doctor of philosophy in management
TRANSCRIPT
A STUDY ON MEDIATING EFFECTS ON SERVICE LOYALTY IN MOBILE SERVICE PROVIDERS IN CAUVERY DELTA DISTRICTS IN TAMILNADU
Thesis submitted to
BHARATHIDASAN UNIVERSITY, TIRUCHIRAPALLI
Partial fulfillment of the requirements for the award of the degree of
DOCTOR OF PHILOSOPHY IN MANAGEMENT
SUBMITTED BY
K. KEERTHI, M.B.A.,
UNDER THE GUIDANCE OF
Dr. A. ARULRAJ, M.A., M.Phil., PGDBA, M.B.A., Ph.D.,
RESEARCH DEPARTMENT OF BUSINESS ADMINISTRATION,
RAJAH SERFOJI GOVERNMENT COLLEGE, (AUTONOMOUS),
THANJAVUR – 613 005, TAMILNADU, INDIA.
JANUARY 2014
CERTIFICATE
This to certify that the thesis entitled “A STUDY ON MEDIATING EFFECTS
ON SERVICE LOYALTY IN MOBILE SERVICE PROVIDERS IN CAUVERY
DELTA DISTRICTS IN TAMILNADU” is submitted by Mrs. K.KEERTHI, a
Full time Ph.D scholar in the Research Department of Business
Administration, Rajah Serfoji Government College, (Autonomous)
Thanjavur – 613 005. The thesis is the outcome of the personal research
done by the candidate under my supervision and guidance and I certify
that the thesis has not formed the basis for the award of any degree or any
other similar title.
Date: (Dr. A. ARULRAJ)
Place:
DECLARATION
I hereby declare that the work embodied in this thesis has been originally
carried out by me under the supervision of Dr. A. ARULRAJ, M.A., M.B.A.,
M.Phil., PGDBA., PhD., Assistant Professor, PG & Research Department of
Economics, Rajah Serfoji Government College (Autonomous), Thanjavurand
this work has not been submitted either in whole or in part for any other
degree or diploma at any university.
Date: Research Scholar
Place:
(K.KEERTHI)
ACKNOWLEDGEMENTS
The completion of this thesis would not have been possible without support
from several respected persons. First of all, I want to thank my research
advisor, Dr.A.ARULRAJ, M.A., M.Phil., PGDBA., M.B.A., Ph.D, for constructive
comments in guiding me through the process of writing the thesis. I thank
him for profusely encouragement from the very beginning and I am grateful
to him for step-by-step guidance and support.
I extend my heartiest thanks to our beloved Principal
Dr.(Mrs.)K.ANBU, M.Sc., M.Phil., Ph.D, Rajah Serfoji Government College,
(Autonomous) Thanjavur – 613 005, for her encouragement and support.
I am also thankful to my doctoral committee members,
Dr.A.ANANTH & Dr.B.PRABAHARAN for their, knowledge, expertise, and
insightful suggestions. And also my special thanks to Dr.A.ANANTH, who
gave first and most important seeds of my interest in this field and gave
opportunity to serve.
I extend my thanks to my research colleagues Prof.J.SWAMINATHAN,
Dr.G.THIYAGARAJAN, Dr.N.SENTHILKUMAR, Dr.D.RAJASEKARAN,
Dr.R.RAMESH, Dr.G.RETHINASIVAKUMAR, Dr.R.THANGAPRASHATH,
Mr.M.SETHURAMAN, Mrs.M.SANTHANALAKSHMI, Mr.A.ANTONYRAJ,
Mr.M.SAKTHIVEL and Mr.R.ILAVENIL who were instrumental in the process of
completing this degree.
I take this opportunity to express the profound gratitude from my deep heart
to my beloved parents in my life and in research, Mr.T.KARTHIKEYAN, and
to my mother, Mrs.K.ARTHY, whose constant support brought me where I
am today and to many thanks to my Husband, Mr.M.SENTHIL KUMAR, for his
continued enduring source of strength and encouragement and I express my
whole heart full thanks to ever supporting my father in Law & Mother in
Law, Mr.R.MURUGAPPA & Mrs.M.VIJAYALAKSHMI.
I would like thank my colleagues & friends Mr.U.GOWRISHANKER, & Mr.K.R.RAMPRAKASH & Mrs.R.RENUKADEVI for his valuable support at the time of data collection that helped me to carry out this thesis.
I extend my deepest thanks to those who indirectly contributed in this research, your kindness means a lot to me. Thank you very much.
K.KEERTHI
CONTENTS List of Tables
List of Figures
S. No. Chapterisation Page No.
1. Chapter I
Introduction
1.1. Telecommunication Sector 1
1.2. Growth and Development of Indian Telecom Industry
2
1.3. Service Quality in Indian Telecom Sector 9
1.4. Performance of Indian Telecom Sector Post Liberalized Period
10
1.5. Background for the Study 17
1.6. Statement of the Problem 25
1.7. Research Objectives 26
1.8. Research Questions 27
1.9. Proposed Conceptualized Research Model 27
1.10. Significance of the Study 29
1.11. Limitation of Study 29
1.12. Structure of the Thesis 29
1.13. Conclusion
31
2. Chapter II
Literature Review
2.1. Introduction 32
2.2. Studies Related on Growth and Development of Telecom Industry in Global and India
32
2.3. Studies Related Customer Relationships in Telecom Industry
39
2.4. Service Quality of Mobile Phone Service Provider
48
2.5. Service Loyalty 59
2.6. Customer Loyalty 62
2.7. Conclusion
72
3. Chapter III
Research Methodology
3.1. Introduction 73
3.2. Service Quality Measurement – Recent trends
73
3.3. Reflective Research Formation Studies 74
3.4. Formative Research Foundation Studies 77
3.5. Research Design 80
3.6. Procedure for Data Analysis 86
3.7. Hypotheses Development 90
3.8. Conclusion
93
4. Chapter IV
Analyses & Interpretation of Data
4.1. Introduction 94
4.2. Trend analysis in Mobile Service Provider 95
4.3. The Regression “Mobile QUAL” Overall Model
121
4.4. Conclusion
158
5. Chapter V
Findings, Strategic Planning & Conclusions
5.1. Introduction 159
5.2. Findings and Conclusion for the Study 159
5.3. Strategic Planning For Improving Mobile Service Provider Loyalty
170
5.4. Limitations and Directions for Further Research
171
5.5. Conclusion 173
References Questionnaire English
List of Tables
S. No. Particulars P. No.
3.1. Reflective Formation Models and Contributors - 75
3.2. Literature review showed the reflective models on mobile telecommunication Industry
- 76
3.3. Formative Formation Models and Contributors - 77
3.3. Sample Size across the Delta Districts of Tamilnadu - 83
3.4. The Sample Size Across The Difference Demographic Variables
- 85
4.1. Growth of Telephones over the years in Telecom Sector in India (2007-2011)
- 95
4.2. Tele Density in Telecom Sector in India (2007-2011) - 100
4.3. Cumulative FDI and Status of Disbursements made and availability of Fund in Telecom Sector in India (2007-2011)
- 104
4.4. Telecom Equipment and Production in India (2007-2011) - 109
4.5. Growth of Telecom Networks in India (2007-2011) - 112
4.6. Fault Rate in Telecom Sector in India (2007-2011) - 115
4.7. Public Sector – Requirement in Telecom Sector in India (2007-2011)
- 117
4.8. Bayesian Convergence Distribution for “Mobile QUAL” Regression Model
- 124
4.9. Summary of the Various Goodness of Fit Statistics and Other Values Corresponding To the Over All Mediated Mobile QUAL Mediated Structural Equation Model
- 137
4.10. Bayesian Convergence Distribution for “Over All Mediated Mobile QUAL” Structural Model
- 138
List of Figures
S. No.
Particulars Page No.
1.1. Conceptual Model for studying Service loyalty in Mobile Service Providers
- 28
3.1. Proposed Hypothetical Model of “Mobile QUAL Model” - 91
4.1. Trend Analysis plot of Wire line phones in Growth of Telecom Sector in India From (2007-2011)
- 96
4.2. Trend Analysis plot of Wireless phones in Growth of Telecom Sector in India From (2007-2011)
- 97
4.3. Trend Analysis plot of Gross total in Growth of Telecom Sector in India From (2007-2011)
- 98
4.4. Trend Analysis plot of Rural Tele Density of Telecom Sector in India From (2007-2011)
- 101
4.5. Trend Analysis plot of Urban Tele Density of Telecom Sector in India From (2007-2011)
- 102
4.6. Trend Analysis plot of Total Tele Density of Telecom Sector in India From (2007-2011)
- 103
4.7. Trend Analysis plot of FDI in Telecom Sector in India From (2007-2011)
- 105
4.8. Trend Analysis plot of Funds Collected as USL in Telecom Sector in India From (2007-2011)
- 106
4.9. Trend Analysis plot of Funds Allocated in Telecom Sector in India From (2007-2011)
- 107
4.10. Trend Analysis plot of Telecom Equipment in Telecom Sector in India From (2007-2011)
- 110
4.11. Trend Analysis plot of Telecom Equipment production in Telecom Sector in India From (2007-2011)
- 111
4.12. Trend Analysis plot of Public Sector Units Telecom network in India From (2007-2011)
- 112
4.13. Trend Analysis plot of Private Sector Units in Telecom network in India From (2007-2011)
- 113
4.14. Trend Analysis plot of Total Telecom Networks in India From (2007-2011)
- 114
4.15. Trend Analysis plot of Fault Rate in New Delhi unit in Telecom Sector in India From (2007-2011).Customer Loyalty
- 115
4.16. Trend Analysis plot of Fault Rate in Mumbai unit in Telecom Sector in India From (2007-2011)
- 116
4.17. Trend Analysis plot of BSNL- Fund Requirement in Telecom Sector in India From (2007-2011)
- 117
4.18. Trend Analysis plot of MTNC- Fund Requirement in Telecom Sector in India From (2007-2011).
- 118
4.19. Shows the AMOS Output with Regression Weights of “Mobile QUAL” Mediated Model
- 122
4.20. Posterior frequency polygon distribution of the Mediating Factor Service Loyalty (SL) and Fringe Benefit Services (FBS) regression weight (W11)
- 125
4.21. Posterior frequency histogram distribution of the Mediating Factor Service Loyalty (SL) and Fringe Benefit Services (FBS) regression weight (W11)
- 126
4.22. Posterior frequency trace plot of the Mediating Factor Service Loyalty (SL) and Fringe Benefit Services (FBS) regression weight (W11
- 127
4.23. Posterior frequency autocorrelation plot of the Mediating Factor Service Loyalty (SL) and Fringe Benefit Services (FBS) regression weight (W11)
- 128
4.24. Two-dimensional surface plot of the marginal posterior distribution of the mediating factor Fringe Benefit Services (FBS) with SQ and SNC
- 129
4.25. Two-dimensional histogram plot of the marginal posterior distribution of the mediating factor Fringe Benefit Services (FBS) with SQ and SNC
- 129
4.26. Two-dimensional contour plot of the marginal posterior distribution of the mediating factor Fringe Benefit Services (FBS) with SQ and SNC
- 130
4.27. Shows AMOS path diagram output for the overall ‘Over All Mediated Mobile QUAL’ Structural Equation Model
- 135
4.28. Posterior frequency polygon distribution of the mediating factor Fringe Benefit Services (FBS) and Service Loyalty (SL) (W49)
- 142
4.29. Posterior frequency histogram distribution of the mediating factor Fringe Benefit Services (FBS) and Service Loyalty (SL) (W49)
- 142
4.30. Posterior trace plot of the Over All Mediated Mobile QUAL s for the mediated factor Fringe Benefit Services (FBS) and Service Loyalty (SL) (W49)
- 143
4.31. Posterior autocorrelation plot of the Over All Mediated Mobile QUAL for the mediated factor Fringe Benefit Services (FBS) and Service Loyalty (SL) (W49)
- 144
4.32. Two-dimensional surface plot of the marginal posterior distribution of the Fringe Benefit Services (FBS) with the Service Loyalty (SL) and Service Quality (SQ) (W49)
- 145
4.33. Two-dimensional histogram plot of the marginal posterior distribution of the Fringe Benefit Services (FBS) with the Service Loyalty (SL) and Service Quality (SQ) (W49).
- 146
4.34. Two-dimensional contour plot of the marginal posterior distribution of the Fringe Benefit Services with the Service Loyalty (SL) and Service Quality (SQ) (W49)
- 146
5.1. Conceptual Model Research Model - 169
5.2. Strategic Planning for The Mobile Service Provider Loyalty - 170
1
CHAPTER – I
INTRODUCTION
1.1. Telecommunication Sector
The development of world class telecommunication infrastructure is the key
to rapid economic growth and to bring social change of the country. The
service quality is a playing a vital role in developing in Indian telecom
sector Indian telecommunication sector has undergone a major process of
transformation through significant policy reforms, particularly beginning
with the announcement of National Telecom Policy( NTP) 1994 and was
subsequently re-emphasized and carried forward under NTP 1999. Driven
by various policy initiatives, the Indian telecom sector witnessed a complete
transformation in the last decade. It has achieved a phenomenal growth
during the last few years and is poised to take a big leap in the future also.
Such rapid growth in the communication sector has become necessary for
further modernization of Indian economy through rapid development
in Information Technology. Indian Telecommunication sector is playing a
vital role in development of economic and social change in rural India.
Nowadays, the rural India depends upon the mobile services for the rural
people communication for their livelihood developments and other
agriculture activities. The service quality is very essential for the
sustainability for telecommunication in India.
2
Mobile phone services are the fast growing services in telecommunication
industry in India. This sector is showing an inspiring growth in last few
years. Land phone market has no competency to compete with mobile phone
market. Land phone market faces some problems such as weak and
inadequate infrastructure, corruption, long procedures, limited income of
consumers etc. But mobile phone service charges in India were high before
2005 because of weak regulatory systems, restricted openness, and
concentrated market orientation. Effective regulation, more openness, and
entrance of competitive firms including launching a new state owned mobile
phone service company foster competition in this sector since 2005. It is
assumed that, currently the number of mobile phone subscriber is more than
46 million and expected it will cross 60 million by 2012.
Telecommunication sector of a country can tremendously affect the society
with different products and services.
1.2. Growth and Development of Indian Telecom Industry
The history of the Indian Telecom sector goes way back to 1851, when
the first operational landlines were laid by The 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. DoT (Department of
Telecommunications) was formed in 1985 when the Department of Posts
3
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.
1.2.1. Telecom Regulatory Authority of India (TRAI)
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
s i t u a t i o n a s t h e sector witness’s 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.
4
1.2.2. 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 or 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 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 Tele printers 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.
5
1.2.3. Private Participation in Telecom
For the provision of basic services, the entire country was divided into 21
telecom circles, excluding Delhi and Mumbai (Singh et. al. 1999). with
telecom markets opened to competition, DoT and MTNL were joined by
private operators but not in all parts of the country. By mid-2001, all
six of the private operators in the basic segment had started operating.
The number of village public telephones issued by private licensees
by 2002.After a recent licensing exercise in 02, there competition in
most service areas. However, the market is still dominated by the
i n c u m b e n t . In December 2002, the private sector provided
approximately 10 million telephones in fixed, WLL (Wireless Local
Loop) and cellular lines compared to 0.88 million cellular lines in March
1998 DoT Annual Report, (2002). 72 per cent of the total private
investment in telecom has been in cellular mobile services followed
by 22 percent in basic services. After the recent changes, the stage is
now set for greater competition in most service areas for cellular
mobile over time; the rise in coverage of cellular mobile will imply
increased competition even for the basic service market because of
competition among basic and cellular mobile services.
6
1.2.4. Tele density and Village Public Phones (VPTS)
India's rapid population increase coupled with its progress in telecom
provision has landed India's telephone network in the sixth position in the
world and second in Asia (ITU). The much publicized statistic
about telecom development in India is that in the last five years, the
lines added for basic services is 1.5 times those added in the last five
decades! The annual growth rate for basic services has been 22 percent
and over 100 percent for internet and cellular services. As Dossani
(2002) argues, the comparison of teledensity of India with other regions
of the world should be made keeping in mind the affordability issues.
Assuming households have a per capita income of $350 and are willing
to spend 7 percent of that total income on communications, then only
about 1.6 percent of households will be able to afford $30 (for a $1000
investment per line). Teledensity has risen to 4.9 phones per 100 persons
in India compared to the average 7.3 mainlines per 100 people around the
world. The government has made efforts to connect villages through
village public telephones (VPT) and Direct Exchange Lines (DEL).
This coverage increased from 4.6 lakhs in March 2002 to 5.10 lakhs
in December 2002 for VPT and from 90.1 lakhs in March to 106.6 lakhs
in December 2002 for DELs. BSNL has been mainly responsible for
providing VPTs; more than 84 percent of the villages were connected by
7
503610 VPTs with private sector also providing 7123 VPTs. The overall
telecom growth rate is likely to be high for some years, given the increase
in demand as income levels rise and as the share of services in overall
GDP increases. The growth rate will be even higher due to the price
decrease resulting from a reduction in cost of providing telecom services.
A noteworthy feature of the growth rate is the rapid rate at which
the subscriber base for cellular mobile has increased in the last few years
of the 1990s, which is not surprising in view of the relatively lower
subscriber base for cellular mobile.
1.2.5. Foreign Participation
India has opened its telecom sector to foreign investors up to 100 percent
holding in manufacturing of telecom equipment, internet services, and
infrastructure providers (e-mail and voice mail), 74 percent in radio-
paging services, internet (international gateways) and 49 percent in
national long distance, basic telephone, cellular mobile, and other value
added services (FICCI, 2003). Since 1991, foreign direct investment
(FDI) in the telecom sector is second only to power and oil - 858 FDI
proposals were received during 1991-2002 totaling Rs. 56,279 crores
(DoT Annual Report, 2002). Foreign investors have been active
participants in telecom reforms even though there was some
frustration due to initial dithering by the government. Until now, most of
8
the FDI has come in the cellular mobile sector partly due to the fact that
there have been more cellular mobile operators than fixed service
operators. For instance, during the period 1991-2001, about 44 percent of
the FDI was in cellular mobile and about 8 percent in basic service
segment. This total FDI includes the categories of manufacturing and
consultancy and holding companies.
1.2.6. Tariff-Setting
An essential ingredient of the transition from a protected market to
competition is the alignment of tariffs to cost-recovery prices. In basic
telecom for example, pricing of the kind that prevailed in I n d i a
p r i o r t o the reforms, led to a high degree of cross-subsidization and
introduced inefficient decision-making by both consumers and service-
providers. Traditionally, DoT tariffs cross-subsidized the costs of access
(as reflected by rentals) with domestic and international long distance
usage charges (Singh et. al. 1999). Therefore, re-balancing of tariffs
- reducing tariffs that are above costs and increasing those below costs -
was an essential pre- condition to promoting competition among g
different service providers and efficiency in general. TRAI issued its first
directive regarding tariff-setting following NTP 99 aimed at re-balancing
tariffs and to user in an era of competitive service provision.
Subsequently, it conducted periodic reviews and made changes in the
9
tariff levels, if necessary. Re-balancing led to a reduction in cross-
subsidization in the fixed service sector. Cost based pricing, a major
departure from the pre-reform scenario, also provides a basis for
making subsidies more transparent and better targeted to specific
social objectives.
1.3. Service Quality in Indian Telecom Sector
One of the main reasons for encouraging private participation in the
provision of infrastructure rests on its ability to provide superior quality
of service. In India, as in many developing countries, low teledensity
resulted in great emphasis being laid on rapid expansion often at the
cost of quality of service. One of the benefits expected from the private
sector's entry into telecom is an improvement in the quality of
service to international standards. Armed with financial and technical
resources, and greater incentive to make profits, private operators are
expected to provide consumers value for their money. Telephone faults
per 100 main lines came down to 10.32 and 19.14 in Mumbai and Delhi
respectively in 2002-03 compared to 11.72 and 26.6 in 1997-98. Quality
of service was identified as an important reform agenda and TRAI has
devised QOS (Quality of Service) norms that are applicable across the
board to all operators (Singh et. al. 99).
10
1.4. Performance of Indian Telecom Sector Post Liberalized Period
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 privatization 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
11
National Long Distance (NLD) and International Long Distance (ILD) at the
start of the current decade.
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 sect oral 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 those 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
12
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.
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
13
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 Ruche 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
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
14
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 per cent.
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 frantic 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, cell phones, 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.
15
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.
According 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.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.
16
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.
According to Rohit Prasad & V.Sridhar (2007) this is one of the first such
attempts to analyse the tradeoffs between low market power and economics
of scale for sustained growth of mobile services in the country. Our analysis
of the data on mobile services in India indicates the existence of economies
of scale in this sector. We also calculate the upper bound on the optimal
number of operators in each license service area so that policies that make
appropriate tradeoffs between competition and efficiency can be formulated.
Narinder K Chhiber ( 2008) the mobile telecommunication technology is
evolving rapidly in the world as more people demand mobile services with
longer bandwidth and new innovative services like connectivity anywhere,
anytime for feature like T.V., Multimedia, Interoperability and seamless
connectivity with all types of protocols and standards, while the 3G services
are yet to fully come up.
17
1.5. Background for the Study
Within the last two decades, service quality has become a main concern in
the business world especially in services sector. The key to success in
winning the global battle now and in future is to have high standards of
service. Hence, it is helpful for service organizations to know the customer
service quality perceptions in order to overcome the competitors and attract
and retain the customers. Because of the globalization and liberalization of
Indian economy, Indian service sector has been opened for Multinational
companies. In order to overcome the competition and to retain the world
class service standards, Indian companies have been forced to adopt quality
management programs.
Services are defined as: the activities, which are involved in producing
intangible products as education, entertainment, food and lodging,
transportation, insurance, trade, government, financial, real estate, medical,
consultancy, repair and maintenance like occupation.
Quality has become a strategic tool in obtaining efficiency in operations and
improved performance in business. This is true for both the goods and
services sectors. Quality has been defined differently by various authors.
Some prominent definitions include ‘conformance to requirements’ (Crosby,
1990), ‘fitness for use’ or ‘one that satisfies the customer’. According to
production philosophy of Japan, quality has been defined as ‘zero defects’ in
18
the firm’s offerings. Quality has become a strategic tool for obtaining
efficiency in operations and improved business performance (Babakus and
Boller, 1992).
This is true for the services sector too. Several authors have discussed the
unique importance of quality to service firms and have demonstrated its
positive relationship with profits, increased market share, return on
investment, customer satisfaction, and future purchase intentions (Rust and
Oliver, 1994). One obvious conclusion of these studies is that firms with
superior quality products outperform those marketing inferior quality
products.
In services marketing literature, service quality has been concisely defined
as the overall assessment of a service by the customers. Service quality is
playing an increasingly important role in the present environment where
there is no further scope for the companies to differentiate themselves other
than the quality of the service provided by them. Delivering superior service
quality than the competitors is the key for the success of any organization.
But, the companies face difficulties in measuring the quality of services
offered to the customers.
Because unlike measuring the quality of goods, the measurement of the
quality of services offered by the companies is difficult due to the three
unique features of services viz. intangibility, heterogeneity, and
19
inseparability. Hence the only way of measuring the quality of services
offered by the service provider is the measurement of the customers’
perceptions of the quality of service they are experiencing from their service
providers.
Though initial efforts in defining and measuring service quality emanated
largely from the goods sector, a solid foundation for research work in the
area was laid down in the mid-eighties by Parasuraman, Zeithaml and Berry,
(1985). They were amongst the earliest researchers to emphatically point out
that the concept of quality prevalent in the goods sector is not extendable to
the services sector. Being inherently and essentially intangible,
heterogeneous, perishable and entailing simultaneity and inseparability of
production and consumption, services require a distinct framework for
quality explication and measurement.
As against the goods sector where tangible cues exist to enable consumers to
evaluate product quality, quality in the service context is explicated in terms
of parameters that largely come under the domain of ‘experience’ and
‘credence’ properties and are as such difficult to measure and evaluate
(Parasuraman, Zeithaml and Berry, 1985). One major contribution of
Parasuraman, Zeithaml and Berry (1988) was to provide a concise definition
of service quality. According to these authors, service quality means relating
the superiority of the service with the global judgement of a person about it
20
and explicated it as involving evaluations of the outcome (i.e., what the
customer actually receives from service) and process of service act (i.e., the
manner in which service is delivered).
In line with the propositions put forward by Gronroos (1984) and
Parasuraman, Zeithaml and Berry (1985, 1988) posited and operationalized
service quality as a difference between consumer expectations of ‘what they
want’ and their perceptions of ‘what they get.’ Based on this
conceptualization and operationalization, they proposed a service quality
measurement scale called ‘SERVQUAL’. Nerurkar (2000) analyzed the
SERVQUAL (a service quality measurement scale developed by
Parasuraman, Zeithaml, and Berry, 1985) dimensions in India and concluded
that service quality should form the basis for all customer retention
strategies.
With a large population, low telephone penetration levels, a considerable
rise in consumers’ income, and spending owing to strong economic growth,
India has emerged as an attractive business market in the world. In case of
India, the mobile telecommunication industry turned highly competitive
since the government deregulated this sector. This decision of regulation
opened the doors for private and foreign players to operate in the Indian
market. The growth of operators in the Indian market has accelerated rapidly
from one operator in public sector to fifteen operators in all over India.
21
Consequently, the competition among these telecommunication players in
India in obtaining and maintaining customers remains critical in spite of the
fact that the customers have been very selective now in determining their
choices based on the costs paid to receive the services and benefits obtained.
In order to attract new customers and to retain the existing customers,
mobile telecommunication service providers in Indian market are employing
a variety of ways such as providing customers with excellent services,
modern looking equipments, courteous, skilful, well trained personnel, and
supportive operative systems. Service providers expect that with excellent
service, customers will be satisfied and if satisfied, they will become loyal
customers for the organization.
The significant growth of service providers in the field of mobile
telecommunication sector has caused the appearance of buyer’s market.
Buyer’s market is that type of market, where supply exceeds demand. In this
situation of buyer’s market, the customers get more bargaining power.
Therefore in this situation, the service providers have to be very effective
and efficient in their operations because customers now have choices in
determining the service provider they want. In the context of customers, the
need for excellent services always keeps on changing. With the passage of
time, the level of service quality also varies.
22
There is no guarantee that what is excellent service quality today is also
applicable for tomorrow or day after tomorrow. Besides this, in the last two
decades the use of technology in the delivery of services has also changed
significantly. The use of latest world class innovative technology in terms of
various value added services has also increased the war among service
providers. To win the battle of global competition in the service industries
and to be able to exist, these service providers will need to bring into play
new contemporary strategies in providing service that will satisfy the
continuous demanding customers. Because of this reason services marketing
and telecommunication marketing gaining prominence in marketing
literature (Kotler, 2001).
The interest in services marketing research on service quality and customer
satisfaction has grown tremendously. A good number of researches have
been conducted by applying related theories and methods in the service
industry. SERVQUAL and SERVPERF (an unweighted performance only
measure of service quality developed by Cronin and Taylor, 1992)
frameworks have been tested by various researchers in different service
setups to get reliability and validity, and also to suggest the superiority of
one scale over other. Many researchers from all over the world tried to
develop different scales to measure service quality and customer satisfaction
in different service environments.
23
Still there are continuing demands for refining the existing theories that are
suitable for multifaceted service setup. One way for refining the theories is
to consider variables within the existing model which are potentially
powerful in making prediction about the dependent variable. As a stepping
stone to this notion of refining the theories, Cronin, Brady, and Hult (2000)
conducted an empirical study to assess the effects of service quality, value,
and customer satisfaction on behavioural intentions in the context of
different service industries. They suggested in their findings that there is
need to include additional decision-making variables like tangibility aspect
of service quality, customers’ expectations and quality of service
environment. Also, suggested replication of similar study in another service
setting.
Caruana (2002) attempted to examine the model in which service quality is
linked to service loyalty via customer satisfaction. After examining this
model, he suggested the need to consider the role of customer value and
reputation of the company in predicting loyalty. The present study will try to
address the doubts raised by the researchers like Cronin, Brady, and Hult
(2000), Caruana (2002) etc.
The telecommunications sector in India was liberalized in the early 1990s.
Attack of private as well as foreign direct investment in the sector started
afterwards. With taut margins and ephemeral customer loyalty, the mobile
24
phone service providers are now operating in a highly competitive
environment. Profitability of the service providers is being curbed by factors
like; revenue leakage, customer churn, and ineffective customer service. The
Indian mobile telecommunication services operators are facing a number of
significant challenges, because of changing dynamics:
First, retaining existing customers mainly in a pre-paid and high
churn market has become more difficult and costly.
Second, new customer acquisition is becoming more elusive than ever
as potential customers have more options to choose from and mobile
phone operators offer attractive deals to lure prospect customers.
Third, as mobile phone operators have had to incur additional cost in
keeping existing customers and acquiring new ones, their
AverageRevenue Per User (ARPU) has declined, leading to
worsening of their financial performance.
In light of above mentioned challenges, mobile telecommunication services
providers need to make customer satisfaction a strategic priority. Moreover,
satisfied customers have a higher propensity to stay with their existing
service provider than the less satisfied ones (Cronin et al., 2000) and are
more likely to recommend the service provider to others, leading to
improved bottom line for the company. Thus, it is very important that Indian
mobile telecommunication services operators gain a better understanding of
25
the relationship between the performance of service quality attributes,
customer value, satisfaction, and loyalty.
1.6. Statement of the Problem
In the last ten years, the mobile revolution has truly change the socio
economic landscape of India and played a pivotal role in the growth and
development of economy. According to cellular operator Association of
India (COAI) states that India ranks between the top ten telecommunication
in the world and second largest in Asia. India is also one of the fastest
growing markets in mobile communications. India is home to a number of
Global mobile operators’ working with local companies and mobile market
has consistently experienced very high annual growth rates.
The telecommunication sector, especially the mobile phone sector, in India
is one of the fastest growing business segments of the country which provide
a lot of value additional to the society with its service and creation of
employment opportunities. At present there are fifteen mobile phone
operators in the country – Bharati Airtel Limited (bharti) , Reliance
Communications Limited(Reliance), Vodafone Essar Limited (Vodafone),
Bharat Sanchar Nigam Limited(BSNL)-Govt of India owned public sector
company, Tata Teleservices Limited (TATA), Idea Cellular Limited
(IDEA), Aircel Limited (Aircel), unitech wireless Limited, Mahanagar
telephone Nigam Limited (MTNL) etc., All of them compete with each
26
other to grab customers by providing wide range of services. They not only
offer basic services of cell phone but also produce other value added
services. Along with the normal services all of the operators are now offer
internet facilities (Technology Adoption) which enable the subscribers to
reach the whole world through internet easily and their services includes
prepaid, post paid, internet, value added services roaming and devices. The
hasty growth and development in information technology and mobile
devices has made the Indian mobile phone service markets more and more
competitive. It is assumed by all mobile service providers that value added
services increase the customer loyalty. But does value added services fulfill
all the customer needs and it is the only factor that plays a significant role in
maintaining and building up the loyalty of the customer. On the other hand
according to Lee et al (2001) the mobile providers should build up customer
commitment by providing good quality service to their customer.
1.7. Research Objectives
1) To examine performance of Indian Telecom Industry post liberalised
period
2) To find out the relationship between the dimensions of service loyalty
on mobile service providers in Cauvery Delta Districts in Tamil
Nadu.
3) To identify the meditated effects on service loyalty on mobile phone
service providers Cauvery Delta Districts in Tamil Nadu.
27
4) To suggest suitable strategic model for improving service loyalty on
mobile service providers Cauvery Delta Districts in Tamil Nadu.
1.8. Research Questions
The following research questions are quite relevant to the crucial purpose of
the study and seeking to understand the mediating effects of Service Loyalty
(Customer Loyalty) in mobile service providers in Cauvery Delta Districts
in Tamil Nadu.
1) What are the various factors/service dimensions affecting Service
Loyalty (Customer Loyalty) in mobile service providers in Cauvery
Delta Districts in Tamil Nadu?
2) What is the mediating factor (service dimension) for Service Loyalty
(Customer Loyalty) in mobile service providers in Cauvery Delta
Districts in Tamil Nadu?
3) What are all the relationship between the Customer Satisfaction and
Service Loyalty (Customer Loyalty)?
4) What are all the most influential factor(s) for Customer Satisfaction?
1.9. Proposed Conceptualized Research Model
There are 7 dimensions were framed for this study. Those are; i) Service
Network Communication, ii) Technology Adoption, iii) Customer Care
Services, iv) Service Quality, v) Brand Switching Attitude & MNP , vi)
28
Fringe Benefit Services, and vii) Service Loyalty. Here Demographic
variables, Service Network Communication, Technology Adoption,
Customer Care Services, Service Quality, Brand Switching Attitude &
MNP, are independent variables and Fringe Benefit Services and Service
loyalty are the dependent variable. It is studied that how and what extent the
independent variables make changes in the dependent variable. The
proposed conceptual research model shows the process of research as
follows:
Fig: 1.1: Conceptual Model for studying Service loyalty in Mobile Service Providers
Technology Adoption
Customer Care Services
Service Quality
Brand Switching Attitude & MNP
Demographic Variable Fringe Benefit
Services
Service Loyalty
Service Network Communication
29
1.10. Significance of the Study
The proposed empirical research is an attempt to study about the various
service quality (Customer Satisfaction) dimensions and the service loyalty
of mobile service providers. And on the other side, finding out the mediating
factor for the service loyalty in mobile service providers. The present
research pays its attention to identify the dimensions of Service Loyalty
(Customer Loyalty) that ensures maximum satisfaction for the customers in
the mobile service providers. The Customer Satisfaction is the ultimate
determinant of Customer Loyalty (CL) and it decides the motivated loyal
customers for mobile service providers.
1.11. Limitation of Study
1) This study restricts in to the Cauvery Delta Districts (Thanjavur,
Thiruvarur and Nagappattinam) in Tamilnadu.
2) This study is considered in to the social and economical life style of
the beneficiaries only.
1.12. Structure of the Thesis
The study is structured into five chapters organized to present the study
utilizing methodology that allows it to flow from a basic introduction to
empirical findings.
30
Chapter I: This chapter deals with a general introduction and background
of the study about global, national and regional trends in Healthcare
Services. Besides the above, this chapter gives a brief account of the
institutional factors, significance of the study, statement of problem of the
study, limitations of the present study and finally outlines of the structure of
the study.
Chapter II: Reviews literature with respect to the Service Loyalty, mobile
service provider’s quality and the Customers’ Satisfaction. Presents various
important factors affecting the performance contained in works of several
researchers, identifies the gap in past research, the previous empirical
findings and thoroughly examines the models developed to analyse.
Chapter III: Presents a detailed discussion of research design, the research
hypotheses to be tested and the methodology used to test the critical factors
affecting performances and its hypotheses present a simple conceptual
model for testing the critical dimensions.
Chapter IV: Summarizes the outcomes of the statistical and econometrical
analysis that are used to test the hypotheses.
Chapter V: Identifies the findings of the study pertaining to the hypotheses,
the implications for the sector as a whole and individually, drawn from the
findings of the research, recommendations for future research and
conclusions of the study.
31
1.13. Conclusion
This chapter examined mobile service providers after independence in India.
The Research problem is discussed with the objectives for the study and the
variables associated with conceptual model, significance of the study are
clearly defined. The next chapter the researcher will discuss the review of
literature about service quality and service loyalty.
32
CHAPTER – II
LITERATURE REVIEW
2.1. Introduction
Review of literature is a systematic survey on the facts and figures of
previous researches on a particular topic. It is a collection of major findings
of past researches on a particular topic. It is useful to understand what has
happened in the topic during the past period. In every research, there are
certain preliminary works and the review of literature is one of them. A
detailed literature on service loyalty on mobile service providers and other
related issues is given below.
2.2. Studies Related on Growth and Development of Telecom Industry in Global and India
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.
33
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 indigenization program in the
switching segment carried out by C-DOT was successful in the introduction
of rural exchanges designed especially 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 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 technological y 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
34
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. 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.
35
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.
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 up gradation
programmes.
36
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.
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 orders in which
structural adjustments take place determine the effectiveness.
Donaldson (1994), 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
37
the twin challenges of spurring internal growth and competing in global
economy.
According to Stephen Y. Walters (2003) the telecommunications industry is
being rocked by change fuelled 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.
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 take by
38
government in lien of liberalization, privatization and de-monopolization
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.
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.
39
OECD (2007) by increasing competition uptake can be mainly realized by
then following incentives ; (1) bundling of services, such as offering
telephone line plus broadband access to internet ADSL at significantly
reduced price, introducing triple play services on the subscriber line and
promoting digital T.V. as a revenue source for the fixed line operator. These
would however depend on the distance of the subscriber line from the local
exchange and the quality of the copper line. Reducing cost for the second
line would also be effective. This would lead to reduce prices for the
consumer and reduce churn. (2) Increasing competition between broadband
service providers. (3) Reducing the monthly rates of increased speed internet
access using ADSL. (4) increasing awareness of the benefits of ADSL to
the society.(5) increasing the local content on the internet so to attract more
users in attempt to find killer application that would attract user to
indispensable ADSL experience.(6) adopting convergence between wireless
or mobile and fixed services.
2.3. Studies Related Customer Relationships in Telecom Industry
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.
40
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 in touch after the sale is made.
(Vavra1992). A more popular approach with recent application of
information technology is to focus on individual or one-to-one relationship
with customers that integrate database knowledge with a long-term customer
retention and growth strategy.
(Peppers and Rogers, 1993), 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).
41
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,
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 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 enter’ of the firm and marketing should be
devoted to building and enhancing such relationships.
Morgan and Hunt (1994), draw upon the distinction made between
transactional exchanges and relational exchanges by Dwyer,Schurr, and Oh
42
(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. F. Robert Dwyer, Paul H.
Schurr and Sej 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 behavior 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
43
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 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) 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
44
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
45
contracts and master purchasing agreements, forcing major vendors to
develop key account management programs Similarly, in the current era of
hyper-competition, marketers are forced to be more concerned with
customer retention and loyalty (Dick and Basu, 1994). 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
(Hayeset al., 1998; Spekman, 1988).
In addition, several marketers are also concerned with keeping customers for
life, rather than making a onetime 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. Fuelled 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).
46
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 cool
aborative 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.
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 wel 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.
Kalavani (2006) in their study analyzed that majority of the respondents
have given favourable opinion towards the services but some problems
47
exist that deserve the attention of the service providers. They need to bridge
the gap between the services promised and services offered. The overall
customers’ attitude towards cell phone services is that they are satisfied with
the existing services but still they want more services to be provided.
Seth et al (2008), in their study titled “Managing the Customer Perceived
Service Quality for Cellular Mobile Telephone: an Empirical Investigation”
analyzed that there is relative importance of service quality attributes and
showed that responsiveness is the most importance dimension followed by
reliability, customer perceived network quality, assurance, convenience,
empathy and tangibles. This would enable the service providers to focus
their resources in the areas of importance. The research resulted in the
development of a reliable and valid instrument for assessing customer
perceived service quality for cellular mobile services.
Kalpana and Chinnadurai (2006) in their study titled “Promotional
Strategies of Cellular Services: A Customer Perspective” analyzed that the
increasing competition and changing taste and preferences of the customer’s
all over the world are forcing companies to change their targeting strategies.
The study revealed the customer attitude and their satisfaction towards the
cellular services in Coimbatore city.
Rick (2008): in his study found that companies with sound customer
strategies can use that ultimate loyalty program as a differentiator in an
48
increasingly muddled market. In an increasingly competitive market,
customer loyalty efforts can play a major part in the attraction of new
customers and the retention of current ones. As consumers' choices expand,
the importance of a sound customer relationship strategy becomes more and
more important for the success of the company.
Shikha Ojha (2009) conducted a study on “Consumer Awareness of VAS of
Telecom Sector of India”. She analyzed the contribution of the mobile
phone services not only at the national or state level, but also its
involvement in an individual's life. She found out that the less number of
users are aware of all the VAS provided by the service providers and thus
the companies should focus on the awareness campaign.
Shirshendu Ganguli (2008) conducted a study on “Drivers of Customer
Satisfaction in Indian Cellular services Market “in which he discussed the
impact of service quality and features on customer satisfaction from the
cellular users viewpoint.
2.4. Service Quality of Mobile Phone Service Provider
Government of India –Department of Telecommunication’s data shows that
both BSNL and MSNL are losing market share to private operators in the
mobile telephony segment. Investigators have also found customers,
satisfaction from a multidimensional nature and view overall satisfaction as
a function of satisfaction with multiple experiences with the service
49
provider. In general satisfaction ion is developed on the information from all
prior experiences with the service supplier and is consider as a function of
all prior transaction and information (parasuraman et al; 2000).
Nowadays cellular mobile is a very necessary product for our daily
communication. Customer are mainly purchase this product for instant
communication and various service provided by the companies services
mainly depend on some factors and customers are always try to buy that
product which has many factors or attributes fulfilling their desire, Recently
the concept of customer satisfaction has received much attention .In cellular
mobile market ,customers bring higher expectations for communication
from its service providers and if companies are not able to meet this
expectations.
The general definition of quality according to the American Society for
Quality is "A subjective term for which each person has his or her own
definition. In technical usage, quality can have two meanings, (a) The
characteristics of a product or service that bear on its ability to satisfy stated
or implied needs and (b) A product or service free of deficiencies."
Services are defined as "Social act(s) which take place in direct contact
between the customer and representatives of the service company". Service
quality is more difficult to measure objectively than product quality because
service characteristics include intangibility, heterogeneity, and inseparability
50
of the production and consumption of services. These characteristics render
service quality a more abstract and elusive construct than product quality.
Quality is recognized as a multidimensional construct. (1) Performance, (2)
Features, (3) Reliability, (4) Conformance, (5) Durability, (6) Serviceability,
(7) Aesthetics and (8) Perceived Quality, trace the development of the
dimensionality of Quality Garvin' 1987 developed a list of 8 dimensions of
product quality. Garvin suggest that these dimensions are applicable to both
products and services. However, difficulties arise when one tries to
operationalize these dimensions in the service sector because service
characteristics differ from product characteristics.
In dynamic business environment, the role of customer is changing
(Prahalad & Ramaswamy, 2000). The changing paradigm of business has
made the provision of quality of services as top priority for organizations.
Customer-focused strategy has become a means of competitive advantage
and survival for organizations (Taylor & Baker, 1994). Perceived service
quality and its measurement has become essential focus for the organization
in designing and implementing a customer oriented strategy (MacStravic,
1977). Reichheld and Sasser (1990) concluded that customer satisfaction is
vital in attracting new customer and retaining the existing customers.
Researchers have emphasized distinct conceptualizations of quality
(Holbrook, 1994). In operation management, reliability and fitness of use
51
define quality; whereas in marketing and economics, attributes of products
constitute quality. In services, quality is concerned with the overall
assessment of the services (Parasuraman et al., 1988). Garvin (1988)
identified performance, features, conformance, reliability, durability,
serviceability, aesthetics, and customer perception of quality based on
service provider’s image.
Measuring service quality enables organization to know its position in the
market and provides a strategic advantage to enhance its competitiveness.
Measurement of service quality presents areas of strengths and weaknesses
that offer opportunities to the organizations to initiate appropriate response
to focus and improve salient attributes of customer perceived service
quality. Through formal surveys of customers in different industries and
focus group, Parasuraman et al., (1988) developed a list of characteristics
that define quality in general. They combined these attributes into five major
dimensions of service quality, namely; tangible, assurance, responsiveness,
empathy, and responsiveness. These authors subsequently tested these
dimensions through SERVQUAL; a 22-items scale measuring customers’
expectations and perception on five dimensions to evaluate service quality.
Berry et al., (1994) argued that SERVQUAL is an effective tool to steer
organization in its pursuits of quality improvement by focusing on those
areas that significantly contributes toward improvement.
52
Objective measurement of service quality is difficult because of unique
characteristics of services (Zhao et al., 2002). Researchers have used
different instruments to measure service quality. The most widely used
instrument is SERVQUAL scale. This instrument has been used in different
industries and cultures. Researchers have found this instrument valid and
reliable in numerous studies (Babakus & Boller, 1992; Brown & Swartz,
1989; Cronin & Taylor, 1992, 1994).Some of these studies did not support
the five factor structure of the instrument. Some researchers have criticized
the instrument because of “its use of gap scores, negative wording used,
measurement of expectations, positively and negatively worded items, the
generalizability of its dimensions, and the defining of a baseline standard for
good quality (Lai et al., 2007) SERVQUAL primarily focuses on gap-based
scale to measure services quality; whereas Cronin and Taylor (1992, 1994)
emphasized to use performance only index (SERVPERF). The SERVPERF
measure has found strong support in the other studies (Babakus & Mangold,
1992; Teas, 1993; Brown et al., 1993). The researchers have argued that
cultural difference is an important aspect that affects the customers’
expectations of service quality (Donthu & Yoo, 1998; Kettinger et al., 1994;
Mattila, 1999); hence the relevancy of SERVQUAL in different cultures is
also an issue. To improve reliability and validity of SERVQUAL, some
researchers have merged expectations and perceptions into a single measure
and tested it with excellent results (Babakus & Boller, 1992; Andaleeb &
53
Basu, 1994; Dabholkar et al., 2000). Dabholkar et al., (2000) and Wang et
al., (2000) proposed factors associated with service quality (e.g. tangible,
reliability, assurance, responsiveness and empathy) and have described as
antecedents of customers’ perceived service quality and validated and tested
these factors.
SERVQUAL has been widely used in telecommunication industries in
different cultural context with high reliability and validity (Hoffman &
Bateson, 2001; Tyran & Ross, 2006; Stafford et al., 1998; Sureschander et
al., 2002). In a study of mobile telecommunication in South Africa, Van der
Wal et al., (2002) used SERVQUAL with some modifications. The modified
instrument resulted scale reliability of 0.95. In their study of service quality
in telecommunication services, Ward and Mullee (1997) used reliability,
availability, security, assurance, simplicity, and flexibility as criteria of
service quality. They argued that, from customers’ perspective, it is not
appropriate to separate network quality from the other dimensions of
quality.
Numerous studies have investigated the perspective of mobile phone users
with regard to the quality aspects. These have been discussed in succeeding
paragraphs. These studies provide insight to the quality dimensions that
mobile phone operators need to consider remaining competitive in changing
environment.
54
Global System for Mobile Communication (GSM) Association identified a
list of indicators for mobile phone quality of services. These indicators
included network access; service access, service integrity, and service retain
ability (Sutherland, 2007, p. 20).
J.D. Power and Associates Survey (2009) studied the mobile phone users’
satisfaction in the United Kingdom. The study used a sample of 3325 mobile
phone customers throughout United Kingdom. Important dimensions of
service quality included in the survey were coverage, call quality,
promotions and offerings of incentives and rewards, prices of service,
billing, customer, bundled services. The study showed rising customer
expectations with regard to the additional features and services from the
mobile operators.
Based on the survey of 22052 users of wireless phone in the United States in
2008, the Wireless Phone Users’ Satisfaction Index of United States of
America indicated that important dimensions of service quality included
customer satisfaction, billing, brand image; call quality, cost of service and
options for service plans (Customer Satisfaction Index, 2009).
A qualitative (focus groups) and quantitative (consumer surveys) research
study about consumer satisfaction was undertaken by Australian
Communications and Media Authority, ACMA (2008). The study reported
55
highest levels of dissatisfaction with mobile phone services (35 per cent),
citing problems such as drop-outs, poor call quality and interference.
Accenture (2008) carried out survey of 4189 consumers in Australia, Brazil,
Canada, China, France, Germany, India, United States, and United
Kingdom. More than 67% respondents confirmed poor customer services as
the core reason for leaving the operators. The survey also found the rising
expectations of customers in mature and growing markets.
In 2008, Telecom Regulatory Authority India carried out quality of service
survey of mobile operators based on users’ satisfaction. The sample
consisted of 1318 mobile phone users. The important dimensions of
regulatory services benchmark dimensions of service quality included
billing, customer care, availability of network, value-added services and pre-
sales and sales dimensions. Out of 11 operators, only five operators
achieved the 90% service quality benchmark (Survey, 2008).
Souki and Filho (2008) carried out a study based on 434 customers in Brazil.
The study focused on satisfaction of mobile phone users. The results of the
study indicated high rating of customers’ services, quality of connections,
ambience of outlets, and the coverage provided. A study of 10 regions in
Japan measured the customer satisfaction among 7500 individual mobile
telephone service users. The important dimensions of service quality of
mobile service providers included handset, price, quality of call, coverage of
56
area, non-voice functions and services, and customer contact strength in that
order of priority (Mobile Phone Survey, 2004).
Barnhoorn (2006) carried out a study in 2008 in South Africa indicated the
ever increasing expectations of customers with regard to the services of
mobile phone operators. The salient dimensions of quality of service
accorded priority by mobile phone users included courteous and facilitating
role of front-line personnel, ease of availability for cards and recharge
services, availability of products and services at the company outlets,
accurate information and facts about services, affordable prices of the
packages, and customized services.
A study by Sukumar (2007), using a sample of 104 mobile phone
subscribers, measured the mobile phone users’ preferences for selection of
an operator. The result of the study found important dimensions as brand
image, customer care, services availability, credit facility for connection,
deposit amount, and prices in that order of priority.
In Canada, the consumers’ satisfaction survey in 2007 based on the
responses of 6000 mobile phone users indicated the essential elements of
service quality of mobile operators as quality of calls, prices, billing,
customers’ services, and diversity of bundled options of services (Customer
Satisfaction, 2007).
57
A study was undertaken in 2007 on Consumer Satisfaction in
Telecommunication markets in the Organization of Economic Cooperation
and Development (OECD) countries by the Directorate for Science,
Technology, and Industry (DSTI) Committee on Consumer Policy. The
study found imperfect information on quality and price, lack of transparency
in roaming charges for international in service and contractual binding in
changing the operators affect consumer behaviour. The study focused on
mobile phone users and identified and found that quality of service and price
were two major factors for switching over to new operators. The study
further highlighted that major factors affecting mobile phone users’
dissatisfaction included lack of differentiation in United Kingdom, prices
and quality of services in Portugal, early termination fee and unsolicited
calls and inaccurate billing in United States, and lack of meeting and
exceeding customer’s satisfaction in Australia (DSTI, 2007).
A study of mobile phone customers satisfaction about quality dimensions
was undertaken in 2006 in Finland and other Scandinavian (Denmark,
Sweden) and Baltic (Lithuania and Latvia) countries. The important drivers
of customers’ perception of quality emerged product and service in
Scandinavian and Baltic countries. The results found that the significant
aspects of quality of service included attributes of service, image of the
operators, and value-added services. Pricing of the services emerged as the
most important dimension of quality (ESPI, 2006).
58
Sigala (2006) noted, in a study of mobile phone users in Greece that
customization of service, pleasing interaction of staff and customers,
company’s image and differentiated features were the important dimensions
of service quality of mobile phone users. In Turkey, a study was undertaken
to determine the National Customer Satisfaction Index of mobile phone
users based on a sample of 1950 mobile phone subscribers. The dimensions
that emerged in customer satisfaction included meeting customers’ pre-
purchase expectations, perceived quality (coverage, responsiveness to
customers complaints, value-added services, promotional activities and their
fulfillment), and complaint handling (Ozer & Aydin, 2005) Consumer
Surveys (Cap Gemini, 2005; McKinsey Quarterly, 2004; Consumer Reports,
2005) found that network quality based on data services and voice services
strongly influence customer satisfaction and loyalty with regard to the use of
mobile phone.
Muhammad Asif Khan (2010) had adapted SERVQUAL with additional
dimensions that were found to be a valid instrument to measure service
quality in mobile phone services. The dimensions of tangible, assurance,
responsiveness, empathy, convenience, and network quality found to
have positive and statistically significant relationship with mobile
phone users’ perceived service quality. Convenience and network quality
dimensions found to be relatively most important dimensions affecting
59
users’ perception. The dimension of reliability did not reflect significant
effect on customers’ perception of quality.
2.5. Service Loyalty
Loyalty is a deeply held commitment to rebuy or repatronize a preferred
product/service consistently in the future, thereby causing repetitive same-
brand or same brand-set purchasing, despite situational influences and
marketing efforts having the potential to cause switching behaviour (Oliver,
1999). There has been considerable debate over the differences and
similarities between customer satisfaction and service quality (Iacobucci et
al. 1995; Johnston, 1995; Oh and Parks, 1997), with the concepts having
been treated as interchangeable by some service researchers (Iacobucci et al.
1995; Oh and Parks, 1997).This perceived confusion reflects service quality
reflecting functional, rather than technical quality, and, as such, being closer
to satisfaction (Caruana, 2002).There is, however, general consensus in the
literature that service quality and customer satisfaction are different
constructs (Cronin and Taylor, 1992; Iacobucci et al. 1995; Oh, 1999; Oh
and Parks, 1997), but that a positive correlation exists between them (Buttle,
1996; Cronin and Taylor, 1992; Oh and Parks, 1997; Parasuraman et al.
1985; 1988; Selnes, 1993). The debate over the constructs has, to a large
extent, revolved around sequential, definitional and measurement issues.
60
The sequential aspect essentially relates to superiority, the question being
whether customer satisfaction with a service encounter is antecedent to
perceived service quality, or does perceived service quality contribute to
customer satisfaction. Although early service quality researchers defined
satisfaction as an antecedent of service quality (Iacobucci et al. 1995), it has
now generally been accepted that service quality is antecedent to customer
satisfaction (Caruana, 2002; Cronin and Taylor, 1992; Teas, 1994) and that
customer satisfaction acts as a mediating variable between service quality
and loyalty (Caruana, 2002).
Confusion also arises between the terms, as both customer satisfaction and
service quality have been defined, and measured, as the difference between
the expectations held prior to purchase, and the post consumption
performance evaluations. This is known as the gap model for service quality
measurement and as the disconfirmation paradigm for customer satisfaction
measurement (Iacobucci et al. 1995).
The principal means of measuring service quality has been SERVQUAL
(Parasuraman et al. 1985; 1988) and although there is widespread
acceptance of the contribution of this scale there has also been some
criticism over a range of methodological and operational aspects of the
measure (Buttle, 1996; Carman., 1990; Cronin and Taylor, 1992; Teas,
1993). Customer satisfaction measures are widespread but derive from the
61
work originally postulated by Oliver (1993). The measurement process for
both service quality and customer satisfaction was founded on the basis of a
disconfirmation paradigm (Iacobucci et al. 1995), although other methods
have been postulated. Pizam and Ellis (1999) identified nine different
approaches to the measurement of customer satisfaction. However, the
significant difference has been the approach to identifying the
disconfirmation, with satisfaction researchers using a better than/worse than
scale originally specified by Oliver (1980), whilst service quality researchers
mathematically identify disconfirmation through the collection of
expectations and performance separately, based on the approach to service
quality measurement identified by Parasuraman et al.(1985, 1988).
Although the use of the disconfirmation approach has generally been
accepted in customer satisfaction measurement, there has been, however,
considerable debate in the literature over the inclusion of expectations in
service quality measurement (Carman. 1990; Cronin and Taylor, 1992;
1994; Parasuraman et al. 1991; 1994; Teas, 1993; 1994). This has resulted in
a general agreement that performance only measures are superior (Cronin
and Taylor, 1994; Teas, 1994).
Service organizations are continually looking for ways to increase customer
loyalty. Although loyalty to tangible goods (i.e., brand loyalty) has been
studied extensively by marketing scholars, relatively little theoretical or
62
empirical research has examined loyalty to service organizations (i.e.,
service loyalty).This study extends previous loyalty research by examining
service loyalty and factors expected to influence its development. In
particular, a literature review is combined with analysis of qualitative data
from over forty depth interviews to develop a model of service loyalty that
includes three antecedents))satisfaction, switching costs, and interpersonal
bonds (Dwayne D. Gremlera and Stephen W. Brown 1996).
(Chao-Chan Wu, 2011) examines the relationship among hospital brand
image, service quality, patient satisfaction, and loyalty. Survey data gathered
from large private hospitals in Taiwan are used to test the relationship. The
results reveal that hospital brand image has both direct and indirect effects
on patient loyalty. It means that a positive hospital brand image not only
increases patient loyalty directly, but it also improves patient satisfaction
through the enhancing of perceived service quality, which in turn increases
the re-visit intention of patients. Hospital brand image indeed serves as a
lead factor in enhancing service quality, patient satisfaction, and patient
loyalty. In addition, the results imply that the path from service quality to
patient satisfaction is a key avenue for the impact of hospital brand image on
patient loyalty.
2.6. Customer Loyalty
Baumann et al. (2011) pursed an alternative study with the main purpose “to
model both current behaviour (measured as share of wallet) and future
63
intentions as measures of customer loyalty, to quantify the link between
current and future behaviour” (Baumann et al., 2011).This investigation has
direct reference to the banking industry. The methodological approach
chosen by the researchers consisted of building a new hybrid model, which
combined formative and reflective constructs and explained the
phenomenon of customer loyalty. It is important to note that the hybrid
model identifies three main determinants of customer loyalty, namely
resistance to change, variety seeking and risk taking behaviour.
However, it may be argued that the empirical investigation pursued by
Baumann et al. (2011) is associated with a number of limitations. First, the
researchers attempted to view the problem exclusively from a customer
perspective having ignored company related determinants of customer
loyalty. Second the link between the future and current behaviour of bank
customers is not clear. At the same time, the main strength of the
investigation conducted by Baumann et al. (2011) is a unique model
incorporating formative and reflective constructs.
Ganguli and Roy (2011) made an attempt to evaluate the role of company-
related or external factors, which may influence customer loyalty of bank
clients in India. The researchers implemented the methodology of
exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to
achieve the primary research aim of the investigation. In their study they
64
found “four generic service quality dimensions in the technology-based
banking services – customer service, technology security and information
quality, technology convenience, and technology usage easiness and
reliability” (Ganguli and Roy, 2011).
The researchers also estimated the role of technological factors influencing
customer loyalty. Their study refers predominantly to on-line banking and e-
payment technologies, which are gaining more and more popularity today.
However, Ganguli and Roy (2011) failed to identify specific dimensions of
service and information quality, which should be given particular attention.
Finally, the outcomes of the empirical investigation are only limited to the
country in question.
In another empirical research, Sangeetha and Mahalingam (2011) identified
the most important factors having impact on consumer loyalty in Islamic
financial institutions. The researchers implemented a complex methodology,
which was two-fold. First of all, Sangeetha and Mahalingam (2011)
developed as many as 14 service quality models, which can be applied to the
banking sector. Secondly, primary data was collected from more than 500
respondents using the survey research strategy. The researchers arrive at the
conclusion that the most significant determinants of customer loyalty with
the reference to Islamic banking are perceived quality of service, positive
recommendations of friends and relatives, personal experience, security of
65
on-line banking, customer involvement and efficient customer relationship
management (Sangeetha and Mahalingam, 2011). It may be critically
remarked that the findings obtained by Sangeetha and Mahalingam (2011)
are consistent with the theoretical statements of Kracklauer et al. (2004).
These researchers argued that customer loyalty is a result of a set of factors.
At the same time, the obtained results are limited from the viewpoint of
generalisation as they refer to Islamic banking only.In this sense, the
importance of recommendations is overestimated by the researchers
(Sangeetha and Mahalingam, 2011).
Dick and Basu (1994), in their conceptual paper, point out that while the
loyalty concept applies to a variety of contexts, most researchers have
focused on issues related to the measurement of loyalty. They introduced the
notion of “relative attitude” as a means to provide better theoretical
grounding to the loyalty construct. Relative attitude refers to “a favourable
attitude that is high compared to potential alternatives” (Dick and Basu,
1994).They suggest that loyalty maybe an outcome of both a more
favourable attitude towards a brand (as compared to alternatives) and repeat
patronage. Furthermore, they state that low relative attitude with low repeat
purchase connotes absence of loyalty, while low relative attitude with high
repeat purchase indicates spurious loyalty.
66
Buttle and Burton (2002) argued that loyalty is probably better seen as
attitude than behaviour. In spite of the arguments about whether loyalty
should be conceptualized as attitude, behaviour or both, it is apparent that
most studies have conceptualized loyalty as a behavioural intention or
behavioural response (Shukla, 2004). Several studies used re-visit intention
as a surrogate for patient loyalty in the health care environment (Boshoff
and Gray, 2004; Kim et al. 2008). Patient loyalty may be more appropriate
viewed as a behavioural intention. Regardless of whether the discussion
focuses on patient loyalty in the health care context or customer loyalty in
the general service context, there is no question that the same benefits of
customer loyalty apply to a hospital as they do to a bank or retail business.
In fact, loyalty has been illustrated as the market place currency for the
twenty-first century (Singh and Sirdeshmukh, 2000). Hence, patient loyalty
acts as a competitive asset for the hospital.
Bloemer and Kasper (1995) suggested that one should “Explicitly take into
account the degree of a consumer’s commitment to a brand when he/she
repurchases a brand”. Thus repeat purchasing behaviour alone does not
imply a consumer is loyal to a brand. True loyalty implies a commitment
towards a brand and not just repurchasing due to inertia (Bloemer and
Kasper, 1995).Consumers that repurchase a brand due to inertia may be
easily induced to switch brands when offered a pricecut, or coupon. Thus, a
favourable relative attitude and not just repurchase is a prerequisite for
67
loyalty. The relationship between customer satisfaction and brand loyalty is
well established in the literature at both the “transaction specific” level and
the “Overall” level (Oliver, 1999; Bitner and Hubbert, 1994).Their research
findings have offered robust evidence in this respect demonstrating a
definite positive relationship between customer satisfaction and behavioural
intentions. Similarly, Anderson and Sullivan (1993) found that stated
repurchase intentions are strongly related to stated satisfaction across
product categories.
It is generally recognised that there are linkages between service quality,
customer satisfaction and loyalty (Bloemer and Kasper, 1995; Buttle, 1996;
Caruana, 2002; Chiou, 2004; McDougall and Levesque, 2000; Oliver,
1980). However Oliver (1999) stated that the suggestion that satisfaction
generates loyalty is erroneous, with between 65% and 85% of satisfied
customers defecting to other suppliers.There have been a number of studies
that have looked at the antecedents of loyalty, including value, levels of
functional and emotional risk, and brand reputation, trust, affect and
preference. A number of studies by various researchers (Bloemer and
Kasper, 1995; Bowen and Chen, 2001; Caruana, 2002; Delgado-Ballester
and Munuera-Alemán, 2000; Dick and Basu, 1994; Oliver, 1999) have
contributed to the understanding of the relationship between the consumer
and provider.Javalgi and Moburg (1997) suggested that, due to the
intangibility and heterogeneity of services, there is an increased likelihood
68
of loyalty in a service context, resulting from a risk reduction strategy
associated with selection of a new provider.This section continues by
looking at the key antecedents of loyalty including satisfaction and the
brand.
Seyed Yaghoub Hosseini, Manijeh Bahreini Zade, Alireza Ziaei Bideh
(2013) has empirically developed that a reliable and valid model specifically
for measuring mobile telecommunication service quality. A multidimensional
measurement model (MS-Qual) has been proposed based on an extensive
literature review and then, to assess the model validity, convergent and
discriminate validity have been established based on the survey data gathered
from 363 of Iranian mobile phone subscribers. Findings of this study showed
that customers form their service quality perceptions based on their
evaluations of seven primary dimensions including: network quality, value-
added service, pricing plans, employees‟ competency, billing system,
customer services, and service convenience. This study h a s several
practical implications. First, practitioners could use developed MS-Qual scale
for measuring and managing service quality in the mobile
telecommunication sector. Second, this study showed that customers‟
evaluation of value-added service, pricing plans and service convenience are
most important factors in their overall perceived service quality. Mobile
phone operators could use these results to set their priorities for the
development of service quality, to better utilize their resources.
69
In mobile telecommunication literature, service quality has been
conceptualized in different ways. Some of the researchers measured
mobile service quality as customers” overall evaluation of their experience
with the service provider, and did not consider it as a multidimensional
construct (Akroush et al., 2011; Aydin & Özer,2005; Edward et al.,
2010; Liu et al., 2011; Shin & Kim, 2008; Lai etal., 2009). Nonetheless,
most researchers considered mobile service quality as a multidimensional
concept. However, the number and content of these dimensions are
different across studies. Some of them used and adapted generic models
like SERVQUAL to measure mobile service quality (Boohene &
Agyapong, 2011; Leisen & Vance, 2001; Negi, 2009; Wang & Lo, 2002),
Moreover, SERVQUAL or SERVPERF, as very general instruments, are
inadequate to measure mobile service qualities in making satisfactory
service related decisions because the dimensions of service quality
depends on the type of service offered (Babakus & Boller, 1992). For
example, Wang and Lo (2002) employed a modified version of
SERVQUAL model to measure service quality of mobile phone operators
in China. They added network quality dimension to the model based on
focus group discussions and expert opinions. According to their findings
based on structural equation modeling, the most important service quality
dimensions in predicting customers‟ overall satisfaction was assurance,
followed by reliability and network quality. But they found no evidence to
70
support the influence of responsiveness and empathy on customer
satisfaction (Wang & Lo, 2002).
Similarly, Negi (2009) tried to modify SERVQUAL scale to best fit in the
context of mobile telecommunication market in Ethiopia. In a pilot study,
respondents were asked about additional service quality dimensions by
using open-ended questions. Three additional dimensions were derived
including network quality, compliant handling and service convenience.
According to regression analysis, network quality scored the highest in
predicting overall customer satisfaction followed by reliability, empathy
and assurance (Negi, 2009).
Some researches in mobile telecommunication industry extended the
traditional definition of service quality and incorporated aspects
particularly relevant to mobile services. For example, Eshghi et al. (2008)
used literature review to identify thirty two attributes relevant to mobile
telecommunication industry. Six factors were derived using factor analysis
including relational quality, competitiveness, reliability, reputation,
customer support and transmission quality. These factors were taken as
service quality dimensions. Based on regression analysis, competitiveness
and reliability had the greatest effect on customer satisfaction followed by
relational quality and transmission quality. Also, a regression analysis
was done to identify most important service quality dimensions in
71
predicting repurchase intension of customers. Results indicated that
relational quality and reliability are the most determinant factors in
customers‟ purchase decisions (Eshghi et al., 2008).
In another study on the perceptions of mobile phone operators‟ service
quality, Santouridis and Trivellas (2010) suggested that customers evaluate
service quality of their mobile phone operators based on quality of six
dimensions including network, value-added services, mobile devices,
customer service, pricing structure and billing system. This scale was
administered to two hundred five residential non-business mobile phone
users in Greece. Their findings show that customer service, pricing
structure and billing system are the service quality dimensions that have
the most significant positive effect on customer satisfaction, which in turn
have significant positive impact on customer loyalty (Santouridis &
Trivellas, 2010).
Moreover, Lu et al. (2009) developed a multidimensional and hierarchical
model to measure mobile service quality. They proposed that mobile
service quality was composed of three primary dimensions, which are
interaction quality, environment quality and outcome quality. Each primary
dimensions further included sub-dimensions. An instrument was
developed and empirically tested using data collected from four hundred
thirty eight mobile brokerage service users (Lu et al., 2009). Also
72
recently, Zhao et al. (2012) used this model to assess the effect of mobile
telecommunication service quality on customer satisfaction and the
continuance intention of mobile value-added services. Their findings
showed that all three dimensions of service quality have significant and
positive effect on customers‟ satisfaction and continuance intention (Zhao
et al., 2012). The review of literatures reveal that the service quality and
service loyalty are key factor for sustainable mobile communication
industry in global as well as India.
2.7. Conclusion
This chapter has covered a review of relevant literature regarding the
constructs of the proposed model. The chapter began with reviews of the
Empirical Studies of Service Quality, followed by Service Loyalty and with
Patients Satisfaction. In the next chapter deals with research designing data
gathering procedures and development of Hypothesis Model etc,.
73
CHAPTER – III
RESEARCH METHODOLOGY
3.1. Introduction
The purpose of this chapter is to address the methodology adopted in this
study. Items that will be addressed include the research design, population
and sample, instrumentation, reliability and validity of the instrumentation,
scoring techniques, data gathering procedures and the development of the
model for the measurement of Service Loyalty for mobile service providers.
3.2. Service Quality Measurement – Recent trends
Based on this perspective, Parasuraman et al. (1988, 1991) developed a scale
for measuring service quality, which is mostly popular as SERVQUAL. This
scale operationalizes service quality by calculating the difference between
expectations and perceptions, evaluating both in relation to the 22 items that
represent five service quality dimensions known as ‘tangibles’, ‘reliability’,
‘responsiveness’, ‘assurance’ and ‘empathy’. The SERVQUAL scale has
been tested and/or adapted in a great number of studies conducted in various
service settings, cultural contexts and geographic locations like the quality
of service offered by a hospital (Babakus and Mangold, 1992), a CPA firm
(Bojanic, 1991), a dental school patient clinic, business school placement
center, tire store, and acute care hospital (Carman, 1990), pest control, dry
cleaning, and fast food (Cronin and Taylor, 1992), banking (Cronin and
74
Taylor, 1992; Spreng and Singh, 1993; Sharma and Mehta, 2004) and
discount and departmental stores (Finn and Lamb, 1991; Teas, 1993;
Dabholkar et al., 1996, Mehta et al., 2000, Vazquez et al., 2001; Kim and
Byoungho 2002). All these studies do not support the factor structure
proposed by Parasuraman et al. (1988). The universality of the scale and its
dimensions has also been the subject of criticisms (Lapierre et al., 1996) and
it is suggested that they require customization to the specific service sector
in which they are applied (Vazquez et al., 2001). Senthilkumar.N and
Arulaj.A (2011) empirically studied the service quality and service
measurement through employability in education institutional in India.
These research studies are empirically studied for the sustainability of the
markets. The authors have developed a new approach for measurement
service quality in their home country consumer’s behaviours.
3.3. Reflective Research Formation Studies
The following table (table 3.1) conducted a comprehensive study to review
19 models of reflective research formations of service quality used till now
in different studies in order to measure the service quality in different
service environment. These studies showed that there is a significant
association between service quality and customer satisfaction.
75
Table 3.1: Reflective Formation Models and Contributors Sl.No Service Quality Model Author
1. Technical and Functional Quality Model Gro’nroos, 1984 2. GAP Model Parasurman et. al. 1985 3. Attribute Service Quality Model Haywood-Farmer, 1988 4. Synthesized Model of Service Quality Brogowiczet, al., 1990 5. Performance Only Model (SERVPERF) Cronin and Taylor, 1992 6. Ideal Value Model of Service Quality Mattsson, 1992 7. Evaluated Performance and Normed Quality
Model Teas 1993
8. IT Alignment Model Berkley and Gupta, 1994 9. Attribute and Overall Affect Model Dabholkar, 1996 10. Model of Perceived Service Quality and
Satisfaction Spreng and Mackoy 1996
11. PCP Attribute Model Philip and Hazlett 1997 12. Retail Service Quality and Perceived Value
Model Sweeney et al., 1997
13. Service Quality, Customer Value and Customer Satisfaction Model
Oh, 1999
14. Antecedents and Mediator Model Dabholkar, et.al 2000 15. Internal Service Quality Model Frost and Kumar, 2000 16. Internal Service Quality DEA Model Soteriouand Stavrinides,
2000 17. Internet banking model Broderick and
Vachirapornpuk, 2002 18. IT Based Model Zhu, et.al. 2002 19. Model of e service quality Santos, 2003
Source: See References The above table 3.1 shows the comprehensive study to review 19 models of
reflective research formations of service quality used till now in different
studies in order to measure the service quality in different service
environment. These studies showed that there is a significant association
between service quality and customer satisfactions for the sustainability of
market economy. The researcher has reviewed above stated models on
service quality before constructions of questionnaires and a developed
proposed conceptual model in this research.
76
Table 3.2: Literature review showed the reflective models on mobile telecommunication Industry
Sl.No Dimensions Researches 1) Net Work Quality Wang and Lo (2002); M. K. Kim et al.
(2004); H. S. Kim & Yoon (2004); Kassim (2006); Lim et al. (2006); Eshghi (2008); Ling & De Run (2009); Negi (2009); Pezeshki, Mousavi & Grant (2009); Santouridis & Trivellas (2010); Wong (2010);Gunjan et al. (2011); Gautam (2011); Liang, Ma & Qi (2012)
2) Value Added Services M. K. Kim et al. (2004); H. S. Kim & Yoon (2004); Lim et al. (2006); Santouridis & Trivellas (2010); Gunjan et al. (2011); Jahanzeb, Fatima & Khan (2011)
3) Pricing Plan M. K. Kim et al. (2004); Lim et al. (2006); Ling & De Run(2009); Santouridis & Trivellas (2010); Gunjan et al. (2011)
4) Employees Competency Eshghi et al. (2008); Krishnan & Kothari (2008); Jahanzeb et al. (2011)
5) Billing System Lim et al. (2006); Krishnan & Kothari (2008); Pezeshki et al. (2009); Santouridis & Trivellas (2010)
6) Customer Service H. S. Kim & Yoon (2004); M. K. Kim et al. (2004); Lim et al. (2006); Kassim (2006); Pezeshki et al. (2009); Negi (2009); Negi & Ketema (2010); Y. E. Kim & Lee (2010); Santouridis & Trivellas (2010); Gautam (2011); Gunjan et al. (2011); Jahanzeb et al. (2011); Khaligh, Miremadi & Aminilari (2012)
7) Convenience M. K. Kim et al. (2004); Ling & De Run (2009); Negi (2009); Liang et al. (2012)
Source: See Reference Literature review showed the reflective models on mobile
telecommunication industry, researchers used different models with several
technical and functional dimensions to measure service quality. However,
most of them agreed that perceptions of mobile operators “service quality
are of a multidimensional nature. In this study, based on literature review a
formative multidimensional model has been developed (Mobile -Qual) that
77
determines customers” perceived service quality in mobile
telecommunication industry in Tamil Nadu.
3.4. Formative Research Foundation Studies
The conducted research is basically a survey on the mediating effects of
service loyalty on mobile service providers in Tamil Nadu. For this research,
almost all districts capital, public and private hospitals were selected. Since
the research is constructed on the basis of formative research model. The
following table (table 3.2) shows the unique formative research models.
Table 3.3 : Formative Research Models and Contributors
Sl.No Model Authors 1) BEM- ESW (NW) Model, PROFIT-COST Model,
TEM-AFC Model, ESW (NW) – PROFIT Model, (Performance of Asset Finance Companies in Non-Banking Financial Sector in Tamil Nadu Model)
Arulraj.A and Thiyagarajan.G 2008
2) SQM-HEI Model (Service Quality Mediated Higher Education India Model)
Arulraj, A. and Senthilkumar, N 2009
3) HFSQ Model (Housing Finance Service Quality Model)
Arulraj, A. and Sureshkumar, V 2010
4) TNTOURQUAL Model (Tamil Nadu Tourism Service Quality Model)
Arulraj, A. and Prabaharan, B 2010
5) SF-Cost Model (Share Holders Funds Model) Arulraj, A and Sarangarajan, V 2010
6) SEM-CPD Model (Structural Equation Modeling Consumer Purchasing Decision Model)
Arulraj, A. and Parthiban, B 2010
7) FERTQUAL Model (Fertilizer Retail Service Quality Model)
Arulraj, A. and Sukumaran, A 2010
8) BANKQUAL Model (Banking Service Quality Model)
Arulraj, A. and Ananth, A 2011
9) INSURELOYAL Model (Life Insurance Loyalty Model)
Arulraj, A and Ramesh, R 2012
10) IMQUAL (Investment Management Service Quality Model)
Arulraj, A and Lourthuraj, S.A 2012
11) THL Model (Tamil Nadu Healthcare Loyalty Model)
Arulraj, A and Rethina Sivakumar, G 2012
12) RETAIL QUAL Model (Retailing Service Loyalty Model)
Arulraj,A and Thanga Prashath, R 2012
78
13) MGNREGP QUAL (Mahatma Gandhi National Rural Employment Guarantee Programme Model)
Arulraj.A and Sethuraman.M 2013
14) SHGs QUAL (Self Help Groups Quality Model) Arulraj.A and Santhanalakshmi.M 2013
15) NW – INCOME Model (Strategic Financial Performance of Public Banks in India Model)
Arulraj.A and Ilavenil.R 2013
Source: See Reference The researcher reviewed above stated formative research models, before
developing the proposed hypothetical model in the present research. From
the above empirical quality researches the researcher formulated the Mobile
QUAL (Mobile Service Providers Quality) Model examines the relative
importance of Fringe Benefit Services as a mediating factor for Service
Loyalty to Tamil Nadu, India. The Mobile QUAL Model includes the
measurement of sub dimensions of quality of mobile service providers as
follows: I. Service Network Communication (SNC): The distributions of
telecom services to appropriate individuals in done actively on time (SNC1),
Do personalized dealing are made in a frequent manner (SNC2), The
distribution of coverage network speed is good (SNC3), Service provide
without waiting of call services during business hours (SNC4), and Clarity
in communication network (SNC5); II. Technology Adoption (TA): The
company regularly updates newer technologies (advanced) available in the
market (TA1), New technologies like broadband 2G & 3G etc., (TA2),
Mobile phone makes you feel secure and where always in touch with our
dear ones (TA3), Do low cost handsets will be able to provide a secure
communication channel (TA4), Branded mobile phones allow you to conduct
79
communication on a secure basis (TA5), If mobile phone is lost it is easily
traced by company using new technology (TA6), The cost of adopting new
technologies is higher for old customers (TA7), Education would enhance
the proficiency in mobile phone technology (TA8) and Is the company
committed to training and educating the customers on the operation of
relevant technologies (TA9); III. Customer Care Services (CCS): A
service provider does not tell customers exactly when services will be
performed (CCS1), I don’t receive prompt service from customer service
staff (CCS2), Customer service staff are not always willing to help
customers (CCS3), Customer service staff are too busy to respond to
customer requests promptly (CCS4), I can trust customer service staff
(CCS5), I feel safe in your transactions with customer service staff (CCS6),
Customer service staff are polite (CCS7), Customer service staff get
adequate support form a service provider to do their jobs well (CCS8),
Company is customer friendly always (CCS9), Whether your feedback are
accepted and upgraded by telecom company (CCS10) and Individual care
and special attention is given for old customer (CCS11); IV. Fringe Benefit
Services (FBS): Rate Cuter Schemes (FBS1), Festival offer Schemes
(FBS2), Internet pocket facility (FBS3), Free SMS facility (FBS4), Free
MMS facility (FBS5), E-Recharge Facilities (FBS6) and Sharing of Amount
(Talk time) (FBS7); V. Service Quality (SQ): Overall Service Network
Communication (SQ1), Overall Technology Adoption (SQ2), Overall
80
Customer care Services (SQ3), Overall Fringe Benefit Services (SQ4) and
Overall Brand Switching Process & MNP (OP5); VI. Brand Switching
Attitude & MNP (BSA): For Network failure (BSA1), For call service
failure (BSA2), For message failure (BSA3), For technology failure (BSA4),
For tariff system (BSA5), Rate cutters and recharge (BSA6), For poor
customer care (BSA7), Mobile number Portability facility (BSA8) and
Promotional Calls & SMS disturbing me to change (BSA9) and VII. Service
Loyalty (SL): I will continue my existing service network in future (SL1), I
will suggest to my other family member (SL2), I will recommend to my
friends & colleagues (SL3) and Some time Introduction MNP induce me to
change the provider (SL4).
3.5. Research Design
The research employed a cross sectional methodological approach.
Methodology described as cross sectional “is one used to collect data on all
relevant variables at one time” (O’Sullivan and Rassel, 1999).This approach
is particularly useful for studies designed to collect information on attitudes
and behaviours of large geographically diverse populations (O’Sullivan and
Rassel, 1999).The survey design is regarded as the most appropriate
research design to measure the perceptions of the respondents in this study.
A survey is the most appropriate research design as it can enable the
researcher to collect information from a large population. The information
obtained from the sample can then be generalized to an entire population
81
(Kerlinger and Lee, 2000).Survey research is usually a qualitative method
that requires standardized information in order to define or describe
variables or to study the relationships between variables.
Surveys generally fall into one of two categories, descriptive or relational.
Descriptive surveys are designed to provide a snapshot of the current state of
affairs while relational surveys are designed to empirically examine
relationships among two or more constructs either in an exploratory or in a
confirmatory manner. The current study is a relational survey that seeks to
explore the relationship between the Service Network Communication
(SNC), Technology Adoption (TA), Customer Care Services (CCS), Service
Quality (SQ), Brand Switching Attitude & MNP (BSA), Fringe Benefit
Services (FBS) is the Mediating factor and outcome is the Service Loyalty
(SL) on mobile service providers.
3.5.1. Pilot Study
Prior to beginning actual data collection with the procedure described above,
the researcher utilized similar procedures to conduct a pilot study to ensure
that the survey materials and procedure were clear and did not provoke any
confusion or problems for participants. The feedback received was rather
ambiguous thus only minor changes were made. For instance, technical
jargon was rephrased to ensure clarity and simplicity. The revised
questionnaire was subsequently submitted to three experts (an academician,
82
a researcher and a Mobile Service Providers) for feedback before being
administered for a full-scale survey. These experts indicated that the draft
questionnaire was rather lengthy, which in fact coincided with the
preliminary feedback from customers.
3.5.2. Construct Measures and Data Collection
Data were collected by means of a structured questionnaire comprising nine
dimensions namely (1)Service Network Communication (SNC),
(2)Technology Adoption (TA), (3)Customer Care Services (CCS), (4) Fringe
Benefit Services (FBS) (5)Service Quality (SQ), (6)Brand Switching Attitude
& MNP (BSA) and (7)Service Loyalty (SL), Service Network
Communication (SNC) consists of Five Questions, Technology Adoption
(TA) consists of Nine Questions, Customer Care Services (CCS) consists of
Eleven Questions, Fringe Benefit Services (FBS) consists of Seven
Questions, Service Quality (SQ) consists of Five Questions, Brand
Switching Attitude & MNP (BSA) consists of Nine Questions, and Service
Loyalty (SL) consists of Four Questions. Finally in the Eleven Questions
pertaining to respondents demographic profile information was given. All
the dimensions were presented as statements on the questionnaire, with the
same rating scale used throughout and measured on a seven point, Likert-
type scale that varied from 1 highly dissatisfied to 7 highly satisfied and
Strongly Disagree to Strongly Agree. For conducting an empirical study,
data were collected from respondents in Cauvery Delta Districts in Tamil
83
Nadu. Assurance was given to the respondents that the information collected
from them will be kept confidential and will be used only for academic
research purposes. Data had been collected using the “Personal-Contact”
approach as suggested by Suresh chandar et al. (2002) whereby “Contact
Persons” (Patients) have been approached personally and the survey was
explained in detail. The final questionnaire together with a cover letter
handed over personally to the “Contact Persons”, who in turn distributed it
randomly to customers among the Mobile Service Provider’s.
A total of 750 nos. of questionnaire were circulated to Customer of the
Cauvery Delta Districts in Tamil Nadu of these 750 were collected. Out of
the questionnaires that were collected 36 were not usable due to insufficient
and/or incomplete data. As a result, a total of 714 valid questionnaires were
used for the analysis, leading to a response rate of 95.2 percentages. Hence,
the sample size for the analysis is 714.The following table (table 3.3) gives a
view of the sample size across the Cauvery Delta Districts in Tamil Nadu.
Table 3.4 : Sample Size across the Delta Districts of Tamilnadu
Districts Region
Thanjavur
Thiruvarur
Nagappattinam Total
South 50 50 50 150 East 50 50 50 150 Centre 50 50 50 150 West 50 50 50 150 North 50 50 50 150 Total 250 250 250 750
Source: Primary Data
The sampling procedure used for the study was stratified random sampling.
The stratification has been done based on the Delta Districts Thanjavur,
84
Thiruvarur, and Nagappatinam for the nature of region south, east, centre,
west and north while selecting the customer of Mobile Service Providers
from each category, non-probabilistic convenience and judgmental sampling
technique was used. However, within such District, the respondents were
selected by stratified random sampling. The data collected were analyzed for
the entire sample.
3.5.3. Respondent’s Characteristics The demographical characteristics of the sample of respondents are
presented in order to get a clear picture of the sample. Demographic
variables that were measured from the respondents were as follows:
1. Name 2. Age 3. Sex 4. Religion 5. Community 6. Education Qualification 7. Occupation 8. Annual Income in Rs. 9. Service Provider 10. Type service 11. How often have you use Mobile 12. Preferring the Provider 13. No of SIM Cards have 14. Reason why?
The following table (table 3.5) gives the breakup of the sample size across
the different demographic variables.
85
Table 3.5:The Sample Size Across The Difference Demographic Variables
S. No. Demographic Dimensions No. of
Respondents Percentage of Respondents
1) Sex Male 472 62.50 Female 278 37.50
Total 750 100 2) Age 18.yrs. to 27 yrs. 156 20.80
28.yrs. to 37 yrs. 216 28.80 38 yrs. to 48 yrs. 181 24.14 48 yrs. to 57 yrs. 102 13.60 58.yrs. to 67 yrs. 76 10.13 68 yrs and above. 19 02.53
Total 750 100 3) Religion Hindu 678 90.40
Muslim 27 03.60 Christian 45 06.00
Total 750 100 4) Community BC 202 26.93
MBC 85 11.33 SC 463 61.74
Total 750 100 5) Educational
Qualifications School Dropout 76 10.13 SSLC 124 16.53 HSC 80 10.67 Diploma 112 14.94 UG 177 23.60 PG 181 24.13
Total 750 100 6) Occupation Unemployed 124 16.53
Farmer 80 10.67 Private Employee 177 23.60 Government Employee 112 14.94 Business 76 10.13 Professional 80 10.67
Total 750 100 7) Annual Income
` (Rupees) Below 50000 173 23.00 50000- 150000 186 24.80 150001 – 250000 192 25.60 250001- 350000 147 19.60 Above 350000 53 7.00
Total 750 100 8) Service Provider` BSNL 112 14.93
Airtel 161 21.47 Aircel 137 18.27 Reliance 80 10.67 MTS 67 8.93 Vodafone 114 15.20 Idea 43 5.73 Tata Docomo 36 4.80
Total 750 100 9) Type service Post Paid 273 36.40
Pre Paid 387 51.60 CDMA 56 7.47 GSM 34 4.53
Total 750 100 Source: Primary Data
86
3.6. Procedure for Data Analysis
The data collected were analysed for the entire sample. Data analyses were
performed with Statistical Package for Social Sciences (SPSS) using
techniques that included descriptive statistics, Correlation analysis and
Analysis of Moment Structures (AMOS) package for Structural Equation
Modeling and Bayesian estimation and testing.
3.6.1. Structural Equation Modeling
The main study used Structural Equation Modelingbecause of two
advantages: “(1) Estimation of Multiple and Interrelated Dependence
Relationships, and (2) The Ability to Represent Unobserved Concepts in
These Relationships and Account for Measurement Error in the Estimation
Process” (Hair et al., 1998).Therefore simultaneously estimated multiple
regressions; the direct and indirect effects were identified (Tate,
1998).However, a series of separate multiple regressions had to be
established based on “theory, prior experience, and the research objectives
to distinguish which independent variables predict each dependent variable”
(Hair et al., 1998).In addition, because SEM considers a measurement error,
the reliability of the predictor variable was improved. AMOS 7.0
(Senthilkumar. N and Arulraj. A, 2011; Arbuckle and Wothke, 2006), a
computer program for formulating, fitting and testing Structural Equation
87
Models (SEM) to observed data was used for SEM and the data preparation
was conducted with SPSS 13.0.
Linear Structural Equation Models (SEMs) are widely used in sociology,
econometrics, management, biology, and other sciences. A SEM (without
free parameters) has two parts: a probability distribution (in the Normal case
specified by a set of linear structural equations and a covariance matrix
among the “error” or “disturbance” terms), and an associated path diagram
corresponding to the causal relations among variables specified by the
structural equations and the correlations among the error terms.It is often
thought that the path diagram is nothing more than a heuristic device for
illustrating the assumptions of the model. However, in this research, the
researcher will show how path diagrams can be used to solve a number of
important problems in structural equation modeling.
Structural Equation Models with latent variables (SEM) are more and more
often used to analyse relationships among variables in marketing and
consumer research (see for instance Bollen, 1989; Schumacker and Lomax,
1996, or Batista-Foguet and Coenders, 2000, for an introduction and
Bagozzi, 1994 for applications to marketing research).Some reasons for the
widespread use of these models are their parsimony (they belong to the
family of linear models), their ability to model complex systems (where
simultaneous and reciprocal relationships may be present, such as the
88
relationship between quality and satisfaction), and their ability to model
relationships among non-observable variables (such as the domains in the
THL Model) while taking measurement errors into account (which are
usually sizeable in questionnaire data and can result in biased estimates if
ignored).
As is usually recommended, a Confirmatory Factor Analysis (CFA) model
is first specified to account for the measurement relationships from latent to
observable variables. In our case, the latent variables are the four perception
dimensions and the observed variables the 30 perception items. The
relationships among latent variables cannot be tested until a well-fitting
CFA model has been reached. In our case, the relationships among Service
Loyalty (SL) of Mobile Service Provider, the mediating impact of Fringe
Benefit Services (FBS) with the SNC, TA, CCS, SQ, BSA, and SL
dimensions are of interest. This modeling sequence stresses the importance
of the goodness of fit assessment. As a combination of regression, path and
factor analyses, in SEM, each predictor is used with its associated
uncontrolled error and, unlike regression analyses; predictor multi-co
linearity does not affect the model results.
3.6.2. Evaluation of Model Fit
According to the usual procedures, the goodness of fit is assessed by
checking the statistical and substantive validity of estimates, the
89
convergence of the estimation procedure, the empirical identification of the
model, the statistical significance of the parameters, and the goodness of fit
to the covariance matrix. Since complex models are inevitably mis specified
to a certain extent, the standard test of the hypothesis of perfect fit to the
population covariance matrix is given less importance than measures of the
degree of approximation between the model and the population covariance
matrix. The Root Mean Squared Error of Approximation (RMSEA) is
selected as such a measure. Values equal to 0.05 or lower are generally
considered to be acceptable (Browne and Cudeck, 1993).The sampling
distribution for the RMSEA can be derived, which makes it possible to
compute confidence intervals.
These intervals allow researchers to test for close fit and not only for exact
fit, as the statistics does.If both extremes of the confidence interval are
below 0.05, then the hypothesis of close fit is rejected in favour of the
hypothesis of better than close fit. If both extremes of the confidence
interval are above 0.05, then the hypothesis of close fit is rejected in favour
of the hypothesis of bad fit.
Several well-known goodness-of-fit indices were used to evaluate model fit:
the chi-square, The Comparative Fit Index (CFI), The Unadjusted
Goodness-of-Fit Indices (GFI), The Normal Fit Index (NFI), The Tucker-
Lewis Index (TLI), The Root Mean Square Error of Approximation
90
(RMSEA) and The Standardized Root Mean Square Error Residual
(SRMR).
3.6.3. Bayesian Estimation and Testing in SEM
With modern computers and software, a Bayesian approach to structural
equation modeling (SEM) is now possible. Posterior distributions over the
parameters of a structural equation model can be approximated to arbitrary
precision with AMOS, even for small samples. Being able to compute the
posterior over the parameters allows us to address several issues of practical
interest. First, prior knowledge about the parameters may be incorporated
into the modeling process in AMOS. Second, we need not rely on
asymptotic theory when the sample size is small, a practice which has been
shown to be misleading for inference and goodness-of-fit tests in SEM
(Boomsma, 1983).Third, the class of models that can be handled is no
longer restricted to just identified or over identified models. Whereas each
identifying assumption must be taken as given in the classical approach, in a
Bayesian approach some of these assumptions can be specified with perhaps
more realistic uncertainty.
3.7. Hypotheses Development
Mediation refers to a process or mechanism through which one variable (i.e.,
exogenous) causes variation in another variable (i.e., endogenous).Studies
designed to test for moderation may provide stronger tests of mediation than
91
the partial and whole covariance approaches typically used (e.g. Baron and
Kenny, 1986; Bing, Davison, LeBreton, and LeBreton, 2002; James and
Brett, 1984). It is useful to distinguish between moderation and mediation.
Moderation carries with it no connotation of causality, unlike mediation,
which implies a causal order. Based on the arguments discussed in the
previous chapters and this chapter the researcher formulated the following
hypotheses.
Figure 3.1 : Proposed Hypothetical Model of “Mobile QUAL Model”
Technology Adoption
Customer Care Services
Service Quality
Brand Switching Attitude & MNP
Fringe Benefit Services
Service Loyalty
Service Network Communication
H6H1
H10
H7
H9
H8
H12H11
H2
H3
H4
H5
The dimensions of Mobile Service Providers were influenced by
the mediating factor Fringe Benefit Services.
The dimensions of Mobile Service Providers were positively
influenced by the Fringe Benefit Services.
A mediator hypothesis is supported if the interaction path (SNC, TA, CCS,
SQ, BSA, SL and FBS) are significant. There may also be significant main
92
effects for the predictor (Service Loyalty) and mediator Fringe Benefit
Services (FBS). Therefore, this research seeks to explore whether the
relationship between Service Loyalty (SL) and SNC, TA, CCS, SQ, BSA,
and SL are fully or partially mediated by Fringe Benefit Services (FBS).
Hypothesis 1: The service Loyalty dimension Service Network
Communication (SNC) is mediated by Fringe Benefit Services (FBS)
towards attainment of Service Loyalty to the Mobile Service Providers.
Hypothesis 2: The service Loyalty dimension Technology Adoption (TA) is
mediated by Fringe Benefit Services (FBS) towards attainment of Service
Loyalty to the Mobile Service Providers.
Hypothesis 3: The service Loyalty dimension Customer Care Services
(CCS) is mediated by Fringe Benefit Services (FBS) towards attainment of
Service Loyalty to the Mobile Service Providers.
Hypothesis 4: The service Loyalty dimension Service Quality (SQ) is
mediated by Fringe Benefit Services (FBS) towards attainment of Service
Loyalty to the Mobile Service Providers.
Hypothesis 5: The service Loyalty dimension Brand Switching Attitude &
MNP (BSA) is mediated by Fringe Benefit Services (FBS) towards
attainment of Service Loyalty to the Mobile Service Providers.
Hypothesis 6: The service Loyalty dimension Service Network
Communication (SNC) positively influences the Service Loyalty to the
Mobile Service Providers.
Hypothesis 7: The service Loyalty dimension Technology Adoption (TA)
positively influences the Service Loyalty to the Mobile Service Providers.
93
Hypothesis 8: The service Loyalty dimension Customer Care Services
(CCS) positively influences the Service Loyalty to the Mobile Service
Providers.
Hypothesis 9: The service Loyalty dimension Service Quality (SQ) positively
influences the Service Loyalty to the Mobile Service Providers.
Hypothesis 10: The service Loyalty dimension Brand Switching Attitude &
MNP (BSA) positively influences the Service Loyalty to the Mobile Service
Providers.
Hypothesis 11: The services Loyalty mediating dimension Fringe Benefit
Services (FBS), positively influence the Service Loyalty (SL) to the Mobile
Service Providers.
Hypothesis 12: Including the interaction between dimensions of the service
Loyalty and Fringe Benefit Services (FBS) will explain more of the variance
in Service Loyalty (SL) than the direct influence of dimensions of service
Loyalty or Fringe Benefit Services (FBS) on their own.
3.8. Conclusion
In this chapter the research methodology adopted for this research was
explained with the research design followed by the explanation of the
population and the sample, respondents’ characteristics, survey instruments
and scoring procedures, data collection procedure and data analysis were
briefed respectively. In the following chapter the developed hypotheses will
be empirically tested.
94
CHAPTER – IV
ANALYSES AND INTERPRETATION OF DATA
4.1. Introduction
In this chapter result of the statistical analysis done for testing hypothesis are
presented and interpreted. Both primary and secondary data were analysed.
The data collected were analyzed for the entire sample. Data analysis were
performed with Statistical Package for Social Sciences (SPSS) using
techniques that included descriptive statistics, correlation analysis and
AMOS package for structural equation modeling (SEM) and Bayesian
estimation and testing (Senthilkumar. N and Arulraj. A, 2011), AMOS 20.0
(Arbuckle and Wothke 2006), a computer programme for formulating,
fitting and testing SEM to observed/primary data, was used for SEM and the
data preparation was conducted with SPSS 18.0 and Minitab – 16 was used
for secondary data analysis.
The analysis presents the constructions and validation of Structural Equation
Modeling (SEM) of ‘Mobile QUAL’ mediated model with the dimensions
of Service Network Communication (SNC), Technology Adoption (TA),
Customer Care Services (CCS), Service Quality (SQ) and Brand Switching
Attitude & MNP (BSA) and the mediating parameter Fringe Benefit
Services (FBS) and the outcome of Service Loyalty (SL) for Mobile Service
Provider within the AMOS graphics environment.
95
4.2. Trend analysis in Mobile Service Provider 4.2.1. Trend analysis in Growth of Telecom Sector in India
The opening of the sector has not only led to rapid growth but also benefited
the consumers through low tariffs as a result of intense competition.
Telecom sector has witnessed a continuous rising trend in the total number
of telephone subscribers. From a mere 22.81 million telephone subscribers
in 1999, the number increased to 846.33 million at the end of March, 2011.
The total number of telephones stands at 926.55 million at the end of
December'11 showing addition of 80.22 million during the period from
April to December'11. Wireless telephone connections have contributed to
this growth as their number rose from 165.09 million in 2007 to 811.60
million in March, 2011 and 893.86 million at the end of December'11. The
wire line connections have however, declined from 40.77 million in 2007 to
34.73 million in March, 2011 and 32.69 million in December'11. (Table 1)
Table 4.1: Growth of Telephones over the years in Telecom Sector in India (2007-2011) Year Wire line phones (in
millions) Wireless phones (in
millions) Gross Total (in millions)
2007 40.77 165.09 205.87 2008 39.41 261.08 300.49 2009 37.97 391.76 429.73 2010 36.96 584.32 621.28 2011 34.21 852.27 886.44
Source: Annual Report in MoCIT (Year: 2011 – 2012) 4.2.1.1. Wire line vs. Wireless
The growth of wireless services has been substantial, with wireless
subscribers growing at a compounded annual growth rate (CAGR) of 42.7%
96
since 2007. Wireless has overtaken wire lines. The share of wireless phones
has increased from 80.19% in 2007 to 96.47% in December'11. On the other
hand, the share of wire line has steadily declined from 19.81% in 2007 to
3.53% in December'11. Wireless phones have increased as they are
preferred because of their convenience and affordability.
Figure 4.1: Trend Analysis plot of Wire line phones in Growth of Telecom Sector in
India From (2007-2011).
20112010200920082007
41
40
39
38
37
36
35
34
Year
Wir
elin
e ph
ones
(in
mill
ions
)
MAPE 0.832504MAD 0.303600MSD 0.154526
Accuracy Measures
ActualFits
Variable
rend Analysis Plot of Wire line phones in Growth of Telephones over the years (2007-2011Linear Trend Model
Yt = 42.535 - 1.55700*t
Trend analysis figure 4.1 reveals the trends in the Wire line phones in
Growth of Telecom Sector in India. The trend plot that shows the original
data, and the fitted trend line, the output also displays the fitted trend
equation Yt = 42.535-1.55700*t and three measures help to determine the
accuracy of the fitted values: 0.832504, 0.303600, and 0.154526. The Wire
line phones data show a general down trend, though with an evident cyclic
97
factor. The trend model appears to fit well to the overall trend. The above
chart shows the amount of Wire line phones in Growth of Telecom Sector in
India (in millions) from 2007 - 2011.
Figure 4.2: Trend Analysis plot of Wireless phones in Growth of Telecom Sector in
India From (2007-2011).
20112010200920082007
900
800
700
600
500
400
300
200
100
Year
Wir
e le
ss p
hone
s (i
n m
illio
ns)
MAPE 13.76MAD 46.22MSD 2386.15
Accuracy Measures
ActualFits
Variable
Trend Analysis Plot of wire less phones Growth of Telephones over the years (2007-2011)Linear Trend ModelYt = -58.4 + 170*t
Trend analysis figure 4.2 reveals the trends in the Wireless phones in
Growth of Telecom Sector in India. The trend plot that shows the original
data, and the fitted trend line, the output also displays the fitted trend
equation Yt = 58.4+170*t and three measures help to determine the
accuracy of the fitted values: 13.76, 46.22, and 2386.15. The Wireless
phones data show a general upward trend, though with an evident cyclic
factor. The trend model appears to fit well to the overall trend. The above
98
chart shows the amount of Wireless phones in Growth of Telecom Sector in
India (in millions) from 2007 - 2011.
Figure 4.3: Trend Analysis plot of Gross total in Growth of Telecom Sector in India
From (2007-2011).
20112010200920082007
900
800
700
600
500
400
300
200
100
Year
Gros
s To
tal (
in m
illio
ns)
MAPE 11.81MAD 45.91MSD 2355.79
Accuracy Measures
ActualFits
Variable
Trend Analysis Plot of Gross total in growth of Telephones overthe years (2007-2011)Linear Trend ModelYt = -15.8 + 168*t
Trend analysis figure 4.3 reveals the trends in the Gross total of phones in
Growth of Telecom Sector in India. The trend plot that shows the original
data, and the fitted trend line, the output also displays the fitted trend
equation Yt = -15.8+168*t and three measures help to determine the
accuracy of the fitted values: 11.81, 45.91, and 2355.79. The Gross total
data show a general upward trend, though with an evident cyclic factor. The
trend model appears to fit well to the overall trend. The above chart shows
the amount of Gross total of phones in Growth of Telecom Sector in India
(in millions) from 2007 - 2011.
99
4.2.2. Trend analysis in Tele-density
Tele-density is an important indicator of telecom penetration in the country.
There has been phenomenal growth of tele-density in the country with the
evolution of new wireless technologies.
The tele-density which was 18.22% in March 2007 increased to
70.89% March, 2011and 76.86% in December'11. Thus there has
been continuous improvement in the overall tele-density of the
country.
The rural tele-density which was 5.89% in March 2007 increased to
33.83% in March, 2011and 37.52% at the end of December'11.
The urban tele-density increased from 48.10% in March 2007 to
156.94% in March, 2011 and stands at 167.46% at the end of
December'11.
For economic and social development of rural areas, rapid increase in rural
tele-density is of utmost importance. With the introduction of wireless
phones in rural areas, there is increasing trend in rural tele-density also. The
Government is taking various measures under USOF for expansion of
mobile network in remote and rural areas. As the urban areas have got
largely saturated, private service providers are also looking for further
opportunities in rural areas. All these factors have led to increasing trend in
rural tele-density.
100
4.2.2.1. Shifting Focus on Rural Telephones
The rural telephone connections increased from 47.10 million in March
2007 to 282.29 million in March, 2011 and further to 315.39 million in
December'11. The share of rural phones in the total telephones has
constantly increased, from 22.88% in 2007 to 34.04% in December'11. The
wireless connections have contributed substantially to total rural telephone
connections. Their share in the rural telephones increased from 73.33% in
March, 2007 to 96.90% in March, 2011 and further to 97.53% in
December'11. During 2011-12 (upto December), the growth rate of rural
telephone was 11.73% as against the growth of 8.35% of urban telephones.
The private sector has also contributed to the growth of rural telephones as
its share was 86.78% in December'11 up from 51.87% in 2007.
Table 4.2: Tele Density in Telecom Sector in India (2007-2011)
Year Rural (in percentage) Urban (in percentage) Total Density (in percentage)
2007 5.89 48.10 18.22 2008 9.46 66.39 26.22 2009 15.11 88.84 36.98 2010 24.31 119.45 52.74 2011 36.16 162.21 74.35
Source: Annual Report in MoCIT (Year: 2011 – 2012)
101
Figure 4.4: Trend Analysis plot of Rural Tele Density of Telecom Sector in India From (2007-2011).
20112010200920082007
40
30
20
10
0
Year
Rur
al (
in p
erce
ntag
e)
MAPE 18.7934MAD 2.2712MSD 5.7999
Accuracy Measures
ActualFits
Variable
Trend Analysis Plot of rural in tele density Linear Trend ModelYt = -4.43 + 7.54*t
Trend analysis figure 4.4 reveals the trends in the Rural Tele Density in
Telecom Sector in India. The trend plot that shows the original data, and the
fitted trend line, the output also displays the fitted trend equation Yt = -
4.43+7.54*t and three measures help to determine the accuracy of the fitted
values: 18.7934, 2.2712, and 5.7999. The Rural Tele Density data show a
general upward trend, though with an evident cyclic factor. The trend model
appears to fit well to the overall trend. The above chart shows the amount of
Rural Tele Density of Telecom Sector in India (in percentage) from 2007 -
2011.
102
Figure 4.5: Trend Analysis plot of Urban Tele Density of Telecom Sector in India From (2007-2011).
20112010200920082007
175
150
125
100
75
50
Year
Urba
n (i
n pe
rcen
tage
)
MAPE 7.6977MAD 6.5256MSD 47.8541
Accuracy Measures
ActualFits
Variable
Trend Analysis Plot of urban in tele densityLinear Trend ModelYt = 12.61 + 28.1*t
Trend analysis figure 4.5 reveals the trends in the Urban Tele Density in
Telecom Sector in India. The trend plot that shows the original data, and the
fitted trend line, the output also displays the fitted trend equation Yt =
12.61+28.1*t and three measures help to determine the accuracy of the fitted
values: 7.6977, 6.5256, and 47.8541. The Urban Tele Density data show a
general upward trend, though with an evident cyclic factor. The trend model
appears to fit well to the overall trend. The above chart shows the amount of
Urban Tele Density of Telecom Sector in India (in percentage) from 2007 -
2011.
103
Figure 4.6: Trend Analysis plot of Total Tele Density of Telecom Sector in India From (2007-2011).
20112010200920082007
80
70
60
50
40
30
20
10
Year
Tota
l tel
e D
ensi
ty (
in p
erce
ntag
e)
MAPE 10.8618MAD 3.6664MSD 15.0269
Accuracy Measures
ActualFits
Variable
Trend Analysis Plot of Total Tele DensityLinear Trend ModelYt = 0.07 + 13.9*t
Trend analysis figure 4.6 reveals the trends in the Total Tele Density in
Telecom Sector in India. The trend plot that shows the original data, and the
fitted trend line, the output also displays the fitted trend equation Yt =
0.07+13.9*t and three measures help to determine the accuracy of the fitted
values: 10.8618, 3.6664, and 15.0269. The Total Tele Density data show a
general upward trend, though with an evident cyclic factor. The trend model
appears to fit well to the overall trend. The above chart shows the amount of
Total Tele Density of Telecom Sector in India (in percentage) from 2007 -
2011.
104
4.2.3. Trend Analysis of FDI in Telecom Sector
Telecom Sector is considered to be one of the most attractive sectors for
foreign direct investment. Telecom is the third major sector attracting FDI
inflows after services and computer software sector. At present 74% to
100% FDI is permitted for various telecom services. This has helped the
telecom sector to grow. Actual Inflow of FDI in Telecom Sector from April
2000 to September 2011 is US $12456 in million.
Table 4.3: Cumulative FDI and Status of Disbursements made and availability of Fund in
Telecom Sector in India (2007-2011) Cumulative FDI in Telecom Sector Status of Disbursements made and availability of
Funds Year FDI in Telecom Sector (in
US $ millions) Funds Collected as
USL (in crore) Funds Allocated (in crore)
2007 2581 3940.73 1500 2008 3782 5405.80 1290 2009 6392 5515.14 1600 2010 8924 5778.00 2400 2011 11505 6114.56 3100
Source: Annual Report in MoCIT (Year: 2011 – 2012)
105
Figure 4.7: Trend Analysis plot of FDI in Telecom Sector in India From (2007-2011).
20112010200920082007
12000
10000
8000
6000
4000
2000
Year
FDI
in T
elec
om S
ecto
r (i
n US
$ m
illio
ns)
MAPE 8MAD 325MSD 147194
Accuracy Measures
ActualFits
Variable
Trend Analysis Plot of FDI in Telecome Sector in IndiaLinear Trend ModelYt = -260 + 2299*t
Trend analysis figure 4.7 reveals the trends in the FDI (Foreign Direct
Investment) in Telecom Sector in India. The trend plot that shows the
original data, and the fitted trend line, the output also displays the fitted
trend equation Yt = -260+2299*t and three measures help to determine the
accuracy of the fitted values: 8, 325, and 147194. The FDI data show a
general upward trend, though with an evident cyclic factor. The trend model
appears to fit well to the overall trend. The above chart shows the amount of
FDI in Telecom Sector in India (in millions) from 2007 - 2011.
106
Figure 4.8: Trend Analysis plot of Funds Collected as USL in Telecom Sector in India From (2007-2011).
20112010200920082007
6500
6000
5500
5000
4500
4000
Year
Fund
s Co
llect
ed a
s US
L (i
n cr
ore)
MAPE 6MAD 276MSD 111290
Accuracy Measures
ActualFits
Variable
Trend Analysis Plot of Status of Disbursements collected of Fund in Telecom Sector in India Linear Trend ModelYt = 3935 + 472*t
Trend analysis figure 4.8 reveals the trends in the Funds Collected as USL in
Telecom Sector in India. The trend plot that shows the original data, and the
fitted trend line, the output also displays the fitted trend equation Yt =
3935+472*t and three measures help to determine the accuracy of the fitted
values: 6, 276 and 111290. The Funds Collected as USL data show a general
upward trend, though with an evident cyclic factor. The trend model appears
to fit well to the overall trend. The above chart shows the amount of Funds
Collected as USL in Telecom Sector in India (in crore) from 2007 - 2011.
107
Figure 4.9: Trend Analysis plot of Funds Allocated in Telecom Sector in India From (2007-2011).
20112010200920082007
3000
2500
2000
1500
1000
Year
Fund
s A
lloca
ted
(in
cror
e)
MAPE 15.6MAD 257.6MSD 84814.0
Accuracy Measures
ActualFits
Variable
Trend Analysis Plot Funds Allocated in Telecom Sector in IndiaLinear Trend ModelYt = 685 + 431*t
Trend analysis figure 4.9 reveals the trends in the Funds Allocated in
Telecom Sector in India. The trend plot that shows the original data, and the
fitted trend line, the output also displays the fitted trend equation Yt =
685+431*t and three measures help to determine the accuracy of the fitted
values: 15.6, 257.6 and 84814.0. The Funds Allocated data show a general
upward trend, though with an evident cyclic factor. The trend model appears
to fit well to the overall trend. The above chart shows the amount of Funds
Allocated in Telecom Sector in India (in crore) from 2007 - 2011.
4.2.4. Trend analysis in Telecom Equipment and Production
To bring the issues relating to telecom manufacturing in India, TRAI issued
a pre-consultation in May 2010. Based on the comments received and
further study, a consultation paper on 'Encouraging the Telecom Equipment
108
Manufacturing in India' was issued on 28 December 2010 for obtaining
views of the stakeholders. After analysis of the comments and OHDs, TRAI
issued recommendations on the 'Telecom Equipment Manufacturing Policy'
on 12 April 2011. In these recommendations, the specific targets that seek to
achieve would be:
To meet 45% of the domestic demand through domestically
manufactured products by the year 2015 and 80% by the year 2020.
To provide market access to Indian products to the extent of 25% by
the year 2015 and 50% by the year 2020.
To increase value addition in domestic manufactured products to 35%
by the year 2015 and 65% by the year 2020.
4.2.4.1. Green Telecommunications
Telecom Regulatory Authority of India (TRAI) issued a pre-consultation
paper on “Green Telecom” on 18 June, 2010 for obtaining views of the
stakeholders. Based on the comments received from the stakeholders, TRAI
issued consultation paper on 'Green Telecommunications' on 03.02.2011.
Based on the comments received during the consultation and its own
analysis, TRAI released it’s the recommendations on Approach towards
Green telecommunications on 12 April 2011. The key recommendations are:
Measures towards greening the sector should be part of National
Telecom Policy.
109
In the next 5 years – 50% of all rural towers and 33% of all urban
towers to be powered by hybrid power (Renewable energy sources +
Grid power)
All equipments, products and services deployed in the sector should
be energy and performance assessed and certified “Green passport”
by 2015.
All mobile phones should be free of brominates, chlorinated
compounds and antimony trioxide by 2015.
All mobile manufactures / distributors should place collection bins at
appropriate places across the country for collection of e-waste –
mobile phones, batteries, chargers etc.
Table 4.4: Telecom Equipment and Production in India (2007-2011)
Year Telecom Equipment (in millions) Telecom Equipment Production (in millions)
2007 236560 18980 2008 412700 81310 2009 488000 110000 2010 510000 135000 2011 520000 158380
Source: Annual Report in MoCIT (Year: 2011 – 2012)
110
Figure 4.10: Trend Analysis plot of Telecom Equipment in Telecom Sector in India From (2007-2011).
20112010200920082007
600000
500000
400000
300000
200000
Year
Tele
com
Equ
ipm
ent
(in
mill
ions
)
MAPE 12MAD 44138MSD 2281846968
Accuracy Measures
ActualFits
Variable
Trend Analysis Plot of Telecome Equipment in Telecom Sector in IndiaLinear Trend Model
Yt = 234198 + 66418*t
Trend analysis figure 4.10 reveals the trends in the Telecom Equipment in
Telecom Sector in India. The trend plot that shows the original data, and the
fitted trend line, the output also displays the fitted trend equation Yt =
234198+66418*t and three measures help to determine the accuracy of the
fitted values: 12, 44138 and 2281846968. The Telecom Equipment data
show a general upward trend, though with an evident cyclic factor. The
trend model appears to fit well to the overall trend. The above chart shows
the amount of Telecom Equipment in Telecom Sector in India (in millions)
from 2007 - 2011.
111
Figure 4.11: Trend Analysis plot of Telecom Equipment production in Telecom Sector in India From (2007-2011).
20112010200920082007
180000
160000
140000
120000
100000
80000
60000
40000
20000
0
Year
Tele
com
e Eq
uipm
ent
Prod
ucti
on (
in m
illio
ns)
MAPE 22MAD 9643MSD 117825422
Accuracy Measures
ActualFits
Variable
Trend Analysis Plot of Telecom Equipment ProductionLinear Trend ModelYt = 987 + 33249*t
Trend analysis figure 4.11 reveals the trends in the Telecom Equipment
Production in Telecom Sector in India. The trend plot that shows the
original data, and the fitted trend line, the output also displays the fitted
trend equation Yt = 987+33249*t and three measures help to determine the
accuracy of the fitted values: 22, 9643 and 117825422. The Telecom
Equipment Production data show a general upward trend, though with an
evident cyclic factor. The trend model appears to fit well to the overall
trend. The above chart shows the amount of Telecom Equipment Production
in Telecom Sector in India (in millions) from 2007 - 2011.
112
4.2.5. Trend analysis in Growth of Telecom Networks in India
Table 4.5: Growth of Telecom Networks in India (2007-2011) Year PSU Telecom Network (in
Lakh) Private Telecom
Network (in lakh) Total Network (in lakh)
2007 713.90 1344.76 2058.67 2008 795.49 2209.43 3004.92 2009 895.46 3401.79 4297.25 2010 1058.71 5154.09 6212.80 2011 1275.09 7589.29 8864.38
Source: Annual Report in MoCIT (Year: 2011 – 2012) Figure 4.12: Trend Analysis plot of Public Sector Units Telecom network in India From
(2007-2011).
20112010200920082007
1300
1200
1100
1000
900
800
700
600
Year
PSU
Tele
com
Net
wor
k (i
n la
kh)
MAPE 4.03MAD 37.41MSD 1615.61
Accuracy Measures
ActualFits
Variable
Trend Analysis Plot of Public Sector Units Telecom network in IndiaLinear Trend ModelYt = 532.1 + 139*t
Trend analysis figure 4.12 reveals the trends in the Public Sector Units in
Telecom Network in India. The trend plot that shows the original data, and
the fitted trend line, the output also displays the fitted trend equation Yt =
532.1+139*t and three measures help to determine the accuracy of the fitted
values: 4.03, 37.41 and 1615.61. The Public Sector Units Telecom network
data show a general upward trend, though with an evident cyclic factor. The
113
trend model appears to fit well to the overall trend. The above chart shows
the amount of Public Sector Units in Telecom network in India (in lakh)
from 2007 - 2011.
Figure 4.13: Trend Analysis plot of Private Sector Units in Telecom network in India
From (2007-2011).
20112010200920082007
8000
7000
6000
5000
4000
3000
2000
1000
0
Year
Priv
ate
Tele
com
Net
wor
k (i
n la
kh)
MAPE 15MAD 422MSD 198235
Accuracy Measures
ActualFits
Variable
Trend Analysis Plot of Private Telecom Networks Linear Trend ModelYt = -690 + 1543*t
Trend analysis figure 4.13 reveals the trends in the Private Sector Units in
Telecom network in India. The trend plot that shows the original data, and
the fitted trend line, the output also displays the fitted trend equation Yt = -
690+1543*t and three measures help to determine the accuracy of the fitted
values: 15, 422 and 198235. The Private Sector Units Telecom network data
show a general upward trend, though with an evident cyclic factor. The
trend model appears to fit well to the overall trend. The above chart shows
114
the amount of Private Sector Units in Telecom network in India (in lakh)
from 2007 - 2011.
Figure 4.14: Trend Analysis plot of Total Telecom Networks in India From (2007-2011).
20112010200920082007
9000
8000
7000
6000
5000
4000
3000
2000
1000
Year
Tota
l Net
wor
ks (
in la
kh)
MAPE 12MAD 459MSD 235578
Accuracy Measures
ActualFits
Variable
Trend Analysis Plot of Total Network in Telecom sector in IndiaLinear Trend ModelYt = -158 + 1682*t
Trend analysis figure 4.14 reveals the trends in the Total Telecom network
in India. The trend plot that shows the original data, and the fitted trend line,
the output also displays the fitted trend equation Yt = -158+1682*t and three
measures help to determine the accuracy of the fitted values: 12, 459 and
235578. The Private Sector Units Telecom network data show a general
upward trend, though with an evident cyclic factor. The trend model appears
to fit well to the overall trend. The above chart shows the amount of Private
Sector Units in Telecom network in India (in lakh) from 2007 - 2011.
115
4.2.6. Trend Analysis in Fault Rate in Telecom Sector in India
Table 4.6: Fault Rate in Telecom Sector in India (2007-2011) Year New Delhi Unit Mumbai Unit 2007 7.20 11.38 2008 6.71 9.10 2009 7.71 7.25 2010 11.01 6.17 2011 6.58 8.04
Source: Annual Report in MoCIT (Year: 2011 – 2012)
Figure 4.15: Trend Analysis plot of Fault Rate in New Delhi unit in Telecom Sector in India From (2007-2011).
20112010200920082007
11
10
9
8
7
6
Year
New
Del
hi U
nit
MAPE 13.7827MAD 1.1448MSD 2.4807
Accuracy Measures
ActualFits
Variable
Trend Analysis Plot of Fault Rate in New Delhi unit in Telecom Sector in IndiaLinear Trend ModelYt = 6.92 + 0.306*t
Trend analysis figure 4.15 reveals the trends in the Fault rate New Delhi
Units in Telecom network in India. The trend plot that shows the original
data, and the fitted trend line, the output also displays the fitted trend
equation Yt = -6.92+0.306*t and three measures help to determine the
accuracy of the fitted values: 13.7827, 1.1448 and 2.4807. The Fault rates
New Delhi Units Telecom network data show a general upward trend,
though with an evident cyclic factor. The trend model appears to fit well to
116
the overall trend. The above chart shows the amount of Fault rate New Delhi
Units in Telecom network in India from 2007 - 2011.
Figure 4.16: Trend Analysis plot of Fault Rate in Mumbai unit in Telecom Sector in
India From (2007-2011).
20112010200920082007
12
11
10
9
8
7
6
Year
Mum
bai U
nit
MAPE 13.5570MAD 1.0576MSD 1.3119
Accuracy Measures
ActualFits
Variable
Trend Analysis Plot of Mumbai Unit in Telecom Sector in IndiaLinear Trend Model
Yt = 11.27 - 0.961000*t
Trend analysis figure 4.16 reveals the trends in the Fault rate Mumbai Units
in Telecom network in India. The trend plot that shows the original data, and
the fitted trend line, the output also displays the fitted trend equation Yt =
11.27-0.961000*t and three measures help to determine the accuracy of the
fitted values: 13.5570, 1.0576 and 1.3119. The Fault rates Mumbai Units
Telecom network data show a general downward trend, though with an
evident cyclic factor. The trend model appears to fit well to the overall
trend. The above chart shows the amount of Fault rate Mumbai Units in
Telecom network in India from 2007 - 2011.
117
4.2.7. Trend analysis in Public Sector-Requirement in Telecom Sector in India
Table 4.7: Public Sector – Requirement in Telecom Sector in India (2007-2011) Year BSNL (in crore) MTNC (in crore) 2007 19203 1912.25 2008 20892 1513.98 2009 22497 1895.84 2010 24319 1429.73 2011 26143 8576.31
Source: Annual Report DoT Year (2011 – 2012)
Figure 4.17: Trend Analysis plot of BSNL- Fund Requirement in Telecom Sector in India From (2007-2011).
20112010200920082007
27000
26000
25000
24000
23000
22000
21000
20000
19000
Year
BSNL
(in
cro
re) MAPE 0.24
MAD 54.52MSD 4296.78
Accuracy Measures
ActualFits
Variable
Trend Analysis Plot of BSNL Requirement Fund in Telecom Sector in IndiaLinear Trend Model
Yt = 17418.7 + 1731*t
Trend analysis figure 4.17 reveals the trends in the BSNL-Fund
Requirement Telecom network in India. The trend plot that shows the
original data, and the fitted trend line, the output also displays the fitted
trend equation Yt = 17418.7+1731*t and three measures help to determine
the accuracy of the fitted values: 0.24, 54.52 and 4296.78. The BSNL-Fund
Requirement Telecom network data show a general upward trend, though
118
with an evident cyclic factor. The trend model appears to fit well to the
overall trend. The above chart shows the amount of BSNL-Fund
Requirement in Telecom network in India (in crore) from 2007 - 2011.
Figure 4.18: Trend Analysis plot of MTNC- Fund Requirement in Telecom Sector in India From (2007-2011).
20112010200920082007
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
Year
MTN
C (i
n cr
ore)
MAPE 79MAD 1743MSD 4122013
Accuracy Measures
ActualFits
Variable
Trend Analysis Plot of MTNC Reuirement in Telecom Sector in indiaLinear Trend ModelYt = -908 + 1324*t
Trend analysis figure 4.18 reveals the trends in the MTNC-Fund
Requirement Telecom network in India. The trend plot that shows the
original data, and the fitted trend line, the output also displays the fitted
trend equation Yt = -908+1324*t and three measures help to determine the
accuracy of the fitted values: 79, 1743 and 4122013. The MTNC-Fund
Requirement Telecom network data show a general upward trend, though
with an evident cyclic factor. The trend model appears to fit well to the
119
overall trend. The above chart shows the amount of MTNC-Fund
Requirement in Telecom network in India (in crore) from 2007 - 2011.
4.2.8. Managerial Implication for Trend Analysis
The Indian mobile services industry has been the fastest growing mobile
services market in the world, registering a CAGR of more than 50% in terms
of subscribers and 15% in terms of gross revenues over the past decade. The
industry is presently the second largest globally by subscriber base with the
total subscriber base of 913.5 million as on July 2012. The strong growth in
the industry can be attributed primarily to the country’s large population,
healthy economic growth, affordable handsets, and most importantly low
tariffs.
Increasing price competition and aggressive customer acquisitions by the
telecom operators (telcos) led to frequent migrations between plans/telcos
by the price-sensitive subscribers, leading to proliferation of inactive
connections. In such a scenario, ‘active subscribers’ is a more accurate
representation of the subscriber base in the country, and stands at 76% of the
total subscribers as on July 2012, translating into ‘active tele density’ of
57%.
The tower industry is awaiting clarity on reduction in the limit on foreign
direct investment (FDI) in the sector and the inclusion of Tower Company’s
120
within the purview of licensing. Currently, some telcos are even exploring
the possibility of divesting their equity stake in tower companies in order to
meet funding requirements.
Cellular mobile telephones subscribers in India increased from 77 thousand
in 1995 to 3.6 million in 2000. By March 2002, it has grown to 6.4 million.
Cellular subscribers in proportion to total number of telephone subscribers
(basic plus cellular) have increased from 0.6 percent in 1995 to 14.6 percent
in 2002. This is still lower than the average of 24.6 percent achieved by the
low-income countries in 2001. The corresponding ratio for lower middle-
income countries is 41.8 percent, 52.8 percent for upper middle-income
countries and 50.2 percent for high-income countries. India is yet to
experience mobile explosion of the scale other countries have seen. One
would expect a rapid growth in mobile telephony in coming decades. India
has also achieved significant quality up gradation of its network in the 90s.
Digital lines in proportion to total number of main telephone lines have
increased from 87 per cent in 1995 to 99.8 percent in 1999.
One notable break with the past is that with opening up of the developing
economies and widespread sectoral reforms, catching up process has
become faster. Developing countries with liberal policies have much better
opportunity to leapfrog than before. Mobile experience of the low-income
countries bears testimony to this process. India is a participant in this global
121
process. There is tremendous appetite to absorb new technology. At the
higher end of the market, India will mimic the most sophisticated telecom
technology of the world and face all types of uncertainties that are
associated with any new technology anywhere in the world. It will take time
for the market for new technologies to consolidate. ‘Market maturing’ will
be a continuous process at some of the segments of telecom sector. This
holds good even today.
4.3. The Regression “Mobile QUAL” Overall Model 4.3.1. Regression Model of the “Mobile QUAL” Mediated Structural Model
In hierarchical regression, the predictor variables are entered in sets of
variables according to a pre-determined order that may infer some causal or
potentially mediating relationships between the predictors and the dependent
variable (Francis, 2003). Such situations are frequently of interest in the
social sciences. The logic involved in hypothesizing mediating relationships
is that “The Independent Variable Influences the Mediator Which, In Turn,
Influences the Outcome” (Holmbeck, 1997). However, an important pre-
condition for examining mediated relationships is that the independent
variable is significantly associated with the dependent variable prior to
testing any model for mediating variables (Holmbeck, 1997). Of interest is
the extent to which the introduction of the hypothesized mediating variable
reduces the magnitude of any direct influence of the independent variable on
122
the dependent variable. Hence the researcher empirically tested the
hierarchical regression for the model conceptualized in the figure 4.19
within the AMOS 20.0 graphics environment.
Figure 4.19: Shows the AMOS Output with Regression Weights of “Mobile QUAL”
Mediated Model
The Regression analyses conducted, the parameter estimates are then viewed
within AMOS graphics and it displays the standardized parameter estimates.
The regression analysis revealed that the Fringe Benefit Services on the
various dimensions of Mediated Model Mobile Service Provider, Fringe
123
Benefit Services (FBS) influenced 0.11 of the Service Loyalty (SL),
followed by Service Quality (SQ) which explains 0.40 of the Fringe Benefit
Services (FBS) the R2 value of 0.11 is displayed above the box Service
Loyalty (SL) in the AMOS graphics output. The visual representation of
results suggest that the relationships between the dimensions of Mobile
Service Provider, procedure and formalities (Service Quality (SQ) => Fringe
Benefit Services (FBS) = 0.40) resulted significant impact on the mediated
factor Fringe Benefit Services (FBS). Service Network Communication
(SNC), Technology Adoption (TA), Customer Care Services (CCS), Service
Quality (SQ), and Brand Switching Attitude & MNP (BSA) are resulted
very limited influence on the Fringe Benefit Services (FBS). It shows that
the Customer perception towards the Technology Adoption (TA) and
Customer Care Services (CCS) towards outcome of Mobile Service Provider
in insignificant whereas the impact of the same is very high on mediating
variable.
4.3.2. Bayesian Estimation and Testing for Regression Model of “Mobile QUAL” Mediated Structural Equation Model
The research model is a SEM, while many management scientist are most
familiar with the estimation of these models using software that analyses
covariance matrix of the observed data (e.g. LISREL, AMOS, EQS), the
researcher adopt a Bayesian approach for estimation and inference in AMOS
20.0 environment (Senthilkumar. N and Arulraj. A, 2011; Arbuckle and
124
Wothke, 2006). Since, it offers numerous methodological and substantive
advantages over alternative approaches.
Table 4.8: Bayesian Convergence Distribution for “Mobile QUAL” Regression Model
Regression weights Mean S.E. S.D. C.S. Skewness Kurtosis Min Max Name
FBS<--SNC 0.004 0.001 0.05 1.000 0.025 0.03 -0.187 0.212 W1 FBS<--TA 0.296 0.001 0.039 1.000 -0.023 0.109 0.14 0.441 W2
FBS<--CCS 0.184 0.001 0.033 1.000 -0.042 0.047 0.029 0.303 W3 FBS<--SQ 0.402 0.001 0.065 1.000 0.034 0.068 0.15 0.659 W4
FBS<--BSA 0.013 0.001 0.032 1.000 -0.047 -0.06 -0.113 0.146 W5 SL<--BSA 0.051 0 0.017 1.000 -0.027 0.046 -0.021 0.12 W6 SL<--SQ 0.141 0.001 0.036 1.000 -0.049 0.074 -0.002 0.269 W7
SL<--CCS 0.054 0 0.018 1.000 0.021 0.004 -0.019 0.125 W8 SL<--TA 0.048 0.001 0.023 1.000 0.023 0.03 -0.048 0.135 W9
SL<--SNC 0.149 0.001 0.027 1.000 -0.017 0.031 0.039 0.281 W10 SL<--FBS 0.105 0 0.021 1.000 0.009 0.102 0.016 0.19 W11
Means Mean S.E. S.D. C.S. Skewness Kurtosis Min Max Name
SNC 22.365 0.007 0.251 1.000 0.052 0.059 21.378 23.432 M1 TA 40.37 0.006 0.33 1.000 0.016 0.044 39.079 41.765 M2
CCS 47.16 0.01 0.415 1.000 -0.065 -0.124 45.275 48.652 M3 SQ 22.809 0.006 0.212 1.000 -0.009 -0.005 22.018 23.665 M4
BSA 35.74 0.01 0.36 1.000 0.01 -0.024 34.14 37.015 M5 Intercepts
Mean S.E. S.D. C.S. Skewness Kurtosis Min Max Name FBS 1.995 0.048 1.637 1.000 0.021 0.056 -4.181 8.688 I1 SL 2.099 0.021 0.913 1.000 -0.027 0.013 -1.724 5.568 I2
Covariances Mean S.E. S.D. C.S. Skewness Kurtosis Min Max Name
SNC<->BSA 3.724 0.048 2.348 1.000 0.045 0.055 -5.384 14.736 C1 BSA<->TA 15.286 0.079 3.194 1.000 0.048 0.038 1.597 27.671 C2 BSA<->CCS 38.5 0.112 4.212 1.000 0.148 0.129 22.726 57.56 C3 BSA<->SQ 18.731 0.047 2.154 1.000 0.095 0.01 9.935 28.336 C4 SNC<->SQ 13.807 0.033 1.452 1.000 0.165 -0.047 8.396 19.898 C5 TA<->SQ 21.865 0.067 2.04 1.001 0.123 -0.098 14.578 29.848 C6
CCS<->SQ 30.662 0.067 2.627 1.000 0.225 0.188 20.732 43.424 C7 SNC<->CCS 24.947 0.069 2.926 1.000 0.193 0.012 14.512 38.295 C8 TA<->CCS 43.823 0.127 3.994 1.001 0.261 0.322 30.795 67.788 C9 SNC<->TA 25.102 0.06 2.327 1.000 0.163 0.07 16.372 35.334 C10 Variances
Mean S.E. S.D. C.S. Skewness Kurtosis Min Max Name SNC 42.433 0.055 2.232 1.000 0.232 0.111 35.077 52.231 V1 BSA 92.477 0.097 4.852 1.000 0.181 -0.076 75.772 115.103 V2 TA 74.473 0.128 4.01 1.001 0.225 0.134 60.545 91.118 V3
CCS 122.585 0.198 6.57 1.000 0.26 0.209 96.428 156.995 V4 SQ 31.028 0.05 1.689 1.000 0.177 -0.015 24.849 38.61 V5 e2 54.348 0.102 2.872 1.001 0.19 0.011 44.56 65.208 V6 e1 16.322 0.02 0.874 1.000 0.232 0.098 13.363 19.905 V7
Source: Amos 18 output
125
4.3.3. Posterior Diagnostic Plots of ‘Mobile QUAL’ Mediated Regression Model
To check the convergence of the Bayesian MCMC method the posterior
diagnostic plots are analysed. The following figure (figure 4.20 and 4.21)
shows the posterior frequency polygon of the distribution of the parameters
across the 99000 samples. The Bayesian MCMC diagnostic plots reveals
that for all the figures the normality is achieved, so the structural equation
model fit is accurately estimated.
Figure 4.20: Posterior frequency polygon distribution of the Mediating Factor
Service Loyalty (SL) and Fringe Benefit Services (FBS) regression weight (W11)
126
Figure 4.21: Posterior frequency histogram distribution of the Mediating Factor Service Loyalty (SL) and Fringe Benefit Services (FBS) regression weight (W11)
The trace plot also called as time-series plot shows the sampled values of a
parameter over time. This plot helps to judge how quickly the MCMC
procedure converges in distribution. The following figures (figure4.22)
show the trace plot of the mediated Mobile QUAL Model for the mediated
factor Fringe Benefit Services (FBS) to Service Loyalty (SL) dimension
across 99000 samples. If we mentally break up this plot into a few
horizontal sections, the trace within any section would not look much
different from the trace in any other section. This indicates that the
convergence in distribution takes place rapidly. Hence the mediated Mobile
QUAL MCMC procedure very quickly forgets its starting values.
127
Figure 4.22: Posterior frequency trace plot of the Mediating Factor Service Loyalty (SL) and Fringe Benefit Services (FBS) regression weight (W11)
To determine how long it takes for the correlations among the samples to die
down, autocorrelation plot which is the estimated correlation between the
sampled value at any iteration and the sampled value k iterations later for k
= 1, 2, 3,…. is analysed for the Mobile QUAL regression model. The figure
4.23 shows the correlation plot of the Mobile QUAL model for the mediated
factor Fringe Benefit Services (FBS) to Service Loyalty (SL) dimension
across 99000 samples. The figure exhibits that at lag 100 and beyond, the
correlation is effectively 0. This indicates that by 90 iterations, the MCMC
procedure has essentially forgotten its starting position. Forgetting the
starting position is equivalent to convergence in distribution. Hence it is
ensured that convergence in distribution was attained and that the analysed
samples are indeed samples from the true posterior distribution.
128
Figure 4.23: Posterior frequency autocorrelation plot of the Mediating Factor Service Loyalty (SL) and Fringe Benefit Services (FBS) regression weight (W11)
Even though marginal posterior distributions are very important, they do not
reveal relationships that may exist among the two parameters. The summary
table given in table 4.8 and the frequency polygons given in the figure 4.24
and figure 4.25 describe only the marginal posterior distributions of the
parameters. Hence to visualize the relationships among pairs of Parameters
in two-dimensional. The surface plots following figures (figure 4.24 and
figure 4.25) provides bivariate marginal posterior plots of the Mobile QUAL
model for the mediated factor Fringe Benefit Services (FBS) with other
dimensions across 99000 samples. From the two figures it is revealed that
the two dimensional surface plots also signifies the interrelationship
between the mediating variable Fringe Benefit Services (FBS) with the other
dimensions Service Loyalty (SL) and Service Network Communication
(SNC).
129
Figure 4.24: Two-dimensional surface plot of the marginal posterior distribution of the mediating factor Fringe Benefit Services (FBS) with SQ and SNC
Figure 4.25: Two-dimensional histogram plot of the marginal posterior distribution of the mediating factor Fringe Benefit Services (FBS) with SQ and
SNC
The following figure 4.26 displays the two-dimensional plot of the bivariate
posterior density across 99000 samples. Ranging from dark to light, the
three shades of gray represent 50%, 90% and 95% credible regions,
130
respectively. From the figure, it is revealed that the sample respondent’s
responses are normally distributed.
Figure 4.26: Two-dimensional contour plot of the marginal posterior distribution
of the mediating factor Fringe Benefit Services (FBS) with SQ and SNC
The various diagnostic plots featured from figure 4.20 to figure 4.26 of the
Bayesian estimation of convergence of MCMC algorithm confirms the fact
that the convergence takes place and the normality is attained. Hence
absolute fit of the Mobile QUAL regression model. From the Mobile QUAL
regression model which is empirically tested with mediating factor with the
dimensions Service Loyalty (SL) and Service Network Communication
(SNC) it is evident that the Mobile service provider should concentrate on
the Fringe Benefit Services (FBS) as the mandatory aspect of Mobile
Service Provider in Cauvery Delta Districts in Tamil Nadu.
131
4.3.4. Structural Equation Modeling of “Overall Mediated Mobile QUAL” Mediated Model
Since the Service loyalty in Overall Mediated Inclusive Model for Mobile
Service Provider is theoretical construct, researcher has defined its
dimension based on the setting used to explore the construct. If Mediated
“Overall Mediated Mobile QUAL” Model is to be applicable in the Indian
context, the dimensions and the sub dimensions on Mobile Service Provider
have to be reliable and valid in measuring Service Loyalty in Mobile Service
Provider. The model examines the relative importance of dimensions of
Service Loyalty (SL) and Fringe Benefit Services (FBS) in Mobile Service
Provider in Cauvery Delta Districts in Tamil Nadu.
The “Overall Mediated Mobile QUAL” Model examines the relative
importance of Fringe Benefit Services (FBS) as a mediating factor for
Service Loyalty to Cauvery Delta Districts in Tamil Nadu. “The Overall
Mediated Mobile QUAL” Mediated Model includes the measurement of sub
dimensions of Service Loyalty of Mobile Service Provider as follows:
I. Service Network Communication (SNC): The distributions of telecom
services to appropriate individuals in done actively on time (SNC1), Do
personalized dealing are made in a frequent manner (SNC2), The
distribution of coverage network speed is good (SNC3), Service provide
without waiting of call services during business hours (SNC4), and Clarity
in communication network (SNC5); II. Technology Adoption (TA): The
132
company regularly updates newer technologies (advanced) available in the
market (TA1), New technologies like broadband 2G & 3G etc., (TA2),
Mobile phone makes you feel secure and where always in touch with our
dear ones (TA3), Do low cost handsets will be able to provide a secure
communication channel (TA4), Branded mobile phones allow you to conduct
communication on a secure basis (TA5), If mobile phone is lost it is easily
traced by company using new technology (TA6), The cost of adopting new
technologies is higher for old customers (TA7), Education would enhance
the proficiency in mobile phone technology (TA8) and Is the company
committed to training and educating the customers on the operation of
relevant technologies (TA9); III. Customer Care Services (CCS): A
service provider does not tell customers exactly when services will be
performed (CCS1), I don’t receive prompt service from customer service
staff (CCS2), Customer service staff are not always willing to help
customers (CCS3), Customer service staff are too busy to respond to
customer requests promptly (CCS4), I can trust customer service staff
(CCS5), I feel safe in your transactions with customer service staff (CCS6),
Customer service staff are polite (CCS7), Customer service staff get
adequate support form a service provider to do their jobs well (CCS8),
Company is customer friendly always (CCS9), Whether your feedback are
accepted and upgraded by telecom company (CCS10) and Individual care
and special attention is given for old customer (CCS11); IV. Fringe Benefit
133
Services (FBS): Rate Cuter Schemes (FBS1), Festival offer Schemes
(FBS2), Internet pocket facility (FBS3), Free SMS facility (FBS4), Free
MMS facility (FBS5), E-Recharge Facilities (FBS6) and Sharing of Amount
(Talk time) (FBS7); V. Service Quality (SQ): Overall Service Network
Communication (SQ1), Overall Technology Adoption (SQ2), Overall
Customer care Services (SQ3), Overall Fringe Benefit Services (SQ4) and
Overall Brand Switching Process & MNP (OP5); VI. Brand Switching
Attitude & MNP (BSA): For Network failure (BSA1), For call service
failure (BSA2), For message failure (BSA3), For technology failure (BSA4),
For tariff system (BSA5), Rate cutters and recharge (BSA6), For poor
customer care (BSA7), Mobile number Portability facility (BSA8) and
Promotional Calls & SMS disturbing me to change (BSA9) and VII. Service
Loyalty (SL): I will continue my existing service network in future (SL1), I
will suggest to my other family member (SL2), I will recommend to my
friends & colleagues (SL3) and Some time Introduction MNP induce me to
change the provider (SL4).
After identifying a potential model that best explains the data in terms of
theory and model fit, a Confirmatory Factor Analysis (CFA) using
Structural Equation Modeling (SEM) was used to test the invariance of the
factorial model. All tests of model invariance begin with a global test of the
equality of covariance structures across groups (Joreskog, 1971). The data
for all groups were analysed simultaneously to obtain efficient estimates
134
(Bentler, 1995). The constraints used include, from weaker to stronger: (1)
Model Structure, (2) Model Structure and Factor Loadings, and (3) Model
Structure, Factor Loadings, and Unique Variance.
4.3.4.1. Evaluation of Model Fit
According to the usual procedures, the goodness of fit is assessed by
checking the statistical and substantive validity of estimates, the
convergence of the estimation procedure, the empirical identification of the
model, the statistical significance of the parameters, and the goodness of fit
to the covariance matrix (Senthilkumar.N and Arulraj.A, 2011). The root
mean squared error of approximation (RMSEA) is selected as such a
measure. Values equal to 0.05 or lower are generally considered to be
acceptable (Browne and Cudeck, 1993). The sampling distribution for the
RMSEA can be derived, which makes it possible to compute confidence
intervals. These intervals allow researchers to test for close fit and not only
for exact fit, as the X2 does. If both extremes of the confidence interval are
below 0.05, then the hypothesis of close fit is rejected in favor of the
hypothesis of better than close fit. If both extremes of the confidence
interval are above 0.05, then the hypothesis of close fit is rejected in favor of
the hypothesis of bad fit (Senthilkumar. N and Arulraj. A, 2011). Several
well-known goodness-of-fit indices (GFI) were used to evaluate model fit:
the chi-square X2, the comparative fit index, the unadjusted GFI, the normal
fit index
standardiz
Figure
(NFI), th
zed root m
e 4.27: ShowMediat
he Tucker
mean square
ws AMOS ted Mobile
135
r-Lewis i
e error resi
path diagrQUAL’ St
5
ndex (TL
idual.
am output tructural E
LI), the R
for the ovequation M
RMSEA a
erall ‘Overodel
and the
All
136
Figure 4.27 shows Amos’s path diagram output for the Over All Mediated
Mobile QUAL Structural Equation model., You can see that the, Service
Network Communication (SNC) consists of sub dimensions, Technology
Adoption (TA) consists of Nine sub dimensions, Customer Care Services
(CCS) consists of Eleven sub dimensions, Fringe Benefit Services (FBS)
consists of Seven sub dimensions, Service Quality (SQ) consists of Five sub
dimensions, Brand Switching Attitude & MNP (BSA) consists of Nine sub
dimensions, and Service Loyalty (SL) consists of Four sub dimensions. The
RMSEA fit statistics for the model was 0.05, which was considered as a best
fit model (Brown and Cudeck, 1993; Diamantopoulos and Siguaw, 2000).
The path diagram shows the Fringe Benefit Services (FBS) is the mediating
factor for Service Loyalty. The regression co-efficient 0.31 signifies the
impact of mediating factor Fringe Benefit Services (FBS) on the other
Dimensions towards Service Loyalty of the Mobile Service Provider.
4.3.5. Evaluation of Over All Mediated Mobile QUAL Mediated Model
The following table 4.9 gives the summary of the various goodness-of-fit
statistics and other values corresponding to the Over All Mediated Mobile
QUAL Mediated Structural Equation Model. Also the last column in the
table provides the acceptable level for the various goodness-of-fit statistics
and other values.
137
Table.4.9: Summary of the Various Goodness of Fit Statistics and Other Values Corresponding To the Over All Mediated Mobile QUAL Mediated Structural
Equation Model
S.No. Measures of fit Over All Mediated
Mobile QUAL
Acceptable level for good fit
1. Chi-square (x2) at p 0.01 2677.017 Significant 2. Degree of freedom (d.f) 1159 Accepted 3. Comparative Fit Index (CFI) .801 >0.90 4. Bentler – Bonett Indes or Normed Fit
Index (NFI) .699 >0.90
5. Root Mean Squared error of Approximation (RMSEA)
.043 <0.05
Accepted 6. Non Centrality Parameter (NCP) 1518.017 Accepted 7. Non Centrality Parameter, Lower
Boundary (NCPLO 90) 1371.144 Accepted
8. Non Centrality Parameter, Upper Boundary (NCPHI 90)
1672.555 Accepted
9. Parsimony adjusted NFI (PNFI) .635 Accepted 10. Parsimony adjusted CFI (PCFI) .728 Accepted 11. Minimum value of Discrepancy
(FMIN) 3.755 Accepted
12. Lower Limit of FMIN (LO90) 1.923 Accepted 13. Upper Limit of FMIN (HI90) 2.346 Accepted 14. Browne-Cudeck Criterion (BCC) 3034.594 Accepted 15. ECVI 4.22 Accepted 16. LO90 4.014 Accepted 17. HI90 4.437 Accepted 18. MECVI 4.250 Accepted 19. HOELTER .05 331 <= 20. HOELTER .01 346 Atleast 200
Source: Amos 20.0 output From the above table it is revealed that all the criterions of goodness-of-fit
statistics and other measures of statistics are acceptable for the Over All
Mediated Mobile QUAL Structural Equation Model.
4.3.6. Bayesian Estimation and Testing of “Over All Mediated Mobile QUAL” Structural Equation Model
The table 4.10 shows the Bayesian convergence distribution of the “Over
All Mediated Mobile QUAL” structural equation model. In this research the
138
researcher has adopted for the procedure of assessing convergence of
MCMC algorithm of maximum likelihood.
Table 4.10 : Bayesian Convergence Distribution for “Over All Mediated Mobile QUAL”
Structural Model Regression weights
Mean S.E. S.D. C.S. Skewness Kurtosis Min Max Name
CCS2<--CCS 0.84 0.003 0.095 1.001 0.205 -0.035 0.497 1.199 W1 CCS3<--CCS 1.022 0.005 0.112 1.001 0.26 0.086 0.655 1.595 W2 CCS4<--CCS 1.04 0.005 0.114 1.001 0.279 -0.019 0.655 1.568 W3 CCS5<--CCS 1.023 0.006 0.11 1.001 0.314 0.039 0.647 1.476 W4 CCS6<--CCS 0.946 0.005 0.125 1.001 0.149 -0.062 0.531 1.422 W5 CCS7<--CCS 1.037 0.005 0.11 1.001 0.311 -0.083 0.693 1.48 W6 CCS8<--CCS 1.086 0.005 0.111 1.001 0.294 0.051 0.728 1.532 W7 CCS9<--CCS 1.22 0.005 0.117 1.001 0.285 0.009 0.842 1.725 W8 CCS10<--CCS 0.977 0.005 0.105 1.001 0.245 0.122 0.625 1.408 W9 CCS11<--CCS 0.972 0.004 0.105 1.001 0.253 0.099 0.613 1.511 W10 TA8<--TA 1.157 0.011 0.199 1.001 0.684 1.05 0.585 2.276 W11 TA7<--TA 0.867 0.009 0.152 1.002 0.526 0.621 0.324 1.641 W12 TA6<--TA 1.02 0.01 0.179 1.001 0.517 0.505 0.463 1.762 W13 TA5<--TA 1.21 0.01 0.184 1.001 0.655 0.773 0.634 2.095 W14 TA4<--TA 1.183 0.01 0.189 1.001 0.962 2.778 0.665 2.4 W15 TA3<--TA 1.391 0.011 0.209 1.001 0.84 1.397 0.759 2.389 W16 TA2<--TA 1.478 0.012 0.211 1.002 0.783 1.381 0.864 2.561 W17 TA1<--TA 1.437 0.012 0.206 1.002 0.697 0.908 0.798 2.332 W18
BSA2<--BSA 1.23 0.003 0.105 1.000 0.196 0.037 0.843 1.656 W19 BSA3<--BSA 1.105 0.003 0.09 1.000 0.205 0.091 0.8 1.515 W20 BSA4<--BSA 1.066 0.002 0.088 1.000 0.224 -0.018 0.785 1.391 W21 BSA5<--BSA 0.824 0.002 0.082 1.000 0.188 0.083 0.546 1.145 W22 BSA6<--BSA 0.513 0.003 0.103 1.001 0.005 0.088 0.104 0.975 W23 BSA7<--BSA 0.62 0.003 0.08 1.001 0.228 0.205 0.355 1 W24 BSA8<--BSA 0.382 0.003 0.074 1.001 0.15 0.006 0.122 0.747 W25 BSA9<--BSA 0.371 0.003 0.072 1.001 0.211 0.345 0.09 0.736 W26 SQ2<--SQ 0.821 0.002 0.069 1.000 0.187 0.103 0.545 1.123 W27 SQ3<--SQ 0.941 0.003 0.078 1.001 0.339 0.474 0.665 1.405 W28 SQ4<--SQ 0.764 0.002 0.075 1.001 0.162 0.106 0.504 1.079 W29 SQ5<--SQ 0.622 0.003 0.077 1.001 0.119 0.102 0.344 0.929 W30
SNC4<--SNC 0.634 0.005 0.114 1.001 0.42 0.08 0.281 1.103 W31 SNC3<--SNC 1.043 0.009 0.157 1.002 0.553 0.171 0.607 1.698 W32 SNC2<--SNC 0.93 0.008 0.145 1.002 0.564 0.252 0.531 1.589 W33 SNC1<--SNC 1.125 0.01 0.176 1.001 0.575 0.162 0.687 1.804 W34 FBS2<--FBS 0.843 0.002 0.079 1.000 0.191 0.03 0.551 1.155 W35 FBS3<--FBS 1.013 0.002 0.076 1.000 0.144 -0.044 0.77 1.313 W36 FBS4<--FBS 1.266 0.003 0.093 1.001 0.217 0.147 0.903 1.673 W37 FBS5<--FBS 1.039 0.003 0.083 1.001 0.192 0.128 0.733 1.421 W38 FBS6<--FBS 1.067 0.002 0.079 1.000 0.19 0.015 0.807 1.448 W39 FBS7<--FBS 0.963 0.003 0.08 1.001 0.204 0.046 0.679 1.287 W40 SL3<--SL 2.131 0.019 0.332 1.002 0.7 0.735 1.253 3.653 W41 SL2<--SL 2.349 0.02 0.373 1.001 0.7 0.79 1.304 4.086 W42 SL1<--SL 2.532 0.023 0.41 1.002 0.789 1.101 1.442 4.502 W43
FBS<--SNC -0.122 0.007 0.14 1.001 -0.386 0.494 -0.767 0.356 W44 FBS<--TA 0.818 0.014 0.25 1.001 0.441 0.59 -0.16 1.955 W45
FBS<--CCS 0.247 0.003 0.1 1.000 0.091 0.041 -0.135 0.631 W46
139
FBS<--BSA -0.051 0.002 0.05 1.001 -0.124 0.214 -0.308 0.142 W47 FBS<--BSA -0.051 0.002 0.05 1.001 -0.124 0.214 -0.308 0.142 W47 FBS<--SQ 0.361 0.002 0.093 1.000 0.237 0.242 0.031 0.771 W48 SL<--FBS 0.306 0.002 0.05 1.001 0.2 -0.123 0.144 0.521 W49
Intercepts Mean S.E. S.D. C.S. Skewness Kurtosis Min Max Name
CCS1 3.877 0.002 0.067 1.000 -0.038 -0.098 3.611 4.121 I1 CCS2 4.093 0.002 0.064 1.000 0.018 -0.002 3.834 4.348 I2 CCS3 4.195 0.002 0.07 1.001 0.051 -0.01 3.944 4.475 I3 CCS4 4.107 0.002 0.071 1.000 0.02 -0.066 3.842 4.373 I4 CCS5 4.228 0.002 0.066 1.000 0.032 -0.083 3.988 4.479 I5 CCS6 4.428 0.002 0.084 1.000 0.096 0.057 4.107 4.808 I6 CCS7 4.625 0.002 0.065 1.000 -0.009 -0.07 4.367 4.867 I7 CCS8 4.518 0.002 0.065 1.001 0.006 -0.15 4.269 4.786 I8 CCS9 4.554 0.003 0.068 1.001 -0.035 0.071 4.298 4.827 I9
CCS10 4.387 0.002 0.064 1.001 0.014 -0.04 4.13 4.63 I10 CCS11 4.133 0.002 0.066 1.000 -0.045 -0.055 3.846 4.397 I11
TA9 4.143 0.003 0.069 1.001 0.068 0.047 3.836 4.412 I12 TA8 4.425 0.003 0.082 1.001 0.012 0.044 4.058 4.733 I13 TA7 4.004 0.002 0.066 1.001 -0.048 0.099 3.687 4.252 I14 TA6 4.218 0.002 0.078 1.000 -0.017 0.217 3.908 4.536 I15 TA5 4.749 0.002 0.064 1.000 -0.028 -0.055 4.48 4.991 I16 TA4 4.145 0.003 0.069 1.001 0.028 0.101 3.861 4.468 I17 TA3 5.047 0.002 0.067 1.000 0 0.083 4.789 5.347 I18 TA2 4.778 0.003 0.068 1.001 -0.026 0.247 4.517 5.112 I19 TA1 4.865 0.002 0.067 1.000 0.038 -0.044 4.631 5.142 I20
BSA1 3.556 0.002 0.069 1.000 0.004 0.066 3.296 3.883 I21 BSA2 3.733 0.002 0.091 1.000 0 0.017 3.378 4.094 I22 BSA3 3.668 0.002 0.068 1.000 0.071 0 3.418 3.97 I23 BSA4 3.704 0.002 0.068 1.000 -0.029 0.041 3.412 3.956 I24 BSA5 3.963 0.002 0.068 1.000 0.016 -0.014 3.69 4.237 I25 BSA6 4.481 0.002 0.093 1.000 -0.093 0.021 4.093 4.831 I26 BSA7 4.043 0.002 0.068 1.001 0 -0.137 3.781 4.289 I27 BSA8 4.484 0.002 0.066 1.000 0.014 0.042 4.234 4.737 I28 BSA9 4.106 0.002 0.064 1.001 0.01 0.048 3.804 4.357 I29 SQ1 4.761 0.002 0.065 1.000 0.055 -0.126 4.53 5.01 I30 SQ2 4.689 0.002 0.058 1.001 0.033 -0.012 4.453 4.911 I31 SQ3 4.589 0.002 0.065 1.001 -0.024 0.14 4.323 4.845 I32 SQ4 4.472 0.002 0.062 1.001 0.038 0.024 4.24 4.796 I33 SQ5 4.317 0.002 0.071 1.000 -0.073 0.013 3.971 4.565 I34
SNC5 4.658 0.004 0.133 1.000 -0.004 -0.018 4.118 5.154 I35 SNC4 4.086 0.002 0.067 1.001 -0.009 -0.007 3.822 4.328 I36
SNC3 4.721 0.002 0.064 1.001 -0.059 -0.21 4.452 4.942 I37 SNC2 4.315 0.002 0.058 1.000 0.028 0.016 4.1 4.565 I38 SNC1 4.584 0.002 0.068 1.001 -0.042 0.002 4.27 4.836 I39 FBS1 4.69 0.002 0.07 1.001 -0.023 -0.036 4.366 4.943 I40 FBS2 4.45 0.003 0.075 1.001 -0.084 0.153 4.116 4.782 I41 FBS3 4.866 0.002 0.07 1.000 -0.031 -0.139 4.554 5.139 I42 FBS4 4.529 0.002 0.079 1.000 -0.045 0.017 4.2 4.844 I43 FBS5 4.317 0.002 0.075 1.000 -0.083 0.085 3.992 4.596 I44 FBS6 4.905 0.002 0.067 1.000 -0.092 0.14 4.616 5.142 I45 FBS7 4.595 0.002 0.07 1.001 -0.053 0.062 4.273 4.858 I46 SL4 4.181 0.002 0.072 1.000 -0.022 0.102 3.864 4.446 I47 SL3 4.657 0.002 0.065 1.001 -0.008 -0.009 4.389 4.931 I48 SL2 4.772 0.002 0.063 1.001 0.017 -0.001 4.525 5.009 I49 SL1 4.707 0.002 0.068 1.001 0.02 0.01 4.443 4.994 I50
140
Covariances
Mean S.E. S.D. C.S. Skewness Kurtosis Min Max Name CCS<->BSA
0.328 0.002 0.058 1.001 0.255 0.075 0.112 0.589 C1
BSA<->SQ 0.375 0.002 0.07 1.001 0.23 0.062 0.145 0.698 C2 SNC<->BSA
-0.026 0.003 0.063 1.001 -0.145 0.296 -0.335 0.215 C3
TA<->BSA 0.071 0.002 0.042 1.001 0.236 0.301 -0.09 0.263 C4 CCS<->TA 0.357 0.003 0.058 1.001 0.241 0.037 0.179 0.601 C5
SNC<->CCS
0.504 0.004 0.089 1.001 0.353 0.121 0.256 0.907 C6
CCS<->SQ 0.659 0.003 0.077 1.001 0.205 0.02 0.373 0.973 C7 SNC<->TA 0.511 0.005 0.098 1.001 0.305 -0.13 0.228 0.902 C8 TA<->SQ 0.481 0.004 0.073 1.002 0.279 0.512 0.224 0.853 C9
SNC<->SQ 0.674 0.006 0.117 1.001 0.313 -0.06 0.346 1.171 C10 Variances
Mean S.E. S.D. C.S. Skewness Kurtosis Min Max Name
CCS 0.763 0.006 0.123 1.001 0.33 0.05 0.392 1.29 V1 TA 0.425 0.005 0.099 1.001 0.303 0.105 0.138 0.85 V2
BSA 1.27 0.006 0.171 1.001 0.187 -0.066 0.701 1.903 V3 SQ 1.189 0.007 0.145 1.001 0.197 0.023 0.699 1.828 V4
SNC 0.995 0.014 0.27 1.001 0.385 -0.133 0.338 2.052 V5 e52 0.478 0.003 0.073 1.001 0.307 0.049 0.263 0.788 V6 e51 0.149 0.002 0.044 1.001 0.494 0.056 0.038 0.362 V7 e1 2.546 0.004 0.151 1.000 0.184 0.053 2.012 3.152 V8 e2 2.413 0.005 0.135 1.001 0.213 0.072 1.957 2.987 V9 e3 2.704 0.005 0.153 1.001 0.154 0.008 2.141 3.29 V10 e4 2.645 0.005 0.152 1.000 0.162 -0.021 2.111 3.22 V11 e5 2.184 0.004 0.128 1.000 0.236 0.221 1.719 2.798 V12 e6 4.459 0.01 0.248 1.001 0.183 0.02 3.577 5.67 V13 e7 2.226 0.004 0.128 1.001 0.358 0.668 1.794 2.974 V14 e8 2.13 0.003 0.121 1.000 0.215 -0.01 1.725 2.641 V15 e9 2.053 0.004 0.126 1.001 0.221 0.015 1.633 2.578 V16
e10 2.237 0.004 0.131 1.000 0.273 0.023 1.807 2.852 V17 e11 2.434 0.004 0.132 1.000 0.252 0.053 1.918 3.022 V18 e12 2.791 0.003 0.154 1.000 0.129 -0.017 2.215 3.398 V19 e13 3.883 0.006 0.218 1.000 0.25 -0.044 3.22 4.778 V20 e14 2.696 0.004 0.146 1.000 0.287 0.313 2.146 3.414 V21 e15 3.842 0.008 0.211 1.001 0.229 0.115 3.078 4.879 V22 e16 2.379 0.004 0.139 1.000 0.226 0.045 1.912 2.968 V23 e17 2.731 0.005 0.152 1.001 0.18 -0.066 2.219 3.341 V24 e18 2.439 0.005 0.145 1.001 0.165 0.009 1.938 3.051 V25 e19 2.457 0.004 0.148 1.000 0.224 0.18 1.859 3.116 V26 e20 2.334 0.003 0.134 1.000 0.164 0.073 1.899 2.986 V27 e21 2.23 0.005 0.143 1.001 0.136 0.039 1.625 2.9 V28 e22 3.839 0.008 0.24 1.001 0.179 0.058 2.857 4.782 V29 e23 1.869 0.005 0.132 1.001 0.184 0.04 1.409 2.407 V30 e24 1.83 0.005 0.125 1.001 0.201 0.205 1.383 2.428 V31 e25 2.318 0.005 0.138 1.001 0.146 0.093 1.857 3.013 V32 e26 5.895 0.011 0.319 1.001 0.187 0.014 4.755 7.242 V33 e27 2.854 0.005 0.158 1.001 0.123 -0.185 2.312 3.46 V34 e28 2.957 0.005 0.164 1.000 0.161 0.068 2.344 3.747 V35 e29 2.906 0.005 0.155 1.000 0.183 0.027 2.357 3.516 V36 e30 1.751 0.004 0.117 1.001 0.181 -0.122 1.333 2.237 V37 e31 1.634 0.004 0.101 1.001 0.197 0.047 1.277 2.092 V38 e32 1.863 0.003 0.12 1.000 0.202 0.073 1.439 2.419 V39
141
e33 2.111 0.003 0.127 1.000 0.276 0.063 1.701 2.657 V40 e34 3.204 0.005 0.179 1.000 0.197 0.043 2.574 3.919 V41 e34 3.204 0.005 0.179 1.000 0.197 0.043 2.574 3.919 V41 e36 2.796 0.004 0.155 1.000 0.226 -0.017 2.239 3.502 V43 e37 1.982 0.005 0.13 1.001 0.174 -0.14 1.565 2.589 V44 e38 1.668 0.004 0.108 1.001 0.214 0.19 1.215 2.174 V45 e39 2.13 0.004 0.147 1.000 0.124 -0.076 1.554 2.751 V46 e40 2.233 0.005 0.133 1.001 0.281 0.079 1.732 2.812 V47 e41 3.062 0.006 0.173 1.001 0.24 0.144 2.426 3.809 V48 e42 2.245 0.005 0.137 1.001 0.229 0.304 1.754 2.902 V49 e43 2.595 0.006 0.162 1.001 0.209 -0.012 2.06 3.366 V50 e44 2.526 0.005 0.15 1.001 0.169 -0.077 2.011 3.134 V51 e45 1.775 0.004 0.112 1.001 0.229 0.071 1.397 2.263 V52 e46 2.295 0.004 0.136 1.001 0.215 0.104 1.832 2.886 V53 e47 3.359 0.005 0.18 1.000 0.222 0.167 2.706 4.292 V54 e48 1.864 0.005 0.122 1.001 0.177 0.199 1.428 2.511 V55 e49 1.357 0.004 0.108 1.001 0.161 0.109 0.968 1.784 V56 e50 1.713 0.004 0.13 1.000 0.094 -0.115 1.198 2.197 V57
Source: AMOS 18 output
The table 4.10 shows the Bayesian convergence distribution of the “Over
All Mediated Mobile QUAL” Regression Model. In this research the
researcher has adopted for the procedure of assessing convergence of
MCMC algorithm of maximum likelihood. To estimate the MCMC
convergence the researcher has adopted two methods namely, convergence
in distribution, convergence of posterior summaries. The values of posterior
mean accurately estimate the Over All Mediated Mobile QUAL SEM
model. From the above table the highest value of Convergence Statistics
(C.S) is 1.001 which is less than the 1.002 conservative measures (Gelman
et al. 2004).
4.3.7. Posterior Diagnostic Plots of “Over All Mediated Mobile QUAL” Model
To check the convergence of the Bayesian MCMC method the posterior
diagnostic plots are analysed. The following figures (figure to 4.28 and 4.29)
142
show the posterior frequency polygon of the distribution of the parameters
across the 69000 samples. The Bayesian MCMC diagnostic plots reveals
that for all the figures the normality is achieved, so the structural equation
model fit is accurately estimated.
Figure 4.28: Posterior frequency polygon distribution of the mediating factor
Fringe Benefit Services (FBS) and Service Loyalty (SL) (W49)
Figure 4.29: Posterior frequency histogram distribution of the mediating factor Fringe Benefit Services (FBS) and Service Loyalty (SL) (W49)
The trace plot also called as time-series plot shows the sampled values of a
parameter over time. This plot helps to judge how quickly the MCMC
Fre
quen
cyF
requ
ency
143
procedure converges in distribution. The following figure (figure 4.30)
shows the trace plot of the Over All Mediated Mobile QUAL for the
mediated factor Fringe Benefit Services with Service Loyalty dimension
across 69000 samples. If we mentally break up this plot into a few
horizontal sections, the trace within any section would not look much
different from the trace in any other section. This indicates that the
convergence in distribution takes place rapidly. Hence the Over All
Mediated Mobile QUAL MCMC procedure very quickly forgets its starting
values.
Figure 4.30: Posterior trace plot of the Over All Mediated Mobile QUAL s for
the mediated factor Fringe Benefit Services (FBS) and Service Loyalty (SL) (W49)
To determine how long it takes for the correlations among the samples to die
down, autocorrelation plot which is the estimated correlation between the
sampled value at any iteration and the sampled value k iterations later for k
= 1, 2, 3,…. is analysed for the Over All Mediated Mobile QUAL
144
regression model. The figure (figure 4.31) shows the correlation plot of the
Over All Mediated Mobile QUAL model for the mediated factor Fringe
Benefit Services with Service Loyalty dimension across 69000 samples.
The figure exhibits that at lag 100 and beyond, the correlation is effectively
0. This indicates that by 90 iterations, the MCMC procedure has essentially
forgotten its starting position. Forgetting the starting position is equivalent
to convergence in distribution. Hence it is ensured that convergence in
distribution was attained, and that the analysis samples are indeed samples
from the true posterior distribution.
Figure 4.31: Posterior autocorrelation plot of the Over All Mediated Mobile
QUAL for the mediated factor Fringe Benefit Services (FBS) and Service Loyalty (SL) (W49)
Even though marginal posterior distributions are very important, they do not
reveal relationships that may exist among the two parameters. The summary
table given in table 4.10 and the frequency polygons given in the figure 4.32
and figure 4.33 describe only the marginal posterior distributions of the
parameters. Hence to visualize the relationships among pairs of Parameters
Cor
rela
tion
145
in two-dimensional. The surface plots following figures (figure 4.32 and
figure 4.33) provides bivariate marginal posterior plots of the Over All
Mediated Mobile QUAL model for the mediated factor Fringe Benefit
Services with other dimensions across 69000 samples. From the two figures
it reveals that the two-dimensional surface plots also signifies the
interrelationship between the variable of Fringe Benefit Services (FBS) with
the other dimensions Service Loyalty (SL) and Service Quality (SQ).
Figure 4.32: Two-dimensional surface plot of the marginal posterior distribution
of the Fringe Benefit Services (FBS) with the Service Loyalty (SL) and Service Quality (SQ) (W49).
146
Figure 4.33: Two-dimensional histogram plot of the marginal posterior distribution of the Fringe Benefit Services (FBS) with the Service Loyalty (SL)
and Service Quality (SQ) (W49).
The following figure 4.34 displays the two-dimensional plot of the bivariate
posterior density across 69000 samples. Ranging from dark to light the three
shades of gray represent 50%, 90%, and 95% credible regions, respectively.
From the figure, it reveals that the sample respondent’s responses are
normally distributed.
Figure 4.34: Two-dimensional contour plot of the marginal posterior distribution of the Fringe Benefit Services with the Service Loyalty (SL) and Service Quality
(SQ) (W49).
147
The various diagnostic plots featured from figure 4.28 to figure 4.34 of the
Bayesian estimation of convergence of MCMC algorithm confirms the fact
that the convergence takes place and the normality is attained. Hence
absolute fit of the Over All Mediated Mobile QUAL regression model. From
the Over All Mediated Mobile QUAL regression model which is empirically
tested with mediating factor Fringe Benefit Services (FBS) with the
dimensions of Service Network Communication (SNC), Technology
Adoption (TA), Customer Care Services (CCS), Service Quality (SQ),
Brand Switching Attitude & MNP (BSA) and Service Loyalty (SL) it is
evident that the Mobile Service Provider should concentrate on the Fringe
Benefit Service (FBS) as the most important aspect of Service Loyalty on
Mobile Service Provider in Cauvery Delta Districts in Tamil Nadu.
4.3.8. Hypotheses testing for the Mobile QUAL Model:
A mediator hypothesis is supported if the interaction path (SNC, TA, CCS,
SQ, BSA, SL and Fringe Benefit Services) are significant. There may also
be significant main effects for the predictor (Service Loyalty) and mediator
(Fringe Benefit Services). Therefore, this research seeks to explore whether
the relationship between Service Loyalty (SL) and SNC, TA, CCS, SQ,
BSA, SL are fully or partially mediated by Fringe Benefit Services.
Hypothesis 1: The service Loyalty dimension Service Network
Communication (SNC) is mediated by Fringe Benefit Services (FBS)
towards attainment of Service Loyalty to the Mobile Service Providers.
148
Hypothesis 2: The service Loyalty dimension Technology Adoption (TA) is
mediated by Fringe Benefit Services (FBS) towards attainment of Service
Loyalty to the Mobile Service Providers.
Hypothesis 3: The service Loyalty dimension Customer Care Services
(CCS) is mediated by Fringe Benefit Services (FBS) towards attainment of
Service Loyalty to the Mobile Service Providers.
Hypothesis 4: The service Loyalty dimension Service Quality (SQ) is
mediated by Fringe Benefit Services (FBS) towards attainment of Service
Loyalty to the Mobile Service Providers.
Hypothesis 5: The service Loyalty dimension Brand Switching Attitude &
MNP (BSA) is mediated by Fringe Benefit Services (FBS) towards
attainment of Service Loyalty to the Mobile Service Providers.
Hypothesis 6: The service Loyalty dimension Service Network
Communication (SNC) positively influences the Service Loyalty to the
Mobile Service Providers.
Hypothesis 7: The service Loyalty dimension Technology Adoption (TA)
positively influences the Service Loyalty to the Mobile Service Providers.
Hypothesis 8: The service Loyalty dimension Customer Care Services
(CCS) positively influences the Service Loyalty to the Mobile Service
Providers.
Hypothesis 9: The service Loyalty dimension Service Quality (SQ) positively
influences the Service Loyalty to the Mobile Service Providers.
149
Hypothesis 10: The service Loyalty dimension Brand Switching Attitude &
MNP (BSA) positively influences the Service Loyalty to the Mobile Service
Providers.
Hypothesis 11: The services Loyalty mediating dimension Fringe Benefit
Services (FBS), positively influence the Service Loyalty (SL) to the Mobile
Service Providers.
Hypothesis 12: Including the interaction between dimensions of the service
Loyalty and Fringe Benefit Services (FBS) will explain more of the variance
in Service Loyalty (SL) than the direct influence of dimensions of service
Loyalty or Fringe Benefit Services (FBS) on their own.
4.3.9. Hypotheses Verification for the Mobile QUAL Model
Hypothesis 1: Service Network Communication (SNC) r 2 of 0.00 mediated
through Fringe Benefit Services (FBS) with r 2 of 0.11 positively influence
Mobile Service Providers in Cauvery Delta Districts in Tamil Nadu.
Hypothesis is accepted.
Hypothesis 2: Technology Adoption (TA) r 2 of 0.30 mediated through
Fringe Benefit Services (FBS) with r 2 of 0.11 positively influence Mobile
Service Providers in Cauvery Delta Districts in Tamil Nadu. Hypothesis is
accepted.
Hypothesis 3: Customer Care Services (CCS) r 2 of 0.18 mediated through
Fringe Benefit Services (FBS) with r 2 of 0.11 positively influence Mobile
Service Providers in Cauvery Delta Districts in Tamil Nadu. Hypothesis is
accepted.
150
Hypothesis 4: Service Quality (SQ) r 2 of 0.40 mediated through Fringe
Benefit Services (FBS) with r 2 of 0.11 positively influence Mobile Service
Providers in Cauvery Delta Districts in Tamil Nadu. Hypothesis is accepted.
Hypothesis 5: Brand Switching Attitude & MNP (BSA) r 2 of 0.01 mediated
through Fringe Benefit Services (FBS) with r 2 of 0.11 positively influence
Mobile Service Providers in Cauvery Delta Districts in Tamil Nadu.
Hypothesis is accepted.
Hypothesis 6: Service Network Communication (SNC) has very high
insignificant influence over Mobile Service Providers in Cauvery Delta
Districts in Tamil Nadu Service Loyalty directly with r 2 of 0.15. Hypothesis
is accepted but trivial for the study.
Hypothesis 7: Technology Adoption (TA) has very low insignificant
influence over Mobile Service Providers in Cauvery Delta Districts in Tamil
Nadu Service Loyalty directly with r 2 of 0.05. Hypothesis is accepted but
trivial for the study.
Hypothesis 8: Customer Care Services (CCS) has very low insignificant
influence over Mobile Service Providers in Cauvery Delta Districts in Tamil
Nadu Service Loyalty directly with r 2 of 0.05. Hypothesis is accepted but
trivial for the study.
Hypothesis 9: Service Quality (SQ) has very high insignificant influence
over Mobile Service Providers in Cauvery Delta Districts in Tamil Nadu
Service Loyalty directly with r 2 of 0.14. Hypothesis is accepted but trivial
for the study.
Hypothesis 10: Brand Switching Attitude & MNP (BSA) has very low
insignificant influence over Mobile Service Providers in Cauvery Delta
151
Districts in Tamil Nadu Service Loyalty directly with r 2 of 0.05. Hypothesis
is accepted but trivial for the study.
Hypothesis 11: Mediator Fringe Benefit Services (FBS) has an r 2 of 0.11
with the outcome Mobile Service Providers in Cauvery Delta Districts in
Tamil Nadu and its mediating effect along with other variables with Mobile
Service Providers in Cauvery Delta Districts in Tamil Nadu is confirmed.
Hypothesis 12: There is no significance for the relationships between All
Hypotheses are accepted in Mobile QUAL models of Mobile Service
Providers in Cauvery Delta Districts in Tamil Nadu.
4.3.10. Managerial Implications for Mobile Service Provider
Mobile-QUAL is a valid instrument to measure service quality in cellular
mobile telephone operators in Tamil Nadu. Inclusion of additional
dimensions and items make it more comprehensive for application in
telecommunication services. The dimensions of Service Network
Communication (SNC), Technology Adoption (TA), Customer Care
Services (CCS), Service Quality (SQ) and Brand Switching Attitude &
MNP (BSA) and the mediating parameter Fringe Benefit Services (FBS)
and the outcome of Service Loyalty (SL) are important aspects that need
managerial attention to attract and retain customers. The regulators in
telecommunication industry should take appropriate measure to include
these dimensions in undertaking objective assessment of quality of
152
service of cellular mobile telephone operators in Tamil Nadu in
safeguarding customers’ interest.
The adapted Mobile-QUAL with additional dimensions was found to be a
valid instrument to measure service quality in mobile phone services. The
dimensions of tangible, assurance, responsiveness, empathy, convenience,
and network quality found to have positive and statistically significant
relationship with mobile phone users’ perceived service quality.
Convenience and network quality dimensions found to be relatively most
important dimensions affecting users’ perception. The dimension of
reliability did not reflect significant effect on customers’ perception of
quality.
The competitive environment in mobile phone industry in Tamil Nadu
has become intense. Mobile operators are vigorously investing in network
coverage, upgradation, and quality, competitive pricing, and diversified
offering to attract new customers and retain the existing customers. The
results of this study substantiate the response strategy of mobile phone
operators to enhance quality of network, the tangible, responsiveness, and
assurance, empathy, and convenience dimensions of services that are vital
to affect the customers’ perception of quality of service.
The proactive role of Indian Telecom Sector, consumers’ awareness to
higher quality of services, and the prospects of new entrants in the market
153
will enhance the existing level of competition. The emerging competitive
market environment will offer challenges to mobile phone operators to
proactively pursue customer focused strategy for building and sustaining
competitive advantage based on benchmark quality of service dimensions
that the results of this research indicate.
The results of the study reflect that the issue of provisioning of promised
service, timely, accurately, and dependably will need highest priority.
Earlier researches indicate that reliability positively and significantly
affects customers’ perception of service quality of mobile phone users.
Because the reliability has been established as the driver of mobile phone
service quality, Mobile operators will need to pursue two pronged
strategy with internal focus on improved processes, and external focus
on customers’ needs. An aggressive strategy is needed to enhance the
trustworthiness of mobile phone operators by keeping customers’ best
interest at heart, providing customized services and exemplary behaviour of
contact personnel to make the interaction a memorable experience. The
mobile operators should also focus on other dimensions of tangible;
responsiveness, assurance, and empathy because these aspects significantly
affect customers’ perception of service quality of mobile phone service
provider.
154
Employees play a leading role in telecommunication service. The role of
frontline staff becomes extremely important in making the interaction with
customer pleasing. The staffs need to know the importance of their role in
service delivery. Management should ensure that human resources
dimensions are addressed to optimize the service delivery by staff.
The study established that Mobile-QUAL with additional dimensions is a
reliable instrument for measurement of service quality dimensions in
telecommunication industry in Tamil Nadu. Changing customers have
made the service quality a fluid phenomenon. The competitive environment
demand constant assessment of service quality to meet rapid changes in
customers’ demand. It is essential that service quality of mobile phone
users be evaluated on regular basis to identify weaknesses, and emerging
trends in the service. The regular service quality assessment enables
organizations to align to the changing customers needs (Dutka & Frankel,
1993). Because of the growing level of competition that can be observed in
Iranian telecommunication industry, mobile phone operators should make
efforts to continuously improve the level of service quality offered to
their subscribers. However, a basic principle of quality management is that
to improve quality, it must first be measured. On the basis of the need to
develop specific measurement tools for different services), this study aimed
at developing and validating a model specifically for measuring mobile
telecommunication service quality. A multidimensional model has been
155
proposed (Mobile-QUAL) based on an extensive literature review and
then tested and validated by the survey data collected through Indian
mobile phone subscribers in Tamil Nadu. This model provides a very
useful tool, for both researchers and practitioners, for measuring and
managing service quality in mobile telecommunication sector.
Finding of this study showed that mobile phone subscribers for m their
service quality perceptions based on their evaluations of seven primary
dimensions including: network quality, value-added service, pricing
plans, employees’ competency, billing system, customer services and
service convenience. According to developed Mobile – QUAL scale,
mobile telecommunication service quality is a second-order factor
underlying these seven dimensions. Each of the seven identified and
ve r i f i ed d imens ions h a d s ign i f i can t l o a d i n g on second-order
factor. For practitioners, the twenty one items across seven factors can
serve as a useful diagnostic purpose. They can use the validated scale to
measure and improve service quality.
The results of confirmatory factor analysis indicated that value- added
services is the most important factor driving customers’ perceived service
quality (Mobile-QUAL), followed by pricing plans and service
convenience. These findings indicate that enhancing quality of value-
added services can provide mobile phone operators with competitive
156
advantages over their competitors. Iranian mobile phone operators have
been struggling over the past several years to improve their network quality
through massive equipment investments. However, the results of this
study show that network quality is the least important factor in customers‟
perception of service quality. Thus, mobile service providers must
concentrate their efforts on developing value-added services, diversifying
pricing plans and increasing service convenience to improve service quality
and achieve customer satisfaction.
They did not find any significant relationship between customers
evaluation of value-added services and their overall perceived service
quality neither their satisfaction. But in contrast, they concluded that
network quality is the most effecting factor on customer satisfaction and
loyalty. On the other hand, findings similar to the results of this study
were reported by Kim et al. (2004) and Lim et al. (2006) that confirm a
positive effect of value- added services on customer satisfaction.
Mobile technology has developed rapidly and provided a wealth of
opportunities for mobile service providers. As a result, many mobile phone
users enjoy access to value-added services in addition to basic voice
communication. Value-added services could be separated into four main
types including communicating services, system based services, downloads
and subscription services and internet access services (MoEA, 2007).
157
Communicating services refer to services that subscribers use other than
traditional voice calls to communicate through video, pictures or text such
as SMS, MMS and video call. System based services refer to services
provided through setup on the operators such as ring back tones and two
phone ringing. Downloads and subscription services refer to services such
as downloading ringtones, wallpaper and games or subscription to
newsletter and weather forecasting information. Internet access services
refer to mobile internet provided by operators through WAP, GPRS or 3G
internet access. Through developing and improving quality of mentioned
value-added services, a mobile phone operator will stand a much better
chance of retention and acquisition of more subscribers.
Furthermore, findings of this study showed that customers‟ evaluation of
pricing plans and service convenience has important role in forming their
overall perceived service quality. These results are similar to the findings
of Santouridis and Trivellas (2010) which found pricing plans as a
significant determinant in customer satisfaction and also similar to the
findings of Negi (2009) which confirmed the importance of service
convenience in driving customers perceived service quality. Thus, mobile
phone operators must try to offer various pricing plans that meet
customers’ need, provide easy procedures for changing plans and deliver
required information about pricing plans to improve customers‟ evaluation
of pricing plans. Also, they must give great attention to issues such as
158
sufficient number of retailers or kiosks, sufficient methods and locations
for bill payment and ease of subscribing and changing services. The
multination firm should take of Indian socio-economic culture before fixing
the price and other formation activities in telecom sector.
4.4. Conclusion
The researcher has empirically analysed the objectives with help of
hypotheses and statistical tool for the study. The study reveals that the
conceptual research models are empirically proved. These findings are
interpreted in the chapter for the strategic economic planning for Indian
Mobile Service Provider.
159
CHAPTER V
FINDINGS, STRATEGIC PLANNING AND CONCLUSIONS
5.1. Introduction
The findings obtained from the statistical test performed on the hypotheses,
The Structural Equation Model Mobile QUAL Mediated Model and
Regression Model are given. Based on the findings the policy frameworks
for the stakeholders to enhance the quality of Mobile Service Provider in
Tamil Nadu, India are summarized in this chapter. Final section will bring
the scope for future research.
5.2. Findings and Conclusion for the Study
The findings of this study have a number of implications for managers. The
government and private stakeholders need not competent each other in
Mobile Service Provider Sector. They have to create their own niche market.
This is possible only under non price competition especially consistent
development of their service quality and delivery of the customized service.
The study reveals that customers’ satisfaction is most significant predictor of
Mobile Service Provider sector.
5.2.1. Findings from the Trend analysis of Mobile Service Provider
Trend analysis figure 4.1 reveals the trends in the Wire line phones in
Growth of Telecom Sector in India. The trend plot that shows the
160
original data, and the fitted trend line, the output also displays the
fitted trend equation Yt = 42.535-1.55700*t and three measures help
to determine the accuracy of the fitted values: 0.832504, 0.303600,
and 0.154526. The Wire line phones data show a general down trend,
though with an evident cyclic factor. The trend model appears to fit
well to the overall trend. The above chart shows the amount of Wire
line phones in Growth of Telecom Sector in India (in millions) from
2007 - 2011.
Trend analysis figure 4.2 reveals the trends in the Wireless phones in
Growth of Telecom Sector in India. The trend plot that shows the
original data, and the fitted trend line, the output also displays the
fitted trend equation Yt = 58.4+170*t and three measures help to
determine the accuracy of the fitted values: 13.76, 46.22, and
2386.15. The Wireless phones data show a general upward trend,
though with an evident cyclic factor. The trend model appears to fit
well to the overall trend. The above chart shows the amount of
Wireless phones in Growth of Telecom Sector in India (in millions)
from 2007 - 2011.
Trend analysis figure 4.3 reveals the trends in the Gross total of
phones in Growth of Telecom Sector in India. The trend plot that
shows the original data, and the fitted trend line, the output also
displays the fitted trend equation Yt = -15.8+168*t and three
161
measures help to determine the accuracy of the fitted values: 11.81,
45.91, and 2355.79. The Gross total data show a general upward
trend, though with an evident cyclic factor. The trend model appears
to fit well to the overall trend. The above chart shows the amount of
Gross total of phones in Growth of Telecom Sector in India (in
millions) from 2007 - 2011.
Trend analysis figure 4.4 reveals the trends in the Rural Tele Density
in Telecom Sector in India. The trend plot that shows the original
data, and the fitted trend line, the output also displays the fitted trend
equation Yt = -4.43+7.54*t and three measures help to determine the
accuracy of the fitted values: 18.7934, 2.2712, and 5.7999. The Rural
Tele Density data show a general upward trend, though with an
evident cyclic factor. The trend model appears to fit well to the
overall trend. The above chart shows the amount of Rural Tele
Density of Telecom Sector in India (in percentage) from 2007 - 2011.
Trend analysis figure 4.5 reveals the trends in the Urban Tele Density
in Telecom Sector in India. The trend plot that shows the original
data, and the fitted trend line, the output also displays the fitted trend
equation Yt = 12.61+28.1*t and three measures help to determine the
accuracy of the fitted values: 7.6977, 6.5256, and 47.8541. The Urban
Tele Density data show a general upward trend, though with an
evident cyclic factor. The trend model appears to fit well to the
162
overall trend. The above chart shows the amount of Urban Tele
Density of Telecom Sector in India (in percentage) from 2007 - 2011.
Trend analysis figure 4.6 reveals the trends in the Total Tele Density
in Telecom Sector in India. The trend plot that shows the original
data, and the fitted trend line, the output also displays the fitted trend
equation Yt = 0.07+13.9*t and three measures help to determine the
accuracy of the fitted values: 10.8618, 3.6664, and 15.0269. The
Total Tele Density data show a general upward trend, though with an
evident cyclic factor. The trend model appears to fit well to the
overall trend. The above chart shows the amount of Total Tele
Density of Telecom Sector in India (in percentage) from 2007 - 2011.
Trend analysis figure 4.7 reveals the trends in the FDI (Foreign Direct
Investment) in Telecom Sector in India. The trend plot that shows the
original data, and the fitted trend line, the output also displays the
fitted trend equation Yt = -260+2299*t and three measures help to
determine the accuracy of the fitted values: 8, 325, and 147194. The
FDI data show a general upward trend, though with an evident cyclic
factor. The trend model appears to fit well to the overall trend. The
above chart shows the amount of FDI in Telecom Sector in India (in
millions) from 2007 - 2011.
Trend analysis figure 4.8 reveals the trends in the Funds Collected as
USL in Telecom Sector in India. The trend plot that shows the
163
original data, and the fitted trend line, the output also displays the
fitted trend equation Yt = 3935+472*t and three measures help to
determine the accuracy of the fitted values: 6, 276 and 111290. The
Funds Collected as USL data show a general upward trend, though
with an evident cyclic factor. The trend model appears to fit well to
the overall trend. The above chart shows the amount of Funds
Collected as USL in Telecom Sector in India (in crore) from 2007 -
2011.
Trend analysis figure 4.9 reveals the trends in the Funds Allocated in
Telecom Sector in India. The trend plot that shows the original data,
and the fitted trend line, the output also displays the fitted trend
equation Yt = 685+431*t and three measures help to determine the
accuracy of the fitted values: 15.6, 257.6 and 84814.0. The Funds
Allocated data show a general upward trend, though with an evident
cyclic factor. The trend model appears to fit well to the overall trend.
The above chart shows the amount of Funds Allocated in Telecom
Sector in India (in crore) from 2007 - 2011.
Trend analysis figure 4.10 reveals the trends in the Telecom
Equipment in Telecom Sector in India. The trend plot that shows the
original data, and the fitted trend line, the output also displays the
fitted trend equation Yt = 234198+66418*t and three measures help
to determine the accuracy of the fitted values: 12, 44138 and
164
2281846968. The Telecom Equipment data show a general upward
trend, though with an evident cyclic factor. The trend model appears
to fit well to the overall trend. The above chart shows the amount of
Telecom Equipment in Telecom Sector in India (in millions) from
2007 - 2011.
Trend analysis figure 4.11 reveals the trends in the Telecom
Equipment Production in Telecom Sector in India. The trend plot that
shows the original data, and the fitted trend line, the output also
displays the fitted trend equation Yt = 987+33249*t and three
measures help to determine the accuracy of the fitted values: 22, 9643
and 117825422. The Telecom Equipment Production data show a
general upward trend, though with an evident cyclic factor. The trend
model appears to fit well to the overall trend. The above chart shows
the amount of Telecom Equipment Production in Telecom Sector in
India (in millions) from 2007 - 2011.
Trend analysis figure 4.12 reveals the trends in the Public Sector
Units in Telecom Network in India. The trend plot that shows the
original data, and the fitted trend line, the output also displays the
fitted trend equation Yt = 532.1+139*t and three measures help to
determine the accuracy of the fitted values: 4.03, 37.41 and 1615.61.
The Public Sector Units Telecom network data show a general
upward trend, though with an evident cyclic factor. The trend model
165
appears to fit well to the overall trend. The above chart shows the
amount of Public Sector Units in Telecom network in India (in lakh)
from 2007 - 2011.
Trend analysis figure 4.13 reveals the trends in the Private Sector
Units in Telecom network in India. The trend plot that shows the
original data, and the fitted trend line, the output also displays the
fitted trend equation Yt = -690+1543*t and three measures help to
determine the accuracy of the fitted values: 15, 422 and 198235. The
Private Sector Units Telecom network data show a general upward
trend, though with an evident cyclic factor. The trend model appears
to fit well to the overall trend. The above chart shows the amount of
Private Sector Units in Telecom network in India (in lakh) from
2007-2011.
Trend analysis figure 4.14 reveals the trends in the Total Telecom
network in India. The trend plot that shows the original data, and the
fitted trend line, the output also displays the fitted trend equation Yt =
-158+1682*t and three measures help to determine the accuracy of
the fitted values: 12, 459 and 235578. The Private Sector Units
Telecom network data show a general upward trend, though with an
evident cyclic factor. The trend model appears to fit well to the
overall trend. The above chart shows the amount of Private Sector
Units in Telecom network in India (in lakh) from 2007 - 2011.
166
Trend analysis figure 4.15 reveals the trends in the Fault rate New
Delhi Units in Telecom network in India. The trend plot that shows
the original data, and the fitted trend line, the output also displays the
fitted trend equation Yt = -6.92+0.306*t and three measures help to
determine the accuracy of the fitted values: 13.7827, 1.1448 and
2.4807. The Fault rates New Delhi Units Telecom network data show
a general upward trend, though with an evident cyclic factor. The
trend model appears to fit well to the overall trend. The above chart
shows the amount of Fault rate New Delhi Units in Telecom network
in India from 2007 - 2011.
Trend analysis figure 4.16 reveals the trends in the Fault rate Mumbai
Units in Telecom network in India. The trend plot that shows the
original data, and the fitted trend line, the output also displays the
fitted trend equation Yt = 11.27-0.961000*t and three measures help
to determine the accuracy of the fitted values: 13.5570, 1.0576 and
1.3119. The Fault rates Mumbai Units Telecom network data show a
general downward trend, though with an evident cyclic factor. The
trend model appears to fit well to the overall trend. The above chart
shows the amount of Fault rate Mumbai Units in Telecom network in
India from 2007 - 2011.
Trend analysis figure 4.17 reveals the trends in the BSNL-Fund
Requirement Telecom network in India. The trend plot that shows the
167
original data, and the fitted trend line, the output also displays the
fitted trend equation Yt = 17418.7+1731*t and three measures help to
determine the accuracy of the fitted values: 0.24, 54.52 and 4296.78.
The BSNL-Fund Requirement Telecom network data show a general
upward trend, though with an evident cyclic factor. The trend model
appears to fit well to the overall trend. The above chart shows the
amount of BSNL-Fund Requirement in Telecom network in India (in
crore) from 2007 - 2011.
Trend analysis figure 4.18 reveals the trends in the MTNC-Fund
Requirement Telecom network in India. The trend plot that shows the
original data, and the fitted trend line, the output also displays the
fitted trend equation Yt = -908+1324*t and three measures help to
determine the accuracy of the fitted values: 79, 1743 and 4122013.
The MTNC-Fund Requirement Telecom network data show a general
upward trend, though with an evident cyclic factor. The trend model
appears to fit well to the overall trend. The above chart shows the
amount of MTNC-Fund Requirement in Telecom network in India (in
crore) from 2007 - 2011.
5.2.2. Findings from the Regression Model of the “Mobile QUAL”
Mediated Structural Model
The regression analysis revealed that the Fringe Benefit Services on
the various dimensions of Mediated Model Mobile Service Provider,
168
Fringe Benefit Services (FBS) influenced 0.11 of the Service Loyalty
(SL), followed by Service Quality (SQ) which explains 0.40 of the
Fringe Benefit Services (FBS) the R2 value of 0.11 is displayed above
the box Service Loyalty (SL).
The regression analysis revealed that the Fringe Benefit Services on
the various dimensions of Mediated Model Mobile Service Provider,
Fringe Benefit Services (FBS) influenced 0.11 of the Service Loyalty
(SL), followed by Service Quality (SQ) which explains 0.40 of the
Fringe Benefit Services (FBS) the R2 value of 0.11 is displayed above
the box Service Loyalty (SL).
The regression analysis results suggest that the relationships between
the dimensions of Mobile Service Provider, procedure and formalities
(Service Quality (SQ) => Fringe Benefit Services (FBS) = 0.40)
resulted significant impact on the mediated factor Fringe Benefit
Services (FBS).
5.2.3. Findings from the Regression Model of the “Over All Mediated
Mobile QUAL” Model
The regression co-efficient 0.31 signifies the impact of mediating
factor Fringe Benefit Services (FBS) on the other Dimensions
towards Service Loyalty of the Mobile Service Provider.
Regression Model of the “Over All Mediated Mobile QUAL” Model
is revealed that all the criterions of goodness-of-fit statistics and other
169
measures of statistics are acceptable for the Over All Mediated
Mobile QUAL Structural Equation Model.
The Root Mean Squared error of Approximation (RMSEA) Value is
.043<0.05 Accepted level of good fit.
5.2.4. Findings from Conceptual Model
The conceptual research model empirically proved (figure 5.1). Thus, the
present study has focused on Mobile Service Provider Customer (Service)
Loyalty which is considered as an important indicator for both Government
(Public) and Private. The researcher identify that the Fringe Benefit
Services is the mediating factor for the Mobile Service Provider (Telecom)
sector in the study area. Hence Mobile Service Provider (Telecom) Sector
would be concentrated on Fringe Benefit Services to improve the Customer
loyalty for the growth of Mobile Service Provider (Telecom) Sector.
Figure 5.1 : Conceptual Model Research Model
Technology Adoption
Customer Care Services
Service Quality
Brand Switching Attitude & MNP
Demographic Variable Fringe Benefit
Services
Service Loyalty
Service Network Communication
170
5.3. Strategic Planning For Improving Mobile Service Provider Loyalty
In India, the Telecom Sector face a challenge of providing services to a
broad range of customers, which varies from suave community and high net
worth individuals to low-end publics who are catered to by stakeholders.
Over time, a series of initiatives have been taken to improve the quality of
customer service comprises the following in the figure:
Fig 5.2 Strategic Planning for The Mobile Service Provider Loyalty
Strategic Planning for the Promoting Mobile Service Provider
Service Loyalty
Service Quality
Service Quality
Service Loyalty
Technology Adoption
Service Net Work
Communication
Brand Switching Attitude & MNP
Customer Care
Service
s
Fringe Benefit Services
171
5.4. Limitations and Directions for Further Research Academician point of view
This study has some limitations on the generalize ability of the findings.
First, since the data were gathered in a specific geographic area of Tamil
Nadu, the results may be specific for this area. In order to generalize the
proposed model, further researches should replicate this model in other
populations and provinces. Second, the possibility to generalize the results
to other countries with different characteristics (such as different cultural
context, different level of economic development) needs to be verified, by
re-testing the proposed model. Another limitation of this study could be the
significant difference between the population of men and women in survey
sample. This happened because women were less likely to cooperate with
interviewers and complete the questionnaire.
Further researchers could examine the relationship between Mobile- QUAL,
customers‟ satisfaction and other relevant variables such as customer
loyalty. Also, future research could focus on the antecedents of mobile
telecommunication service quality and how customers form their
perceptions about each of the Mobile- QUAL in Indian context.
172
5.5. Conclusion
Quality is generally regarded as being a key factor in the creation of worth
and in influencing customer satisfaction. Hence, the telecommunication
industry in India has to be strategically positioned to provide quality
services to satisfy customers. To provide improved quality service,
telecommunication companies need to investigate degree of customers’
sensitivity and expectations toward service quality. Armed with such
information, telecommunication outfits are then able to strategically focus
service quality objectives and procedures to fit the Indian market.
References Accenture. (2008). High Performance in the Age of Customer Centricity, Customer
Satisfaction Research.
ACMA. (2008). Consumers express Consumers express overall satisfaction with telecommunication services but mobile and internet services of concern to rural sector.
ACMA. (2008). Consumers express Consumers express overall satisfaction with telecommunication services but mobile and internet services of concern to rural sector.
Aisha Khan and Ruche Chaturvedi (2005) Customer Relationship Management – An India Perspective, Excel Books, Indian Infrastructure, Oct. 2005, pg. 26.
Akroush, M. N., Al-Mohammad, S. M., Zuriekat, M. I., & Abu-Lail, B. N. (2011). An empirical model of customer loyalty in the Jordanian mobile telecommunications market. International Journal of Mobile Communications, 9(1), 76-101.
Akwule R U (1992), “Telecommunications in Kenya; Development and policy issue” in telecommunication policy, sept- oct.pp.603-11.
Andaleeb, S.S., & Basu, A.K. (1994). Technical complexity and consumer knowledge as moderators of service quality evaluation in the automobile service industry. Journal of Retailing, 70(4), 367-81.
Anderson, EW & Sullivan, M. (1993) “The Antecedents and Consequences of Consumer Satisfaction for Firms”, Mark Sci.12:pp.125–43 (spring).
Anita seth (2007), “quality of service parameters in cellular mobile communication”, international journal of mobile communications, vol-5, issue 1, jan-2007.
Annual Report in MoCIT (Year: 2011 – 2012), Department of Telecommunications Ministry of Communications & Information Technology, Government of India, New Delhi.
Arbuckle, J. L., &Wothke, W. (2006) Amos 7.0 user's Guide, Chicago: Small Waters Corporation.
Arulraj, A & Lourthuraj, S.A. (2012) “Investment Management Service Quality in Equities in Secondary Market”. Serial Publication, New Delhi.
Arulraj, A & Sarangarajan, V (2010), “Strategic Measurement Financial Performance”-. Serial Publication, New Delhi.
Arulraj, A & Sukumaran, A. (2010), “A Study on Mediating Service Quality in Fertilizer Marketing in Tamilnadu, India, (Unpublished Doctoral dissertation, Bharathidasan University, Trichy, India).
Arulraj, A. & Ananth, A. (2011), Strategic Approach on Service Quality Management in Rural Banking, Serial Publication, New Delhi.
Arulraj, A. & Parthiban, B. (2010) “A Study on Mediating Effects on Purchase Decision of Bikes in Kancheepuram Dt., Tamil Nadu”, (Unpublished Doctoral dissertation, Bharathidasan University, Trichy, India)
Arulraj, A. & Prabaharan, B. (2010) Methodology for Strategy Service Quality in Tourism, Serials Publication, New Delhi
Arulraj, A. & Ramesh, R (2012), Strategic Planning for Service Loyalty on Insurance, Serials Publication, New Delhi.
Arulraj, A. & Suresh Kumar, V. (2010), Social Empowerment through Housing Finance Service Quality, Serials Publication, New Delhi.
Arulraj,A & Ilavenil, R (2013) “A Study On Strategic Financial Performance Of Public Sector Banks In India”. (Unpublished Doctoral Dissertation, Bharathidasan University, Trichy, TN, India).
Arulraj,A & Rethina Sivakumar, R (2012) “A Study on Mediating Effects on Service Loyalty on Healthcare Services in Tamilnadu, India”. (Unpublished Doctoral Dissertation, Bharathidasan University, Trichy, TN, India)
Arulraj,A & Santhanalakshmi, M, (2013) “A Study Of Mediating Effects On Economic Service Quality For The Sustainability Of Self Help Groups In Central Districts Of Tamil Nadu”. (Unpublished Doctoral Dissertation, Bharathidasan University, Trichy, TN, India).
Arulraj,A & Sethuraman, M (2013) “A Study On Mediating Effects On Poverty Reduction In Mahatma Gandhi National Rural Employment Guarantee Programme At Cauvery Delta Districts In Tamilnadu”. (Unpublished Doctoral Dissertation, Bharathidasan University, Trichy, TN, India).
Arulraj,A & Thanga Prasath, R (2012) “A Study on Mediating Effects on Service Loyalty in Grocery Retailing in Thanjavur District, Tamilnadu”. (Unpublished Doctoral Dissertation, Bharathidasan University, Trichy, TN, India)
Arulraj,A. and Senthil Kumar, N. (2009) Methodology for Service Quality Measurement of higher education in India, Serials Publication, New Delhi.
Arulraj. A, Thiyagarajan .V, (2012) "Strategies Measurement of non-banking finance sector in India", Serials Publication, New Delhi.
As Navin (1995), “impact of strength of Ethnic identification on Hispanic shopping Behavior”, Journal of Retailing, Vol-70(4), 383-393.
ASEAN India synergy sectors Report 2005.
Associated chambers of commerce and industry of india 2005.
Aydin, S., & Özer, G. (2005). The analysis of antecedents of customer loyalty in the Turkish mobile telecommunication market. European Journal of Marketing, 39(7/8), 910-925.
Babakus, E., & Boller, G. W. (1992). An empirical assessment of the SERVQUAL scale. Journal of Business Research, 24, 235-268.
Babakus, E., & Mangold, W.G. (1992). Adapting the SERVQUAL scale to hospital services: an empirical investigation. Health Service Research, 26(6), 767-80.
Babakus, Emin, and Gregory W. Boiler. 1992. An empirical assessment of the SERVQUAL scale. Journal of Business Research, 24, 253-268.
Bagozzi, R.P. (1994): Structural equation models in marketing research: basic principles. In R.P. Bagozzi (Ed.): Principles of Marketing Research. Cambridge: Basil Blackwell, 317-385.
Barnhoorn, C. (2006). Customer satisfaction increases in the Telecommunications Industry.
Baron, R. M. & Kenny, D. A. (1986) “The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical.
Batista-Foguet, J.M. and Coenders, G. (2000): Modelos de Ecuaciones Estructurales [Structural Equation Models]. Madrid, Spain: La Muralla.
Baumann, C., Elliott, G. &Hamin, H. (2011) ‘Modelling Customer Loyalty in Financial Services: A Hybrid of Formative and Reflective Constructs’, International Journal of Bank Marketing, Vol. 29, Issue 3: pp. 247-267.
Bentler PM. (1995) EQS: Structural Equations Program Manual, Version 5.0, BMDP Statistical Software, Los Angeles.
Bepko and Charlene Pleger (2002), “Service Intangibility and Its Impact on Consumer Expectations of Service Quality”, Journal of Service Marketing, Vol. 14, No. 2, pp. 9-26.
Berkley, B.J. & Gupta, A. (1994) “Improving Service Quality with Information Technology”, International Journal of Information Management, Vol. 14: pp. 109-21.
Berry, Leonard L. and A. Parsuraman (1991), Marketing Services – Competing Through Quality, New York: Free Press.
Berry, L.L. (1995), “Relationship marketing of services: growing interests, emerging perspectives”, Journal of the Academy of Marketing Science, Vol.23, No.4, pp.236-45.
Berry, L.L., Parasuraman, A., Zeithaml, V.A., & Adsit, D. (1994). Improving service quality in America: lessons learned. Academy of Management Executive, 8(2), 32-52.
Bhatia, N. & Bhatia, A., (2000), Lending To Groups, Yojana, New Delhi.
Bickert, Jock (1992), “The Database Revolution,” Target Marketing, May, pp. 14-18.
Bing, M. N., Davison, H. K., LeBreton, D. L., & LeBreton, J. M. (2002) Issues and improvements in tests of mediation, Poster presented at the annual meeting of the Society for Industrial and Organizational Psychology, Toronto, Canada.
Bitner, M.J. &Hubbert, A.R. (1994), “Encounter satisfaction versus overall satisfaction versus quality”, in Rust, R.T. and Oliver, R.L. (Eds), Service Quality: New Directions in Theory and Practice, Sage Publications, Thousand Oaks, CA, pp. 72-94.
Bjorn welenius and Peter A Stern(2001) “Implementing Reforms in the Telecommunications sector”, The world bank, Washington DC.
Bloemer, J.M. & Kasper, H. (1995) “The Complex Relationship between Consumer Satisfaction and Brand Loyalty”, Journal of Economic Psychology, Vol. 16 No. 2: pp. 311-29.
Bojanic, D.C. (1991). Quality measurement in professional services firms, Journal of Professional Services Marketing, 7(2) 27-37.
Bollen, K.A. (1989), Structural Equations with Latent Variables. New York: Wiley.
Boohene, R., & Agyapong, G. (2011). Analysis of the antecedents of customer loyalty of telecommunication industry in Ghana: The case of Vodafone (Ghana). International Business Research, 4(1), 229-240.
Boomsma, A. (1983) On the robustness of LISREL (maximum likelihood estimation against small sample size and non-normality. Amsterdam: Socio-metric Research Foundation, (Doctoral dissertation, University of Groningen, The Netherlands).
Boshoff, C. &Gray, B. (2004)“The Relationships Between Service Quality, Customer Satisfaction and Buying Intentions in the Private Hospital Industry”, South African Journal Business Management, Vol. 35 No. 4: pp. 27-38.
Bowen, J. T. & Chen, S.-L. (2001) "The Relationship between Customer Loyalty and Customer Satisfaction", International Journal of Contemporary Hospitality Management, 13(4): pp.213-217.
Boyd, Walker & Larréché (1995). Advertising, personal selling and salesPromotion, Define PR on (p 352) as non-paid, non-personal stimulation of demand for a product, service or business unit by planting significant news about it or favourable presentation of it in the media (looks suspiciously similar to other definitions of publicity). Later on publicity and PR is used interchangeably (p374/375).
Braff Adam, Passmore William,J, and Simpson Michael (2003), “Going the distance with telecom customers”, The Mckinsey Quarterly,No.4,Pg.83.
Broderick, A.J. & Vachirapornpuk, S. (2002) “Service Quality in Internet Banking: the Importance of Customer Role”, Marketing Intelligence & Planning, Vol. 20 No. 6: pp. 327-35.
Brogowicz, A.A., Delene, L.M. & Lyth, D.M. (1990) “A Synthesised Service Quality Model with Managerial Implications”, International Journal of Service Industry Management, Vol. 1 No. 1: pp. 27-44.
Brown, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models. Newbury Park, CA: Sage Publications.
Brown, S.W., & Swartz, T. (1989). A gap analysis of professional service quality. Journal of Marketing, 53(2), 92-8.
Brown, T., Churchill, G., & Peter, J. (1993). Improving the measurement of service quality. Journal of Retailing, 69(1), 127-39.
Browne, M.W. & Cudeck, R. (1993) Alternative ways of assessing model fit. In K.A. Bollen and J.S. Long (Eds.): Testing Structural Equation Models. Thousand Oaks: Sage: pp. 136-162.
Browne, M.W. and Cudeck, R. (1993): Alternative ways of assessing model fit. In K.A. Bollen and J.S. Long (Eds.): Testing Structural Equation Models. Thousand Oaks: Sage, 136-162.
Business & Economy, “Telecom Czar” 30 July 2005.
Business Today (1992), India Today Group.
Buttle, F. (1996) “SERVQUAL: Review, Critique, Research Agenda”. European Journal of Marketing, 30(1): pp. 8–35.
Buttle, F.&Burton, J. (2002) “Does Service Failure Influence Customer Loyalty?” J. Consumer Behavior. 1(3): pp.217-227.
BW Marketing white book, 2005, pg.54. Cannie and Caplin, 1991, “A Marketing approach to Customer retention”, Journal
of Consumer Marketing (Spring), pp.
Cap Gemini. (2005). Wireless phone users demand more than lower price plans according to German Mobile Cellular Telecommunications Markets. Telecommunication Policy, 25, 249.
Carman, J. M. (1990) “Consumer Perceptions of Service Quality: An Assessment of the SERVQUAL Dimension”, Journal of Retailing, 66(1):pp. 33-55.
Carman, J.M. (1990). Consumer perceptions of service quality: an assessment of the SERVQUAL dimensions. Journal of Retailing, 66(1) 33-55.
Carsten Fink, Aaditya Mattoo and Randeep Rathindran (2001), “Liberalizing Basic Telecommunications: The Asian Experience”, World Bank Policy Research Working Paper 2718.
Caruana, A. (2002) "Service Loyalty: The Effects of Service Quality and the Mediating Role of Customer Satisfaction." European Journal of Marketing, 36(7/8): pp. 811-830.
Caruana, Albert. 2002. Service Loyalty, the effects of service quality and the mediating role of customer satisfaction. European Journal of Marketing, 36 (7/8), 811-828.
Chao-Chan Wu, (2011) “The Impact of Hospital Brand Image On Service Quality, Patient Satisfaction and Loyalty”. African Journal of Business Management Vol. 5(12): pp. 4873-4882.
Chiou, J. S. (2004) "The Antecedents of Consumers’ Loyalty toward Internet Service Providers", Information & Management, 41(6): pp.685-695.
Consumer Report. (2005). There are differences among major carriers.
Cronin, J. and Stephen A. Taylor. 1992. Measuring service quality: A re-examination and extension. Journal of Marketing, 56 (July), 55-68.
Cronin, J. Joseph, Jr., Michael K. Brady and G. Thomas M. Hult. 2000. Assessing the effects of quality, value, and customer satisfaction on behavioural intentions in service environments. Journal of Retailing, 76 (2), 193-218.
Cronin, J.J. & Taylor, S.A. (1994) “SERVPERF versus SERVQUAL: Reconciling Performance-Based and Perceptions-Minus-Expectations Measurement of Service Quality”, Journal of Marketing, Vol. 58: pp. 125-31.
Cronin, J.J., & Taylor, S.A. (1994). SERVPERF versus SERVQUAL: reconciling performance-based and perceptions-minus-expectations measurement of service quality. Journal of Marketing, 58(1), 125-31.
Crosby, L. A., K. R. Evans, and D. Cowles. 1990. Relationship quality in services selling: An interpersonal influence perspective. Journal of Marketing, 54, 68-81.
Crosby, Lawrence, A. and Nancy Stephens (1987), “Effects of Relationship Marketing and Satisfaction, Retention, and Prices in the Life Insurance Industry,” Journal of Marketing Research, (November), pp. 404-411.
Crosby, Lawrence, A., Kenneth R Teas, and Deboprah Gowles, (1993),“Relationship Qualityin Services Selling – An interpersonal influence Perspective”, Journal of Marketing, 52 (April), pp. 21-34.
Customer Satisfaction Index. (2009). U.S. Wireless Contract Regional Customer Satisfaction.
Customer Satisfaction. (2007). Customer Satisfaction with Wireless Service Providers and Wireless Phone Manufacturers in Canada Declines Significantly.
Dabholkar, P. A., Thorpe, D. I., & Rentz, J. O. (1996), A measure of service quality for retail stores: Scale development and validation. Academy of Marketing Science Journal, 24(1), 3-16.
Dabholkar, P.A., Shepherd, C.D. and Thorpe, D.I. (2000) “A Comprehensive Frame-Work for Service Quality: An Investigation of Critical Conceptual and Measurement Issues through A Longitudinal Study”, Journal of Retailing, Vol. 76 No. 2: pp. 131-9.
David L. Kurtz, Kenneth L. Clow (2002). Services Marketing, John Wiley & Sons.
Delgado-Ballester, E. &Munuera-Alemán, J. (2000) "Brand Trust in the Context of Consumer Loyalty", European Journal of Marketing, 35(11/12): pp.1238-1258.
Dhar, Ravi and Klaus Wertenbroach 2006), “Consumer choice between hedonic and utilitarian goods”, Journal of consumer research ,37 (Feb.),pp.60-71.
Dhar, Ravi.Stephen M.Nowlis, and steven J Sherman (1999), “comparison effect on preference construction”, Journal of consumer research 26th dec.pp.293-306.
Diamantopoulos, A. &Siguaw, J.A. (2000) Introducing LISREL, London: Sage Publications.
Dick, Alan S. and Kunal Basu (1994), “Customer Loyality: Toward an Integrated Conceptual Framework”,Journal of the Academy of Marketing Science,22 (Spring), pp. 99-113.
Dirks and Danniel (1991), Advertising and promotion; an integrated marketing communications perspective, 3rd Canadian edition.
Donald A Snyder (2006), “Technology transfer; Lessons from experience – The telecommunication process” 16.dec.2006.
Donald H (1994) , “ Telecommunication libralisation and privatization ; the New Zealand experience in B.Wellenius and P.A.Stern (ed) Implementing reforms in the telecom sector, world bank, Washington, DC, PP.253-60.
Donath B (1999), “Consumer marketing trends”, marketing news 28, pp.14-27.
Donthu, N., & Yoo, B. (1998). Cultural influences on service quality expectations. Journal of Service Research, 1(2), 178-86.
Dossani, R. 2002. Telecommunications Reform in India. Westport, CT: Greenwood Press.
DOT- Annual Report, (2002) Government of India- Department of Telecommunication.
DSTI. (2007). Enhancing Competition in Telecommunication: Protecting and Empowering Consumers.
Dutka, S. and L. R. Frankel. 1991. In Paul Biemer et al, eds., Measurement Errors in Surveys. New York: Wiley, pp. 113-123.
Dutt and Sundaram, Indian Economy, Edition, 2004, Carlsson Jeanette and Arias Salvador, “Transforming Wire line Telecom”,E- business, Feb. 2004, pg. 13.
Dwayne D. Gremlera and Stephen W. Brown, (1996) “Service Loyalty: Its Nature, Importance, and Implications”, Advancing Service Quality: A Global perspective, ISQA, NY.
E Pedersen and Methlie (2002), A taxonomy of intermediately integration strategies in online markets, presented at the 15th Bled Electronic Commerce Conference, Bled, Slovenia, June-pp.17-19.
Economic Commission for Europe, “The Telecommunication Industry – Growth & Structural Change – United Nations, New York.
Economic survey, GOI, 2002 – 2003.
Economics Times 2005.
Edward, M., George, B. P., & Sarkar, S. K. (2010). The Impact of switching costs upon the service quality-perceived value-customer satisfaction- service loyalty chain: A study in the context of cellular services in India. Services Marketing Quarterly, 31(2), 151-173.
Eshghi, A., Roy, S. K., & Ganguli, S. (2008). Service quality and customer satisfaction: An empirical investigation in Indian mobile telecommunication services. Marketing Management Journal, 18(2), 119-144.
ESPI. (2006). Extended Performance Satisfaction Index.
F. Robert Dwyer, Paul H. Schurr and Sej Oh (1987), “Developing Buyer Seller Relationships”, Journal of Marketing, Vol 51, (April, 1987), pp. 11-27.
FICCI (2003), Federation of Indian Chambers of Commerce & Industry Report.
Finn, David W. and Charles Lamb Jr., (1991), “An Evaluation of the SERVQUAL Scales in a Retailing Setting,” Advances in Consumer Research, Vol. 18, No. 1, pp. 483-490.
Francis, G. (2003) Multiple Regressions: Swinburne University Press. Frazier, Gary, Robert E. Spekman, and Charles O‟Neal (1988), “Just in Time
Exchange System and Industrial Marketing”, Journal of Marketing, 52 (October), pp. 52-67.
Frost, F.A. & Kumar, M. (2000) “INTSERVQUAL: An Internal Adaptation of the GAP Model in A Large Service Organization”, Journal of Services Marketing, Vol. 14 No. 5: pp. 358-77.
Ganesan, S. (1994), “Determinants of long-term orientation in buyer-seller relationships” Journal of Marketing, Vol. 58, pp. 1-19.
Ganguli, S. & Roy, S. (2011) “Generic Technology-Based Service Quality Dimensions In Banking: Impact on Customer Satisfaction and Loyalty”, International Journal of Bank Marketing, Vol. 29, Issue 2: pp. 168-189.
Ganguli,Shirshendu (2008),” changing face of relationship marketing valuation of CRM to EMM “, Effective executive,april-pp.54.
Gautam, V. (2011). An empirical study to understand the different antecedents of relationship quality in the Indian context with reference to the mobile telecommunication sector. RRM, 1, 29- 43.
Gelman, A, Carlin J.B, Stern H.S, and Rubin D.B (2004) “Bayesian Data Analysis”, 2nd ed, Boca Raton: Chapman and Hall/CRC.
Girish Taneja and Neeraj Kaushik(2007) “customer perception towards mobile service providers; An analytical overview”, The IUP Journal of services marketing,pp.14.
Gremler, D. (1995). The effect of satisfaction, switching costs, and interpersonal bonds on service loyalty. Unpublished doctoral dissertation, Arisona State University, Tucson, Arizona.
Grisaffe, Doug (2001), “Loyalty-Attitude, Behavior, and Good Science: A Third Take on the Neal-Brandt Debate,” Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 14, 55-59.
Gronroos 1984. A service quality model and its implications. European Journal of Marketing, 18, 36-44.
Gronroos, Christian (1990), “Relationship Approach to Marketing in Service.
Gummerson, E. (1987), “The new marketing developing long-term interactive relationships”, Long-range Planning, Vol.20, No.4, pp.10-20.
Gunjan, M., Amitava, M., Abhishek, N., & Soumyadeep, S. (2011). Consumer behavior towards mobile phone service provider: An empirical research on mobile number portability in India. Advances In Management, 4(6), 44-49.
Hair, J. E., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate Data Analysis. Delhi, India: Pearson Education, Inc.
Hal Mather, “How to Profitably Delight your Customers”, Butterworth Heinemann.
Haywood-Farmer, J. (1988), “A Conceptual Model Of Service Quality”, International Journal of Operations & Production Management, Vol. 8 No. 6: pp. 19-29.
Heman Requelme (2001), “ Do consumers know what they want? “, Journal of consumer marketing, Vol.18,Iss-5,pp 437- 448.
Hoffman, K.D., & Bateson, J.E.G. (2001). Essentials of Service Marketing. The Dryden Press, Hinsdale, IL.
Holbrook, M.B. (1994). The nature of customer value: an axiology of services in the consumption experience. In Rust, R.T., Oliver, R.L. (Eds), Service Quality: New Directions in Theory and Practice, Sage Publications, Inc., Thousand Oaks, CA, pp.21-71.
Holmbeck, G. N. (1997), Toward terminological, conceptual, and statistical clarity in the study of mediators and moderators: Examples from the child-clinical and
pediatric psychology literatures. Journal of Consulting and Clinical Psychology, 4, 599-610.
Iacobucci, D., Ostrom, A. & Grayson, K. (1995) "Distinguishing Service Quality and Customer Satisfaction: The Voice of the Consumer." Journal of Consumer Psychology 4(3): pp.277-303.
Indian infrastructure report (2005), The Infrastructure Sector in India, p.g.29. Indian Telecommunication Statistics 2002, Ministry of Communications, Government
of India.
J.D. Power & Associates Reports. (2009). Wireless traditional mobile phone satisfaction. [Online Available: http://businesscenter.jdpower.com/news/ pressrelease.aspx? ID=2009082. ( February 16, 2010).
J.D.Power and associate reports; Exceptional service satisfaction enhances dealer and manufacturer profibility through improved customer retention,”
Jacksopn, Barbara B. (1985). Winning and Keeping Industrial Customers: The Dynamics of Customer Relationships, Lexington, MA: D.C. Health and Company.
Jacoby, Jacob and Robert W. Chestnut (1978), Brand Loyalty: Measurement and Management, New York: Wiley.
Jahanzeb, S., Fatima, T., & Khan, M. B. (2011). An empirical analysis of customer loyalty in Pakistan's telecommunication industry. Journal of Database Marketing & Customer Strategy Management, 18(1), 5-15.
Jain and Chhokar (1993), “Reorganization of telecom sector, Past and Future”, Vikas Publication, New delhi.
James, L. R. & Brett, J. M. (1984) “Mediators, Moderators and Tests of Mediation”, Journal of Applied Psychology, 69:pp. 307-321.
Javalgi, R.G. and Moburg, C.R. (1997), “Service loyalty: implications for service pro-viders”, Journal of Services Marketing, Vol. 11 No. 3, pp. 165-79.
Joe alba, “knowledge calibration; what consumer know and what they think they know,” journal of consumer research , spring 2006.
Johnston, R. (1995) "The Determinants of Service Quality: Satisfiers and Dissatisfiers" International Journal of Service Industry Management, 6(5): pp. 53-72.
Joreskog, K.G. (1971) “Statistical Analysis of Sets of Cogeneric Tests,” Psychometrika,36, 109-136.
Kalavani (2006), “To study the gap between service promised and service offered by service provider”.
Kalpana and Chinnadurai (2006), “Promotional strategies of cellular services: A customer perspective”, Indian Journal of marketing, may- 2006.pp.29.
Kassim, N. M. (2006). Telecommunication industry in Malaysia: Demographics effect on customer expectations, performance, satisfaction and retention. Asia Pacific Business Review, 12(4), 437-463.
Keller, Kevin L. (1993) ``Conceptualizing, measuring and managing consumer-based brand equity'' Journal of Marketing.
Kerlinger, F.N., & Lee, H.B. (2000) Foundations of behavioral research, 4th Ed., New York: Harcourt Publishers.
Khaligh, A. A., Miremadi, A., & Aminilari, M. (2012). The impact of eCRM on loyalty and retention of customers in Iranian telecommunication sector. International Joumal of Business and Management, 7(2), 150-162.
Kim YK, Cho CH, Ahn SK, Goh IH, Kim HJ (2008) A study on medical services quality and its influence upon value of care and patient satisfaction – Focusing upon outpatients in a large-sized hospital. Total Qual. Manag. Bus. Excel. 19(11): pp.1155-1171.
Kim, H. S., & Yoon, C. H. (2004). Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market. Telecommunications Policy, 28(9-10), 751-765.
Kim, M. K., Park, M. C., & Jeong, D. H. (2004). The effects of customer satisfaction and switching barrier on customer loyalty in Korean mobile telecommunication services. Telecommunications Policy, 28(2), 145-159.
Kim, Soyoung, Byoungho Jin. (2002). Validating the retail service quality scale for US and Korean customers of discount stores: an exploratory study. Journal of Services Marketing, 7(2) 223-237.
Kotler &Armstrong (1997). Advertising, personal selling and sales promotion (p 428) direct marketing is added as a growth trend (p 444).
Kotler, Philip 2001. Marketing Management. The Millennium Edition. Prentice Hall International, Inc.
Kracklauer, A., Mills, D. and Seifert, D. (2004) Collaborative Customer Relationship Management: taking CRM to the next level, New York: Springer.
Krishnan, R., & Kothari, M. (2008). Antecedents of customer relationships in the telecommunication sector: An empirical study. The Icfai University Press, 38-59.
Kushan Mitra (2005), Business Today, 5 June 2005.
Lai, F., Griffin, M., & Babin, B. J. (2009). How quality, value, image, and satisfaction create loyalty at a Chinese telecom. Journal of Business Research, 62(10), 980-986.
Lai, F., Hutchinson, J., Li, D., & Bai, C. (2007). An empirical assessment and application of SERVQUAL in mainland China’s mobile communication
industry. International Journal of Quality & Reliability Management, 24(3), 244-262.
Lapierre, J., P. Filiatrault, J. Perrien. (1996). Research on service quality evaluation: evolution and methodological issues. Journal of Retailing and Consumer Services, 3(2) 91 98.
Lee, J., Lee, J., & Feick, L. (2001). The impact of switching costs on the customer satisfaction–loyalty link: Mobile phone service in France. Journal of Services Marketing, 15, 35–48.
Leisen, B., & Vance, C. (2001). Cross-national assessment of service quality in the telecommunication industry: Evidence from the USA and Germany. Managing Service Quality, 11(5), 307-317.
Liang, D., Ma, Z., & Qi, L. (2012). Service quality and customer switching behavior in China's mobile phone service sector. Journal of Business Research.
Lim, H., Widdows, R., & Park, J. (2006). M-loyalty: Winning strategies for mobile carriers. Journal of Consumer Marketing, 23(4), 208-218.
Ling, C. E., & De Run, E. C. (2009). Satisfaction and loyalty: Customer perceptions of Malaysian Telecommunication service providers. The Icfai University Press, 6-18.
Liu, C. T., Guo, Y. M., & Lee, C. H. (2011). The effects of relationship quality and switching barriers on customer loyalty. International Journal of Information Management, 31(1), 71-79.
Lu, Y., Zhang, L., & Wang, B. (2009). A multidimensional and hierarchical model of mobile service quality. Electronic Commerce Research and Applications, 8(5), 228-240.
MacStravic, S. (1997). Questions of value in health care. Marketing Health Services, Chicago, 18(4), 50-3.
Maran K., Madhavi C. and Thilagavathi K. (2004), „Customer's Perception on Telephone: A Study with Special Reference to Chennai City‟, Journal of Marketing Management, ICFAI Press.
Marine Souheil and Blanchard Jean-Marie (2005), “Bridging the Digital Divide”,E-Business.
Mattsson, J. (1992), “A Service Quality Model Based On Ideal Value Standard”, International Journal of Service Industry Management, Vol. 3 No. 3: pp. 18-33.
McCaslin, Martin (2001), “Customer Loyalty is the Holy Grail for Insurance Businesses Today,” National Underwriter (Life & Health/Financial Services Edition), 50, 2001.
McDougall, G. H. G. & Levesque, T. (2000) "Customer Satisfaction with Services: Putting Perceived Value into the Equation." Journal of Services Marketing, 14(5): pp. 392-410.
McKenna, Regis (1991), Relationship Marketing : Successful Strategies for Age of the Customers, Addison Wesley Publishing Company.
McKinsey Quarterly. (2004). Mobile Dissatisfied Customers. Quarterly. com/article_print.aspx?L2=22 & L3=77&r=1425. (April 15, 2008).
McMullan, Rosalind and Audrey Gilmore (2003), “The Conceptual Development of Customer Loyalty Measurement: A Proposed Scale,” Journal of Targeting, Measurement & Analysis for Marketing, 11 (3), 230-243.
Mehta, S.C., Lalwani, A.K. and Han, S.L. (2000), “Service quality in retailing: relative efficiency of alternative measurement scales for different product-service environments”, International Journal of Retail & Distribution Management, Vol. 28 No. 2, pp. 62-72.
Melody .W.H. (1990), Communication policy in the global information economy. Whither the public interest? In M.Ferguson(ed) public communication; the new imperatives, pp.16-30.
Melody .W.H. (1994), “The information society; implications for economic institutions and market theory”. In E Comer (ed) , The global political economy of communication,pp.21-36.
Michael Meltzer (2005), “ are your customer profitable and segment your customer based on profitability.
Miettila, A. and Moller, K. (1990), “Interaction perspective into professional business services: a conceptual analysis”, paper presented at the Research Development on International Industrial Marketing and Purchasing, Milan.
Mobile Phone Survey. (2004). Au Ranks Highest in Customer Satisfaction with Mobile Telephone Service inJapan.
MoEA, 2007, Inward and Outward Direct Investment Statistics (December, 2007) (In Traditional Chinese), Investment Commission, Ministry of Economic Affairs, Taiwan. http://www.moeaic.gov.tw/system_external/home.html.
Morgan, R.M. and Hunt, S.D. (1994), “The commitment-trust theory of relationship marketing, Journal of Marketing, Vol.58, July, pp.20-30.
Motto (1990), “Privatization and Reorganization of Nippon telegraph and telephone” in in restructuring and managing the telecommunication sector, pp.67-69.
MTNL Report-1991.
Muhammad Asif Khan ( 2010) An Empirical Assessment of Service Quality of Cellular Mobile Telephone Operators in Pakistan, Asia social Science, Vol. 6, No. 10.
Mukherjee Arindham (2006), “Mobile service providers-perspective and Mpractice” , ICFAI University press.
Mutoh (1994), “The Database Revolution,” Target Marketing, May, pp. 14-18.
N.P. Singh and R.K. Gupta, “Use of Data Mining Tools”, Effective Executive, Nov. 2004, pg. 59-67.
Naidu, G.M., Atul Parvatiyar, Jagdish N. Sheth and Lori Westgate (1999). “Does Relationship Marketing Pay? An Empirical Investigation of Relationship Marketing Practices in Hospitals,” 46(3), pp. 207-218.
Narindar K Chhiber (2008) “Fast growth of mobile communication in india: lesson for emerging markets” excel books, new delhi.
National Telecom Policy (1999), Government of India.
Negi, R. (2009). Determining customer satisfaction through percieved service quality: A study of Ethiopian mobile users. International Journal of Mobile Marketing, 4(1), 31-38.
Negi, R., & Ketema, E. (2010). Relationship Marketing and customer loyalty: The Ethiopian mobile communication perspective. IJMM Summer, 5(3), 113-124.
Nerurkar, O. 2000. A preliminary investigation of SERVQUAL dimensions in India. Proceedings of the International Conference on Delivering Service Quality – Managerial Challenges for the 21st Century, New Delhi, 571-80.
O’Sullivan,E. & Rassel, G.R. ( 1999). Research methods for Public Administrators, New York, NY: Longman.
OECD (2007) “Mobile multiple play; new service pricing and policy implications,OECD digital economy papers no.126.
Oh, H. & Parks, S. C. (1997) "Customer Satisfaction and Service Quality: A Critical Review of the Literature and Research Implications for the Hospitality Industry." Hospitality Research Journal, 20(3): pp. 35-64.
Oh, H. (1999) “Service Quality, Customer Satisfaction and Customer Value: A Holistic Perspective”, International Journal of Hospitality Management, Vol. 18, pp. 67-82.
Oh, H. (1999), Store Image Issues in Retailing Literature: A Review and Discussion, Kyungsung University School of Business Administration, Pusan, available at: www.idrc.re.kr/data
Oliver R.L. (1980) “A Cognitive Model Of The Antecedents And Consequences Of Satisfaction Decisions”, Journal of Marketing Research 4: pp. 460–69.
Oliver, Richard L. (1999), “Whence Consumer Loyalty?” Journal of Marketing, 63, 33- 44.
Oliver, R. (1999) “Whence Consumer Loyalty”, Journal of Marketing, Vol. 63 No. 4: pp. 33-44.
Oliver, R.L. (1993) “A Conceptual Model of Service Quality and Service Satisfaction: Compatible Goals, Different Concepts”, in Swartz, T.A., Bowen, D.E. and
Brown, S.W. (Eds), Advances in Services Marketing and Management, Vol. 2, JAI Press, New York, NY: pp. 65-85.
Ozer, G., & Aydin, S. (2005). National customer satisfaction indices: an implementation in the Turkish mobile telephone market. Marketing Intelligence & Planning, 23(5), 486-504.
P.S.Saran (2004) “Developing Buyer Seller Relationships”, Journal of Marketing, Vol 51,
Parasuraman, A. Valari A. Zeithaml, and Leonard L. Berry. 1988. SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, (Spring), 12-40.
Parasuraman, A. 2000. “Technology Readiness Index (TRI): A Multiple Item Scale to Measure Readiness to Embrace New Technologies.” Journal of Services Research 2 (4): 307- 320.
Parasuraman, A. Valari A. Zeithaml, and Leonard L. Berry. 1985. A conceptual model of service quality and its implications for future research. Journal of Marketing, 49 (Fall), 41-50.
Parasuraman, A., Zeithaml, V. and Berry, L. (1991), “Refinement of expectations as a comparison standard in measuring service quality: implications for further research”, Journal of Retailing, Vol. 67 No. 4, pp. 420-50.
Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1988), “SERVQUAL: a multiple-item scale for measuring consumer perceptions of service quality”, Journal of Retailing, Vol. 64 No. 1, pp. 12-40.
Parasuraman, A., Zeithaml, V.A., Berry, L.L. (1985), "A conceptual model of service quality and its implications for future research", Journal of Marketing, Vol. 49 No.4, pp.41-50.
Peppers, Don and Martha Rogers (1993), The One to One Future Building Relationships One Customer at a Time, New York, NY: Doubleday.
Pezeshki, V., Mousavi, A., & Grant, S. (2009). Importance-performance analysis of service attributes and its impact on decision making in the mobile telecommunication industry. Measuring Business Excellence, 13(1), 82 - 92.
Philip, G. and Hazlett, S.A. (1997) “The Measurement of Service Quality: A New PC- P Attributes Model”, International Journal of Quality & Reliability Management, Vol. 14 No. 3: pp. 260-86.
Pizam, A. & Ellis, T. (1999) "Customer Satisfaction and Its Measurement in Hospitality Enterprises" International Journal of Contemporary Hospitality Management, 11(7): pp.326-339.
Prahalad, C.K., & Ramaswamy, V. (2000). Co-opting customer competencies. Harvard Business Review, 79-87.
Pratibha A Dabholkar (1995), “ A contingency Frame work for predicting causality between customer satisfaction and service quality,” Advances in consumer research , Vol.22, 1995 ; Pg.101- 108.
Rajan Bharti Mittal(2005),Joint Managing Director,Bharti televenture limited, Trends and Development, feb-15,2005.
Reichheld, F.F., & Sasser, W.E. (1990). Zero defections: quality comes to services. Harvard Business Review, 68, 301-7.
Rick Kazman (2008). “The Affective and Cognitive Impacts of Perceived Touch on Online Customers‟ Intention to Return in the Web-based eCRM environment,” Chapter 5.19 in S. A. Backer ed., Electronic Commerce: Concepts, Methodologies, Tools and Applications, Information Science Reference, 2008.
Robert C. Ford, Cherill P. Heaton, Stephen W. Brown (2001), “Delivering Excellent Service. Lessons From The Best Firms”, California Management Review, Vol. 44, No.1, Fall.
Rohit Prasad and V.Sridhar (2007), “Optimal number of mobile service provider in India: international journal of business data communications and networking, volume-4, issue-3, pg.no.69-88.
Rustr and Richard L. Oliver. 1994. Service quality: Insights and managerial implications from the frontier. In Service Quality: New Directions in Theory and Practice. Roland T. Rust and Richard L. Oliver (Eds.). New York: Sage Publications, Inc., 1-19.
Ruth N. Bolton (1991), „A Dynamic Model of the Duration of the Customer‟s Relationship with a Continuous Service Provider: The Role of Satisfaction‟, The Maryland Business School, University of Maryland.
Salegna, Gary J. and Stephen A. Goodwin (2005), “Consumer Loyalty to Service Providers: An Integrated Conceptual Model,” Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 18, 51-67.
Sangani Priyanka (2005), “Cell commix” Mythology comes to the mobile”,Business Today, Feb., pg. 18.
Sangeetha, J. &Mahalingam, S. (2011) “Service Quality Models In Banking: A Review”,International Journal of Islamic and Middle Eastern Finance and Management, Vol. 4, Issue 1, pp. 83-103.
Santos, J. (2003) “E-Service Quality: A Model of Virtual Service Quality Dimensions”, Managing Service Quality, Vol. 13 No. 3, pp. 233-46.
Santouridis, I., & Trivellas, P. (2010). Investigating the impact of service quality and customer satisfaction on customer loyalty in mobile telephony in Greece. The TQM Journal, 22(3), 330 - 343.
Schumacker, R. E., & Lomax, R. G. (2004). A beginner's guide to structural equation modeling. Mahwah, New Jersey: Lawrence Erlbaum Associates, Inc.
Schumacker, Randall E. & Richard G. Lomax, (1996) A Beginner’s Guide to Structural Equation Modeling, Mahwah, NJ: Earlbaum Associates.
Selnes, F. (1993) “An Examination of the Effect of Product Performance on Brand Reputation, Satisfaction and Loyalty”, European Journal of Marketing, 27(9): pp. 19-35.
Senthikumar, N. & Arulraj, A. (2011), SQM-HEI determination of service quality measurement of higher education in India, Journal of Modelling in Management, 6(1), 60-78.
Seth et al (2008), “Managing customer perceived service quality for cellular mobile telephony; an empirical investigation” Vikalp.2008.
Seyed Yaghoub Hosseini, Manijeh Bahreini Zade, Alireza Ziaei Bideh (2013) Providing a Multidimensional Measurement Model for Assessing Mobile Telecommunication Service Quality (MS-Qual), Iranian Journal of Management Studies (IJMS) Vol.6, No.
Shanthi N.M. (2005), “Effectiveness of Predictive Churn Models for sustaining market share in telecom industry –An Appraisal”, ICFAI Journal of Services Marketing, September 2005.
Shapiro, B.P. and R.S. Posner (1979), “Making the Major Sale”, Harvard Business Review (March- April), pp. 68-79.
Sharma, Alka, Versha Mehta. (2004), Service Quality in Financial Services – A case study of Banking Services, Journal of Services Research, 4(2) 205-222.
Sheth, Jagdish N. and Rajendra S. Sisodia (1995), “Improving Marketing Productivity”, in Encyclopedia of Marketing in the year 2000. J. Heilbrunn,Ed., Chicago, IL: American Marketing Association/NTC Publishing.
Shikha Oja (2009), “Customer awareness of VAS of telecom sector of India”, The ICFAI University, Journal of Infrastructure,pp;1-14.
Shin, D. H., & Kim, W. Y. (2008). Forecasting customer switching intention in mobile service: An exploratory study of predictive factors in mobile number portability. Technological Forecasting and Social Change, 75(6), 854-874.
Shukla P (2004)“Effect of Product Usage, Satisfaction and Involvement on Brand Switching Behaviour”, Asia Pac. J. Mark., Log., 16(4): pp.82-104.
Shymal Ghosh (2003), “The Resurging Telecom Sector”, pib.nic.in, April.
Sigala, M. (2006). Mass customization implementation models and customer value in mobile phones services: Preliminary findings from Greece. Managing Service Quality, 16(4), 395-420.
Singh BK & Kuhad RC (1999) Biodegradation of lindane by the white-rot fungus Trametes hirsutus. Lett Appl Microbiol 28: 238–241.
Singh J &Sirdeshmukh D, (2000)“Agency and Trust Mechanisms in Consumer Satisfaction snd Loyalty Judgements”, J. Acad. Mark. Sci., 28(1): pp. 150-167.
Smith A.K. and Bolton R.N. (1998) "An Experimental Investigation of Customer Reactions to Service Failures: Paradox or Peril?" Journal of Service Research, Vol 1 No 1, pp. 65-81
Soteriou, A.C. & Stavrinides, Y. (2000) “An Internal Customer Service Quality Data Envelope Analysis Model for Bank Branches”, International Journal of Bank Marketing, Vol. 18 No. 5: pp. 246-52.
Souki, G.Q., & Filho, C.G. (2008). Perceived quality, satisfaction and customer loyalty: an empirical study in the mobile phones sector in Brazil. International Journal of Internet and Enterprise Management, 5(4), 298 – 312.
Spreng, R.A. & Mackoy, R.D. (1996) “An Empirical Examination of a Model of Perceived Service Quality and Satisfaction”, Journal of retailing, Vol. 722: pp. 201-14.
Spreng, R.A., A.K. Singh. (1993). An empirical assessment of the SERVQUAL scale and the relationship between service quality and satisfaction. in Cravens, D.W. and Dickson, P. (Eds), Enhancing Knowledge Development in Marketing, American Marketing Association, Chicago, IL, 1-6.
Stafford, M.R., Stafford, T.F., & Wells, B.P. (1998). Determinants of service quality and satisfaction in the auto casualty claims process. Journal of Services Marketing, 12(6), 426-40.
Stanton, Etzel & Walker (1994). Advertising, personal selling and sales promotion (p 456).
Stephen M. Watters, The New Telephony – Technology, Convergence, Industry Collision, Prentice Hall PTR, NJ07458.
Stone, M., Woodcock, N. and Machtynger, L. (2000) Customer Relationship Marketing: Get To Know Your Customers And Win Their Loyalty, 2nd ed., London: Kogan Page Publishers.
Storbacka, Kaj (2000), “Customer Profitability: Analysis and Design Issues”, in Handbook of Relationship Marketing,Jagdish N. Sheth and Atul Parvatiyar, Eds., Thousand Oaks, CA: Sage Publications, pp. 565-586.
Sukumar. (2007). A study of consumers preference and satisfaction towards various cell phone service providers.
Sureshchandar, G.S., Rajendran, C., & Anantharaman, R.N. (2002). Determinants of customer perceived service quality: a confirmatory factor analysis approach. Journal of Services Marketing, 16(1), 9-34.
Survey. (2008). Virgin Mobile tops regulator’s service benchmark: Survey.
Sutherland, E. (2007). The regulation of the quality of service in mobile networks. Info, 9(6), 17-34.
Swadeshkumar Samanta (2007), “Impact of price on mobile subscription and revenue”, Dept. of computing and electronic systems, University of essex, Colchester,UK.
Sweeney, J.C., Soutar, G.N. & Johnson, L.W. (1997) “Retail Service Quality and Perceived Value”, Journal of Consumer Services, Vol. 4 No. 1: pp. 39-48.
T.V.Ramchandran (2005), Director-Genaral, Cellular operators association of india, Trends and Development, may-15, 2005.
Tate, R. (1998). An introduction to modeling outcomes in the behavioral and social sciences. Boston, MA: Pearson Custom Publishing.
Tate, R. (1998). An introduction to modeling outcomes in the behavioral and social sciences. Boston, MA: Pearson Custom Publishing.
Taylor, S.A., & Baker, T.L. (1994). An assessment of the relationship between service quality and customer satisfaction in the formation of consumers’ purchase intentions. Journal of Retailing, 70(2), 163-78.
Teas, K.R. (1993): “Expectations, performance evaluation, and consumers’ perceptions of quality”, Journal of Marketing, Vol. 57: pp. 18-34.
Teas, R. K. (1994) "Expectations As A Comparison Standard in Measuring Service Quality: An Assessment of A Reassessment." Journal of Marketing.58 (1): pp. 132-140.
Tyran, C.K., & Ross, S.C. (2006). Service quality expectations and perceptions: use of the SERVQUAL instrument for requirements analysis. Issues in Information Systems, 7(1), 357-62.
Van der Wal, R.W.E., Pampallis, A., & Bond, C. (2002). Service quality in cellular telecommunications company: a South-African experience. Managing Service Quality, 12(5), 323-35.
Vargo, Stephen L. and Robert F. Lusch (2004a), “Evolving to a New marketing strategy,” Journal of management and research.
Vavra, Terry G. (1992), after marketing: How to Keep Customers for Life through Relationship Marketing, Homewood, IL: Business One-Irwin.
Vazquez, Rodolfo, Ignacio A. Bosque, Ana M. Diaz, Agustin V. Ruiz. (2001). Service quality in supermarket retailing: identifying critical service experiences. Journal of Retailing and Consumer Services, 8 1-14
Vikas sinh and chintaganta (1994), “Strengthening the Satisfaction-Profit Chain,” Journal of Service Research, 3 (2), 107-120.
Virat Bahri (2006), “The Database Revolution,” Target Marketing, May, pp. 14-18.
VSNL 16th annual report 2002, (Government of India).
Wang, Y., & Lo, H. -P. (2002). Service quality, customer satisfaction and behavior intentions: Evidence from China‟s telecommunication industry. Info, 4(6), 50-60.
Wang, Y., & Lo, H. -P. (2002). Service quality, customer satisfaction and behavior intentions: Evidence from China’s telecommunication industry. Info, 4(6), 50-60.
Wang, Y., & Lo, H.P. (2000). Service quality, customer satisfaction and behavior intentions. Info, 4(6), 50-60.
Ward, K.E., & Mullee, A.W. (1997). Quality of Service in Telecommunications. The Institution of Electrical Engineers Press, Stevenage.
Wilska(2001), “ New technology and young people‟s consumer identities; Acomparative study between Finland and Brazil” – Seniar researcher in economic sociology at the Turku School of economics, Finland.
Wong, K. K. K. (2010). Fighting churn with rate plan right-sizing: A customer retention strategy for the wireless telecommunications industry. The Service Industries Journal, 30(13), 2261-2271.
World telecommunication development report 2002.
Yip, George S. and Tammy L. Madsen (1996), “Global Account Management:The Frontier in Relationship Marketing,” International Marketing Review,13(3), pp. 24-42.
Zhao, L., Lu, Y., Zhang, L., & Chau, P. Y. K. (2012). Assessing the effects of service quality and justice on customer satisfaction and the continuance intention of mobile value-added services: An empirical test of a multidimensional model. Decision Support Systems, 52(3), 645-656.
Zhao, Y., Pugh, K., Sheldon, S., & Byers, J. (2002). Conditions for Classroom Technology Innovations. Teachers College Record, 104 (3).
Zhu, F.X., Wymer, W.J. & Chen, I. (2002) “IT-Based Services And Service Quality In Consumer Banking”, International Journal of Service Industry Management, Vol. 13 No. 1: pp. 69-90.
A Study on Service loyalty on Mobile communication Industry in India
Research scholar Research GuideK.Keerthi Dr.A.ARULRAJ, R.S.Govt .College, Thanjavur
Questionnaire
This questionnaire seeks your expectations and Perceptions on what makes an excellent Telecommunication network Satisfaction. Thank you for your assistance as this survey will allow us to understand your needs and improving the delivery to your satisfaction. This questionnaire is voluntary; all replies are confidential and anonymous. The research work carried for the purpose of academic development and not for any others. Please indicate by circling the appropriate level of Satisfaction for the factors mentioned below.
1. Highly dissatisfied 2.Dissatisfied 3.Some what ok 4.Undesired 5.Some what satisfied
6. Satisfied 7. Highly satisfied s.no Dimensions Highly Dissatisfied – highly
Satisfied Service Network Communication
1. The distributions of telecom services to appropriate individuals in done actively on time.
(1) (2) (3) (4) (5) (6) (7)
2. Do personalized dealing are made in a frequent manner.
(1) (2) (3) (4) (5) (6) (7)
3. The distribution of coverage network speed is good.
(1) (2) (3) (4) (5) (6) (7)
4. Service provide without waiting of call services during business hours.
(1) (2) (3) (4) (5) (6) (7)
5. Clarity in communication network. (1) (2) (3) (4) (5) (6) (7) Technology Adoption
6. The company regularly updates newer technologies (advanced) available in the market.
(1) (2) (3) (4) (5) (6) (7)
7. New technologies like broadband 2G & 3G etc.,
(1) (2) (3) (4) (5) (6) (7)
8. Mobile phone makes you feel secure and where always in touch with our dear ones.
(1) (2) (3) (4) (5) (6) (7)
9. Do low cost handsets will be able to provide a secure communication channel.
(1) (2) (3) (4) (5) (6) (7)
10. Branded mobile phones allow you to conduct communication on a secure basis.
(1) (2) (3) (4) (5) (6) (7)
11. If mobile phone is lost it is easily traced by company using new technology.
(1) (2) (3) (4) (5) (6) (7)
12. The cost of adopting new technologies is higher for old customers
(1) (2) (3) (4) (5) (6) (7)
13. Education would enhance the proficiency in mobile phone technology.
(1) (2) (3) (4) (5) (6) (7)
14. Is the company committed to training and educating the customers on the operation of relevant technologies
(1) (2) (3) (4) (5) (6) (7)
Customer care Services 15. A service provider does not tell customers
exactly when services will be performed. (1) (2) (3) (4) (5) (6) (7)
16. I don’t receive prompt service from customer service staff.
(1) (2) (3) (4) (5) (6) (7)
17. Customer service staff is not always willing to help customers.
(1) (2) (3) (4) (5) (6) (7)
18. Customer service staff is too busy to respond to customer requests promptly.
(1) (2) (3) (4) (5) (6) (7)
19. I can trust customer service staff. (1) (2) (3) (4) (5) (6) (7)20. I feel safe in your transactions with customer
service staff. (1) (2) (3) (4) (5) (6) (7)
21. Customer service staff is polite. (1) (2) (3) (4) (5) (6) (7)22. Customer service staff gets adequate support
form a service provider to do their jobs well. (1) (2) (3) (4) (5) (6) (7)
23. Company is customer friendly always. (1) (2) (3) (4) (5) (6) (7)24. Whether your feedback are accepted and
upgraded by telecom company. (1) (2) (3) (4) (5) (6) (7)
25. Individual care and special attention is given for old customer.
(1) (2) (3) (4) (5) (6) (7)
Fringe Benefit Services 26. Rate Cuter Schemes. (1) (2) (3) (4) (5) (6) (7)27. Festival offer Schemes. (1) (2) (3) (4) (5) (6) (7)28. Internet pocket facility. (1) (2) (3) (4) (5) (6) (7)29. Free SMS facility. (1) (2) (3) (4) (5) (6) (7)30. Free MMS facility. (1) (2) (3) (4) (5) (6) (7)31. E-Recharge Facilities. (1) (2) (3) (4) (5) (6) (7)32. Sharing of Amount. (Talk time) (1) (2) (3) (4) (5) (6) (7)
Service Quality 33. Overall Service Network Communication. (1) (2) (3) (4) (5) (6) (7)34. Overall Technology Adoption. (1) (2) (3) (4) (5) (6) (7)
35. Overall Customer care Services. (1) (2) (3) (4) (5) (6) (7)36. Overall Fringe Benefit Services. (1) (2) (3) (4) (5) (6) (7)37. Overall Brand Switching Process & MNP. (1) (2) (3) (4) (5) (6) (7)
Brand Switching Attitude & MNP 38. For Network failure. (1) (2) (3) (4) (5) (6) (7)39. For call service failure. (1) (2) (3) (4) (5) (6) (7)40. For message failure. (1) (2) (3) (4) (5) (6) (7)41. For technology failure. (1) (2) (3) (4) (5) (6) (7)42. For tariff system. (1) (2) (3) (4) (5) (6) (7)43. Rate cutters and recharge. (1) (2) (3) (4) (5) (6) (7)44. For poor customer care. (1) (2) (3) (4) (5) (6) (7)45. Mobile number Portability facility. (1) (2) (3) (4) (5) (6) (7)46. Promotional Calls & SMS disturbing me to
change.
Service Loyalty S Disagree S Agree 47. I will continue my existing service network in
future. (1) (2) (3) (4) (5) (6) (7)
48. I will suggest to my other family member. (1) (2) (3) (4) (5) (6) (7)49. I will recommend to my friends & colleagues. (1) (2) (3) (4) (5) (6) (7)50. Some time Introduction MNP induces me to
change the provider. (1) (2) (3) (4) (5) (6) (7)
Personal Information
1) Name : 2) Age : 3) Sex : (a) Male (b) Female 4) Religion : (a) Hindu (b) Muslim (c) Christian 5) Community : (a) BC (b) MBC (c) SC 6) Education
Qualification : (a) No formal
Education (b) Schooling (c) Diploma
(d) Degree (e) PG Degree (f) Professional Qualification 7) Occupation : (a) Unemployed (b) Farmer (c) Private
Employee
(d) Government Employee
(e) Business (f) Professional 8) Annual
Income in Rs. : (a) Below 50,000 (b) 50,000 –
1,00,000 (c) 1,00,001 – 1.50.000
(d) 1,50,000 – 2,00,000
(e) 2,00,001- 3,00,000
(f) 3,00,001 above
9) Service Provider
: (a) BSNL (b) AIRTEL (c) AIRCEL (d) Vodafone (e) TATA (f) IDEA (g) MTS (h) Reliance 10) Type service : (a) Prepaid (b) Post
Paid (a) CDMA (b) GSM
Thanks for your response