subscriber base remains the largest GSM operator
followed by BSNL, Hutch, and Idea with subscriber base of 24.80 million, 22.20
million and 10.65 million, respectively. The subscriber base of all the GSM
operators except of BSNL in Punjab has increased.
Table 3.8: Subscriber Base of CDMA Operators for the period 2004-06
Sr. No. CDMA Operator Number of Subscribers in Millions
2004 2005 2006
1. Reliance Communications 6.474 9.115 17.41
2. Tata Tele Services 0.625 0.805 6.85
3. BSNL 2.022 0.4337 0.49
4. MTNL 0.102 0.181 0.11
5. HFCL 0.03 0.0485 0.06
6. Shyam Telelink 0.0276 0.025 0.03
Source: www.trai.gov.in as on 20th Dec,2006
100
The Table 3.8 explains the CDMA Subscribers Base for the period of
2004-2006.It has reached 25.95 million during the year 2006 as against 6.474
million for the year 2004. Reliance remains the largest CDMA operator followed
by Tata Teleservices and BSNL with subscribers’ base of 6.85 million, 0.49
million respectively. CDMA subscriber base recorded a growth of 33.2% during
this period.
Bharti, a part of Bharti Enterprises, was the first to launch its cellular
service on July 7, 1995. Bharti's cellular services were launched under the brand
name ‘Airtel’ and were categorized as pre-paid services and post-paids services.
The postpaid service was launched under the brand name ‘Airtel’ whereas its
prepaid services were launched under the brand name "Magic". As of March
2005, the company operated in 23 telecom circles and had the largest subscriber
base of all service providers at over 9.8 million.
In 1995, came Hutchinson Max Telecom – a joint venture between
Hutchinson and Max India Limited. Hutchinson Max Telecom launched its
cellular services in Mumbai under the brand name "Max Touch". In 2000,
Hutchinson entered into a joint venture with Essar called Hutchinson-Essar to
further expand its operations in India. The venture acquired cellular licenses in
Delhi, Kolkata and Gujarat to become one of the largest cellular businesses in
the country by the year 2000. In 2001, Hutchinson-Essar acquired a license to
extend its operations to Chennai, Kamataka and Andhra Pradesh. In 2002, the
company brought all its cellular services across India under the brand name
"Hutch". Hutch operated in 13 telecom circles with a subscriber base of over
seven million in March 2005.
The Tata group of companies also launched their cellular service called
Tata Cellular in the year 1995. In 1996, the Tata group was the first to launch
CDMA mobile services in India, under the brand name Tata Teleservices
(TTSL). TTSL's telephony services included mobile services, Fixed Wireless
Phones (FWP), public booth telephony and wireline services. The company
launched a prepaid card under the Tata Indicom brand with the tagline "100%
Sacchai True Paid". Later in May 2002, Tata Cellular was merged with Birla-
101
AT&T giving rise to a brand called "Idea Cellular" (Idea). The conglomerate's
prepaid service was launched under the brand name "Idea Chit-Chat". As of
2005, Idea operated in 8 telecom circles with a subscriber base of over 4.6
million.
Reliance Infocomm, a telecommunications subsidiary of Reliance
Industries limited, launched its cellular service called Reliance India Mobile (RIM)
on December 28, 2002. The service was launched under the "Dhirubhai Ambani
Pioneer Offer" wherein customers could avail handsets along with the mobile
connection by paying in monthly installments. This one-of-its-kind offer to the
customers generated a huge response. In February 2004, RIM launched its
prepaid service, extending its network through the GSM technology. Following
table 3.9 explains the reach of the selected telecom service providers.
Table 3.9: Coverage of Leading Mobile Service Providers
No. of cities/town
Company/ Group
Mar’06 Mar’07
No. of retailers as on Mar’06
Cumulative Investment as on Mar’06 (Rs crore)
Technology No. of Circles
Bharti 4,000 5,200 400,000 15,923 GSM 23
Hutch NA NA NA NA GSM 16
Reliance 3,824 5,200 255,000+ 14,799 GSM,CDMA 23
Tata Tele 2,500 4,000 NA 10,000+ GSM,CDMA 20 NA stands for not available Source: Cyber Media Research
3.9.1 Airtel
With liberalization of the Indian economy in the early nineties the telecom
industry was thrown open to the private sector. This saw the entry of a number of
major players into the services division. The Bharti group was one of the first to
seize the opportunity provided by the government of India and established Bharti
Tele-Ventures to provide the full range of telephony services for both mobile and
landline connections.
Bharti Tele-Ventures Limited (BTVL) was incorporated on July 7, 1995
with its subsidiaries operating across India. The company was started to promote
102
investments in telecommunications services. With just 4 mobile operators In the
country, the potential for Bharti is immense. Through its subsidiaries, it has
licenses to provide GSM services in 15 of the 22 telecom circles in India.
AirTel, its mobile arm, cakes care of all cellular services co ensure that
communication is faultless and cost efficient. The strategic objective is co
achieve and maintain leadership in the industry within the country. In September
2004, Bharti Televentures limited has brought all its telecom services under the
‘AirTel'’ brand umbrella. With the result of this unified branding strategy, all the
other supplementary brands like Touchtel, IndiaOne and Mantra cease to exist.
Under this the services would be called AirTel Mobile Services, AirTel Telephone
& Broadband services, AirTel Long Distance Services, AirTel Enterprise
Services. This strategy has been in accordance with the government policy to
launch unified licensing, which will allow cellular and landline companies to offer
their individual services under the same license.Like any other telecom service
provider, Bharti also considers information technology a key driver of its
business.
Gartner analyst, Kobita Desai says, “Developed markets like Australia and
Hong Kong have a mnthly churn rate of 3 percent, which usually occurs in the
postpaid segment. India, being an emerging market, is driven by the prepaid
segment.” The churn in telecom industry is due to many factors, viz., age,
profession, usage pattern, etc., which the present CRM system cannot address
effectively. Hence, there is a need for exclusive CRM software which can handle
other factors also. Both prepaid and post-paid sections are inherent in mobile
telephony business. According to industry experts, the prepaid segment is a
crucial growth area as they provide up to 55 percent of the operators' revenue
3.9.2 Tata Teleservices Limited
The telecom industry today is all about big numbers and volumes-read
customer satisfaction. This spells good news for telecom service provider TTSL,
which believes in top-of-class customer services. Tata Teleservices Limited
(TTSL), a part of the Tata Group – one of India’s largest business houses with
more than 80 companies in the fold-is a premium telecom services provider.
103
TTSL was established by Tata Industries to leverage the liberalised regime in the
telecom sector and to provide quality telecom (data/voice) services to retail as
well as corporate customers. TTSL provides basic telephony services and
complements and competes with the state-owned Bharat Sanchar Nigam
Limited (BSNL) in the circles it operates in. In the past two years the company
has been successful in entering major towns in Andhra Pradesh. Around a year
ago, it started providing basic and WLL-based services in New Delhi. TTSL has
also applied for fresh basic and WLL licenses in eight more telecom circles.
TTSL has so far been able to provide approximately two lakh connections
in Andhra Pradesh alone. In this circle, the number of subscribers is growing at
more than 70 percent year-an-year. As part of its expansion plans, TTSL
successfully bid to establish its high quality telecom infrastructure in five other
circles (states). TTSL has already invested $400 million for rolling out services in
Andhra Pradesh and plans to invest another $1.7 billion over the next five years
to expand its services across the planned service areas. TTSLS plans for service
roll-out cover 56 percent of the incumbents (BSNL/MTNL) telephone subscriber
base, and accounts for about 65 percent of the incumbents' revenues. If
estimates do work in the expected direction, the company is certain to have
nearly five to six million customers over the next five years.
With a significant presence across the telecom value chain and the
synergies after tile acquisition of VSNL by the Tata group, TTSL is planning to
expand the range of its coverage and services; advanced communication
solutions now include seamless integration of voice, video, data and IP systems.
As a basic telephone services provider, TTSL provides the backbone for India’s
corporate leaders such as GE Capital, Wipro, Magnacom, Citicorp Overseas
software (now called Orbitech), Dr. Reddy’s Labs, Standard Chartered Bank,
Motorola India Electronics, TCS and Satyam, in addition to servicing the telecom
needs of retail customers.
But the entire telecom infrastructure was of little use until customers were
serviced satisfactorily. After putting in place the required equipment and
infrastructure, TTSL was committed to setting up a CRM solution that would
104
carer to the varied needs of its vast customer base, “Oracle E-Business Suite
has played an important role in helping TTSL meet its customer service needs,
thereby making it a joy for customers to interact with our company,” says S
Ramakrishnan, managing director, Tata Teleservices.
In order to build its customer base, the TTSL management understood the
need to have robust infrastructure in place to ensure quality customer service.
This was to be a key differentiator for TTSL in the competitive landscape it was
operating in. This was also a compelling need for the private sector service
provider, as customer expectations were very high.
The TTSL management had the foresight to realise the potential of
leveraging customer interaction to attract, maintain and enhance tile lifetime
value of the customer. The growth and wide acceptance of the service presented
TTSL with all excellent opportunity to use customer interaction 'to enhance
customer relationships as well as leverage the opportunity to upsell and cross-
sell flew products and offerings. To meet the strategic objectives of the customer
care experience there was a need to have integrated processes and systems in
place to:
• Ensure that customers could access and get information about various
services easily.
• Provide quick and accurate resolution of customer requests.
• Meet all the commitments.
• Get things right the first time, all the time.
• Constantly find ways to reduce costs and increase productivity to
generate and safeguard company revenues and profitability.
• Ensure that every interaction with the customer results in delight,
produces joy and enhances the customer’s relationship with TTSL.
Translating the corporate mission was a challenging task. TTSL realised
very early that a CRM strategy required robust and scaleable tools. There was
also a need to align all processes towards customer-centricity ethics and also to
ensure that this got, translated into faster, quicker and better service levels
105
3.9.3 Hutchison Essar Limited
In September 2005, Hutchison Essar snapped up BPL Communicatinos
for $ 1.154 billion, including cash considerations and assumed debt. It also
signed a conditional agreement to acquire Essar Spacetel for $6 million. This
marked another phase of consolidation in the Indian telecom industry. For
Hutchison Essar, this deal is a win-win situation. In addition to acquiring BPL’s
subscribers base, the footprint expands to seven new service areas. It has been
taken over by Vodafone in February, 2007.
At the moment, Hutchison Essar operates in 13 circles. The merger
creates a pan-India presence covering 23 circles, bringing it on par with Bharti,
BSNL and Reliance Infocomm. This deal also offers an additional 2.8 million BPL
subscribers, automatically hiking Hutch’s total subscriber base to 12.5 million
(Hutchison Essar’s subscriber base as of August 2005 was 9.7 million). This
pushes it to second position behind Bharti with its 14.07 million subscribers.
BSNL is thus relegated to third position in GSM.
In the past year, five entities have been clubbed under Hutchison Max
Te1ecom Limited (HMTL), which has now been rechristened Hutchison Essar
Limited. This is the holding company of all five 'Hutch affiliates in India. The
group has brought its services under the “Hutch” banner, except in Mumbai circle
where it uses the “Orange” brand name.
After completion of this internal consolidation and after formalisation of the
acquisition, a long-awaited initial public offers (IPO) could be on the cards.
According to company sources, the first quarter of 2006 is being considered as
an appropriate time for an IPO.
Hutchison is also keenly focused on increasing its subscriber base. It has
recently launched services in Uttaranchal. Also on the drawing board are plans
to step up investments in West Bengal (excluding Kolkata circle) where it has
earmarked Rs. 4 billion. Apart from expansions, Hutch has also introduced a
range of innovative products and value-added services.
106
3.9.4 Reliance Infocomm Limited
In May 2004, Reliance Infocomm Limited, one of the leading in the fastest
growing telecommunications companies in the country, was conferred the ‘Most
Promising Service Provider of the Year 2003 (Asia Pacific) award at the Asia
Pacific Technology Awards instituted by Frost & Sullivan. Given that Reliance
Infocomm Limited received the award within the first year of the launch of its
telecommunications services in India, this international recognition was
considered on mean achievement for the company.
Analysts felt that the Primary reason for the success of Reliance
Infocomm in the Indian telecom industry was its aggressive marketing strategy.
Reliance Infocomm used all the tactics which it could to win over the hearts of
millions of customers (10.3 million subscribers as per reports of January 2005) in
India. It offered a first of its kind technology in India (The CDMA technology)
attractive products.
Reliance Infocomm provides a host of communication services. From
restrictive wireline connectivity to the all pervasive wireless technology; from
public service telephone network to broadband; from analogue to digital
transmissions; and from plain voice telephony to virtual networks. The company
aims to set up a nationwide, world-class broadband communication
infrastructure comparable with the best in the world.
The first phase begins in 2003 and aims at providing the Reliance India
Mobile service through a nationwide wireless network reaching out to nearly 90%
of India’s population. The second phase beginning in mid 2003, aims at bringing
about enterprise netway revolution, by providing 100 mbps Ethernet links. The
final phase begins in end 2003, with the objective of bringing about a consumer
convergence revolution, by providing high speed Ethernet links to enlighten
every home an entire range of television channels, high-speed telephony, audio
conferencing, videoconferencing and video on demand.
Priced to suit, the common man's budget, and backed these up with
massive aggressive promotion campaigns, using every possible medium of
communication. It continuously flooded the markets with new offers, new
107
schemes, particularly during the festive seasons, and thereby ensured that it
always caught the attention of people. In the process, Reliance Infocomm
Limited demonstrated the importance that marketing discipline holds in the
success of a company to the academic and corporate community alike. In 2006,
Reliance Infocomm has been renamed as Reliance Communications Limited.
3.10 CONCLUSION
As a result of the liberalization, privatization and demonpolisation
initiatives taken by the government of India, the telecom sector is experiencing a
historical growth. The trend is expected to continue in the segment, as prices are
falling as a result of competition in the segment. The beneficiaries of the
competition are the consumers, who are given a wide variety of services. In the
years to come, the country is predicted to witness a communication revolution,
which would increase the teledensity to match that of the developed world.
108
CHAPTER – 4
A CONCEPTUAL FRAMEWORK OF TRUST AND COMMITMENT
4.1 INTRODUCTION
“Building bridges between manufacturers and their suppliers requires unprecedented levels of trust and commitment in placing the fate of company in the hands of people who aren’t even employees”.
Davidow and Malone (1992)
“Trust is the sine quo non of the digital economy”
Japcott, Ticoll and Lowy (2000)
The past decade has witnessed a major directional change in both
marketing theory and practice considered by Webster (1992) to represent a
“fundamental reshaping of the field” and by other a genuine paradigm shift
(Kotler, 1991; Parvityar, Sheth and Whittington, 1992) the turn is towards
relationship marketing, a concept that encompassing relation contracting
(Macaneil, 1980), relational marketing (Dwyer, Schurr and Oh, 1987), working
partnership (Anderson and Narus, 1990) symbiotic marketing (Varadrajan and
Rajaratnam, 1986), strategic Alliances (day, 1990) co-marketing alliances
(Bucklin and Sengupta, 1993) and internal marketing (Berry and Parsuraman,
1991) which recognizes that global competition occurs increasingly between
networks of firms (Thorelli, 1986). Indeed, Achrol (1991) forecasts the rise of
“true market companies” within networks. A fundamentally specialized
organizations whose inter relationships, being driven are “held together and
coordinated by market-driven focal organizations by means of “norms of sharing
and commitment based on trust. “To be an effective competitor in the global
economy one requires to be trusted co-operator. As McKinsey & Co. strategists
(Bleeke and Ernst, 1993) put it, “For most global businesses, the days of flat out,
predatory competition are over. In place of predation, many multinational
companies are learning that must collaborate to compete.”
109
Relationship marketing has been defined in several ways. Berry (1983)
defined relationship marketing as attracting, maintaining and enhancing
customer relationships. He advocated a number of relationship strategies,
including a core service strategy, customisation; relationship pricing, and internal
marketing. Similarly, Jackson (1985) referred to industrial relationship marketing
as efforts oriented towards strong, lasting relationships with individual accounts.
Predominant among most definitions of relationship marketing is the view that
buyer-seller encounters accumulate over time, and opportunities exist to
transform individual and discrete transactions into relational partnerships
(Czepiel, 1990). This view supports the notion that a relationship exists when an
individual exchange is assessed not in isolation, but as a continuation of past
exchanges likely to continue into the future. Perceived in this way, customer
acquisition is only an intermediate step in the marketing process, with the
ultimate goal being to strengthen already strong relationships, and to convert
indifferent customers into loyal ones (Berry and Parasuraman, 1991).
Consequently, relationship marketing may be used to describe a plethora of
marketing relationships, such as those between a firm and its buyers, suppliers,
employees and regulators (Morgan and Hunt, 1994).
In order for me concept of relationship marketing to be useful, Berry
(1983) identified three conditions should exist, and they are:
1. the customer must have an ongoing or periodic desire for me service;
2. the service customer must control me selection of me supplier; and
3. there must be an alternate supplier
The importance of relationship marketing has emerged as a major focal
point for business strategy during the past decade, and this can be attributed to
factors such as the blurring boundaries between markets or industries (Day,
2000), an increasing fragmentation or markets (Buttle, 1999), shorter product life
cycles, rapid changing customer buying patterns and more knowledgeable and
sophisticated customers (Buttle, 1999; Gronroos, 1996). In addition, other
explanations for the shift towards relationship orientation in marketing include the
continuing growth of the service economy as well as the increasing competition
110
in the current marketplace (Christopher et al., 1991; Lehtinen, 1996). Regarding
practice, firms are also considering the adoption of relationship marketing
strategies as critical for sustaining a competitive advantage (Sharma et al.,
1999). Due to above reasons, the philosophy of relationship marketing is being
advocated more and more strongly by marketers (Gummesson, 1994).
The management of customer relationships in the service industry is
critical for many reasons. Firstly, as Lovelock (1983) points out, many services
by their very nature require ongoing membership (e.g. insurance, cable
television). Even when membership is not required, customers may seek on-
going relationships with service providers to reduce their perceived risk in
evaluating services characterised by intangibility and credence properties. In
addition, due to the intangibility of services, customer evaluative criteria are less
well articulated, and the appraisal of the value received is much more subjective
(Berry, 1980; Zeithaml et al., 1993). Therefore, customers are more likely to form
relationships with individuals and with the organisations they represent than with
goods (Bendapudi and Berry, 1997). Finally, employees play a major role in
shaping the service experience as the interface between the service and its
provider is inseparable; therefore, the service setting is especially conducive to
customers forming relationships in services. Consequently, there have been
calls for greater attention to the role of relationships in services (Gronroos, 1990;
Gummesson, 1987a; Sheaves and Barnes, 1996).
However, despite the growing importance and emphasis on relationship
marketing, the operationalisation of this concept is still unclear. While much work
has been focused on generic issues, little concentrates on areas s that are
influenced by the nature of the industry or transaction concerned (Pressey and
Mathews, 2000). In addition, there has been a lack of studies that examined
relationship of variables at different levels of relationships (Macintosh and
Lockshin, 1.997). Splitting the relationship variables into different levels will allow
for the identification of basic differences in person-to-person (interpersonal) as
well as person-to-firm (company) relationships. The recognition of these
differences has practical implications for managers as they can direct their
111
efforts to improving important attributes on both the interpersonal as well as
company level. In doing so, firms striving for true customer intimacy can find
ways to bridge the perceptual gap between the two levels of relationships
highlighted above.
One of the objectives of current marketing practices is building up
relationships. This is based on the belief that it costs several times more to find a
new customer than to retain an existing customer. A customer who leaves, is
taking away the profit-making potential of the company. Therefore, it is argued,
that it is worthwhile to try to retain existing customers, to build strong long-term
relationships with them, so that they remain loyal. If there is a normal attrition of
say, 25 per cent in customers, reducing that by 5 per cent can mean substantial
savings by way of increased purchases, reduced operating costs, and more
references. A study by the All India Management Association showed that
among service firms, reducing customer defections by just 5 per cent boosted
profits by 25 per cent to 35 per cent.
Relationships of a long standing nature, are built on trust and confidence
that one’s interests are safe in the hands of the other. Attempts to exploit the
other’s compulsions mitigate against this principle. When a supplier refuses to
take advantage of the customer’s difficulties, customer’s trust is created.
Relationships are based on closeness and trust. Local retailers have this
advantage as their customer loyalties are strong. It is possible for them to source
well-known products directly from the manufacturers and sell them under their
own brands, thus displacing national brands with their own local brands
4.1.1 Concept of Trust and Commitment
The notion of trust as a critical success factor in service relationship was introduced by Parasuraman et al. (1985), who suggested that customers should be able to trust their service providers, feel safe in their dealings with their service providers, and be assured that their dealings are kept confidential. Many conceptualisations and operationalisations of trust in relationships have been offered in the literature (e.g. Crosby et al., 1990; Doney and Cannon, 1997; Moorman et al., 1993). This research conceptualisation is consistent with extant
112
research in that trust is defined in terms of a customer’s perceptions of service representative confidentiality, honesty, integrity and high ethical standards.
The perceived level of trust between exchange partners is an important criterion for understanding the strength of marketing relationships which has been defined in a variety of related ways. Trust is a fundamental relationship model building block and is included in most relationship models (Wilson, 1995). Trust has been defined in various ways in the relationship marketing literature: “as a willingness to rely on an exchange partner in whom one has confidence” (Moorman et al., 1992); and as “the belief that a partner’s word or promise is reliable and a party will fulfil his/her obligations in the relationship” (Schurr and Ozanne, 1985).
These two definitions of trust draw on Rotter’s (1967) classic view that trust is:
“…a generalized expectancy held by an individual that the word of another … can be relied on.”
Both definitions also stress the importance of confidence on the part of the trusting partner. Anderson and Narus (1984) focus on the perceived outcome of trust when they define it as:
“… a partner’s belief that the other partner will perform actions that will result in positive outcomes, as well as not take unexpected actions that would result in negative outcomes.”
Indeed, one would expect a positive outcome from a partner on whose integrity one can confidently rely on (Morgan and Hunt, 1994). Other authors have defined trust in terms of shared values (Heide and John, 1992; Morgan and Hunt, 1994), mutual goals (Wilson, 1995), opportunistic behavior (Dwyer et al., 1987; Morgan and Hunt, 1994), making and keeping promises (Bitner, 1990, 1995; Bitner et al., 1994) and uncertainty (Crosby et al., 1990; Parasuraman et al., 1985). Trust is also viewed as central in studies conducted by the Industrial Marketing and Purchasing Group (Ford, 1990; Hakansson, 1982). Using a similar definition of trust, Gwinner et al. (1998) find the psychological benefit of confidence and trust to be more important than special treatment or social benefits in consumer relationships with service firms.
113
Finally, Gronroos (1996) suggests that:
“…the relationship philosophy approach relies on … a trusting relationship
with customers … instead of an adversarial approach to customers…”
As can be seen, trust is an important construct in relational exchange
because relationships characterised by trust are so highly valued that parties will
desire to commit themselves to such relationships (Hrebiniak, 1974).
Undeniably, because commitment encompasses vulnerability, parties will seek
only trustworthy partners (Morgan and Hunt, 1994). To support this notion, trust
has been posited as a major determinant of relationship commitment (Achrol,
1991; Miettilia and Moller, 1990; Morgan and Hunt, 1994). Further, Moorman et
al. (1992) find that trust by marketing research users in their research providers
significantly affected user commitment to the research relationship.
Similar to trust, commitment appears to be one of the most important
variables for understanding the strength of a marketing relationship, and it is a
useful construct for measuring the likelihood of customer loyalty as well as for
predicting future purchase frequency (Dwyer et al., 1987; Gundlach et al., 1995;
Morgan and Hunt, 1994). While commitment is the most common dependent
variable used in buyer-seller relationship studies (Wilson, 1995), there has been
no agreement as to the proper measurement scale to use for this
multidimensional construct (Hocutt, 1998). Furthermore, to date, there has been
a lack of attention to the paucity of consumer research on the definition and
measurement of commitment (Kelley and Davis, 1994; Morgan and Hunt, 1994).
In the relationship marketing literature, commitment has been described in
many ways. Moorman et al. (1992), define commitment as an enduring desire to
maintain a valued relationship. The term “valued relationship” emphasized the
belief that commitment exists only when the relationship is considered important.
This implies a higher level of obligation to work a relationship and to make it
mutually satisfying and beneficial (Gundlach et al., 1995; Morgan and Hunt,
1994). Since commitment is higher among individuals who believe that they
receive more value from a relationship, highly committed customers should be
willing to reciprocate effort on behalf of a firm due to past benefits received
114
(Mowday et al., 1982). This perspective is consistent with Dwyer et al.’s (1987)
definition of commitment in a buyer-seller relationship as the existence of:
“…an implicit or explicit pledge of relational continuity between exchange
partners.”
A great deal of research in the services literature has documented the
importance of trust in maintaining satisfactory service provider/customer
relationships (e.g. Crosby et al., 1990; Doney and Cannon, 1997; Dorsch et al.,
1998).
4.2 DISCRETE VS. RELATIONAL EXCHANGE
In relationship marketing, relationships can be classified into two types i.e.
discrete transactions and relational exchange. Whereas discrete transaction
relationship is one which has a distinct beginning, short duration, and sharp
ending by performance. And in relational exchange “traces to previous
agreements reflecting an going process” categorized with reference to a focal
firm and relational exchange in supplier, lateral, buyer and internal partnerships.
Figure 1 shows ten discrete forms of relationships marketing.
Figure 4.1: Discrete Forms of Relationship Marketing
Focal Firm
Goods Suppliers
Business Units
Employees
Financial
Intermediate Customers
Ultimate Customers
Government
Competitors
Service Suppliers
Supplier Partnerships
Buyer Partnerships Internal Partnerships
115
Figure 4.1 explains the relational exchanges between the focal firm and
various agencies for building discrete relationships.
1. The partnering involved in relational exchanges between manufacturers
and their goods suppliers as in “just-in-time” procurement and total quality
management.
2. Relational exchanges involving service providers as between advertising
and research agencies and their clients.
3. Strategic alliances between firms and their competitors as in technology
alliances; co-marketing alliances and global strategic alliances.
4. Alliances between an firm and non-profit organizations as in public-
purpose partnerships.
5. Partnerships for joint research and development as between firms and
local, state governments.
6. Long-term exchanges between firms and ultimate customers, as
particularly recommended in the services marketing area.
7. Relational exchanges of working partnerships as in channels of
distribution.
8. Exchanges involving functional departments.
9. Exchanges between a firm and its employees, as in internal marketing.
10. Within firm relational exchanges involving such business units as
subsidiaries, divisions and strategic business units.
Relationship marketing is probably most advanced within the telecom
sector. In UK, BT has worked hard at defending itself against the price war
waged by Mercury. Both of them introduced various loyalty schemes to retain the
existing customers. Both of them are strongly emphasizing on various
relationship marketing practices. There highlight the need to balance existing
and new customers in order to achieve maximum long-term profitability.
116
Figure 4.2: The Hypothesized Realm of Buyer Seller Relationship
Relationship marketing has been defined in different areas. For example
in services marketing area, Berry (1983) states, “Relationship marketing is
attracting, maintaining and in multi-service organization enhancing customer
relationships”. In industrial marketing, Jackson (1985) refer to relationship
marketing as “marketing oriented toward strong, lasting relationships with
individual accounts. The Hypothesized realm of buyer seller relationship also
supports the need of both seller maintained relationship as well as buyer’s
motivational investment.
4.3 THE RELATIONSHIP DEVELOPMENT PROCESS
Relationships evolve through five general phases identified as (1)
Awareness (2) Exploration (3) Expansion (4) commitment (5) Dissolution. Each
phase represents a major transition in how parties regard one another.
High
Low
Discrete exchange (spot contract)
Seller’s Market
Buyer maintained relation High
Buyers Motivational Investment in relationship
Bilateral relationship maintenance
Seller maintained relation
Buyer’s market
117
4.3.1 Phase-I Awareness
Awareness refers to party’s recognition that another party is a feasible
exchange partner. Situational proximity between the parties facilitates
awareness. Just as a family is more likely to be acquainted with adjacent
neighbour than those down the street, buyers are apt to become aware of local
merchants and brands advertised in frequently viewed media.
Figure 4.3: Stages in buyer and seller relationship development (based on Dwyer, F.R., P.H. Schurr and S. Oh (1987) ‘Developing Buyer and Seller
Relationships’, Journal of Marketing, 51, April, pp. 11-27
Time
4.3.2 Phase-II Exploration
In this phase, potential exchange partner first considers obligations,
benefits, burdens and possibility of exchange. The exploration phase is
conceptualized in five sub processes.
• Attraction
• Communication and bargaining
• Development and exercise of power
• Norm development
• Expectation development
Awar
enes
s
Expl
orat
ion
Expa
nsio
n
Dis
solu
tion
Com
mitm
ent
Stre
ngth
of R
elat
ions
hip
118
4.3.3 Phase-III Expansion
Expansion refers to the continual increase in benefits obtained by
exchange partners and to their increasing interdependence. The five sub
processes introduced in the exploration process also operate in expansion
phase. The range and depth of mutual dependence also increases. The
intensive business growth strategies of market penetration and product
development depend on the process of expansion. For example, Procter and
Gamble depended its relationship with telemarketing distribution.
4.3.4 Phase-IV Commitment
Commitment refers to a implicit or explicit pledge of relational continuity
between exchange partners. At the most advanced phase of buyer-seller inter-
dependence the exchange partners have achieved a level of satisfaction from
the exchange process that virtually precludes other primary exchange partners
who could provide similar benefits .Three measurable criteria of commitment are
inputs, durability and consistency. The first criteria of commitment is that the
parties provide relatively high inputs to the association in terms of economic,
communication ad emotional resources. Durability presumes the parties can
discern the benefits attributable to the exchange relation and anticipate an
environment that will abet continued effective exchange. Given these
expectation the parties can bond themselves such way as to encourage their
continued investment in the relation .The third aspect of commitment is the
consistency with which the inputs are made to the association. In spite of
increased costs of transaction, decreased obstacles and changing personal
needs the consistency accrue benefits of certainty, efficiency and effectiveness.
4.3.5 Phase-V Dissolution
The relationship development process has great consequence when they
occur after parties have reached the status of interdependence characteristic of
the status of the expansion and commitment phases. Termination of personal
relationships is a significant source of psychological , emotional and physical
stress.
119
4.4 TRUST AND COMMITMENT AS KEY MEDIATING VARIABLES
This research labels trust and commitment as key mediating variables of
relationship marketing which focuses on one party in the relational exchange and
that party’s relationship commitment and trust. Commitment and trust are “key”
because they encourage marketers to
a) Work at preserving relationship investments by co-operating with
exchange partners.
b) Resist attractive short-term alternatives in favour of the expected long-
term benefits of staying with existing partners and
c) View potentially high risk action as being prudent because of the belief
that their partners will not act opportunistically.
Therefore, when both commitment and trust-not just one or the other-are
present, they produce outcomes that promote efficiency, productivity and
effectiveness. In short, commitment and trust lead directly to co-operative
behaviours that are conductive in relationship marketing success.
The pioneering work by Reichheld, a partner at the US consulting firm and
by Sesser, a professor at the Harvard Business School (Reich held and session
1990) suggested that there is a high correlation between customer retention an
company profitability. They investigated the impact on profitability of improved
customer retention in a number of organizations and found that customer
retention improvement in a number of organizations and found that customer
retention improvement of just a few percentage points had a dramatic impact on
improvement of profitability. They found five percentage points increase in
customer retention, for a range of service businesses, yielded improvement in
profitability, in net present value (NPV) terms, from 20% to 85%.
Reichheld and Sasser identified of reasons why there is such as dramatic
effect on profitability by improved retention rates. Factors include:
− Retained business
− Sales, marketing and set up costs are amortised over a longer customer
lifetime
120
− Increased expenditure over time
− Repeat customers often cost less to service
− Satisfied customers can provide referrals
− Satisfied customers may be willing to pay a price premium
Figure 4.4: Benefits of Loyal Customers
In some cases such managers work within service business where the
profit impact of retention has already been identified such as banking, credit
cards, insurances broking, commercial laundries, etc. (Reichheld and Sasser
1990). However, uncomfortable with direct analogies being made between their
business and other industrial sectors. In particular, they wish to learn whether a
given improvement in retention (for example, five percentage points will have a
low improvement in NPV profitability (say 10-20%) or a high improvement in
NPV profitability (say 80-100%)
4.5 CATEGORIES OF TRUST
Trust is part of a large set of interpersonal risk assessment perspectives,
which can be categorized into three groups: (1) Calculative Trust (assessing how
incentives affect another person’s behavior); (2) Empathetic Trust (merging
one’s behavior); and (3) Knowledge based Trust (using our personal
CRM Program
• Better responsiveness to customer needs
• Increased customer satisfaction
Customer Database
Data Mining
Cross-selling
Better Target Marketing
Market Research
• Increase ARPU*
• Cost reductions • More target communications
• New customer insight • Early warning system
LOYAL CUSTOMERS • Increased ARPU* • Stronger brand attitude • Less price sensitive • Educe customer chum
Relationship
121
recollections or other reliable information to help understand a person’s or
group’s competencies, preferences, and moral habits).
4.5.1 Calculative Trust
The economic approach treats trust as risk analysis that focuses on
human behavior. This “calculative” view defines trust as a level of subjective
probability with which and agent (A) assesses that another agent or group of
agents will perform another agent or group of agents (B) will perform a particular
action that affects A’s own actions.
Economist Oliver Williamson (1993) calls this “calculative trust,” “an
economic view of behavior that has merit to the extent that others behaviors can
be well under stood as rational and measurable. In these cases, trust can
smooth the progress of private information sharing. Will beyond levels that sheer
market calculative ness could do.
4.5.2 Empathetic trust
Empathetic trust assumes that one party has fully internalized the other
party’s preferences, and demands that both parties commit to building and
maintaining a personal relationship. Thus, information sharing and joint problem
solving can occur with very little transaction cost-if empathetic trust rules.
Edward Deming (1986), have `long argued that using rewards and punishments
tends destroy the internalization of others; preferences destroy the internalization
of others preferences essential for deep trust.
4.5.3 Knowledge-based Trust
More recently, Francis Fukuyama (1995) described economic networks a
fundamentally bade on reciprocal moral obligations, a generalized trust among
unrelated people. Certainly a capacity for honoring commitments ranks highly as
a desired character trait, and in the fast paced e-business economy the
willingness of and individual to share private information quickly and completely
is greatly valued.
122
4.6 TRUST AND PROFITABILITY
The November 2000 issue of the business magazine executive excellent
contained four articles titled: “Customer Love,” “Customer Intimacy,” “Customers
for life,” and “Customer Loyalty.” Each consultation author extolled the virtues of
retaining customers for the longest period possible by developing strong
relationship with them.
However, there is considerable anecdotal evidence to suggest that many
customers do not want a relationship with most of the products and services
(and thus the companies) that they buy. People simply don’t have the tie,
products and services. The reason for this is that relationships are special. They
involve two way trust, commitment, the sharing of information, partnership
among people of equal standing, and so on.
Contrary to the line of argument that long-life customers are more
profitable, Grahame Dowling and Mark Uncles caution that loyal customers will
often be less profitable. One reason for this is that they may expect a reward for
their loyalty. This may be in the form of a price discount (for accumulated
volume) or extra free services.
Various studies indicate that it is the amount of money that a customer
spends with a company drives their lifetime value to the company regardless of
how long they have been or are likely to be a customer of the company. Hence,
marketing strategy should be focused on revenue generation and transaction
cost management in preference to the creation of loyal customers. Database-
driven CRM has claimed significant improvements in identifying profitable (and
unprofitable customers, increasing the efficiency and effectiveness of target
marketing, and increasing customer satisfaction. Critics have argued that:
gathering an extensive amount of information about customers (“a 360 degree
view”) raises concerns about privacy: managers concentrate less on what
customers really want (their latent and expressed needs) and more on what the
data patterns suggest they may want; and relationships seldom develop beyond
satisfaction into rapport because they start with the seller “targeting” the
customer and then attempting to seduce them. CRM programs have also
123
experienced some significant implementation problems. Examples include, the
high turnover rates of staff in call centers, the frequent cost blowouts associated
with constructing a data warehouse, problems implementing new information
technology systems, and the high cost involved in designing anew information
The top row of effects leads to building relationships with customers and
thus established customer loyalty. Depending on the type of product (e.g., high
or low involvement), this relationship can be based on an affective association.
124
CHAPTER – 5
RESEARCH FINDINGS OF THE STUDY
5.1 BRIEF OVERVIEW
Trust and commitment and related key mediating variables – shared
value, communication and opportunistic behavior play significant role between
both prepaid and postpaid subscribers. These key-mediating variables of trust
and commitment vary telecom service provider-wise and city-wise.
The above hypotheses are studied using normal-curve, Cronbach α,
ANOVA, chi-square, t-test, spearman’s rank correlation and Pearson’s
correlation coefficient. With the help of these techniques various hypotheses can
be tested.
Part-A presents results of trust and commitment from the subscriber’s
perspective. Part-B discusses findings from dealers’ viewpoint and Part-C
explains results of data of telecom service provider.
(PART-A)
5.2 RELIABILITY FOR DATA COLLECTED FROM SUBSCRIBERS
Prior to analysis of the results, the research instrument was tested for its
reliability. Several measures of reliability can ascertain the reliability of a
measuring instrument. These include test-retest method, equivalent forms, split
halves method and internal consistency method. Of all the above methods, the
internal consistency method requires only one administration and consequently
is supposed to be most effective, especially in field studies. Moreover, this
method is considered to be the most general form of reliability analysis. In this
method, reliability is operationalised as internal consistency, which is the degree
of inter correlation among the items that constitute a scale. Internal consistency
is estimated using a reliability coefficient called Cronbach’s alpha. An alpha
value of 0.60 and 0.70 or above is considered to be the criterion for
demonstrating internal consistency of new scales and established scales
125
respectively. Reliability of the measurements was determined using Cronbach’s
Coefficient alpha. In this research, following are the research for the data
collected from subscribers:
RELIABILITY ANALYSIS - SCALE (ALPHA)
Number of Cases = 311.0 Number of Items = 61
Cronbach Alpha = .9676
Cronbach's α (alpha) is an important psychometric instrument to measure
the reliability of data. The reliability coefficient indicates that the scale for
measuring trust and commitment is a reliable. So, various statistical tools can be
applied and tested.
5.3 RELATIONSHIP BETWEEN KEY-MEDIATING VARIABLES OF PREPAID AND POSTPAID SUBSCRIBERS OF SELECTED TELECOM SERVICE OPERATORS
Degree of relationship was studied between key mediating variables –
shared value, communication, opportunistic behavior, trust and commitment for
prepaid and postpaid subscriber. It covers selected telecom service providers i.e.
Reliance, Airtel, Hutch and Tata Indicom.
Table 5.1: Evaluation of Relationship between Key Mediating Variables of Reliance Prepaid Subscribers
Variables Shared Value Communication Opportunistic
Behaviour Trust Relationship Commitment
Shared Value 1 .694(**) .697(**) .793(**) .620(**)
Communication 1 .588(**) .665(**) .610(**)
Opportunistic Behaviour 1 .843(**) 0.27
Trust 1 .419(*)
Relationship Commitment 1
**Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).
126
The above table 5.1 shows Pearson’s correlation between key mediating
variables of Reliance prepaid subscribers. The correlation between shared value
and communication (.694), shared value and opportunistic behaviour (.697),
shared value and trust (.793) and shared value and relationship commitment
(.620) is highly significant amongst prepaid subscribers of Reliance. Similarly,
the relationship between communication and opportunistic behaviour (.588),
communication and trust (.665) and communication and relationship commitment
(.610) is highly significant. The correlation between opportunistic behaviour and
trust (.843) is highly significant and trust and relationship commitment (.419) is
significant amongst prepaid subscribers of Reliance. However, correlation
between opportunistic behavior and relationship commitment is non-significant.
Table 5.2: Evaluation of Relationship between Key Mediating Variables of Airtel Prepaid Subscribers
Variables Shared Value Communication Opportunistic
Behaviour Trust Relationship Commitment
Shared Value 1 0.327 .644(**) 0.289 .430(*)
Communication 1 .680(**) .845(**) .591(**)
Opportunistic Behaviour 1 .566(**) .641(**)
Trust 1 .550(**)
Relationship Commitment 1
** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.051 level (2-tailed).
The above table 5.2 shows Pearson’s correlation between key mediating
variables of Airtel prepaid subscribers. The above table depicts highly significant
at .01 level correlations between shared value and opportunistic behavior (.644)
and significant correlation between shared value and relationship commitment
amongst prepaid subscribers of Airtel. However, it has non-significant
relationship with communication and trust. Communication is highly significant in
relation to opportunistic behaviour (.680) and trust (.845) and relationship
commitment (.591). Opportunistic behaviour is highly significant in relation to
127
trust (.566) and relationship commitment (.641). Trust is highly significant in
relation to relationship commitment (.550) of prepaid subscribers of Airtel.
Table 5.3: Evaluation of Relationship between Key Mediating Variables of Hutch Prepaid Subscribers
Variables Shared value Communication Opportunistic
Behaviour Trust Relationship Commitment
Shared Value 1 .717(**) .699(**) .645(**) .554(**)
Communication 1 .670(**) .740(**) .730(**)
Opportunistic Behaviour 1 .714(**) .677(**)
Trust 1 .619(**)
Relationship Commitment 1
** Correlation is significant at the 0.01 level (2-tailed).
The above table 5.3 shows Pearson’s correlation between key mediating
variables of Hutch prepaid subscribers The above table shows highly significant
correlation between shared value and communication (.717), shared value and
opportunistic behaviour (.699),shared value and trust(.645) and shared value
and relationship commitment (.554) of prepaid subscribers of Hutch. Similarly,
the correlation between communications and opportunistic behaviour (.670),
communication and trust (.740) and communication and relationship commitment
(.730) is highly significant. The correlation between opportunistic behaviour and
trust (.714) and opportunistic behaviour and relationship commitment (.677) is
highly significant. Similarly, the relationship between trust and commitment
(.619) is highly significant amongst prepaid subscribers of Hutch.
128
Table 5.4: Evaluation of Relationship between Key Mediating Variables of Tata Indicom Prepaid Subscribers
Variables Shared Value Communication Opportunistic
Behaviour Trust Relationship Commitment
Shared Value 1 .895(**) .874(**) .808(**) .848(**)
Communication 1 .900(**) .884(**) .862(**)
Opportunistic Behaviour 1 .873(**) .866(**)
Trust 1 .807(**)
Relationship Commitment 1
** Correlation is significant at 0.01 level (2-tailed).
The above table 5.4 shows Pearson’s correlation between key mediating
variables of Tata prepaid subscribers. The above correlation matrix explains
highly significant relationship between shared value and communication (.895),
shared value and opportunistic behaviour (.874), shared value and trust (.808)
and shared value of shared value and relationship commitment (.848). Similarly,
the relation between communication and opportunistic behaviour (.900),
communication and trust (.844) and communication and relationship commitment
(.862) is highly significant at 99level. The correlation between opportunistic
behaviour and trust (.873), opportunistic behaviour and relationship commitment
(.866) and trust and relationship commitment is highly significant amongst
prepaid subscribers of Tata Indicom.
129
Table 5.5: Evaluation of Relationship between Key Mediating Variables of Reliance Postpaid Subscribers
Variables Shared Value CommunicationOpportunistic
Behaviour Trust Relationship Commitment
Shared Value 1 .822(**) .765(**) .803(**) .747(**)
Communication 1 .806(**) .911(**) .809(**)
Opportunistic Behaviour 1 .767(**) .607(**)
Trust 1 .830(**)
Relationship Commitment 1
** Correlation is significant at 0.01 level (2-tailed).
The above table 5.5 shows Pearson’s correlation between key mediating
variables of Reliance postpaid subscribers. As can be checked from the table,
Pearson’s correlation is highly significant amongst postpaid subscribers of
Reliance between shared value and communication (.822), shared value and
opportunistic behaviour (.765), shared value and trust (.803) and shared value
and relationship commitment (.747). Similarly, the relation between
communication and opportunistic behaviour (.806), communication and trust
(.911) and communication and relationship commitment (.809) is highly
significant at 99level. The correlation between opportunistic behaviour and trust
(.767), opportunistic behaviour and relationship commitment (.607) and trust and
relationship commitment (.830) is highly significant amongst postpaid
subscribers of Reliance.
130
Table 5.6: Evaluation of Relationship between Key Mediating Variables of Airtel Postpaid Subscribers
Variables Shared Value Communication Opportunistic
Behaviour Trust Relationship Commitment
Shared Value 1 .643(**) .341(**) .593(**) .508(**)
Communication 1 .426(**) .668(**) .519(**)
Opportunistic Behaviour 1 .648(**) .462(**)
Trust 1 .638(**)
Relationship Commitment 1
** Correlation is significant at 0.01 level (2-tailed)
The above table 5.6 shows Pearson’s correlation between key mediating
variables of Airtel postpaid subscribers. As can be checked from the table,
Pearson’s correlation is highly significant amongst postpaid subscribers of Airtel
between shared value and communication (.643), shared value and opportunistic
behaviour (.341), shared value and trust (.593) and shared value and
relationship commitment (.508). Similarly, the relation between communication
and opportunistic behaviour (.426), communication and trust (.668) and
communication and relationship commitment (.519) is highly significant. The
correlation between opportunistic behaviour and trust (.648), opportunistic
behaviour and relationship commitment (.462)) and trust and relationship
commitment (.638) is highly significant amongst postpaid subscribers of Airtel.
131
Table 5.7: Evaluation of Relationship between Key Mediating Variables of Hutch Postpaid Subscribers
Variables Shared Value Communication Opportunistic
Behaviour Trust Relationship Commitment
Shared Value 1 .514(**) .548(**) .435(**) .285(*)
Communication 1 .777(**) .636(**) .590(**)
Opportunistic behaviour 1 .587(**) .560(**)
Trust 1 .534(**)
Relationship commitment 1
** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed).
The above table 5.7 shows Pearson’s correlation between key mediating
variables of Hutch postpaid subscribers. As can be seen from the table,
Pearson’s correlation is highly significant amongst postpaid subscribers of Hutch
between shared value and communication (.514), shared value and opportunistic
behaviour (.548), and shared value and trust (.435) But the relationship between
shared value and relationship commitment (.285) is significant at .05 level.
Similarly, the relation between communication and opportunistic behaviour
(.777), communication and trust (.636) and communication and relationship
commitment (.590) is highly significant at 99% level. The correlation between
opportunistic behaviour and trust (.587), opportunistic behaviour and relationship
commitment (.560)) and trust and relationship commitment (.534) is highly
significant amongst postpaid subscribers of Hutch.
132
Table 5.8: Evaluation of Relationship between Key Mediating Variables of Tata Indicom Postpaid Subscribers
Variables Shared value Communication Opportunistic
behaviour Trust Relationship commitment
Shared value 1 .807(**) .853(**) .861(**) .784(**)
Communication 1 .901(**) .934(**) .868(**)
Opportunistic behaviour 1 .896(**) .879(**)
Trust 1 .796(**)
Relationship commitment 1
** Correlation is significant at the 0.01 level (2-tailed)
The above table 5.8 shows Pearson’s correlation between key mediating
variables of Tata Indicom postpaid subscribers. As can be seen from the table,
Pearson’s correlation is highly significant amongst postpaid subscribers of Tata
Indicom between shared value and communication (.807), shared value and
opportunistic behaviour (.853), and shared value and trust (.861) and shared
value and relationship commitment (.784) is significant at .01 level. Similarly, the
relation between communication and opportunistic behaviour (.901),
communication and trust (.934) and communication and relationship commitment
(.868) is highly significant at 99% level. The correlation between opportunistic
behaviour and trust (.896), opportunistic behaviour and relationship commitment
(.879)) and trust and relationship commitment (.796) is highly significant amongst
postpaid subscribers of Tata Indicom.
133
5.4 THE EMPIRICAL TEST
5.4.1 Reliability Analysis - Scale (Alpha) for Key Mediating Variable -Shared Value
RELIABILITY COEFFICIENTS
N of Cases = 367.0 N of Items = 3 Cronbach Alpha = .7839
Cronbach α (alpha) has an important use of a psychometric instrument to
measure reliability. The data for analysis is reliable one as its value is >0.6.So,
various statistical tools can be applied and tested.
5.4.1.1 Results for Shared Value between Service Provider and Prepaid and Postpaid Subscribers
In order to test the difference between Telecom Service Provider (TSP),
prepaid/postpaid subscribers and their interaction following hypotheses are
specified:
Ho1: There is no significant difference for shared value between all the
four TSP’s.
HoA: There is significant difference for shared value between all the four
TSP’s.
Ho2: There is no significant difference for shared value between prepaid
and postpaid subscribers.
HoA: There is significant difference for shared value between prepaid
and postpaid subscribers.
Ho3: There is no interaction for shared value between TSP and prepaid
and postpaid subscribers.
HoA: There is interaction for shared value between TSP and prepaid and
postpaid subscribers.
These hypotheses can be tested using two-way ANOVA and the results
are presented in Table 5.9.
134
Table 5.9: Two way ANOVA for Service Provider and Prepaid and Postpaid Subscribers
Source Type III Sum of Squares df Mean Square F-value p-value
Corrected Model 15.636 7 2.234 3.674 0.001
Intercept 2733.799 1 2733.799 4496.536 0.001
TSP 2.171 3 0.724 1.19 0.313
Pr/Po 12.028 1 12.028 19.783 0.001
TSP * Pr/Po 2.522 3 0.841 1.383 0.248
Error 231.64 381 0.608
Total 3579.218 389
Corrected Total 247.276 388
TSP: Telecom Service Providers; Pr/Po: Prepaid and Postpaid
The table 5.9 explains ANOVA results for shared value between Prepaid
and Postpaid subscribers (Pr/Po) for selected Telecom Service Providers (TSP).
It is observed that the variation in the shared value is significant in the prepaid
and postpaid services.
Shared Value and its Dimensions of Prepaid and Postpaid Subscribers
has been further analysed by Using t-test below.
Table 5.10: Comparison of Shared Value and its Dimensions for Reliance Subscribers
Variables Prepaid/postpaid Mean Std. Deviation t -value p -value
Prepaid 2.4617 0.9969 Shared Value
Postpaid 3.1093 0.605 3.15 0.003**
Prepaid 2.5417 1.1413 Privacy
Postpaid 2.9455 0.7798 1.58 0.124
Prepaid 2.44 1.193 Security
Postpaid 3.1311 0.8461 2.637 0.012*
Prepaid 3.5556 0.8864 Ethics
Postpaid 2.8433 0.5434 3.891 .001**
* Significant at .05 level ** Significant at .01 level
135
It is evident from table 5.10, that there is significant difference between
prepaid and postpaid subscribers for shared value, security and ethics of
Reliance. However, these differences for privacy were found to be non-
significant. In case of shared value and ethics the results are highly significant as
compared to security. Shared value and security are more important for the
postpaid subscribers as compared to prepaid subscribers but as for as ethics is
concerned prepaid subscribers are better than postpaid subscribers.
Table 5.11: Comparison of Shared Value and its Dimensions for Airtel Subscribers
Variables Prepaid/postpaid Mean Std. Deviation t -value p –value Prepaid 2.7333 0.6859
Shared Value Postpaid 3.0169 0.7066
1.821 0.072
Prepaid 2.7241 0.8408 Privacy
Postpaid 2.9538 1.1379 0.974 0.333
Prepaid 2.9655 1.0851 Security
Postpaid 3.0476 0.9743 0.362 0.718
Prepaid 3.4897 0.4916 Ethics
Postpaid 3 0.6946 3.437 0.001**
** Significant at .01 level
It is evident from table 5.11, that there is significant difference between
prepaid and postpaid for ethics of Airtel. However, these differences for shared
value, privacy and security were found to be non-significant. In case of ethics are
more important for prepaid (3.48) subscribers as compared to the postpaid (3)
one.
Table 5.12: Comparison of Shared Value and its Dimensions for Hutch Subscribers
Variables A2.2 Mean Std. Deviation t p-value Shared Value Prepaid 2.8922 0.689
Postpaid 3.0647 0.5059 1.411 0.161
Privacy Prepaid 2.9355 0.9978 Postpaid 3.2206 0.7091
1.625 0.107
Security Prepaid 2.871 1.0565 Postpaid 2.9559 0.8713
0.42 0.675
Ethics Prepaid 3.1359 0.7686 Postpaid 2.9824 0.5361
1.02 0.313
136
It is evident from table 5.12, that there is not significant difference
between prepaid and postpaid for shared value, privacy, security and ethics of
Hutch.
Table 5.13: Comparison of Shared Value and its Dimensions for Tata Indicom Subscribers
Variables Prepaid/postpaid Mean Std. Deviation t -value p -value
Shared Value Prepaid 2.5796 1.0458
Postpaid 2.9915 1.0268 1.907 0.059
Privacy Prepaid 2.6944 1.2147
Postpaid 3.0968 1.1694 1.619 0.109
Security Prepaid 2.2778 1.2786
Postpaid 2.8387 1.2307 2.144 0.035*
Ethics Prepaid 3.2333 1.0855
Postpaid 2.9619 0.9863 1.27 0.207
It is evident from table 5.13, that there is significant difference between
prepaid and postpaid subscribers for security of Tata Indicom. However, these
differences for shared value, privacy and ethics were found to be non-significant.
Security is more important for the postpaid(2.83) subscribers as compared to
prepaid(2.27) subscribers.
5.4.1.2 Results of Prepaid and Postpaid Subscribers and City-Wise Results for Shared Value
In order to test the difference between selected cities and
prepaid/postpaid subscribers and their interaction following hypotheses are
specified:
Ho1: There is no significant difference for shared value between all the
four cities.
HoA: There is significant difference for shared value between all the four
cities.
Ho2: There is no significant difference for shared value between prepaid
and postpaid subscribers.
137
HoA: There is significant difference for shared value between prepaid
and postpaid subscribers.
Ho3: There is no interaction for shared value between city and prepaid
and postpaid subscribers.
HoA: There is interaction for shared value between city and prepaid and
postpaid subscribers.
These hypotheses can be tested using two-way ANOVA and the results
are presented in Table 5.14.
Table 5.14: Two Way ANOVA for Selected Cities and Prepaid and Postpaid Subscribers
Source Type III Sum of Squares df Mean Square F-value p-value
Corrected Model 36.780 7 5.254 9.51 0.001
Intercept 2753.05 1 2753.05 4983.057 0.001
City 17.313 3 5.771 10.445 0.001
Pr/Po 9.977 1 9.977 18.058 0.001
City*Pr/Po 7.12 3 2.373 4.296 0.005
Error 210.496 381 0.552
Total 3579.218 389
Corrected Total 247.276 388
Pr/Po: Prepaid and Postpaid
Table 5.14 exhibits ANOVA results for shared value between Prepaid and
postpaid subscribers for selected cities. It is observed that the variation in the
shared value is significant in the prepaid and postpaid services as well as in the
selected cities. The interaction between selected city and prepaid and postpaid
subscribers is also significant. Further, a Post-Hoc Scheffe’s multiple
comparisons was carried out.
138
Table 5.15: Multiple Comparisons (Selected Cities)
95% Confidence Interval
(I) City (J) City
Mean Difference (I-J)
Std. Error
p-value Lower
Bound Upper Bound
Amritsar .4809(*) 0.1062 0.001 0.1827 0.7791
Patiala .5457(*) 0.1068 0.001 .0.2459 0.8455 Ludhiana
Chandigarh .5023(*) 0.1059 0.001 0.2049 0.7997
Above table 5.15 explain the results of a post-hoc test for multiple
comparisons among selected cities. After applying the test, it was observed that
there are significant differences between Ludhiana and other cities. But
differences are not significant amongst other cities. One of the reason could be
that Ludhiana is an industrial town and business community is very specific for
selecting the particular service provider.
Shared value of Prepaid and Postpaid subscribers has been further
analysed by using t-test below:
Table 5.16: Comparison of Prepaid and Postpaid Subscribers for Shared Value and its Dimensions in Ludhiana City
Variables Prepaid/postpaid Mean Std.
Deviation t -value p -value
Prepaid 3.1679 1.0147 Shared Value Postpaid 3.3583 0.7166
1.045
0.299
Prepaid 3.1852 1.0755 Privacy
Postpaid 3.4366 0.8406 1.222
0.225
Prepaid 3.1852 1.0755 Security
Postpaid 3.3056 0.8663 0.575
0.566
Prepaid 2.8667 1.0827 Ethics
Postpaid 2.6639 0.6837 1.109
0.27
It is evident from table 5.16, that there is no significant difference between
prepaid and postpaid subscribers for shared value, privacy, security and ethics in
Ludhiana city.
139
Table 5.17: Comparison of Prepaid and Postpaid Subscribers for Shared Value and its Dimensions in Amritsar City
Variables Prepaid/postpaid Mean Std. Deviation t -value p -value
Prepaid 2.3054 0.8822 Shared Value Postpaid 3.0698 0.5571
4.428 0.001**
Prepaid 2.5333 1.2794 Privacy Postpaid 3.0333 1.0079
1.87 0.068
Prepaid 2.0645 1.1528 Security Postpaid 2.9846 0.9269
3.885 0.001**
Prepaid 3.7097 0.8822 Ethics
Postpaid 2.8492 0.6288 5.5
0.001**
It is evident from table 5.17, that there is significant difference between
prepaid and postpaid subscribers for shared value, security and ethics of
Amritsar city. However, these differences for privacy were found to be non-
significant. In case of shared value, security and ethics the results are highly
significant. Shared value and security are more important for the postpaid
subscribers as compared to prepaid subscribers but as for as ethics is
concerned prepaid subscribers are better than postpaid subscribers.
Table 5.18: Comparison of Prepaid and Postpaid Subscribers for Shared Value and its Dimensions in Chandigarh City
Variables Prepaid/postpaid Mean Std. Deviation t -value P –value
Prepaid 2.5205 0.793 Shared Value
Postpaid 2.9616 0.7408 2.755 0.007**
Prepaid 2.3333 0.9242 Privacy
Postpaid 3 1.0269 3.067 0.003**
Prepaid 2.6061 1.2976 Security
Postpaid 2.7593 1.0628 0.599 0.551
Prepaid 3.3814 0.6998 Ethics
Postpaid 3.0103 0.6305 2.684 0.009**
** Significant at .01 level
140
It is evident from table 5.18, that there is significant difference between
prepaid and postpaid subscribers for shared value, privacy and ethics of
Chandigarh city. However, these differences security were found to be non-
significant. In case of shared value, privacy and ethics the results are highly
significant. Shared value and privacy are more important for the postpaid
subscribers as compared to prepaid subscribers but as for as ethics is
concerned prepaid subscribers are better than postpaid subscribers.
5.4.1.3 Results of Shared Value and its Dimensions for City-Wise and Telecom Service Operator-Wise
In order to test the difference between selected cities and selected
telecom service providers and their interaction, following hypotheses are
specified:
Ho1: There is no significant difference for shared value between all the
four cities.
HoA: There is significant difference for shared value between all the four
cities.
Ho2: There is no significant difference for shared value between telecom
service providers.
HoA: There is significant difference for shared value between telecom
service providers.
Ho3: There is no interaction for shared value between city and telecom
service providers.
HoA: There is interaction for shared value between city and telecom
service providers.
These hypotheses can be tested using two-way ANOVA and the results
are presented in Table 5.19.
141
Table 5.19: Two Way ANOVA for Selected Cities and Telecom Service Providers
Source Type III Sum of Squares df Mean Square F-value p-valueCorrected Model 82.641 15 5.509 12.482 0.001 Intercept 3322.893 1 3322.893 7528.377 0.001 City 19.958 3 6.653 15.072 0.001 TSP 1.193 3 0.398 0.901 0.441 City*TSP 61.934 9 6.882 15.591 0.001 Error 164.636 373 0.441 Total 3579.218 389 Corrected Total 247.276 388
TSP: Telecom Service Provider
The table 5.19 explains ANOVA results for shared value for selected
telecom service provider and for selected cities. It is observed that the variation
in the shared value is significant in the selected cities and the interaction
between different telecom service providers and selected cities is also
significant.
Table 5.20: Multiple Comparisons (Selected Cities)
95% Confidence Interval
(I) City (J) City
Mean Difference
(I-J) Std. Errorp-value
Lower Bound Upper Bound
Amritsar .4809(*) .0949 0.05 0.2143 0.7474 Patiala .5457(*) .0954 0.05 0.2777 0.8137 Ludhiana
Chandigarh .5023(*) .0947 0.05 0.2365 0.7682
Table 5.20 purses the results of a post-hoc test for multiple comparisons
among selected cities. It was carried out using Scheffe’s multiple comparisons.
After applying the test, it was observed that there are significant differences
between Ludhiana and other cities. But differences are not significant amongst
other cities.
To study the relationship amongst cities and telecom service providers
further, one –way ANOVA is applied and discussed.
142
Table 5.21: One-Way ANOVA - Ludhiana and Telecom Service Provider
Source Of Variation Sum of Squares df Mean
Square F-value p-value
Between Groups 28.728 3 9.576 25.838 0.01
Within Groups 35.209 95 0.371
Shared value
Total 63.937 98
Table 5.21 exhibits that there are significant differences between Reliance,
Airtel, Hutch and Tata Indicom for Ludhiana city. Scheffe’s Post-Hoc test was
carried for multiple comparisons in order to see the significant differences
between the combination of the groups.
Table 5.22: Multiple Comparison for Shared Value between Telecom Service Providers for Ludhiana City
95% Confidence
Interval
Dependent Variable (I) TSP (J) TSP
Mean Difference
(I-J) Std.
Error p-
value Lower Bound
Upper Bound
Reliance 1.2554(*) 0.174 0.001 0.7603 1.7506 Airtel 1.3301(*) 0.174 0.001 0.8349 1.8253 Tata
Indicom Hutch 1.1621(*) 0.174 0.001 0.6669 1.6573 TSP: Telecom Service Provider
Table 5.22 depicts results of Post-Hoc Test. It was carried out to test the
significant differences for shared value amongst telecom service providers for
Ludhiana city. Differences are significant between Reliance and Tata Indicom,
Airtel and Tata Indicom, Hutch and Tata Indicom and Tata Indicom with other
operators. All other combinations of differences were found to be non-significant.
Table 5.23: One-Way ANOVA - Amritsar and Telecom Service Providers
Source Of Variation Sum of Squares Df Mean
Square F-value P-value
Between Groups 10.058 3 3.353 6.809 0.001
Within Groups 45.79 93 0.492 Shared Value
Total 55.849 96
143
Table 5.23 exhibits that there are significant differences between
Reliance, Airtel, Hutch and Tata Indicom for Amritsar city. Scheffe’s Post-Hoc
test was carried for multiple comparisons in order to see the significant
differences between the combination of the groups.
Table 5.24: Multiple Comparison for Shared Value between Telecom Service Providers for Amritsar City
95% Confidence
Interval Dependent Variable (I) TSP (J) TSP
Mean Difference
(I-J)
Std. Error
p-value Lower
Bound Upper Bound
Shared Value
Tata Indicom Reliance -.8697(*) 0.1985 0.001 -1.4348 -0.3046
TSP: Telecom Service Provider
Table 5.24 exhibits results of One-way ANOVA. It was carried out to test
the significant differences for shared value amongst telecom service providers
for Amritsar city. Differences are significant between Reliance and Tata Indicom.
All other combinations of differences were found to be non-significant.
Table 5.25: One-Way ANOVA – Patiala and Telecom Service Providers
Source Of Variation Sum of Squares Df Mean
Square F-value p-value
Between Groups 18.414 3 6.138 18.657 0.01 Within Groups 29.937 91 0.329 Shared
Value Total 48.351 94
Table 5.25 exhibits that there are significant differences between
Reliance, Airtel, Hutch and Tata Indicom for Patiala city. Scheffe’s Post-Hoc test
was carried for multiple comparisons in order to see the significant differences
between the combination of the groups.
Table 5.26: Multiple Comparisons for Shared Value between Telecom Service Providers for Patiala City
95% Confidence Interval
Dependent Variable (I) TSP (J) TSP
Mean Difference
(I-J) Std. Error
p-value Lower
Bound Upper Bound
Reliance -.9765(*) 0.1698 0.001 -1.4601 -0.4929Airtel -.9539(*) 0.1639 0.001 -1.4208 -0.487 Shared
Value Tata Indicom Hutch -1.0533(*) 0.1622 0.001 -1.5155 -0.5912
TSP: Telecom Service Provider
144
Table 5.26 depicts results of Post-Hoc test. It was carried out to test the
significant differences for shared value amongst telecom service providers for
Patiala city. Differences are significant between Reliance and Tata Indicom,
Airtel and Tata Indicom, Hutch and Tata Indicom and Tata Indicom with other
operators. All other combinations of differences were found to be non-significant.
Table 5.27: One-Way ANOVA – Chandigarh and Telecom Service Providers
Source Of Variation Sum of Squares df Mean
Square F-value p-value
Between Groups 6.083 3 2.028 3.55 0.017
Within Groups 53.699 94 0.571 Shared Value
Total 59.782 97
Table 5.27 exhibits that there are significant differences between
Reliance, Airtel, Hutch and Tata Indicom for Chandigarh city. Scheffe’s Post-Hoc
test was carried for multiple comparisons in order to see the significant
differences between the combination of the groups.
Table 5.28: Multiple Comparison for Shared Value between Telecom Service Providers for Chandigarh City
95% Confidence
Interval
Dependent Variable (I) TSP (J)
TSP
Mean Difference
(I-J) Std. Error
p-value Lower
Bound Upper Bound
Shared Value Reliance Airtel -.6800(*) 0.2184 0.026 -1.3017 -.05840
TSP: Telecom Service Provider
Table 5.28 exhibits results of One-way ANOVA. It was carried out to test
the significant differences for shared value amongst telecom service providers
for Chandigarh city. Differences are significant between Reliance and Airtel. All
other combinations of differences were found to be non-significant.
5.4.1.4 Results of Telecom Service Provider-Wise, City-Wise and Service-Wise Results of Shared Value
In order to test the difference between prepaid and postpaid subscribers
and selected telecom service providers and their interaction, following
hypotheses are specified:
145
Ho1: There is no significant difference for shared value between all the
four cities.
HoA: There is significant difference for shared value between all the four
cities.
Ho2: There is no significant difference for shared value between telecom
service providers.
HoA: There is significant difference for shared value between telecom
service providers.
Ho3: There is no significant difference for shared value between prepaid
and postpaid subscribers.
HoA: There is significant difference for shared value between prepaid
and postpaid subscribers.
Ho4: There is no interaction for shared value between city and telecom
service providers.
HoA: There is interaction for shared value between city and telecom
service providers.
Ho5: There is no interaction for shared value between city and prepaid
and postpaid subscribers.
HoA: There is interaction for shared value between city and prepaid and
postpaid subscribers.
Ho6: There is no interaction for shared value between TSP and prepaid
and postpaid subscribers.
HoA: There is interaction for shared value between TSP and prepaid and
postpaid subscribers.
Ho7: There is no interaction for shared value among TSP, prepaid and
postpaid subscribers and city.
HoA: There is interaction for shared value among TSP, prepaid and
postpaid subscribers and city.
146
These hypotheses can be tested using two-way ANOVA and the results
are presented in Table 5.29.
Table 5.29: Multiple Factor ANOVA for Prepaid and Postpaid Subscribers, Selected Telecom Provider and Selected Cities
Source Type III Sum of Squares df Mean Square F Sig.
Corrected Model 106.240(a) 31 3.427 8.675 0.001
Intercept 2724.971 1 2724.971 6897.607 0.001
City 15.423 3 5.141 13.013 0.001
TSP 1.709 3 0.57 1.442 0.23
Pr/Po 9.161 1 9.161 23.188 0.001
City*TSP 55.467 9 6.163 15.6 0.001
City*Pr/Po 5.773 3 1.924 4.871 0.002
TSP*Pr/Po 1.888 3 0.629 1.593 0.191
TSP*Pr/Po*City 6.068 9 0.674 1.707 0.086
Error 141.037 357 0.395
Total 3579.218 389
Corrected Total 247.276 388
Table 5.29 explains the ANOVA results for shared value for prepaid and
postpaid services of selected telecom service providers and for selected cities. It
is observed that the variation in the shared value is significant in the selected
cities and the interaction between different telecom service providers and
selected cities.
To study the relationship amongst telecom service provider, city and
prepaid and postpaid services, t-test is further applied and discussed.
Table 5.30: Comparison of Shared Value its Dimensions for Amritsar Hutch
Variable Prepaid/ Postpaid
Mean Std. Deviation
Std. Error Mean t-value p-value
Prepaid 2.4917 .56 .198 Shared Value Postpaid 3.1451 .5095 .1236
2.901 0.008**
147
Table 5.31: Comparison of Shared Value its Dimensions for Amritsar Tata Indicom
Variable Prepaid/ Postpaid
Mean Std. Deviation
Std. Error Mean t-value p-value
Prepaid 1.7407 0.8784 0.2928 Shared Value Postpaid 2.75 0.3783 9.46E-02
3.28** 0.009
Tables 5.30 and 5.31 indicate t-test results for prepaid and postpaid
subscribers among all telecom operators and selected cities. There are
significant differences between prepaid and postpaid subscribers of Ludhiana
Reliance, Ludhiana Airtel, Ludhiana Hutch, Amritsar Tata Indicom and Patiala
Reliance. However, the differences between other combinations are found to be
non-significant.
5.4.3 Reliability Analysis - Scale (Alpha) for Key Mediating Variable Communication
RELIABILITY COEFFICIENTS
Number of Cases = 386.0 Number of Items = 3 Cronbach Alpha = .8520
The reliability coefficient indicates that the scale for measuring the key
mediating variable communication is a reliable one as its value is >0.6.
5.4.3.1 Results for Communication between Service Provider and Prepaid and Postpaid Subscribers
In order to test the difference for communication between Telecom
Service Provider (TSP), prepaid/postpaid subscribers and their interaction
following hypotheses are specified:
Ho1: There is no significant difference for communication between all
the four TSP’s.
HoA: There is significant difference for communication between all the
four TSP’s.
148
Ho2: There is no significant difference for communication between
prepaid and postpaid subscribers.
HoA: There is significant difference for communication between prepaid
and postpaid subscribers.
Ho3: There is no interaction for communication between TSP and
prepaid and postpaid subscribers.
HoA: There is interaction for communication between TSP and prepaid
and postpaid subscribers.
These hypotheses can be tested using two-way ANOVA and the results
are presented in Table 5.32.
Table 5.32: Two-Way for ANOVA Service Provider and Prepaid and Postpaid Subscribers
Source Type III Sum of Squares df Mean
Square F-value p-value
Corrected Model 9.785 7 1.398 4.513 0.001
Intercept 2863.117 1 2863.117 9243.75 0.001 TSP 1.334 3 0.445 1.435 0.232 Pr/Po 8.104 1 8.104 26.163 0.001
TSP* PR/Po 0.595 3 0.198 0.64 0.59
Error 118.319 382 0.31 Total 3568.052 390
Corrected Total 128.104 389
Above table 5.32 explains results of ANOVA for communication are
presented in the above table prepaid and postpaid subscribers for selected
telecom service providers. It is observed that the variation in the communication
is significant in the prepaid and postpaid subscribers.
Communication of Prepaid and Postpaid subscribers has been further
analysed by using t-test below.
149
Table 5.33: Comparison of Communication and its Dimensions for Reliance
Variables Prepaid/postpaid Mean Std. Deviation t-value p-value
Prepaid 2.7352 0.6911 Communication
Postpaid 3.101 0.479 2.518 0.016*
Prepaid 2.5359 0.8485 Openness
Postpaid 3.0931 0.6367 3.034 0.004**
Prepaid 2.7933 0.601 Speed
Postpaid 3.1192 0.4939 2.636 0.01**
Prepaid 2.816 0.7158 Quality of information Postpaid 3.0905 0.4681
1.84 0.074
It is evident from table 5.33, that there is significant difference between
prepaid and postpaid subscribers for communication, openness and speed of
response of Reliance. However, these differences for quality of information were
found to be non-significant. In case of openness and speed of response the
results are highly significant as compared to communication. Communication,
openness and speed of response are more important for the postpaid
subscribers as compared to prepaid subscribers.
Table 5.34: Comparison of Communication and its Dimensions of Hutch
Variables Prepaid /Postpaid Mean Std. Deviation t p-value
Prepaid 2.9299 0.534 Communication
Postpaid 3.0976 0.4507 1.634 0.105
Prepaid 2.7143 0.663 Openness
Postpaid 2.9436 0.636 1.659 0.1
Prepaid 3.0755 0.6442 Speed of response
Postpaid 3.1434 0.5024 0.574 0.567
Prepaid 3 0.6364 Quality of information
Postpaid 3.2059 0.4314 1.9 0.06
150
It is evident from table 5.34, that there is no significant difference between
prepaid and postpaid subscribers for communication, openness and speed of
response of Hutch.
Table 5.35: Comparison of Communication and its Dimensions for Tata Indicom
Variables Prepaid /Postpaid Mean Std. Deviation t-value p-value
Prepaid 2.6784 0.7725 Communication
Postpaid 3.0464 0.6213 2.591 0.011*
Prepaid 2.545 0.9846 Openness
Postpaid 2.9764 0.9388 2.161 0.033*
Prepaid 2.7986 0.7579 Speed of response Postpaid 3.0357 0.4663
1.702 0.095
It is evident from table 5.35, that there is significant difference between
prepaid and postpaid subscribers for communication and openness of Tata
Indicom. However, these differences for speed of response of information were
found to be non-significant. In case of openness and communication the results
are highly significant as compared to communication. In case of communication,
openness and speed of response are more important for the postpaid
subscribers as compared to prepaid subscribers.
5.4.3.2 Results of Prepaid and Postpaid Subscribers and City-Wise Results for Communication
In order to test the difference between cities, prepaid/postpaid subscribers
and their interaction following hypotheses are specified:
Ho1: There is no significant difference for communication between all
the four cities.
HoA: There is significant difference for communication between all the
four cities.
Ho2: There is no significant difference for communication between
prepaid and postpaid subscribers.
151
HoA: There is significant difference for communication between prepaid
and postpaid subscribers.
Ho3: There is no interaction for communication between cities and
prepaid and postpaid subscribers.
HoA: There is interaction for communication between cities and prepaid
and postpaid subscribers.
These hypotheses can be tested using two-way ANOVA and the results
are presented in Table 5.36.
Table 5.36: Two way ANOVA Prepaid and Postpaid Subscribers and Selected Cities
Source Type III Sum of Squares df Mean
Square f-value p-value
Corrected Model 19.965 7 2.852 10.075 0.001
Intercept 2870.223 1 2870.223 10139.027 0.001
City 7.759 3 2.586 9.136 0.001
Pr/Po 7.207 1 7.207 25.46 0.001
City*Pr/Po 3.644 3 1.215 4.291 0.005
Error 108.139 382 0.283
Total 3568.052 390
Corrected Total 128.104 389
Pr/Po: Prepaid and Postpaid
In the above table 5.36 the ANOVA results for communication are
presented for selected cities. It is observed that the variation in the
communication is significant in the selected cities and the interaction between
different selected cities and services is also significant.
152
Table 5.37: Multiple Comparisons (Selected Cities)
95% Confidence interval(I) City (J) City Mean Difference
(I-J) Std. Error p-value Lower Bound
Upper Bound
Amritsar .3293(*) .0760 0.01 0.1159 0.5428
Patiala .3844(*) .0762 0.01 0.1704 0.5984 Ludhiana
Chandigarh .3234(*) .0758 0.01 0.1105 0.5363
A post-hoc test for multiple comparisons among selected cities was
carried out using Scheffe’s multiple comparisons. After applying the test, it was
observed that there are significant differences for communication between
Ludhiana and other cities. But differences are not significant amongst other
cities.
Communication of Prepaid and Postpaid subscribers among selected
cities has been further analysed by using t-test below.
Table 5.38: Comparison of Communication and its Dimensions for Ludhiana City
Variables Prepaid/postpaid Mean Std. Deviation t-value p-value
Prepaid 3.0813 0.6301 Communication
Postpaid 3.2826 0.5032 1.652 0.102
Prepaid 3.1111 0.8756 Openness
Postpaid 3.371 0.7283 1.495 0.138
Prepaid 2.9907 0.5696 Speed of response Postpaid 3.2639 0.4689
2.431 0.017*
Prepaid 3.142 0.645 Quality of information Postpaid 3.213 0.4478
0.619 0.537
It is evident from table 5.38, that there is significant difference between
prepaid and postpaid subscribers for speed of response of Ludhiana city.
However, these differences for communication, openness and quality of
information were found to be non-significant. In case of speed of response the
153
results are significant. Speed of response is more important for the postpaid
subscribers as compared to prepaid subscribers.
Table 5.39: Comparison of Communication and its Dimensions for Amritsar City
Variables Pre-pad/Postpaid Mean Std.
Deviation t-value p-value
Prepaid 2.4822 0.7573 Communication
Postpaid 3.0939 0.4351 4.185 0.001**
Prepaid 2.2309 0.8494 Openness
Postpaid 2.9771 0.6267 4.863
0.001**
Prepaid 2.793 0.9055 Speed of response
Postpaid 3.0568 0.5363 1.795
0.076
Prepaid 2.4226 0.8241 Quality of information Postpaid 3.2477 0.472
5.19 0.001**
It is evident from table 5.39, that there is significant difference between
prepaid and postpaid subscribers for communication, openness and quality of
information of Amritsar city. However, these differences for speed of response
were found to be non-significant. In case of communication, openness and
quality of information the results are highly significant. Communication,
openness and quality of information are more important for the postpaid
subscribers as compared to prepaid subscribers.
Table 5.40: Comparison of Communication and its Dimensions for Chandigarh City
Variables Prepaid/Postpaid Mean Std. Deviation t-value p-value
Prepaid 2.7034 0.5757 Communication
Postpaid 3.016 0.5189 2.748 0.007**
Prepaid 2.5577 0.6779 Openness
Postpaid 2.8825 0.6516 2.331 0.022*
Prepaid 2.8358 0.691 Speed of response
Postpaid 3.0492 0.518 1.704 0.092
Prepaid 2.7476 0.5905 Quality of information Postpaid 3.1138 0.5581
3.048 0.003**
154
It is evident from table 5.40, that there is significant difference between
prepaid and postpaid subscribers for communication, openness and quality of
information of Chandigarh city. However, these differences for speed of
response were found to be non-significant. In case of communication, openness
and quality of information the results are highly significant. Communication,
openness and quality of information are more important for the postpaid
subscribers as compared to prepaid subscribers.
5.4.3.3 Findings of a Comparison of City-Wise and Telecom Service Provider-Wise Results of Communication
In order to test the difference between cities and telecom service provider
and their interaction following hypotheses are specified:
Ho1: There is no significant difference for communication between all
the four cities.
HoA: There is significant difference for communication between all the
four cities.
Ho2: There is no significant difference for communication between the
selected for TSP.
HoA: There is significant difference for communication between the
selected for TSP.
Ho3: There is no interaction for communication between cities and TSP.
HoA: There is interaction for communication between cities and TSP.
These hypotheses can be tested using two-way ANOVA and the results
are presented in Table 5.41.
155
Table 5.41: Two Way ANOVA for Selected Telecom Provider and Selected Cities
Source Type III Sum of Squares df Mean Square F-value p-value
Corrected Model 36.794(a) 15 2.453 10.047 0.001
Intercept 3431.87 1 3431.87 14056.783 0.001 City 9.185 3 3.062 12.54 0.001 TSP 1.049 3 0.35 1.432 0.233
City*TSP 26.68 9 2.964 12.142 0.001
Error 91.31 374 0.244 Total 3568.052 390
Corrected Total 128.104 389
TSP: Telecom Service Providers
In the above table 5.41, the ANOVA results for selected telecom service
providers and for selected cities. It is observed that the variation in the
communication is significant in the selected cities and the interaction between
different telecom service providers and selected cities.
Communication of selected cities and selected TSPs has been further
analysed by using a Post-Hoc test below.
Table 5.42: Multiple Comparisons (Selected Cities)
95% Confidence Interval(I) City (J) City Mean Difference
(I-J) Std. Error p-valueLower Bound
Upper Bound
Amritsar .3293(*) .0706 0.01 0.1311 0.5276 Patiala .3844(*) .0708 0.01 0.1857 0.5832 Ludhiana Chandigarh .3234(*) .0704 0.01 0.1257 0.5211
Table 5.42 purses results of a post-hoc test for multiple comparisons
among selected cities .It was carried out using Scheffe’s multiple comparisons.
After applying the test, it was observed that there are significant differences for
communication between Ludhiana and other cities. But differences are not
significant amongst other cities.
156
On the basis of multiple comparisons, communication between selected
TSPs and selected cities has been further analysed by using one-way ANOVA
as below:
Table 5.43: One-Way ANOVA for Ludhiana and Selected Telecom Service Providers
Source Of Information Sum of Squares df Mean
Square F-
value p-
value Between Groups 11.288 3 3.763 20.074 0.001
Within Groups 17.807 95 0.187 Communication
Total 29.095 98
Table 5.43 exhibits that there are significant differences between
Reliance, Airtel, Hutch and Tata Indicom for communication in Ludhiana city.
Scheffe’s Post-Hoc test was carried for multiple comparisons in order to see the
significant differences between the combination of the groups.
Table 5.44: Multiple Comparisons for Communication between Telecom Service Providers for Ludhiana City
95%
Confidence Interval
Dependent Variable (I) TSP (J) TSP
Mean Difference
(I-J) Std. Error
p-value Lower
Bound Upper Bound
Reliance .7771(*) 0.1237 0.01 0.4249 1.1292Airtel .8434(*) 0.1237 0.01 0.4913 1.1956Communication Tata
Indicom Hutch .7247(*) 0.1237 0.01 0.3726 1.0769TSP: Telecom Service Providers
Above table 5.44 explains the results of a Post-Hoc test. It was carried out
to test the significant differences for communication amongst telecom service
providers for Amritsar city. Differences are significant between Reliance and
Tata Indicom, Airtel and Tata Indicom, Hutch and Tata Indicom and Tata Indicom
with other operators. All other combinations of differences were found to be non-
significant.
157
Table 5.45: One-way ANOVA for Amritsar and Selected Telecom Operators
Source Of Variation Sum of Squares df Mean
Square F-
value p-
value Between Groups 4.045 3 1.348 3.759 0.013 Within Groups 33.355 93 0.359
Communication
Total 37.4 96
Table 5.45 exhibits that there are significant differences between
Reliance, Airtel, Hutch and Tata Indicom for communication in Amritsar city.
Scheffe’s Post-Hoc test was carried for multiple comparisons in order to see the
significant differences between the combination of the groups.
Table 5.46: Multiple Comparisons for Communication between Telecom Service Providers for Amritsar City
95% Confidence
Interval
Dependent Variable (I) TSP (J) TSP
Mean Difference
(I-J) Std.
Error p-
value Lower Bound
Upper Bound
Reliance -.5392(*) 0.1694 0.022 -1.0215 .0569 Airtel -0.1357 0.1751 0.896 -0.6342 .0036 Tata
Indicom Hutch -0.3145 0.1694 0.334 -0.7968 .0167 TSP: Telecom Service Providers
Above table 5.46 explains results of a Scheffe’s Post-Hoc test. It was
carried out to test the significant differences for communication amongst telecom
service providers for Amritsar city. Differences are significant between Reliance
and Tata Indicom, Airtel and Tata Indicom, Hutch and Tata Indicom and Tata
Indicom with other operators. All other combinations of differences were found to
be non-significant.
Table 5.47: One-Way ANOVA: Patiala and Selected Telecom Operators
Source of Variation Sum of Squares df Mean
Square F-
value p-
value Between Groups 9.11 3 3.037 21.001 0.001
Within Groups 13.303 92 0.145 Communication
Total 22.413 95
158
Table 5.47 exhibits that there are significant differences between
Reliance, Airtel, Hutch and Tata Indicom for communication in Patiala city.
Scheffe’s Post-Hoc test was carried for multiple comparisons in order to see the
significant differences between the combination of the groups.
Table 5.48: Multiple Comparisons for Communication between Telecom Service Providers for Patiala City
95%
Confidence Interval
Dependent Variable (I) TSP (J) TSP
Mean Difference
(I-J) Std. Error
p-value
Lower Bound
Upper Bound
Reliance -.8005(*) 0.1126 0.01 -1.121 -
0.4799
Airtel -.6046(*) 0.1076 0.01 -
0.9109 -
0.2983Communication Tata Indicom
Hutch -.6667(*) 0.1076 0.01 -0.973 -
0.3604
TSP: Telecom Service Providers
Above table 5.48 explains results of Scheffe’s Post-Hoc test. It was
carried out to test the significant differences for shared value amongst telecom
service providers for Patiala city. Differences are significant between Reliance
and Tata Indicom, Airtel and Tata Indicom, Hutch and Tata Indicom and Tata
Indicom with other operators. All other combinations of differences were found to
be non-significant.
Table 5.49: One Way ANOVA: Chandigarh and Selected Telecom Operators
Source Of Variation Sum of Squares df Mean
Square F-
value p-
value Between Groups 3.314 3 1.105 3.869 0.012
Within Groups 26.845 94 0.286 Communication
Total 30.159 97
Table 5.49 exhibits that there are significant differences between
Reliance, Airtel, Hutch and Tata Indicom for communication in Chandigarh city.
Scheffe’s Post-Hoc test was carried for multiple comparisons in order to see the
significant differences between the combination of the groups.
159
Table 5.50: Multiple Comparisons for Communication between Telecom Service Providers for Chandigarh City
95% Confidence
Interval
Dependent Variable
(I) TSP (J) TSP
Mean Difference
(I-J) Std. Error
p-value Lower
Bound Upper Bound
Communication Hutch Reliance .5065(*) 0.1544 0.017 .06702 0.9461 TSP: Telecom Service Providers
Above table 5.50 shows results of Post-Hoc test. It was carried out to test
the significant differences for communication amongst telecom service providers
for Chandigarh city. Differences are significant between Reliance and Hutch
operators. All other combinations of differences were found to be non-significant.
5.4.4.4 Results of Telecom Service Provider-Wise, City-Wise and Service-Wise Results of Communication
In order to test the difference between prepaid and postpaid subscribers
and selected telecom service providers and their interaction for communication,
following hypotheses are specified:
Ho1: There is no significant difference for communication between all
the four cities.
HoA: There is significant difference for communication between all the
four cities.
Ho2: There is no significant difference for communication between
telecom service providers.
HoA: There is significant difference for communication between telecom
service providers.
Ho3: There is no significant difference for communication between
prepaid and postpaid subscribers.
HoA: There is significant difference for communication between prepaid
and postpaid subscribers.
Ho4: There is no interaction for communication between city and
telecom service providers.
160
HoA: There is interaction for communication between city and telecom
service providers.
Ho5: There is no interaction for communication between city and prepaid
and postpaid subscribers.
HoA: There is interaction for communication between city and prepaid
and postpaid subscribers.
Ho6: There is no interaction for communication between TSP and
prepaid and postpaid subscribers.
HoA: There is interaction for communication between TSP and prepaid
and postpaid subscribers.
Ho7: There is no interaction for communication among TSP, prepaid and
postpaid subscribers and city.
HoA: There is interaction for communication among TSP, prepaid and
postpaid subscribers and city.
These hypotheses can be tested using two-way ANOVA and the results
are presented in Table 5.51.
Table 5.51: Multi-factor ANOVA for Selected Telecom Provider, Prepaid and Postpaid and Selected Cities
Source Type III Sum of Squares df Mean Square F-value p-value
Corrected Model 54.306 31 1.752 8.498 0.001 Intercept 2838.492 1 2838.492 13769.854 0.001 City 6.638 3 2.213 10.734 0.001 TSP 1.173 3 0.391 1.897 0.13 Pr/Po 6.523 1 6.523 31.641 0.001 City*TSP 26.186 9 2.91 14.114 0.001 City*Pr/Po 2.98 3 0.993 4.82 0.003 TSP*Pr/Po 0.367 3 0.122 0.593 0.62 City* TSP*Pr/Po 7.168 9 0.796 3.864 0.001 Error 73.797 358 0.206 Total 3568.052 390 Corrected Total 128.104 389
Pr/Po: Prepaid and Postpaid; TSP: Telecom Service Providers
161
Above table 5.51 exhibits the ANOVA results for communication. They are
presented for selected telecom service provider and prepaid and postpaid
services in selected cities. It is observed that the variation in the communication
is significant in the selected cities and the interaction between different prepaid
and postpaid subscribers of selected telecom service providers and selected
cities.
To study the relationship amongst telecom service provider, city and
prepaid and postpaid services for communication, t-test is further applied and
discussed.
Table 5.52: Comparison of Communication and its Dimensions for Reliance Ludhiana
Variable Prepaid/Postpaid Mean Std. Deviation
Std. Error Mean t-value p-
valuePrepaid 2.4451 0.3393 0.1385
Communication Postpaid 3.2316 0.5218 0.1197
3.442** 0.002
** Significant at .01 level
Table 5.53: Comparison of Communication and its Dimensions for Airtel Ludhiana
Variable Prepaid/Postpaid Mean Std. Deviation
Std. Error Mean t-value p-
valuePrepaid 2.7963 0.1361 0.5980
Communication Postpaid 3.0334 0.5362 0.8260
2.325** 0.03
** Significant at .01 level
Table 5.54: Comparison of Communication and its Dimensions for Hutch Amritsar
Variable Prepaid/Postpaid Mean Std. Deviation
Std. Error Mean
t-value p-value
Prepaid 2.4023 .4828 .1707 Communication
Postpaid 3.2255 .6285 .1524 3.266** 0.003
** Significant at .01 level
162
Table 5.55: Comparison of Communication and its Dimensions for Tata Indicom Amritsar
Variable Prepaid/Postpaid Mean Std. Deviation
Std. Error Mean
t-value p-value
Prepaid 2.0263 .8876 .2959 Communication Postpaid 2.997 .3048 .0762
3.177** 0.011
** Significant at .01 level
Table 5.56: Comparison of Communication and its Dimensions for Reliance Patiala
Variable A2.2 Mean Std. Deviation
Std. Error Mean t-value p-value
Prepaid 3.3074 .3405 .1287 Communication Postpaid 3.0527 .1674 .0447
2.329** .031
Table 5.57: Comparison of Communication and its Dimensions for Airtel
Chandigarh
Variable A2.2 Mean Std. Deviation
Std. Error Mean t-value p-value
Prepaid 2.6448 .3887 .1374 Communication Postpaid 3.1506 .4523 .1097
2.71** .012
Above table 5.57 indicates t-test results for communication of prepaid
and postpaid subscribers among all telecom operators and selected cities. There
are significant differences between prepaid and postpaid subscribers of
Ludhiana Reliance, Airtel Ludhiana, Hutch Amritsar, Tata Indicom Amritsar,
Patiala Reliance and Airtel Chandigarh. All other combinations are found
insignificant.
5.4.5 Reliability Analysis – Scale (Alpha) for Key Mediating Variable
Opportunistic Behavior
RELIABILITY COEFFICIENTS Number of Cases = 374.0 Number of Items = 2 Alpha = .7719
Cronbach α (alpha) is an important psychometric instrument to measure
the reliability. In this research, the data for analysis is reliable one as its value is
>0.6. So, various statistical tools can be applied and tested.
163
5.4.5.1 Results for Opportunistic Behaviour between Telecom Service Providers and Prepaid and Postpaid Subscribers
In order to test the difference between Telecom Service Provider (TSP),
prepaid/postpaid subscribers and their interaction for opportunistic behaviour,
following hypotheses are specified:
Ho1: There is no significant difference for opportunistic behaviour
between all the four TSP’s.
HoA: There is significant difference for opportunistic behaviour between
all the four TSP’s.
Ho2: There is no significant difference for opportunistic behaviour
between prepaid and postpaid subscribers.
HoA: There is significant difference for opportunistic behaviour between
prepaid and postpaid subscribers.
Ho3: There is no interaction for opportunistic behaviour between TSP
and prepaid and postpaid subscribers.
HoA: There is interaction for opportunistic behaviour between TSP and
prepaid and postpaid subscribers.
These hypotheses can be tested using two-way ANOVA and the results
are presented in Table 5.58.
Table 5.58: Two-way ANOVA for Prepaid and postpaid subscribers and Selected Telecom Provider
Source Type III Sum of Squares df Mean
Square F-value p-value
Corrected Model 12.738 7 1.82 3.388 0.002 Intercept 2807.116 1 2807.116 5225.968 0.01 TSP 2.442 3 0.814 1.515 0.21 Pr/Po 8.215 1 8.215 15.293 0.001 TSP* Pr/Po 2.889 3 0.963 1.793 0.148 Error 205.19 382 0.537 Total 3588.117 390 Corrected Total 217.929 389 Pr/Po: Prepaid and Postpaid; TSP: Telecom Service Providers
164
The above table 5.58 shows ANOVA results for opportunistic behavior.
They are presented in the above table selected telecom service provider and
prepaid and postpaid services. It is observed that the variation in the
opportunistic behaviour significant in the prepaid and postpaid services.
Opportunistic behaviour of Prepaid and Postpaid subscribers has been
further analysed by using t-test below.
Table 5.59: Comparison of Opportunistic Behaviour and its Dimensions for Reliance
Variables Prepaid/ Postpaid Mean
Std. Deviation t-value p-value
Prepaid 2.6989 0.8825 Opportunistic behavior Postpaid 3.0871 0.6299
2.082 0.044*
Prepaid 2.94 0.939 Regulatory control
Postpaid 3.0603 0.8537 0.572 0.569
Prepaid 2.5644 0.8742 Information asymmetry Postpaid 3.0796 0.5537
2.841 0.007**
It is evident from table 5.59, that there is significant difference between
prepaid and postpaid subscribers for opportunistic behaviour and information
asymmetry of Reliance. However, these differences for regulatory control were
found to be non-significant. In case of information asymmetry the results are
highly significant as compared to opportunistic behaviour. Opportunistic
behaviour and information asymmetry are more important for the postpaid
subscribers as compared to prepaid subscribers.
Table 5.60: Comparison of Opportunistic Behaviour and its Dimensions for Airtel
Variables Prepaid/ Postpaid Mean
Std. Deviation t-value p-value
Prepaid 2.7188 0.6681 Opportunistic behavior Postpaid 3.0031 0.6158
2.047 0.043*
Prepaid 2.7759 0.7269 Regulatory control
Postpaid 2.9769 0.8946 1.063 0.291
Prepaid 2.7208 0.693 Information asymmetry Postpaid 2.9988 0.6286
1.95 0.054
165
It is evident from table 5.60, that there is significant difference between
prepaid and postpaid subscribers for opportunistic behaviour of Airtel. However,
these differences for regulatory control and information asymmetry were found to
be non-significant. Opportunistic behaviour is more important for the postpaid
subscribers as compared to prepaid subscribers.
Table 5.61: Comparison of Opportunistic Behaviour and its Dimensions for Hutch
Variables Prepaid/ Postpaid Mean
Std. Deviation t-value p-value
Prepaid 2.9896 0.517 Opportunistic behavior
Postpaid 3.0295 0.544 0.348 0.729
Prepaid 3.0323 0.5764 Regulatory control
Postpaid 2.9559 0.6732 0.547 0.586
Prepaid 2.9271 0.6574 Information asymmetry
Postpaid 3.1032 0.5456 1.408 0.162
It is evident from table 5.61, opportunistic behaviour, regulatory control
and information asymmetry are non-significant for prepaid and postpaid
subscribers of Hutch.
Table 5.62: Comparison of Opportunistic Behaviour and its Dimensions for Tata Indicom
Variables Prepaid/ Postpaid Mean
Std. Deviation t-value p-value
Prepaid 2.5104 0.9656 Opportunistic behavior Postpaid 3.0466 0.984
2.626 0.01**
Prepaid 2.3333 1.1402 Regulatory control
Postpaid 2.9516 1.1619 2.557 0.012*
Prepaid 2.6875 Information asymmetry Postpaid 3.1488 0.8836
2.491 0.014*
It is evident from table 5.62, that there is significant difference between
prepaid and postpaid subscribers for opportunistic behaviour, regulatory control
and information asymmetry of Tata Indicom. In case of opportunistic behaviour
the results are highly significant as compared to opportunistic behaviour and
information asymmetry. Opportunistic behaviour, regulatory control and
166
information asymmetry are more important for the postpaid subscribers as
compared to prepaid subscribers.
5.4.5.2 Results for Opportunistic Behaviour between Selected Cities and Prepaid and Postpaid Subscribers
In order to test the difference between cities, prepaid/postpaid subscribers
and their interaction following hypotheses are specified:
Ho1: There is no significant difference for opportunistic behaviour
between all the four cities.
HoA: There is significant difference for opportunistic behaviour between
all the four cities.
Ho2: There is no significant difference for opportunistic behaviour
between prepaid and postpaid subscribers.
HoA: There is significant difference for opportunistic behaviour between
prepaid and postpaid subscribers.
Ho3: There is no interaction for opportunistic behaviour between cities
and prepaid and postpaid subscribers.
HoA: There is interaction for opportunistic behaviour between cities and
prepaid and postpaid subscribers.
These hypotheses can be tested using two-way ANOVA and the results
are presented in table 5.63.
Table 5.63: Two Way ANOVA for Prepaid and Postpaid Subscribers and Selected Cities
Source Type III Sum of Squares df Mean Square F p-value
Corrected Model 31.107 7 4.444 9.087 0.001 Intercept 2811.012 1 2811.012 5747.781 0.001 City 15.641 3 5.214 10.66 0.001 Pr/Po 7.033 1 7.033 14.38 0.001 City* Pr/Po 3.508 3 1.169 2.391 0.068 Error 186.821 382 0.489 Total 3588.117 390 Corrected Total 217.929 389
167
The table 5.63 depicts ANOVA results for opportunistic behavior for
selected cities and prepaid and postpaid services. It is observed that the
variation in the opportunistic behaviour is significant in the prepaid and postpaid
services.
Opportunistic behaviour of Prepaid and Postpaid subscribers has been
further analysed by using a Post-Hoc test. The results of which are given below.
Table 5.64: Multiple Comparisons (Selected Cities)
95% Confidence Interval (I) City (J) City Mean Difference (I-J)Std. Errorp-valueLower Bound Upper Bound
Amritsar .3228(*) 0.0836 0.002 0.0879 0.5577 Patiala .6094(*) 0.1000 0.001 0.3281 0.8449 Ludhiana Chandigarh .4870(*) 0.0997 0.001 0.2072 0.7213 Ludhiana -.3228(*) 0.0999 0.016 -0.6033 0-.0879 Amritsar Patiala .2866(*) 0.1007 0.045 0.0038 0.5233 Ludhiana -.6094(*) 0.1002 0.001 -0.8906 -0.3738 Patiala Amritsar -.2866(*) 0.1007 0.045 -0.5693 -0.499
Chandigarh Ludhiana -.4870(*) 0.0997 0.001 -0.7668 -0.2527
The above table 5.64 explains a post-hoc test for multiple comparisons among selected cities by using Scheffe’s multiple comparisons. After applying the test, it was observed that there are significant differences a)between Ludhiana and other cities (b) Amritsar and Ludhiana (c) Patiala and Amritsar and (d) Chandigarh and Ludhiana. But differences are not significant amongst other cities.
Opportunistic behaviour of Prepaid and Postpaid subscribers has been further analysed by using t-test. The results of which are given below.
Table 5.65: Comparison of Opportunistic Behaviour and its Dimensions for Amritsar City
Variables Prepaid/ Postpaid Mean
Std. Deviation t-value p-value
Prepaid 2.5533 0.8036 Opportunistic behaviour Postpaid 3.165 0.6335
4.061 0.001**
Prepaid 2.5645 0.9725 Regulatory control Postpaid 3.1371 0.8692
2.878 0.005**
Prepaid 2.5421 0.8243 Information asymmetry Postpaid 3.181 0.5241
3.956 0.001**
** Significant at .01 level
168
It is evident from table 5.65, that there is significant difference between
prepaid and postpaid subscribers for opportunistic behaviour, regulatory control
and information asymmetry of Amritsar city. Opportunistic behaviour, regulatory
control and information asymmetry are more important for the postpaid
subscribers as compared to prepaid subscribers but as for as ethics is
concerned prepaid subscribers are better than postpaid subscribers.
Table 5.66: Comparison of Opportunistic Behaviour and its Dimensions for Chandigarh City
Variables Prepaid/ Postpaid Mean
Std. Deviation t-value p-value
Prepaid 2.6163 0.7095 Opportunistic behaviour Postpaid 2.9103 0.6259
2.123 0.036*
Prepaid 2.4545 0.8136 Regulatory control
Postpaid 2.7589 0.7977 1.726 0.088
Prepaid 2.7565 0.7565 Information asymmetry Postpaid 2.9952 0.6448
1.65 0.102
* Significant at .05 level It is evident from table 5.66, that there is significant difference between
prepaid and postpaid subscribers for opportunistic behaviour in Chandigarh city.
However, these differences for regulatory control and information asymmetry
were found to be non-significant. Opportunistic behaviour is more important for
the postpaid subscribers as compared to prepaid subscribers but as for as ethics
is concerned prepaid subscribers are better than postpaid subscribers.
5.4.5.3 Results for Opportunistic Behaviour between Selected Cities and Telecom Service Providers (TSP)
In order to test the difference between cities, TSPs and their interaction
following hypotheses are specified:
Ho1: There is no significant difference for opportunistic behaviour
between all the four cities.
HoA: There is significant difference for opportunistic behaviour between
all the four cities.
169
Ho2: There is no significant difference for opportunistic behaviour
between TSPs.
HoA: There is significant difference for opportunistic behaviour between
TSPs.
Ho3: There is no interaction for opportunistic behaviour between cities
and TSPs.
HoA: There is interaction for opportunistic behaviour between cities and
TSPs.
These hypotheses can be tested using two-way ANOVA and the results
are presented in Table 5.67.
Table 5.67: Two-way ANOVA for Telecom Service Providers and Selected Cities
Source Type III Sum of Squares df Mean Square F-value p-value
Corrected Model 78.950 15 5.263 14.164 0.001 Intercept 3371.841 1 3371.841 9073.82 0.001 City 20.246 3 6.749 18.161 0.001 TSP 1.304 3 0.435 1.169 0.321 City* TSP 57.058 9 6.34 17.061 0.001 Error 138.979 374 0.372 Total 3588.117 390 Corrected Total 217.929 389
TSP: Telecom Service Providers
The ANOVA results for opportunistic behaviour presented in the above
table selected cities and selected telecom service providers. It is observed that
the variation in the opportunistic behaviour is significant in the selected cities and
the interaction between selected telecom service providers and selected cities.
On the basis of multiple comparisons, further one-way ANOVA was
carried out. The results of which are given below:
170
Table 5.68: One-way ANOVA for Opportunistic Behaviour Ludhiana and Selected Telecom Operators
Source Of Information Sum of Squares df Mean
Square F-
value p-
value Between Groups 29.816 3 9.939 27.402 0.01 Within Groups 34.456 95 0.363 Opportunistic
Behaviour Total 64.272 98
Table 5.68 exhibits that there are significant differences between
Reliance, Airtel, Hutch and Tata Indicom for opportunistic behaviour in Ludhiana
city. Scheffe’s Post-Hoc test was carried for multiple comparisons in order to see
the significant differences between the combination of the groups.
Table 5.69: Multiple Comparison for Opportunistic Behaviour between Telecom Service Providers for Ludhiana City
95%
Confidence Interval
Dependent Variable (I) TSP (J) TSP
Mean Difference
(I-J) Std. Error
p-value
Lower Bound
Upper Bound
Reliance 1.3353(*) 0.1721 0.05 0.8455 1.8252 Airtel 1.2978(*) 0.1721 0.05 0.808 1.7877 Opportunistic
Behaviour Tata Indicom
Hutch 1.1903(*) 0.1721 0.05 0.7005 1.6802
* Significant at .05 level; TSP: Telecom Service Providers Table 5.69 explains Scheffe’s Post-Hoc results. It was carried out to test
the significant differences for opportunistic behaviour amongst telecom service
providers for Ludhiana city. Differences are significant between Reliance and
Tata Indicom, Airtel and Tata Indicom, Hutch and Tata Indicom. All other
combinations of differences were found to be non-significant.
Table 5.70: One-way ANOVA for Amritsar and Selected Telecom Operators
Source Of Information Sum of Squares df Mean
Square F-
value p-
value Between Groups 5.317 3 1.772 3.431 0.02 Within Groups 48.038 93 0.517 Opportunistic
Behaviour Total 53.355 96
171
Table 5.70 exhibits that there are significant differences between
Reliance, Airtel, Hutch and Tata Indicom for opportunistic behaviour in Amritsar
city. Scheffe’s Post-Hoc test was carried for multiple comparisons in order to see
the significant differences between the combination of the groups.
Table 5.71: Multiple Comparison for Opportunistic Behaviour between Telecom Service Providers for Amritsar City
95% Confidence Interval
Dependent Variable (I) TSP (J) TSP
Mean Difference
(I-J) Std. Error
P-value Lower
Bound Upper Bound
Tata Indicom Reliance -.6083(*) 0.2033 0.035 -1.1872 -.02952
* Significant at .05 level; TSP: Telecom Service Providers
Table 5.71 explains Scheffe’s Post-Hoc results. It was carried out to test
the significant differences for opportunistic behaviour amongst telecom service
providers for Amritsar city. Differences are significant between Reliance and
Tata Indicom. All other combinations of differences were found to be non-
significant.
Table 5.72: One Way ANOVA for Patiala and Selected Telecom Operators
Source Of Information Sum of Squares df Mean
Square F-
value p-
value Between Groups 17.933 3 5.978 29.678 0.001 Within Groups 18.53 92 0.201 Opportunistic
Behaviour Total 36.463 95
Table 5.72 exhibits that there are significant differences between
Reliance, Airtel, Hutch and Tata Indicom for opportunistic behaviour in Patiala
city. Scheffe’s Post-Hoc test was carried for multiple comparisons in order to see
the significant differences between the combination of the groups.
172
Table 5.73: Multiple Comparison for Opportunistic Behaviour between Telecom Service Providers for Patiala City
95%
Confidence Interval
Dependent Variable (I) TSP (J) TSP
Mean Difference
(I-J) Std.
Error P-
valueLower Bound
Upper Bound
Airtel .6143(*) 0.1328 0.001 0.236 0.9926 Reliance Tata
Indicom 1.1643(*) 0.1328 0.001 0.786 1.5426
Reliance -.6143(*) 0.1328 0.001-
0.9926 -0.236 Tata Indicom .5500(*) 0.1269 0.001 0.1885 0.9115 Airtel
Tata Indicom .9025(*) 0.1269 0.001 0.541 1.264
Reliance -1.1643(*) 0.1328 0.001-
1.5426 -0.786
Airtel -.5500(*) 0.1269 0.001-
0.9115 -
0.1885
Opportunistic Behaviour
Tata Indicom
Hutch -.9025(*) 0.1269 0.001 -1.264 -0.541
* Significant at .05 level; TSP: Telecom Service Providers
Table 5.73 explains Scheffe’s Post-Hoc results. It was carried out to test
the significant differences for opportunistic behaviour amongst telecom service providers for Patiala city. Differences are significant between Reliance, Airtel and Tata Indicom, Hutch and Tata Indicom and Tata Indicom with other operators. All other combinations of differences were found to be non-significant.
Table 5.74: One-way ANOVA for Chandigarh and Selected Telecom Operators
Source Of Information Sum of Squares df Mean
Square F-
value p-
value Between Groups 5.391 3 1.797 4.45 0.006 Within Groups 37.954 94 0.404 Opportunistic
Behaviour Total 43.345 97
Table 5.74 exhibits that there are significant differences between
Reliance, Airtel, Hutch and Tata Indicom for opportunistic behaviour in
Chandigarh city. Scheffe’s Post-Hoc test was carried for multiple comparisons in
order to see the significant differences between the combination of the groups.
173
Table 5.75: Multiple Comparison for Opportunistic Behaviour between Telecom Service Providers for Chandigarh City
95%
Confidence Interval
Dependent Variable
(I) TSP (J) TSP
Mean Difference
(I-J) Std.
Error P-
value Lower Bound
Upper Bound
Reliance .5242(*) 0.1836 0.049 0.0015 1.0468 Opportunistic
Behaviour Airtel Tata Indicom .5325(*) 0.1797 0.038 0.0202 1.0441
* Significant at .05 level; TSP: Telecom Service Providers Table 5.75 explains Scheffe’s Post-Hoc results. It was carried out to test
the significant differences for opportunistic behaviour amongst telecom service providers for Chandigarh city. Differences are significant between Reliance and Airtel and Airtel and Tata Indicom. All other combinations of differences were found to be non-significant.
5.4.5.4 Results for Opportunistic Behaviour between Prepaid and Postpaid Subscribers, Selected Cities and Telecom Service Providers (TSP)
a) In order to test the difference between prepaid and postpaid subscribers,
selected city and selected telecom service providers (TSPs) and their
interaction, following hypotheses are specified:
Ho1: There is no significant difference for opportunistic behaviour
between all the four cities.
HoA: There is significant difference for opportunistic behaviour between
all the four cities.
Ho2: There is no significant difference for opportunistic behaviour
between telecom service providers.
HoA: There is significant difference for opportunistic behaviour between
telecom service providers.
Ho3: There is no significant difference for opportunistic behaviour
between prepaid and postpaid subscribers.
HoA: There is significant difference for opportunistic behaviour between
prepaid and postpaid subscribers.
174
Ho4: There is no interaction for opportunistic behaviour between city and telecom service providers.
HoA: There is interaction for opportunistic behaviour between city and
telecom service providers.
Ho5: There is no interaction for opportunistic behaviour between city and
prepaid and postpaid subscribers.
HoA: There is interaction for opportunistic behaviour between city and
prepaid and postpaid subscribers.
Ho6: There is no interaction for opportunistic behaviour between TSP
and prepaid and postpaid subscribers.
HoA: There is interaction for opportunistic behaviour between TSP and
prepaid and postpaid subscribers.
Ho7: There is no interaction for opportunistic behaviour among TSP,
prepaid and postpaid subscribers and city.
HoA: There is interaction for opportunistic behaviour among TSP, prepaid and postpaid subscribers and city.
These hypotheses can be tested using two-way ANOVA and the results
are presented in table 5.76.
Table 5.76: Multiple Factor ANOVA for Telecom Service Providers, Selected Cities and Prepaid and Postpaid Services
Source Type III Sum of Squares df Mean
Square F Sig.
Corrected Model 95.262 31 3.073 8.968 0.01 Intercept 2793.179 1 2793.179 8151.865 0.001 City 13.674 3 4.558 13.302 0.001 TSP 1.893 3 0.631 1.842 0.139 Pr/Po 6.386 1 6.386 18.637 0.001 City* TSP 50.39 9 5.599 16.34 0.001 City* Pr/Po 2.722 3 0.907 2.648 0.049 TSP* Pr/Po 1.764 3 0.588 1.716 0.163 City* TSP* Pr/Po 4.937 9 0.549 1.601 0.113 Error 122.666 358 0.343 Total 3588.117 390 Corrected Total 217.929 389
Pr/Po: Prepaid and Postpaid; TSP: Telecom Service Providers
175
The above table 5.76 exhibits ANOVA results for opportunistic behaviour
for prepaid and postpaid services of selected cities and selected telecom service
providers. It is observed that the variation in the opportunistic behaviours
significant in the selected cities and the interaction between selected telecom
service providers and selected cities.
Opportunistic behaviour of Prepaid and Postpaid subscribers between
selected cities has been further analysed by using a Post-Hoc test. The results
of which are given below.
Table 5.77: Multiple Comparisons (Selected Cities)
(I) City (J) City Mean Difference (I-J) Std. Error p-value Lower
Bound Amritsar .3228(*) 0.0836 0.002 .08792 Patiala .6094(*) 0.0839 0.01 0.3738 Ludhiana Chandigarh .4870(*) 0.0834 0.01 0.2527 Ludhiana -.3228(*) 0.0836 0.002 -0.5577
Amritsar Patiala .2866(*) 0.0843 0.01 .04992 Ludhiana -.6094(*) 0.0839 0.01 -0.8449
Patiala Amritsar -.2866(*) 0.0843 0.01 -0.5233
Chandigarh Ludhiana -.4870(*) 0.0834 0.01 -0.7213
The above table 5.77 explains a post-hoc test for multiple comparisons
among selected cities. It was carried out using Scheffe’s multiple comparisons.
After applying the test, it was observed that there are significant differences between Ludhiana and other cities and Patiala and Amritsar. But other
differences are not significant amongst other cities.
On the basis of multiple comparisons, opportunistic behaviour of prepaid
and postpaid subscribers between selected telecom operators has been further analysed by using t-test. The results of which are given below.
Table 5.78: Comparison of Opportunistic Behaviour and its Dimensions for Airtel Ludhiana
Variable Prepaid/ Postpaid Mean Std.
DeviationStd. Error
Mean t-value p-value
Prepaid 2.5833 0.1094 .0447 Opportunistic behavior Postpaid 3.0789 0.4934 .1132
2.408** 0.024
176
Above table 5.78 indicates t-test results for opportunistic behaviour of
prepaid and postpaid subscribers among all Airtel subscribers of Ludhiana city.
There are significant differences between prepaid(2.58) and postpaid (3.08)
subscribers of Ludhiana Airtel.
Table 5.79: Comparison of Opportunistic Behaviour and its Dimensions for Hutch Ludhiana
Variable Prepaid/ Postpaid Mean Std.
Deviation Std. Error
Mean t-value p-value Prepaid 3.2656 0.8925 0.3156 Opportunistic
behavior Postpaid 2.9743 0.4409 0.1069 .874** 0.405
Above table 5.79 indicates t-test results for opportunistic behaviour of
prepaid and postpaid subscribers among all Ludhiana Hutch subscribers. There
are significant differences between prepaid(3.26) and postpaid (2.98)
subscribers of Ludhiana Hutch.
Table 5.80: Comparison of Opportunistic Behaviour and its Dimensions for Hutch Amritsar
Variable Prepaid/ Postpaid Mean Std.
DeviationStd. Error
Mean t-value p-value
Prepaid 2.7031 .1145 .0405 Opportunistic behavior Postpaid 3.2684 .7221 .1751
3.145** 0.006
Above table 5.80 indicates t-test results for opportunistic behaviour of
prepaid and postpaid subscribers among all Amritsar Hutch subscribers. There
are significant differences between prepaid(2.70) and postpaid (3.27)
subscribers of Amritsar Hutch.
Table 5.81: Comparison of Opportunistic Behaviour and its Dimensions for Tata Indicom Amritsar
Variable Prepaid/ Postpaid Mean Std.
DeviationStd. Error
Mean t-value p-value
Prepaid 1.8819 0.8235 0.2745 Opportunistic behavior Postpaid 2.9844 0.5744 0.1436
3.559** 0.004
177
Table 5.81 indicates t-test results for opportunistic behaviour of prepaid
and postpaid subscribers among all Amritsar Tata Indicom subscribers. There
are significant differences between prepaid (1.88) and postpaid
(2.98)subscribers of Amritsar Tata Indicom.
Table 5.82: Comparison of Opportunistic Behaviour and its Dimensions for Reliance Patiala
Variable Prepaid/ Postpaid Mean Std.
Deviation Std. Error
Mean t-value p-value
Prepaid 3.0893 1.0501 0.3969 Opportunistic behavior Postpaid 3.2768 0.1746 .04672
0.469** 0.655
Above table 5.82 indicates t-test results for opportunistic behaviour of
prepaid and postpaid subscribers among all Patiala Reliance subscribers. There
are significant differences between prepaid(3.08) and postpaid (3.28)subscribers
of Patiala Reliance.
Table 5.83: Comparison of Opportunistic Behaviour and its Dimensions for Hutch Patiala
Variable Prepaid/ Postpaid Mean Std.
DeviationStd. Error
Mean t-value p-value
Prepaid 2.9141 0.1375 0.0486 Opportunistic behavior Postpaid 2.9706 0.2662 0.0646
.699** 0.491
Above table 5.83 indicates t-test results for opportunistic behaviour of
prepaid and postpaid subscribers among all Patiala Hutch. There are significant
differences between prepaid(2.91) and postpaid(2.97)subscribers of Patiala
Hutch.
5.4.6 Reliability Analysis - Scale (Alpha) for Key Mediating Variable-Trust
RELIABILITY COEFFICIENTS
Number of Cases = 383.0 Number of Items = 3 Alpha = .8621
Cronbach's α (alpha) is an important psychometric instrument to measure
the reliability. The data for analysis is reliable one as its’ value is >.6.So, various
statistical tools can be applied and tested.
178
5.4.6.1 Results for Opportunistic Behaviour between Prepaid and Postpaid Subscribers and Telecom Service Providers (TSPs)
a) In order to test the difference between cities, prepaid/postpaid subscribers
and their interaction for trust, following hypotheses are specified:
Ho1: There is no significant difference for trust between prepaid and
postpaid subscribers.
HoA: There is significant difference for trust between prepaid and
postpaid subscribers.
Ho2: There is no significant difference for trust between TSPs.
HoA: There is significant difference for trust between TSPs.
Ho3: There is no interaction for trust between TSPs and prepaid and
postpaid subscribers.
HoA: There is interaction for trust between TSPs and prepaid and
postpaid subscribers.
These hypotheses can be tested using two-way ANOVA and the results
are presented in Table 5.84.
Table 5.84: Two Way ANOVA for Selected Service Provider and Prepaid and Postpaid Subscribers
Source Type III Sum of Squares df Mean
Square F-value p-value
Corrected Model 10.167 7 1.452 3.31 0.002
Intercept 2738.775 1 2738.775 6241.078 0.01
TSP 1.177 3 0.392 0.894 0.444
Pr/Po 8.423 1 8.423 19.195 0.01
TSP* Pr/Po 1.873 3 0.624 1.423 0.236
Error 167.633 382 0.439
Total 3481.611 390
Corrected Total 177.8 389
Pr/Po: Prepaid and Postpaid; TSP: Telecom Service Providers
179
The above table 5.84 explains ANOVA results for trust for prepaid and
postpaid subscribers of selected telecom service providers. It is observed that
the variation in the trust significant in the prepaid and postpaid subscribers.
Table 5.85: Comparison of Trust and its Dimensions for Reliance
Variables Prepaid/ Postpaid Mean Std.
Deviation t-value p-value
Prepaid 2.5207 0.859 2.918 0.006** Trust
Postpaid 3.0454 0.5792 Prepaid 2.5212 0.7734 3.873 .001**
Perceived risk Postpaid 3.1079 0.6164 Prepaid 2.6 1.1456 1.183 0.246 Technology
Orientation Postpaid 2.8923 0.7474 Prepaid 2.6194 0.8118 2.94 0.006**
Reputation Postpaid 3.1329 0.5881
It is evident from table 5.85, that there is significant difference between
prepaid and postpaid subscribers for trust, perceived risk and reputation of
Reliance. However, these differences for technology orientation were found to be
non-significant. Trust, perceived risk and reputation are more important for the
postpaid subscribers as compared to prepaid subscribers.
Table 5.86: Comparison of Trust and its Dimensions for Hutch
Variables Prepaid/ Postpaid Mean Std.
Deviation t-value p-value
Prepaid 2.8424 0.7584 Trust
Postpaid 2.9821 0.4774 0.957 0.344
Prepaid 2.7993 0.7251 Perceived risk Postpaid 3.0364 0.4841
1.682 0.1
Prepaid 2.9194 0.9924 Technology orientation Postpaid 2.9254 0.7295
0.034 0.973
It is evident from table 5.86, that there is not significant difference
between prepaid and postpaid subscribers for trust, perceived risk and
reputation of Hutch.
180
Table 5.87: Comparison of Trust and its Dimensions for Tata Indicom
Variables Prepaid/ Postpaid Mean Std.
Deviation t-value p-value
Prepaid 2.8005 0.8921 Trust
Postpaid 3.0113 0.8342 1.18 0.241
Prepaid 2.7341 0.9126 Perceived risk
Postpaid 3.0839 0.8983 1.853 0.067
Prepaid 2.7917 1.3059 Technology orientation Postpaid 2.9841 1.0586
0.798 0.427
Prepaid 2.8757 0.6403 Reputation
Postpaid 2.966 0.6864 0.645 0.52
It is evident from table 5.87, that there is no significant difference between
prepaid and postpaid subscribers for trust, perceived risk and reputation of Tata
Indicom.
5.4.6.2 Results for Trust between Selected Cities and Prepaid and Postpaid Subscribers
a) In order to test the difference between selected cities, prepaid/postpaid
subscribers and their interaction following hypotheses are specified:
Ho1: There is no significant difference for trust between all the four
cities.
HoA: There is significant difference for trust between all the four cities.
Ho2: There is no significant difference for trust between prepaid and
postpaid subscribers.
HoA: There is significant difference for trust between prepaid and
postpaid subscribers.
Ho3: There is no interaction for trust between cities and prepaid and
postpaid subscribers.
HoA: There is interaction for trust between cities and prepaid and
postpaid subscribers.
181
Table 5.88: Two-way ANOVA for Trust in Selected cities and Prepaid and Postpaid Subscribers
Source Type III Sum of Squares df Mean Square F-value p-value
Corrected Model 30.844 7 4.406 11.454 0.001
Intercept 2762.344 1 2762.344 7180.471 0.001
City 18.553 3 6.184 16.076 0.001
Pr/Po 6.253 1 6.253 16.255 0.001
City* Pr/Po 2.517 3 0.839 2.181 0.09
Error 146.956 382 0.385
Total 3481.611 390
Corrected Total 177.8 389
Pr/Po: Prepaid and Postpaid
The above table 5.88 shows ANOVA results for trust for prepaid and postpaid services of selected cities. It is observed that the variation in the trust is significant in the selected cities and prepaid and postpaid subscribers. The interaction between prepaid and postpaid subscribers and selected cities is also significant.
Trust of prepaid and postpaid subscribers and selected cities has been further analysed by using a Post-Hoc test. The results of which are given below.
Table 5.89: Multiple Comparisons (Selected Cities)
95% Confidence Interval
(I) City (J) City Mean
Difference (I-J)
Std. Error p-value
Lower Bound
Upper Bound
Amritsar .4187(*) 0.0886 0.05 0.1699 0.6676
Patiala .6250(*) 0.0888 0.05 0.3756 0.8745 Ludhiana
Chandigarh .5131(*) 0.0884 0.05 0.265 0.7613
The above table 5.89 explains results for post-hoc test. It was carried out
using Scheffe’s multiple comparisons among selected cities. After applying the
test, it was observed that there are significant differences between Ludhiana and
other cities. But other differences are not significant amongst other cities.
182
On the basis of multiple comparisons, further t-test was carried out. The
results of which are given below:
Table 5.90: Comparison of Trust and its Dimensions for Amritsar City
Variables Prepaid/ Postpaid Mean Std.
Deviation t-value p-value
Prepaid 2.5155 0.7832 Trust
Postpaid 3.0494 0.5376 3.435 0.001**
Prepaid 2.5538 0.7688 Perceived risk
Postpaid 3.1002 0.5281
4.085
0.001**
Prepaid 2.4194 1.1626 Technology orientation Postpaid 2.947 0.7551
2.308 0.026*
Prepaid 2.5733 0.6046 Reputation
Postpaid 3.101 0.6416
3.846
0.001**
It is evident from table 5.90, that there is significant difference between
prepaid and postpaid subscribers for trust, perceived risk, technology orientation
and reputation of Amritsar city. In case of trust, perceived risk and reputation the
results are highly significant as compared to technology orientation. Trust,
perceived risk, technology orientation and reputation are more important for the
postpaid subscribers as compared to prepaid subscribers.
Table 5.91: Comparison of Trust and its Dimensions for Patiala City
Variables Prepaid/ Postpaid Mean Std.
Deviation t-value p-value
Prepaid 2.6018 0.6273 Trust
Postpaid 2.7078 0.4621 0.937 0.351
Prepaid 2.3895 0.5318 Perceived risk
Postpaid 2.6328 0.5419 2.087 0.04*
Prepaid 2.8167 0.8146 Technology orientation Postpaid 2.5781 0.6313
1.553 0.124
It is evident from table 5.91, that there is significant difference between
prepaid and postpaid subscribers for perceived risk of Patiala city. For trust and
technology orientation the results are non-significant. Perceived risk is more
important for the postpaid subscribers as compared to prepaid subscribers.
183
Table 5.92: Comparison of Trust and its Dimensions for Chandigarh City
Variables Prepaid/ Postpaid Mean Std. Deviation t-value p-value
Prepaid 2.5762 0.6898 Trust
Postpaid 2.9 0.5801 2.473 0.015
Prepaid 2.6454 0.697 Perceived risk
Postpaid 3.0014 0.5854 2.692 0.008**
Prepaid 2.4394 0.8638 Technology orientation Postpaid 2.8583 0.8442
2.271 0.025*
Prepaid 2.6646 0.7235 Reputation
Postpaid 2.8343 0.5628 1.2 0.235
It is evident from table 5.92, that there is significant difference between
prepaid and postpaid subscribers for perceived risk and technology orientation of
Chandigarh city. For trust and reputation the results are non-significant. In case
of perceived risk, the results are highly significant as compared to technology
orientation. Perceived risk and technology orientation are more important for the
postpaid subscribers as compared to prepaid subscribers.
5.4.6.3 Results for Opportunistic Behaviour between Selected Cities and Telecom Service Providers (TSP)
a) In order to test the difference between cities, TSPs and their interaction
following hypotheses are specified:
Ho1: There is no significant difference for trust between all the four
cities.
HoA: There is significant difference for trust between all the four cities.
Ho2: There is no significant difference for trust between TSPs.
HoA: There is significant difference for trust between TSPs.
Ho3: There is no interaction for trust between cities and TSPs.
HoA: There is interaction for trust between cities and TSPs.
These hypotheses can be tested using two-way ANOVA and the results
are presented in table 5.93.
184
Table 5.93: Two Way ANOVA for Selected Cities and Selected Telecom Service Providers
Source Type III Sum of Squares df Mean
Square F-value P-value
Corrected Model 68.006 15 4.534 15.443 0.001
Intercept 3298.342 1 3298.342 11235.338 0.001
City 22.135 3 7.378 25.134 0.001
TSP 0.361 3 0.12 0.409 0.746
City* TSP 45.718 9 5.08 17.304 0.001
Error 109.795 374 0.294
Total 3481.611 390
Corrected Total 177.8 389
TSP: Telecom Service Providers
The ANOVA results for trust presented in the above table of selected
cities and selected telecom service providers. It is observed that the variation in
the trust is significant in the selected cities and the interaction between selected
telecom service providers and selected cities.
Trust of selected cities and selected telecom service providers has been
further analysed by using one-way ANOVA. The results of which are given
below.
Table 5.94: One Way ANOVA for Ludhiana and Selected Telecom Operators
Source of Variation Sum of Squares df Mean
Square F-value P-value
Between Groups 23.372 3 7.791 30.822 0.01
Within Groups 24.012 95 0.253 Trust
Total 47.384 98
Table 5.94 exhibits that there are significant differences between
Reliance, Airtel, Hutch and Tata Indicom for trust in Ludhiana city. Scheffe’s
Post-Hoc test was carried for multiple comparisons in order to see the significant
differences between the combination of the groups.
185
Table 5.95: Multiple Comparison for Trust between Telecom Service Providers for Ludhiana City
95% Confidence Interval
Dependent Variable (I) TSP (J) TSP
Mean Difference
(I-J) Std.
Error P-
value Lower Bound
Upper Bound
Reliance 1.1893(*) 0.1437 0.01 0.7804 1.5982 Airtel 1.1940(*) 0.1437 0.01 0.7851 1.603 Trust Tata
Indicom Hutch .9407(*) 0.1437 0.01 0.5318 1.3496
TSP: Telecom Service Providers
The above table 5.95 explains Scheffe’s Post-Hoc results. It was carried
out to test the significant differences for trust amongst telecom service providers
for Ludhiana city. Differences are significant between Reliance and Tata
Indicom, Airtel and Tata Indicom, Hutch and Tata Indicom and Tata Indicom with
other operators. All other combinations of differences were found to be non-
significant.
Table 5.96: One Way ANOVA for Amritsar and Selected Telecom 0perators
Source of Variation Sum of Squares df Mean
Square F-value P-value
Between Groups 5.649 3 1.883 4.663 0.004 Within Groups 37.554 93 0.404 Trust
Total 43.202 96
Table 5.96 exhibits that there are significant differences between
Reliance, Airtel, Hutch and Tata Indicom for trust in Amritsar city. Scheffe’s Post-
Hoc test was carried for multiple comparisons in order to see the significant
differences between the combination of the groups.
Table 5.97: Multiple Comparison for Trust between Telecom Service Providers for Amritsar City
95% Confidence Interval
Dependent Variable (I) TSP (J) TSP
Mean Difference
(I-J) Std. Error
P-value Lower
Bound Upper Bound
Hutch Reliance -.5895(*) 0.1797 0.017 -1.1012 .0777 Trust Tata
Indicom Reliance -.5590(*) 0.1797 0.026 -1.0708 .0472
* Significant at .05 level; TSP: Telecom Service Providers
186
The above table 5.97 explains Scheffe’s Post-Hoc results. It was carried
out to test the significant differences for trust amongst telecom service providers
for Amritsar city. Differences are significant between Reliance and Tata Indicom
and Reliance and Hutch. All other combinations of differences were found to be
non-significant.
Table 5.98: One-way ANOVA for Patiala and Selected Telecom 0perators
Source of Variation Sum of Squares df Mean
Square F-value P-value
Between Groups 11.099 3 3.7 23.008 0.01 Within Groups 14.794 92 0.161 Trust
Total 25.893 95
Table 5.98 exhibits that there are significant differences between
Reliance, Airtel, Hutch and Tata Indicom for trust in Patiala city. Scheffe’s Post-
Hoc test was carried for multiple comparisons in order to see the significant
differences between the combination of the groups.
Table 5.99: Multiple Comparison for Trust between Telecom Service Providers for Patiala City
95% Confidence Interval
Dependent Variable (I) TSP (J) TSP
Mean Difference
(I-J) Std.
Error P-
value Lower Bound
Upper Bound
Reliance -.8851(*) 0.1187 0.01 -1.2231 -0.5471 Airtel -.5825(*) 0.1134 0.01 -0.9056 -0.2595 Trust Tata
Indicom Hutch -.7649(*) 0.1134 0.01 -1.0879 -0.4419
* Significant at .05 level; TSP: Telecom Service Providers
The above table 5.99 explains Scheffe’s Post-Hoc results. It was carried
out to test the significant differences for trust amongst telecom service providers
for Patiala city. Differences are significant between Reliance and Tata Indicom,
Airtel and Tata Indicom, Hutch and Tata Indicom. All other combinations of
differences were found to be non-significant.
187
Table 5.100: One Way ANOVA for Chandigarh and Selected Telecom 0perators
Source of Variation Sum of Squares df Mean
Square F-value P-value
Between Groups 5.962 3 1.987 5.588 0.001 Within Groups 33.435 94 0.356 Trust
Total 39.398 97
Table 5.100 exhibits that there are significant differences between
Reliance, Airtel, Hutch and Tata Indicom for trust in Chandigarh city. Scheffe’s
Post-Hoc test was carried for multiple comparisons in order to see the significant
differences between the combination of the groups.
Table 5.101: Multiple Comparison for Trust between Telecom Service Providers for Chandigarh City
95% Confidence Interval
Dependent Variable (I) TSP (J) TSP
Mean Difference
(I-J) Std.
Error P-
Value Lower Bound
Upper Bound
Airtel -.5481(*) 0.1723 0.022 -1.0386 -.0575 Trust Reliance
Hutch -.6562(*) 0.1723 0.004 -1.1468 -0.1657
* Significant at .05 level; TSP: Telecom Service Providers
Scheffe’s Post-Hoc test was carried out to test the significant differences
for trust amongst telecom service providers for Chandigarh city. Differences are
significant between Reliance and Airtel and Reliance and Hutch. All other
combinations of differences were found to be non-significant.
5.4.6.4 Results for Trust between Selected Cities, Prepaid and Postpaid Subscribers and Telecom Service Providers (TSPs)
In order to test the difference between cities, prepaid/postpaid subscribers
and TSPs their interaction following hypotheses are specified:
Ho1: There is no significant difference for trust between all the four
cities.
HoA: There is significant difference for trust between all the four cities.
188
Ho2: There is no significant difference for trust between telecom service
providers.
HoA: There is significant difference for trust between telecom service
providers.
Ho3: There is no significant difference for trust between prepaid and
postpaid subscribers.
HoA: There is significant difference for trust between prepaid and
postpaid subscribers.
Ho4: There is no interaction for trust between city and telecom service
providers.
HoA: There is interaction for trust between city and telecom service
providers.
Ho5: There is no interaction for trust between city and prepaid and
postpaid subscribers.
HoA: There is interaction for trust between city and prepaid and postpaid
subscribers.
Ho6: There is no interaction for trust between TSP and prepaid and
postpaid subscribers.
HoA: There is interaction for trust between TSP and prepaid and
postpaid subscribers.
Ho7: There is no interaction for trust among TSP, prepaid and postpaid
subscribers and city.
HoA: There is interaction for trust among TSP, prepaid and postpaid
subscribers and city.
These hypotheses can be tested using two-way ANOVA and the results
are presented in Table 5.102.
189
Table 5.102: Multiple Factor ANOVA for Selected Cities, Selected Service Providers and Prepaid and Postpaid Services
Source Type III Sum of Squares df Mean
Square F-value p-value
Corrected Model 83.117 31 2.681 10.138 0.01
Intercept 2725.888 1 2725.888 10306.666 0.01
City 16.843 3 5.614 21.228 0.01
TSP 1.308 3 0.436 1.648 0.178
Pr/Po 6.364 1 6.364 24.062 0.01
City* TSP 42.724 9 4.747 17.949 0.01
City* Pr/Po 1.922 3 0.641 2.423 0.066
TSP* Pr/Po 1.91 3 0.637 2.408 0.067
City* TSP* Pr/Po 4.897 9 0.544 2.057 0.033
Error 94.683 358 0.264
Total 3481.611 390
Corrected Total 177.8 389
Pr/Po: Prepaid and Postpaid; TSP: Telecom Service Providers
The above table 5.102 explains ANOVA results for trust between prepaid
and postpaid services of selected cities and selected telecom service providers.
It is observed that the variation in the trust is significant in the selected cities and
the interaction between selected TSPs and selected cities.
Table 5.103: Comparison of Trust and its Dimensions for Reliance Ludhiana
Variable Prepaid/ Postpaid Mean Std.
DeviationStd. Error
Mean t-value p-value
Prepaid 2.3214 0.6852 0.2797 Trust
Postpaid 3.1454 0.4939 0.1133 3.25** 0.004
** Significant at .01 level
Above table 5.103 indicates t-test results for trust of prepaid and postpaid
subscribers among all Reliance subscribers of Ludhiana city. There are
significant differences between prepaid (2.32) and postpaid(3.15) subscribers of
Ludhiana Reliance.
190
Table 5.104: Comparison of Trust and its Dimensions for Airtel Amritsar
Variable Prepaid/ Postpaid Mean Std.
DeviationStd. Error
Mean t-value p-value
Prepaid 2.59233 0.6281 0.2221 Trust
Postpaid 3.1533 0.5582 0.1492 2.169** 0.042
** Significant at .01 level
Above table 5.104 indicates t-test results for trust of prepaid and postpaid
subscribers among all Airtel subscribers of Amritsar city. There are significant
differences between prepaid (2.59) and postpaid (3.15) subscribers of Amritsar
Airtel.
Table 5.105: Comparison of Trust and its Dimensions for Hutch Amritsar
Variable Prepaid/ Postpaid Mean Std.
DeviationStd. Error
Mean t-value p-value
Prepaid 2.0357 .6576 .2325 Trust
Postpaid 2.9412 .6003 .1456 3.415** 0.002
** Significant at .01 level
Above table 5.105 indicates t-test results for trust of prepaid and postpaid
subscribers among all Hutch Indicom subscribers of Amritsar city. There are
significant differences between prepaid (2.03) and postpaid (2.94) subscribers of
Amritsar Hutch.
5.4.7 Reliability Analysis - Scale (Alpha) for Key Mediating Variable Relationship Commitment
RELIABILITY COEFFICIENTS Number of Cases = 387.0 Number of Items = 2 Alpha = .8464
Cronbach's α (alpha) is an important psychometric instrument to measure
the reliability. The data for analysis is reliable one as its’ value is >.6.So various
statistical tools can be applied and tested.
191
5.4.7.1 Results for Relationship Commitment between Selected Cities and Prepaid and Post Paid Subscribers
In order to test the difference between cities, prepaid/postpaid subscribers
and their interaction following hypotheses are specified:
Ho1: There is no significant difference for relationship commitment
between all the four cities.
HoA: There is significant difference for relationship commitment between
all the four cities.
Ho2: There is no significant difference for relationship commitment
between prepaid and postpaid subscribers.
HoA: There is significant difference for relationship commitment between
prepaid and postpaid subscribers.
Ho3: There is no interaction for relationship commitment between cities
and prepaid and postpaid subscribers.
HoA: There is interaction for relationship commitment between cities and
prepaid and postpaid subscribers.
These hypotheses can be tested using two-way ANOVA and the results
are presented in Table 5.106.
Table 5.106: Two Way ANOVA Prepaid and Postpaid Subscribers and Selected Cities
Source Type III Sum of Squares df Mean
Square F-value P-value
Corrected Model 7.701 7 1.1 3.93 0.01
Intercept 2770.245 1 2770.245 9897.358 0.01 TSP 1.244 3 0.415 1.481 0.219 Pr/Po 6.65 1 6.65 23.758 0.01
TSP* Pr/Po 0.236 3 7.86E-02 0.281 0.839
Error 106.921 382 0.28 Total 3435.921 390
Corrected Total 114.622 389
Pr/Po: Prepaid and Postpaid; TSP: Telecom Service Providers
192
The above table 5.106 explains ANOVA results for relationship commitment for prepaid and postpaid services. It is observed that the variation in the relationship commitment is significant.
Table 5.107: Comparison of Relationship Commitment and its Dimensions for Reliance
Variables Prepaid/ Postpaid Mean Std.
Deviation t-value p-value
Prepaid 2.6878 0.7158 Relationship commitment Postpaid 3.015 0.4219
2.225 0.033*
Prepaid 2.6543 0.7072 Degree and length of association Postpaid 3.0264 0.4401
2.542 0.016*
Prepaid 2.619 0.714 Sense of belongingness Postpaid 3.0037 0.4639
2.504 0.018*
It is evident from table 5.107, that there is significant difference between
prepaid and postpaid subscribers for relationship commitment, degree and
length of association and sense of belongingness of Reliance. Relationship
commitment, degree and length of association and sense of belongingness and
security are more important for the postpaid subscribers as compared to prepaid
subscribers.
Table 5.108: Comparison of Relationship Commitment and its Dimensions for Airtel
Variables Prepaid/ Postpaid Mean Std.
Deviation t-value p-value
Prepaid 2.6181 0.5116 Relationship commitment Postpaid 2.956 0.4792
3.143 0.002**
Prepaid 2.6933 0.6362 Degree and length of association Postpaid 2.9567 0.5417
2.095 0.039*
Prepaid 2.5961 0.4287 Sense of belongingness Postpaid 2.9552 0.4956
3.39 0.001**
It is evident from table 5.108, that there is significant difference between
prepaid and postpaid subscribers for relationship commitment, degree and
length of association and sense of belongingness of Airtel. In case of relationship
193
commitment and sense of belongingness, results are highly significant as
compared to degree and length of association. Relationship commitment, degree
and length of association and sense of belongingness are more important for the
postpaid subscribers as compared to prepaid subscribers.
Table 5.109: Comparison of Relationship Commitment and its Dimensions for Hutch
Variables Prepaid/ Postpaid Mean Std.
Deviation t-value p-value
Prepaid 2.7665 0.5235 Relationship commitment Postpaid 2.9805 0.3614
2.09 0.042*
Prepaid 2.8641 0.5554 Degree of association Postpaid 3.0184 0.3432
1.447
0.155
Prepaid 2.6689 0.5506 Sense of belongingness Postpaid 2.9426 0.4549
2.62 0.01**
It is evident from table 5.109, that there is significant difference between
prepaid and postpaid subscribers for relationship commitment and sense of
belongingness for Hutch. However, these differences for degree of association
were found to be non-significant. In case of sense of belongingness, the results
are highly significant as compared to relationship commitment. Relationship
commitment and sense of belongingness are more important for the postpaid
subscribers as compared to prepaid subscribers.
5.4.7.2 Results for relationship commitment between Prepaid and Postpaid subscribers Selected Cities
In order to test the difference between cities, prepaid/postpaid
subscribers and their interaction following hypotheses are specified:
Ho1: There is no significant difference for relationship commitment
between all the four cities.
HoA: There is significant difference for relationship commitment between
all the four cities.
Ho2: There is no significant difference for relationship commitment
between prepaid and postpaid subscribers.
194
HoA: There is significant difference for relationship commitment between
prepaid and postpaid subscribers.
Ho3: There is no interaction for relationship commitment between cities
and prepaid and postpaid subscribers.
HoA: There is interaction for relationship commitment between cities and
prepaid and postpaid subscribers.
These hypotheses can be tested using two-way ANOVA and the results
are presented in Table 5.110.
Table: 5.110: Two Way ANOVA for Prepaid and Postpaid Subscribers and Selected Cities
Source Type III Sum of Squares df Mean
Square F-value P-value
Corrected Model 24.746 7 3.535 15.025 0.001 Intercept 2780.96 1 2780.96 11819.973 0.001 City 14.962 3 4.987 21.198 0.001 Pr/Po 5.556 1 5.556 23.613 0.001 City* Pr/Po 3.131 3 1.044 4.436 0.004 Error 89.876 382 0.235 Total 3435.921 390 Corrected Total 114.622 389
Pr/Po: Prepaid and Postpaid; TSP: Telecom Service Providers
The above table 5.110 explains ANOVA results for relationship
commitment for prepaid and postpaid services of selected cities. It is observed
that the variation in the relationship commitment is significant in the selected
cities and the interaction between prepaid and postpaid services and selected
cities.
Relationship commitment of Prepaid and Postpaid subscribers in selected
cities has been further analysed by using a Post-Hoc test. The results of which
are given below.
195
Table 5.111: Multiple Comparisons (Selected Cities)
95% Confidence Interval
(I) City (J) City Mean
Difference (I-J)
Std. Error p-value Lower
Bound Upper Bound
Amritsar .2370(*) .0693 0.009 .04252 0.4316 Patiala .5676(*) .0695 0.05 0.3725 0.7627 Ludhiana
Chandigarh .2132(*) .0691 0.024 .0191 0.4073 Ludhiana -.2370(*) .0693 0.009 -0.4316 -.0425
Amritsar Patiala .3306(*) .0698 0.05 0.1345 0.5267
Ludhiana -.5676(*) .0695 0.05 -0.7627 -0.3725 Amritsar -.3306(*) .0698 0.05 -0.5267 -0.1345 Patiala
Chandigarh -.3544(*) .0697 0.05 -0.55 -0.1589 Ludhiana -.2132(*) .0691 0.024 -0.4073 -.0191
Chandigarh Patiala .3544(*) .0697 0.05 0.1589 0.55
The above table 5.111 explains the results of post-hoc test. It was carried
out for multiple comparisons among selected cities using Scheffe’s multiple
comparisons. After applying the test, it was observed that there are significant
differences between Ludhiana and other cities, Amritsar and Patiala, Patiala and
Chandigarh. But other differences are not significant amongst other cities.
Relationship commitment of Prepaid and Postpaid subscribers has been
further analysed by using a Post-Hoc test. The results of which are given below.
Table 5.112: Comparison of Relationship Commitment and its Dimensions for Amritsar City
Variables Prepaid/ Postpaid Mean Std.
Deviation t-value p-value
Prepaid 2.5439 0.5668 Relationship commitment Postpaid 3.1164 0.3899
5.087 0.001**
Prepaid 2.5532 0.5402 Degree and length of association Postpaid 3.1508 0.414
5.996 0.001**
Prepaid 2.5346 0.6546 Sense of belongingness Postpaid 3.0821 0.5088
4.499 0.001**
196
It is evident from table 5.112, that there is significant difference between
prepaid and postpaid subscribers for relationship commitment, degree and
length of association and sense of belongingness of Amritsar City. In case of
relationship commitment, degree and length of association and sense of
belongingness are highly significant. Relationship commitment, degree and
length of association and sense of belongingness are more important for the
postpaid subscribers as compared to prepaid subscribers.
Table 5.113: Comparison of Relationship Commitment and its Dimensions for Patiala City
Variables Prepaid/ Postpaid Mean Std.
Deviation t-value p-value
Prepaid 2.434 0.4823 Relationship commitment Postpaid 2.6873 0.4303
2.611 0.011*
Prepaid 2.4781 0.4995 Degree and length of association Postpaid 2.6953 0.4732
2.081 0.04
Prepaid 2.3825 0.3274 Sense of belongingness Postpaid 2.6793 0.4405
3.652 0.001**
It is evident from table 5.113, that there is significant difference between
prepaid and postpaid subscribers for relationship commitment and sense of
belongingness of Patiala City. However, these differences for degree and length
of association were found to be non-significant. In case of sense of
belongingness are highly significant as compared to relationship commitment.
Relationship commitment and sense of belongingness are more important for the
postpaid subscribers as compared to prepaid subscribers.
Table 5.114: Comparison of Relationship Commitment and its Dimensions for Chandigarh City
Variables Prepaid/ Postpaid Mean
Std. Deviation t-value p-value
Prepaid 2.8824 0.5297 Relationship commitment Postpaid 2.9989 0.5056
1.074 0.285
Prepaid 3.0533 0.6627 Degree and length of association Postpaid 3.0519 0.4914
0.013 0.99
Prepaid 2.6737 0.5404 Sense of belongingness Postpaid 2.946 0.6109
2.178 0.032*
197
It is evident from table 5.114, that there is significant difference between
prepaid and postpaid subscribers for sense of belongingness of Chandigarh City.
However, these differences for relationship commitment and degree and length
of association were found to be non-significant. Sense of belongingness is more
important for the postpaid subscribers as compared to prepaid subscribers.
5.4.7.3 Results for Relationship Commitment between Selected Cities and Telecom Service Providers (TSP)
In order to test the difference between cities, TSP and their interaction
following hypotheses are specified:
Ho1: There is no significant difference for relationship commitment
between all the four cities.
HoA: There is significant difference for relationship commitment between
all the four cities.
Ho2: There is no significant difference for relationship commitment
between TSPs.
HoA: There is significant difference for relationship commitment between
TSPs.
Ho3: There is no interaction for relationship commitment between cities
and TSPs.
HoA: There is interaction for relationship commitment between cities and
TSPs.
These hypotheses can be tested using two-way ANOVA and the results
are presented in Table 5.115.
198
Table: 5.115 Two Way ANOVA for Selected Telecom Service Providers and Selected Cities
Source Type III Sum of Squares df Mean
Square F-value P-value
Corrected Model 38.545 15 2.57 12.633 0.001 Intercept 3314.659 1 3314.659 16295.223 0.001 City 15.523 3 5.174 25.437 0.001 TSP 0.994 3 0.331 1.63 0.182 City* TSP 21.582 9 2.398 11.789 0.001 Error 76.076 374 0.203 Total 3435.921 390 Corrected Total 114.622 389
TSP: Telecom Service Providers
The above table 5.115 explains ANOVA results for relationship commitment for prepaid and postpaid services of selected cities and selected telecom service providers. It is observed that the variation in the relationship commitment is significant in the selected cities and the interaction between selected telecom service providers and selected cities.
Relationship commitment of selected cities and selected Telecom Service
Providers has been further analysed by using one way ANOVA. The results of
which are given below.
Table 5.116: One Way ANOVA: Ludhiana and Selected Telecom 0perators
Source of Variation Sum of Squares df Mean
Square F-
value p-value
Between Groups 14.137 3 4.712 36.995 0.01
Within Groups 12.101 95 0.127 Relationship Commitment
Total 26.238 98
Table 5.116 exhibits that there are significant differences between
Reliance, Airtel, Hutch and Tata Indicom for relationship commitment in
Ludhiana City. Scheffe’s Post-Hoc test was carried for multiple comparisons in
order to see the significant differences between the combination of the groups.
199
Table 5.117: Multiple Comparison for Relationship Commitment between Telecom Service Providers for Ludhiana City
95% Confidence Interval
Dependent Variable (I) TSP (J) TSP
Mean Difference
(I-J) Std.
Error P-
value Lower Bound
Upper Bound
Reliance .8955(*) 0.102 0.001 0.6052 1.1858 Airtel .9530(*) 0.102 0.001 0.6627 1.2433 Tata
Indicom Hutch .7387(*) 0.102 0.001 0.4484 1.029
* Significant at .05 level; TSP: Telecom Service Providers
Table 5.117 exhibits the results of Scheffe’s Post-Hoc test. It was carried
out to test the significant differences for relationship commitment amongst
telecom service providers for Ludhiana city. Differences are significant between
Reliance and Tata Indicom, Airtel and Tata Indicom, Hutch and Tata Indicom. All
other combinations of differences were found to be non-significant.
Table 5.118: One Way ANOVA: Patiala and Selected Telecom 0perators
Source of Variation Sum of Squares Df Mean
Square F-
value p-value
Between Groups 3.916 3 1.305
Within Groups 16.328 92 0.177 7.356 .001 Relationship
Commitment
Total 20.244 95
Table 5.118 exhibits that there are significant differences between
Reliance, Airtel, Hutch and Tata Indicom for relationship commitment in Patiala
city. Scheffe’s Post-Hoc test was carried for multiple comparisons in order to see
the significant differences between the combination of the groups.
Table 5.119: Multiple Comparison for Shared Value between Telecom Service Providers for Patiala City
95% Confidence
Interval Dependent
Variable (I) TSP (J) TSP
Mean Difference
(I-J) Std.
Error P-
value Lower Bound
Upper Bound
Airtel .3789(*) 0.1247 0.031 .02382 0.734 Relationship Commitment Reliance Tata
Indicom .5658(*) 0.1247 0.01 0.2106 0.9209
* Significant at .05 level; TSP: Telecom Service Providers
200
Above Table 5.119 explains results of Scheffe’s Post-Hoc test. It was
carried out to test the significant differences for relationship commitment
amongst telecom service providers for Patiala city. Differences are significant
between Reliance and Airtel and between Reliance and Tata Indicom. All other
combinations of differences were found to be non-significant.
5.4.7.4 Results for Relationship Commitment between Selected Cities, Prepaid & Postpaid Subscribers and Telecom Service Providers (TSP)
In order to test the difference between cities, prepaid/postpaid subscribers
and selected telecom service providers (TSPs) and their interaction following
hypotheses are specified:
Ho1: There is no significant difference for relationship commitment
between all the four cities.
HoA: There is significant difference for relationship commitment between
all the four cities.
Ho2: There is no significant difference for relationship commitment
between prepaid and postpaid subscribers.
HoA: There is significant difference for relationship commitment between
prepaid and postpaid subscribers.
Ho3: There is no significant difference for relationship commitment
between TSPs.
HoA: There is significant difference for relationship commitment between
TSPs.
Ho4: There is no interaction for relationship commitment between cities
and prepaid and postpaid subscribers.
HoA: There is interaction for relationship commitment cities and prepaid
and postpaid subscribers.
Ho5: There is no interaction for relationship commitment between TSPs
and prepaid and postpaid subscribers.
201
HoA: There is interaction for relationship commitment between TSPs and
prepaid and postpaid subscribers.
Ho6: There is no interaction for relationship commitment between TSPs
and cities.
HoA: There is interaction for relationship commitment between TSPs and
cities.
Ho7: There is no interaction for relationship commitment among TSPs,
prepaid & postpaid subscriber and cities.
HoA: There is interaction for relationship commitment among TSPs,
prepaid & postpaid subscriber and cities.
These hypotheses can be tested using two-way ANOVA and the results
are presented in Table 5.120.
Table 5.120: Multifactor ANOVA for Prepaid and Postpaid Subscribers, TSPs and Selected Cities
Source Type III Sum of Squares df Mean
Square F-value P-value
Corrected Model 50.625 31 1.633 9.135 0.001
Intercept 2748.021 1 2748.021 15372.543 0.001 City 13.602 3 4.534 25.364 0.001 TSP 1.328 3 0.443 2.476 0.061 Pr/Po 5.574 1 5.574 31.181 0.001 City* TSP 20.387 9 2.265 12.672 0.001 City* Pr/Po 2.245 3 0.748 4.186 0.006 TSP* Pr/Po 0.32 3 0.107 0.597 0.617 City* TSP* Pr/Po 3.521 9 0.391 2.188 0.022 Error 63.997 358 0.179 Total 3435.921 390 Corrected Total 114.622 389
Pr/Po: Prepaid and Postpaid; TSP: Telecom Service Providers
Table 5.120 presents the ANOVA results for relationship commitment for
prepaid and postpaid services of selected cities and selected telecom service
202
providers. It is observed that the variation in the relationship commitment is
significant in the interaction amongst selected telecom service providers and
selected cities, prepaid & postpaid subscribers and cities. The interaction
amongst city, Telecom service provider and prepaid & post paid subscribers is
also significant one.
Table 5.121: Comparison of Relationship Commitment and its Dimensions for Ludhiana Reliance
Variable Prepaid/ Postpaid Mean Std.
DeviationStd. Error
Mean t-value p-value
Prepaid 2.5631 .4981 .2034 Relationship commitment Postpaid 3.04 .3072 .0705
2.87** .009
Table 5.121 indicates t-test results for relationship commitment of prepaid
and postpaid subscribers among all Reliance subscribers of Ludhiana city. There
are significant differences between prepaid (2.56) and postpaid (3.04)
subscribers of Ludhiana Reliance
Table 5.122: Comparison of Relationship Commitment and its Dimensions for Amritsar Airtel
Variable Prepaid/ Postpaid Mean Std.
DeviationStd. Error
Mean t-value p-value
Prepaid 2.6125 .631 .2231 Relationship commitment Postpaid 3.127 .4956 .1325
2.123** .046
Above table 5.122 indicates t-test results for relationship commitment of
prepaid and postpaid subscribers among all Airtel subscribers of Amritsar city.
There are significant differences between prepaid (2.61) and postpaid (3.12)
subscribers of Amritsar Airtel.
Table 5.123: Comparison of relationship commitment and its dimensions for Amritsar Hutch
Variable Prepaid/ Postpaid Mean Std.
DeviationStd. Error
Mean t-value p-value
Prepaid 2.4344 .3051 .1079 Relationship commitment Postpaid 3.0044 .3357 .0814
4.218** .001
203
Above table 5.123 indicates t-test results for relationship commitment of
prepaid and postpaid subscribers among all Hutch subscribers of Amritsar city.
There are significant differences between prepaid (2.43) and postpaid (3.00)
subscribers of Amritsar Hutch.
Table 5.124: Comparison of Relationship Commitment and its Dimensions for Amritsar Tata Indicom
Variable Prepaid/ Postpaid Mean Std.
DeviationStd. Error
Mean t-value p-value
Prepaid 2.2444 .4593 .1531 Relationship commitment Postpaid 3.1393 .5037 .1259
4.394** .001
** Significant at .01 level
Above table 5.124 indicates t-test results for relationship commitment of
prepaid and postpaid subscribers among all Tata Indicom subscribers of
Amritsar city. There are significant differences between prepaid (2.24) and
postpaid (3.13) subscribers of Amritsar Tata Indicom.
204
Figure 5.1: Regression Results of Reliance
205
5.4.7.5 Results of Regression Values of Reliance
For Reliance various β-values represent the closeness amongst various
dependent and independent variables. The model diagram represents that in
shared value security, privacy and ethics are highly significant. Security (β=.424)
plays more significant role as compared to privacy (β=.373) and ethics (β=.295).
In opportunistic behavior information asymmetry is comparatively highly
significant as compared to regulatory control (β=.475). Similarly in
communication openness (β=.442) plays more important role as compared to
speed of response (β=.325) and quality of information (β=.331). In trust,
opportunistic behavior (β=.345) plays more important role as compared to
communication (β=.322) and shared value (β=.312). For trust, technology
orientation (β=.441) is highly significant as compared to highly significant values
of perceived risk (β=.337) and reputation (β=.335). Similarly for relationship
commitment shared value (β=.520) has more significant role to play as compare
to trust (β=.340). For relationship commitment sense of belongingness (β=.542)
is more closely related as compared to degrees and length of association
(β=.513).
206
Figure 5.2: Regression Results of Airtel
* - Significant at .05 level
207
5.4.7.6 Results of Regression Values of Airtel For Airtel various β-values represent the closeness amongst various
dependent and independent variables. The model diagram represents that in
shared value security, privacy and ethics are highly significant. Privacy (β=.504)
plays more significant role as compared to security (β=.475) and ethics (β=.315).
In opportunistic behavior information asymmetry (β=.705) is comparatively highly
significant as compared to regulatory control (β=.514). Similarly in
communication quality of information (β=.538) plays more important role as
compared to speed of response (β=.406) and openness (β=.406). In trust,
opportunistic behavior (β=.538) plays more important role as compared to
communication (β=.287) and shared value (β=.091). For trust, technology
orientation (β=.481) is highly significant as compared to highly significant values
of perceived risk (β=.375) and reputation (β=.392). Similarly for relationship
commitment trust (β=.446) as compared to shared value (β=.274). For
relationship commitment degree and length of association (β=.579) is more
closely related as compared to sense of belongingness (β=.526).
208
Figure 5.3: Regression Results of Huch
209
5.4.7.7 Results of Regression Values of Hutch For Hutch various β-values represent the closeness amongst various
dependent and independent variables. The model diagram represents that in
shared value security, privacy and ethics are highly significant. Security (β=.537)
plays more significant role as compared to privacy (β=.472) and ethics (β=.361).
In opportunistic behavior information asymmetry (β=.604) is comparatively highly
significant as compared to regulatory control (β=.551). Similarly in
communication openness (β=.449) plays more important role as compared to
speed of response (β=.379) and quality of information (β=.354). In trust,
opportunistic behavior (β=.434) plays more important role as compared to
communication (β=.207) and shared value (β=.159). For trust, technology
orientation (β=.463) is highly significant as compared to perceived risk (β=.331)
and reputation (β=.321). Similarly for relationship commitment trust (β=.497) is
highly significant as compared to shared value (β=.160). For relationship
commitment, sense of belongingness is highly significant (β=.584) as compared
to degree and length of association (β=.496).
210
Figure 5.4: Regression Results of Tata Indicom
* - Significant at .05 level
211
5.4.7.8 Results of Regression Values of Tata Indicom For Tata Indicom various β-values represent the closeness amongst
various dependent and independent variables. The model diagram represents
that in shared value security, privacy and ethics are highly significant. Security
(β=.403) plays more significant role as compared to privacy (β=.379) and ethics
(β=.327). In opportunistic behavior regulatory control (β=.587) is comparatively
highly significant as compared to information asymmetry (β=.452). Similarly in
communication, openness (β=.464) plays more important role as compared to
speed of response (β=.284) and quality of information (β=.338). In trust,
opportunistic behavior is highly significant (β=.523) plays more important role as
compared to communication (β=.255) and shared value (β=.179). For trust,
technology orientation (β=.448) is highly significant as compared to perceived
risk (β=.356) and reputation (β=.260). Similarly for relationship commitment
shared value (β=.475) is highly significant as compared to trust (β=.403). For
relationship commitment, sense of belongingness (β=.552) is highly significant
as compared to degree and length of association (β=.508).
212
Figure 5.5: Regression Results of Prepaid Subscribers
* - Significant at .05 level
213
5.4.7.9 Results of Regression Values of Prepaid Subscribers For prepaid subscribers various β-values represent the closeness
amongst various dependent and independent variables. The model diagram
represents that in shared value security, privacy and ethics are highly significant.
Security (β=.447) plays more significant role as compared to privacy (β=.398)
and ethics (β=.323). In opportunistic behavior, regulatory control (β=.598) is
comparatively highly significant as compared to information asymmetry (β=.496).
Similarly in communication, openness (β=.416) plays more important role as
compared to quality of information (β=.366) and speed of response (β=.362). In
trust, opportunistic behavior is highly significant (β=.433) plays more important
role as compared to communication (β=.312) and shared value (β=.147). For
trust, technology orientation (β=.483) is highly significant as compared to
perceived risk (β=.327) and reputation (β=.288). Similarly for relationship
commitment shared value (β=.461) is highly significant as compared to trust
(β=.279). For relationship commitment, degree and length of association
(β=.544) is highly significant as compared to sense of belongingness (β=.532).
214
Figure 5.6: Regression Results of Postpaid Subscribers
* - Significant at .05 level
215
5.4.7.10 Results of Regression Values of Postpaid Subscribers For postpaid subscribers various β-values represent the closeness
amongst various dependent and independent variables. The model diagram
represents that in shared value security, privacy and ethics are highly significant.
Security (β=.450) plays more significant role as compared to privacy (β=.440)
and ethics (β=.320). In opportunistic behavior, regulatory control (β=.634) is
comparatively highly significant as compared to information asymmetry (β=.469).
Similarly in communication, openness (β=.457) plays more important role as
compared to quality of information (β=.340) and speed of response (β=.336). In
trust, communication (β=.432) is highly significant plays more important role as
compared to opportunistic behavior (β=.308) and shared value (β=.210). For
trust, technology orientation (β=.442) is highly significant as compared to
perceived risk (β=.360) and reputation (β=.327). Similarly for relationship
commitment trust (β=.555) is highly significant as compared to shared value
(β=.233). For relationship commitment, degree and length of association
(β=.558) is highly significant as compared to sense of belongingness (β=.521).
216
Figure 5.7: Regression Results of Combined Prepaid and Postpaid Subscribers
* - Significant at .05 level
217
5.4.7.11 Results of Regression Values of Combined Prepaid and Postpaid Subscribers
For overall β-values of selected telecom service providers in selected city
represent the closeness amongst various dependent and independent variables.
The model diagram represents that in shared value security, privacy and ethics
are highly significant. Security (β=.445) plays more significant role as compared
to privacy (β=.419) and ethics (β=.322). In opportunistic behavior, regulatory
control (β=.617) is comparatively highly significant as compared to information
asymmetry (β=.481). Similarly in communication, openness (β=.440) plays more
important role as compared to quality of information (β=.349) and speed of
response (β=.341). In opportunistic behavior (β=.436) is highly significant plays
more important role as compared to communication (β=.305) and shared value
(β=.188). For trust, technology orientation (β=.454) is highly significant as
compared to perceived risk (β=.349) and reputation (β=.312). Similarly for
relationship commitment trust (β=.437) is highly significant as compared to
shared value (β=.344). For relationship commitment, sense of belongingness
(β=.549) is highly significant as compared to degree and length of association
(β=.535).
218
Table 5.125: Effectiveness of Source of Information between Prepaid and Postpaid Subscribers
PLACE
Ludhiana Amritsar Patiala Chandigarh Total p-value
Yes 72 57 24 43 196 <0.001** Advertisements (Media): source of information of Mobile Services
No 27 41 74 54 196
Yes 63 47 71 49 230 <0.001** Word Of mouth: source of information of Mobile Services
No 36 51 27 48 162
Yes 56 19 8 7 90 Company Outlet source of information of Mobile Services
No 43 79 90 90 302 <0.001**
Yes 1 3 1 2 7 Internet: source of information of Mobile Services
No 98 95 97 95 385 0.655
Chi-square for advertisement=15.97, word of mouth=89.60, * Significant at .05 level; ** Significant at .01 level
On pursing above table 5.125, it is evident that results are highly
significant for advertisements as medium of information (<0.001), word of mouth
(<0.001) and company outlet (<0.001) between prepaid and postpaid
subscribers. The results for internet as source of information of mobile services
has been found non-significant.
Table 5.126: Subscription of Other Services of Selected Telecom Operators
PLACE Telecom Service Provider
Ludhiana Amritsar Patiala ChandigarhTotal
p-value
Yes 47 10 21 12 90 <0.001** Reliance No 49 88 79 86 302 Yes 51 19 16 34 120 Airtel No 45 78 84 64 271
<0,001**
Yes 19 16 7 14 56 Hutch No 77 82 93 84 336
0.07
Yes 8 2 2 7 19 Tata Indicom No 87 96 98 91 372
0.067
Chi-square Reliance=52.278, Airtel=39.220 * Significant at .05 level; ** Significant at .01 level
219
The above table 5.126 explains people’s preference for the subscription of other services along with mobile services of various mobile operators. Highly
significant results are obtained for subscription of other services of Reliance
(0.001) and Airtel (0.001) amongst various cities covering Ludhiana, Amritsar,
Patiala and Chandigarh. For Hutch and Tata Indicom the results are found to be non-significant for the subscription of other services along with the mobile one.
Table 5.127: Subscription between Prepaid and Postpaid Subscribers
Prepaid/PostpaidLudhiana Amritsar Patiala Chandigarh Total p-value
Prepaid 21 26 26 34 107 Services Postpaid 73 34 10 42 159
0.001**
** Significant at .01 level , Chi-square = 28.718
It is evident from the above table 5.127 that significant differences are found between prepaid and postpaid subscribers (0.001) amongst selected four cities- Ludhiana, Amritsar, Patiala and Chandigarh.
Table 5.128: Evaluation of Various Purposes of Mobile Usage
S.No. Statements Services N Mean Std.
Deviation t-value p-value
Prepaid 118 4.18 0.95 0.042 0.967 1 To stay in touch Postpaid 252 4.18 0.99 Prepaid 122 3.85 1.05 0.668 0.505 2 For Business or
Professional Requirement Postpaid 248 3.93 1.08
Prepaid 119 3.07 1.26 1.08 0.281 3 As a status symbol Postpaid 245 3.21 1.12 Prepaid 114 3.96 0.83 1.231 0.219 4 It is advantageous
over Landline Postpaid 244 4.08 0.84 Prepaid 117 4.03 0.92 2.458 0.014* 5 It adds to Mobility Postpaid 239 4.26 0.78 Prepaid 120 3.98 0.91 0.916 0.361 6 For services Postpaid 238 4.08 0.9 Prepaid 120 4.36 0.76 0.01 0.992 7 Convenience of
calling anytime Postpaid 263 4.36 0.82 Prepaid 121 4.12 0.98 2.109 0.036* 8 Makes you easily
accessible Postpaid 251 4.31 0.73
* Significant at .05 level
220
On pursing table 5.128, it is evident that the results are significant for all
the four operators namely Reliance, Airtel, Hutch and Tata Indicom regarding the
purpose of usage of mobile phones. Significant differences are found between
prepaid and postpaid users who consider mobile usage as advantageous over
the landline (.0014) and mobile usage makes easily accessible (0.036). For the
rest of purposes, no significant differences are found among prepaid and
postpaid operators.
Figure 5.8: Purposes of Mobile Usage
00.5
11.5
22.5
33.5
44.5
5
Mea
n S
core
s
1 2 3 4 5 6 7 8
Reliance Airtel Hutch Tata Indicom
1. To stay in touch 5. It adds to Mobility 2. For Business or Professional Requirement 6. For services 3. As a status symbol 7. Convenience of calling anytime 4. It is advantageous over Landline 8. Makes you easily accessible
Above figure 5.8 mobile phones are used for used by subscribers for
different purposes of selected mobile service provided. In the above graph,
different purposes are taken on x-axis and mean scores of selected telecom
service providers on y-axis. For the purpose to stay in touch, there are not
significant differences between Reliance and Tata Indicom. But there are
significant differences between Reliance and Airtel and Reliance and Hutch.
Being professional requirement, there are significant differences among Tata
Indicom and Reliance, Airtel and Tata Indicom. In context of status symbol, there
221
are significant differences between Reliance, Tata Indicom and Airtel and Hutch.
Having advantage over the landline is another purpose where Reliance and Tata
Indicom has highest scores. Since mobile usage enhances your mobility, the
significant mean scores of Tata Indicom.
Table 5.129: Evaluation of Different Mobile Services between Prepaid and Postpaid Subscribers
S.
No. Statements Services N Mean Std. Deviation t-value p-value
Prepaid 122 4.07 1.12 1.371 0.171 1 Short Message
Services (SMS) Postpaid 263 3.89 1.16
Prepaid 120 2.22 1.21 0.306 0.76 2
Multi Media Message Services
(MMS) Postpaid 253 2.26 1.18
Prepaid 120 2.33 1.39 0.653 0.514 3 GPRS / Internet
Postpaid 245 2.23 1.3
Prepaid 119 2.26 1.32 0.11 0.912
4
Call Diverting Feature (Call
Forwarding / Call Diverting )
Postpaid 246 2.28 1.28
Prepaid 118 2.24 1.2 0.422 0.673 5 Information based
services Postpaid 240 2.3 1.25
Prepaid 118 2.35 1.25 0.631 0.528 6
Getting News Update (Sports /
Others ) Postpaid 239 2.26 1.15
Prepaid 118 3.63 1.04 0.165 0.869 7 For STD calling
Postpaid 257 3.65 1.01
Prepaid 122 4.4 0.8 0.421 0.674 8 For Local calling
Postpaid 263 4.44 0.88
Telecom operators are providing various mobile services covering short
message service, multi-media service, GPRS, internet, call diverting features,
information based services, getting news update etc. No significant differences
are found between prepaid and postpaid subscribers regarding the availing of
various services. It implies that both types of subscribers are using mobile
phones to avail almost same kind of services. Results of each operator for
various purposes are shown in following figure 5.9.
222
Figure 5.9: Different Mobile Services between Prepaid and Postpaid Subscribers
00.5
11.5
22.5
33.5
44.5
5
Mean Scores
1 2 3 4 5 6 7 8
Reliance Airtel Hutch Tata Indicom
1. Short Message Services (SMS) 5. Information based services 2. Multi Media Message Services (MMS) 6. Getting News Update (Sports / Others ) 3. GPRS / Internet 7. For STD calling 4. Call Diverting Feature (Call Forwarding
/ Call Diverting ) 8. For Local calling
The above figure 5.9 shows the significant differences between the
means of various types of services of selected telecom service provider. With
regard to usage of SMS services, there are significant differences between Tata
Indicom and Airtel. Whereas Reliance has got maximum mean score for multi-
media message services. Reliance and Tata Indicom have almost same mean
score for GPRS/internet. For call diverting feature, there are significant
differences between the mean scores of Reliance and Airtel. Reliance has
maximum mean score value for information- based services as compare to Tata
Indicom. For getting news update, Reliance has got significant mean scores
followed by Tata Indicom, Hutch and Airtel. For STD calling, there are significant
differences between the means of Reliance and Tata Indicom. For local calling,
there are not significant differences between the means of selected operators.
223
Table 5.130: Evaluation of Executive’s Knowledge About the Product
Yes/No Ludhiana Amritsar Patiala Chandigarh Total p-value
Yes 78 65 82 65 290 Executive's knowledge of the Product No 2 18 1 9 30
<0.001
Chi-square=26.456, ** Significant at .01 level
Table 5.130 explains customer care’s knowledge about the product is
highly significant (<0.001) for both prepaid and postpaid subscribers amongst all
selected cities i.e. Ludhiana, Amritsar, Patiala and Chandigarh.
Table 5.131: Considerations Prior to Purchase Decision for a Mobile Service Provider
S. No. Parameters Prepaid/
Postpaid N Mean Std. Deviation t-value p-value
Prepaid 119 3.4 0.96 0.16 0.873 1 Brand Image
Postpaid 254 3.39 1 Prepaid 120 4.13 0.86 0.995 0.32 2 Network Connectivity Postpaid 257 4.03 0.9 Prepaid 118 4.22 0.8 1.562 0.119
3 Coverage Postpaid 258 4.08 0.8 Prepaid 119 4.07 0.79 0.509 0.611
4 Call tariff Postpaid 255 4.02 0.87 Prepaid 116 4.13 0.73 0.481 0.631
5 Service Quality Postpaid 255 4.09 0.83 Prepaid 117 4.09 0.82 1.501 0.134
6 Reliability Postpaid 256 3.95 0.81 Prepaid 118 3.03 1.16 1.057 0.291
7 Advertisement Postpaid 254 3.15 1.05 Prepaid 119 3.16 1.2 1.736 0.084
8 Sales Promotion Postpaid 251 3.38 0.97 Prepaid 120 3.63 1.06 0.688 0.492
9 Value Added Services Postpaid 255 3.71 0.87 Prepaid 118 3.87 0.92 0.21 0.834 10 Ease of availability and
recharge Postpaid 257 3.85 0.88 Prepaid 122 3.66 1.03 1.656 0.099
11 Customer Care ServicesPostpaid 256 3.84 0.83 Prepaid 120 3.88 0.87 0.809 0.419
12 Roaming facility Postpaid 257 3.95 0.82 Prepaid 113 3.88 0.91 0.53 0.597 13 Rebate and discount Postpaid 254 3.83 0.9
224
S. No. Parameters Prepaid/
Postpaid N Mean Std. Deviation t-value p-value
Prepaid 121 3.98 0.84 0.1 0.921 14 Voice clarity
Postpaid 257 3.98 0.84 Prepaid 117 3.79 1.04 2.124 0.035*
15 Transparency in billing Postpaid 255 4.02 0.78 Prepaid 119 3.44 0.89 2.033 0.043*
16 Dealer services Postpaid 253 3.64 0.9 Prepaid 118 3.22 1.09 3.209 0.002** 17 Word of Mouth Postpaid 246 3.59 0.83 Prepaid 119 3.36 0.99 0.708 0.479
18 Friends’ advice Postpaid 258 3.44 1.04
*significant at .01 level **Significant at .05 level
As can be seen from the above table 5.131, the purchase decision is
influenced by various factors- Brand Image, network Connectivity, coverage, call
tariff, service quality, reliability, advertisement, sales promotion, value added
services, ease of availability and recharge customer care services, roaming
facility, and discount, Voice clarity Transparency in billing, dealer services, and
word of Mouth.
• Significant results are obtained for transparency in billing (0.035) between
prepaid (3.79) and postpaid (4.02) subscribers.
• Significant results are obtained for Dealer services (0.043) between
prepaid (3.44) and postpaid (3.64) subscribers.
• Highly significant results can be seen for word of mouth (0.002) between
prepaid (3.22) and postpaid (3.59) subscribers.
• For the rest of considerations, no significant differences are found
between prepaid and postpaid subscribers.
Further, consideration prior to purchase decision for a mobile service
provider has been studied in the following figure 5.10.
225
Figure 5.10: Considerations Prior to Purchase Decision for a Mobile Service Provider
2.5
3
3.5
4
4.5
1 3 5 7 9 11 13 15 17
Mea
n Sc
ores
Reliance Airtel Hutch Tata Indicom
The above figure 5.10 explains the significant mean scores for the option
of selection of mobile service provider. Following are the results of various
parameters.
• The first consideration is brand image and network connectivity where
Tata Indicom has the maximum mean score as compared to Reliance.
For coverage Tata Indicom has significant value as compared to Airtel.
• For network connectivity, Hutch and Tata Indicom have significant mean
scores as compare to Airtel and Reliance.
• For coverage, Hutch, Reliance and Tata Indicom have maximum values
of mean as compare to Airtel.
• For call tariff, Tata Indicom has significant maximum mean value and
Reliance being the minimum one.
• For service quality and reliability parameter, Tata Indicom has the
significant mean value as compare to the minimum value of Airtel.
• For Advertisement and sales promotion, Reliance and Tata Indicom has
maximum mean values as compared to Airtel.
226
• For value added services, ease of availability and recharge facility. Tata
Indicom, Hutch and Reliance have significant mean values as compare to
Airtel.
• For customer care services, Tata Indicom has maximum mean values as
compare to Airtel.
• For roaming facility, Reliance has significant mean score as compare to
other operators.
• For rebate and discount, Tata Indicom has the maximum mean score and
Airtel has the least one.
• For voice and clarity, there are significant differences between mean
scores of Tata Indicom being maximum and Airtel as the least ones.
• For transparency in billing, Hutch has significant mean score followed by
Reliance, Tata Indicom and Airtel.
• For dealer services, Reliance and Hutch has higher mean values in
relation to Airtel.
• For word of mouth, Reliance has significant mean score as compare to
other operators.
• For friend’s advice, Hutch has highest mean score as compare to Airtel
being the minimum one.
Table 5.132: Evaluation of Awareness of Company’s Offices
Yes/No LudhianaAmritsar Patiala Chandigarh Total p-value
Yes 82 64 89 78 313 Awareness of Company Offices No 3 27 14 44
0.001**
Chi-square = 44.645, **Significant at .01 level.
The above table 5.132 indicates that there are significant differences.
relating to information about awareness of company’s offices between those who
agree and who do not agree amongst selected four cities is significant at 99%
level.
227
Table 5.133: Response About Service Providers’ Touch Points
S. No. Statements Prepaid/
Postpaid N Mean Std. Deviation t p-value
Prepaid 102 3.42 0.87 1 Ambience of the company outlet Postpaid 211 3.56 0.79
1.441 0.15
Prepaid 103 3.39 1.02 2 Availability of Literatures/ Brochures Postpaid 222 3.48 0.9
0.797 0.426
Prepaid 106 3.63 1.01 3 Inter-personal skills of the Executive at company outlet Postpaid 227 3.56 0.86
0.68 0.497
Prepaid 105 3.59 1.02 4 Handling Product related Queries Postpaid 229 3.63 0.9
0.386 0.7
Prepaid 109 3.7 0.97 5 Complaint Handling at company outlet Postpaid 236 3.64 0.94
0.522 0.602
Prepaid 106 3.58 0.98 6 Overall Rating of company outlet Postpaid 235 3.57 0.88
0.05 0.961
Table 5.133 explains the results of response about service providers’
touch points. It can be evaluated from various aspects covering-ambience,
availability of literature, inter-personal skills, handling of product queries and
complaint handling. No significant differences are found between the users of
both prepaid and postpaid subscribers.
Further, for each operator the differences between mean scores for
response about service providers’ touch points are explained in the following
figure 5.11.
Figure 5.11: Evaluation of Service Providers’ Touch Points
2
2.5
3
3.5
4
4.5
1 2 3 4 5 6
Mea
n S
core
s
Reliance Airtel Hutch Tata Indicom
228
The above figure 5.11 explains the evaluation of service providers’ touch
points for individual operator.
Reliance has been assigned significant mean scores and Airtel the
minimum one for ambience of company outlet, availability of literature/brochures,
inter-personals bills of the executive at company outlet, handling product related
queries, complaints handling at company outlet. For overall rating of company
outlet, Reliance and Hutch are followed by Tata Indicom and Airtel.
Table 5.134: Evaluation of Customer Care Services
S. No. Statements A2.2 N Mean Std.
Deviation T p-value
Prepaid 122 3.49 1.16 1 Easily Accessible
Postpaid 261 3.68 1.02 1.593 0.112
Prepaid 122 3.71 0.99 2 Humble and Soft Spoken Postpaid 261 3.81 1.01
0.902 0.368
Prepaid 121 3.51 1.16 3 Ability for Complete Resolution Postpaid 261 3.67 1.05
1.354 0.177
Prepaid 120 3.5 1.01 4 Customer Care Services Postpaid 254 3.61 0.97
1.045 0.297
The table 5.134 explains the results of customer care services. They are
evaluated on the grounds of easily accessibility, humble and soft- spoken
executive, ability to resolve the problem and customer care services. No
significant differences are found for both prepaid and postpaid services .It
implies that these are equally important for both kinds of subscribers.
Further, for each operator customer care services are studied in the
following figure 5.12.
229
Figure 5.12
Evaluation of Customer Care services for Selected Mobile Service Provider
33.13.23.33.43.53.63.73.83.9
4
Mea
n Sc
ores
1 2 3 4
Reliance Airtel Hutch Tata Indicom
The above figure 5.12 explains the significant results with regard to
customer care services regarding the accessibility of the executive Tata Indicom
had maximum mean scores as compared to Airtel. Secondly, in relation to
humble and softness of executive Hutch has maximum mean score as compared
to Airtel. For providing the complete resolution, Hutch has maximum mean score
as compared to other operators.
Table 5.135: Efficient Solution of Query Handling
Yes/No LudhianaAmritsar Patiala Chandigarh Total p-value
Yes 98 58 92 60 308 Contact for query solving No 38 4 35 77
<0.001
Yes 92 45 91 48 276 Efficient solution of query(s)/complaints No 6 34 2 32 74
<0.001
Chi-square 1=79.394, Chi-square 2=73.159, ** Significant at .01 level
Table 5.135 explains the results regarding the handling of query solution
and its efficient solutions. The results are highly significant. The above table
explains the highly significant values (<0.001) between ‘yes’ and ‘no’
respondents amongst different selected cities covering Chandigarh, Ludhiana,
Amritsar and Patiala.
230
Table 5.136: Effectiveness of After-Sales Services
Yes/No LudhianaAmritsar Patiala Chandigarh Total p-value
Yes 45 48 56 45 194 Problem faced in the handset No 51 45 39 50 185
0.312
Yes 31 11 15 18 75 Warranty claimed
No 61 85 77 73 296 0.001**
Yes 25 8 15 16 64 Claim settled within promised time No 2 2 0 0 4
0.13
Yes 74 66 91 65 296 Awareness about the Handset Service Centers No 17 31 2 30 80
<0.001**
Yes 51 34 88 49 222 Visit to the Service Centers No 40 60 5 41 146
< 0.001**
Yes 36 29 88 41 194 Promptness in problem solving No 46 19 2 28 95
< 0.001**
**significant at .01 level
The above table 5.136 indicates the highly significant results between the
‘yes ‘respondents and ‘no’ respondents. There are highly significant results for warranty claimed (0.001), awareness about handset service centers (<0.001), visit to service centres and promptness in problem-solving (<0.001) amongst selected cities covering Chandigarh, Ludhiana, Amritsar and Patiala.
Table 5.137: Evaluation of Service Quality
Services N Mean Std. Deviation t p-value
Prepaid 108 3.4 0.74 Overall Services
Postpaid 243 3.48 0.77 0.899 0.369
The above table 5.137 explains the results with regard to the evaluation of
overall service quality. The differences are not significant one between the
prepaid and post paid subscriber of selected telecom service providers.
231
Figure 5.13: Evaluation of Service Quality
3.73
3.44
3.31 3.33
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
Mea
n Sc
ores
Reliance Airtel Hutch Tata Indicom
The above figure 5.13 explains the overall rating of after sale services of
all the selected four operators. Reliance has maximum mean score (3.73)
followed by Airtel (3.44), Tata Indicom (3.33) and Hutch (3.31). In relation to
evaluation of overall evaluation of service quality, significant differences are not
found between both prepaid and postpaid subscribers.
Table 5.138: Evaluation of Satisfaction Level of Network Quality
S. No. Parameters Prepaid/ Postpaid N Mean Std.
Deviation t p-value
Prepaid 122 3.57 1 0.592 0.555 1 Tariff / Price / Call rate
Postpaid 258 3.63 0.86 Prepaid 120 3.71 0.89 1.519 0.13
2 Network Connectivity Postpaid 264 3.85 0.7 Prepaid 124 3.8 0.92 1.309 0.192
3 Coverage Postpaid 260 3.92 0.67 Prepaid 117 3.74 0.76 0.451 0.652
4 Value Added Service Postpaid 253 3.7 0.68 Prepaid 119 3.47 0.99 1.593 0.112
5 Roaming facility Postpaid 262 3.64 0.93 Prepaid 117 3.53 0.9 0.129 0.897
6 Transparency I n Billing Postpaid 251 3.52 0.8 Prepaid 116 3.94 0.76 2.26 0.024*
7 Ease of Availability / Recharge Postpaid 247 3.76 0.7
Prepaid 119 3.39 0.94 1.498 0.135 8 Customer Care Services
Postpaid 256 3.54 0.87
232
S. No. Parameters Prepaid/ Postpaid N Mean Std.
Deviation t p-value
Prepaid 117 3.37 0.85 0.164 0.87 9 Sales Promotion Offers
Postpaid 253 3.38 0.88 Prepaid 120 3.3 0.98 0.07 0.944
10 Advertisement Postpaid 257 3.31 0.94 Prepaid 120 3.52 1.02 0.959 0.339
11 Voice Clarity Postpaid 257 3.62 0.82 Prepaid 118 3.47 0.87 0.598 0.55
12 Dealer Network Postpaid 245 3.53 0.82 Prepaid 93 3.75 0.76 0.829 0.408
13 Overall Satisfaction Level Postpaid 191 3.83 0.6 Prepaid 121 2.88 1.03 4.011 0.001**
14 Continuation of Services Postpaid 256 2.48 0.84
* significant at .01 level **Significant at .05 level
In the above table 5.138, the satisfaction level of network quality has been
evaluated on the basis of tariff /call rate, network connectivity, coverage, value
added services, roaming facility, transparency in billing, customer care services,
sales promotion, advertisement, voice clarity, dealer network, overall satisfaction
level and continuation of services. Ease of availability and recharge facility
(0.024) has shown significant differences between prepaid (3.94) and postpaid
subscribers (3.76). There are highly significant results for continuation of
services (0.001) between prepaid (2.88) and postpaid (2.48) services.
Figure 5.14: Satisfaction Level of Network Quality
2
2.5
3
3.5
4
4.5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Mea
n Sc
ores
Reliance Airtel Hutch Tata Indicom
233
The above figure 5.14 explains the significant results for the satisfaction
level of network quality of selected service provider.
• For Tariff/Price/Call rate, Reliance has maximum mean score as compare
to Airtel.
• For network connectivity, Reliance has maximum mean score as compare
to Hutch.
• For coverage, Reliance, Airtel, Hutch have maximum mean score as
compare to Airtel.
• For value added service, Reliance has maximum mean score as compare
to Hutch.
• For roaming facility, transparency in billing and ease of availability /
recharge, Reliance has maximum mean score as compare to Airtel.
• For customer care services, Tata Indicom has significant mean score as
compare to Airtel.
• For sales promotion offers and advertisement, Reliance has maximum
mean score as compare to Hutch.
Table 5.139: Evaluation of Correlation between Pre-Purchase and Post-Purchase Behaviour
S.No. Factors Correlation 1 Tariff / Price / Call rate .071 2 Network Connectivity .256 (**). 3 Coverage 215 (**) 4 Value Added Service .226 (**) 5 Roaming facility .257(**) 6 Transparency I Billing .099 7 Ease of Availability / Recharge .361(**) 8 Customer Care Services .195(**) 9 Sales Promotion Offers .199(**)
10 Advertisement .207(**) 11 Voice Clarity .342(**) 12 Dealer Network .200(**)
** Correlation is significant at the 0.01 level (2-tailed).
234
The above table 5.139 evaluates correlation between pre-purchase and
post-purchase behaviour of subscribers. Amongst various parameters, there is
high correlation in ease of availability and recharge (.361) factor for pre-purchase
and post-purchase.
5.5 SOCIO DEMOGRAPHIC PROFILE OF SUBSCRIBERS
i) Age
Table 5.140: Age-Wise Composition of Subscribers
Service Provider
Reliance Airtel Hutch Tata Indicom Total
Age <18 2 4 0 4 10 Age 18-25 24 32 45 48 149 Age 25-40 55 46 35 24 160
AGE (in yrs)
Age 40+ 7 13 19 22 61 Total 88 95 99 98 380
In the above table 5.140 of age-wise composition, significant results are
obtained for different age groups of selected telecom operators. It explains that
maximum number of mobile users are in the category of 18-25 years and 25-40
years as compare to less than 18 years and above 40. Following figure 5.15
explains age-wise composition of prepaid and post paid subscribers.
Figure 5.15: Age-Wise Composition of Prepaid and Postpaid Subscribers
2 8
52
97
50
110
19
42
020406080
100120
Freq
uenc
y
Age <18 Age 18-25 Age 25-40 Age 40+
Age wise
Prepaid Postpaid
235
In figure 5.15, for below 18 years category, there are only 2 prepaid
subscribers as compare to 8 postpaid subscribers. Whereas maximum number
of postpaid subscribers are in the age group of 25-40 years.
Table 5.141: Occupation-Wise Composition of Subscribers
Service Provider
Reliance Airtel Hutch Tata Indicom Total
Student 3 14 30 33 80 Government. Employee 3 3 0 2 8
Private employee 16 22 11 18 67
Professional 21 7 16 9 53 Self-employed 26 34 28 26 114
Occupation
Others 12 12 5 6 35 Total 81 92 90 94 357
The above 5.141 explains the occupational pattern of various subscribers
it points that mobile usage is more prevalent among self-employed
professionals, followed by students, private employees, professionals and
others.
Figure 5.16: Occupation-Wise Composition of Prepaid and Postpaid Subscribers
27
53
2 621
46
2033 33
81
1124
0
20
40
60
80
100
Freq
uenc
y
1 2 3 4 5 6
Occupation
Prepaid Postpaid
236
Whereas the figure 5.16 explains the occupation composition between
prepaid and post paid subscribers. It represents that self-employed are more into
postpaid (81) as compare to prepaid (33), and least number is of government
employee for both postpaid (6) and prepaid (2).
Table 5.142: Gender-Wise Composition of Subscribers
Service Provider
Reliance Airtel Hutch Tata Indicom Total
Male 72 79 58 68 277 Gender
Female 16 15 40 27 98
Total 88 94 98 95 375 The above table 5.142 reflects the gender composition of subscribers of
selected telecom providers. The graph for gender comprise more of males (277)
and category lesser females (90). Following figure 5.17 shows the gender-wise
composition of mobile subscribers.
Figure 5.17: Gender-Wise Composition of Prepaid and Postpaid Subscribers
82
39
195
59
0
50
100
150
200
Freq
uenc
y
1 2
Genderwise
Male Female
Above figure 5.17 explains that more males are using in both prepaid and
postpaid categories as compare to females.
237
Table 5.143: Education-Wise Composition of Subscribers Amongst Selected Cities
Service Provider Reliance Airtel Hutch Tata Indicom
Total
Metric 3 0 3 3 9 Education 10+2 8 8 27 25 68
Graduate 48 58 45 66 217 Postgraduate 30 25 24 4 83 Total 89 91 99 98 377
Table 5.144: Income-Wise Composition of Subscribers Amongst Selected Cities
Service Provider
Reliance Airtel Hutch Tata Indicom Total
<5000 9 10 18 31 68 Income 5000-10000 20 20 27 13 80
10000-20000 23 30 21 36 110 20000+ 12 20 14 5 51 Total 64 80 80 85 309
The above tables 5.143, 5.144 related to education and income reflect the
significant results of various mobile subscribers. Maximum graduates (217) are
using mobile phone followed by postgraduate (83), 10+2 (68) and metric group
(9). Following figure 5.18 explains income –wise composition of prepaid and post
paid subscribers of mobile usage.
Figure 5.18: Income-Wise Composition of Prepaid and Postpaid Subscribers
22
46
29
51
35
75
14
37
0
20
40
60
80
Freq
uenc
y
<5000 5000-10000 10000-20000 20000+
Incomewise
Prepaid Postpaid
238
Whereas in income profile, most of the users are in income group of
10,000-20,000 (110) as compare to other income classes. On the same
direction, the results between prepaid and postpaid subscribers can be seen.
Maximum number of postpaid subscribers are in income group of 10,000 –
20,000 and least in for prepaid (14) in the 20,000 and above income group.
Table 5.145: Experience-Wise Composition of Subscribers Amongst Selected Cities
Service Provider
Reliance Airtel Hutch Tata Indicom
Total
<1 year 7 6 11 13 37 Experience
1-2 year 21 24 29 22 96 >2 year 61 63 60 63 247 Total 89 93 100 98 380
The above table 5.145 explains usage of mobile phones experience in
terms number of years. Most number of subscribers are using mobile phones for
more than two years. Following figure 5.19 explains the number of year of
experience of mobile usage.
Figure 5.19: Experience-Wise Composition of Prepaid and Postpaid Subscribers
17 20 3066 77
170
0
50
100
150
200
Freq
uenc
y
<1 year 1-2 year >2 year
Experience
Prepaid Postpaid
The figure 5.19 explains the ratio between prepaid and postpaid
subscribers is almost same for the subscribers with less than one year
239
experience. Most of the subscribers for both prepaid and postpaid category are
falling in the category with experience of more than two years.
Table 5.146: Time-Spent on Mobile-Usage of Subscribers Amongst Selected Cities
Service Provider
Reliance Airtel Hutch Tata Indicom
Total
< 10 min 6 19 17 23 65 Time
10-20 min 21 21 11 24 77 >20 min 62 54 71 51 238 Total 89 94 99 98 380
The table 5.146 explains the time spent on usage of mobile during the
day. Most of the subscribers (238) are using mobile phones for more than 20
minutes. Around 65 customers are spending less than 10 minutes on usage.
Following figure 5.20 explains the time spent on for mobile usage single call
duration.
Figure 5.20: Time-Spent on Mobile-Usage of Subscribers Amongst Selected Cities
2441
2453
75
163
0
50
100
150
200
Freq
uenc
y
< 10 min 10-20 min >20 min
Time
Prepaid Postpaid
The figure 5.20 explains the proportion of prepaid and postpaid
subscribers for time spent for one call. In all the three categories, postpaid
subscribers are more using the mobile phones as compare to prepaid
240
subscribers. In more than 20 minutes categories, 163 postpaid subscribers are
using mobile phones as compare to 75 prepaid subscribers.
Table 5.147: Place-Wise Mobile-Usage of Subscribers Amongst Selected Cities
Service Provider
Reliance Airtel Hutch Tata Indicom
Total
Yes 60 66 84 94 304 Domestic
No 29 27 16 4 76 National Yes 62 58 43 32 195 No 27 35 57 66 185 International Yes 11 6 8 10 35 No 78 87 91 88 344
The above table 5.147 explains the results with regard to places where
calls are generally made. 75% of the respondents use mobile phones for making
local calls. 195 subscribers use mobile phones for making national calls and
around 10% are using mobile phones for making international calls.
(PART-B)
RESEARCH FINDING OF THE DATA COLLECTED FROM DEALERS
5.6 RELIABILITY OF DATA COLLECTED FROM DEALERS
RELIABILITY ANALYSIS - SCALE (ALPHA)
Reliability Coefficients
Number of Cases = 22.0 N of Items = 55
Alpha = .8535
Table 5.148: Dealership of Telecom Operators Operator Yes No Reliance 16 24
Airtel 37 3
Hutch 12 28
Tata Indicom 23 17
241
Figure 5.21: Dealership of Telecom Operators
16
24
37
3
12
28
23
17
0
5
10
15
20
25
30
35
40Fr
eque
ncy
Reliance Airtel Hutch Tata Indicom
Yes No
The above table 5.148 and figure 5.21, explains the results of dealers’
preference for services for various telecom service providers. Out of the total
sample size of 40, most of the dealers (37) preferred the dealership of Airtel as
compare to Tata Indicom (23), Reliance (16) and Hutch (12).
Figure 5.22: Evaluation of Company’s Directions with to Dealers
28
12
17
231921
9
31
2
38
4
36
1
39
0
5
10
15
20
25
30
35
40
Freq
uenc
y
Sales T
arget
Customer
Service
Discount
Satisfa
ction
Area C
overed
Adverti
semen
t
Others
Yes No
242
Table 5.149: Evaluation of Company’s Directions with to Dealers
Areas Yes No
Sales target 28 12
Customer service 17 23
Discount/Schemes 19 21
Consumer Satisfaction 9 31
Areas to be covered 2 38
Advertisement Planning 4 36
Any other (specify ) 1 39
In the above figure 5.22 and table 5.149, various areas in which dealer’s
get directions from companies have been explained. Most of the telecom service
providers are focused on sales target (28), discount and schemes (19),customer
services (17). Lesser weightage is given to other areas like consumer
satisfaction (9), areas to be covered (2), advertisement planning (4).
Figure 5.23: Dealers’ Participation in Marketing Decisions
10
15
11
0
2
4
6
8
10
12
14
16
Freq
uenc
y
Regularly Sometimes Occasionally
243
The above figure 5.23 explains dealers’ participation in marketing
decision. Most of the dealers (15) agreed that they participate in marketing
decisions sometimes followed by occasionally (1) and regularly (10).
Table 5.150: Evaluation of Dealers
Operators Channel Margins
Channel Penetration
Channel Delivery
Channel Support
Quality of Product
Technical support
Airtel 16 20 13 16 19 22
Hutch 10 5 8 5 4 2
Reliance 9 8 8 8 8 6
Tata Indicom 5 7 11 11 9 10
Above table 5.150 explain results with regard to evaluation of dealers for
best channel margins and best channel penetration. For best channel margins
and best channel penetration, Airtel and Hutch are preferred telecom service
providers as compare to Reliance and Tata Indicom. In context of best channel
delivery and best channel support, Airtel and Tata Indicom have shown
significant results as compare to Hutch and Reliance. For best quality of the
product results are more significant for Airtel, Tata Indicom and Reliance as
compare to Hutch.
Table 5.151: Evaluation of Company’s Product Attributes
Attributes Product Features
Product Quality
Product Range
Low Price Advertisement Dealers’
Influence Premium
Price CustomerService
Product Features 1 .887(**) .544(**) .675(**) .467(*) 0.077 .519(**) .595(**) Product Quality 1 .635(**) .503(**) 0.378 0.106 .519(**) 0.378 Product Range 1 0.239 0.296 -0.059 .396(*) 0.144 Low Price 1 .693(**) 0.125 0.324 0.376
Advertisement 1 0.12 0.295 0.091 Dealers’ Influence 1 0.13 0.315
Premium Price 1 .487(*)
Customer Service 1
244
The above table 5.151 explains various product attributes and their
correlation. There is highly significant correlation between Product features and
product quality, product range and product features, low price and product
features, premium price and product features and customer service and product
features. Results are highly significant for product quality and product range, low
price and product quality, premium price and product quality and low price and
advertisement. Correlations are significant for advertisement and product
features, product range and premium price and for premium price and customer
service.
Figure 5.24: Customer Feedback and After Sale Service
1413
1817
02468
1012141618
Freq
uenc
y
Yes No
After Sale Service Forward of Feedback
The above figure 5.24 explains dealer’s opinion that feedback from the
customer after the sale of product is important one and most of them agree that
this feedback is forwarded to company.
245
Figure 5.25: Effectiveness of Product Strategy
.
25
1513
27
7
33
6
34
0
40
0
5
10
15
20
25
30
35
40
Freq
uenc
y
Quality Range Features BrandImage
AnyOther
Yes No
The above figure 5.25 explains various aspects of product strategy in the
current market scenario. Quality of the product (25) has significant role to play as
compare to any other aspect for product strategy.
Figure 5.26: Effectiveness of Price Strategy
26
14
5
35
3
37
0
40
05
10152025303540
Freq
uenc
y
Price Cuts Cost Cuts PaymentTerms
Any Other
Yes No
In figure 5.26 various aspects of price strategy are taken into account.
Price cuts (26) play most significant role of price strategy as compare to cost
cuts (5), payment and credit terms (3).
246
Table 5.152: Evaluation of Promotional Strategy
Yes No Advertisements 14 26
Discounts and schemes 14 26
Dealer incentives 14 26
Any other 0 40
For promotional strategy, advertisement, discounts and dealer incentives
have equal role to play.
Figure 5.27: Compensation and Seller Support
.
16
24
1723
15
252020
15
25
9
31
2
38
05
10152025303540
Freq
uenc
y
Margin
Allowan
ce
Exclusiv
ity
Continuity
Develo
pment
Credit
Others
Yes No
The above graph explains the dealer’s response for the satisfaction of
compensation the company for seller support. Various parameters for dealers’
satisfaction studied are gross margin and overhead contribution, Promotional
allowance and below-the line benefits, Distribution Exclusivity, Continuity of
Supply, Market Development and credit. Continuity of supply (20) distribution
exclusively (15), promotional allowance (17), gross margins (16) and market
developments have more significance as compare to credit and other support
aspects.
247
(PART C)
ANALYSIS OF DATA COLLECTED FROM SELECTED TELECOM SERVICE PROVIDERS.
5.7 RELIABILITY FOR TELECOM SERVICE PROVIDERS
RELIABILITY ANALYSIS - SCALE (ALPHA)
Reliability Coefficients
N of Cases = 40.0 N of Items = 41
Alpha = .7250
The data for telecom service providers has been collected from the
executives at the touch points, officials dealing with customers care service.
Data was collected from the head office, web world offices and touch points.
Figure 5.28: Telecom Service Providers’ Collaboration
24%
28%
44%
4%
Reliance Airtel Hutch Tata Indicom
The above figure 5.28 explains telecom service provider’s foreign
collaboration in terms of both technical and foreign support. Hutch has shown
significant results (44%), followed by Airtel (28%), Reliance (24%) and Tata
Indicom (4%).
Further, significant results of the kind of services provided by telecom
service provider with regard to mobile are obtained.
248
i) Voice mail service: Call waiting, call hold, call divert, call line identification
and presentation, international SMS, Net connect and call identification
and restriction. The results are almost same for each operator as each of
them is providing this service.
ii) 3 way call conferencing: Reliance has significant results as compare to
Airtel.
Figure 5.29: Telecom Service Provider and Call Line Identification Services
27%
33%9%
31%
Reliance Airtel Hutch Tata Indicom
iii) Call line identification and Restriction: Figure explains that Tata Indicom
has significant value (33%) as compare to Hutch (9%) amongst all
operators.
Table 5.153: Functional Domain of Products and Services
Telecom Service Operators Banking Manufacturing Insurance Health Retail Individuals
Reliance 9 9 11 9 12 14
Airtel 0 13 0 0 0 15
Hutch 3 3 3 3 3 13
Tata Indicom 0 0 0 0 0 13
With regard to functional domain of products and services, individuals are
most covered for the mobile phones. It is followed by manufacturing and other
sectors.
249
Table 5.154: Export of Services
Telecom Service Operators Yes No Total
Reliance 7 5 12
Airtel 0 14 14
Hutch 0 15 15
Tata Indicom 2 9 11
Total 9 43 52
Table 5.155: Localisation of Products and Services
Telecom Service Operators Yes No Total
Reliance 6 0 6
Airtel 5 9 14
Hutch 13 0 13
Tata Indicom 0 4 4
Total 24 13 37
The above tables 5.154 and 5.155 explain the exporter services and
localization aspect of products and services according to Indian conditions. None
of the telecom service operator is into major export of services. Whereas for
localization of products and services, Hutch and Reliance have significant results
as compare to Airtel and Tata Indicom.
Table 5.156: Promotional Activities
Television Internet Print Ads
Discounts Seminars/Exhibitions
Market Survey
PersonalSelling
Reliance 15 11 15 14 9 9 11
Airtel 6 6 6 0 0 0 0
Hutch 13 12 11 7 8 9 6
Tata Indicom 14 14 14 14 14 14 14
Total 48 43 46 35 31 32 31
In the above table, various promotional activities of selected telecom
service providers are discussed.
250
• With regard to television as medium of advertisement Reliance and Hutch
have significant results followed by Tata Indicom and Airtel.
• With relation to internet advertisement as promotional activity, Tata
Indicom and Hutch have shown significant results.
• For the print ads, Reliance and Tata Indicom have shown maximum
values.
• For customer incentives/discounts, Reliance and Tata Indicom have
significant values as compare to Hutch and Airtel.
• For seminar and exhibitions and personal selling as promotional activity,
Tata Indicom and Reliance have significant values.
• For market survey and research, Tata Indicom, Reliance and Hutch are
with the maximum values.
Table 5.157: Effectiveness of Marketing Channels and Distribution Networks
Telecom Service Operators
Direct Marketing
Company Outlets Internet
Association with other companies
Dealer/ distributor
Reliance 6 12 0 0 9
Airtel 6 4 0 0 12
Hutch 7 7 6 2 7
Tata Indicom 8 3 0 0 7
In table 5.157 the marketing channels and distribution networks have
shown significant results. Reliance, Airtel and Hutch have significant values as
compare to Tata Indicom.
Table 5.158: Evaluation of Pre and After-Sale Services
Telecom Service Operators Yes No Total
Reliance 14 1 15
Airtel 7 8 15
Hutch 13 2 15
Tata Indicom 15 0 15
251
The table 5.158 explains results for pre and post sales services. Reliance,
Tata Indicom and Hutch have significant values as compare to Airtel for pre and
post sales services.
Table 5.159: Availability of After-Sale Service Station
Telecom Service Operator Yes No
Reliance 14 1
Airtel 6 9
Hutch 12 3
Tata Indicom 10 5
The table 5.159 shows the results for the availability of after sales service
stations. Reliance, Tata Indicom and Hutch have shown significant results as
compare to Airtel.
Table 5.160: Fixation of After-Sale Charges
Telecom Service Operator Yes No
Reliance 8 7
Airtel 9 6
Hutch 10 5
Tata Indicom 14 1
Total 32 9
The above table 5.160 indicates the results for the allocation amount for
after-sales service charges. Tata Indicom is investing significant amounts for
providing after sales service as:
• Tata Indicom and Hutch use mailing as medium of feedback to the
maximum as compare to other telecom service providers.
• Visits by company sales executive are used as medium of feedback by
Tata Indicom and Hutch.
Tata Indicom, Reliance and Hutch are using internet as medium for
feedback to the maximum s compare to Airtel.
252
Table 5.161: Customer Care and After-Sale Services
Telecom Service Operator Yes No Total
Reliance 10 5 15
Airtel 6 9 15
Hutch 13 2 15
Tata Indicom 15 0 15
The table 5.161 explains the role of service stations in terms of customer
service and customer relations. Hutch and Tata Indicom have shown significant
results for customer care services as compare to other operators.
Table 5.162: Maintenance of Customer Database
Telecom Service Operator Yes No
Reliance 14 1
Airtel 6 9
Hutch 13 2
Tata Indicom 15 0
The above table 5.162 explains results with regard to maintenance of
customer database. Tata Indicom, Reliance and Hutch have shown significant
results as compare to Airtel.
Table 5.163: Customer Feedback After Sale Service
Telecom Service Operator Yes No Reliance 10 5
Airtel 5 10 Hutch 13 2
Tata Indicom 15 0
The table 5.163 explains the company’s policy of customer feedback after
the sale of the services. Hutch and Tata Indicom take regularly feedback from
customers after sales service of the mobile services as compare to other
operators.
253
Table 5.164: Feedback Channels of Telecom Service Providers
Telephonic calls Mails Visits by executive Websites
Reliance 12 4 4 10
Airtel 6 0 0 0
Hutch 15 9 12 10
Tata Indicom 15 14 15 14
The table 5.164 indicates significant results with regard to feedback
channels.
• Telephone calls is significant for feedback channel by Tata Indicom,
Reliance and Hutch to Tata Indicom for Direct Marketing to end-user.
• Reliance and Hutch have maximum values for distribution through
national and international offices.
• Reliance and Hutch have shown significant results for dealership network.
Table 5.165: Usage of Indian Technology vis-à-vis Foreign Technology
Telecom Service Operator Purely Indian Foreign and Indian both
Reliance 2 10
Airtel 6 9
Hutch 4 11
Tata Indicom 0 15
The table 5.165 indicates the results of usage of Indian technology vis-à-
vis foreign technology. Significant results are shown for Reliance, Hutch and
Tata Indicom. These telecom service operators employing both foreign and
Indian technology.
254
Figure 5.30: Methods of marketing of products
12
3
5
10
13
2
15
00
2
4
6
8
10
12
14
16
Freq
uenc
y
Reliance Airtel Hutch Tata Indicom
Yes No
The figure 5.30 reflects the various methods of marketing of products. The
results that Tata Indicom, Hutch and Reliance use sales personnel as medium of
marketing in relation to other sources.
Table 5.166: Company’s Policy with regard to Training Programme
Telecom Service Operator Products &Policies Sales Techniques and Products
Information
Reliance 15 0
Airtel 15 0
Hutch 11 2
Tata Indicom 15 0
This table 5.166 evaluates companies’ policies with regard to training
programme. Telecom Service providers are imparting training for products and
policies, sales techniques and product information. All the four selected telecom
operators are focusing on products and policies as compare to sales technique
and product information. These selected telecom service operators impart
training for 7 days.
255
Table 5.167: Areas of Marketing Strategy
Telecom Service Operator Product Price
Promotional Activities
Distribution Network
Customer Service
Reliance 1 2 5 1 13
Airtel 0 0 0 0 6
Hutch 7 11 3 10 6
Tata Indicom 1 14 1 0 0
Table 5.167 explains the areas of improvement for marketing strategy.
Telecom service provider can work in the areas of product, price, promotional
activities, distribution network and customer service. It is clear from above table
that companies should focus more on customer service and price as compare to
other areas.
5.8 IMPACT OF TRUST AND COMMITMENT IN TELECOM SECTOR
This research tests the impact of key antecedent factors, which influence
consumer trust in mobile usage of various telecom service providers, which in
turn affects relationship commitment. The model results and hypothesis tests are
furnished below.
Some of the key findings from the data analysis are highlighted below:
• Trust has a significant positive influence on relationship commitment.
Thus, H1 is established.
• Shared value has a significant positive impact in developing relationship
commitment. Thus H2 is established.
• Opportunistic behaviour is most important determinant of trust and has a
significant positive relationship with trust. Thus, H3 is established. Within
opportunistic behaviour, information asymmetry and regulatory control are
the most significant variables.
• Communication plays a significant positive role on trust. Thus, H4 is
established. Speed of response is most critical to communication.
256
• Opportunistic behavior tends to have a critical impact on trust. Thus, H5 is
established. Regulatory control and information asymmetry plays a
significant role in controlling opportunistic behavior.
• Reputation is a critical component of trust and is most affected by the
antecedents.
• Commitment can be improved by prolonged interaction and intense
association between the telecom service provider and the customer.
• Trust and commitment play significant role from dealers’ as well as
telecom service providers’ perspective.
The primary objective of this research is to explore the impact of trust of
telecom service providers through an antecedent-consequences approach. The
research confirms that trust significantly affects customers’ commitment.
257
CHAPTER – 6
SUMMARY, SUGGESTIONS AND RECOMMENDATIONS
6.1 INTRODUCTION
The summary, managerial implications conclusions of the study and
directions for future research are presented in this chapter. Section 6.2 covers
the need and significance of the study. Section 6.3 covers research objectives of
the study. Section 6.4 contains research methodology and section 6.5 explains
the summary of the findings. Section 6.6 covers discussions of results of the
study and section 6.7 covers the managerial implications. Section 6.8 covers the
limitations of the study and section 6.9 explains scope for further research.
6.2 NEED AND SIGNIFICANCE OF STUDY
It is well perceived fact that customer trust and commitment have become
the important factors of business success. However many conclusions have
been drawn with regard to trust and commitment. But there are very few studies
related with trust and commitment in telecom sector with special reference to
India. The relevance of Trust and Commitment Theory for mobile users of Indian
Telecom Sector has yet to be established.
This research is grounded in the well-known commitment-trust theory of
relationship marketing, originally proposed by Morgan and Hunt (1994). Morgan
and Hunt showed that relationship marketing as the act of establishing,
developing, and maintaining successful relational exchanges. It constitutes a
major shift in marketing theory and practice. According to the theory (Morgan
and Hunt, 1994), trust is central to successful relationship marketing, because it
encourages marketers to:
• Work at preserving relationship investments by co-operating with
exchange partners;
• Resist attractive short-term alternatives in favour of the expected long-
term benefits; and
258
• View potentially high-risk options as being prudent because of the belief
that their partners will not act opportunistically.
Trust, according to Speakman (1988) is so important to relational
exchange that it is “the cornerstone of the strategic partnership” between the
seller and the buyer.
This research attempts to test an adaptation of the commitment-trust
theory of relationship marketing in the telecommunication context. Although the
main variables were mostly borrowed from the commitment-trust framework
(Morgan and Hunt, 1994), the dimensions and the items were adapted
significantly to the context of cellular users of telecom sector. Therefore, one of
the primary contributions of this research is to test the applicability and
extendibility of the commitment-trust theory to the domain of telecom service
providers
In Indian Telecom Sector mobile telephony is called as “sun-rise industry.
It is one of the growing industries in the country rapid growing with rate of
subscriber base teledensity and traffic. Indian telecom sector holds huge
potential for growth because of following reasons.
• Liberalisation and privatization has brought around 12 majors in both
GSM and CDMA sectors. With intense competition, companies try to woo
and retain customers for longer period of time. For which, trust and
commitment are key variables to maximize the average revenue per user.
• Impact of technology
• Impact of FDI flows
• Trust and commitment model given by Morgan and Hunt comprises
various key dimensions-shared value, communication, and opportunistic
behaviour. How these variables affect trust and commitment among
mobile users of Indian telecom sectors has further to be studied.
• Companies are spending heavily on acquisition and retention of both pre
paid and post-paid subscribers. To what extent, different telecom services
259
providers are able to build trust with both pre-paid and post-paid require
further analysis.
6.3 RESEARCH OBJECTIVES OF THE STUDY
The study has following research objectives :
1. To study the rapid growth of Indian telecom sector.
2. To explore the impact of key dimensions of trust and commitment for
mobile services in the telecom sector.
3. To study the relationship between trust and commitment in both pre-paid
and post-paid services in selected telecom companies.
4. To analyse the level of trust and commitment in selected telecom service
providers across Punjab and Chandigarh.
5. To suggest managerial implications to Indian Telecom Industry to build
trust and commitment.
6.4 RESEARCH METHODOLOGY
This research has selected four telecom service providers --- Reliance
Communications, Airtel, Hutch and Tata Indicom from four cities covering
Ludhiana, Amritsar, Patiala and Chandigarh. It is based on selected sample of
pre-paid and post-paid subscribers, dealers and telecom service providers. This
research adopts combination of primary and secondary source of information.
This study is based on primary and secondary data. The main sources of
secondary data are published reports of World Telecommunications
Development, Department of Telecommunications, Indian Telecom Policy, Year
book of Statistics, Journals, Books and various websites of the mobile operators.
The primary data is collected through the questionnaire and personal Interviews.
4 major telecom operators are selected: Reliance Infocomm, Airtel, Tata
Telecom and Hutch. Through a systematic random sampling, a sample 400
respondents are selected for administering questionnaire at the touch-points of
these selected telecom operators in the selected towns. Besides this, a sample
260
of 60 questionnaires is collected from employees and of 40 from the dealers of
selected telecom operators.
The data collected will be analysed using various statistical tools. The
basic framework of the model consisted of unobservable theoretical constructs,
which would not be measured directly. So, this study used a set of indicator
variables, which measured the unobservable constructs.
To tackle the problem of measuring the latent variables, usually two
strategies are followed. The first is selection of a single indicator variable for
each theoretical construct. However, in most cases, it is unrealistic to assume
that a single indictor variable provide a reliable measure of the latent variables.
The second method is to assign pre- determined weights to different indictor
variables. However, both these methods are prone to error. Dillon and Goldstein
(1984), showed that when such measures are used in linear models, the
coefficients would have an unknown bias.
Tests for significance are less useful in small samples (less than 30) and
quite sensitive in large samples (exceeding 1,000 observations) (Hair et al.,
1995). Thus, both graphical and statistical tests are carried out using SPSS
version 10.0 for Windows to assess the actual degree of departure from
normality the mean, standard deviation and reliability estimate of each model
construct.
6.5 SUMMARY OF THE FINDINGS
6.5.1 Growth of Indian Telecom Sector
Indian mobile telephony may be called as “the sun-rise industry” of the
Indian economy because of outstanding performance on various parameters.
• Rate of growth in mobile subscriber base has been substantially higher
than growth in population, indicating a rapid proliferation of mobile
telephone and adoption by non-users/first-time users.
• Teledensity has also gone up which is a reflection of the above
discussion.
261
• From 2000 to 2006, traffic or usage of mobile phones increased, but the
increase was not uniform during the period. Till 2003, mobile usage went
up gradually but year 2004 onwards saw an era of speedy increase in
mobile traffic. It was probably because of the reasons that in initial days of
mobile telephony, call rates were high and the number of services offered
by cellular operators was limited. However 2003 onwards, because of the
slashed prices and add-on-services resulted in sudden increase in traffic.
Private operators have also shown remarkable growth in a highly
competitive environment. The overall growth in the subscriber base of private
operators during 1998-2006 was 78.36 million comprising 7.92 million fixed
subscribers and 70.44 million mobile subscribers. Private operators have
contributed very largely to post 1998 growth primarily in mobile services due to
the obvious cost and fast deployment advantages. Competition, foreign direct
investment and privatization have following implications for Indian Telecom
Industry.
• Lower prices – It has been witnessed across most industries that
competition helped in lowering the prices.
• Increased efficiency – In deregulated industries or license based cost plus
regime there was no incentive to shed inefficient assets and reduce
overheads. The advent of competition forces an industry to eliminate
inefficient and unproductive assets.
• Greater innovation – The deregulation in telecom industry gave birth to
the greatest innovation period in the past 20 years. New technologies,
standards, data services, new devices, CRM solutions and creative
bundling have all been the result of competition in telecom industry.
• Telecom is high technology industry. Telecom equipment is highly
complex to design and needs the expertise of both telecom and
computing fields. With the advent of competition, tremendous innovation
in services was made possible by improving the quality of -technology by
the players.
262
• Telecom industry is services industry, hence the good quality services to
the customer and the customer relationship management is the key and
competition has totally changed the definition of service in Indian telecom
industry. The quality of service has improved by leaps and bounds.
• The transition-trend in the economy that had resulted out of liberalization,
privatization and demonopolisation placed huge capital requirements,
especially for private ventures, which could not be met from domestic
sources alone. Hence, foreign investors were invited to take part in
contributing to the capital requirements of the telecom companies.
• As per the FDI policy for the Telecom Sector, investment up to 49% is
permitted in Basic, Cellular and other value added services, which is
hiked to 74% in 2003-04 budget; upto 74% is permitted in Internet,
infrastructure and radio paging services and up to 100% is permitted in
manufacturing, Internet service, voice and electronic mail, based on
certain conditions for fulfilment as a part of licensing and security
requirements, laid down by the Department of Telecommunications,
Government of India.
• The private participation was initially witnessed only in the cellular
segment. Here also, they had a very limited role to play, since it took a
very long time to penetrate the market, due to very high instrument costs,
tariff and licensing regulations and very high infrastructure costs. The total
number of telephone lines provided by private players, which was
restricted only to cellular phones, was 0.88 million in March 1998. But the
scenario changed totally with the announcement of the NTP in 1999,
which had broadened the scope for private players.
• Today, private players contribute to 64% of the total telecom network of
the country, with a major contribution in the cellular segment. Out of the
62 million phones provided in the country for the period 2005-06, 50
million phones provided by private players alone.
263
• The role of private sector in the Indian Telecom Industry is expected to
increase at a higher rate in the years to come due to high existing
potential for cellular growth in urban and rural India. Year 2003 onwards, it
has witnessed a dramatic increase in the number of mobile user in India.
It was, largely propelled by decreasing tariffs and entry cost and
increasing coverage. Every month, around 2 million mobile wireless
customers were added, translating into a monthly tele-density increase of
0.2%.
6.5.2 Trust and Commitment as Key Mediating Variable
The concept of trust has been explained as when one party has
confidence in an exchange partner’s reliability and integrity. There are three
dimensions of trust: perceived risk, technology orientation and reputation.
Shared value has been treated as a multi-dimensional construct. In the
telecommunication context, shared value symbolises the extent to which the
company and the customers share common beliefs on critical values like ethics,
security, and privacy. Communication, especially timely communication fosters
trust by assisting in resolving disputes and ambiguities, and aligning perceptions
and expectations. The multi-dimensional constructs that constitute
communication are openness, speed of response, and quality of information.
Opportunistic behaviour concerns the integrity of the telecom service
providers and adherence to expected roles and obligations. It depends on the
extent of regulatory control and information asymmetry. Relationship
commitment is an enduring desire to maintain a valued relationship. It is a critical
complement of trust in exchange relationships. Trust influences relationship
commitment. The dimensions of commitment are the degree of association,
length of association and sense of belongingness. This research labels trust and
commitment as key mediating variables of relationship marketing which focuses
on one party in the relational exchange and that party’s relationship commitment
and trust. Therefore, when both commitment and trust-not just one or the other-
are present, they produce outcomes that promote efficiency, productivity and
264
effectiveness. In short, commitment and trust lead directly to co-operative
behaviour that is conductive in relationship success.
6.5.3 Impact of Trust and Commitment in Indian Telecom Sector
This research tests the impact of key antecedent factors, which influence
consumer trust in mobile usage of various telecom service providers, which in
turn affects relationship commitment. The model results and hypothesis tests are
furnished below.
Some of the key findings from the data analysis are highlighted below:
• Trust has a significant positive influence on relationship commitment.
Thus, H1 is established.
• Shared value has a significant positive impact in developing relationship
commitment. Thus H2 is established. Shared value is influenced by
privacy, security and ethics. For both pre-paid and post-pad subscribers
security is more significant as compare to security and ethics.
• Opportunistic behaviour is most important determinant of trust and has a
significant relationship with trust. Thus, H3 is established. Within
opportunistic behaviour, information asymmetry and regulatory control are
the significant variables. For both pre-paid and post- paid subscribers
regulatory control is more significant as compare to information
asymmetry.
• Communication plays a significant positive role on trust. Thus, H4 is
established. Openness is most critical to communication as compare to
quality of information and speed of response.
• Opportunistic behavior tends to have a critical impact on trust. Thus, H5 is
established. Regulatory control and information asymmetry plays a
significant role in controlling opportunistic behavior.
• Technology orientation is a critical component of trust and is most
affected by the antecedents.
265
• Commitment can be improved by prolonged interaction and intense
association between the telecom service provider and the customer.
The primary objective of this research is to explore the impact of trust of
telecom service providers through an antecedent-consequences approach. The
research confirms that trust significantly affects customers’ commitment.
6.6 DISCUSSIONS OF THE FINDINGS
Some discussions on the key antecedents and consequences are
presented below:
• To reach out to new consumers in rural and remote areas, the sharing of
infrastructure must be encouraged by the government. It would, indeed,
be wasteful for every operator to duplicate costly infrastructure.
Infrastructure sharing on fair, transparent and commercial terms will
ensure that consumers in rural areas get choice of service, quality as well
as affordability. While the nation achieves aggressive rollout and
improved tele-density, the operators get an attractive commercial
proposition and an opportunity to expand the coverage and reach of their
services.
• In essence only 34.5 million mobile customers are active in the mobile
industry. The challenges for the industry to make the balance 10 million
customers also actively use their mobile, connection, which is not easy,
considering that several of these customers have shifted to another
mobile connection during the grace period, due to better scenes/tariffs.
However, thorough concerted efforts, at least a third of the 10 million
customers can be brought back to the active and paying customers list. In
the post-paid category, “Zero” usage billing customers (only rental paid)
exist to the extent of 5% of the total base, thus adding limited revenue to
operators.
• The new mobile companies, especially the CDMA operators, have learnt
a bitter lesson from this experience and tightened the controls from
January 2004. Tighter control over documentation, customer profile
266
verification, payment capacity and transferring most of the customers to
the pre-paid segment have reduced the possibility of “junk” customers
coming in to the network.
• All the activities are focused on tariff charges, launch of value added
services to enhance usage and revenue, increase or decrease of various
charges/tariff, instead of working towards market expansion. The next
revolution in the mobile industry can happen only when the telecom
companies work towards market expansion rather than price/tariff
changes. However, in the pursuit to beat each other, the focus of each
operator is only on price/tariff changes instead of working collectively to
acquire more new customers, who are confused due to the constant
changes and delay in entry.
• Markets like Gujarat and Punjab have expanded dramatically due to the
expansion of the distribution network of the handset vendors. In spite of
distribution, the handset vendors are still not offering them at an
affordable price for the Indian population. The prices are still high vis-à-vis
the affordability index of Indians and hence the growth is limited. If the
handset prices cannot be reduced further, the vendors should at least
work towards marketing second-hand handsets in order to attract a large
number of small town customers. Second-hand sets sold by reputed
handset vendors will certainly guarantee the quality of the product.
• In Punjab, the total mobile population has crossed 8.21 million by October
2006 for a population of just 34.35 million. Most states, which are
industrially advanced are at just 6% mobile penetration, clearly illustrating
the lead achieved by Punjab. Today; the mobile network covers every
urban town and most of the rural villages and all highways in Punjab,
which has enabled the state's economy to grow substantially. The
aggressive "marketing of the services by operators has ensured that most
of the youth, executives and families in Punjab own a mobile.
• The research reveals that shared value is the significant determinant of
trust. Shared value also leads to increased commitment from the
267
customer. The customer looks for a better association with the telecom
service provider it is dealing with. Shared value enhances the feeling of
association, developing a bonding and nurturing an associative long-term
relationship. This leads to the birth of trust.
• Communication is found to play a relatively secondary influence in
building a customer trust relationship. Speed of response, quality of
information and openness are important.
• Opportunistic behaviour is measured by attributes like distortion of
information and violation of rules and regulations influence users’ trust. It
plays most significant role because of intense competition. Coherence is
required in formulating their strategic intentions so as to minimize their
perceived opportunistic behaviour, and to get a share of the mind of the
customers.
6.7 MANAGERIAL IMPLICATIONS
To grow exponentially above 100 million customers (it is just 10%
penetration nationally against 13% achieved already by Punjab), the service
providers have to specifically target the customer segments and arrive at
aggressive marketing programmes to reach out to them. However, it needs
support in following areas:
• To ensure that every youth has a mobile, service providers have to offer
services like SMS/MMS at low cost/free and ensure that the total mobile
bill for the youth does not cross Rs.300-400 per month, which is the
maximum this segment of customers can afford from their pocket money;
• In the same way, for executives/businessmen, to tap the full potential, it is
essential that services like Closed User Group, National Closed User
Groups, low STD/ISD rates, Fixed cost for Network calling etc., are
offered so that they can lap up the services and go mobile soon.
• To ensure that every household has a mobile connection, it is essential
that the utility of mobile phones is increased through better STD and ISD
268
rates vis-à-vis landline, friends and family offers, special rates to landlines
etc., with easy/low deposit schemes to acquire these facilities.
• To ensure that the penetration targeted in towns and villages is achieved,
service providers have to invest in network expansion and reach out on
priority; to exploit the untapped potential in these markets.
• To expand the network to a large number of towns and villages by all the
operators, network sharing should be allowed by BSNL and the
government has allowed 74% FDI in mobile companies for easy access to
funds.
• Both service providers and handset vendors have to combine their
strengths and address the issue of market expansion on priority and
launch aggressive programmes to make mobile phones affordable and
available to all.
• The construct of trust is important for cellular users of telecom industry.
As such, it has implications for value added services, market
segmentation, and customer retention strategies. Theoretically,
conceptualising and modelling trust in telecom sector help to expand
scholars’ knowledge of interactive consumer behaviour in this emerging
discipline.
• This study also shows that technology orientation is a significant factor of
mobile users’ trust. Therefore, telecom service provider needs to ensure
that it provides the best network quality and value added services.
• The study tests that shared value, and security in particular, is the
important determinant of trust. To earn a higher level of trust in
customers, many steps can be taken by telecom service providers. One
approach would be highlighting shared value and co-operative interaction
with customers, by recognizing the customer’s right to data ownership.
They can introduce some customer-oriented information security model,
which allows the customers to control their personal information. The use
of the security programmes or models will help customers to identify the
269
concern for customer security, to check the telecom service providers’
reliability and to evaluate the company’s trustworthiness.
• Trust is shown to lead to relationship commitment. The degree and length
of association and the sense of belonging between customers and the
telecom service provider are critical. Strong bond of trust with customers
boosts valuable resources for promoting commitment by providing
reviews, hints, tips and advice to customers. Trust is heavily linked to the
development, fostering and maintenance of relationships. Empowering
the customer to perform various other uses also increases commitment.
• Developing the brand and the telecom service providers’ reputation will
also act as assurance to the customers. It implies enhancing, developing
and maintaining customer relationships remain a priority for organisation.
This research reinforces the importance of trust as a key driver to
developing relationship among cellular users of telecom industry.
6.8 LIMITATIONS OF THE STUDY
Like any other study, this one is also not without limitations. Since the
scope of the study was limited to four major cities only. This study can be
replicated in other cities of Indian, in other industries or for cross study and
contexts for greater generalisability. Another area can be an analysis of
acquisition costs and overall profitability of the customers with strong trust and
commitments.
6.9 DIRECTIONS FOR FUTURE RESEARCH
• How trust and related variables affect the length of the customer/service
provider relationship?
• Feasibility of customer clubs with trust and commitment and trust.
• How the relationship commitment can be improved and what are its
outcomes?
• Implications of trust and commitment in other telecom services –
broadband, internet telephony and many more.
270
6.10 CONCLUSION
As a result of the liberalization, privatisation, and de-monopolisation
initiatives taken by the government of India, the telecom sector is experiencing a
historical growth. The trend is expected to continue in the segment, as prices are
falling as a result of competition in the segment. The beneficiaries of the
competition are the consumers, who are given a wide variety of services. In the
years to come the country is predicted to witness a communication revolution,
which would increase the teledensity to match that of the developed world. The
need of the time is a new revolution in mobile telephony and it is imperative that
service providers work towards the same and make it a reality.
An important contribution of this study is how trust is developed and
sustained over different levels of customer relationship in telecommunication
sector. The future commitment of the customers to organisation depends on
perceived trust. The issue of trust is therefore increasingly recognized as a
critical success factor in the emerging scenario.
271
BIBLIOGRAPHY
Research Journals and Articles
Achrol, R. (1991), “Evolution of the marketing organization: New Forms for
turbulent environments”, Journal of Marketing, Vol. 55, No. 4, pp. 77-93.
Adrian Payne and Pennie Frow (1999), “Relationship Marketing: key Issues for
the Utilities sector”, Journal of Marketing.
Akhil Gupta (2002), “Importance of Competition in Telecommunication”,
Nasscom Telecom Conference 2002, in partnership with The Hindustan
Times and Stanford University, New Delhi, Nov.
Akhil Gupta (2001), “Telecom – Is the Future Finally Near?”, 2001 Asian venture
forum, India, Dec.
Alam Srinivas (2006), “Techno Serfs”, Outlook, 3rd April.
Anderson, E. and Weitz, B. (1989), “Determinants of continuity in conventional
industrial channel dyads”, Marketing Science, Vol. 8, No. 4, pp. 310-323.
Anderson, J.C. and Narus, J.A. (1984), “A model of the distributor’s perspective
of distributor-manufacturer working relationships”, Journal of Marketing,
Vol.48, Fall, pp.62-74.
Anderson, J.C. and Narus, J.A. (1990), “A model of distributor firm and
manufacturer firm working partnerships”, Journal of Marketing, Vol.54,
January, pp.42-58.
AT Kearney (2005), “How Wireless changes the way we work”, E-Business,
June, pg. 23.
Ba, S. (2001), “Establishing online trust through a community responsibility
system”, Decision Support Systems, Vol.31, No.3, pp.323-36.
Bagozzi, R.P. (1995), “Reflections on relationship marketing in consumer
markets”, Academy of Marketing Science, Vol.23, No.4, pp.227-7.
272
Bailey, J.P. and Bakos, Y. (1997), “An exploratory study of the emerging role of
electronic intermediaries”, International Journal of Electronic Commerce,
Vol.1, No.3, pp.7-20.
Barnes, J.G. (1994). ‘Close to the Customer: But is it Really a Relationship?’,
Journal of Marketing Management, Vol. 10, No. 7, pp. 561-70.
Barnes, J.G. (1997), “Closeness, strength and satisfaction: examining the nature
of relationships between providers of financial services and their retail
customers”, Psychology and Marketing, Vol.14, No.8, pp.765-90.
Beatty, S.E. and Kahle, L.R. (1988), “Alternative hierarchies of the attitude –
behaviour relationship: the impact of brand commitment and habit”,
Journal of the Academy of Marketing Science, Vol.16, No.2, pp.1-10.
Becker, H.S. (1960), “Notes on the concept of commitment”, American Journal of
Sociology, Vol.66, No.1, pp.32-40.
Becker, H.S. (1960). “Notes on the concept of commitment”, American Journal of
Sociology, Vol. 66, pp. 32-42.
Benassi, P. (1999), “TRUSTe: an online privacy seal program”, Communications
of the ACM, Vol.42, No.2, pp.56-9.
Bendapudi, N. and Berry, L.L. (1997), “Customers’ motivations for maintaining
relationships with service providers”, Journal of Retailing, Vol.73, No.1,
pp.15-37.
Berry, L.L. (1983). “relationship marketing”, in Berry, L.L., Shostack, G.L. and
Upah, G.D. (Eds), Emerging Perspectives on Services Marketing,
American Marketing Association, Chicago, IL, pp. 25-28.
Berry, L.L. (1995), “Relationship marketing of services: growing interests,
emerging perspectives”, Journal of the Academy of Marketing Science,
Vol.23, No.4, pp.236-45.
Bickert, Jock (1992), “The Database Revolution,” Target Marketing, May, pp. 14-
18.
273
Bitner, Mary Jo (1995), “Building Service Relationships: It’s All About Promises,”
Journal of the academy of Marketing Science (Fall), pp. 246-251.
Braff Adam, Passmore William, J, and Simpson Michael (2003), “Going the
distance with telecom customers”, The Mckinsey Quarterly, No. 4, Pg. 83.
Business & Economy, “Telecom Czar” 30th July 2005.
Business & Economy, 26th Aug-8th Sept. 2005.
Business & Economy, 27th Jan-9th Feb. ph. 66.
Business & Economy, 4th Nov. 17th Nov. 2005.
Business & Economy, 9th Sept-22Sept. 2005, pg. 26.
Butler, J.K. (1991). “Toward understanding and measuring conditions of trust:
evolution of the conditions of trust inventory”, Journal of Management,
Vol. 17, pp. 643-63.
Buttle, F. (1999), “The Scope of relationship management, International Journal
of Customer Relationship Management, Vol. 1, No. 4, pp. 327-36.
C.K. Prahland and VenkatramRamaswamy, “Co-opting Customer Competence”,
Harvard Business Review, Jan-Feb. 2000, pg. 79-87.
Carlsson Jeanette and Arias Salvador, “Transforming Wireline Telecom”, E-
business, Feb. 2005, pg. 13.
Carsten Fink, Aaditya Mattoo and Randeep Rathindran, “An Assessment of
Telecommunication Reform in Developing Countries”, World Bank
Research Working Paper No.2909, October 2002. www.worldbank.org.
Charted Financial Analyst, Special Issue, Dec. 2006.
Clay, K. and Strauss, R. (2000), “Trust, risk and electronic commerce: nineteenth
century lessons for the 21st century”, paper presented at the 93rd Annual
Conference on Taxation, National Tax Association, Session on Taxation
and E-commerce, 9 November 2000.
274
Crosby, L.A. Evans, K.R. and Cowles, D. (1990), “Relationship quality in
services selling: an interpersonal influence perspective”, Journal of
Marketing, Vol.54, July, pp.68-81.
Crosby, Lawrence, A. and Nancy Stephens (1987), “Effects of Relationship
Marketing and Satisfaction, Retention, and Prices in the Life Insurance
Industry,” Journal of Marketing Research, (November), pp. 404-411.
Crosby, Lawrence, A., Kenneth R Evans, and Deboprah Gowles (1990),
“Relationship Quality in Services Selling – An interpersonal influence
Perspective”, Journal of Marketing, 52 (April), pp. 21-34.
Das Naryandas, “Building Loyalty in Business Markets”, Harvard Business
Review, pg. 131, Sept. 2005.
Days, G.S. (2000). “Managing Market relationships” Academy of Marketing
Science, Viol 28, No.1, pp. 24-30.
Desiree Blankenburg Holm and Kent Eriksson, “The character of bridgehead
relationships”, International Business Review, 9 (2000), pp. 191-210.
Deutsch, M. (1960), “The effect of motivational orientation on trust and
suspicion”, Human Relations, Vol.13, pp.123-39.
Dhananjayan G. “Looking Beyond Price in Mobile Services”, Indian
Management, January 2005, pg. 7.
Diane Sinclair, Laurie Hunter and Phil Beaumount, “Models of Customer
Supplier Relations”, Journal of General Management, Vol 22, No.2,
Winter 1996, pg. 56-76
Dick, Alan S. and Kunal Basu (1994), “Customer Loyality: Toward an Integrated
Conceptual Framework”, Journal of the Academy of Marketing Science,
22 (Spring), pp. 99-113.
Dillard B. Tinsley, “Relationship Marketing’s Strategic Array”. Business Horizons,
Jan-Feb. 2002, pp. 70-75.
275
Doney, P.A. and Cannon, J.P. (1997), "An examination of the nature of trust in
buyer-seller relationships", Journal of Marketing, Vol. 61, April, pp. 35-51.
Donnelly, J.H. and George, W.R. (Eds), Marketing of Services, American
Marketing Association, Chicago, 1L, pp. 5-9.
Dorsch, M.J., Swanson, S.R. and Kelly, S.W. (1998), "The role of relationship
quality in the stratification of vendors as perceived by customers", Journal
of the Academy of Marketing Science, Vol. 26, Spring., pp. 128-42.
Douglas A. Houston, “Trust in the Networked Economy: Doing business on Web
Time”, Business Horizons, March – April, 2001, pp. 38-43.
Doyle, Stephen X. and Roth George Thomas (1992), “Selling and Sales
Management in Action: The Use of Insight Coaching to Improve
Relationship Selling,” Journal of Personal Selling & Sales Management,
(Winter), pp. 59-64.
Dr. V. Venkataramana and G. Samayajuly, “Custiomer Relationship
Management – A Key Success Factor for Telecom Sector”, Journal of
Telecom Finance and Management, pp. 51-59.
Earl Naumann and Patrick Shanno, “What is customer Driven Marketing”,
Business Horizons, Nov. Dec. 1992, Vol. 35, No. 6, pp. 44-52.
Economy Bureau, “Every 12th Indian has a Phone”, Business Standard, Feb. 9,
2005, pg. 3.
Economy Bureau, “Non voice segment Trumps for Booming mobile market”,
Business Standard, March 2005, pg. 2.
Economy Bureau, “Phone Users approaching 100m”, Business Standard, March
23, 2005, pg.3.
Egger, F.N. (2000), “Towards a model of trust for e-commerce system design”,
working paper, IPO, Center for User-System Interaction, Eindhoven
University of Technology, The Netherlands.
276
Ellen Garbarino and Mark S. Johnson, “The Different Roles of Satisfaction, Trust
and Commitment in Customer Relationships”, Journal of Marketing, April
1999, Vol. 63, pp. 70-85.
Enrico Benni, Klemens Hjartar, and Jurgen Laartz, “The IT factor in Mobile
Services”, The Mckinsey Quarterly, 2003, No.3, pp. 87-93.
Etgar, M. (1979), “Sources and types of intrachannel conflict”, Journal of
Retailing, Vol.55, pp.77-8.
F. Robert Dwyer, Paul H. Schurr and Sej Oh, “Developing Buyer Seller
Relationships”, Journal of Marketing, Vol 51, (April, 1987), pp. 11-27.
Frazier, Gary, Robert E. Spekman, and Charles O’Neal (1988), “Just in Time
Exchange System and Industrial Marketing”, Journal of Marketing, 52
(October), pp. 52-67.
Ganesan, S. (1994), “Determinants of long-term orientation in buyer-seller
relationships” Journal of Marketing, Vol. 58, pp. 1-19.
Gefen, D. and Straub, D. (2001), “Managing user trust in B2C e-services”, e-
Service Journal, Vol.1, No.1.
Grahame Dowling, “Customer Relationship Management in B2C Markets, Often
Less is More”, California Management Review, Vol. 44, NO. 3, Spring
2002, pp. 87-103.
Gronroos, C. (1989) ‘Defining Marketing: A Market-Oriented Approach’,
European Journal of Marketing, Vol. 23, No.1, pp. 52-60.
Gronroos, C. (1996), “Relationship marketing logic”, Asia-Australia Marketing
Journal, Vol. 4, No.1, p. 7-18.
Gronroos, Christian (1990), “Relationship Approach to Marketing in Service
Contexts: The Marketing and Organizational Behaviour Interface,” Journal
of Business Research, 20 (January), pp. 8-11.
Gummerson, E. (1987), “The new marketing developing long-term interactive
relationships”, Long-range Planning, Vol.20, No.4, pp.10-20.
277
Gummesson, E. (1994), “Making relationship marketing operational”,
International Journal of Service Industry Management, Vol. 5, No. 5, pp.
5-20.
Gundlach, G.T., Achrol, R.S. and Mentzer, J.T. (1995), “The structure of
commitment in exchange”, Journal of Marketing, Vol.59, No.1, pp.78-93.
Gwinner, K.P., Gremler, D.D. and Bitner, M.J. (1998), “Relational benefits in
services industries: the customers perspective”, Academy of Marketing
Science, Vol.26, No.2, pp.101-14.
Heide, J.B. and John, G. (1992), “Do norms matter in marketing relationships?”,
Journal of Marketing, Vol.56, April, pp.32-45.
Hocutt, M.A. (1998), “Relationship dissolution model: antecedents of relationship
commitment and the likelihood of dissolving a relationship”, International
Journal of Service Industry Management, Vol.9, No.2, pp.189-200.
Hot Sectors for 2005, 4Ps, Mega Issue, 2005.
Houn-Gee Chen, Edward T. Chen, Ayi Yeh, “The Effects of Relationship
Commitment and Trust on Business to Consumer Commerce – The Case
of Taiwan”, Communications of the International Information Management
Association, Vol. 3, Issue 1, pp. 35-45.
Hrebiniak, L.G. (1974), “Effect of job level and participation on employee
attitudes and perceptions of influence”, Academy of Management Journal,
Vol.17, pp.649-62.
Huemer, L. (1998), Trust in Business Relations: Economic Logic or Social
Interaction? Borea Bokforlag, Umea.
Humphrey, J. and Schmitz, H. (1998), “Trust and inter-firm relations in
developing and transition economies”, The Journal of Development
Studies, Vol.34, No.4, pp.32-61.
278
Ik Whan A Kwon, Taewon Suh, “Factors Affecting the level of Trust and
Commitment in Supply Chain Relationship”, The Journal of Supply Chain
Management, Spring, 2004, pg. 3.
Itamor Simonson, “Get Close to your customers by Understanding How They
Make Choices” California Management Review, Summer 1993, pg. 68-83.
Jain, Rajesh, “The many Users of Mobile Phones”, Iceworld-Business Standard,
March 2005, pg.2.
James C. Anderson, James A Narus and Wouter Van Russum, “Customer Value
Propositions in Business Markets”, Harvard Business Review, March
2006, pg. 90.
Jeanette Carlsson, Salvador Arias, “Transforming Wireline Telecom”, E-
Business, February 2005, pg. 12.
Jeffrey F. Rayport, Bernard J. Jawo-rski, “Best Face Forward”, Harvard Business
School Press, 2005.
Jeffrey Shuman, Janica Twombly, “Every-one is a Customer”, Dearborn Trade, a
Kaplan Professional Company, 2002.
John B. Cullen, Jean L. Johnson, and Tomoaki Sakano, “Success Through
Commitment and Trust, The Soft Side of Strategic Alliance Management”,
Journal of World Business, 35 (3), 2000, pp. 224-239.
Joseph P. Cannon and William D. Perreault Jr., “Buyer Seller Relationships in
Business Markets”. Journal of Marketing Research, Nov. 1999, Vol.
XXXVI, pg. 397-412.
Kathleen Seiders, Glen B. Voss, Dhruv Grewal and Andrea Godfrey, “Do
Satisfied Customers Buy More? Examining Moderating Influences in a
Retailing Context”, Journal of Marketing, Vol. 69 (October 2005), pg. 26-
43.
Keaveney, S.M. (1995), "Customer switching behavior in service industries: an
exploratory study", Journal of Marketing, Vol. 59, April, pp. 71-82.
279
Kelley, S.W., Donnelly, J.H. and Skinner, S.J.J. (1990), “Customer participation
in service production and delivery”, Journal of Retailing, Vol.22, No.1,
pp.52-61.
Kent Grayson and Tim Anbler, “The Dark side of Long-term Relationships in
Marketing Services”, Journal of Marketing Research, Vol. XXXVI (Feb.
99), pp. 132-141.
Klang, M. (2001), “Who do you trust? Beyond encryption, secure e-business”,
Decision Support Systems, Vol.31, No.3, pp.293-302.
Kohli Vanita and Anup Jayaram, “Broad-band in India”, E-Business, Nov. 2004,
pg. 63.
Kotler, P. (1997), Marketing Management: Analysis, Planning, Implementation
and Control, 9th ed., Prentice Hall, Englewood Cliff, N.J.
Krishna kumar Aparna, “God now just an SMS way” Business Standard, May 21,
2005, pg.1.
Krishnakumar Aparna, “Comics on Your Mobile” Iceworld, Business Standard,
March 9, 2005, Pg. 3.
Lane F. Cooper, Beth McHugh and Susan Aluise, “Managing the Mobility
Imperative”, E-Business, Feb. 2005, pg. 29.
Lane, F. Cooper, Beth Mchugh, Susan Aluise, “Managing the Mobility
Imperative, E-Business, Feb. 2005, pg. 13.
Lee, M.K.O. and Turban, E. (2001), “A trust model for consumer Internet
shopping”, International Journal of Electronic Commerce, Vol.6, No.4,
pp.75-91.
Lehtinen, U. (1996), “Our present state of ignorance in relationship marketing”,
Asia-Australia Marketing Journal, Vol. 4, No.1, pp. 43-51.
Len Tiu Wright, Merlin Stone and Julie Abbotte, “The CRM Imperative Practice
vs. Theory in Telecommunications Industry”, Journal of Database
Marketing, 2002, Vol. 9, pp. 339-349.
280
Levitt, Theodore (1983), “After the Sale is Over”, Harvard Business Review,
(September- October), pp. 87-93.
Lewis, J.D. and Weigert, A.J. (1985), “Trust as a social reality”, Social Forces,
Vol. 63 No. 4, pp. 967-85.
Liljander, V. and Standvik, T. (1995), “The nature of customer relationship in
services”, in Swartz T.A., Bowen, D.A. and Brown S.W., Advances in
Services Marketing and Management, Jai Press, London, Vol.4, pp.141-
167.
Liljander, V. and T. Strandvik (1995). ‘The Nature of Customer Relationships in
Services’, in Advances in Services Marketing and Management, T.A.
Swartz, D.E. Bowen and S. W. Brown (eds), Vol. 4, JAI Press, London.
Linda L. Price and Eric J. Arnould, “Commercial Friendships: Service Provider-
client Relationships in Context”, Journal of Marketing, Oct. 99, Vol. 63,
No. 4, pp. 38-56.
Lindskold, S. (1978), “Trust development, the GRIT proposal and the effects of
conciliatory acts as conflict and cooperation”, Psychological Bulletin, Vol.
85, No. 4, pp. 772-93.
Lovelock, C.H. (1981), “Why management needs to be different for services”, in
Donnelly, J.H. and George, W.R. (Eds.), Marketing of services, American
Marketing Association, Chicago, IL, pp.5-9.
Lovelock, C.H. (1983), “Classifying services to gain strategic marketing insights”,
Journal of Marketing, Vol. 47, Summer, pp. 9-20.
Management, 1997, 13, pp. 463-477.
Managing Moments of Truth, Effective Executive, June 2006, pg. 38.
Marc Beanjean, Jonathan Davidson, Stacey Madge (2006), “The Movement of
Truth in Customer Service”, The McKinsey Quarterly, No.1, pg 63-73.
Marine Souheil and Blanchard Jean-Marie (2005), “Bridging the Digital Divide”,
E-Business.
281
Matthias Homner and Andr’e Krause (2004), “Profiting from Prepaid phone
customers”, The Mckinsey Quarterly, No.2, pp. 12.
Mayer, R.C. Davis, J.H. and Schoorman, F.D. (1995), “An integrative model of
organizational trust”, Academy of Management Review, Vol.20, March,
pp.709-34.
Miettila, A. and Moller, K. (1990), “Interaction perspective into professional
business services: a conceptual analysis”, paper presented at the
Research Development on International Industrial Marketing and
Purchasing, Milan.
Moorman, C., Deshpande, R. and Zaltman, G. (1993), “Factors affecting trust in
market research relationships”, Journal of Marketing, Vol.57, No.1, pp.81-
101.
Moorman, c., Zaltman, G. and Deshpande, R. (1992), “Relationships between
providers and users of market research: the dynamics of trust within and
between organizations”, Journal of Marketing Research, Vol.29, August,
pp.314-28.
Morgan, R.M. and Hunt, S.D. (1994), “The commitment-trust theory of
relationship marketing, Journal of Marketing, Vol.58, July, pp.20-30.
Murray, K.B. and Schlacter, J.L. (1990), “The impact of services versus goods on
consumers assessment of perceived risk and variability”, Journal of the
Academy of Marketing Science, Vol.18, January, pp.51-5.
N.P. Singh and R.K. Gupta, “Use of Data Mining Tools”, Effective Executive,
Nov. 2004, pg. 59-67.
Naidu, G.M., Atul Parvatiyar, Jagdish N. Sheth and Lori Westgate (1999). “Does
Relationship Marketing Pay? An Empirical Investigation of Relationship
Marketing Practices in Hospitals,” 46(3), pp. 207-218.
Nain Chandra, “Myriad Thoughts, Meaning Insights” The Financial Express, 1-
28, 2004.
282
Nevin John R. (1995). “Relationship Marketing and Distribution Channels:
Exploring Fundamental Issues”, Journal of the Academy Marketing
Sciences, (Fall), pp. 327-334.
Novak, T.P., Hoffman, D.L. and Peralta, M. (1999), “Building consumer trust in
online environments: the case for information privacy”, Communications
of the ACM, Vol.40, No.4, pp.80-5.
O’Neal, Charles R. (1989), “JIT Procurement and Relationship Marketing”,
Industrial sMarketing Management, 18 (February), pp.55-63.
Palmer, A. and D. Bejou (1994). ‘Buyer-Seller Relationships: A Conceptual
Model and Empirical Investigation’, Journal of Marketing Management,
Summer, Vol. 6, No. 10, pp. 495-512.
Pankaj Doval (2005), “Reigning Strict”, Management Compass, pp.50-55.
Parsuraman, A., Zeithaml, V.A. and Berry, L. (1985), "A conceptual model of
service quality and its implications for future research", Journal of
Marketing, Vol. 49, Fall. pp.41-50.
Patricia M. Doney, Joseph P. Cannon and Michael R. Mullen (1998),
“Understanding the Influence of National Culture on the Development of
Trust”. Academy of Management Review, Vol. 23, No. 3, pp. 601-620.
Paul, Terry (1988), “Relationship Marketing for Health Care Providers”, Journal
of Health Care Marketing, 8, pp. 2-25.
Peggy Salz- Trautman (2000), “Changing Face–Communication Industry
focuses on customer relations”. Communications International.
Philip Joji Thomas (2005), “Average Mobile Bill Below Rs. 400”. Business
Standard, Feb. 11, pg. 10.
Philip Joji Thomas (2005), “More BSNL lines surrendered”, Business Standard,
March 28, pg. 4.
283
Pritchard, M.P., Havitz, M.E. and Howard, D.R. (1999), “Analyzing the
commitment-loyalty link in service contexts”, Academy of Marketing
Science, Vol. 27, No.3, pp.333-48.
Rajagopalan (2006), “The Digital Divide”, Effective Executive, June, p.8.
Ranjana Kaushal (2006), “Sharing the Spoils”, Outlook Business, Aug. 5.
Rao Girish (2006), “SMS on Airline Ticket” The Economic Times, May 26, Pg.
15.
Ratter, J.B. (1967), J.B. (1967), “A new scale for the measurement of
interpersonal trust”, Journal of Personality, Vol.35, No.4, pp.651-65.
Reichheld, F. (1996). ‘Learning from Customer Defections’, Harvard Business
Review (March – April), pp. 56-69.
Reichheld, F., W. Sasser and J. Earl (1990). ‘Zero Defections’, Harvard Business
Review, Vol. 68, No. 5, pp. 105-11.
Rekha Jain (1997), “Operationalising a Regulatory Framework in India”, Vikalpa,
Vol. 22, No.3, July-Sept., pg. 29.
Revathi S. and Dr. Padma Vathy (2005), “Preference in cellular service providers
in post liberalization era”, Indian Journal of Marketing, Feb., pp. 6-10.
Reynold, K.E. and Arnold, M.J. (2000), “Customer loyalty to the salesperson and
the store: examining relationship of customers in an upscale retail
context”, Journal of Personal Selling and Sales Management, Vol.20,
No.2, pp.89-98.
Robert C. Ford, Cherill P. Heaton, Stephen W. Brown (2001), “Delivering
Excellent Service. Lessons From The Best Firms”, California
Management Review, Vol. 44, No.1, Fall.
Rosenberg, Larry and John Czepiel (1984), “A Marketing approach to Customer
retention”, Journal of Consumer Marketing (Spring), pp. 45-51.
284
S. Manikutty (1997), “Telecom Services in Urban and Corporate Segments: A
consumer Perspective”, Vikalpa, Vol. 22, No. 3/ July-Sept., pg.15.
S.R. Saxena (1997), Financing of Indian Telecom in Competitive Environment,
Vikalpa, Vol. 22, No.3, July-Sept., pg. 65.
Sangani Priyanka (2005), “Cell commix” Mythology comes to the mobile”,
Business Today, Feb., pg. 18.
Schurr, P.H. and Ozanne, J.L. (1985), “Influence on exchange processes:
buyer’s preconceptions of a seller’s trustworthiness and bargaining
toughness”, Journal of Consumer Research, Vol. 11, No.4, pp.939-53.
Shani, David and Sujana Chalasani (1992), “Exploiting Niches Using
Relationship Marketing,” Journal of Consumer Marketing, 9(3), pp. 33-42.
Shanker Ganesan (1994), “Determinants of Long-term Orientation in Buyer-
Seller Relationships”, Journal of Marketing, Vol. 58, (April), pp. 1-19.
Shanthi N.M. (2005), “Indian Telecom: Growth and Transition”, Business Today,
January 2.
Shapiro, B.P. and R.S. Posner (1979), “Making the Major Sale”, Harvard
Business Review (March- April), pp. 68-79.
Shapiro, Benson P. (1988), “Close Encounters of the Four Kinds: Managing
Customers in a Rapidly Changing Environment”, HBS Working Paper No.
9-589-015, Harvard Business School, Boston, MA.
Shapiro, Benson P. Ronald T. Moriarty, Jr. (1980), National Account
Management, Cambridge, MA: Marketing Science Institute.
Sharma, A., Tzokas, N., Saren, M., and Kyziridis, P. (1999). “Antecedents and
consequences of relationship marketing: insights from business service
salespeople”, Industrial Marketing Management, Vol. 28, No. 5, pp. 601-
11.
Sheaves, D.E. and Barnes, J.G. (1996), “The fundamentals of relationships: an
exploration of the concept to guide marketing implementation”, in Swartz,
285
T.A., Bowen, D.E. and Brown, S. (Eds), Advances in Services Marketing
and Management, Jai Press Inc., Greenwich, CT, Vol.5, pp.215-45.
Sheth, Jagdish N. and Rajendra S. Sisodia (1995), “Improving Marketing
Productivity”, in Encyclopedia of Marketing in the year 2000. J. Heilbrunn,
Ed., Chicago, IL: American Marketing Association/NTC Publishing.
Sidharth Sinha (1997), “The Risks of Financing Telecom Projects”, Vikalpa, Vol.
22, No.3, July-Sept., Pg. 55.
Siva V. Gabbita (2004), “The CRM Illusion: The Ghost of Marketing Past”,
Marketing Mastermind, Nov., p. 17.
Souheil Marine and Jean Marie Blanchard (2005), “Bridging the Digital Divide”,
E-Business, Feb., pg. 58.
Storbacka, K., Strandvik, T. and Gronroos, C. (1994), “Managing customer
relationships for profit: the dynamics of relationship quality, International
Journal of Service Industry Management, Vol.5, No.5, pp.21-38.
Strub, P.J. and Priest, T.B. (1976). “Two patterns of establishing trust: the
marijuana user”, Sociological Focus, Vol. 9, No. 4, pp. 399-411.
Sugato Hazra (2005), “Players brace for next phase”, The Hindu Survey of
Indian Industry, pg. 137.
Swaminathan, V., Lepkowska-White, E. and Rao, B.P. (1999), “Browsers or
buyers in cyberspace? An investigation of factors influencing electronic
exchange”, Journal of Computer-Mediated Communication, Vol.5, No.2.
Techno Serfs (2006), Outlook, April 3, pg. 56.
Thakur Rajeshwari Adappa (2005), “Short Message takes a big leap”, The
Economic Times, May 30, pg.12.
The Hindu (2005), “Increased FDI Ceiling in telecom sector”, Nov. 8.
286
Thomas W. Greun, John O. Summers and Frank Acito, “Relationship Marketing
Activities, Commitment and Membership Behaviours in Professional
Associations”, Journal of Marketing, 2000, pg. 34-49.
Voice & Data, Jan. 2007, Vol. 13, Issue 7.
Voice & Data, Vol. 13, Issue 6, Dec. 2006, Special Issue.
Werner Reinartz, Jacqnelyn S. Thomas and V. Kumar (2005), “Balancing
Acquisition and Retention Resources to maximize customer profitability”,
Journal of Marketing, Vol. 69, Jan., pp. 63-79.
Williamson, O.E. (1991), “Calculativeness, trust and economic organization”
Journal of Law and Economics, Vol. 26, pp. 453-86.
Wilson, D.T. (1995), “An integrated model of buyer-seller relationships”, Journal
of the Academy of Marketing Science, Vol.23, No.4, pp.335-45.
Yip, George S. and Tammy L. Madsen (1996), “Global Account Management:
The Frontier in Relationship Marketing,” International Marketing Review,
13(3), pp. 24-42
Books
AL Golin, “Trust or Consequences Build Trust Today or Lose Your Market
Tomorrow”, AMACOM, 2004.
Alfredo Zingale, Mathhias Arndt, “New Economy Emotion – Engaging Customer
Passion with eCRM”. John Wiley & Sons.
Andrew Sturdy, Irena Gruglis, Hugh Willmott, “Customer Service –
Empowerment and Entrapment”. Palgrave.
Barber, B. (1983), The Logics and Limits of Trust, New Rutgers University Press,
Brunswick, NJ
Berry, Leonard L. and A. Parsuraman (1991), Marketing Services – Competing
Through Quality, New York: Free Press.
287
Bjorn Wellenius and Peter A Stern, “Implementing Reforms in the
Telecommunications Sector”, The World Bank, Washington DC.
Brandley T. Gale, Robert Chapman Wood. “Managing Customer Value”. The
Free Press.
Bryan Bergeron, “Essentials of CRM – A Guide to Relationship Management”.
Wiley.
BW Marketing whitebook, 2005, pg. 54.
Carl Sewell and Paul B. Brown, “Customer for Life, How to Turn that One Time
Buyer into Life time Customer”, Currency, 1998.
Charles A Snydel, Management of Telecommunications, Tata McGraw Hill
Publish Company Ltd., New Delhi.
Christopher H. Lovelock (1999), Understanding services, Prentice Hall,
International Edition.
Christopher, M., Payne, A. and Ballantyne, D. (1991), Relationship Marketing:
Bringing Quality, Customer Service and Marketing Together, Butterworth-
Heinemann, London.
Dasgupta, P. (1988), “Trust as a commodity”, in Gambetta, D. (ed.), Trust:
Making and Breaking Cooperative Relations, Basil Blackwell, Inc. New
York, NY.
David L. Kurtz, Kenneth L. Clow (2002). Services Marketing, John Wiley & Sons.
Dutt and Sundaram, Indian Economy, Edition, 2005
Economic Commission for Europe, “The Telecommunication Industry – Growth &
Structural Change – United Nations, New York.
Ford, D. (1990), Understanding Business Markets: Interaction, Relationships,
and Networks, Academic Press, London.
Gerald Zaltman, “How Customers Think”, Harvard Business School, Press,
2003.
288
Govind Apte, “Services Marketing”, Oxford University Press.
Grove and Fisk, "An Analytical Fremework for Service Marketing, Prentice Hall,
USA, 2001.
Gummesson, E. (1999) Total Relationship Marketing, Butterworth-Heinemann,
London.
Hakansson, H. (1982), International Marketing and Purchasing of Industrial
Goods: An Interaction Approach, John Wiley and Sons Ltd., Chester.
Hal Mather, “How to Profitably Delight your Customers”, Butterworth Heinemann
Hayes, Robert H., Steven C. Wheelwright, and Kim B. Clarke (1988), Dynamic
Manufacturing, New York: The Free Press.
Hoffman and Bateson, "Essentials of Services marketing, 2002, Thomson-South
Western, Singapore.
India 2005, pg. 166.
Jacksopn, Barbara B. (1985). Winning and Keeping Industrial Customers: The
Dynamics of Customer Relationships, Lexington, MA: D.C. Health and
Company.
Jan Carlzon, "Moments of Truth" 1987, Ballinger, New York.
Jay Curry, Wil Wurtz, Conny Zijlstra, “Customer Marketing – How to Improve
Profitability of your Customer Base”. Kogan Page.
Jeffrey F. Rayport, Bernard J. Jaworski (2005), “Best Face Forward: Why
companies must Improve Their Service Interfaces with Customers”,
Harvard Business School Press.
Jeffrey Shuman, Janica Twombly, “Every-one is a Customer”, Dearborn Trade, a
Kaplan Professional Company, 2002.
Jell Dyche, The CRM Handbook – A Business Guide to Customer Relationship
Management, Pearson Education, Asia.
289
Jill Graffin, Customer Loyalty – How to Learn it, How to Keep it, Jossey-Bass.
John Abram, Paul Hawkes. “The Seven Myths of Customer Management – How
to be Customer-driven without being Customer-led John Wiley & Sons
Ltd.
Johnson, M.D. (1998), Customer orientation and Market Action, Prentice Hall,
Englewood Cliffs, N.J.
Judith W. Kincard, “Customer Relationship Management – Getting it
Management”, HP.
Karen P. Goncalves. “Services Marketing – A Strategic Approach, Prentice Hall
International.
KenBlanchard (2004), “Customer Mania, It’s Never Too Late to Build a Customer
– Focused Company”, Free Press.
Kiesler, C.A. (1971), The Psychology of Commitment, Academic Press, New
York, NY.
Kotler Philip (2004), Marketing Management, Tenth Edition, New Delhi, Prentice
Hall India Private Limited.
Kotler, P. (1997), Marketing Management: Analysis, Planning, Implementation,
and Control, 9th ed., Prentice-Hall, Englewood Cliffs, NJ.
Lewicki, R.J. and Bunker, B.B. (1995), “Trust in relationships: a model of
development and decline”, in Bunker, B.B. and Rubin, J.Z. (Eds.),
Conflict, Cooperation and Justice, Jossey-Bass, San Francisco, CA
Lewis, R. and Booms B.H. (1983), “The marketing aspect of service quality”, in
Berry, L., Shostack, G. and Upah, G. (Eds.), Emerging Perspective of
Services Marketing, American Marketing Association, Chicago, IL, pp. 99-
107.
M.K. Rampal & S.L. Gupta (2002), Services Marketing: Concept Applications
and Cases, Galgotia Publishing Company.
290
M.R. Superant, C.F. Czepiel, J.A. & Gatman, "A Role Theory Perspective on
dyadic Interactions: The Service Encounter" Journal of Marketing, No. 49,
Winter pp. 99-111.
McKenna, Regis (1991), Relationship Marketing : Successful Strategies for Age
of the Customers, Addison Wesley Publishing Company.
Merlin Stone, Neil Woodcock, Liz Maehtynger, “Handbook of Customer
Relationship Marketing’. Crest Publishing House.
Mowday, R., Porter, L. and Steers, R. (1982), Organizational Linkages: The
Psychology of Commitment, Absenteeism, and Turnover, Academic Press
Inc., New York, NY.
Mukesh Chaturvedi, Abhinav Chaturvedi, Customer Relationship Management –
An India Perspective, Excel Books.
Oliver Stehmann, “Network Competition for European Telecommunications”,
Oxford University Press.
Patnek Molineux, “Exploiting CRM – Connecting with Customers”. Hodder &
Stonghton.
Patricia B. Seybold, Mitchell Kramer and Dr. Muhamed Muneer, “Capitalising on
Customers”, Vikas Publishing House Private Limited.
Patricia Seybold and Ronni T. Marshak (2001), The Customer Revolution.
Paul de Bijh and Martin Pertz, “Regulation and Entry into Telecommunications
Markets”, Cambridge University Press.
Payne, A., M. Christopher, H. Peck and M. Clark (1999). Relationship Marketing,
Strategy and Implementation, Butterworth-Heinemann, London.
Peppers, Don and Martha Rogers (1993), The One to One Future Building
Relationships One Customer at a Time, New York, NY: Doubleday.
Peter Curwen & Jason Whalley, “Telecommunications Strategy – Cases, Theory
and Applications, Routledge London.
291
Phillip E. Mahfood, “The Customer Crisis – Turning an Unhappy Customer into a
Life-Long Chent”, East West Press Pvt. Ltd.
Porter, M.E. (1985), Competitive Advantage, The Free Press, New York, NY.
Rajendra Nargundkar, Tapan K. Panda, “Managing Customer Relationships in
Service Industries”, Excel Books.
Rakesh Seth and Kirti Seth (2005), Creating Customer Delight: The How and
Why of CRM, Response Books.
Rampal, M.K. and Gupta, S.L. (2002), “Services Marketing: Concept,
applications and cases”, Galgttal Publishing Co.
Regis McKenna, Real Time (Boston: Harvard Business School Press, 1997), p.
11.
Reichheld, F. and Teal, T. (1996), The loyalty effect: The hidden force behind
growth, Profits and Lasting Value, Harvard Business School Press,
Boston.
Richard A. Buckingham (2004), “Customer Once, Client Forever, 12 Tolls for
Building Lifetime Business Relationships”, Kiplinger Books.
Roger Blackwell and Kristina Stephan (2001), “Customers Rule”, Crown
Business.
S. Balachandran (2004), “Customer Driven Services Marketing, Second Edition,
Response Books.
Stephen M. Watters, The New Telephony – Technology, Convergence, Industry
Collision, Prentice Hall PTR, NJ07458.
Stere Baron, Kim Harris. Services Marketing – Text & Cases, Palgrave.
Steven Shepard, “Telecommunications Convergence”, McGraw Hill.
Storbacka, Kaj (2000), “Customer Profitability: Analysis and Design Issues”, in
Handbook of Relationship Marketing, Jagdish N. Sheth and Atul
Parvatiyar, Eds., Thousand Oaks, CA: Sage Publications, pp. 565-586.
292
Teresa A Swartz, David E Bowen, Stephen W. Brown. “Services Marketing and
Management. Jai Press Inc. London.
V. Kumar, Werner J. Reinartz, “Customer Relationship Management – A
Databases Approach, John Wiley & Sons.
Valarie A. Zeithaml, Mary Jo Bitner, Services Marketing, Integrating Customer
Focus Acsosthe Firm, Tata McGraw Publishing Co. Ltd.
Vavra, Terry G. (1992), Aftermarketing: How to Keep Customers for Life through
Relationship Marketing, Homewood, IL: Business One-Irwin
Voice & Data, Jan. 2007, Vol. 13, Issue 7.
William G. Zikmund, Raymond Mcleod, Jr. Faye W Gilbert, “Customer
Relationship Management – Integrated Marketing Strategy and
Information Technology”, Wiley.
William H. Davidow, Bro Uttal. “Total Customer Service – The Ultimate Weapon”.
Harper Perennial.
Reports
Annual report on Telecommunications 2003-04, Department of
Telecommunications, Government of India, www.dotindia.com.
Bjorn Wellenius and Peter A Stern, “Implementing Reforms in the
Telecommunications Sector”, The World Bank, Washington DC.
Economic Commission for Europe, “The Telecommunication Industry – Growth &
Structural Change – United Nations, New York.
“ICT and Millennium Development Goals”, World Telecom Development Report
2006, www.itu.int.
“Indian Telecommunication Statistics 2004”, Ministry of Communications,
Government of India.
Indian Infrastructure, Oct. 2005, pg. 26.
293
Indian Infrastructure, Oct. 2005, pg. 41.
Industry Monitor, Telecommunications, May 2006, Pg. 430.
“New Telecom Policy – 1999”, www.trai.gov.in
“Opportunity India – Telecom Industry”, CII Report, 2006.
“Telecom Sector update”, Aug 2004, www.indianfoline.com.
“Telecommunications: Accessibility & Service Performance, Infrastructure”,
CMIE March 2004.
Websites
Bob Thomson (2004), “Successful CRM: Turning Customer Loyalty into
Profitability”, www. Rightnowtechnology.com Oct.
Challapalli Sravanthi (2002), Hutch Says Hi, Catalyst, www.blonnet.com, June 6.
Das Ranjan (2003), Race to the Future: How Indian Telcos Have Made Their
Initial Moves, www.economictimes.com, March 13.
Dhawan Radhika (2002), Hutch the Contrarian Network,
www.businessworldindia.com, November 1, 2004 India Telecom,
www.convergencelus.com, Sep. 4.
Jain Rekha, “Review of Policy Changes in the Indian Telecom Sector”
uniahd.ernt.in
Jayaram Anup (2004), Win-Win for BSNL, www.businessworldindia.com,
February 2.
Kaushik Neha (2002), Idea Cellular Draws Up Strategy, www.blonnet.com, July
11.
“LCT and Millennium Development Goals” (2003), World Telecom Development
Report, www.itu.int
Nair Sanjeev (2003), More freebies while crackers bust, www.domain-b.com,
October 24.
294
Shashidhar Ajita and Srinivasan Sriram (2003), Where Names Don’t Matter,
Catalyst, www.blonnet.com, August 21.
Shymal Ghosh (2003), “The Resurging Telecom Sector”, pib.nic.in, April.
The Renaissance (2004), Coverstory, www.businessworldindia.com, August 23,.
The Year of the Child (2004), www.rediff.com, January 02.
www.adexindia.com
www.airtelworld.com
www.blonnet.com
www.businessworldindia.com
www.domainb.com
www.economictimes.com
www.economywatch.com
www.exchange4media.com
www.hutch.co.in
www.ibef.org
www.ideacellular.com
www.indiantelevision.com
www.relianceinfo.com
www.trai.gov.in
i
ANNEXURE – I LIST OF SERVICE PROVIDERS
Details of service providers: - The list of all the mobile service providers along with their
licensed service area is as under:
ii
ANNEXURE – II QUESTIONNAIRE FOR SUBSCRIBER
Dear Respondent,
I am conducting research at University Business School, Panjab University, Chandigarh for the study of Trust and Commitment in Telecom Service Sector for which I intend to pose a questionnaire aimed at checking the trust level of the subscribers.
Your cooperation is deeply solicitated to provide the relevant information. I assure that information will be kept confidential.
Name of Subscriber:_______________________
Mobile No._______________________________
Mobile Company__________________________
Address:_________________________________
Please tick at the place that matches your opinion.
A 1. Through which source did you come to know about Mobile Services? Advertisements (Media) Friends/Relatives Company Outlet Internet
2. Have you subscribed to any other service also (presently/previously)? Yes No
2.1 If Yes, then which one
Service Provider Age of the Connection Reliance Airtel Hutch Tata Indicom
(a) Prepaid (b) Post paid 3 Why do you need a Mobile ? S.No. Statement Strongly
Disagree Disagree Neutral Agree Strongly
Agree 1 To stay in touch 2 For Business or Professional Requirement 3 As a status symbol 4 It is advantegeous over Landline 5 It adds to Mobility 6 For services 7 Convenience of calling anytime 8 Makes you easily accessible
iii
4 Which of the following services do you avail of ? S.No. Name of Services Never Rarely Sometimes Often Always
1 Short Message Services (SMS)
2 Multi Media Message Services (MMS)
3 GPRS / Internet
4 Call Diverting Feature (Call Forwarding / Call Diverting )
5 Information based services
6 Getting News Update (Sports / Others )
7 For STD calling
8 For Local calling
5. Was the Dealer/Customer Care Executive knowledgeable about the Product?
Yes No
6. What factors would you consider while opting for a Mobile Service Provider ? S.No. Statement Strongly
Disagree Disagree Neutral Agree Strongly
Agree
1 I would give special consideration to Brand Image
2 Network Connectivity would be an important factor
3 I would give special attention to coverage
4 I would give special consideration to Call tariff
5 Service Quality would be an important factor
6 Reliability would be an important factor
7 I would give special consideration to advertisement
8 Sales Promotion offers would be an important factor
9 I would give special consideration to Value Added Services (SMS , MMS , CLIP)
10 Ease of availability and recharge facility would be an important factor
11 I would give special consideration to Customer Care Services
12 Roaming facility would be an important factor
iv
S.No. Statement Strongly Disagree
Disagree Neutral Agree Strongly Agree
13 I would give special consideration to rebate and discount on calls
14 I would give special consideration to Voice clarity
15 I would give special consideration to transparency in billing
16 For me dealer services would be an important consideration
17 For me Word of Mouth would be an important consideration
18 Advice of friends and relatives influence my Purchase Decision
B) TOUCH POINTS
7. Are you aware of the Company Offices ?
Yes No 8. Have you visited one?
Yes No
Strongly Disagree
Disagree Neutral Agree Strongly Agree
Ambience of the company outlet
Availability of Literatures/Brochures
Inter-personal skills of the Executive at company outlet
Handling Product related Queries
Complaint Handling at company outlet
Overall Rating of company outlet
C). Customer Care Services:
Rate the customer care services according to you from 1 to 5, where 5 is extremely good and 1 is extremely bad.
9 Are the Executive 1 2 3 4 5 a. Easily Accessible
b. Humble and Soft Spoken
c. Able to provide Complete Resolution
v
9.1 Rate Overall Customer Care Services 10. Has any Company executive ever Contacted you for solving your query(s)?
Yes No 10.1 If Yes, Do they solve your query(s)/complaints efficiently?
Yes No D) AFTER SALES SERVICE
11. Have you ever faced any problem in your handset?
Yes No
11.1 If Yes, what type of problem you faced? Keypad Internet Network Voice Battery Any Other (Specify)__________________________
12. Have you ever claimed Warranty? Yes No
12.1 If Yes, was the claim settled within promised time? Yes/No
12.2 If No, specify (with reasons)________________________
13. Are you aware about the Handset Service Centers? Yes No
13.1 If Yes, have you visited any of the Service Centers? Yes No
13.2 If Yes, then was the problem properly taken care of? Yes No 13.3 How do you perceive your Service Provider (Using Semantic Scale 1-7 , 1 being the lowest , 7 being the highest ) 7 1
RELIABLE UNRELIABLE
POWERFUL WEAK (Brandname )
INNOVATIVE RIGID
TRENDY OLD FASHIONED
ECONOMICAL EXPENSIVE
USER FRIENDLY HOSTILE
vi
CUSTOMER FOCUSSED UNCONCERNED
TRANSPARENT NOT TRANSPARENT
PROACTIVE REACTIVE
14. Please, Rate Overall After Sale Services Extremely Good Moderately Good Good Bad Worst
E) NETWORK QUALITY
How far are you satisfied with the following factors for your Service Provider
S.No. Factors Very Dissatisfied
Dissatified Neutral Satisfied Very Satisfied
1 Tariff / Price / Call rate
2 Network Connectivity
3 Coverage
4 Value Added Service
5 Roaming facility
6 Transparency I Billing
7 Ease of Availability / Recharge
8 Customer Care Services
9 Sales Promotion Offers
10 Advertisement
11 Voice Clarity
12 Dealer Network
Overall Satisfaction Level
15. Overall – Are You Satisfied with your mobile phone? Extremely Satisfied Moderately Satisfied Satisfied Moderately Dissatisfied Extremely Dissatisfied If below Satisfied level, Specify Reason(s)? _________________________________________ _________________________________________
vii
16. Do you plan to use the same services provider in future ?
(a) Definitely would use (b) Probably would use (c) Might Use
(d) Probably would not use
(e) Definitely would not use
17. Any Suggestions or Areas where must improve? _________________________________________
_________________________________________
F) TRUST AND COMMITMENT LEVEL
G) Rank the following from 1 to 5 where 1 means strongly disagree, 2- moderately disagree, 3-agree, 4-moderately agree, 5-strongly agree.
1 2 3 4 5
1. I believe that long-term relationship with this company will be profitable.
2. Maintaining a long-term relationship with this company is important to me.
3. I am willing to co-operate and make adjustments from time to time.
4. I am concerned with outcomes in this relationship.
5. I expect this company to continue its operations in the long-run.
6. Relationship between me and my company is very committed one.
7. I am proud to belong in this company.
8. This company has been co-operative with me.
9. This company cares for me.
10. In times of problem, this company’s representative has gone out of way in help.
11. Company’s representative is like a friend.
12. I trust my company is do-things, which I can’t do myself.
13. I am well aware about the new products and scheme.
14. I frankly share my opinion with company.
viii
15. My complaints are patiently heard and tackled.
16. I get timely information.
17. Information provided to me is upto date.
18. Information given to me is relevant one.
19. I have to remind about my difficulties again.
20. I can approach concerned person easily.
21. Company’s gets feedback from me from time to time.
22. I share information about my mobile services with my friends and relatives.
23. My mobile company takes into account my requirements.
24. I have got genuine relationship.
25. Other companies could also provide what I get from this company.
26. This company has monopoly for its services.
27. This is one of the few suppliers I could use for this product.
28. No other supplier has equivalent capabilities.
29. My mobile company follows various rules and regulations issued from time to time.
30. This company changes its policies very quickly.
31. Company shares with me its various products and schemes.
32. Company advertises about its products.
33. I am well informed about the various the new product.
34. I am given with detailed information.
35. Information provided by me is kept secret.
36. This company is honest and transparent.
37. This company has good reputation in the market.
ix
38. I enjoy discussing my mobile services with other people.
39. A feel the ‘part of the family’ of this company.
40. I have little attachment with this company.
41. Under all the circumstances, I will continue to stick to this company’s mobile services.
42. Company takes into account customers’ benefits while taking decisions.
43. My mobile company understands my problems.
44. I am concerned that the services are not worth the money.
45. The quality of services of this mobile company is consistently high.
46. The services of this company are reliable ones.
47. I cannot always trust upon this mobile company.
48. This company has always met my expectations.
49. I feel sense of belongingness.
50. We–me and company want to carry this relationship for indefinite period.
51. I am the loyal patron of this company.
52. This company’s representative has been open in dealing with me.
53. Promises made by this company are reliable ones.
54. This company’s representative is knowledgeable regarding its products.
55. This company is honest about problems.
56. This company’s claims are not false ones.
57. Company’s representative has problems in answering my questions.
58. This company meets my expectations.
59. This company’s representative displays a sound understanding with me.
60. I plan to subscribe for my mobile in future.
61. I am keen to know the future of the company.
x
G. CUSTOMER’S PROFILE (kindly tick the suitable box)
19. Age:
<18 18-25 25-40 40 and above
20. Occupation: Student Professional
Govt. Employee Self-Employed Pvt. Employee Other (mention)
21. Gender: Male Female
22. Education: Matriculation Senior Secondary School Bachelor Degree Post Graduate Degree 23. Disposable Income
<5000 5000 < 10000 10000 < 20000 20000 and above
24. Experience of Using the Mobile:
Less than 1 year 1-2 years More than 2 years
25. Time spent on usage of mobile during the day: Less than 10 minute 10-20 minute
More than 20 minutes 26. Place where generally calls are made: Domestic National International
(Thanks for Co-operation)
xi
ANNEXURE – III QUESTIONNAIRE FOR DEALERS
Dear Respondent,
I am conducting research at University Business School, Panjab University, Chandigarh for the study of Trust and Commitment in Telecom Service Sector for which I intend to pose a questionnaire aimed at checking the Satisfaction level of the subscribers.
Your cooperation is deeply solicitated to provide the relevant information as per the contents of the questionnaire. Name : _____________________________________
Address : _____________________________________________________
1. Which of the following companies / brands do you cater to? (Tick whichever is appropriate) a) Reliance Infocomm b) Airtel c) Hutch d) Tata Indicom e) Any other _________ 2. Kindly rank the following dealer objectives on a scale of 1-5 (1 being the most important and 5
being the least). a) To enhance the Market/Segment Share b) To achieve the targeted profit objectives c) To enhance the consumer brand loyalty d) Any other specify ________
3. In which of the following areas do you receive directions from the companies? a) Sales target b) Customer service c) Discount/Schemes d) Consumer Satisfaction e) Areas to be covered f) Advertisement Planning g) Any other (specify) ________ 4. Do you take part in Marketing Decision making activity of the company? If Yes, How often do you take part. a) Regularly b) Sometimes c) Occasionally 5. Which of the companies (names mentioned in Q.No.1) gives the: a) Best channel margin ________________________ b) Best channel penetration _________________________ c) Best channel delivery speed to customers ________________________ d) Best channel support ________________________ e) Best quality of the product _________________________ f) Best technical support ________________________ 6. Which of the following company product attributes appeal most of the customers? Kindly rank
them on a scale of 1-10 (1 for the most appealing and 10 for the least). a) Product features b) Product quality c) Product range d) Low price e) Ads on TV/Newspaper f) Convincing power of dealers
xii
g) Premium price f) Customer service (presales and after sales) 7. Do you take feedback from the customer after the sale of the product? Yes/No Do you forward the feedback to the company? Yes/No
8. In how many cases (percentages) do you feel the company takes decisions based on the feedback received from you? __________________________________
9. In the current market scenario which of the following areas need to be more emphasized and developed, in order to make a difference in the service sector (Tick whichever attribute is appropriate).
a) Product Strategy: Quality Range Features Brand image
Any other (specify) ____________ b) Price Strategy: Price cuts Cost cuts Payment and credit terms Any other (specify) ____________ c) Promotion Strategy:
Advertisements Discounts and schemes Dealer incentives Any other (specify) ____________
d) Distribution Network: Number of Dealers/distributors Channel Margins Channel Support Channel Delivery speed to the customers Any other (Specify) ____________
d) Customer Service: Pre-paid Post paid
10. Are you satisfied by the kind of compensation received from the company for providing seller support? Yes/No
a) Gross margin and overhead contribution b) Promotional allowance and below-the line benefits c) Distribution Exclusivity d) Continuity of Supply e) Market Development f) Credit g) Any other (Specify)
(Thank you for your cooperation in this important study)
xiii
ANNEXURE – IV QUESTIONNAIRE (TELECOM SERVICE PROVIDER)
Dear Respondent,
I am conducting research at University Business School, Panjab University, Chandigarh for the study of Trust and Commitment in Telecom Service Sector for which I intend to pose a questionnaire aimed at checking the trust level of the subscribers vis-à-vis telecom service provider.
Your cooperation is deeply solicitated to provide the relevant information as per the contents of the questionnaire. Information provided by you will be confidential.
Name : _____________________________________
Address : _____________________________________________________
Please put a tick mark in the appropriate boxes wherever required.
• Kindly consult the other departments/divisions of the company wherever required. • Please do not leave any question unanswered.
A) General
1. Name of the Company________________________________________________
2. For how many years has this company been in operation_________________
3. Does the company have any foreign collaboration – Yes/No
If yes, a) what is the percent foreign equity_____________________
b) What is the nature of collaboration – Technical/Financial
B) Product
1. Your company provides: (specify services).
Voice mail service:
Call waiting: Call hold:
Call divert:
3-way call conferencing:
CLIP (Caller Line Identification Presentation)
CLIR (Call Line Identification Restriction)
International SMS
Net Connect 4. Which functional domain do your products and services address? (Kindly specify the
percentage, in case of more than one domain) 1. Banking __________ 2. Manufacturing __________ 3. Insurance & Other financial services __________ 4. Health Care __________ 5. Retail and Distribution __________ 6. Others __________
xiv
5. Does the company export any of its services? Yes/No If Yes, a) Specific the services_____________________________________ ______________________________________________________
b) What is the proportion of exports vis-à-vis the domestic sale?_____________ ______________________________________________________________
6. For Foreign Companies only (Collaboration/Joint Venture / Wholly Owned Subsidiary)
a) Have your product/services been localized according to the Indian Market demands? Yes/No
b) If Yes, Please specify the measures undertaken ______________________________________________________________
7. For Indian Companies only
a) What are the advantages you enjoy over the foreign companies operating in India?
8. Which of these promotional activities does your company undertake? a) TV Ads b) Internet Ads c) Print Ads d) Customer Incentives/Discounts e) Seminars/Exhibitions f) Market Survey/Research g) Personal Selling h) Any other (Specify)
9. Approximate Advertisement Budget (p.a.)
____________________________________________________________________
10. Approximate marketing expense (p.a.)
11. Which of the following marketing channel does your company use to distribute your products/services? Also kindly mention the approximate revenue earned through each channel?
Revenue Earned
a) Direct marketing to end user __________________
b) Through your own national and __________________ international offices
c) Through the Internet __________________
d) By forming associations with other __________________ companies
e) End user through Principal __________________
f) Dealer/Distributor __________________
g) Any other (Specify) __________________
xv
12. Do you believe that pre and after-sales service is important?
Yes/No
13. Does the company have any authorized after-sales service stations? Yes/No
14. Has the company fixed any specific amount of after-sales service charges? Yes/No
15. Is the service station given any kind of directions from the company in terms of customer service and customer relations?
Yes/No
16. Does the company maintain customer database? Yes/No
17. Does the company take feedback from the customer after the sale of the product? Yes/No
If Yes, feedback is taken- a) Regularly b) Sometimes c) Never
18. Feedback is taken through a) Telephonic calls b) Mailing Letters/Questionnaires c) Visits by company sales executives d) Websites/Internet e) Any other (Specify)
19. What kind of manpower technology does the company use: a) Purely Indian b) Purely Foreign c) Foreign and Indian both d) Any other Specify______________________
20. The marketing of your services is carried out by: a) Sales Personnel b) Professionals c) Internet d) Any other Specify______________________
If option (a) is correct, then
The sales personnel recruited possess, which of the following qualifications: a) Diploma in marketing and sales b) MBA (Marketing) c) MBA (IT) d) B.E. e) Any other Specify______________________ 21. What kind of training do you provide to the new Marketing Personnel?
_____________________________________________________________________ _____________________________________________________________________ _____________________________________________________________________ 22. Stipulated training period for new marketing personnel. _____________________________________________________________________
xvi
23. Compensation to the marketing personnel is given in the form of: a) Fixed Salary b) Fixed Salary cum commission on sales c) Only commission on sales and incentives d) Any other (specify)__________________________
24. Are the Marketing personnel given any refresher training courses? Yes/No If Yes, then after how much time period___________________________ Please indicate your position in the company (i.e. job title) Low Average High How would you rate your own knowledge of your company’s marketing programs, strategies and policies?
1 2 3 4 5 6 7
How would rate your own involvement in your company’s marketing programs and strategies?
1 2 3 4 5 6 7
How would you rate your own knowledge of your major competitors marketing programs, strategies and policy?
1 2 3 4 5 6 7
25. In which of these areas would you like your company to improve its marketing strategy: • Product • Price • Promotional Activities • Distribution Network • Customer Service
26. Did you face any difficulty while filling up the questionnaire (please specify) ___________________________________________________________________________ ___________________________________________________________________________ ___________________________________________________________________________ Please attach your business card if you would like to receive a copy of the study findings.
(Thank you for your cooperation in this important study)