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

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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

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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

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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.

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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.

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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

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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

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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.

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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

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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

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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

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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

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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

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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

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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

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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

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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.

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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:

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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%.

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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;

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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

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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.

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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

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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.

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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

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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

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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-

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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

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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.

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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

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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

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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.

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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

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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.

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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.”

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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

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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

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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.

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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

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(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

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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.

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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

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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

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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.

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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

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− 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

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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.

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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

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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.

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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

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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).

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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

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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.

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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.

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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.

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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.

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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.

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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

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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

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www.ibef.org

www.ideacellular.com

www.indiantelevision.com

www.relianceinfo.com

www.trai.gov.in

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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:

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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

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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

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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

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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

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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)? _________________________________________ _________________________________________

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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.

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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.

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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.

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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)

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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

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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)

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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 __________

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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) __________________

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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. _____________________________________________________________________

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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)