100 CHAPTER IV RESEARCH METHODOLOGY 4.1 INTRODUCTION Performance evaluation of an organization depends upon the type and the objectives lying behind it. Performance evaluation of commercial banks is different from commercial undertakings. Banks have the responsibility of increasing their economic efficiency and satisfy certain social obligations. Economic norms are not the only norms, which have to be applied to judge the performance; rather social norms too have to be applied to judge the same. Thus the present study aims at evaluating the performance of commercial banks in India as an aftermath of the introduction of Information Technology in the Indian Banking Sector. 4.2 RESEARCH DESIGN The Present study “Impact of Information Technology on the Performance of Banking Sector in India” involves both Primary and Secondary data. 4.2.1 Collection of Data Secondary data was collected from following publications; i) Performance Highlights, Various Issues, IBA (Mumbai) 19998-99 to 2009- 2010 ii) IBA Bulletin (Special Issues), 1998-99 to 2009-2010 iii) Report on Trend and Progress of Banking in India, 2000 to 2010 iv)Indian Banking at a Glance, 2006 v) Annual Reports of these Banks. Various other RBI publications, The Financial Express, The Economic Times, and the Monthly Review of the Banks have also been consulted for the required data. The primary data was collected through pre-tested and well draft questionnaire from bank customers. An attempt has been made to compare the performance of Commercial banks in India between the partially computerized era (1998-2004) and IT enabled era (2004-2010).
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100
CHAPTER IV
RESEARCH METHODOLOGY
4.1 INTRODUCTION
Performance evaluation of an organization depends upon the type and the
objectives lying behind it. Performance evaluation of commercial banks is different
from commercial undertakings. Banks have the responsibility of increasing their
economic efficiency and satisfy certain social obligations. Economic norms are not
the only norms, which have to be applied to judge the performance; rather social
norms too have to be applied to judge the same. Thus the present study aims at
evaluating the performance of commercial banks in India as an aftermath of the
introduction of Information Technology in the Indian Banking Sector.
4.2 RESEARCH DESIGN
The Present study “Impact of Information Technology on the Performance of
Banking Sector in India” involves both Primary and Secondary data.
4.2.1 Collection of Data
Secondary data was collected from following publications;
i) Performance Highlights, Various Issues, IBA (Mumbai) 19998-99 to 2009-
2010
ii) IBA Bulletin (Special Issues), 1998-99 to 2009-2010
iii) Report on Trend and Progress of Banking in India, 2000 to 2010
iv)Indian Banking at a Glance, 2006
v) Annual Reports of these Banks.
Various other RBI publications, The Financial Express, The Economic Times,
and the Monthly Review of the Banks have also been consulted for the required data.
The primary data was collected through pre-tested and well draft questionnaire from
bank customers.
An attempt has been made to compare the performance of Commercial banks
in India between the partially computerized era (1998-2004) and IT enabled era
(2004-2010).
101
The first stage of study is confined only to specific areas like:
a. Earnings efficiency
b. Profitability
c. Managerial efficiency
d. Asset quality
The selections of indicators for the present study have been decided with the
view of analyzing the impact of information technology on Indian banking sector. In
order to analyze the data and draw conclusions, various statistical tools like growth
rate, Correlation and paired‘t’ test have been employed through EXCEL and SPSS
Software.
The first stage of the study is a diagnostic approach which examines the
performance of Indian banking sector by dividing it into five groups as Nationalized
banks, SBI & its Associates, Old private sector banks, New private sector banks and
Foreign banks in terms of Earnings efficiency Profitability, Managerial efficiency and
Asset quality by applying ratio analysis . In the second stage the bank customer’s
perception towards e- delivery channel was analyzed by selecting sample respondents
of 304. Variance analysis, Factor analysis has been calculated to analyze the perception
of 304 bank customers regarding some selected aspects. Multiple regression analysis had
been employed to study the level of satisfaction towards selected aspects. The operational
efficiency of e – delivery channels had been studied using ANOVA analysis.
4.2.2 Sample framework
The universe of the present study is the Scheduled commercial banks of India.
The Indian Banking sector has been divided into five groups and a representative
sample of 30% has been selected from each group based on its profitability.
i)Nationalized bank Group:
a. Punjab National Bank (PNB),
b. Canara Bank (CB),
c. Bank of India (BOI),
d. Bank of Baroda (BOB)
e. Indian Overseas Bank (IOB )
f. Oriental Bank of Commerce (OBC).
102
ii) SBI & its Associates bank Group:
a.State Bank of India (SBI)
b. State Bank of Indore (SBID).
iii) Old private sector bank Group:
a.Federal Bank Ltd (FB)
b. Jammu and Kashmir Bank Ltd (J&KB)
c.Karnataka Bank ltd (KB)
d. South Indian Bank (SIB)
e. Tamil Nadu Mercantile Bank Ltd., (TMB).
iv) New private sector bank Group:
a.ICICI Bank ltd (ICICI)
b. HDFC Bank Ltd (HDFC).
v) Foreign banks:
a.Standard Chartered Bank (SCB)
b. Citibank NA (CIB)
c.HSBC Ltd (HSBC)
d. ABN – Ambro Bank NV (ABNB)
e.Deutsche Bank AG (DB)
f. Bank of America (BOA)
g. JP Morgan Chase Bank (JPMCB)
h. Barclays Bank PLC (BB)
For the empirical study on the bank customer’s perception towards e – delivery
channels a sample of 304 respondents were selected at random using convenience
sampling technique. The selected samples were provided with a well structured
questionnaire to collect information regarding e – delivery channels. The bank
customers from different socio-economic background (age, income, occupation,
education and gender) were surveyed from different branches.
4.2.3 Significance of the study period
The Software Packages for Banking Applications in India had their beginnings
in the middle of 80s, when the banks started computerizing the branches in a limited
manner. The early 90’s saw the plummeting hardware prices and advent of cheap and
inexpensive but high powered PC’s and services had made the banks to use Total
103
Branch Automation (TBA) packages. The middle 90’s witnessed the tornado of
financial reforms, deregulation, and globalization etc. coupled with rapid revolution in
communication technologies and evolution of novel concept of convergence of
communication technologies, like internet, mobile/cell phones etc. Indian banking
sector especially public sector and old private sector banks accepted computerization
since 1993, more out of sheer compulsion and necessity to cope up increasing
overload and incompatibility of the manual system to sustain further growth. From
then technology has continuously played an important role in the working of banking
institutions and the services provided by them. In this study, the reference period is
twelve years from 1998-1999 to 2010-2011.This period has been identified because,
banking sector in India resorted to speedy reforms, liberalization and computerization,
since the mid of nineties. The study is classified into two segments based on the
implementation of information technology i.e., from 1998-1999 to 2010-2011.
4.2.4 Factors considered for analysis
The study uses Ratio analysis to compare the performance of different
categories of banks. Ratio analysis is a powerful tool of financial analysis. In financial
analysis ratios are used as benchmarks for evaluating a firm’s position or
performance. The absolute values may not provide us meaningful values until and
unless they are related to some relevant information.
Four parameters are considered for analysis:
1. EARNINGS EFFICIENCY PARAMETERS
Earnings efficiency is evaluated by looking at ratios which involves net
income, net interest income, non-interest income, non-interest expense, and
provision for loan losses. Earnings can also be measured by using a bank’s
ROA (Return on Assets)
For measuring the earnings efficiency of commercial banks, the study
employs the following indicators.
a. Deposit per branch (Deposits/Branches); Since deposit mobilization is one of
the major objectives of a bank, its efficiency is reflected in term of deposits
per branch.
b. Advances per branch (Advances/Branches); higher is the credit deployment
per branch, higher is going to be the profits per branch. Higher credit
104
deployment also reflects the contribution of the bank in the process of
economic development. Therefore Advances per branch is an important
indicator of earnings efficiency of a branch.
c. Interest income per branch (Interest income/Branches); It denotes the
income earned by banks by way of granting loans and advances. It is the major
source of income of a banking institution.
d. Interest expenses per branch (Interest expenses/Branches); Interest expenses
are the expenses incurred by banks by way of paying interest on deposits with
it.
e. Non-interest income per branch (Non-Interest income/Branches); The
income derived from discount, commission, exchange and brokerage.
f. Non-interest expenses per branch (Non-Interest expenses/Branches); It
includes operational expenses of the bank such as salaries, allowances,
Measures of Summary statistics had been applied to measure mean and, Standard Deviation
between the financial parameters of sample banks.
i) Mean
Arithmetic Mean is the total of the values of the items divided by their number. A.M is the
abbreviation and x (read as x-bar) is the symbol for arithmetic mean. The terms ‘mean’ and
‘average’ (singular) also refer to arithmetic mean.
108
X = x
N
x denote a given value. x denotes the sum of all x. (read, sigma) is a symbol which is used
to denote the sum or the total of the values given after the symbol
ii) Standard Deviation
Standard deviations are taken from actual mean. The following formula is applied:
= x2/N __
Calculate the actual mean of the series, i.e., X. Take the deviations of the items from the
mean, i.e., find (X -X). Denote these deviations by x. Square these deviations and obtain the
total x2.Divide x2 by the total number of observations, and extract the square-root. This
gives us the value of standard deviation.
iii) Annual Compound Growth Rate
The Annual Compound Growth Rate help the researcher to measure the average
annual growth of individual sample banks for the various variables measured and analyzed.
Very frequently summary judgments as to the growths are to be made to interpret time series
on the variables. Estimates of trend are not only of academic interest they are of considerable
significance to the policy maker. Computation of growth rates is the most prevalent method
for this purpose. The method of computation should be such which uses the entire series of
observations. The basic approach is to specify the variable under study as a fraction of time.
To understand the concept of compound growth rate, let us assume that the value of Y in base
period (t=0) is 100 and it grows over time at the rate 10% every the value of Y at different
points of time shall be as follows.
^B =yt -
(y2) (t)n
t- (t)^2)n
iv) Pearson's Correlation
Karl Person's Correlation has been applied to analyze the correlation between the
individual banks performances for all the four parameters viz, earnings efficiency,
profitability, managerial efficiency and asset quality
The most common measure of correlation is the Karl Pearson product-moment
coefficient of correlation (r). This measure expresses both the strength and direction of linear
correlation. This is measured by the formula:
r =N XY – (X) (Y)[NX2 -- (X)2] [N Y2 – (Y)2]
Where
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R = the Pearson correlation coefficient
N = the total number of pairs of X and Y
X = raw score on the X variable
Y = raw score on the y variable
v) Paired ‘’t’’ test
Paired t-test is a way to test for comparing two related samples, involving small
values of n that does not require the variances of the two populations to be equal, but the
assumption that the two populations are normal and must continue to apply. For a paired t-
test, it is necessary that the observations in the two samples be collected in the form of what is
called matched pairs i.e. ‘’each observation in the one sample must be paired with an
observation in the other sample in such a manner that these observations are somehow
‘’matched’’ or related, in an attempt to eliminate extraneous factors which are not of interest
in test’’. This test was considered appropriate for this study to measure the performance of
various bank group and to analyze the perception of bank customers towards It enabled
banking services. To apply this test, the differences between the variables were calculated- D,
along with the sample variance of the difference score. If the values farm the two matched
samples are denoted as Xi and Yi and differences by Di (Di=Xi-Yi), then the mean of the
differences i.e.,
__D =
(Di)
nand the variance of the differences or
( diff)2 =(Di2—D 2).n)n-1
Assuming the above said differences to be normally distributed and independent, the
paired t-test was applied for judging the significance of mean of differences to work out the
test statistic t as under:
t =__D -- 0 with (n-1) degrees of freedom diff /n
__Where, D = Mean of differences
diff = Standard deviation of differences
n =Number of matched pairs
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4.2.6 Data analysis techniques for empirical study
The empirical analysis was carried out to find out customers’ perception about IT
enabled services in Coimbatore city. The chapter demonstrates the acceptance of e-channels
among the customers, their satisfaction, and suggestions to further improve IT enabled
services in Indian banking. For analyzing the customer’s perception, 304 customers using e-
delivery channels has been selected. Variance, Factor analysis has been calculated to analyze
the perception of 304 bank customers regarding some selected aspects. Multiple regression
analysis had been employed to study the level of satisfaction towards selected aspects. The
operational efficiency of e – delivery channels had been studied using ANOVA analysis
4.3 National Status of the Present Research
At a time when the economy, is undergoing a radical transformation due to the all
pervasive influence of IT and it is growing at a fast pace, a number of changes has occurred
in the total economy like work culture, structure, systems etc. One sector that has undergone
fundamental changes as a consequence of the application of IT has been financial sector and
banking is not an exception. The new technology has radically altered the traditional ways of
doing banking business. We realize that in the coming days IT will contribute substantially to
banking industry’s efficiency. If Indian banks are to compete globally, the time is opportune
for them to institute sound and robust risk management practices.The current research work is
a comprehensive study regarding the various issues and challenges faced by the banking
industry and also explore the various opportunities by using IT in managing transformation in
banking industry. In this study a comprehensive survey has been conducted to know the
perceptions and extent of acceptability of IT among the bank customers. The study highlights
the extent of awareness in society regarding the use of IT in banks. To cap it all, this study
will be helpful to the society and also to the nation. After studying various aspects of
introduction of information technology in banking sector, comprehensive policy regarding the
managing of the bank transformation through IT could be initiated. This study will surely
capture the attention of all the concerned experts because excellent changes and growth has
been observed during this study. It will help to make policies to make our banks globally
competitive by the full adoption of IT.
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4.4 INTERPRETATION OF DIAGONISTIC ANALYSIS
4.4.1 Introduction
In the present chapter an attempt has been made to study and compare the
performance of commercial banks in the partially computerized era and Information
Technology enabled era. The impact of information technology is well reflected in
terms of behaviour of various indicators.
In order to measure and compare the performance of banks, the researcher had
applied two different types of analysis. They are
1. Ratio Analysis
2. Statistical Analysis
The ratio analysis was carried out for all the indicators. The ratio analysis was
presented in a comparative statement, which represented both partially computerized
era and Information technology enabled era. The values presented in the tables were
average values of each period have been taken into consideration. The performance of
banks was analyzed using Growth Rate and Compound Growth Rate. These two
analyses had helped the researcher to draw conclusions on the growth achieved by
Banks. The comparison between bank’s growth rate was carried out by using both
inter group and intra group analysis. When this study was carried out, certain values
were not available, thus the growth rate and CGR could not be computed.
The Statistical analysis was carried out for all the indicators. For this purpose,
mean, standard deviation and correlation analysis was undertaken. These tools were
employed to find out the impact of Information Technology on the five Bank groups
under study. The paired “t” test was carried out to discover whether the introduction
of information technology had significantly affected the performance of the five bank
groups. Even though correlation analysis and paired “t” test was aimed to reveal the
impact of Information Technology on the performance of the five bank groups taken
under study, the results of the two analyses varied for certain indicators .
112
4.4.2 EARNINGS EFFICIENCY: In order to analyze the impact of informationtechnology on the parameter earnings efficiency, the following analysis had beencarried out. Certain indicators were considered for this analysis. These indicators werecompared as between partially computerized era and IT Enabled era.
I. DEPOSITS PER BRANCHTABLE: 4.1
DEPOSITS PER BRANCH(Value in lakhs) (Value in lakhs)
Bank
Partially computerized era(1998-2004)
IT Enabled era(2005-2010)
ValueGrowth
Rate CGR ValueGrowth
Rate CGR
Nationalized BanksBank of Baroda 46842 115 7.15 120733 390 17.48Bank of India 45531 107 2.32 117476 386 17.48Canara Bank 32387 117 7.15 85325 344 14.81Indian Overseas Bank 22712 111 4.71 63193 348 17.48Oriental Bank of Commerce 18565 132 12.20 61389 422 20.22Punjab Nationalized Bank 43009 116 7.15 135124 389 17.48
SBI & its Associates BanksState Bank of India 178298 116 7.15 461279 297 14.81State Bank of Indore 4381 127 12.20 17972 387 17.48
Old Private Sectors BanksFederal Bank 6672 95 -2.27 20235 407 17.48Jammu & Kashmir Bank 7433 146 20.22 23934 288 12.20Karnataka Bank 4643 118 7.15 13766 339 14.81South Indian Bank 3374 125 9.64 11959 335 17.48Tamilnadu Mercantile Bank 2256 130 12.20 6349 312 14.81
New Private Sectors BanksHDFC Bank 4749 290 58.48 71319 948 31.82ICICI Bank 7334 163 25.89 145397 630 28.82
Foreign BanksBank of America 3169 72 -14.88 2899 288 14.81Bank of Nova Scotia 619 117 7.15 2346 187 9.64Barclays Bank 174 135 14.81 3633 688 99.52Citibank 9689 108 2.32 32553 357 17.48Hongkong& ShanghaiBanking Corpn.
7072 133 14.81 29616 452 23.02
JP Morgan Chase Bank 239 112 4.71 1989 898 54.48Standard Chartered Bank 5234 94 -2.27 28837 511 17.48
Source: Statistics Published by RBI of various YearsNote: Base Year: Partially computerized era: 1998-99Note: Base Year: IT Enabled era: 2004-05
113
TABLE: 4.2PAIRED SAMPLES STATISTICS
DEPOSITS PER BRANCHGroups Variables Mean SD Std Error Correlation
SBI & its Associates Banks(Partially computerized)and SBI & its AssociatesBanks (IT Enabled)
.228 .321 .227 1.004Not
Significant
Group3
Old Private Sectors Banks(Partially computerized)and Old Private SectorsBanks (IT Enabled)
.012 .007 .003 3.464 Significant
Group4
New Private Sectors Banks( Partially computerized)and New Private SectorsBanks (IT Enabled)
.000 .002 .002 .000Not
Significant
Group5
Foreign Banks (PartiallyComputerized) and ForeignBanks (IT Enabled)
3.747 9.839 3.719 1.008Not
Significant
Level of Significance: 5 per cent
175
The event of computerization had opened up new opportunities for the whole
banking industry, but at the same time the pressures of competition have led to
narrowing the spreads, consolidation and restructuring of the private sector and
foreign banks which has further affected the overall profit making of the banks.
Hence, banks with a view to maximize their profits have been largely focusing on
core competencies to maximize their profits in the IT enabled era.
It was documented in Table 4.46 that in the Nationalized Bank group (Group
1)in the highest growth rate for the indicator profitability ratio was achieved by the
Bank of Baroda with a growth rate of 100 and a CGR of 25.89%. But the growth rate
of all the other nationalized banks revealed negative values. As far as the SBI & its
Associates bank group (Group 2) are concerned, the State Bank of India had a growth
rate of -101, and the State Bank of Indore also revealed negative growth rate of -
86.The reasons for negative growth in profitability could be the emphasis on priority
sector lending, high statutory liquidity and cash reserves ratios, the mushroom growth
of non-viable branches, the levels of spread and burden, the composition of deposits
credits etc,.
The table further revealed that some of the private sector banks recorded
improved performance in the IT enabled era. In the old private sector Bank group
(Group 3), the Federal Bank registered the highest growth rate of 11 and with a CGR
of 20.22%. In new private sector Bank group (Group 4) the ICICI bank revealed the
highest growth rate of 175 and a CGR of 25.89%. With regard to the foreign bank
group (Group 5), the Bank of America and Bank of Nova Scotia registered the highest
growth rate of 133 and 130 respectively. The reasons for negative growth in
profitability could be the high establishment expenses coupled with their own
business policies which led to poor profitability ratio of both the bank groups under
study.
In Table 4.47, the correlation analysis of the indicator, the profitability ratio
was exhibited .the analysis proved that foreign bank group (Group 5) had the highest
positive correlation of .999 and nationalized bank group had a positive correlation of
.784. The rest of the bank groups also revealed positive correlation thus concluding
that information technology is having a positive impact on the profitability of majority
of bank groups under study.
176
The results of the paired ‘t’ test was registered from Table 4.48.It was evident
that the old private sector bank group (Group 3) had significant‘t’ value of 3.464. It
was followed by nationalized bank group (Group 1) with a ‘t’ value of 2.314. Thus it
was proved that information technology had a significant impact on the entire bank
group under study, with regard to profitability ratio.
Thus it could be concluded from the ratio analysis of the indicator profitability
ratio that in the Nationalized Bank group (Group 1), the Bank of Baroda had a growth
rate of 100 and a CGR of 25.89%, in the SBI & its Associates banks (Group 2), the
State Bank of India had a growth rate of -101. In the old private sector Bank group
(Group 3), the Federal Bank registered the highest growth rate of 11 and with a CGR
of 20.22%. In new private sector Bank group (Group 4) the ICICI bank revealed the
highest growth rate of 175 and a CGR of 25.89%.In the foreign bank group (Group 5),
the Bank of America registered the highest growth rate of 133 in its group. The
correlation analysis of the indicator, the profitability ratio proved that foreign bank
group (Group 5) had the highest positive correlation of .999. The results of the paired
‘t’ test revealed that the old private sector bank group (Group 3) had significant ‘t’
value of 3.464.
177
4.4.3 MANAGERIAL EFFICIENCY PARAMETERS: The managerial efficiencyhad been analyzed by using certain indicators. These indicators were comparedbetween partially computerized era and IT enabled era.
I. CREDIT -DEPOSIT RATIO
TABLE: 4.49CREDIT -DEPOSIT RATIO(Value in ratio) (Value in ratio)
Bank
Partially computerized era(1998-2004)
IT Enabled era(2005-2010)
ValueGrowth
Rate CGR ValueGrowth
Rate CGR
Nationalized BanksBank of Baroda 49.88 4 1.62 66.92 40 7.15Bank of India 59.29 27 2.32 72.52 7 2.09Canara Bank 44.11 6 -0.68 61.96 4 2.32Indian Overseas Bank 47.81 97 0.18 68.66 53 4.71Oriental Bank of Commerce 47.00 43 4.71 66.30 180 4.71Punjab Nationalized Bank 50.42 11 0.23 68.39 52 4.71
SBI & its Associates BanksState Bank of India 48.15 3 -8.79 71.78 43 4.71State Bank of Indore 55.76 4 0.69 73.50 30 2.32
Old Private Sectors BanksFederal Bank 60.44 9 -1.82 68.33 11 4.71Jammu & Kashmir Bank 40.25 157 14.81 62.34 228 2.32Karnataka Bank 46.02 26 2.32 61.26 47 0.69South Indian Bank 51.49 6 1.15 66.27 88 2.32Tamilnadu Mercantile Bank 46.88 12 1.62 65.19 60 4.71
New Private Sectors BanksHDFC Bank 44.14 84 20.22 62.88 118 2.32ICICI Bank 71.79 131 23.02 91.18 154 2.09
Foreign BanksBank of America 164.00 3 4.71 109.65 57 -16.82Bank of Nova Scotia 116.06 22 -2.27 135.02 5 4.71Barclays Bank 12.09 81 -20.56 47.08 471 151.18Citibank 68.72 97 2.09 81.08 101 -2.05Hongkong& ShanghaiBanking Corpn.
56.70 109 2.32 69.57 129 -
JP Morgan Chase Bank 1.23 100 - 22.81 95 25.89Standard Chartered Bank 82.76 62 7.15 87.92 80 0.69
Source: Statistics Published by RBI of various YearsNote: Base Year: Partially computerized era: 1998-99Note: Base Year: IT Enabled era: 2004-05
178
TABLE: 4.50PAIRED SAMPLES STATISTICS
CREDIT -DEPOSIT RATIOGroups Variables Mean SD Std Error Correlation
Group 1Nationalized Banks (PartiallyComputerized)
49.75 5.19 2.12.900
Nationalized Banks (IT Enabled) 67.46 3.46 1.41
Group 2
SBI & its Associates Banks (Partiallycomputerized)
51.96 5.38 3.811.000
SBI & its Associates Banks (ITEnabled)
72.64 1.22 0.86
Group 3
Old Private Sectors Banks (Partiallycomputerized)
49.02 7.53 3.37.866
Old Private Sectors Banks (ITEnabled)
64.68 2.88 1.29
Group 4
New Private Sectors Banks ( Partiallycomputerized)
57.97 19.55 13.831.000
New Private Sectors Banks (ITEnabled)
77.03 20.01 14.15
Group 5
Foreign Banks (PartiallyComputerized)
71.65 56.81 21.47.893
Foreign Banks (IT Enabled) 79.02 37.48 14.16
Level of Significance: 5 per cent
TABLE: 4.51PAIRED SAMPLES TEST
CREDIT -DEPOSIT RATIOGroups Variables Mean SD Std Error t value Sig
SBI & its Associates Banks(Partially computerized) andSBI & its Associates Banks(IT Enabled)
3.63 0.03 0.02 1.815Not
Significant
Group3
Old Private Sectors Banks(Partially computerized) andOld Private Sectors Banks(IT Enabled)
4.38 2.48 1.11 3.946 Significant
Group4
New Private Sectors Banks( Partially computerized)and New Private SectorsBanks (IT Enabled)
0.70 0.82 0.58 1.207Not
Significant
Group5
Foreign Banks (PartiallyComputerized) and ForeignBanks (IT Enabled)
0.81 1.64 0.62 1.307Not
Significant
Level of Significance: 5 per cent
187
An important indicator used in the analysis of financial performance of banks
is the level of Non-Performing assets (NPAs). The information on NPAs helps the
commercial banking supervisors to monitor and discipline errant banks and helps
investors to decide on the financial worth of the banks.
Since 1985, the Indian commercial banks were required to classify their
advances portfolio under a uniform grading system called the Health Code System,
which indicates the quality or health of the individual advances. This system consists
of 8 codes of which code Nos. 5 to 8 are deemed as non-performing assets. Such non-
performing assets consists of a) debts recalled , b) suit-filed accounts i.e., where suits
have been filed and decree obtained and d) debts classified as bad and doubtful.
The quantum of non-performing assets (NPAs) as a percentage of advances is
one of the critical indicators of the quality of a bank’s loan portfolio and hence of its
overall health. In this connection, one has to make a distinction between gross and net
NPAs of banks. Net NPA is derived from gross NPA by excluding (i) balance in
interest suspense account i.e. interest due but not received, (ii) DICGC/ECGC claim
received and kept in suspense account pending adjustment (for final settlement) , (iii)
part payment received and kept in suspense account and (iv) total provisions held. Net
NPA is the concept which is internationally recognized as relevant.
It was relevant from Table 4.55 that in the nationalized bank group (Group 1),
the Bank of Baroda had the lowest growth rate of 79 and a CGR of (-30.81%).
Similarly, the Oriental Bank of Commerce had also a growth rate of 79 and a CGR of
(-4.50%).These banks had performed well to regulate its Net NPAs. In the SBI & its
Associates bank group (Group 2), the State Bank of India had performed better than
compared to its counterpart, State Bank of Indore with a growth rate of 68 and a CGR
of -7.95% in the IT enabled era.
Similarly, in the old private sector bank group (Group 3) of the five banks
taken under study, the Karnataka bank had the lowest growth rate of 43 and a CGR of
-16.82%, thus it was proved that it was able to control its NPAs efficiently. As far as
the new private sector bank group (Group 4) was concerned, the HDFC had a growth
rate of 37, which was lower than ICICI Bank (106). Further in the Foreign bank group
188
(Group 5), the HSBC bank had a growth rate of 34. The Bank of Nova Scotia (616)
had the highest growth rate with regard to Net NPAs to Net Advances ratio.
The statistical analysis of Ratio of Net NPAs to Net Advances was depicted in
Table 4.56. It was clear that the old private sector bank group (Group 3) had the
highest positive correlation of .981 followed by the Nationalized bank group (Group
1) with a correlation co-efficient of .601. Thus the introduction of information
technology had a positive impact Group 1 and Group 3 with regard to the indicator,
Ratio of Net NPAs to Net Advances.
Further Table 4.57 revealed the results of paired‘t’ test. It was evident that the
old private sector bank group (Group 3) had a significant value of 3.946. The bank
groups that had significant values were Group 2 (1.815), Group 5 (1.307) and groups
Group 4 (1.207). The nationalized bank group (Group 1) revealed insignificant ‘t’
value. Thus it was concluded that the introduction technology had a significant impact
on the majority of bank groups under study.
Thus it was concluded from the ratio analysis that in the nationalized bank
group (Group 1), the Bank of Baroda had the lowest growth rate of 79 and a CGR of
(-30.81%). In the SBI & its Associates bank group (Group 2), had a growth rate of 68
and a CGR of -7.95% in the IT enabled era, in the old private sector bank group
(Group 4) , the Karnataka bank had the lowest growth rate of 43 and a CGR of -
16.82%. Similarly, in the new private sector bank group (Group 4), the HDFC had a
growth rate of 37. Among all the banks in the Foreign bank group (Group 5), the
HSBC bank had the lowest growth rate of 34. The paired‘t’ test revealed that the old
private sector bank group (Group 3) had a significant value of 3.946.
189
4.4.4 ASSET QUALITY: In order to analyze the impact of information technologyon the parameter asset quality, certain indicators were taken into account andanalyzed for partially computerized era and IT enabled era
I. RATIO OF NET INTEREST INCOME TO TOTAL ASSETS (NETINTEREST MARGIN)
TABLE: 4.58
RATIO OF NET INTEREST INCOME TO TOTAL ASSETS(NET INTEREST MARGIN)
(Value in ratio) (Value in ratio)
Bank
Partially computerized era(1998-2004)
IT Enabled era(2005-2010)
Value GrowthRate
CGR Value GrowthRate
CGR
Nationalized BanksBank of Baroda 3.09 96 -1.59 2.75 24 -6.67Bank of India 2.61 44 4.71 2.57 9 2.09Canara Bank 2.66 906 28.82 2.46 44 -14.88Indian Overseas Bank 3.39 26 -2.05 2.49 47 -8.79Oriental Bank of Commerce 3.39 25 2.32 2.49 37 -10.87Punjab Nationalized Bank 3.47 48 2.32 3.27 18 -2.27
SBI & its Associates BanksState Bank of India 2.83 6 -0.20 2.80 8 -6.67State Bank of Indore 3.37 56 2.32 2.57 99 -8.79
Old Private Sectors BanksFederal Bank 2.69 11 4.71 3.27 7 2.32Jammu & Kashmir Bank 3.05 31 2.32 2.71 12 1.39Karnataka Bank 1.65 420 25.89 2.41 468 -2.27South Indian Bank 2.39 99 7.15 2.77 736 -0.13Tamilnadu Mercantile Bank 3.05 450 25.89 3.80 315 -6.67
New Private Sectors BanksHDFC Bank 3.59 4 -2.05 4.26 32 4.71ICICI Bank 1.76 2 -4.50 2.02 35 1.85
Foreign BanksBank of America 3.30 35 -8.79 3.84 40 20.22Bank of Nova Scotia 2.66 37 -6.67 2.05 4 12.20Barclays Bank 1.77 1569 20.22 3.65 4738 25.89Citibank 4.54 98 -0.45 4.69 69 -4.50Hongkong& ShanghaiBanking Corpn.
3.11 88 -1.37 4.35 124 -
JP Morgan Chase Bank 2.50 331 25.89 3.01 200 2.32Standard Chartered Bank 4.30 106 2.32 4.14 131 -0.22
Source: Statistics Published by RBI of various YearsNote: Base Year: Partially computerized era: 1998-99Note: Base Year: IT Enabled era: 2004-05
190
TABLE: 4.59PAIRED SAMPLES STATISTICS
RATIO OF NET INTEREST INCOME TO TOTAL ASSETS (NET INTERESTMARGIN)
Groups Variables Mean SD Std Error Correlation
Group 1Nationalized Banks (PartiallyComputerized)
3.10 0.38 0.16.431
Nationalized Banks (IT Enabled) 2.67 0.31 0.13
Group 2
SBI & its Associates Banks (Partiallycomputerized)
3.10 0.38 0.27-1.000
SBI & its Associates Banks (ITEnabled)
2.69 0.16 0.12
Group 3
Old Private Sectors Banks (Partiallycomputerized)
2.57 0.58 0.26.676
Old Private Sectors Banks (ITEnabled)
2.99 0.55 0.24
Group 4
New Private Sectors Banks ( Partiallycomputerized)
2.68 1.29 0.921.000
New Private Sectors Banks (ITEnabled)
3.14 1.58 1.12
Group 5Foreign Banks (PartiallyComputerized)
3.17 0.99 0.37.605
Foreign Banks (IT Enabled) 3.68 0.90 0.34Level of Significance: 5 per cent
TABLE: 4.60PAIRED SAMPLES TEST
RATIO OF NET INTEREST INCOME TO TOTAL ASSETS (NET INTERESTMARGIN)
4. Courteous Behaviour Of The Bank Staff 1.000 .862
5. Easy Procedure And Less Formalities For Loan / Advances 1.000 .619
6. High Rate Of Interest On Deposits 1.000 .825
7. Low Penalty Charges 1.000 .820
8. More And Appropriate E-Channels 1.000 .844
9. Minimum Balance Required To Maintain In Deposit Accounts 1.000 .888
10. Sound Reputation Of Bank 1.000 .663
Extraction Method: Principal Component Analysis.
Table 4.67(a)Total Variance ExplainedComponent Initial Eigen values Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %1 3.068 30.681 30.681 3.068 30.681 30.6812 1.898 18.976 49.657 1.898 18.976 49.6573 1.345 13.447 63.104 1.345 13.447 63.1044 1.178 11.781 74.885 1.178 11.781 74.8855 .829 8.287 83.172
6 .610 6.102 89.274
7 .411 4.113 93.387
8 .345 3.453 96.839
9 .215 2.151 98.990
10 .101 1.010 100.000
Extraction Method: Principal Component Analysis.
When considering the factors which were important in selecting a bank (Table 4.67) ,
Of those various factors which were taken for study and analyzed through factor analysis, the
variable X9- minimum balance required to maintain in deposit accounts have secured the
extraction value of .883, the variable X4 –courteous behavior of the bank staff had a extraction
207
value of .862, the variable X8 –more and more appropriate e-channels had a factor loading of
.862, the variable X6-high rate of interest on deposits, X7- low penalty charges had a factor
loadings of .825, .820 respectively. The variable X2-better customer relationship management
had a factor loadings of .802, variable X3 – convenient location of a bank had a factor
loadings of .697, the variable X10- sound reputation of the bank, variable X5- easy procedure
and less formalities for loan/advances, variable X1- availability of more products/services had
a factor loading of .619 and .470 respectively.
When the total variance were explained in Table 4.67(a), the variable X1-
availability of more products/services, X2-better customer relationship management, X3-
convenient location of a bank, X4-courteous behavior of the bank staff constituted the overall,
factor loadings of 74.885 percent and can be grouped as “prime location and effective service
quality practices”, the other factors constituted the remaining 25.115 percent of the loadings
and thus all the factors of variables X5 to X10 can be grouped together and can be termed as
“operational strategies with good reputation”.
When the factors were made in to component matrix with four classification
components, the variable X2-better customer relationship management, X7-low penalty
charges, X8-more and more appropriate e-channels, X9-minimum balance required to maintain
in deposit accounts had a factor loadings of .585, .852, .890, .807 respectively falls in
component I, under component II, the variables X3-convenient location of a bank, X6-high
rate of interest on deposits had a factor loadings of .808, .652 respectively, under the III
component the variable X5-easy procedure and less formalities for loan/advances, variable
X10-sound reputation of the bank had a factor loadings of .605 , in the component IV, the
variable X4-courteous behavior of the bank staff had a factor loadings of .873.
Table 4.68 Factors concentrated by the respondents while selecting e-channels
S.No Factors SA A UD DA SDA Total Rank1. Convenient Accessibility Of E-Channels 540 308 218 10 35 1111 II2. Convenient Location Of ATM’s 376 176 200 150 60 962 III3. Easy Availability Of E-Channels 290 182 107 162 125 866 IV4. Low Hidden Cost For Services 50 192 691 180 70 1183 I5. Number Of Facilities Provided By E-
Channels60 348 116 176 145 845 V
6. Security/Less Risk To Use 305 138 125 48 67 683 VISource: Primary Data
Table 4.68 reveals that the variable X4-low hidden cost for services has
secured the Ist rank with a total score of 1183, variable X1-convenient accessibility of
e-channels has secured the IInd rank with a total score of 1111, the variable X2-
208
convenient location of ATM’s has secured the IIIrd rank with a total score of 962, the
factor X4- easy availability of e-channels has secured the IV rank with a total score of
866, the Vth and VIth rank was secured by the variable X5-number of facilities
provided by e-channels, X6-security/less risk to use with a total score of 845 and 683
respectively.
Hence it could be understood very clearly that the factor low hidden cost for services
and convenient accessibility of e-channels has influenced the customer while selecting e-
channel services.
Figure 4.6 Factors concentrated by the respondents while selecting e-channels
Table 4.69 Motivational factors encouraging the customers to prefer particular
e-channels
S.No Factors SA A UD DA SDA Total Rank1. Cost Effective 587 420 30 180 90 1307 VI2. Convenient Accessibility 1038 376 178 43 85 1720 IV3. Provide Accurate
Information1236 786 216 30 320 2588 II
4. Provide Efficient Services 2978 323 96 120 75 3592 I5. Provide Security For Threats
It was inferred from the above table 4.71 that 180 respondents have opined
that Partially IT – oriented banks took more than 30 days to meet the demand for
cash, whereas it was less than 10 days in case of e – banks (180 respondents). 204
respondents were of the opinion that e- banks took less than 10 days to encash bank
draft, while 200 respondents revealed that Partially IT – oriented banks took more
than 30 days to encash bank draft. To get a new cheque book, 115 respondents were
of the view that no response was made by Partially IT – oriented banks, while 126
respondents have opined that the e – banks took only 10-30 days to issue a new
cheque book.138 respondents were of the view that it took 10-30 days to open a fixed
deposit account, while 158 respondents opined that it took less than 10 days to open a
fixed deposit account in an e – bank.
Thus it was concluded from the above discussion that in the opinion of therespondents the e- banks had performed better in respect to the services provided tothe customers by banks.
4.72 Time taken by the banks to respond towards customer’s chequetransactions.
S.No Services
Partially IT orientedbanks e-banks
Sam
e D
ay
2-3
days
3-5
days
No
resp
onse
Tot
al
Sam
e da
y
2-3
day
s
3-5
day
s
No
resp
onse
Tot
al1.
To credit local chequescredit to customer accounts
5 214 85 - 304 118 169 17 - 304
2.To credit outstation chequescredit to customer accounts
- 256 48
-
304 212 82 10 - 304
Source: Primary Data
It was inferred from the above table 4.72 that in the case of Partially IT –
oriented banks, 214 respondents were in the view that it has taken 2 – 3 days to credit
local cheques to customer’s accounts and 169 respondents have opined that e –
banks also took 2 – 3 days to credit local cheques to customer’s accounts. 256
respondents were in the view that it took 2 – 3 days to credit outstation cheques to
customer’s accounts, while in the case of e – banks 212 respondents have opined that
the cheques were credited on the same day in which it was deposited.
212
Thus it was concluded that the e – banks had performed better in handling
customer’s cheque transactions.
RATING OF HIDDEN SERVICE CHARGES FOR UTILISING E-
CHANNELS.
Regression is a statistical fool used to find out the relationship between two or
more variables. In simple regression there will be only two variables, one variable is
caused by the behavior of another variable. The former variable is defined as an
independent variable and the latter is defined as the dependent variable. Multiple
regressions are applied when there are two or more independent variables, especially,
to predict the variability of the dependent variable based on its co-variance with all
the independent variables. It is useful to predict the level of dependent phenomena
through multiple regression models, the level of independent variables are given.
In the following analysis, the level of effectiveness perceived by the
respondents in rating the opinion of the hidden service charges towards e – delivery
channels were discussed. Among the various e-channels the relationship of age with 7
independent variables were studied. It was found that, out of the 7 independent
variables, 5 variables are closely associated with the dependent variable (ie) the
satisfaction towards hidden service charges. They are:
1. ATM2. Credit card3. Debit card4. Internet banking5. Mobile banking6. Smart card7. Tele-banking
Multiple regression analysis of respondent’s level of satisfaction towards the
hidden service charge for various product categories was carried out in order to
measure the interdependence of independent variable and their total contribution to
the level of satisfaction towards the hidden service charges for various product
categories, a step wise multiple regression models was used. The result of the analysis
(simple regression analysis) and the details are shown in Table No 4.73.
213
Table 4.73 Multiple Regression Analysis on the hidden charges for utilizing e –channels.
The multiple linear regression components (dependent variables) were found
statistically a good fit since R2 value is 0.801. It shows that independent variables
contribute to about 80.1% to the variation of the opinion on the hidden charges
towards services of e-channel and this is statistically significant at 1% and 5%
respectively. All the variables viz., ATM, Debit card, Internet banking, Mobile
banking, Smart card, were found statistically significant at 5% level, and the variables
credit card and tele-Banking services were not significant at 1% and 5% level.
CUSTOMERS AGREEABILITY LEVEL TOWARDS VARIOUS
DIMENSIONS OF E-CHANNELS
Factor analysis is a statistical method used to describe variability among observed,
correlated variables in terms of a potentially lower number of unobserved variables called
factors. In other words, it is possible, that variations in three or four observed variables mainly
reflect the variations in fewer unobserved variables. Factor analysis searches for such joint
variations in response to unobserved latent variables. The observed variables are modeled as
linear combinations of the potential factors, plus "error" terms. The information gained about
the interdependencies between observed variables can be used later to reduce the set of
variables in a dataset. Computationally this technique is equivalent to low rank approximation
of the matrix of observed variables. Factor analysis originated in psychometrics, and is used
in behavioral sciences, social sciences, marketing, product management, operations research,
and other applied sciences that deal with large quantities of data. As the e-channels are
214
gaining momentum as there was a need raised to check the agreeability level of
customers towards the various dimensions on e-channel. For the purpose of analyzing
the various dimensions factor analysis was employed and twelve factors were taken
and accounted for the analysis.
4.74 (a) Table showing Communalities of the customers agreeability leveltowards various dimensions of e-channels
S.no Factors Initial Extraction1. e-channels do not ensure privacy 1.000 .7052. e-channels ensure more transparency 1.000 .7843. e-channels are creating more confusion for customers 1.000 .8824. e-channels have bright future in global age 1.000 .9075. e-channels improve the quality of customer services n banks 1.000 .7676. e-channels are necessary in the competitive, global and new
economy of India1.000 .567
7. e-channels make online purchase of goods and services easier 1.000 .7558. e-channels are creating more social relations among the bank
customers and bank employees1.000 .840
9. e-channels are fulfilling all our requirements in e-age 1.000 .82210.e-banks charge more hidden cost 1.000 .68911.More formalities are required to get e-channels issued from the
banks1.000 .489
12.Online banking helps t o manage transformation in banks moreefficiently
1.000 .794
13. Smart card sometime creates technical hurdles to make payments 1.000 .816
Table 4.74 (b)Total Variance Explained
Com
pone
nt
Initial Eigenvalues Extraction Sums of SquaredLoadings
From the above analysis it is clear that the factor X1- e-channels do not ensure
privacy has secured the maximum factor loadings of .705, the factor X2- e-channels
ensure more transparency has been loaded with .784, the factor X3-e-channels are
creating more confusion for customer has got the maximum loadings of .882, the
factor X4-e-channels have bright future in global age has got the factor loadings of
.907, the factor X5- e-channels improve the quality of customer services in e-banks
has got maximum factor loadings of .767, the factor X6-e-channel are necessary in the
competitive, global and new economy of India has secured the maximum factor
loadings of .567, the factor X7—channels make online purchase of goods & services
easier with a loadings of .755, the factor X8-e-channel are creating more social
relations among the bank customers and bank employees with a factor loadings of
.840, the factor X9-e-channels are fulfilling all our requirements in e-age, X10-e- banks
charge more hidden cost, X11-more formalities are required to get e-channels issued
from banks, X12-online banking helps to manage transformation in banks more
efficiently and X13-smart card sometime creates technical hurdles to make payments
has secured the factor loadings of .822, .689, .489, .794 and .816 respectively.
When the factors were cumulated and the total variance was explained the
factor X1- e-channels do not ensure privacy, X2- e-channels ensure more transparency,
the factor X3-e-channels are creating more confusion for customer and X4-e-channels
have bright future in global age together accounted for 75.523 percentage. The rest of
the factors X5- e-channels improve the quality of customer services in e-banks, the
factor X6-e-channel are necessary in the competitive, global and new economy of
India, the factor X7—channels make online purchase of goods & services easier, the
factor X8-e-channel are creating more social relations among the bank customers and
bank employees, the factor X9-e-channels are fulfilling all our requirements in e-age,
X10-e- banks charge more hidden cost, X11-more formalities are required to get e-
channels issued from banks, X12-online banking helps to manage transformation in
banks more efficiently and X13-smart card sometime creates technical hurdles to make
payments has accounted with 24.477 percentage. The component matrix was
classified into four components, the variables has designed and supplemented with
individual weights and appraised the actual loadings of factor. The four factors which
have been assigned with 75.523 percentages can be grouped as “Holistic Environment
216
and Best Motivation Practices” and the remaining factors were grouped as “Marketing
Strategies clubbed with social welfare programmes”.
Thus it could be concluded from the above discussion that “Holistic
Environment and Best Motivation Practices” factors were considered as the factors
responsible for respondent’s level of satisfaction towards the hidden service charges
for various product categories than compared to ‘Marketing Strategies clubbed with
social welfare programmes” factors.
Table 4.75 Table showing the operational efficiency of e – delivery channels
S .no Statements SA A UD DA SDA Total Rank
1 Bank employees give the information
regarding products and services that best suits
the customer’s needs.
260 436 324 70 - 1050 VI
2 Employees of e-banks are not fully trained. 350 304 270 136 - 1060 V
3 General environment of the partially
computerized banks is better than in e-banks.
190 444 126 218 4 982 IX
4 Motivation levels in partially IT oriented
banks regarding savings mobilization are
better than e-banks.
200 168 636 8 6 1018 VII
5 The customers can easily understand the
procedure of partially IT oriented banks as
compared to e-banks.
580 168 216 78 35 1077 III
6 The partially IT oriented banks are committed
for ''social welfare programmes'' in society
whereas e-banks are only profit motivated.
1095 292 30 4 - 1221 II
7 The market-strategies of partially IT oriented
banks are less effective as compared to e-
banks.
70 280 648 4 2 1004 VIII
8 The bank employees explain various features
of e-channels provided by the e-banks.
200 308 123 284 4 919 X
9 The bank manager regularly organizes
meetings with the customer to learn about his
needs, ideas and complaints.
1000 368 126 37 12 1543 I
10 The bank regularly provides the customer
with the required literature to explain the new
methods, products and services.
620 160 200 56 40 1076 IV
Source: Primary Data
217
Operational efficiency of e-delivery channels
An attempt was made to analyze/rate the partially IT oriented banks and e-
banks towards their operational efficiency. The listed statements were rated through a
five point scaling technique with a score of 5-Strongly Agree 4-Agree 3-Undecided 2-
Disagree 1-Strongly disagree and the total scores were cumulated and presented to
give the ultimate rank for the factors which were selected. The factors selected were.
X1-Bank employees give the information regarding products and services
that best suits your needs.
X2- Employees of e-banks are not fully trained.
X3-General environment of the partially computerized banks is better than
in e-banks.
X4-Motivation levels in partially IT oriented banks regarding savings
mobilization are better than in e-banks.
X5-The customers can easily understand the procedure of partially IT
oriented banks as compared to e-banks.
X6- The partially IT oriented banks are committed for ''social welfare
programmes'' in society whereas e-banks are only profit motivated
X7The market-strategies of partially IT oriented banks are less effective as
compared to e-banks.
X8 - The bank employees explain various features of e-channels provided
by the e-banks.
X9 - The bank manager regularly organizes meetings with the customers to
learn about his needs, ideas and complaints.
X10- The bank regularly provides the customer the required literature to
explain the new methods, products and services.
When analyzing the factors, the factor- X9 “The bank manager regularly
organize meetings with the customer to learn about his needs, ideas and complaints
have secured the Ist rank with a total score of 1543, the factor X6-the partially IT
oriented banks are committed for ”Social Welfare Programmes” in society where as
banks are only profit motivated has gained the IInd rank with a total score of 1221, the
factor X5-the customers can easily understand the procedure of partially IT oriented
banks as compared to e-banks has gained the IIIrd rank with a total score of 1077, the
factor X10- the bank regularly provides the customer the required literature to explain
218
the new methods, products, and services has secured the IVth rank with a total score of
1076 the factor X2-employees of e-banks are not fully trained has secured the Vth
Rank with a total score of 1060 the VIth rank was secured by the factor X1-bank
employees give the information regarding products and services that best suit the
customers’ needs, with a total score of 1050, the factor X4-“Motivation Levels” in
partially IT oriented banks regarding saving mobilization is better than e-banks has
secured the VIIIth rank with a total score of 1004, the IXth rank was secured by the
factor X3-“General Environment” of the partially computerized banks is better than e-
banking with a total score of 982. The factor X8-the bank employees explain various
features of e-channels provided by the e-banks has secured the Xth rank with a total
score of 919.
The statements of operational efficiency was tested through ANOVA with a
hypothesis Ho: There is no significant association between the age of the respondents and the
operational efficiency of partially IT oriented banks and e-banks.
219
Table 4.76 Table showing the Operational efficiency of e-delivery channels
S.No Factors of operational efficiency Sum ofSquares
d.f MeanSquare
F Sig. Result
1.Bank employees give the informationregarding products and services that bestsuits your needs
BetweenGroups
15.358 2 7.679 9.945 .000
AcceptedWithinGroups
232.418 301 .772
Total 247.776 303
2. Employees of e-banks are not fully trained
BetweenGroups
.377 2 .188 .161 .851
RejectedWithinGroups
351.570 301 1.168
Total 351.947 303
3.General environment of the partiallycomputerized banks is better than e-banking
BetweenGroups
25.173 2 12.587 5.470 .005
AcceptedWithinGroups
692.603 301 2.301
Total 717.776 303
4.Motivation levels in partially IT orientedbanks regarding savings mobilization isbetter than e-banks
BetweenGroups
2.736 2 1.368 1.115 .329
AcceptedWithinGroups
369.146 301 1.226
Total 371.882 303
5.The customers can easily understand theprocedure of partially IT oriented banks ascompared to e-banks
BetweenGroups
1.499 2 .749 1.177 .309
AcceptedWithinGroups
191.541 301 .636
Total 193.039 303
6.
The partially IT oriented banks arecommitted for ''social welfare programmes''in society whereas e-banks are only profitmotivated
BetweenGroups
67.487 2 33.743 19.238 .000
AcceptedWithinGroups
527.957 301 1.754
Total 595.444 303
7.The market-strategies of partially IT orientedbanks are less effective as compared to e-banks
BetweenGroups
70.694 2 35.347 49.989 .000
AcceptedWithinGroups
212.833 301 .707
Total 283.526 303
8.The bank employees explain various featuresof e-channels provided by the e-banks
BetweenGroups
17.901 2 8.951 33.319 .000
AcceptedWithinGroups
80.859 301 .269
Total 98.760 303
9.The bank manager regularly organizesmeetings with you to learn about your needs,ideas and complaints
BetweenGroups
.545 2 .273 .762 .468
AcceptedWithinGroups
107.613 301 .358
Total 108.158 30310. The bank regulariy provides the customer
with the required literature to explain thenew methods, products and services
BetweenGroups
.445 2 .260 .662 .362
AcceptedWithinGroups
99.63 301 .340
Total100.075 303
Source: Primary Data
Level of significance at 5 %
From the above analysis it was proved that there is no significant association
between the age group of the respondents and the operational efficiency of partially
220
IT oriented banks and e-banks except for the factor - X2 – Employees of e – banks are
not fully trained which has significant association.
Table 4.77 Traditional Banks Vs E-Banks
S. No Comparative statements
Traditionalbanks
e-banks BothTotal
%%
%1 24 hours facilities are provided 70 23 138 45.39 96 31.57 3042 Complaints are more 146 48.02 121 39.80 37 12.17 3043 Employees behavior is better 211 69.40 76 25 17 5.50 304
4More products/services are providedto customers
43 14.14 236 77.63 25 8.22 304
5New products/services are providedto customers
153 50.32 128 42.10 23 7.56 304
Source: primary Data
An attempt was carried out to compare the traditional banks and e-banks on the
following factors.
X1 - 24 hours facilities are provided.
X2 - Complaints are more.
X3 - Employees behavior is better.
X4 - More products/services are provided to customers.
X5 - New products/services are provided to customers.
The factor X1 revealed that 24 hours facilities are provided in e-banks are
good with a percentage share of (45.39%) and only (23%) with traditional banks, the
factor X2 - proved that complaints are more in traditional banks (48.02%), the factor
X3 - justified that employees behavior is better in traditional banks (69.40%), the
factor X4 - proved that more products/services are provided to customers in e-banks
(77.63%) and new products/services are provided to customer in traditional banks
(50.32%).
Thus it could be concluded that improved services are provided better in e –
banks but new products /services are provided by traditional banks in order to attract
new customers.
221
REGRESSION ANALYSIS BETWEEN AGE AND AGREEABILITYTOWARDS E-CHANNELS.
Regression analysis was carried out find out the relation between age and
agreeability towards e-channels. For this analysis four factors was considered.
Ho: There is no significant difference between Age and Agreeability towards e-
channels.
4.78 Table showing Coefficients between age and agreeability towards e -channels
S.no Model
UnstandardizedCoefficients
StandardizedCoefficients t Sig.
ResultB Std. Error Beta
(Constant) .267 .289 .923 .357 Accepted
1.
Awareness for e-channels will beeffective to manage changingenvironment
.534 .169 .449 3.159 .002Accepted
2.Information technology will helpto improve efficiency
-.069 .152 -.079 -.458 .647Rejected
3.Information technology willmanage the entire bank activities
.084 .092 .116 .916 .361Accepted
4.The people will have no trust in e-channels
.075 .069 .105 1.077 .282Accepted
Source : Primary Data
Result:
The regression analysis strengthened that the hypothesis framed has no
significant difference between the age of the respondents and the Statements
regarding the agreeability towards the future of e – channels, except the factor X3 -
information technology will help to improve efficiency.
ANOVA ANALYSIS BETWEEN AGE AND THE PREFERENCE
TOWARDS E-CHANNEL.
In order to find out that whether there is an association between age and the
preference towards e-channels, ANOVA analysis wad carried out.The associantion
was checked for all seven e-channels take an under study.
Ho: There is no significant association between Age and the preference towards
e- channels.
222
S.No e-channels Sum ofSquares df
MeanSquare F Sig. Result
1 ATM BetweenGroups
59.865 3 19.955 27.144 .000Accepted
WithinGroups
220.543 300 .735
Total 280.408 303
2 Credit card BetweenGroups
9.585 3 3.195 9.918 .000
AcceptedWithinGroups
96.648 300 .322
Total 106.234 303
3 Debit card BetweenGroups
145.875 3 48.625 38.894 .000
AcceptedWithinGroups
375.059 300 1.250
Total 520.934 303
4 Mobile banking BetweenGroups
123.129 3 41.043 54.175 .000
AcceptedWithinGroups
227.279 300 .758
Total 350.408 303
5 Online banking BetweenGroups
24.727 3 8.242 16.595 .000
AcceptedWithinGroups
149.006 300 .497
Total 173.734 303
6 Smart card BetweenGroups
3.001 3 1.000 1.288 .279
AcceptedWithinGroups
232.154 300 .776
Total 235.155 303
7 Tele banking BetweenGroups
31.420 3 10.473 7.737 .000Accepted
WithinGroups 404.765 300 1.354
Total 436.185 303
Result:
The hypothesis has been accepted and it was concluded that there is no
significant association between age group of the respondents and the preference
towards e – channels.
223
Table 4.80 Functions preferred by customers regarding ATM
S.No Factors Total score Rank
1 Balance enquiry 2190 IV2 Cash withdrawal 3640 I3 Deposits 1360 VIII4 Mini statements 1680 VII5 Request for bill payment 2280 II6 Request for issue of
cheque book2120 V
7 Transfer of funds 2220 III8 Loan payments 1890 VI
Source: Primary Data
It was observed from the above table that the factor X2 – Cash withdrawal ranked Ist
among all the other factors for the customers to use ATMs with a total score of 3640,
followed by the factor X5- Request for bill payment which ranked IInd , with a total score of
2280. The factor X7 - Transfer of funds scored the IIIrd rank, followed by X1 - Balance
enquiry. Request for issue of cheque book - X6 got the Vth rank with a total score of 2120, the
factor X8 - Loan payments secured the VIth rank, the factor X4 - Mini statements scored the
VIIth rank, followed by the factor X3 – Deposits which secured VIIIth rank with a total score
of 1360.
Thus it could be concluded that majority of the respondents used the e – channel
ATM for cash withdrawal. Thus it was the most important function performed by the e –
channel ATM.
Figure 4.9 Functions preferred by customers regarding ATM
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Table 4.81 Functions preferred by customers regarding Tele - Banking
S. no Factors Total score Rank1 Balance enquiry 2260 IV2 Demand draft ,ATM card & cheque
book lost report3280 I
3 Request for issue cheque book 1560 VIII4 Statement request 2180 V5 Transfer of funds 2765 II6 Loan payment 2468 III7 Stop payment instructions 1856 VII8 Obtain product information 1890 VI
Source: Primary Data.
It was observed from the above table that the factor X2 - Demand draft, ATM card
& cheque book lost report secured the Ist rank with a total score of 3280 as the most
preferred function performed by Tele – Banking, it was followed by the factor X5 -
Transfer of funds with a total score of 2765. The factor X6 - Loan payment ranked
IIIrd, followed by the factor X1 - Balance enquiry which scored IVth rank with a total
score of 2260. The factor X4 - Statement request ranked Vth, the factor X8 - Obtain
product information ranked VIth with a total score of 1890.The VIIth rank was secured
by the factor X7 - Stop payment instructions with a 1856 and the factor X3 - Request
for issue cheque book scored the VIIIth rank with a total score of 1560.
Thus it was concluded from the above discussions that Demand draft, ATM
card & cheque book lost report was the most preferred function performed by the e –
channel Tele – Banking.
Figure 4.10 Functions preferred by customers regarding Tele – Banking
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Table 4.82 Functions preferred by customers regarding I - Banking
S .no Factors Total score Rank1 Balance enquiry 2157 V2 Request for issue cheque book 2376 III3 Request for draft making &
transferring1596 VII
4 Statement request 3184 I5 Transfer of funds 2763 II6 Transaction date 1890 VI7 Cheque number/transaction
reference number1462 VIII
8 Description of transaction 2260 IV
Source: Primary Data.
It was observed from the above table that the factor X4 - Statement request secured
Ist rank with a total score of 3184 as the most preferred I – Banking function, followed
by the factor X5 - Transfer of funds with a total score of 2763. The factor X2 - Request
for issue cheque book ranked IIIrd, the factor X8 - Description of transaction ranked
IVth, the factor X1 - Balance enquiry ranked Vth and the factor X6 - Transaction date
ranked VIth. The factor X3 - Request for draft making & transferring secured the VIIth
rank with a total score of 1596, followed by the factor X7 - Cheque
number/transaction reference number with a total score of 1462.
Thus it was concluded from the above discussion that the factor X4 - Statement
request was the most preferred function with regard to I – Banking.
Figure 4.11 Functions preferred by customers regarding I - Banking
226
Table 4.83 Functions preferred by customers regarding M - Banking
S no Factors Total score Rank1 Balance enquiry 1852 VI
2 Request for bill payment 3416 II
3 Request for issue cheque book 432 VIII4 Request for statement 3824 I
5 View last three transactions 2156 IV6 Find out the status of a cheque. 1763 VII7 Stop payment on a cheque. 2564 III8 View of fixed deposit details 2121 V
Source: Primary Data
It was evident from the above table that the factor X4 - Request for statement
secured the Ist rank with a total score of 3824 as the most preferred function
performed by M – Banking, followed by the factor X2 - Request for bill payment with
a total score of 3416. The factor X7 -Stop payment on a cheque ranked IIIrd, the factor
X5 - View last three transactions ranked IVth, the factor X8 - View of fixed deposit
details ranked Vth and the factor X6 - Find out the status of a cheque ranked VIIth with
a total score of 1763 and the factor X3 - Request for issue cheque book secured the
least rank with a total score of 432.
Thus it was concluded from the above discussion that the factor X4 - Statement
request was the most preferred function with regard to M – Banking.
Figure 4.12 Functions preferred by customers regarding M - Banking
227
Table 4.84 Problems faced by customers while using e - channels
S.No Factors Total Score Rank1 Inadequate knowledge 1765 V2 Lack of knowledge regarding use
of e- channels.2112 IV
3 Lack of infrastructure 3563 I4 Unsuitable location of ATMs 2342 III5 Number of ATMs are not
sufficient2872 II
6 Poor net work 1063 VI7 Time consuming 978 VII8 No problem at all 543 VIII
Source: Primary Data
It was clear from the above table that the factor X3 - Lack of infrastructure was
considered as the major problem by customers while using e – channels with a total
score of 3563, followed by the factor X5 - Number of ATMs are not sufficient with a
total score of 2872. The factor X4 - Unsuitable location of ATMs ranked IIIrd, the
factor X2 - Lack of knowledge regarding use of e- channels ranked IVth, the factor X5
- Inadequate knowledge ranked Vth, the factor X6 - Poor net work ranked VIth. It was
found that the factor X7 - Time consuming ranked VIIth with a total score of 978 and
the factor X8 - No problem at all ranked VIIIth with a total score of 543.
Thus it was concluded from the above discussion that the problem faced by
most of the customers using e – channel was lack of infrastructure.
Figure 4.13 Problems faced by customers while using e - channels
228
Table 4.85 Strategies to overcome problems faced by customers while using e –channels
S.No Factors Total Score Rank1 Conduct more training programmes for
bank customers3457 I
2 Demo – fairs regarding e – channels. 2800 II3 Information/demo at the counter 1700 V4 More advertisements. 2012 III5 Personal contact programmes. 1765 IVSource: Primary Data
It was observed from the above table that among the strategies to overcome
problems faced by customers while using e – channels given by customers, the factor
X1 - Conduct more training programmes for bank customers ranked first with a total
score of 3457 followed by the factor X2 - Demo – fairs regarding e – channels with a
total score of 2800. The factor X4 - More advertisements ranked IIIrd, the factor X5 -
Personal contact programmes ranked IVth while the factor X3 - Information/demo at
the counter ranked Vth with a total score of 1700.
Thus it was concluded from the above discussion that of the strategies
suggested by customers to overcome problems while using e – channels, the factor X1
- Conduct more training programmes for bank customers ranked first.
Figure 4.15 Strategies to overcome problems faced bycustomers while using e – channels
229
Table 4.86 Frequency of visit to a bank physically in a month
S.No Frequency of Transactions Frequency percentage1 Occasionally once 156 51.312 2- 5 times 56 18.423 6- 10 times 60 19.734 11- 15 times 15 4.935 More than 15 times a month 17 5.59
Total 304 100.00Source: Primary Data
The above table exhibited the frequency of visiting banks physically by
customers and it was concluded that 51.53 percentage of the respondents visited
occasionally once. While 19.73 percentage of respondents visited banks 6- 10 times in
a month, 18.42 percentage of respondents visited 2- 5 times in a month, 5.59
percentage of respondents visited more than 15 times a month, while 4.93 percentage
of respondents visited 11-15 times in a month
Thus it was concluded from the above observation that majority of the
respondents visited banks occasionally once in a month.
Figure 4.15 Frequency of visit to a bank physically in a month
Table 4.87 Major complaints regarding e - channels