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Transcript of mris FINAL.pdf
A PROJECT REPORT
ON
COMPARISON
OF
SERVICE QUALITY BETWEEN
PUBLIC AND PRIVARE SECTOR BANKS
SUBMITTED BY:
NIRAV PATEL (13M54)
RONAK SHARMA (13M59)
SUBMITTED TO:
DR. DARSHNA R. DAVE
G H PATEL POST GRADUATE INSTITUTE OF BUSINESS
MANAGEMENT
SARDAR PATEL UNIVERSITY
VALLABH VIDYANAGAR
2013-2015
PREFACE
G.H. Patel Postgraduate Institute of Business Management is a reputed institute which was
established in 1989. The course of Marketing Research in M.B.A program of this institute
provides opportunities for the students to carry out practical research on the various topics of
their choice for the purpose of survey. We have carried out our research project on the topic
named “Consumer Satisfaction towards Services provided by Indian Railway in region of
Vallabh vidyanagar”.
ACKNOWLEDGEMENT
This research work could never have been submitted without the major contribution of
several people.
Here we take this opportunity to thank Dr. Darshana Dave, our research guide, G H PATEL
POSTGRADUATE INSTITUTE OF BUSINESS MANAGEMENT, who has inspired,
supported and encouraged us throughout the work and has provided numerous suggestions of
great value.
We express our thanks for providing us necessary guidance to complete this course. Without
the help of the various respondents, we could not have been able to succeed in this research.
We would like to declare that mistakes in this project and report, if any, are solely our own.
ABSTRACT
The present study is undertaken to compare the services provided by Public and Private
Bakes. The study was done to find out the level of satisfactions and expectations from the
two Banking service providers and to compare them. This survey was done in Vallabh
Vidyanagar city. The study has equal number of respondents for both the service providers so
one can have a rational comparison. The data was processed using computer aided tools such
as MS-EXCEL, SPSS and descriptive analysis were used for analysis.
Table of Contents PREFACE........................................................................................................................................ 2
ACKNOWLEDGEMENT ............................................................................................................... 3
ABSTRACT ..................................................................................................................................... 3
EXECUTIVE SUMMARY................................................................. Error! Bookmark not defined.
1. INTRODUCTION ...................................................................... Error! Bookmark not defined.
2. LITERATURE REVIEW ........................................................... Error! Bookmark not defined.
3. RESEARCH METHODOLOGY .......................................................................................... 33
3.1 Objectives of the study ........................................................ Error! Bookmark not defined.
3.2 Scope of the study ................................................................ Error! Bookmark not defined.
3.3 Limitations ................................................................................ Error! Bookmark not defined.
1 INTRODUCTION
Banking in India in the modern sense originated in the last decades of the 18th century. The
first banks were Bank of Hindustan (1770-1829) and The General Bank of India, established
1786 and since defunct.
The largest bank, and the oldest still in existence, is the State Bank of India, which originated
in the Bank of Calcutta in June 1806, which almost immediately became the Bank of Bengal.
This was one of the three presidency banks, the other two being the Bank of Bombay and
the Bank of Madras, all three of which were established under charters from the British East
India Company. The three banks merged in 1921 to form the Imperial Bank of India, which,
upon India's independence, became the State Bank of India in 1955. For many years the
presidency banks acted as quasi-central banks, as did their successors, until the Reserve Bank
of India was established in 1935.
In 1969 the Indian government nationalised all the major banks that it did not already own
and these have remained under government ownership. They are run under a structure known
as 'profit-making public sector undertaking' (PSU) and are allowed to compete and operate
as commercial banks. The Indian banking sector is made up of four types of banks, as well as
the PSUs and the state banks; they have been joined since the 1990s by new private
commercial banks and a number of foreign banks.
Generally banking in India was fairly mature in terms of supply, product range and reach-
even though reach in rural India and to the poor still remains a challenge. The government
has developed initiatives to address this through the State Bank of India expanding its branch
network and through the National Bank for Agriculture and Rural Development with things
like microfinance. This also included the 2014 plan by the then prime minister to bring bank
accounts to the estimated 40% of the population that were still unbanked.
Structure of Banking Sector in India
Current period
All banks which are included in the Second Schedule to the Reserve Bank of India Act, 1934
are Scheduled Banks. These banks comprise Scheduled Commercial Banks and Scheduled
Co-operative Banks. Scheduled Commercial Banks in India are categorised into five different
groups according to their ownership and/or nature of operation. These bank groups are:
State Bank of India and its Associates
Nationalised Banks
Private Sector Banks
Foreign Banks
Regional Rural Banks.
In the bank group-wise classification, IDBI Bank Ltd. is included in Nationalised Banks.
Scheduled Co-operative Banks consist of Scheduled State Co-operative Banks and Scheduled
Urban Cooperative Banks.
Growth of Banking in India of Scheduled Commercial Banks
In
dic
ato
rs
31 March of
2005 2006 2007 2008 2009 2010 2011 2012 2013
Num
ber
of
Com
merci
al
Bank
s
284 218 178 169 166 163 163 169 151
Growth of Banking in India of Scheduled Commercial Banks
In
dic
ato
rs
31 March of
2005 2006 2007 2008 2009 2010 2011 2012 2013
Num
ber
of
Bran
ches
70,373 72,072 74,653 78,787 82,897 88,203 94,019 102,377 109,811
Popu
lation
per
Bank
s(in
‘000)
16 16 15 15 15 14 13 13 12
Aggr
egate
Depo
sits
17002
billion(
US$280
billion)
21090
billion(
US$340
billion)
26119
billion(
US$420
billion)
31969
billion(
US$520
billion)
38341
billion(
US$620
billion)
44928
billion(
US$730
billion)
52078
billion(
US$840
billion)
59091
billion(
US$960
billion)
67504.5
4
billion(
US$1.1 t
rillion)
Bank
Credi
t
11004
billion(
US$180
15071
billion(
US$240
19312
billion(
US$310
23619
billion(
US$380
27755
billion(
US$450
32448
billion(
US$530
39421
billion(
US$640
46119
billion(
US$750
52605
billion(
US$850
Growth of Banking in India of Scheduled Commercial Banks
In
dic
ato
rs
31 March of
2005 2006 2007 2008 2009 2010 2011 2012 2013
billion) billion) billion) billion) billion) billion) billion) billion) billion)
Depo
sit as
perce
ntage
to G
NP (a
t
facto
r
cost)
62% 64% 69% 73% 77% 78% 78% 78% 79%
Per
Capit
a
Depo
sit
16281(U
S$260)
19130(U
S$310)
23382(U
S$380)
28610(U
S$460)
33919(U
S$550)
39107(U
S$630)
45505(U
S$740)
50183(U
S$810)
56380 (
US$910)
Per
Capit
a
Credi
10752(U
S$170)
13869(U
S$220)
17541(U
S$280)
21218(U
S$340)
24617(U
S$400)
28431(U
S$460)
34187(U
S$550)
38874(U
S$630)
44028 (
US$710)
Growth of Banking in India of Scheduled Commercial Banks
In
dic
ato
rs
31 March of
2005 2006 2007 2008 2009 2010 2011 2012 2013
t
Credi
t
Depo
sit
Ratio
63% 70% 74% 75% 74% 74% 76% 79% 79%
By 2010, banking in India was generally fairly mature in terms of supply, product range and
reach-even though reach in rural India still remains a challenge for the private sector and
foreign banks. In terms of quality of assets and capital adequacy, Indian banks are considered
to have clean, strong and transparent balance sheets relative to other banks in comparable
economies in its region. The Reserve Bank of India is an autonomous body, with minimal
pressure from the government.
With the growth in the Indian economy expected to be strong for quite some time-especially
in its services sector-the demand for banking services, especially retail banking, mortgages
and investment services are expected to be strong. One may also expect M&As, takeovers,
and asset sales.
In March 2006, the Reserve Bank of India allowed Warburg Pincus to increase its stake
in Kotak Mahindra Bank (a private sector bank) to 10%. This is the first time an investor has
been allowed to hold more than 5% in a private sector bank since the RBI announced norms
in 2005 that any stake exceeding 5% in the private sector banks would need to be vetted by
them.
In recent years critics have charged that the non-government owned banks are too aggressive
in their loan recovery efforts in connexion with housing, vehicle and personal loans. There
are press reports that the banks' loan recovery efforts have driven defaulting borrowers to
suicide.
By 2013 the Indian Banking Industry employed 1,175,149 employees and had a total of
109,811 branches in India and 171 branches abroad and manages an aggregate deposit of
67504.54 billion (US$1.1 trillion or €840 billion) and bank credit of 52604.59
billion (US$850 billion or €650 billion). The net profit of the banks operating in India was
1027.51 billion (US$17 billion or €13 billion) against a turnover of 9148.59
billion (US$150 billion or €110 billion) for the financial year 2012-13.
Adoption of banking technology
The IT revolution has had a great impact on the Indian banking system. The use of computers
has led to the introduction of online banking in India. The use of computers in the banking
sector in India has increased many folds after the economic liberalisation of 1991 as the
country's banking sector has been exposed to the world's market. Indian banks were finding it
difficult to compete with the international banks in terms of customer service, without the use
of information technology.
The RBI set up a number of committees to define and co-ordinate banking technology. These
have included:
In 1984 was formed the Committee on Mechanisation in the Banking Industry
(1984) whose chairman was Dr. C Rangarajan, Deputy Governor, Reserve Bank of India.
The major recommendations of this committee were introducing MICR technology in all
the banks in the metropolises in India.[14] This provided for the use of standardized
cheque forms and encoders.
In 1988, the RBI set up the Committee on Computerisation in Banks (1988) headed by
Dr. C Rangarajan. It emphasized that settlement operation must be computerized in
the clearinghouses ofRBI
in Bhubaneshwar, Guwahati, Jaipur, Patna and Thiruvananthapuram. It further stated that
there should be National Clearing of inter-
city chequesat Kolkata, Mumbai, Delhi, Chennai and MICR should be made operational.
It also focused on computerisation of branches and increasing connectivity among
branches through computers. It also suggested modalities for implementing on-line
banking. The committee submitted its reports in 1989 and computerisation began from
1993 with the settlement between IBA and bank employees' associations.
In 1994, the Committee on Technology Issues relating to Payment systems, Cheque
Clearing and Securities Settlement in the Banking Industry (1994)[17] was set up under
Chairman W S Saraf. It emphasized Electronic Funds Transfer (EFT) system, with the
BANKNET communications network as its carrier. It also said that MICR clearing
should be set up in all branches of all those banks with more than 100 branches.
In 1995, the Committee for proposing Legislation on Electronic Funds Transfer and other
Electronic Payments (1995)[18] again emphasized EFT system.
The total number of automated teller machines (ATMs) installed in India by various
banks as of end June 2012 is 99,218.[19] The new private sector banks in India have the
most ATMs, followed by off-site ATMs belonging to SBI and its subsidiaries and then
by nationalised banks and foreign banks, while on-site is highest for the nationalised
banks of India.
Branches and ATMs of Scheduled Commercial Banks as of end March 2005[16]
Bank type Number of
branches
On-site
ATMs
Off-site
ATMs
Total
ATMs
Nationalised banks 33,627 38,606 22,265 60,871
State Bank of India 13,661 28,926 22,827 51,753
Old private sector
banks 4,511 4,761 4,624 9,385
New private sector
banks 1,685 12,546 26,839 39,385
Branches and ATMs of Scheduled Commercial Banks as of end March 2005[16]
Bank type Number of
branches
On-site
ATMs
Off-site
ATMs
Total
ATMs
Foreign banks 242 295 854 1,149
TOTAL 53,726 85,134 77,409 1,62,543
Expansion of banking infrastructure
Physical as well as virtual expansion of banking through mobile banking, internet banking,
tele banking, bio-metric and mobile ATMs is taking place since last decade and has gained
momentum in last few years. As per the census of 2011, 58.7% of households are availing
banking services in the country. There are 102,343 branches of Scheduled Commercial Banks
(SCBs) in the country, out of which 37,953 (37%) bank branches are in the rural areas and
27,219 (26%) in semi-urban areas, constituting 63% of the total numbers of branches in semi-
urban and rural areas of the country. However, a significant proportion of the households,
especially in rural areas, are still outside the formal fold of the banking system. To extend the
reach of banking to those outside the formal banking system, Government and Reserve Bank
of India (RBI) are taking various initiatives from time to time some of which are enumerated
below:
Opening of bank branches: Government had issued detailed strategy and guidelines on
Financial Inclusion in October 2011, advising banks to open branches in all habitations of
5,000 or more population in under-banked districts and 10,000 or more population in other
districts. Out of 3,925 such identified villages/habitations, branches have been opened in
3,402 villages/habitations (including 2,121 Ultra Small Branches) by end of April, 2013.
Each household to have at least one bank account: Banks have been advised to ensure
service area bank in rural areas and banks assigned the responsibility in specific wards in
urban area to ensure that every household has at least one bank account.
Business Correspondent model: With the objective of ensuring greater financial inclusion
and increasing the outreach of the banking sector, banks were permitted by RBI in 2006
to use the services of intermediaries in providing financial and banking services through
the use of Business Facilitators (BFs) and Business Correspondents (BCs). Business
correspondents are retail agents engaged by banks for providing banking services at
locations other than a bank branch/ATM. BCs and the BC agents (BCAs) represent the
bank concerned and enable a bank to expand its outreach and offer limited range of
banking services at low cost, particularly where setting up a brick and mortar branch is
not viable. BCs as agents of the banks, thus, are an integral part of the business strategy
for achieving greater financial inclusion. Banks had been permitted to engage
individuals/entities as BC like retired bank employees, retired teachers, retired
government employees, ex-servicemen, individual owners of kirana/medical/fair price
shops, individual Public Call Office (PCO) operators, agents of Small Savings Schemes
of Government of India, insurance companies, etc. Further, since September 2010, RBI
had permitted banks to engage "for profit" companies registered under the Indian
Companies Act, 1956, excluding Non-Banking Financial Companies (NBFCs), as BCs in
addition to individuals/entities permitted earlier. According to the data maintained by
RBI, as in December, 2012, there were over 152,000 BCs deployed by Banks. During
2012-13, over 183.8 million transactions valued at 165 billion (US$2.7 billion) had been
undertaken by BCs till December 2012.
Swabhimaan Campaign: Under "Swabhimaan" - the Financial Inclusion Campaign
launched in February 2011, banks had provided banking facilities by March, 2012 to over
74,000 habitations having population in excess of 2000 using various models and
technologies including branchless banking through Business Correspondents Agents
(BCAs). Further, in terms of Finance Minister's Budget Speech 2012-13, the
"Swabhimaan" campaign has been extended to habitations with population of more than
1,000 inNorth Eastern and Hilly States and to habitations which have crossed population
of 1,600 as per census 2001. About 40,000 such habitations have been identified to be
covered under the extended "Swabhimaan" campaign.
Setting up of ultra-small branches (USBs): Considering the need for close supervision
and mentoring of the Business Correspondent Agents (BCAs) by the respective banks
and to ensure that a range of banking services are available to the residents of such
villages, Ultra Small Branches (USBs) are being set up in all villages covered through
BCAs under Financial Inclusion. A USB would comprise a small area of 100 sq ft
(9.3 m2) - 200 sq ft (19 m2) where the officer designated by the bank would be available
with a laptop on pre-determined days. While the cash services would be offered by the
BCAs, the bank officer would offer other services, undertake field verification and follow
up on the banking transactions. The periodicity and duration of visits can be
progressively enhanced depending upon business potential in the area. A total of over
50,000 USBs have been set up in the country by March 2013.
Banking facilities in Unbanked Blocks: All the 129 unbanked blocks (91 in North East
States and 38 in other States) identified in the country in July 2009, had been provided
with banking facilities by March 2012, either through Brick Mortar Branch or Business
Correspondents or Mobile van. As a next step it has been advised to cover all those
blocks with BCA and Ultra Small Branch which have so far been covered by mobile van
only.
USSD Based Mobile Banking: National Payments Corporation of India (NPCI) worked
upon a "Common USSD Platform" for all banks and telcos who wish to offer the facility
of Mobile Banking using Unstructured Supplementary Service Data (USSD) based
Mobile Banking. The Department helped NPCI to get a common USSD Code *99# for
all telcos. More than 20 banks have joined the National Uniform USSD Platform (NUUP)
of NPCI and the product has been launched by NPCI with BSNL and MTNL. Other
telcos are likely to join in the near future. USSD based Mobile Banking offers basic
Banking facilities like Money Transfer, Bill Payments, Balance Enquiries, Merchant
Payments etc. on a simple GSM based Mobile phone, without the need to download
application on a phone as required at present in the IMPS based Mobile Banking.
Steps taken by Reserve Bank of India (RBI) to strengthen the banking
infrastructure
RBI has permitted domestic Scheduled Commercial Banks (excluding RRBs) to open
branches in tier 2 to tier 6 cities (with population up to 99,999 as per census 2001)
without the need to take permission from RBI in each case, subject to reporting.
RBI has also permitted SCBs (excluding RRBs) to open branches in rural, semi-urban
and urban centres in North Eastern States and Sikkim without having the need to take
permission from RBI in each case, subject to reporting.
Regional Rural Banks (RRBs) are also allowed to open branches in Tier 2 to Tier 6
centres (with population up to 99,999 as per Census 2001) without the need to take
permission from RBI in each case, subject to reporting, provided they fulfill the
following conditions, as per the latest inspection report:
CRAR of at least 9%;
Net NPA less than 5%;
No default in CRR / SLR for the last year;
Net profit in the last financial year;
CBS compliant.
Domestic SCBs have been advised that while preparing their Annual Branch Expansion
Plan (ABEP), they should allocate at least 25% of the total number of branches proposed
to be opened during the year in unbanked Tier 5 and Tier 6 centres i.e. (population up to
9,999) centres which do not have a brick and mortar structure of any SCB for customer
based banking transactions.
RRBs have also been advised to allocate at least 25% of the total number of branches
proposed to be opened during a year in unbanked rural (Tier 5 and Tier 6) Centres).
New private sector banks are required to ensure that at least 25% of their total branches
are in semi-urban and rural centres on an ongoing basis.
Private-sector banks
Axis bank Bandhan financial Bank Kotak Mahindra Bank
Catholic Syrian Bank South Indian Bank Karur Vysya Bank
City Union Bank Tamilnadu Mercantile Bank Karnataka Bank
Development Credit Bank Shivalik bank IndusInd Bank
Dhanlaxmi Bank Nainital Bank ICICI Bank
YES Bank RBL Bank Fedral Bank
IDFC Lakshmi Vilas Bank HDFC Bank
Public Sector Banks (Nationalised banks):
State Bank of India (SBI) State Bank of Patiala Canara Bank
State Bank of Bikaner &
Jaipur
State Bank of Saurashtra
Central Bank of India
State Bank of Hyderabad State Bank of Travancore Corporation bank
State Bank of Indore Bank of India Indian Bank
State Bank of Mysore Syndicate Bank Indian overseas bank
UCO Bank Bank of Baroda Oriental Bank of Commerce
Allahabad Bank Bank of Maharashtra Punjab & Sind Bank
Andhra Bank Dena Bank Union Bank of India
United Bank of India Vijaya Bank IDBI Bank
2 Literature review
1) A COMPARATIVE STUDY ON CUSTOMER SATISFACTION IN INDIAN PUBLIC
SECTOR AND PRIVATE SECTOR BANKS (WITH SPECIAL REFERENCE TO DELHI
AND NCR REGION)
MS.PALLAVIGUPTA,DR.CHHAYAMANGAL MISHRA, DR. TAZYN
RAHMAN(International Journal of Social Science & Interdisciplinary Research ISSN
2277 -3630 IJSSIR, Vol. 2 (8), AUGUST (2013))
The banking industry like many other financial service industries is facing a rapidly
changing Market, new technologies, economic uncertainties, fierce competition, and
especially more Demanding customers; and the changing climate has presented an
unprecedented set of Challenges. Customer service is one integral part of any facet of
banking and it defines future of Any banking organization. In banking sector, the whole
range of activity and generation of Income swivels around the customer. From a very
comfortable and peaceful environment, now the Indian Banking Sector is characterized
by stiff competition for the customer‟s satisfaction and profit war between different
banking groups i.e. (Private bank vs. Nationalized Bank). This paper tries to analyze the
comparative analysis of customer satisfaction among these two categories of banks –
public and private sector banks using the list of service attributes based on SERVQUAL
method. Simple random sampling technique is adopted and sample size of the data is 200
from the Delhi and NCR. This study is just a small step in understanding the multi
Dimensional construct of service quality and its implications in today’s
competitive environment.
2) Incorporating attitude towards Halal banking in an integrated service quality,
satisfaction, trust and loyalty model in online Islamic banking context
Muhammad Mohsin Butt, Muhammad Aftab (International Journal of BankMarketing
Vol. 31 No. 1, 2013 pp. 6-23)
The purpose of this paper is to empirically investigate the influence of consumer attitude
towards Halal banking on service quality and satisfaction, in an online Islamic banking
context. The proposed model also aims to investigate the relationships among service
quality, satisfaction, trust and loyalty.
This study enhances our understanding of how specific religious attitudes can positively
influence consumer assessments of a bank’s perceived e-service quality and their overall
e-satisfaction with it.
3) Social value in retail banking”
Juan Carlos Fandos Roig and Marta Estrada Guille´n
(International Journal of Bank Marketing Vol. 31 No. 5, 2013 pp. 348-367)
The aim of this study is to analyze the influence of perceived value on customer loyalty,
going into depth in the special case of social value.
The effect of social value on customer loyalty is also examined in two ways: as a
determinant of the attitude of the individual and as a normative component directly
influencing behavioural intentions. – It highlights the interest of social marketing
programs and corporate social responsibility to maintain the customer’s loyalty.
4) Are you providing the “right” customer experience? The case of Banca Popolare
di Bari
Philipp Klaus Michele Gorgoglione Daniela Buonamassa and Umberto Panniello and Bang
Nguyen,(International Journal of Bank MarketingVol. 31 No. 7, 2013 pp. 506-528)
The purpose of this paper is to model customer experience (CE) as a “continuum”,
labelled customer experience continuum (CEC). The paper adopts a CE quality construct
and scale (EXQ) to determine the effect of CE on a bank’s marketing outcomes. The
paper discusses the study’s theoretical and managerial implications, focusing on CE
strategy design.
paper empirically test a scale to measure customer experience quality (EXQ) for a
retail bank. The paper interviews customers using a means-end-chain approach and soft-
laddering to explore their CE perceptions with the bank. The paper classifies their
perceptions into the categories of “brand experience” (pre-purchase), “service
experience” (during purchase), and “post-purchase experience”. After a confirmatory
factor analysis, the paper conducts a survey on a representative customer sample. The
paper analyses the survey results with a statistical model based on the partial least
squares method. The paper tests three hypotheses first, Customers’ perceptions of brand,
service provider, and post-purchase experiences have a significant and positive effect on
their EXQ, second, EXQ has a significant and positive effect on the marketing
outcomes, namely share of wallet, satisfaction, and word-of-mouth, and third, the
overall effect of EXQ on marketing outcomes is greater than that of EXQ’s individual
dimensions.
5) Private and public banks: a comparison of customer expectations and perceptions”
Peter Kangis, Vassilis Voukelatos (Journal of Bank Marketing, Vol. 15 Issue: 7, pp.279
– 287)
Reports the findings of a survey among customers of private and public sector banks in
Greece on service quality perceptions and expectations. Finds that quality expectations
and evaluation of services received were marginally higher in the private than in the
public sector in most of the dimensions measured; The perception of the profile of
services received was, however, different between sectors, thus suggesting that they did
deliver a different quality of service. Discusses the implications for strategy since
sectoral differentiation in banking is becoming blurred as a result of increasing overlap
between services and competition from related and substitute industries
6) A Comparison of Indian Public and Private Sector Banks Based on Banking
Service Quality Model
Mihir Dash ,Garima Saxena (International Journal of Bank Marketing, Vol. 18 Iss: 4,
pp.144 – 159)
A more competitive banking environment has gradually been achieved through the
deregulation measures and permission granted to many private and foreign banks into
the Indian banking industry. These changes have also caused a compression of profits
and a re-orientation of banking strategy towards quality service provision. The
introduction of new private sector banks and foreign banks has decreased margins and
revenues to banks
The results of the study show that there is a significant difference between the
expectations and the perceptions of banking service quality of the respondents for all of
the variables under the Banking Service Quality (BSQ) model.
It was found that public banks fared better overall than private banks in terms of
perceptions of banking service quality. With the sudden collapse of some of the
oldest and most well-established international banks and the onset of the global financial
meltdown, customers have become more cautious about private banks. At the same time
the trust level on public banks have increased.
7) Consumer Trust in Banking Relationships in Europe
Raija Anneli Järvinen, (International Journal of Bank Marketing, Vol. 32 Issue: 6, pp
28-39)
The purpose of the article is to examine the content of consumer trust in the banking
sector and to find out if there are deviations in consumer trust in banks at the
organisational level, and at a service level, and between distinct services and between
various countries.
The study reveals deviations between various banking services and company level
results regarding consumers’ trust in their banking relationships. Consumer trust is the
highest in banking accounts and the lowest in investments and pensions.
The study also highlights deviations in consumer trust between European countries, and
identifies countries with low, medium and high trust in banking and in distinct banking
services
8) The impact of technology CSFs on customer satisfaction and the role of trust: An
empirical study of the banks in Malaysia
Muhammad Tahir Jan, Kalthom Abdullah,(International Journal of Bank Marketing,
Vol. 32 Iss: 5, pp.429 – 447)
This paper analyses the causal relationship that exists between technology CSFs and
customer satisfaction. It also investigates the mediating role of trust between these two.
For this purpose data were collected quantitatively from 349 employees working in
different banks, through self-administered questionnaire. The data analysis was
conducted using SPSS and AMOS software. Factor analysis was performed to extract
and decide on the number of factors underlying the measured variables of interest.
Structural equation modelling was then used to examine the variables and the fitness of
proposed model.
The result revealed that technology CSFs positively affect customer satisfaction.
Also, trust partially mediates the relationship between technology CSFs and customer
satisfaction. A significant positive impact of technology CSFs on trust, and trust on
customer satisfaction have also been obtained. The significant influence that technology
CSFs have on customer satisfaction and trust shows that technology-related CSFs are
inevitable for the success of customer relationship management (CRM) in financial
services industry, particularly banks. Policy makers of service industry in general and
financial service industry in particular may benefit from the findings of this study.
9) Building brand equity in retail banks: the case of Trinidad and Tobago
Meena Rambocas, Vishnu M. Kirpalani, Errol Simms, (International Journal of Bank
Marketing, Vol. 32 Iss: 4, pp.300 – 320)
The purpose of this paper is to investigate an integrated model mapping the influence of
brand affinity, customer experience, and customer satisfaction on brand equity in retail
banking.
Data were collected from 315 banking customers in Trinidad and Tobago through
personally administered structured questionnaires and analyzed with Structural Equation
Modelling.
The findings showed the mediating role of customer satisfaction in brand equity
relationships. The results also showed the pivotal role of brand affinity, customer
satisfaction, and service experience in explaining brand equity.
The study provides an integrated approach to brand building. It also offers an objective
framework brand owners can use to evaluate marketing investments. It also provides a
clear brand differentiation strategy for bank brands. Finally, it introduces cross-cultural
research in brand equity which can be a useful competitive tool for indigenous banks
and foreign banks seeking market expansion strategies.
10) Customer CSR expectations in the banking industry
Andrea Pérez, Ignacio Rodríguez del Bosque (International Journal of Bank
Marketing, Vol. 32 Iss: 3, pp.223 – 244)
The purpose of this paper is to examine customer corporate social responsibility (CSR)
expectations in the crisis context of the Spanish banking industry. The paper also takes
into consideration the role that corporate governance structure plays in customer CSR
expectations.
Analysing 648 customers of savings banks and 476 customers of commercial banks,
several univariate statistics and two cluster analyses are implemented.
The authors identify significantly steady patterns in the CSR expectations of savings
banks and commercial banks customers. The customers of both types of banking
companies have similar high expectations concerning the CSR oriented to customers,
shareholders and supervising boards, employees, the community and legal and ethical
CSR. Also customers of both types of banking companies can be consistently classified
as customer oriented, legally (customer)-oriented and CSR-oriented customers
depending on their CSR expectations.
11) Linking satisfaction to share of deposits: an application of the Wallet Allocation
Rule",
Lerzan Aksoy, (2014) (International Journal of Bank Marketing, Vol. 32 Iss: 1, pp.28 –
42)
Despite the fact that customer satisfaction is among the most widely used metrics by
managers, the link with share of deposits tends to be weak. Using a recent innovative
approach termed the “Wallet Allocation Rule (WAR)” this research investigates
whether measuring satisfaction relative to other competitors used exhibits a stronger
correlation to share of deposits compared to measuring absolute satisfaction with the
focal firm/product.
A survey approach was used with a sample of 4,712 banking customers across the
USA. Using the WAR, each respondent's satisfaction ratings were transformed into
relative rankings and used to estimate their share of deposits.
The results confirmed that at both the individual and the aggregate level examining
relative ranked satisfaction correlates strongly with customers’ share of deposits. At the
individual level relative satisfaction explains 55 percent of the variance in share of
deposits, as opposed to only 9 percent for absolute satisfaction.
The findings indicate that managers need to rethink their current approach to
satisfaction measurement and consider measuring their customers’ satisfaction relative
to competitors used. Furthermore, using aggregate level absolute satisfaction in
managerial decision making can be misleading.
12) Using a multiple-attribute approach for measuring customer satisfaction with
retail banking services in Kuwait
Abdulkarim S. Al-Eisa, Abdulla M. Alhemoud, (2009 International Journal of Bank
Marketing, Vol. 27 Iss: 4, pp.294 – 314)
The purpose of this paper is to attempt to identify the most salient attributes that
influence customer satisfaction with retail banks in Kuwait and to determine the level
of the overall satisfaction of the customers of these banks
A multiple-attribute approach proposed by Shin and Elliott in 2001 was employed. This
approach was applied in the analysis of data collected from a convenient sample of 863
actual customers of retail banks in Kuwait.
The most crucial attributes for predicting customer satisfaction with retail banks in
Kuwait were fast service, courtesy and helpfulness of employees and availability of
self-banking services. The vast majority of the customers of retail banks in Kuwait
(nearly 81 percent) are either satisfied or very satisfied with the services of their banks.
A number of very important attributes, such as those related to loans and credit cards
were not examined due to social reasons. The unavailability of lists of existing
customers and their contacts made it not possible to draw a random sample from the
target population of this study.
To maintain a competitive edge in the market, the managers of retail banks in Kuwait
need to be updated about technological advances and to invest in those that
satisfactorily enhance technology-based encounters with their customers. These
managers also need to focus on minimizing encounter failures in service delivery. For a
retail bank in Kuwait, sufficient recovery efforts are needed so that dissatisfied
customers do not end up defecting.
13) The role of bank image for customers versus non-customers
Rafael Bravo, Teresa Montaner, José M. Pina, ( International Journal of Bank
Marketing, Vol. 27 Iss: 4, pp.315 – 334)
The purpose of this paper is to analyse the corporate image of financial institutions
and its impact on consumer behaviour. More specifically, it aims to focus on the
differences between customers and non-customers of banking institutions.
Data were collected through five questionnaires involving five major Spanish
commercial banks. The questionnaires were answered by 450 individuals and SEM
methodology was used to test the hypotheses of the study.
Corporate image of commercial banks includes dimensions related to the services
offered, accessibility, corporate social responsibility, global impression, location and
personnel. Two alternative models were validated for customers and non-customers to
explain how corporate associations influence intention to use the bank's services. For
the case of current customers, satisfaction is a key mediating variable.
The study is focused on national commercial banks and corporate image of
individuals. Different stakeholders like employees can hold a different corporate
image. Moreover, the paper only considers intention to use as a dependant variable.
The effect of corporate associations on purchase intentions depends on the specific
type of associations and may be mediated through satisfaction. Results thus indicate
that firms have to use different marketing strategies when considering the individuals'
previous experience.
14) "Attitudes and behaviour in everyday finance: evidence from Switzerland",
Brigitte Fünfgeld, Mei Wang, (2009) International Journal of Bank Marketing, Vol. 27
Iss: 2, pp.108 – 128
In order to classify individuals based on their needs; this paper aims to consider both
self-stated attitudes and behaviours in a comprehensive range of daily financial
affairs. Furthermore, it aims to study the impacts of socio-demographic variables such
as gender, age, and education.
A questionnaire was answered by 1,282 respondents in the German-speaking part of
Switzerland. Factor analysis revealed five components. Based on these components a
two-step cluster analysis (Ward and K-means analyses) identified distinct subgroups.
Linear regressions were used to investigate the impacts of socio-demographic
variables.
Factor analysis revealed five underlying dimensions of financial attitudes and
behaviour: anxiety, interests in financial issues, decision styles, need for precautionary
savings, and spending tendency. Cluster analysis segmented the respondents into five
subgroups based on these dimensions with an ascending order of specific needs for
financial products. Gender, age, and education were found to have significant impacts.
Real consumption behaviour cannot be observed through the survey, which limits the
external validity of the study.
The segmentation identifies different levels of financial competence and needs for
financial products. It allows financial service providers to offer more effective advice
and to meet customers on their own level to improve personal financial management.
15) The role of satisfaction and website usability in developing customer loyalty and
positive word-of-mouth in the e-banking services",
Luis V. Casaló, Carlos Flavián, Miguel Guinalíu, (2008 International Journal of Bank
Marketing, Vol. 26 Iss: 6, pp.399 - 417
Customer loyalty and positive word-of-mouth (WOM) have been traditionally two
main goals aimed at by managers. Focusing on the online banking, the importance of
these concepts is even greater due to the increasing competence in electronic
commerce. Thus, the purpose of this paper is to characterize both concepts in the e-
banking context.
The influence of satisfaction and website usability in developing customer loyalty
and positive WOM in the e-banking business were measured. After the validation of
measurement scales, hypotheses are contrasted through structural modelling.
This research showed that satisfaction with previous interactions with the bank
website had a positive effect on both customer loyalty and positive WOM. In
addition, website usability was found to have a positive effect on customer
satisfaction and, as expected, loyalty was also significantly related to positive WOM.
In order to develop customer loyalty and positive WOM, banks that operate in the
internet should: prioritize ease-of-use in website development, and identify the needs
of online customers (e.g. in terms of services offered) in order to offer them what
they really want.
16) "Demographic influences on behaviour: An update to the adoption of bank delivery
channels",
Ana S. Branca, (International Journal of Bank Marketing, Vol. 26 Iss: 4, pp.237 – 259)
The purpose of this paper is to examine how demographic characteristics contribute to
consumers' decision on bank delivery channels' usage, namely the direct and indirect
demographic influence on channel usage frequency via cognitive and affective
mediators.
The consumer usage frequency pattern concerning the main bank delivery channels
and its determinants are modelled and analysed with a questionnaire sent to 24,000
bank customers. This stage was preceded by a series of in-depth interviews to bank
managers and bank customers.
Empirical evidence suggests that demographic variables' influence over consumers'
usage frequency decision has both a direct and indirect component. These influences
are identified by delivery channel.
The main limitation derives from the nature of empirical results and their
generalization to other samples and contexts. Nevertheless, precautions recommended
in the literature to overcome this limitation were followed.
Bank managers will benefit from knowing, by channel, which demographic
characteristics have the desired direct and indirect impact on usage frequency. This
information will improve bank managers' efforts to encourage customers to favour a
specific delivery channel.
17) “Performance benchmarking and strategic homogeneity of Indian banks”
Avinandan Mukherjee, Prithwiraj Nath, Manabendra Nath Pal (International Journal
of Bank Marketing, Vol. 20 Issue: 3, pp.122 – 139)
Explores the linkage between performance benchmarking and strategic
homogeneity of Indian commercial banks. Defines performance by how a bank is able
to utilize its resources to generate business transactions and is measured by their ratio,
which is then called the efficiency. Clusters banks based on similarity in business
policy which offers a framework for competitive positioning in the target market and
serves as a basis for long-term strategic focus. Finds that the public sector banks
generally outperform the private and foreign banks in this rapidly evolving and
liberalizing sector.
18) “Experience of customer satisfaction: a study of Indian public and private sector
banks”
Vinita Kaura, (International Journal of Bank Marketing, Vol. 31 Iss: 3, pp.167 – 186)
The purpose of this paper is to examine the effect of service quality, perceived price
and fairness and service convenience on customer satisfaction. It also aims to compare
multiple regression models between public and new private sector banks. Significant
difference in beta coefficient is found between public and private sector banks
regarding employee behavior, decision convenience, access convenience and post-
benefit convenience.
Dimensions of service convenience are decision convenience, access convenience,
transaction convenience, benefit convenience and post-benefit convenience.
19) “Customer Satisfaction: A Comparison of Public and Private Banks Of Pakistan”
Waqar ul Haq & Bakhtiar Muhammad(IOSR Journal of Business and
Management,ISSN: 2278-487X Volume 1, Issue 5 (July-Aug. 2012), PP 01-05)
This research is mainly based on primary data which has been collected through a
well-structured questionnaire (adapted from three different studies). The questionnaire
has been distributed to 351 different respondents on different chosen locations. This
paper makes a useful contribution as there are very low number of studies has been
conducted in Pakistan on such areas like price, technology, reliability, customer
service, location and infrastructure. This research shows that customer satisfaction
varies from person to person and, bank managers need to conduct more researches in
order to evaluate customer satisfaction more strongly.
20) “Customer Perception of E- banking services of Indian banks : some survey evidence”
by R.K.Uppal, (ICFAI Journal of Bank Mangement, 2008)
This research paper analyzes the quality of ebanking services in the changing
environment.The sample size of bank customers is 25. The data is collected through
pre-tested and well structured questionnaire in Ludhiana, Punjab in May 2006.The
study concludes that the customers of ebanks are satisfied with the different e-
channels and their services. It also suggests some measures to make ebanking service
more effective in the future. The present study is mainly concerned with the Indian
banking industry in general and particularly those banks that are producing service
through e-channels i.e. ebanks.
21) A study on factors affecting efficiency of Public Sector Banks”
N.Rao & Tiwari, (Journal of Service Research, March,2009.)
Here both the authors study the efficiency of 5 public sector banks selected on the
basis of deposits size in 2005. The study concludes that all employee efficiency
factors have insignificant influence on deposits, assets and advances, from branch
efficiency, only operating profits per branch and from operating efficiency, cost of
deposits have significant and positive impact.
Liquidity influencing factors and ultimate profit factors do not influence deposits,
assets and advances significantly although all profit factors have negative effect. The
study also suggests some measures to improve efficiency.
22) The Relationship between service quality and Customer Satisfaction
G.S. Sureshchandar, C.Rajendran & R.N. Anantharaman, (Journal of Service
Marketing, 2002)
The Authors adopt a different approach and view customer satisfaction as a multi
dimensional construct just as service quality, but argues that customer satisfaction
should be operationalized along the same factors on which service quality is
operationalized. Based on this approach, the link between service quality and customer
satisfaction has been investigated. The results indicate that the two constructs are
indeed independent but are closely related, implying that an increase in one is likely to
lead to an increase in another.
23) Paradigm change : Relationship Marketing and Service quality of Banking
Service” Dhillon, Batra & Dhyani, (Indian Banks’ Bulletin,2003)
This research paper study the impact of relationship marketing and trends of customer
relationship in selected Public Sector Banks (SBI) and private sector banks (ICICI) in
Chandigarh. The study concludes that ICICI bank is doing well in credibility, access,
communication, understanding the customers, tangibles, reliability, responsiveness,
competence and courtesy as their mean value is greater than that of SBI but from security
point of view, SBI is better. The study suggests that Public Sector Banks can also improve
their image by relationship marketing and further this relationship marketing will be
helpful in transforming the Indian banking system.
24) Banker – Customer Relationship in India ”
O.D. Heggade,( Journal of Financial Services research, 2002.)
Heggade O.D. analyzes the range of customer services provided by the banks along with
their impact on customer-banker relations. Study deals with Indian banks in general and
banks of Karnataka. 500 bank customers, 50 bank managers and 50 bank officers and
clerks selected through stratified sampling were surveyed through questionnaires and
interviews. The study concludes that public sector banks, although improved but are far
behind their counterparts mainly because they are operating mostly on labor-intensive
basis rather than computerization of their operations and electronic system. The study also
reveals that banking habits of people in this district are good and majority of the
customers are satisfied with banks’ customer services. A modest degree of customers’
shifting between different public sector banks and different public and private sector
banks has been observed. Employees in majority are satisfied with office space and
communication facilities.
25) Indian Banks & ATMs – An empirical study of consumer perception”
Poonam Garg & Vimi Jham, Strategies of Winning Organization, Excell Books
Publication, 2006.
The authors investigate factors that influence Indian customers to adopt ATMs by
using factor analysis and focused on the influence of demographic and psychological
variables of 296 customers of six selected banks such as SBI, PNB, ICICI, HDFC,
IDBI.
It is examined that most of the respondents are below the age of 35 years and the users
with lesser experience face more problems in comparison to other and they look for
reliability of information. There are problems of dim vision of screen and they use
ATMs maximum for withdrawals and rarely for deposits.
3. RESEARCH METHODOLOGY
Objectives of the Study
Expectation of customer towards their banks.
Satisfaction of customer towards their banks.
Perceptions of customer regarding service offered by banks.
Importance of the study
Satisfaction level of customer in banking sector
Identify the changes that customer needs in their banks
Identify potential customer of public and private banking.
Identify Why customer choose particular banking sector
RESEARCH METHODOLOGY
Research Type – Exploratory & Descriptive
o Why exploratory & descriptive research?
The time and budget constraints have been limited this research to go for
exploratory research design.
This research will provide insights and understanding the consumer’s Perception,
Expectation and Satisfaction level of the services provided by banking sector.
Descriptive research is used to describe characteristics of a population who have
bank accounts.
Data collection method:
Primary data
Structured questionnaire
SAMPLING PROCESS & SAMPLE SIZE
1. Population
a. Element: people selected randomly who have bank accounts.
b. Sampling unit: Customers who have bank accounts.
c. Extent: Vallabh Vidyanagar
d. Time: 15 minutes/respondent
2. Sampling Techniques: Random Sampling
3. Sample size: 200
LIMITATIONS
The study will be restricted to only VVN. Hence, the Findings cannot be
generalized for either the entire region or the country as a whole.
Sample size will be limited to 200 respondents.
RESULT ANALYSIS AND INTERPRETATION
1. Expectation of Public and Private Sector Banks:
Analysis of 29 Statements:
Under the caption ‘expectation level’ in the questionnaire, the respondents were asked
to give their opinion on 29 statements pertaining to services provided by Public and
Private Sector Banks. All the 204 respondents (97 for Public Sector and 107 for Private
Sector Banks) had given their opinion on a five point Likert Scale on all these statements.
Following paragraphs give the various statistical analyses carried out on the responses to
these 29 statements.
Factor Analysis
Analysis of multivariate data is very important. Factor analysis is one of the multivariate
analytical techniques. Factor analysis is a generic name denoting a class of procedures
primarily used for data reduction and summarization. When a research is carried out, it
may contain a large number of variables. Most of these variables may be correlated.
Factor analysis reduces a large number of variables to a small number of factors. This
factor conveys all essential information about the original variables.
Determination of the method of Factor Analysis:
To carry out the factor analysis there are about 6 to 7 methods available, out of which, two
methods are generally used: (1) Principal Component Analysis, and (2) Common Factor
Analysis. An appropriate method is to be selected for the analysis. If, however, the number
of variables is large (greater than 15) both methods result in similar solutions. Since, the
number of variables here are 29, either of the two methods can safely be used. From these
two methods, ‘Principal Component Analysis method’ is selected to carry out Factor
Analysis, as is usually done by different analysts.
Appropriateness of Factor Analysis and number of Factors:
Decision for carrying out Factor Analysis is wholly dependent upon answers to following two
questions:
1. Is factor analysis appropriate for the data?, and
2. How many factors should be extracted?
The answer to the first question is given by (1) Bartlett’s test of sphericity and (2) Kaiser-
Meyer-Olkin (KMO) measures of sampling adequacy. Bartlett’s test of sphericity is used to
test the null hypothesis that variables are uncorrelated in the population. The second is an
index to examine the appropriateness of factor analysis. Generally, the values of ‘KMO
measure of sampling adequacy’, falling between 0.5 to1.0 indicate that factor analysis is
appropriate. Values below 0.5 indicate inappropriateness of the analysis.
Many procedures have been suggested to answer the second question. They include (1)
Priori determination, (2) Determination on the basis of Eigen values, (3) Determination on
the basis of Scree Plot etc.
Factor Analysis using ‘Principal Component Analysis’ method:
Factor analysis was carried out on all the responses to 20 statements using ‘Principal
Components Analysis’ method. The results showed the approximate Chi-Square value of
2129.589 at 406 degree of freedom under the Bartlett’s Test of Sphericity, which is
significant at the 0.05 level. The null hypothesis (that the variables are uncorrelated in the
population, or the correlation matrix is an identity matrix) is, therefore, rejected. The alternate
hypothesis that the variables in the population are correlated is accepted. The Kaiser-Meyer-
Olkin Measure of Sampling Adequacy was 0.800. Thus, factor analysis may be considered
appropriate for analyzing the data.
Further analysis, therefore was carried out. In the final results, total eight factors, out of 29
have Eigen values more than 1.00. As per the approach based on Eigen values, only factors
with Eigen values greater than 1.00 are to be retained. Hence, total eight factors are to be
considered in this data. The results also show that these eight factors account for 62.469
percent of the total variance.
An important output from factor analysis is the factor matrix, also called the factor pattern
matrix. The factor matrix contains the coefficients used to express the standardized variables
in terms of the factors. These coefficients, factor loadings, represent the correlation between
the factors and the variables. A coefficient with a large absolute variable indicates that the
factor and the variable are closely related. Hence, to facilitate interpretation of factors, it is
necessary to identify the variables that have large loadings on the same factor. In the factor
matrix, the highest loading of 0.671 was found for statement one on factor ‘1’. It was decided
to consider factor loading of 0.500 as a cut off point for a statement to be associated with a
factor. When cut off value of loading of 0.500 was considered; seventeen statements were
associated with factor ‘1’, eight statements was associated with factor ‘2’ and there
statements were associated with factor ‘4’, ‘5’ and ‘6’, zero statements were associated with
factor ‘3’, ‘7’ and ‘8’.
Although, the initial or un-rotated factor matrix indicates the relationship between the factors
and individual variables, it rarely results in factors that can be interpreted, because the factors
are correlated with many variables. The factor matrix, therefore, is transformed into a simpler
one through rotation. It is easier to interpret this rotated factor matrix. Again, many methods
are available for rotation. Most commonly used method for rotation is the ‘Varimax’
procedure. Other two popular methods are ‘direct oblimin’ and ‘quartimax’.
Table 4.5 represents Factor Matrix without rotation and Table 4.6 represents Factor Matrix
with Varimax rotation. These two tables are representing the factor loadings. These factor
loadings represent the correlation between the factors and the variables. Analysis based on
these tables is given after these two tables.
Factor Matrix without rotation
Component Matrixa Statements Component
1 2 3 4 5 6 7 8
1 Good .580 .017 -.480 .268 .009 -.011 .048 .228
2 on time .623 .051 -.464 .054 -.003 -.132 -.215 .179
3 Easy .598 .121 -.420 .033 -.060 -.173 -.215 -.137
4 Queries .575 .049 -.142 .197 -.274 -.347 .028 -.127
5 Queue .657 -.023 -.416 .208 .067 .138 .085 .049
6 treat .671 .046 -.217 -.176 .097 .114 .030 .034
7 clean .658 -.131 -.054 -.073 .076 .195 .170 -.116
8 well ma .638 -.056 .004 -.060 .086 .313 .237 -.345
9 upto date .461 .116 -.011 -.061 .141 .479 -.039 .029
10 appealing .623 .036 .046 .012 .014 .279 -.004 -.413
11 promise .486 .045 .250 -.073 .198 -.091 -.403 -.204
12 depend .439 -.104 .385 .438 -.268 .167 -.227 -.016
13 attention .431 -.072 .471 .486 -.139 -.199 -.065 -.027
14 records .527 -.084 .384 .229 .000 .183 .112 .430
15 polite .626 -.074 .307 -.018 -.115 -.113 .363 .025
16 trust .571 -.067 .150 -.216 -.236 -.290 .356 -.136
17 available .544 .004 .067 -.441 .078 -.220 .307 .278
18 safe .542 .025 .249 -.255 .234 -.021 -.128 .456
19 support .569 .011 .339 -.135 .198 -.164 -.311 .014
20 assist .647 -.040 .001 -.121 .053 -.175 -.227 -.131
21 well man -.020 .655 -.056 .058 .160 -.196 .167 -.062
22 no busy -.003 .666 .115 .075 .348 .011 .049 -.101
23 minimal -.013 .627 .017 .155 .194 -.326 -.070 -.058
24 loyalty -.098 .481 .182 .205 .510 .057 .191 -.057
25 ecomm .042 .698 -.036 .138 -.208 -.140 .203 .043
26 etrans .032 .554 .008 .192 -.269 .290 .141 .086
27 informed .071 .534 -.089 .005 -.184 .204 -.222 .299
28 new card .056 .623 .081 -.384 -.242 .162 -.219 -.055
29 clearance .056 .479 .168 -.362 -.463 .075 -.070 -.059
Extraction Method: Principal Component Analysis.
Factor Matrix with Varimax rotation
Rotated Component Matrix Statements Component
1 2 3 4 5 6 7 8
1 Good .772 .178 .026 .050 -.088 .109 -.106 .175
2 on time .792 .090 -.036 .045 .049 .004 .198 .153
3 Easy .703 .173 .030 .115 .101 .004 .289 -.126
4 Queries .541 .037 .027 .407 .047 .301 .143 -.172
5 Queue .679 .421 .007 .065 -.105 .098 -.048 .124
6 treat .466 .438 -.018 .202 .074 -.076 .186 .246
7 clean .290 .573 -.094 .273 -.075 .076 .121 .155
8 well ma .170 .756 -.009 .271 -.041 .088 .108 .000
9 upto date .167 .569 .021 -.115 .156 .055 .080 .288
10 appealing .193 .677 .014 .143 .080 .199 .257 -.108
11 promise .105 .216 .071 .030 .032 .189 .673 .063
12 depend .089 .202 -.149 -.029 .083 .767 .159 .045
13 attention .070 .007 .066 .216 -.114 .771 .218 .030
14 records .092 .213 -.041 .150 -.032 .538 -.020 .589
15 polite .102 .283 -.005 .623 -.030 .331 .072 .238
16 trust .154 .192 -.070 .765 .050 .117 .135 .020
17 available .191 .118 -.001 .612 .036 -.174 .157 .490
18 safe .152 .096 .001 .165 .066 .066 .374 .702
19 support .110 .140 .046 .172 .020 .216 .651 .293
20 assist .372 .225 -.052 .241 .019 .103 .521 .064
21 well man .081 -.066 .677 .090 .193 -.106 -.046 -.058
22 no busy -.073 .110 .729 -.096 .167 -.043 .083 .043
23 minimal .113 -.208 .678 -.009 .152 .017 .156 -.082
24 loyalty -.194 .132 .720 -.135 -.111 .002 -.036 .112
25 ecomm .158 -.113 .545 .173 .431 .099 -.224 -.070
26 etrans .061 .133 .318 -.072 .484 .207 -.352 .018
27 informed .210 -.047 .200 -.239 .563 .055 -.076 .208
28 new card -.059 .082 .199 -.017 .762 -.142 .173 .007
29 clearance -.114 .015 .047 .196 .747 -.021 .062 -.055
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
As discussed earlier, a coefficient with a large absolute variable indicates that the factor and
the variable are closely related. Hence, to facilitate interpretation of factors, it is necessary
to identify the variables that have large loadings on the same factor. It was decided that
loading of absolute value of 0.500 should be considered as a cut off point for a statement to
be associated with a factor. Factor matrices of the five factors obtained under above
referred two different methods were referred to, and a cut off value of loading of 0.500 was
finally considered. Following table shows the number of statements associated with
different five factors under two different methods:
Sr.
No
Rotation
Method
Factors
1 2 3 4 5 6 7 8
1 Without
rotation
1,2,3,4,5,6,7,8,
10,12,14,15,16
,17,18,19,20,
21,22,23,
25,26,27,
28,29
- 13 11 9 - -
2 Varimax
rotation
1,2,3,4,5,6 7,8,9,10 21,22,23
, 24,25
16,17 27,28,29 12,13 11,19,
20
14,18
As discussed earlier, although, the initial or un-rotated factor matrix indicates the
relationship between the factors and individual variables, it seldom results in factors that
can be interpreted, because the factors are correlated with many variables. The factor
matrix, therefore, is transformed into a simpler one through rotation. It is easier to interpret
this rotated factor matrix. It can also be seen that from the above table that more variables
get associated with the factors when the factor matrix is rotated. All the two rotation
methods are giving the same variables associated with each matrix. So, the results of this
method are considered for interpretations of factors.
Interpretation of Factors:
Factor Number 1: Statements number 1, 2, 3, 4, 5, 6 are associated with this factor. These statements are
extracted from the Questionnaire and reproduced below:
Statement Number 1: Services provided should be good.
Statement Number 2: Services should be provided on time.
Statement Number 3: Services should be easy to use.
Statement Number 4: Your bank should be always ready to help you in any
queries.
Statement Number 5: Queue management should be better.
Statement Number 6: Your bank should treat you well.
The six statements stated above reflect dimensions, of ‘Service Quality’. The data can be
summarized by stating that ‘the customers’ satisfaction depends on the services they get
from the banks.’
Factor Number 2
Statements number 7, 8, 9 and 10 are associated with this factor. These statements are
extracted from the Questionnaire and reproduced below:
Statement Number 7: Employees should be neat and clean.
Statement Number 8: Employees should be well mannered.
Statement Number 9: Your bank should have up to date equipments.
Statement Number 10: Their physical facilities should be visually appealing.
The four statements stated above reflect the dimension, of ‘bank equipments and
employees quality’. The data, therefore, can again be summarized by stating that ‘the
customers’ satisfaction is associated with well and clean dressed employees, well mannered
employees, up-to-date equipments and appealing physical facilities be given to the
customers’.
Factor Number 3
Statements number 21, 22, 23, 24, 25 are associated with this factor. These statements are
extracted from the Questionnaire and reproduced below:
Statement Number 21: These facilities of online and phone assistance should be well
managed.
Statement Number 22: Telephones should not be busy.
Statement Number 23: Transactions carried out should take minimal time.
Statement Number 24: Loyalty discounts should be provided.
Statement Number 25: E-commerce facilities should be provided.
The five statements stated above reflect dimension of ‘Quality of Online and Phone
Services’. The data, therefore, can again be summarized by stating that ‘the customers’
satisfaction appears to associate with the quality of the service provided by banks related to
phone and online facilities’.
Factor Number 4
Statements number 16 and 17 are associated with this factor. These statements are
extracted from the Questionnaire and reproduced below:
Statement Number 16: Customers should be able to trust employees of these firms.
Statement Number 17: These firms should have flexible timing and should always be
available for the customers.
The two statements stated above reflect the ‘Employees Dedication towards the
Customers’. The data, therefore, can again be summarized by stating that ‘the customers’
satisfaction is associated with the trust and timings issues’.
Factor Number 5
Statements number 27, 28 and 29 are associated with this factor. These statements are
extracted from the Questionnaire and reproduced below:
Statement Number 27: Customers should be informed about every transaction on paper.
Statement Number 28: Customers should be provided new credit/debit card well in
advance before their current card expires as well as check books.
Statement Number 29: Check clearance should take minimal time.
The three statements stated above reflect the dimension of ‘extra activities’ that are
provided to lure the customers. The data, therefore, can again be summarized by stating
that ‘the customers’ satisfaction is associated with extra things available while customers
are banking with their banks’.
Factor Number 6
Statements number 12 and 13 are associated with this factor. These statements are
extracted from the Questionnaire and reproduced below:
Statement Number 12: These firms should be dependable.
Statement Number 13: Your bank should give you individual attention.
The two statements stated above reflect the dimension of ‘dependability of the customers
towards the banks’. The data, therefore, can again be summarized by stating that ‘the
customers’ satisfaction is associated with dependability on the banks and individual
attention given by the banks to their customers’.
Factor Number 7
Statements number 11, 19 and 20 are associated with this factor. These statements are
extracted from the Questionnaire and reproduced below:
Statement Number 11: When these firms promise to do something by a certain time, they
should do so.
Statement Number 19: Their employees should get adequate support from these firms to
do their jobs well.
Statement Number 20: Phone and online assistance should be provided by these firms.
The three statements stated above reflect the dimension of ‘Employees Duty for their
Customers’. The data, therefore, can again be summarized by stating that ‘the customers’
satisfaction is associated with doing the promised job on time, providing adequate support
and providing phone and online assistance to the customers’.
Factor Number 8
Statements number 14 and 18 are associated with this factor. These statements are
extracted from the Questionnaire and reproduced below:
Statement Number 14: Customers should be able to feel safe in their transactions with
these firms’ employees.
Statement Number 18: They should keep their records accurately.
The two statements stated above reflect the dimension of ‘Safety Measures taken by the
banks’. The data, therefore, can again be summarized by stating that ‘the customers’
satisfaction is associated with Safety issues and keeping the records accurately’.
Sr. No. Factor Name on the basis of Inference
1 Factor 1 Service Quality
2 Factor 2 Bank equipments and employees quality
3 Factor 3 Quality of Online and Phone Services
4 Factor 4 Employees Dedication towards the Customers
5 Factor 5 Extra Activities to lure customers
6 Factor 6 Dependability of the customers towards the banks
7 Factor 7 Employees Duty for their Customers
8 Factor 8 Safety Measures
Public Sector Banks (Satisfaction)
Analysis of 29 statements:
Under the caption ‘satisfaction level’ in the questionnaire, the respondents were asked to
give their opinion on 29 statements pertaining to services provided by Public Sector Banks.
All the 97 respondents had given their opinion on a five point Likert Scale on all these
statements. Following paragraphs give the various statistical analyses carried out on the
responses to these 29 statements.
Factor Analysis:
Analysis of multivariate data is very important. Factor analysis is one of the multivariate
analytical techniques. Factor analysis is a generic name denoting a class of procedures
primarily used for data reduction and summarization. When a research is carried out, it may
contain a large number of variables. Most of these variables may be correlated. Factor
analysis reduces a large number of variables to a small number of factors. This factor conveys
all essential information about the original variables.
Determination of the method of Factor Analysis:
To carry out the factor analysis there are about 6 to 7 methods available, out of which, two
methods are generally used: (1) Principal Component Analysis, and (2) Common Factor
Analysis. An appropriate method is to be selected for the analysis. If, however, the number of
variables is large (greater than 15) both methods result in similar solutions. Since, the number
of variables here are 29, either of the two methods can safely be used. From these two
methods, ‘Principal Component Analysis method’ is selected to carry out Factor Analysis, as
is usually done by different analysts.
Appropriateness of Factor Analysis and number of Factors:
Decision for carrying out Factor Analysis is wholly dependent upon answers to following two
questions:
2. Is factor analysis appropriate for the data?, and
3. How many factors should be extracted?
The answer to the first question is given by (1) Bartlett’s test of sphericity and (2) Kaiser-
Meyer-Olkin (KMO) measures of sampling adequacy. Bartlett’s test of sphericity is used to
test the null hypothesis that variables are uncorrelated in the population. The second is an
index to examine the appropriateness of factor analysis. Generally, the values of ‘KMO
measure of sampling adequacy’, falling between 0.5 to1.0 indicate that factor analysis is
appropriate. Values below 0.5 indicate inappropriateness of the analysis.
Many procedures have been suggested to answer the second question. They include (1) Priori
determination, (2) Determination on the basis of Eigen values, (3) Determination on the basis
of Scree Plot etc.
Factor Analysis using ‘Principal Component Analysis’ method:
Factor analysis was carried out on all the responses to 29 statements using ‘Principal
Components Analysis’ method. The results showed the approximate Chi-Square value of
2325.005 at 406 degree of freedom under the Bartlett’s Test of Sphericity, which is
significant at the 0.05 level. The null hypothesis (that the variables are uncorrelated in the
population, or the correlation matrix is an identity matrix) is, therefore, rejected. The alternate
hypothesis that the variables in the population are correlated is accepted. The Kaiser-Meyer-
Olkin Measure of Sampling Adequacy was 0.750. Thus, factor analysis may be considered
appropriate for analyzing the data.
Further analysis, therefore was carried out. In the final results, total eight factors, out of 29
have Eigen values more than 1.00. As per the approach based on Eigen values, only factors
with Eigen values greater than 1.00 are to be retained. Hence, total nine factors are to be
considered in this data. The results also show that these eight factors account for 63.302
percent of the total variance.
An important output from factor analysis is the factor matrix, also called the factor pattern
matrix. The factor matrix contains the coefficients used to express the standardized variables
in terms of the factors. These coefficients, factor loadings, represent the correlation between
the factors and the variables. A coefficient with a large absolute variable indicates that the
factor and the variable are closely related. Hence, to facilitate interpretation of factors, it is
necessary to identify the variables that have large loadings on the same factor. In the factor
matrix, the highest loading of 0.779 was found for statement two on factor ‘15’. It was
decided to consider factor loading of 0.500 as a cut off point for a statement to be associated
with a factor. When ‘factor matrix’ of the above one factor was referred to, and a cut off
value of loading of 0.500 was considered; eleven statements were associated with factor ‘1’,
five statements were associated with factor ‘2’ and four statements were associated with
factor ‘3’ and two statements were associated with factor ‘4’ and ‘5’, three statements were
associated with factor ‘6’ and ‘7’, three statements were associated with factor ‘8’ and ‘9’.
Although, the initial or un-rotated factor matrix indicates the relationship between the factors
and individual variables, it rarely results in factors that can be interpreted, because the factors
are correlated with many variables. The factor matrix, therefore, is transformed into a simpler
one through rotation. It is easier to interpret this rotated factor matrix. Again, many methods
are available for rotation. Most commonly used method for rotation is the ‘Varimax’
procedure. Other two popular methods are ‘direct oblimin’ and ‘quartimax’.
Table 4.1 represents Factor Matrix without rotation and Table 4.2 represents Factor Matrix
with Varimax rotation. These two tables are representing the factor loadings. These factor
loadings represent the correlation between the factors and the variables. Analysis based on
these tables is given after these two tables.
Component Matrixa Statements Component
1 2 3 4 5 6 7 8
1 Good .618 .061 .057 -.252 .429 -.040 -.033 .010
2 on time .511 -.035 -.017 -.121 -.441 -.094 .252 .157
3 Easy .242 -.141 -.030 .276 .339 .314 .460 -.013
4 Queries .381 .125 -.102 .293 -.229 -.042 -.439 -.387
5 Queue .212 4.116E-5 .221 -.218 .320 -.058 -.405 .549
6 treat .726 -.034 .022 .120 -.188 .012 .265 -.143
7 clean .883 .064 .045 -.038 -.054 .063 -.067 -.116
8 well ma .427 -.138 -.044 .539 .185 .264 -.073 .188
9 upto date -.127 .117 .708 .180 .010 .110 -.045 -.022
10 appealing -.191 .043 .735 .087 -.013 .197 .134 .007
11 promise -.047 .065 .823 .109 -.169 -.053 .046 -.018
12 depend .716 .072 .029 .103 -.249 -.071 -.257 -.051
13 attention .427 .082 .062 -.597 .193 -.131 .298 -.181
14 records .681 -.153 .136 .066 .260 .188 -.218 -.010
15 polite .779 -.008 -.019 -.058 .148 .043 -.076 -.186
16 trust .336 .032 -.156 .547 -.254 -.181 .172 .294
17 available .684 .139 -.047 -.244 -.010 -.080 .139 .310
18 safe .770 -.007 .170 .004 -.031 .065 -.197 .005
19 support .770 .087 .094 -.067 -.142 -.072 .284 -.010
20 assist -.060 .489 .177 -.201 -.070 -.189 -.158 -.179
21 well man .022 .607 .045 -.015 -.244 -.319 -.058 .273
22 no busy -.084 .533 .077 -.023 .075 -.101 .014 .071
23 minimal .015 .558 -.030 .256 .307 -.444 .027 -.044
24 loyalty .024 .577 .038 .378 .267 -.213 .216 -.004
25 ecomm -.003 .656 -.062 .119 .182 .013 .015 -.275
26 etrans .047 .704 -.206 .057 .105 .247 -.015 .144
27 informed -.012 .609 -.259 .004 -.073 .353 -.085 .012
28 new card -.017 .574 -.060 -.220 -.290 .539 .004 .144
29 clearance -.164 .603 .107 -.165 -.053 .224 .091 -.089
Extraction Method: Principal Component Analysis.
Rotated Component Matrixa Statements Component
1 2 3 4 5 6 7 8
1 Good .614 -.057 .169 -.102 .134 -.299 -.085 .323
2 on time .561 .020 -.190 -.045 -.157 .379 -.169 -.138
3 Easy .183 -.018 .063 .025 .697 -.006 -.192 -.142
4 Queries .307 .023 .082 -.076 -.097 .050 .720 -.128
5 Queue .149 -.029 -.011 .068 -.063 -.030 -.069 .821
6 treat .730 -.049 -.014 .010 .171 .197 .057 -.249
7 clean .865 .052 -.012 -.034 .084 .009 .221 .052
8 well ma .240 -.025 .016 .008 .610 .281 .294 .207
9 upto date -.074 .021 .086 .743 .019 -.042 .074 .074
10 appealing -.102 .043 -.024 .780 .067 -.063 -.116 -.007
11 promise .056 -.095 .040 .830 -.153 .060 -.012 -.008
12 depend .668 .009 -.017 -.020 -.094 .207 .399 .079
13 attention .579 -.029 .071 -.097 -.159 -.416 -.416 -.058
14 records .592 -.086 -.074 .047 .337 -.129 .259 .311
15 polite .749 -.044 .038 -.117 .166 -.155 .200 .076
16 trust .219 -.081 .169 -.081 .186 .716 .103 -.060
17 available .696 .104 .039 -.156 -.036 .172 -.245 .243
18 safe .731 -.003 -.066 .085 .073 .040 .258 .215
19 support .814 .005 .055 .031 .034 .166 -.113 -.106
20 assist .043 .219 .315 .150 -.452 -.178 .076 -.013
21 well man .067 .296 .404 .050 -.463 .343 -.052 .132
22 no busy -.040 .295 .420 .093 -.180 .003 -.079 .077
23 minimal -.002 .001 .810 -.042 -.085 .050 .047 .050
24 loyalty .005 .144 .756 .095 .129 .140 -.026 -.055
25 ecomm .022 .386 .578 -8.251E-5 -.014 -.174 .146 -.148
26 etrans .022 .667 .396 -.121 .063 .049 .020 .111
27 informed -.023 .707 .190 -.147 -.003 .007 .131 -.023
28 new card .045 .865 -.093 .056 -.106 .037 -.059 -.014
29 clearance -.060 .576 .235 .181 -.163 -.156 -.101 -.118
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
As discussed earlier, a coefficient with a large absolute variable indicates that the factor and
the variable are closely related. Hence, to facilitate interpretation of factors, it is necessary
to identify the variables that have large loadings on the same factor. It was decided that
loading of absolute value of 0.500 should be considered as a cut off point for a statement to
be associated with a factor. Factor matrices of the five factors obtained under above
referred two different methods were referred to, and a cut off value of loading of 0.500 was
finally considered. Following table shows the number of statements associated with
different five factors under two different methods:
Sr.
No
Rotation
Method
Factors
1 2 3 4 5 6 7 8
1 Without
rotation
1,2,4,6,7,12,13,
14,15,17,18,19
20,21,22,
23,24,25,
26,27,28,29
9,10,11 8 3 9 - 5
2 Varimax
rotation
1,2,6,7,12,13,14
,15,17,18,19
26,27,28,29 23,24,25 9,10,
11
3,8 16,21 4,20 5,22
As discussed earlier, although, the initial or un-rotated factor matrix indicates the
relationship between the factors and individual variables, it seldom results in factors that
can be interpreted, because the factors are correlated with many variables. The factor
matrix, therefore, is transformed into a simpler one through rotation. It is easier to interpret
this rotated factor matrix. It can also be seen that from the above table that more variables
get associated with the factors when the factor matrix is rotated. All the two rotation
methods are giving the same variables associated with each matrix. So, the results of this
method are considered for interpretations of factors.
Interpretation of Factors:
Factor Number 1: Statements number 1, 2, 6, 7, 12, 13, 14, 15, 17, 18 and 19 are associated with this factor.
These statements are extracted from the Questionnaire and reproduced below:
Statement Number 1: Services provided are good.
Statement Number 2: Services are provided on time.
Statement Number 6: Your bank treats you well.
Statement Number 7: Employees are neat and clean.
Statement Number 12: These firms are dependable.
Statement Number 13: Your bank gives you individual attention.
Statement Number 14: They keep their records accurately.
Statement Number 15: Employees of your bank are polite.
Statement Number 17: These firms are having flexible timings and should always
be available for the customers.
Statement Number 18: Customers feel safe in their transactions with these firms’
employees.
Statement Number 19: Employees get adequate support from these firms to do
their jobs well.
The eleven statements stated above reflect dimensions, of ‘Service Quality’. The data can be
summarized by stating that ‘the customers’ satisfaction depends on the services they get
from the banks.’
Factor Number 2
Statements number 26, 27, 28 and 29 are associated with this factor. These statements are
extracted from the Questionnaire and reproduced below:
Statement Number 26: E-transactions are safe enough.
Statement Number 27: Customers are always informed about every transaction on
paper.
Statement Number 28: Customers are provided new credit/debit card well in advance
before their current card expires as well as check books.
Statement Number 29: Check clearance takes minimal time.
The four statements stated above reflect the dimension, of ‘Bank’s Concern for their
Customers’. The data, therefore, can again be summarized by stating that ‘the customers’
satisfaction is associated with parameters that are attracting more customers like safe E-
transactions, taking minimal time for check clearance, providing check books, credit/debit
card which shows that bank takes care of their customers’.
Factor Number 3
Statements number 23, 24, 25 are associated with this factor. These statements are
extracted from the Questionnaire and reproduced below:
Statement Number 23: Transactions carried out are taking minimal time.
Statement Number 24: Loyalty discounts are provided.
Statement Number 25: E-commerce facilities are provided.
The three statements stated above reflect dimension of ‘Extra Activities to attract
Customers’. The data, therefore, can again be summarized by stating that ‘the customers’
satisfaction appears to associate with the minimal time transactions, Loyalty discounts and
E-commerce Facilities’.
Factor Number 4
Statements number 9, 10, 11 are associated with this factor. These statements are extracted
from the Questionnaire and reproduced below:
Statement Number 9: Your bank has up to date equipments.
Statement Number 10: Their physical facilities are visually appealing.
Statement Number 11: Your bank provides its services at the time it promises to do so.
The three statements stated above reflect the ‘Bank Facilities’. The data, therefore, can
again be summarized by stating that ‘the customers’ satisfaction is associated with the
Equipments used and promises made’.
Factor Number 5
Statements number 3 and 8 are associated with this factor. These statements are extracted
from the Questionnaire and reproduced below:
Statement Number 3: Customers Services are easy to use.
Statement Number 8: Employees are well mannered.
The two statements stated above reflect the dimension of ‘Attractive Services’ that are
provided to lure the customers. The data, therefore, can again be summarized by stating
that ‘the customers’ satisfaction is associated with attractive things available while
customers are banking with their banks’.
Factor Number 6
Statements number 16 and 21 are associated with this factor. These statements are
extracted from the Questionnaire and reproduced below:
Statement Number 16: You can trust the employees of your bank.
Statement Number 21: These facilities of online and phone assistance are very well managed.
The two statements stated above reflect the dimension of ‘dependability of the customers
towards the banks’. The data, therefore, can again be summarized by stating that ‘the
customers’ satisfaction is associated with dependability on the trust towards the employees
of the banks and well managed online and phone services.’
Factor Number 7
Statements number 4 and 20 are associated with this factor. These statements are extracted
from the Questionnaire and reproduced below:
Statement Number 4: Your bank is always ready to help you in any queries.
Statement Number 20: Phone and online assistance is provided by these firms.
The two statements stated above reflect the dimension of ‘Bank’s Assistance for any query’.
The data, therefore, can again be summarized by stating that ‘the customers’ satisfaction is
associated with solving the queries every time and providing phone and online assistance’.
Factor Number 8
Statements number 5 and 22 are associated with this factor. These statements are extracted
from the Questionnaire and reproduced below:
Statement Number 5: Queue management is better.
Statement Number 22: Telephones are not always busy.
The two statements stated above reflect the dimension of ‘Value of Time’. The data,
therefore, can again be summarized by stating that ‘the customers’ satisfaction is associated
with better Queue Management and keeping telephone line open for customers’.
Sr. No. Factor Name on the basis of Inference
1 Factor 1 Service Quality
2 Factor 2 Bank’s Concern for their Customers
3 Factor 3 Extra Activities to attract Customers
4 Factor 4 Bank Facilities
5 Factor 5 Attractive Services
6 Factor 6 Dependability of the customers towards the banks
7 Factor 7 Bank’s Assistance for any query
8 Factor 8 Value of Time
Private Sector Banks (Satisfaction)
Analysis of 29 statements:
Under the caption ‘satisfaction level’ in the questionnaire, the respondents were asked to
give their opinion on 29 statements pertaining to services provided by Private Sector Banks.
All the 107 respondents had given their opinion on a five point Likert Scale on all these
statements. Following paragraphs give the various statistical analyses carried out on the
responses to these 29 statements.
Factor Analysis:
Analysis of multivariate data is very important. Factor analysis is one of the multivariate
analytical techniques. Factor analysis is a generic name denoting a class of procedures
primarily used for data reduction and summarization. When a research is carried out, it may
contain a large number of variables. Most of these variables may be correlated. Factor
analysis reduces a large number of variables to a small number of factors. This factor conveys
all essential information about the original variables.
Determination of the method of Factor Analysis:
To carry out the factor analysis there are about 6 to 7 methods available, out of which, two
methods are generally used: (1) Principal Component Analysis, and (2) Common Factor
Analysis. An appropriate method is to be selected for the analysis. If, however, the number of
variables is large (greater than 15) both methods result in similar solutions. Since, the number
of variables here are 29, either of the two methods can safely be used. From these two
methods, ‘Principal Component Analysis method’ is selected to carry out Factor Analysis, as
is usually done by different analysts.
Appropriateness of Factor Analysis and number of Factors:
Decision for carrying out Factor Analysis is wholly dependent upon answers to following two
questions:
4. Is factor analysis appropriate for the data?, and
5. How many factors should be extracted?
The answer to the first question is given by (1) Bartlett’s test of sphericity and (2) Kaiser-
Meyer-Olkin (KMO) measures of sampling adequacy. Bartlett’s test of sphericity is used to
test the null hypothesis that variables are uncorrelated in the population. The second is an
index to examine the appropriateness of factor analysis. Generally, the values of ‘KMO
measure of sampling adequacy’, falling between 0.5 to1.0 indicate that factor analysis is
appropriate. Values below 0.5 indicate inappropriateness of the analysis.
Many procedures have been suggested to answer the second question. They include (1) Priori
determination, (2) Determination on the basis of Eigen values, (3) Determination on the basis
of Scree Plot etc.
Factor Analysis using ‘Principal Component Analysis’ method:
Factor analysis was carried out on all the responses to 29 statements using ‘Principal
Components Analysis’ method. The results showed the approximate Chi-Square value of
2325.005 at 406 degree of freedom under the Bartlett’s Test of Sphericity, which is
significant at the 0.05 level. The null hypothesis (that the variables are uncorrelated in the
population, or the correlation matrix is an identity matrix) is, therefore, rejected. The alternate
hypothesis that the variables in the population are correlated is accepted. The Kaiser-Meyer-
Olkin Measure of Sampling Adequacy was 0.750. Thus, factor analysis may be considered
appropriate for analyzing the data.
Further analysis, therefore was carried out. In the final results, total eight factors, out of 29
have Eigen values more than 1.00. As per the approach based on Eigen values, only factors
with Eigen values greater than 1.00 are to be retained. Hence, total nine factors are to be
considered in this data. The results also show that these eight factors account for 63.302
percent of the total variance.
An important output from factor analysis is the factor matrix, also called the factor pattern
matrix. The factor matrix contains the coefficients used to express the standardized variables
in terms of the factors. These coefficients, factor loadings, represent the correlation between
the factors and the variables. A coefficient with a large absolute variable indicates that the
factor and the variable are closely related. Hence, to facilitate interpretation of factors, it is
necessary to identify the variables that have large loadings on the same factor. In the factor
matrix, the highest loading of 0.779 was found for statement two on factor ‘15’. It was
decided to consider factor loading of 0.500 as a cut off point for a statement to be associated
with a factor. When ‘factor matrix’ of the above one factor was referred to, and a cut off
value of loading of 0.500 was considered; eleven statements were associated with factor ‘1’,
five statements were associated with factor ‘2’ and four statements were associated with
factor ‘3’ and two statements were associated with factor ‘4’ and ‘5’, three statements were
associated with factor ‘6’ and ‘7’, three statements were associated with factor ‘8’ and ‘9’.
Although, the initial or un-rotated factor matrix indicates the relationship between the factors
and individual variables, it rarely results in factors that can be interpreted, because the factors
are correlated with many variables. The factor matrix, therefore, is transformed into a simpler
one through rotation. It is easier to interpret this rotated factor matrix. Again, many methods
are available for rotation. Most commonly used method for rotation is the ‘Varimax’
procedure. Other two popular methods are ‘direct oblimin’ and ‘quartimax’.
Table 4.1 represents Factor Matrix without rotation and Table 4.2 represents Factor Matrix
with Varimax rotation. These two tables are representing the factor loadings. These factor
loadings represent the correlation between the factors and the variables. Analysis based on
these tables is given after these two tables.
Component Matrixa Statements Component
1 2 3 4 5 6 7 8
1 Good .246 -.187 -.230 .266 .452 .353 -.248 .151
2 on time .399 .199 -.089 .204 .088 -.230 .506 .362
3 Easy .208 .131 .166 -.226 .400 -.088 .284 -.611
4 Queries .739 -.003 -.070 .133 -.206 .117 -.190 .046
5 Queue .864 .070 -.024 .024 -.089 -.054 .080 .059
6 treat .418 -.159 -.172 .568 .294 .025 -.070 -.232
7 clean .694 .139 .054 .265 -.227 -.097 .265 -.029
8 well ma .479 .101 .007 -.675 -5.032E-5 .179 -.158 .137
9 Up to date -.115 .143 .501 .082 .274 -.325 -.064 .368
10 appealing -.106 .057 .661 .180 .073 -.071 .037 .234
11 promise .774 .065 .163 .082 -.062 -.120 .079 -.111
12 depend .797 .067 .000 -.021 -.142 .252 -.034 .060
13 attention .803 .076 .027 -.145 .025 .183 .054 .182
14 records -.128 .261 .095 -.036 -.315 .284 .518 -.241
15 polite .277 -.072 -.097 -.165 .656 -.030 .335 .016
16 trust .387 -.091 -.031 .656 -.055 .026 -.184 -.106
17 available .637 .176 .155 -.215 -.211 .128 .053 .047
18 safe .589 -.060 .075 -.321 .266 -.019 -.322 -.138
19 support .456 .032 .063 -.125 .289 -.462 -.108 .030
20 assist -.202 -.181 -.332 .049 .312 .512 .314 .293
21 well man -.138 .078 .624 .075 .136 .394 -.067 -.094
22 no busy .002 .101 .704 .139 .092 .359 -.048 -.044
23 minimal -.047 .734 -.127 -.033 .269 .051 -.065 -.098
24 loyalty -.227 .747 -.071 .132 .104 -.057 -.062 .047
25 ecomm -.052 .722 -.019 .144 .041 -.120 .004 -.030
26 etrans .019 .627 -.069 .141 .008 .066 -.024 -.196
27 informed -.129 .749 -.192 -.017 .144 .181 -.127 .051
28 new card -.108 .692 .008 -.035 -.060 .059 .058 .228
29 clearance .052 .626 -.160 -.028 -.167 -.045 -.235 -.008
Extraction Method: Principal Component Analysis.
Rotated Component Matrixa Statements Component
1 2 3 4 5 6 7 8
1 Good .140 -.045 .378 .010 -.010 .604 .290 -.138
2 on time .318 .117 .081 -.095 .061 .103 -.014 .742
3 Easy .093 .068 .012 .067 .846 -.125 -.087 -.062
4 Queries .731 -.033 .292 -.091 -.149 -.018 .081 -.062
5 Queue .816 -.020 .181 -.114 .093 -.060 .083 .204
6 treat .186 -.068 .757 -.068 .192 .169 .125 .028
7 clean .643 .026 .321 -.034 .050 -.194 -.165 .348
8 well ma .618 .028 -.516 -.073 .105 .104 .194 -.240
9 upto date -.152 .096 -.115 .493 -.030 -.125 .419 .362
10 appealing -.080 -.030 -.031 .664 -.084 -.141 .097 .255
11 promise .702 -.042 .245 .042 .206 -.213 .056 .165
12 depend .837 -.003 .132 -.011 -.021 .091 -.022 -.033
13 attention .828 -.008 -.015 .010 .084 .175 .093 .102
14 records .017 .151 -.138 .087 .108 -.067 -.728 .072
15 polite .122 -.068 -.044 -.057 .595 .406 .209 .273
16 trust .248 -.038 .739 .043 -.152 -.034 .045 .019
17 available .732 .050 -.125 .066 .034 -.093 -.071 .016
18 safe .528 -.071 -.025 -.003 .327 -.003 .429 -.278
19 support .303 .000 .025 -.073 .302 -.191 .522 .200
20 assist -.179 -.092 -.052 -.076 -.030 .791 -.206 .107
21 well man -.043 .034 -.002 .728 .074 .068 -.131 -.216
22 no busy .091 .028 .051 .789 .044 .014 -.116 -.128
23 minimal -.015 .770 -.038 .002 .210 .080 .041 -.036
24 loyalty -.177 .778 -.008 .056 -.034 -.035 .009 .103
25 ecomm -.023 .714 .049 .047 .038 -.147 -.028 .152
26 etrans .057 .634 .134 .017 .089 -.079 -.149 -.042
27 informed -.029 .802 -.090 -.011 -.020 .170 -.012 -.078
28 new card .034 .657 -.226 .101 -.146 .011 -.098 .170
29 clearance .143 .635 -.043 -.130 -.148 -.176 .028 -.099
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
As discussed earlier, a coefficient with a large absolute variable indicates that the factor and
the variable are closely related. Hence, to facilitate interpretation of factors, it is necessary
to identify the variables that have large loadings on the same factor. It was decided that
loading of absolute value of 0.500 should be considered as a cut off point for a statement to
be associated with a factor. Factor matrices of the five factors obtained under above
referred two different methods were referred to, and a cut off value of loading of 0.500 was
finally considered. Following table shows the number of statements associated with
different five factors under two different methods:
Sr.
No
Rotation
Method
Factors
1 2 3 4 5 6 7 8
1 Without
rotation
4,5,7,8,11,12,13
,17,18,19
23,24,25,
26,27,28,29
9,10,21,
22
6,16 1,3,15 20 2,14 -
2 Varimax
rotation
4,5,7,8,11,12,13
,17
23,24,25,
26,27,28,29
6,16 9,10,
21,22
3,15 1,20 18,19 2,14
As discussed earlier, although, the initial or un-rotated factor matrix indicates the
relationship between the factors and individual variables, it seldom results in factors that
can be interpreted, because the factors are correlated with many variables. The factor
matrix, therefore, is transformed into a simpler one through rotation. It is easier to interpret
this rotated factor matrix. It can also be seen that from the above table that more variables
get associated with the factors when the factor matrix is rotated. All the two rotation
methods are giving the same variables associated with each matrix. So, the results of this
method are considered for interpretations of factors.
Interpretation of Factors:
Factor Number 1: Statements number 4, 5, 7, 8, 11, 12, 13 and 17 are associated with this factor. These
statements are extracted from the Questionnaire and reproduced below:
Statement Number 4: Your bank is always ready to help you in any queries.
Statement Number 5: Queue management is better.
Statement Number 7: Employees are neat and clean.
Statement Number 8: Employees are well mannered.
Statement Number 11: Your bank provides its services at the time it
promises to do so.
Statement Number 12: These firms are dependable.
Statement Number 13: Your bank gives you individual attention.
Statement Number 17: These firms are having flexible timings and should always
be available for the customers.
The eight statements stated above reflect dimensions, of ‘Service Quality’. The data can be
summarized by stating that ‘the customers’ satisfaction depends on the services they get
from the banks.’
Factor Number 2
Statements number 23, 24, 25, 26, 27, 28 and 29 are associated with this factor. These
statements are extracted from the Questionnaire and reproduced below:
Statement Number 23: Transactions carried out are taking minimal time.
Statement Number 24: Loyalty discounts are provided.
Statement Number 25: E-commerce facilities are provided.
Statement Number 26: E-transactions are safe enough.
Statement Number 27: Customers are always informed about every transaction on
paper.
Statement Number 28: Customers are provided new credit/debit card well in advance
before their current card expires as well as check books.
Statement Number 29: Check clearance takes minimal time.
The seven statements stated above reflect the dimension, of ‘Private Sector Bank’s
Advantages’. The data, therefore, can again be summarized by stating that ‘the customers’
satisfaction is associated with parameters that are attracting more customers like safe E-
transactions, loyalty discounts, E-commerce Facilities, taking minimal time for check
clearance, providing check books, credit/debit card which shows that bank takes care of
their customers’.
Factor Number 3
Statements number 6 and 16 are associated with this factor. These statements are extracted
from the Questionnaire and reproduced below:
Statement Number 6: Your bank treats you well.
Statement Number 16: You can trust the employees of your bank.
The two statements stated above reflect dimension of ‘Better Treatment for the customers’.
The data, therefore, can again be summarized by stating that ‘the customers’ satisfaction
appears to associate with the treatment the banks give to their customers and they can
trust their employees.
Factor Number 4
Statements number 9, 10, 21 and 22 are associated with this factor. These statements are
extracted from the Questionnaire and reproduced below:
Statement Number 9: Your bank has up to date equipments.
Statement Number 10: Their physical facilities are visually appealing.
Statement Number 21: These facilities of online and phone assistance are very well
managed.
Statement Number 22: Telephones are not always busy.
The four statements stated above reflect the ‘Bank Facilities’. The data, therefore, can again
be summarized by stating that ‘the customers’ satisfaction is associated with the
Equipments used, physical facilities provided and well managed online and phone
assistance’.
Factor Number 5
Statements number 3 and 15 are associated with this factor. These statements are extracted
from the Questionnaire and reproduced below:
Statement Number 3: Customers Services are easy to use.
Statement Number 15: Employees of your bank are polite.
The two statements stated above reflect the dimension of ‘Attractive Services’ that are
provided to lure the customers. The data, therefore, can again be summarized by stating
that ‘the customers’ satisfaction is associated with attractive things available while
customers are banking with their banks’.
Factor Number 6
Statements number 1 and 20 are associated with this factor. These statements are extracted
from the Questionnaire and reproduced below:
Statement Number 1: Services provided are good.
Statement Number 20: Phone and online assistance is provided by these firms.
The two statements stated above reflect the dimension of ‘Better Services’. The data,
therefore, can again be summarized by stating that ‘the customers’ satisfaction is associated
with better banking and online and phone services.’
Factor Number 7
Statements number 18 and 19 are associated with this factor. These statements are
extracted from the Questionnaire and reproduced below:
Statement Number 18: Your Customers feel safe in their transactions with these firms’
employees.
Statement Number 19: Employees get adequate support from these firms to do their jobs
well.
The two statements stated above reflect the dimension of ‘Employees Dedication’. The
data, therefore, can again be summarized by stating that ‘the customers’ satisfaction is
associated with customers feeling safe in their transactions with their banks and employees
getting all the support to help their customers from the banks’.
Factor Number 8
Statements number 2 and 14 are associated with this factor. These statements are extracted
from the Questionnaire and reproduced below:
Statement Number 2: Services are provided on time.
Statement Number 14: They keep their records accurately.
The two statements stated above reflect the dimension of ‘Value of Customers’ Time and
Records’. The data, therefore, can again be summarized by stating that ‘the customers’
satisfaction is associated with serving the customers on time and keeping the records
accurately’.
Sr. No. Factor Name on the basis of Inference
1 Factor 1 Service Quality
2 Factor 2 Private Sector Banks’ advantages
3 Factor 3 Better Treatment for the Customers
4 Factor 4 Bank Facilities
5 Factor 5 Attractive Services
6 Factor 6 Better Services
7 Factor 7 Employees Dedication
8 Factor 8 Value of Customers’ Time and Records
GAP ANALYSIS
1. Services provided are good.
Public Sector Bank = -0.14706 Private Sector Bank = -0.44118
As we can say that both Public and Private Sector Banks are not satisfying their
customers fully of this statement. Still Public Sectors are doing better than Private
Sectors in this factor.
2. Services are provided on time
Public Sector Bank = 0 Private Sector Bank = -0.56373
As we can see that in Public Sector is more satisfying its customer than Private Sector Banks in
this factor.
3. Services are easy to use.
Public Sector Bank = -0.15196 Private Sector Bank = -0.33333
Here in this statement neither of the two sectors is satisfying its customer, but here Public
Sector is better than Private Sector Banks.
4. Your bank is always ready to help you in any queries.
Public Sector Bank = -0.42647 Private Sector Bank = -0.41667
Here as we can see neither of the sectors is satisfying the customers, but here Private Sector is
somewhat better than Public Sector Banks.
5. Queue management is better.
Public Sector Bank = -0.5098 Private Sector Bank = -0.2598
Here as we can see neither of the sectors is satisfying the customers, but here Private Sector is
somewhat better than Public Sector Banks.
6. Your bank treats you well.
Public Sector Bank = -0.46078 Private Sector Bank = -1.42647
Here in this statement neither of the two sectors is satisfying its customer, but here Public
Sector is better than Private Sector Banks.
7. Employees are neat and clean.
Public Sector Bank = 0.004902 Private Sector Bank = 0.014706
Here both the sectors are satisfying its customer to some level and out of them, Private Sector is
better than Public Sector Banks.
8. Employees are well mannered.
Public Sector Bank = -1.26471 Private Sector Bank = 0.897059
Here as we can see, Private Sector Banks are satisfying its customers to a high extent while
Public Sector Banks needs to improve a lot in this area.
9. Your bank has up to date equipments.
Public Sector Bank = 0.039216 Private Sector Bank = 0.25
Here as we can see, both the sectors are satisfying the customers while here Private
Sector is better satisfying its customers than Public Sector.
10. Their physical facilities are visually appealing.
Public Sector Bank = -0.15196 Private Sector Bank = 0.039216
Here Private Sector Banks are satisfying its customers while Public Sector Banks cannot
satisfy their customers in this statement.
11. Your bank provides its services at the time it promises to do so.
Public Sector Bank = 0.196078 Private Sector Bank = -0.45098
Here Public Sector Bank is satisfying its customer while Private Sector Bank is not.
12. These firms are dependable.
Public Sector Bank = -0.25 Private Sector Bank = -0.39216
Here none of the sector is able to satisfy its customers while people still have more faith
on Public Sector Banks than Private Sector Banks.
13. Your bank gives you individual attention.
Public Sector Bank = -0.4951 Private Sector Bank = 0.504902
Here Private Sector Banks are able to satisfy their customers while Public Sector Banks
are not able to.
14. They keep their records accurately.
Public Sector Bank = -0.25 Private Sector Bank = 0.171569
Here Private Sector Banks are able to satisfy their customers while Public Sector Banks
are not able to.
15. Employees of your bank are polite.
Public Sector Bank = -0.54412 Private Sector Bank = -0.40196
Here none of the sector is able to satisfy its customers while Private Sector is better in
this factor than Public Sector Banks.
16. You can trust the employees of your bank.
Public Sector Bank = -0.87255 Private Sector Bank = -0.40196
Here none of the sector is able to satisfy its customers while Private Sector is better than
Public Sector in this factor.
17. These firms are having flexible timings and should always be available for the
customers.
Public Sector Bank = -0.29412 Private Sector Bank = -0.15686
Here none of the sector is able to satisfy its customers while Private Sector is better than
Public Sector in this factor.
18. Customers feel safe in their transactions with these firms’ employees.
Public Sector Bank = -0.37255 Private Sector Bank = 0.357843
Here Private Sector is better than Public Sector Banks in this factor.
19. Employees get adequate support from these firms to do their jobs well.
Public Sector Bank = -0.2451 Private Sector Bank = -0.39216
Here none of the sector is able to satisfy its customers while Public Sector is better than
Private Sector Banks.
20. Phone and online assistance is provided by these firms.
Public Sector Bank = -0.36275 Private Sector Bank = 1.09804
Here Private Sector is far much better than Public Sector Bank in satisfying its customer
related to the phone and online assistance.
21. These facilities of online and phone assistance are very well managed.
Public Sector Bank = -0.05392 Private Sector Bank = -0.01471
Here none of the sector is able to satisfy its customers while Private Sector is better than
Public Sector in this factor.
22. Telephones are not always busy.
Public Sector Bank = 0.112745 Private Sector Bank = 0.308824
Here both the sectors are satisfying its customers while Private Sector Banks are far
much better than Public Sector Banks in this context.
23. Transactions carried out are taking minimal time.
Public Sector Bank = 0.058824 Private Sector Bank = 0.196078
Here both the sectors are satisfying its customers while Private Sector Banks are far
much better than Public Sector Banks in this context.
24. Loyalty discounts are provided.
Public Sector Bank = 0.254902 Private Sector Bank = 0.132353
Here both the sectors are satisfying its customers while Public Sector Banks are far much
better than Private Sector Banks in this context.
25. E-commerce facilities are provided.
Public Sector Bank = -0.01961 Private Sector Bank = -0.08824
Here none of the sector is satisfying its customers while Public Sector is better than
Private Sector banks.
26. E-transactions are safe enough.
Public Sector Bank = 0.215686 Private Sector Bank = 0.098039
Here both the sectors are satisfying its customers while Public Sector Banks are far much
better than Private Sector Banks in this context.
27. Customers are always informed about every transaction on paper.
Public Sector Bank = 0.068627 Private Sector Bank = 0.147059
Here both the sectors are satisfying its customers while Private Sector Banks are far
much better than Public Sector Banks in this context.
28. Customers are provided new credit/debit card well in advance before their current
card expires as well as check books.
Public Sector Bank = 0.171569 Private Sector Bank = 0.188235
Here both the sectors are satisfying its customers while Private Sector Banks are far
much better than Public Sector Banks in this context.
29. Check clearance takes minimal time.
Public Sector Bank = 0.034314 Private Sector Bank = 0.09804
Here both the sectors are satisfying its customers while Private Sector Banks are better
than Public Sector Banks in this context.
Statements Public Sector Satisfying
Private Sector Satisfying
Which is Better?
1 Services provided are good No No Public
2 Services are provided on time Neutral No Public
3 Services are easy to use No No Public
4 Your bank is always ready to help you in any queries No No Private
5 Queue management is better No No Private
6 Your bank treats you well No No Public
7 Employees are neat and clean Yes Yes Private
8 Employees are well mannered No Yes Private
9 Your bank have up to date equipments Yes Yes Private
10 Their physical facilities are visually appealing No Yes Private
11 Your bank provides its services at the time it promises to do so Yes No Public
12 These firms are dependable No No Public
13 Your bank gives you individual attention No Yes Private
14 They keep their records accurately No Yes Private
15 Employees of your bank are polite No No Private
16 You can trust the employees of your bank No No Private
17 These firms are having flexible timings and should always be available for the customers
No No Private
18 Customers feel safe in their transactions with these firms’ employees
No Yes Private
19 Employees get adequate support from these firms to do their jobs well
No No Public
20 Phone and online assistance is provided by these firms No Yes Private
21 These facilities of online and phone assistance are very well managed
No No Private
22 Telephones are not always busy Yes Yes Private
23 Transactions carried out are taking minimal time Yes Yes Private
24 Loyalty discounts are provided Yes Yes Public
25 E-commerce facilities are provided No No Private
26 E-transactions are safe enough Yes Yes Public
27 Customers are always informed about every transaction on paper
Yes Yes Private
28 Customers are provided new credit/debit card well in advance before their current card expires as well as check books
Yes Yes Private
29 Check clearance takes minimal time Yes Yes Private
Total 10/29 15/29
From the above we came to know that, Private Sector Banks are satisfying 15 out of 29
statements and Public Sector Banks are satisfying 10 out of 29 statements. Thus we can say
that, Private Sector banks have succeeded in making their image better in the minds of their
customers than Public Sector banks. With better service quality and better facilities that too
on proper time have helped Private Sector to come up and thus nowadays customers prefer
the Private Sector Banks more.
On the other hand, even though Public Sector Banks are less preferred by many customers,
there are still such factors like less interest rates, trust and safety which customers see in
Public Sector and go for them. If Public Sectors just focus on some service quality and
facilities then they can get much better and be preferred back by the customers.
Findings and Suggestions:
We found out that according to the statements, Private Sector Banks are satisfying in 15 out
of 29 statements and Public Sector banks are satisfying in 10 out of 29 statements. But apart
from this, Private Sector Banks are better than Public Sector Banks in 21 out of 29
statements. This shows that nowadays people highly prefer Private Sector Banks due to
better service and facilities which Private Sector Banks are offering.
Also we found out that though Private Sector Banks are better in service quality and
facilities but still there are parameters which customers prefer which lead them to Public
Sector Banks. People still feel safer while working with the Public Sector Banks than Private
Sector Banks. Thus Private Sector Banks have to work harder to generate an image of safer
bank than Public Sector Banks so that customers prefer them more.
On the other hand, Public Sector Banks are still there in the market in spite of the tough
competition given by Private Sectors due to their goodwill, low interest rates and trust what
customers have on them. Thus if the Public Sector Banks have to lure more customers, then
they have to give better service quality, flexibility and facilities to their customers.
Conclusion:
Thus we can conclude from this research that in Vallabh Vidyanagar, Customers are more
inclined towards Private Sector banks than towards Public Sector banks. Apart from many
people who are having more of Government jobs or old accounts are still with the Public
Sector banks while the new and potential customers are going towards Private Sector banks.
As nowadays competition is too high in the market, in order to retain their position Private
and Public Sector Banks both have to work hard.