chapter iv - shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/33792/4... · iii) r eport on...

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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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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).

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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

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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

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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,

Director’s fees, rent, taxes ,insurance, lighting ,law charges, postage,

telegrams, stationary, printing, advertisement and other expenditure etc.

g. Return on assets (Profits/Assets); Assets include cash in hand, balances with

RBI, balances with banks in India, money at call and short notice. They also

include total investments, total advances, fixed assets (premises, fixed assets

under construction etc.), interest accrued, tax paid, stock of stationary and

stamps etc. Return on assets is the ratio of profit to total average assets. This is

a main indicator of profitability used in international comparisons and also

given under the Reserve Bank of India guidelines for balance sheet analysis.

h. Return on equity (Profit/Equity); Equity includes reserve and paid up capital.

Higher is the return on equity, better is the profitability position of the banks.

i. Return on advances (Profit/Advances); It indicates the return earned on the

advances of the bank. Interest from loaning functions constitutes the most

important item of earnings.

2. PROFITABILITY PARAMETERS:

Profitability is the most important and reliable indicator as it gives a broad

indication of the ability of a bank to raise its income level. Profitability of banks is

affected by a number of factors. Some of these are endogenous, some are exogenous

and yet structural. Changes in policies made by RBI are exogenous to the system.

This includes changes in monetary policy, changes in quantitative credit control like

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changes in CRR, SLR, manipulation of bank rates, qualitative credit controls like

selective credit control measures, C/D ratio, and region wise guidelines on lending to

priority sectors, changes in interest rates on deposits and advances, levy of tax on

interest income etc. Various other factors like careful control of expenditure, timely

recovery of loans are endogenous. Various structural factors include geographical

spread of bank Banking structure and profitability structure of banking system across

countries have a bearing on the profitability of banks. The profitability of banks is

affected one way or the other by these factors, either individually or jointly. Bank

profitability is causing concern to all. After liberalization, profitability has regained its

lost importance. Now efforts are being directed to achieve the profitability targets.

The profitability of public sector banks has been indicating a fast declining trend in

the past and the situation in future may not be different if all the concerned do not

take timely preventive measures before the situation goes out of control. Since all the

banks in the country function under similar environments, the low performance of any

bank can be attributed to a larger extent to their managerial inefficiency and structural

deficiency.

For measuring the profitability of commercial banks the study employs the

following ratios:

a. Interest earned ratio (r) = Total interest earned/Volume of business

b. Interest paid ratio (p) = Total interest paid/Volume of business

c. Non-interest income ratio (n) = Total income- interest income/ Volume of

business

d. Non-interest expenses ratio (o) = Total expenses – interest expenses/

Volume of business

e. Spread ratio (s) = Interest earned ratio – Interest paid ratio (r-p)

f. Burden ratio (b) = Non-interest income ratio - Non-interest expenses ratio

(o-n)

g. Profitability ratio = Spread – Burden (s-b)

3. MANAGERIAL EFFICIENCY PARAMETERS

Managerial efficiency is reflected through the proper functioning of the bank.

Management should identify and control risks through monitoring procedures and

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enforcing policies in the bank. The performance of the bank directly reflects the

quality of the management.

To measure the managerial efficiency the following ratios has been employed

a. Credit deposit ratio (Credit/Deposits); Higher is the credit deposit ratio,

higher will be the credit deployment and resultantly larger profits. However

according to Reserve Bank of India credit deposit ratio of 60 percent is

considered as an ideal business mix.

b. Investment deposit ratio (Investments/Deposits); It is the inverse of credit -

deposit ratio. The deposits received by banks which are not provide as

advances are profitably invested in various avenues by the banks.

c. Ratio of net NPA to net advances (Net NPA/Net advances); The quantum of

non-performing assets (NPA’s) 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. Non – performing assets consist of : (a) debts recalled, (b) suit-

filed accounts i.e., where legal action or recovery proceedings have been

initiated ,(c) decreed debts i.e., where suits have been filed and decree

obtained and (d) debts classified as bad and doubtful.

4. ASSET QUALITY PARAMETERS

Asset quality is an assessment of bank’s existing and potential losses, mainly

in the form loans and other assets. When evaluating asset quality, management is also

evaluated in their ability to create effective loan policies, enforce policies, monitor the

loan portfolios, and maintain an adequate allowance for both loan and lease losses.

The four major ratios focused on when evaluating asset quality are:

a. Ratio of net interest income to total assets (Net interest income /total

assets); Net interest margin is popularly termed as spread. Higher the interest

margin to total assets higher is the level of efficiency of banks. Increase in net

interest income shows increase in interest income of the banks.

b. Ratio of non interest income to total assets (Non interest income /Total

assets); One of the factors contributing to the profitability of the banking

concerns is Non-interest income. If the proportion of non-interest incomes is

more, then the banks can easily utilize this amount to meet the non-interest

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expenses and the surplus increases the profitability and asset quality of the

banks.

c. Ratio of operating profits to total assets (Operating profits /total assets);

operating profits is the difference operating income and operating expenses.

Higher ratio of operating profits to total assets is a clear indication of good

health of the banks.

4.2.5 Data analysis techniques for diagnostic study

To have a clear understanding of the performance of banking sector, the data

provided in the financial statements has been methodically classified and comparisons are

made in tune with the objective of analysis. Only by using the techniques of analysis, the

researcher has found solution to various pertinent questions that are being raised. There are

various techniques for analyzing the performance of banking sector as an aftermath of

introduction of information technology are discussed below:

a. Financial Ratio analysis

The study uses Ratio analysis to compare performance of banks in partially

computerized era and IT enabled era. Ratio analysis is a powerful tool of financial analysis. In

financial analysis ratios are generally 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 other relevant information. Ratios represent the relationship between two

or more variables. Ratios help to summarize large data to draw qualitative judgments about

the firm's performance

b. Statistical analysis

The following statistical tools were applied in addition to the financial rations

calculated: Summary Statistics, compound growth rate. Pearson's Correlation and paired "t"

test.

c. Summary Statistics

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.

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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 .

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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

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TABLE: 4.2PAIRED SAMPLES STATISTICS

DEPOSITS PER BRANCHGroups Variables Mean SD Std Error Correlation

Group1

Nationalized Banks (PartiallyComputerized)

34841.00 12190.65 4976.81.983

Nationalized Banks (IT Enabled) 97206.67 31569.18 12888.07

Group2

SBI & its Associates Banks(Partially computerized)

91339.50 122977.89 86958.50.854

SBI & its Associates Banks (ITEnabled)

239625.50 313465.39 22165.00

Group3

Old Private Sectors Banks(Partially computerized)

4875.60 2175.92 973.10.990

Old Private Sectors Banks (ITEnabled)

15248.60 6937.63 3102.60

Group4

New Private Sectors Banks (Partially computerized)

6041.50 1827.87 1292.50.954

New Private Sectors Banks (ITEnabled)

108358.00 52381.06 37039.00

Group5

Foreign Banks (PartiallyComputerized)

3742.29 3736.42 1412.23.922

Foreign Banks (IT Enabled) 14553.29 14814.71 5599.43Level of Significance: 5 per cent

TABLE: 4.3PAIRED SAMPLES TESTDEPOSITS PER BRANCH

Groups Variables Mean SD Std Error t value Sig

Group1

Nationalized Banks(Partially Computerized)and Nationalized Banks(IT Enabled)

62365.67 20294.41 8285.16 7.527Not

Significant

Group2

SBI & its AssociatesBanks (Partiallycomputerized) and SBI &its Associates Banks (ITEnabled)

148286.00 190487.00 134695.00 1.101Not

Significant

Group3

Old Private SectorsBanks (Partiallycomputerized) and OldPrivate Sectors Banks (ITEnabled)

10373.00 4793.91 2143.90 4.838 Significant

Group4

New Private SectorsBanks ( Partiallycomputerized) and ITEnabled era

102316.50 50553.19 35746.50 2.862Not

Significant

Group5

Foreign Banks (PartiallyComputerized) andForeign Banks (ITEnabled)

10811.00 11460.33 4331.60 2.496Not

Significant

Level of Significance: 5 per cent

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The deposits per branch are documented in Table 4.1. The data was analyzed

through ratio analysis and the results revealed that the new private sector banks have

performed well when compared to all the other banks groups with regard to deposits.

A comparison of partially computerized era and IT enabled era exhibited that the

deposits had increased in the IT enabled era when compared to the partially

computerized era for almost all five groups under study, which can be understood

from the following discussions.

Among the Nationalized Banks group taken under study, the Oriental Bank of

commerce showed an annual growth rate of 422 in the IT enabled era, closely

followed by the Bank of Baroda, Punjab National Bank and the Bank of India. The

reason for higher rates of growth could be attributed to higher rates of deposit

mobilization.

In the SBI & its Associates bank group, the State Bank of Indore has

performed better when compared to State Bank of India. The annual growth rate of

State Bank of Indore was 387 when compared to 297 of SBI in the IT enabled era.

One of the reasons for increased growth rate could be that both the bank groups had

increased its deposit mobilization drive in the already existing branches than that of

setting up new branches for deposit mobilization.

Similarly, in the old private sector banks group, on account of their branch

expansion and deposits policies, they had succeeded in recording higher rate of

growth during the IT enabled era as compared to the partially computerized era. The

table further revealed that the Federal Bank had outperformed all the other banks in its

group, as it registered a growth rate of 407 in the IT enabled era. It was followed by

the Karnataka Bank, and South Indian Bank with a CGR of 14.81% and 17.48%

respectively. As far as the New Private Sector banks are concerned both ICICI bank

and HDFC bank had performed well. The HDFC bank revealed the highest growth

rate of 948 followed by ICICI Bank which showed a growth rate of 630. The private

sector banks high deposits per branch could be attributed to their selective deposits

mobilization policy and branch expansion programmes which were confined to

metropolitan areas only. Another reason could be the quality of services provided by

them, which was also responsible for their high deposits per branch.

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A group-wise analysis of foreign banks revealed that JP Morgan Chase Bank

had outperformed all the other banks in its group, which registered a growth rate of

898 and a CGR of 54.48%. The Barclays Bank also revealed a growth rate of 688 and

a CGR of 99.52%. Except these two banks, all the other banks in the group registered

lower growth rate. The reason for such low growth rates could be attributed to

increased competition from the entry of other foreign banks in the IT enabled era.

A statistical analysis of Deposits per branch was carried out and the mean,

standard deviation and correlation co-efficient of all the bank groups are revealed in

Table 4.2. The analysis revealed that the Group 3 (old private sector bank) had the

highest positive correlation value of .990, closely followed by Group 1 (Nationalized

bank), with a correlation co-efficient of .983. The New Private sector bank group and

foreign bank group also revealed highest positive correlation. This showed that the

indicator deposits per branch had highest relation and the impact of IT can be

positively correlated with the latter period under study (IT enabled era).

The results of paired‘t’ test was depicted in table 4.3 for the indicator deposits

per branch. The paired‘t’ test revealed that Group 3 (old private sector bank) had the

highest‘t’ value of 4.838. Among the entire bank group that had been studied,

lowest‘t’ values was found out for Group 2 (SBI & its Associates) viz., 1.101 and

Group 1(Nationalized bank) revealed insignificant ‘t’value. Thus it could be

concluded that the impact of information technology had positively affected the

indicator, deposits per branch for majority of the bank group under study.

A ratio analysis of the indicator deposit per branch concluded that in Group 1

(Nationalized bank), the Oriental Bank of commerce registered the highest an annual

growth rate of 422 in the IT enabled era, in Group 2 (SBI & its Associates), the State

Bank of Indore has performed better when compared to State Bank of India. In the

Group 3 (old private sector bank) Federal Bank had outperformed all the other banks

in its group, by registering a growth rate of 407 in the IT enabled era, and both the

banks in Group 4 (New private sector bank), HDFC bank and ICICI bank revealed the

highest growth rate. The JP Morgan Chase Bank had outperformed all the other banks

in Group 5 (Foreign Bank), which registered a growth rate of 898 and a CGR of

54.48%. From the Statistical analysis it could be conclude that the Group 3 (old

private sector bank) had the highest positive correlation value of .990.Similarly, the

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paired‘t’ test revealed that Group 3 (old private sector bank) had the highest ‘t’ value

of 4.838. Thus it was concluded that the introduction of Information Technology had

positively impacted the indicator “Deposits per Branch”.

II. ADVANCES PER BRANCHTABLE: 4.4

ADVANCES PER BRANCH

(Value in lakhs) (Value in lakhs)

Bank



Value GrowthRate CGR Value Growth

Rate CGR

Nationalized BanksBank of Baroda 22189 116 7.15 79696 520 23.02Bank of India 24368 100 -0.11 84200 440 20.22Canara Bank 13798 124 9.64 49640 495 23.02Indian Overseas Bank 10599 115 4.71 41580 521 23.02Oriental Bank of Commerce 8244 121 9.64 39898 590 25.89Punjab Nationalized Bank 20219 119 7.15 90474 543 23.02

SBI & its Associates BanksState Bank of India 80937 136 14.81 312115 523 23.02State Bank of Indore 2357 135 14.81 12851 552 23.02

Old Private Sectors BanksFederal Bank 4161 95 -2.05 13646 519 23.02Jammu & Kashmir Bank 3137 120 7.15 14408 359 17.48Karnataka Bank 2178 120 9.64 8078 422 20.22South Indian Bank 1780 122 9.64 7647 490 20.22Tamilnadu Mercantile Bank 1062 131 12.20 3982 467 20.22

New Private Sectors BanksHDFC Bank 2051 242 54.88 48003 184 41.25ICICI Bank 2622 174 31.82 135725 385 23.02

Foreign BanksBank of America 3638 101 0.46 3248 124 1.62Bank of Nova Scotia 698 145 20.22 3053 269 14.81Barclays Bank 47 117 7.15 2883 108 216.22Citibank 5537 133 14.81 19784 362 58.48Hongkong& ShanghaiBanking Corpn.

3294 154 23.02 17694 300 17.48

JP Morgan Chase Bank 145 81 -8.79 1653 337 -Standard Chartered Bank 3690 128 12.20 24978 460 20.22


117


ADVANCES PER BRANCHGroups Variables Mean SD Std Error Correlation

Group 1Nationalized Banks (PartiallyComputerized)

16569.50 6608.42 2697.88.943


Group 2

SBI & its Associates Banks (Partiallycomputerized)

41647.00 55564.45 39290.00.745


162483.00 211611.60 149632.00

Group 3

Old Private Sectors Banks (Partiallycomputerized)

2463.60 1209.13 540.74.930


9552.20 4392.27 1964.28

Group 4

New Private Sectors Banks ( Partiallycomputerized)

2336.50 403.76 285.50.625


91864.00 62028.82 43861.00

Group 5Foreign Banks (PartiallyComputerized)

2435.57 2134.88 806.91.753


TABLE: 4.6PAIRED SAMPLES TEST

ADVANCES PER BRANCHGroups Variables Mean SD Std Error t value Sig

Group1

Nationalized Banks(Partially Computerized)and Nationalized Banks (ITEnabled)

47678.50 16909.76 6903.38 6.907Not

Significant

Group2

SBI & its Associates Banks(Partially computerized)and SBI & its AssociatesBanks (IT Enabled)

120836.00 156047.00 110342.00 1.095Not

Significant

Group3

Old Private Sectors Banks(Partially computerized)and Old Private SectorsBanks (IT Enabled)

7088.60 3298.39 1475.08 4.806 Significant

Group4

New Private SectorsBanks ( Partiallycomputerized) and NewPrivate Sectors Banks (ITEnabled)

89527.50 61625.06 43575.50 2.055Not

Significant

Group5

Foreign Banks (PartiallyComputerized) and ForeignBanks (IT Enabled)

8034.86 8442.14 3190.83 2.518Not

Significant


Advances are considered as the main source of income generation to banks. It

was clearly evident from Table 4.4 that the Advances per Branch have improved in

118

the IT enabled era when compared to the partially computerized era. A group wise

analysis revealed that the Nationalized banks group (Group 1) and SBI & its

Associates bank group (Group 2) registered higher growth rates when compared to the

other groups in the IT enabled era. The growing emphasis on the criteria of safety in

the loans sanctioned and the increase in the amount of NPAs of old private sector

banks are some of the key factors that have affected the rate of growth of credit

deployment in the IT enabled era.

An intra-group analysis of advances per branch of Nationalized Banks (Group

1) revealed that Oriental Bank of Commerce have out-performed all other banks taken

for analysis. It registered an annual growth rate of 590 and a CGR of 25.89%. It was

followed by the Punjab National Bank with an annual growth rate of 543. It was clear

from the table that both SBI and SBI of Indore, in the SBI & its Associates bank

group (Group 2) have registered highest growth rate, with a CGR of 23.02%.

In the Old private sector bank group (Group 3), the Federal Bank revealed

tremendous improvement in the IT enabled era than compared to the partially

computerized era. The bank’s CGR increased to 23.02% in IT enabled era from -

2.05% during partially computerized era. It was followed by the South Indian Bank

and Tamilnadu Mercantile Bank which registered a CGR of 20.22%. In the new

private sector bank group (Group 4), the ICICI Bank outperformed the HDFC Bank in

terms of annual growth rate (viz., 385). It was evident from the above table that in the

Foreign Bank group (Group 5) the Standard Chartered Bank had registered the highest

growth rate in its group with an annual growth rate in credit deployment of 460. The

reason for these exceptionally high advances per branch could be on account of their

restricted branch expansion policies and coupled with the policies of tapping big

clients and financing foreign trade.

It was concluded from Table 4.5 that all the five bank groups under study had

high positive correlation. Especially the Nationalized bank group (Group 1) revealed a

correlation co-efficient of .943 and the Old Private Sector Bank group (Group 3) had

a correlation co-efficient of .930. It was evident from the analysis that information

technology had a positive impact on the parameter advances per branch in all the five

groups of bank under study.

119

The table 4.6 revealed the results of paired‘t’ test analysis. It was evident

from the paired‘t’ test analysis that the Old private sector bank group(Group 3) had

highest significant ‘t’ values with regard to the indicator advances per branch. The

Nationalized bank group (Group 1) revealed insignificant ‘t’ values .Thus it was

concluded that except Group 1 all other bank groups was significantly impacted by

the implementation of Information Technology with regard to the indicator Advances

Per Branch.

Thus it was concluded from the ratio analysis that in group 1 (Nationalized

bank group), the Oriental Bank of Commerce have out-performed all other banks, in

group 2 (SBI& its Associates bank) both SBI and State Bank of Indore have

registered the highest growth rate, with a CGR of 23.02%.Similarly, in Group 3 & 4

(Old private sector bank group) & (New private sector bank group), the Federal Bank

and ICICI bank had outperformed all other banks in their group. Further in group 5

(Foreign bank group) the Standard Chartered Bank had registered the highest growth

rate 460. The correlation analysis revealed that the Nationalized bank group (Group 1)

revealed a correlation co-efficient of .943 and Old private sector bank group (Group3)

had significant impact of Information technology with regard to the indicator

advances per branch.

120

III. INTEREST INCOME PER BRANCH

TABLE: 4.7INTEREST INCOME PER BRANCH


Bank


IT Enabled(2005-2010)

Value GrowthRate

CGR Value GrowthRate

CGR

Nationalized BanksBank of Baroda 3938.71 1642 - 11931.40 4104 23.02Bank of India 3294.00 454 - 12509.00 1149 25.89Canara Bank 4378.71 1242 - 14029.80 2928 20.22Indian Overseas Bank 2112.14 593 - 7572.80 1378 23.02Oriental Bank of Commerce 1883.14 481 - 7044.80 1195 25.89Punjab Nationalized Bank 4660.86 875 - 15175.60 2068 23.02

SBI & its Associates BanksState Bank of India 22758.14 379 28.82 51390.80 148 20.22State Bank of Indore 611.43 738 - 2131.40 1672 20.22

Old Private Sectors BanksFederal Bank 596.86 359 - 2548.20 900 25.89Jammu & Kashmir Bank 1103.71 290 23.02 2416.80 670 17.48Karnataka Bank 544.57 4841 73.78 1558.80 11918 17.48South Indian Bank 400.29 711 - 1330.40 1769 25.89Tamilnadu Mercantile Bank 357.43 408 28.82 811.00 1007 20.22

New Private Sectors BanksHDFC Bank -780.14 145 10748.60 333 41.25ICICI Bank 2363.14 217 24778.00 419 14.81

Foreign BanksBank of America 334.50 -44 - 493.60 24 12.20Bank of Nova Scotia 195.25 38 - 352.80 66 25.89Barclays Bank 31.75 16 - 904.80 4384 99.52Citibank 1100.71 345 - 5264.40 774 17.48Hongkong& ShanghaiBanking Corpn.

605.00 228 - 4436.40 505 23.02

JP Morgan Chase Bank -68.14 115 - 362.00 261 34.89Standard Chartered Bank 1425.71 1353 - 4660.80 2952 14.81


121


INTEREST INCOME PER BRANCHGroups Variables Mean SD Std Error Correlation


3377.93 1166.67 476.29.972


Group 2

SBI & its Associates Banks(Partially computerized)

11684.79 15660.09 11073.36.987


26761.10 34831.66 24629.70

Group 3

Old Private Sectors Banks(Partially computerized)

600.57 298.13 133.33.746


1733.04 737.33 329.75

Group 4

New Private Sectors Banks (Partially computerized)

791.50 2222.63 1571.64.842


17763.30 9920.28 7014.70


517.83 561.08 212.07.883



INTEREST INCOME PER BRANCHGroups Variables Mean SD Std Error t value Sig

Group1

Nationalized Banks(Partially Computerized)and Nationalized Banks(IT Enabled)

7999.31 2237.44 913.43 8.757Not

Significant

Group2

SBI & its AssociatesBanks (Partiallycomputerized) and SBI& its Associates Banks(IT Enabled)

15076.32 19171.56 13556.35 1.112Not

Significant

Group3

Old Private SectorsBanks (Partiallycomputerized) and OldPrivate Sectors Banks(IT Enabled)

1132.47 552.04 246.88 4.587 Significant

Group4

New Private SectorsBanks ( Partiallycomputerized) and NewPrivate Sectors Banks(IT Enabled)

16971.80 7697.65 5443.06 3.118Not

Significant

Group5

Foreign Banks (PartiallyComputerized) andForeign Banks (ITEnabled)

1835.72 1820.72 688.17 2.668Not

Significant


122

Interest income is considered as one of the main source of income that can be

earned by a banking company. Interest income figures reflect income both from

advances and securities. The credit and deposit structure and the degree of vigilance

exercised in the sanctioning of loans by different banks too affect their interest

income. Thus the amount of Interest income could rise of the composition of loans

and securities changes at different banks.

A perusal of Table 4.7 revealed that the Interest income of all Nationalized

banks (Group 1) registered an increasing trend in the It enabled era when compared to

partially computerized era. An intra-group analysis revealed that the Bank of Baroda

ranked first with the maximum growth rate of 4104 followed by the Canara Bank

(2928) and Punjab National Bank (2068).

In the case of SBI & its Associates bank group (Group 2), the SBI had nearly

doubled its interest income in terms of absolute value, in the IT enabled era when

compared to the partially computerized era. But an analysis of growth rate revealed

that, it had decreased from 379 to 148. The fall in the rate of interest could be the

reason for decrease in growth rate of interest income per branch.

It was further evident from the table that the interest income of almost all the

banks in Group 3 (old private sector bank) under study registered an increase in the IT

enabled era when compared to partially computerized era. An intra-bank analysis

revealed that the Karnataka Bank ranked first with a growth rate of 11918 followed by

South Indian Bank and chased by Tamilnadu Mercantile Bank. With regard to

compound growth rate, the Federal Bank and South Indian Bank ranked first in the

old private sector bank group (Group 3).

The ratio analysis further proved that the interest income of both the banks in

the New private sector bank group (Group 4) viz., HDFC Bank and ICICI Bank

revealed a two-fold increase in the IT enabled era when compared to the partially

computerized era. Of the two banks under study the ICICI Bank exhibited the

maximum growth rate of 419 than compared to HDFC Bank. The HDFC bank

exhibited improved performance, viz., from a negative value of -780.14, in the

partially computerized era to a positive growth rate of 333 during the IT enabled era.

123

With regard to the foreign bank group (Group 5), the Barclays Bank exhibited

the maximum growth rate of 4384 .This bank also registered the highest CGR of

99.52% when compared to all the other banks taken for study in this group. The

standard chartered bank ranked second with a growth rate 2952 in the IT enabled era,

followed by the Citi bank. On the contrary, Bank of America and Bank of Nova

Scotia continued to exhibit lowest growth rate in Interest income per branch for both

partially computerized era and IT enabled era.

Table 4.8 revealed the statistical analysis of the indicator interest income per

branch for all the five bank groups under study. It can be gauged from the table that

almost all the 5 bank group under study revealed high positive correlation. Especially

Group 2 (SBI & its Associates) had a correlation co-efficient of .987, followed by

Group 1 (Nationalized bank) with a co-efficient of .972. Thus it could be concluded

that the impact of information technology had positively affected the indicator interest

income per branch for all the five bank groups under study.

The paired‘t’ test analysis exhibited in Table 4.9 revealed that the impact of

information technology had high significance with regard to the Old private sector

bank group (Group 3). The Nationalized bank group (Group 1) had insignificant ‘t’

values. The significance of information technology on interest income had a lighter

effect in the case of Group 2 (SBI & its Associates).Thus the paired ‘t’ test proved

that information technology had significantly affected majority of bank group under

study with regard to the indicator interest income per branch.

The ratio analysis concluded that in Group 1 (Nationalized bank)Bank of

Baroda had the maximum growth rate of 4104,in Group 2 (SBI & its Associates) State

bank of Indore had a growth rate of 1672, in Group 3 (Old private sector bank group)

the Karnataka Bank ranked first with a growth rate of 11918, in group 4 (New private

sector bank) ICICI bank had a growth rate of 419 and in Group 5 (foreign Bank) the

Barclays bank had a growth rate of 4384.The statistical analysis of the indicator

interest income per branch revealed high positive correlation in the case of Group 2

(SBI & its Associates) with a correlation co-efficient of .987.The paired‘t’ test

analysis revealed that the impact of information technology had high significant value

with regard to the Old private sector bank group (Group 3) viz.,4.587.

124

IV. INTEREST EXPENSES PER BRANCH

TABLE: 4.10

INTEREST EXPENSES PER BRANCH


Bank



ValueGrowth


Rate CGR

Nationalized BanksBank of Baroda 2312.57 720 - 7586.20 2032 28.82Bank of India 2031.00 379 - 8197.80 990 28.82Canara Bank 1053.86 223 - 9720.60 465 25.89Indian Overseas Bank 1104.14 309 - 4949.80 806 31.82Oriental Bank of Commerce 876.86 250 - 5070.80 640 31.82Punjab Nationalized Bank 2485.57 497 - 8982.00 1254 28.82

SBI & its Associates BanksState Bank of India 16829.43 162 14.81 32948.00 570 25.89State Bank of Indore 428.57 674 - 1493.20 1919 25.89

Old Private Sectors BanksFederal Bank 571.00 3330 58.48 1566.20 11210 28.82Jammu & Kashmir Bank 781.43 159 14.81 1544.80 427 17.48Karnataka Bank 480.14 408 25.89 1148.40 1558 25.89South Indian Bank 359.57 1122 41.25 901.20 3595 31.82Tamilnadu Mercantile Bank 244.86 220 20.22 506.00 745 25.89

New Private Sectors BanksHDFC Bank 286.86 154 - 5338.60 421 44.54ICICI Bank 3143.43 354 - 17951.60 781 14.81

Foreign BanksBank of America 205.50 59 - 163.40 40 2.32Bank of Nova Scotia 137.00 200 - 223.80 403 14.81Barclays Bank 16.25 69 - 419.80 2307 99.52Citibank 679.86 506 31.82 1891.80 1527 17.48Hongkong& ShanghaiBanking Corpn.

499.71 628 - 1754.20 1670 25.89

JP Morgan Chase Bank -32.71 113 - 158.80 248 34.89Standard Chartered Bank 820.43 241 20.22 1849.20 449 12.20


125


INTEREST EXPENSES PER BRANCHGroups Variables Mean SD Std Error Correlation


1644.00 711.79 290.59.493


Group 2


8629.00 11597.16 8200.43.542


17220.60 22241.90 15727.40

Group 3


487.40 205.34 91.83.920


1133.32 448.42 200.54

Group 4


1715.15 2019.90 1428.29.521


11645.10 8918.74 6306.50


332.29 335.27 126.72.929



INTEREST EXPENSES PER BRANCHGroups Variables Mean SD Std Error t value Sig

Group1


5773.87 1761.23 719.02 8.030Not

Significant

Group2

SBI & its Associates Banks(Partially computerized) andSBI & its Associates Banks(IT Enabled)

8591.60 10644.74 7526.97 1.141Not

Significant

Group3

Old Private Sectors Banks(Partially computerized) andOld Private Sectors Banks(IT Enabled)

645.92 271.67 121.50 5.316Not

Significant

Group4

New Private Sectors Banks( Partially computerized)and New Private SectorsBanks (IT Enabled)

9929.96 6898.84 4878.22 2.036Not

Significant

Group5


590.71 557.85 210.85 2.802 Significant


126

Table 4.10 exhibited the ratio analysis of interest expenses per branch. In the

Nationalized bank group (Group 1) increased considerably in the IT enabled era when

compared to the partially computerized era. The Bank of Baroda had the maximum

interest expenses with a growth rate of 2032 in the IT enabled era from 720 in the

partially computerized era. But the CGR analysis revealed that both Indian Overseas

Bank and Oriental Bank of Commerce had the highest CGR of 31.82%.

On the other hand, a intra-group analysis of the SBI &its Associates Bank

(Group 2), the State Bank of Indore represented a growth rate of 1919. The other bank

in the same group, the State Bank of India revealed a growth rate of 570 which proved

that State Bank of India had lesser growth rate with regard to interest expenses.

As far as the group 3 (Old private sector bank) are concerned the Federal Bank

represented the maximum growth rate of 11210 in the IT enabled era. The reason

could be the high interest rates that prevailed in the economy during the IT enabled

era. There as a threefold increase in Interest paid growth rate in the case of South

Indian Bank (3595) and Karnataka Bank (1558)

.With regard to New private sector bank (Group 4), the ICICI Bank had a CGR

of 14.81% in the IT enabled era, The HDFC Bank’s interest expenses per branch had

also increased with a compound growth rate of 44.54%.The intra-bank analysis of the

indicator Interest expenses for the foreign bank group (Group 5) revealed that the

Barclays Bank had the highest growth rate (2307) with regard to the indicator Interest

expenses in the IT enabled era, followed by HSBC (1670), Citi Bank (1527). In

contrast, the Bank of America (40) had the lowest growth rate followed by JP Morgan

Chase Bank (248), Bank of Nova Scotia (403) and Standard Chartered Bank (449).

The results of statistical analysis for the indicator Interest expense per branch

was depicted in Table 4.11. The correlation analysis of all the five groups under study

revealed that Group 5 (Foreign bank) had the highest correlation value of .929

followed by Group 4 (old private sector bank). Thus it could be concluded that

information technology had a positive impact on the indicator interest expenses of the

entire five bank group in the IT enabled era. It can be concluded that both old private

sector banks and Foreign Banks had the highest intake of deposits in the IT enabled

era thus having highest interest expenses being paid to its customers.

127

Table 4.12 revealed the paired ‘t’test analysis of the indicator interest

expenses per branch at 5% level of significance. The foreign bank group (Group 5)

recorded a ‘t’ value of 2.802 thus revealing the fact that IT had a positive significance

on the indicator interest expenses per branch. The other bank groups that revealed a

significant impact of information Technology on interest expenses per branch were

the New Private sector bank group (Group 4) with a ‘t’ value of 2.036 and the SBI

&its Associates Bank (Group 2) with a ‘t’ value of 1.141. Thus it could be concluded

from the above statistical analysis that with respect to interest expenses indicator, the

introduction of information technology had a positive significance on the majority of

bank group under study, except the nationalized bank group (Group 1) which revealed

insignificant values.

Thus the ratio analysis of the indicator interest expenses per branch proved

that in the Nationalized bank group (Group 1) the Bank of Baroda had the maximum

growth rate of 2032 in the IT enabled era, in the SBI &its Associates Bank (Group 2),

the State Bank of Indore represented a growth rate of 1919, in group 3 (Old private

sector bank) the Federal Bank represented the maximum growth rate of 11210 in the

IT enabled era. In the New private sector bank (Group 5), the ICICI Bank had a

growth rate of 781 and in the foreign bank group (Group 5) the Barclays Bank had the

highest of growth rate (2307). The correlation analysis of all the five groups under

study revealed that Group 5 (Foreign bank) had the highest correlation value of

929.The foreign bank group (Group 5) recorded a ‘t’ value of 2.802 thus revealing the

fact that IT had a positive significance on the indicator interest expenses per branch.

128

V. NON-INTEREST INCOME PER BRANCH

TABLE: 4.13

NON-INTEREST INCOMEPER BRANCH


Bank



ValueGrowth


Rate CGR

Nationalized BanksBank of Baroda 952.57 412 31.82 2024.80 1000 25.89Bank of India 1099.57 141 20.22 2106.60 446 23.02Canara Bank 1349.86 81 12.20 2029.80 235 20.22Indian Overseas Bank 429.29 603 38.03 940.80 1156 31.82Oriental Bank of Commerce 428.14 179 20.22 811.00 563 23.02Punjab Nationalized Bank 974.29 1805 58.48 2297.40 3951 28.82

SBI & its Associates BanksState Bank of India 4279.86 956 44.54 10110.80 2121 20.22State Bank of Indore 110.43 168 - 1182.20 242 -6.67

Old Private Sectors BanksFederal Bank 175.14 342 28.82 392.40 1006 25.89Jammu & Kashmir Bank 212.29 54 -2.27 235.40 134 34.89Karnataka Bank 222.00 19 4.71 248.40 67 20.22South Indian Bank 156.29 30 0.46 138.00 43 28.82Tamilnadu Mercantile Bank 42.86 6700 58.48 117.60 17200 23.02

New Private Sectors BanksHDFC Bank -210.14 141 - 2404.40 341 34.89ICICI Bank 1214.71 327 - 7000.40 598 12.20

Foreign BanksBank of America 157.50 7 - 224.80 311 25.89Bank of Nova Scotia 44.25 44 - 109.80 269 28.82Barclays Bank 96.00 193 - 351.40 247 0.92Citibank 588.86 998 25.89 1471.20 1750 25.89Hongkong& ShanghaiBanking Corpn.

122.57 191 - 1818.20 395 25.89

JP Morgan Chase Bank -1.57 189 - 254.80 3 -Standard Chartered Bank 48.14 152 - 2132.40 383 31.82


129


NON-INTEREST INCOME PER BRANCHGroups Variables Mean SD Std Error Correlation


872.29 371.51 151.67.887


Group 2


2195.15 2948.23 2084.72.652


5646.50 6313.47 4464.30

Group 3


161.72 71.64 32.04.556


226.36 109.29 48.87

Group 4


502.29 1007.52 712.43.785


4702.40 3249.86 2298.00


150.82 200.35 75.73.294



NON-INTEREST INCOME PER BRANCHGroups Variables Mean SD Std Error t value Sig

Group1


829.45 362.51 147.99 5.605Not

Significant

Group2


3451.36 3365.24 2379.59 1.450Not

Significant

Group3


64.64 91.46 40.90 1.580Not

Significant

Group4


4200.12 2242.34 1585.58 2.649 Significant

Group5


758.12 828.09 312.99 2.422Not

Significant


130

The non-interest income of a banking company constitutes income of the bank

other than interest income and it is the main source of income for a bank. Non-interest

income of a bank arises from commission, exchange, and broker’s charges etc. The

results of ratio analysis of the indicator non – interest income had been exhibited in

Table 4.13. Among the Nationalized banks group (Group 1) that had been taken under

study, the analysis revealed that the Punjab National Bank ranked first in terms of

growth rate (3951) and had a compound growth rate (28.82%).It was followed by

Indian overseas bank (1156) and Bank of Baroda (1000). The table further unveiled

that in the SBI and its associates group (Group 2), the State Bank of India revealed the

highest growth rate of 2121 and a CGR of 20.22%, in the IT enabled era. Being the

treasurer of the government, the SBI registered a tremendous growth rate in the IT

enabled era. The reason could be that the overall managerial policies of SBI are more

remunerative and efficient than that of the other bank in the same group, (i.e.) State

Bank of Indore which registered a negative CGR of -6.67%, in the IT enabled era.

With regard to the Non – interest income per branch of old private sector bank

group (Group 3), the ratio analysis revealed that Tamilnadu Mercantile Bank had

recorded the highest growth rate of 17,200 in the IT enabled era, but its CGR had

decreased to 23.02% (IT enabled era) from 58.48% (partially computerized era).

Apart from the above mentioned banks, Federal Bank had registered a growth rate of

1006.The other banks in Group 3 revealed a negligible growth rate in the IT enabled

era.

As far as the new private sector bank group (Group 4) is concerned, out of the

two banks taken for review, the ICICI Bank registered a growth rate of 598 and CGR

of 12.20%. But the HDFC bank registered the highest CGR of 34.89% in the IT

enabled era. The table further revealed the ratio analysis of the indicator, non –

interest of the Foreign Banks (Group 5). It was evident from the analysis that except

the Citi Bank (growth rate of 1750) almost all the other banks under study had a

marginal increase in growth rate. The reason for marginal increase could be on

account of the growing level of NPAs, falling rate of Credit deployment, shrinking

share in banking business operations, decline in income earned from commission,

exchange, brokerage etc. the income of the foreign banks registered a smaller rate of

growth in the IT enabled era. In addition to the aforesaid reasons, due to increased

131

competition from the other bank groups also, the rate of growth in Non-interest

income registered a marginal increase in the second period of the study.

It could be inferred from the correlation analysis presented in Table 4.14 that

Nationalized bank group (Group 1) had the highest correlation co – efficient of

.887,followed by the New Private Sector Bank group(Group 4) with a correlation co –

efficient of .785 The correlation analysis of the five bank groups under study revealed

that information technology had a positive impact on all bank groups except the

Foreign bank group (Group 5) which had a negligible correlation value.

Further a statistical analysis using paired ‘t’ test had revealed in Table 4.15

that with respect to the non – interest income indicator, the New private sector bank

group ( Group 4) had the highest significant value of 2.649 followed by the Foreign

bank group ( Group 5) with a ‘t’ value of 2.422. It could be understood that both old

private sector bank group (Group 3), 1.580 and SBI & its Associates (Group 2) 1.450

had been least affected by the introduction of information Technology with respect to

the indicator Non - interest income per branch of these bank groups. Thus it could be

concluded that introduction of IT had a significant impact with respect to Non-interest

income per branch of all bank groups under study except the Nationalized bank group

(Group 1).

It could be concluded from the ratio analysis of the indicator non – interest

income that, in the Nationalized banks group (Group 1) Punjab National Bank ranked

first in terms of growth rate (3951) and in the SBI and its associates group (Group 2),

the State Bank of India revealed the highest growth rate of 2121 and a CGR of

20.22%, in the IT enabled era. In the old private sector bank group (Group 3), the

ratio analysis revealed that Tamilnadu Mercantile Bank had recorded the highest

growth rate of 17,200 in the IT enabled era. Similarly, in the new private sector bank

group (Group 4) ICICI Bank registered a growth rate of 598 and in the Foreign Banks

(Group 5) the Citi Bank had the highest growth rate of 1750 in the IT enabled era. The

correlation analysis of the five bank groups under study revealed that Nationalized

bank group (Group 1) had the highest correlation co – efficient of .887. It could be

concluded from paired ‘t’ test that introduction of IT had a significant impact with

respect to the indicator Non-interest income per branch of all bank groups under

132

study, with the New private sector bank group ( Group 4) which had the highest

significant value

VI. NON-INTEREST EXPENSES PER BRANCH

TABLE: 4.16

NON-INTEREST EXPENSES PER BRANCH


Bank



ValueGrowth


Rate CGR

Nationalized BanksBank of Baroda 1301.00 388 25.89 3070.00 839 12.20Bank of India 1322.43 256 23.02 2826.00 577 12.20Canara Bank 1488.86 165 14.81 2849.20 337 9.64Indian Overseas Bank 720.57 578 34.89 1708.60 1342 17.48Oriental Bank of Commerce 429.71 4582 69.82 1222.60 9818 14.81Punjab Nationalized Bank 1751.57 344 25.89 3768.40 611 9.64

SBI & its Associates BanksState Bank of India 6399.14 394 28.82 14425.20 896 12.20State Bank of Indore 219.00 188 17.48 441.20 346 4.71

Old Private Sectors BanksFederal Bank 162.71 2343 - 497.60 4936 14.81Jammu & Kashmir Bank 216.86 230 20.22 433.80 489 12.20Karnataka Bank 104.00 9750 86.20 296.20 19200 17.48South Indian Bank 116.29 545 34.89 277.40 1162 12.20Tamilnadu Mercantile Bank 76.14 495 31.82 176.20 1000 14.81

New Private Sectors BanksHDFC Bank -399.71 141 - 3831.00 320 38.03ICICI Bank 1010.86 350 - 6550.20 544 2.32

Foreign BanksBank of America 82.25 2 - 167.80 174 17.48Bank of Nova Scotia 31.75 25 - 43.80 64 9.49Barclays Bank 22.75 107 - 508.80 5100 99.52Citibank 602.86 1192 - 2099.60 2002 12.20Hongkong& ShanghaiBanking Corpn.

263.71 257 - 1779.80 551 17.48

JP Morgan Chase Bank -7.71 139 - 100.40 336 151.18Standard Chartered Bank 242.14 241 - 1915.00 506 20.22


133


NON-INTEREST EXPENSES PER BRANCHGroups Variables Mean SD Std Error Correlation


1169.02 496.04 202.51.979


Group 2


3309.07 4370.02 3090.07.963


7433.20 9888.18 6992.00

Group 3


135.20 55.33 24.74.850

IT Enabled era(Old Private SectorsBanks)

336.24 128.68 57.55

Group 4


305.58 997.42 705.29.843


5190.60 1922.76 1359.60


176.82 216.45 81.81.865



NON-INTEREST EXPENSES PER BRANCHGroups Variables Mean SD Std Error t value Sig

Group1


1405.11 461.97 188.60 7.450Not

Significant

Group2


4124.13 5518.16 3901.93 1.057Not

Significant

Group3


201.04 86.66 38.75 5.188Not

Significant

Group4


4885.03 925.34 654.32 7.466Not

Significant

Group5


768.21 759.56 287.09 2.676 Significant


134

The non-interest expenses per branch of all the five bank groups under study

witnessed a very fast rate of increase during the IT enabled era. The reasons that could

be attributed could be due to factors like undue expenditure made on targeted branch

expansion programs, coming up of a number of non-viable branches, over staffing,

use of outdated work technology leading to poor efficiency and productivity. In

addition to these, political interference, high rate of interest expenses, strong trade

unions, high administration expenses, investment in unviable and doubtful assets and

undue impetus on social banking, product obsolescence due to old machinery and also

on account of unwarranted emphasis on quantity than quality etc. have also led to

high operating expenses.

Table 4.16 represented the ratio analysis of the indicator Non – interest

expenses per branch. Of nationalized bank group (Group 1), the oriental Bank of

Commerce witnesses the highest growth rate of 9818 in the IT enabled era. But it had

performed well in curtailing its operating expenses to a CGR of 14.81% in the IT

enabled era. Both Canara Bank (337) and Bank of India (577) had lower growth rates.

The fall in the rate of growth of expenditure in the IT enabled era can be on account

of the use of Modern Technology in these banks. Further, the Non – interest expenses

per branch of SBI &its Associates (Group 2) revealed that the State Bank of India had

the highest growth rate of 896 in the IT enabled era. This could be on account of the

nature of business and the type of infrastructure, which State Bank of India in had The

State Bank of Indore registered a lowest CGR in the IT enabled era (4.71%).

It was evident from the table that the Non-interest expenses per branch of old

private sector bank (Group 3) registered an increase in terms of growth rate but a

decrease in CGR during the IT enabled era. The reason for the increase in growth rate

could be that these banks with a view to expand their business operations have started

providing a number of new facilities to their clients an account of intensified

competition. Thus the Karnataka Bank had the highest growth rate of 19200 indices

followed by the Federal Bank (4936).

The Non – Interest Expenses of new private sector banks (Group 4) was

unveiled in the above table and the ICICI Bank had the highest growth rate of 544

than the HDFC Bank (320). The reasons that could be attributed for an increase in

expenditure was that they had been focusing on quality customer services by

135

providing on - line services, full-proof security arrangement systems to safe-guard

against fraud hackers, e-banking (e.g. ATM, Tele-banking, Mobile Banking etc.). In

addition, they were spending a lot on supervision and auditing of electronic banking

etc. Thus all these factors had led to a spurt in the Non – Interest expenses per branch

in the IT enabled era.

In the case of foreign bank group (Group 5), it was evident that the Barclays

Bank registered the highest growth rate of 5100 and a CGR of 99.52% in the IT

enabled era. However the Bank of Nova Scotia (64) and the Bank of America (174)

had the lowest growth rate in its group. The reason that could be attributable to the

fall in the rate of growth in expenditure was mainly on account of the drastic

reduction in transaction cost in their day-to-day business operations, which was

facilitated by the improvement in information technology and communication

networking in the IT enabled era.

The correlation analysis had been exhibited in Table 4.17. The analysis

revealed that there had been the highest positive correlation co – efficient for the

Nationalized bank Group (Group 1) of .979 followed by the SBI & its Associates

bank group (Group 2) with a correlation co – efficient of .963. The correlation

analysis exhibited that almost all bank groups under study had high positive

correlation thus it was concluded that in the Information Technology had a positive

impact on Non-interest expenses per branch indicator.

A statistical analysis using’ paired test (Table 4.18) revealed that the Foreign

bank group (Group 5), had the highest‘t’ value of 2.676. The other bank which had

significant‘t’ value was the SBI & it’s Associates (Group 2) with a ‘t’ value of 1.057.

The other entire bank group registered insignificant ‘t’ values. Thus it can be

concluded that the introduction of information technology in the banking sector had a

positive significance on the indicator Non-interest expenses per branch for the

Foreign bank group (Group 5) and the SBI & it’s Associates (Group 2).

Thus it could be concluded from the ratio analysis of the indicator non –

interest among the nationalized bank group (Group 1), the oriental Bank of

Commerce witnesses the highest growth rate of 9818, of the SBI & its Associates

bank group (Group 2) the State Bank of India had the highest growth rate of 896, of

136

the old private sector bank (Group 3) the Karnataka Bank had the highest growth rate

of 19200, of new private sector banks (Group 4) the ICICI Bank had the highest

growth rate of 544, of foreign bank group (Group 5), the Barclays Bank registered the

highest growth rate of 5100. The correlation analysis revealed that the highest positive

correlation co – efficient was for the Nationalized bank Group (Group 1) with a

correlation co – efficient of .979. A statistical analysis using paired test revealed that

the Foreign bank group (Group 5), had the highest‘t’ value of 2.676.

137

VII. RETURN ON ASSETS (NET PROFIT) PER BRANCH

TABLE: 4.19

RETURN ON ASSETS (NET PROFIT) PER BRANCH


Bank



Value GrowthRate


CGR

Nationalized BanksBank of Baroda 0.85 56 4.71 0.92 45 7.15Bank of India 0.65 468 31.82 0.90 292 38.03Canara Bank 0.48 197 14.81 0.64 1400 -12.90Indian Overseas Bank 1.16 1443 54.88 1.24 1571 -1.82Oriental Bank of Commerce 1.16 70 4.71 1.24 12 -16.82Punjab Nationalized Bank 0.83 54 4.71 1.21 99 2.32

SBI & its Associates BanksState Bank of India 0.75 10 2.32 0.94 100 2.09State Bank of Indore 1.17 62 14.81 0.87 18 0.46

Old Private Sectors BanksFederal Bank 0.69 25 17.48 1.21 23 17.48Jammu & Kashmir Bank 0.87 14 17.48 0.91 51 23.02Karnataka Bank 0.99 191 14.81 1.17 98 0.23South Indian Bank 0.66 233 25.89 0.78 537 2.09Tamilnadu Mercantile Bank 0.98 231 23.02 1.57 215 -4.50

New Private Sectors BanksHDFC Bank 1.66 19 -2.27 1.39 28 -2.27ICICI Bank 0.94 58 4.71 1.18 18 -8.79

Foreign BanksBank of America 1.35 260 14.81 2.79 877 23.02Bank of Nova Scotia 1.34 62 20.22 1.46 21 51.35Barclays Bank 0.84 109 17.48 2.42 97 -60.18Citibank 3.35 3 -0.16 2.69 24 -8.79Hongkong& ShanghaiBanking Corpn.

0.96 65 -6.67 1.62 72 -

JP Morgan Chase Bank 2.18 116 - 2.53 108 4.71Standard Chartered Bank 2.76 32 -4.50 2.76 13 9.64


138



Groups Variables Mean SD Std Error Correlation


0.86 0.27 0.11.890


Group 2


0.96 0.30 0.211.000


0.91 0.05 0.04

Group 3


0.84 0.16 0.07.586


1.13 0.31 0.14

Group 4


1.30 0.51 0.361.000


1.29 0.15 0.11


1.83 0.96 0.36.551





Group1


-0.17 0.12 0.05 -3.360Not

Significant

Group2


0.06 0.35 0.25 .224 Significant

Group3


-0.29 0.25 0.11 -2.613Not

Significant

Group4


0.02 0.36 0.26 .059Not

Significant

Group5


-0.50 0.80 0.30 -1.649Not

Significant


139

Assets indicate the total investment made by the Banking companies and

return on assets reflects the profits earned on total assets. The ratio analysis for the

indicator Return on Assets was exhibited in Table 4.19. The analysis revealed that in

the Nationalized Bank group (Group 1) the Indian Overseas Bank had the highest

growth rate followed by the Canara Bank, which had outperformed all the other banks

in its group with a growth rate of 1571 and 1400 respectively. The reasons for higher

growth rate could be the rationalization of branch expansion, deposits credit policies

coupled with efficient customer service, which have helped them to record higher

return on assets. But most of the other banks in this group revealed a negative CGR.

The Bank of Baroda revealed the highest CGR of 38.03%. In the SBI& its Associates

Bank group (Group 2), the State Bank of India had registered the highest growth rate

of 100 and a CGR of 2.09% in its group.

It was evident from the table that the return on assets did not increase in a

consistent manner in the old private sector Bank group (Group 3). The South Indian

Bank recorded the highest growth rate of 537 followed by Tamil Nadu Mercantile

Bank with 215 growth rate. Similarly in the new private Sector Bank group (Group

4), both the HDFC bank and ICICI bank registered a growth rate of 28 and 18

respectively. But a negative CGR of – 2.27 (HDFC Bank) and – 8.79 (ICICI Bank) in

the IT enabled era. In case of the foreign bank group (Group 5), the Bank of America

registered the highest growth rate of 877 which was the highest in its group. The

reason for the poor return on assets in the private sector banks and foreign banks

could be of prudence in Management of Assets. Since these banks were spending

heavily on technological up-gradation and communication structure, it probably might

have reduced their short – term profitability, but would certainly improve their

mechanism to provide better customer services and management of their assets in

future.

It was evident from Table 4.20 that majority of the bank group revealed

positive correlation. The New private sector Bank group (Group 4) and SBI & its

Associates group (Group 2) had a correlation co – efficient of 1. Similarly all the

banks in its group exhibited the highest positive correlation thus indicating that

information Technology had a positive impact on Return of Assets. Thus it was

140

concluded that information Technology had a positive impact on the indicator Return

of Assets

Table 4.21 registered the statistical analysis using paired ‘t’test of return on

Assets of the entire five bank group under study. The SBI & its Associates group

(Group 2) had the highest significant value at 5% level of significance, of .224 .Group

1(Nationalized Bank group),Group 3 (old private sector Bank),Group 3 (New private

sector Bank) revealed negative ‘t’ values followed by old private sector bank group

and Foreign Bank group. Thus it could be concluded that the introduction of

information technology had insignificant impact on the indicator return on assets for

majority of the bank groups under study.

Thus from the above analyses it could be concluded that in the Nationalized

Bank group (Group 1) the Indian Overseas Bank had the highest growth rate of 1571,

in the SBI & its Associates Bank group (Group 2), the State Bank of India had

registered the highest growth rate of 100, in the old private sector Bank group (Group

3) the South Indian Bank recorded the highest growth rate of 537, in the new private

Sector Bank group (Group 4), both the HDFC bank registered a growth rate of 28, in

the foreign bank group (Group 5), the Bank of America registered the highest growth

rate of 877. The correlation analysis revealed that the new private sector Bank group

(Group 4) and SBI & its Associates group (Group 2) had a correlation co – efficient of

1. The statistical analysis using paired‘t’ test of return on Assets revealed that The

SBI & its Associates group (Group 2) had the highest significant value at 5% level of

significance, with a ‘t’ value of .224

141

VIII.RETURN ON EQUITY

TABLE: 4.22

RETURN ON EQUITY


Bank



ValueGrowth


Rate CGR

Nationalized BanksBank of Baroda 15.44 46 4.71 15.40 48 9.64Bank of India 14.65 382 31.82 17.57 211 31.82Canara Bank 7.20 827 - 13.52 374 -Indian Overseas Bank 21.41 48 14.81 13.72 13 -4.50Oriental Bank of Commerce 21.41 30 0.23 13.72 39 -16.82Punjab Nationalized Bank 20.47 36 2.32 19.74 27 2.09

SBI & its Associates BanksState Bank of India 17.56 25 1.15 15.08 73 -16.82State Bank of Indore 27.81 31 9.64 17.29 23 4.71

Old Private Sectors BanksFederal Bank 13.45 4350 58.48 15.54 2233 -6.67Jammu & Kashmir Bank 23.87 302 20.22 13.90 135 23.02Karnataka Bank 19.13 27 2.32 15.98 11 1.39South Indian Bank 18.81 123 14.81 12.54 90 58.48Tamilnadu Mercantile Bank 18.14 19 1.39 16.12 5 -0.13

New Private Sectors BanksHDFC Bank 20.04 33 1.85 17.81 11 -1.37ICICI Bank 14.47 205 7.15 12.29 14 -16.82

Foreign BanksBank of America 16.76 58 -12.90 11.40 43 14.81Bank of Nova Scotia 13.10 9 -8.79 13.18 159 25.89Barclays Bank 0.84 39 0.92 3.32 98 -60.18Citibank 19.91 28 1.62 17.53 63 9.64Hongkong& ShanghaiBanking Corpn.

13.87 68 -2.05 14.29 126 -

JP Morgan Chase Bank 1.95 70 - 14.07 77 -0.16Standard Chartered Bank 28.35 95 2.32 22.28 91 0.46


142


RETURN ON EQUITY



16.76 5.56 2.27.196


Group 2


22.69 7.25 5.131.000


16.19 1.56 1.11

Group 3


18.68 3.70 1.66-.393


14.82 1.55 0.69

Group 4


17.26 3.94 2.791.000


15.05 3.90 2.76


13.54 9.71 3.67.804



RETURN ON EQUITYGroups Variables Mean SD Std Error t value Sig

Group1


1.15 5.64 2.30 .500 Significant

Group2


6.50 5.69 4.02 1.617Not

Significant

Group3


3.86 4.54 2.03 1.903Not

Significant

Group4


2.21 0.04 0.03 0.200Not

Significant

Group5


-0.18 6.11 2.31 -.080Not

Significant


143

One of the more conventional measures of profitability is the return on

shareholders’ funds that is return on equity. The ability of banks to attract fresh

equity depends upon this indicator only. The ratio analysis for the indicator return on

equity was depicted in Table 4.22. It was found that in the Nationalized bank group

(Group 1) the Canara Bank recorded the highest growth rate of 374 in the IT enabled

area, followed by the Bank of India with a growth rate of 211. Except these two

banks, all the other banks in the Nationalized bank group exhibited lower return on

equity. Similarly, in the SBI &its Associates Bank group (Group 2) the State Bank of

India registered highest growth rate in respect of Return on equity of 73 in the

Nationalized bank group (Group 1) the Canara Bank recorded the highest growth rate

of 374, but a negative CGR of – 16.82%. Thus in the IT enabled era, the Nationalized

bank group and SBI Bank group, on an average, marked a negative trend with regard

to return on equity.

In the case of old private sector Bank group (Group 3), the Federal bank

revealed exceptionally higher growth rate of 2233. With an exception to Federal

Bank, all the other banks in this group recorded a lower growth rate with on return on

equity in the IT enabled era. In the New private sector Bank group (group 4) also

both the banks (HDFC Bank & ICICI Bank) registered lower growth rates of11 and

14 respectively, with regard to return on equity, On the other hand, the foreign bank

group (Group 5), on an average, registered minimal growth rate. The bank of Nova

Scotia registered the highest growth rate of 159 in its group. But compared to the

partially computerized era, most of the foreign banks under study registered positive

return on equity, on account of their relatively improved working.

144

The Correlation analysis of the indicator return on equity was exhibited in

table 4.23. The analysis proved that both SBI & its Associates bank group (Group2)

and New private sector bank group (Group 2) had the highest positive correlation. It

was found that the old private sector bank group (Group 3), revealed a negative

correlation co – efficient, while the Nationalized bank group (Group 1) represented

low positive correlation of .196. Thus it could be concluded that information

technology had a positive impact on all bank groups under study, except group 3 with

regard to the indicator return on equity.

It was evident from Table 4.24 that the introduction of information

Technology had a positive significance with respect to return on equity indicator in

the nationalized bank group (group 1) with a‘t’ value of .500. All the bank groups

revealed insignificant values. Thus it could be concluded that the Nationalized bank

group had a significant impact with the introduction of information technology for the

indicator return on equity.

Hence it was concluded from the ratio analysis that in the Nationalized bank

group (Group 1) the Canara Bank recorded the highest growth rate of 374, in the SBI

& its Associates Bank group (Group 2) the State Bank of India registered highest

growth rate of 73, in old private sector Bank group (Group 3), the Federal bank

revealed exceptionally higher growth rate of 2233, in the New private sector Bank

group (group 4) also both the banks (HDFC Bank & ICICI Bank) registered growth

rates of11 and 14 respectively, in the foreign bank group (Group 5), the Bank of Nova

Scotia registered the highest growth rate of 159 in its group. The Correlation analysis

proved that both SBI & its Associates bank group (Group 2) and New private sector

bank group (Group 2) had the highest positive correlation of all the five bank groups

taken under study. The paired ‘t’ test revealed that the nationalized bank group (group

1) had a significant ’t’ value of .500.

145

IX.RETURN ON ADVANCES

TABLE: 4.25

RETURN ON ADVANCES


(Value in lakhs) (Value inlakhs)Bank



ValueGrowth


Rate CGR

Nationalized BanksBank of Baroda 10.45 34 -4.50 8.10 22 4.71Bank of India 9.99 31 -4.50 8.46 37 7.15Canara Bank 9.61 139 7.15 8.72 145 2.32Indian Overseas Bank 11.13 46 -6.67 9.16 41 4.71Oriental Bank of Commerce 11.13 14 -2.27 9.16 2 7.15Punjab Nationalized Bank 10.98 24 2.32 9.14 9 9.64

SBI & its Associates BanksState Bank of India 9.72 25 -4.50 8.47 5 7.15State Bank of Indore 11.00 29 -4.50 9.16 13 7.15

Old Private Sectors BanksFederal Bank 12.22 38 -2.27 10.44 66 7.15Jammu & Kashmir Bank 7.18 52 14.81 9.68 85 7.15Karnataka Bank 11.07 31 2.32 10.06 66 9.64South Indian Bank 11.83 9 -2.27 10.18 13 4.71Tamilnadu Mercantile Bank 11.75 11 2.32 11.01 22 0.23

New Private Sectors BanksHDFC Bank 10.41 22 -4.50 10.92 55 17.48ICICI Bank 10.32 104 -4.50 9.30 99 4.71

Foreign BanksBank of America 10.13 38 -10.87 7.44 35 20.22Bank of Nova Scotia 9.80 30 -4.50 6.57 22 12.20Barclays Bank 8.92 12 -8.79 18.48 285 -10.87Citibank 13.59 59 -12.90 10.58 61 -0.91Hongkong& ShanghaiBanking Corpn.

10.46 90 -2.27 9.86 132 -

JP Morgan Chase Bank 34.61 100 - 4.80 92 298.10Standard Chartered Bank 14.39 42 -6.67 10.29 32 7.15


146


RETURN ON ADVANCES



10.55 0.64 0.26.630


Group 2


10.36 0.91 0.641.000


8.82 0.49 0.35

Group 3


10.81 2.07 0.93.716


10.27 0.49 0.22

Group 4


10.37 0.06 0.051.000


10.11 1.15 0.81


14.56 9.07 3.43.517


TABLE: 4.27PAIRED SAMPLES TESTRETURN ON ADVANCES


Group1


1.76 0.50 0.20 8.605Not

Significant

Group2


1.55 0.42 0.30 5.237Not

Significant

Group3


0.54 1.75 0.78 .684Not

Significant

Group4


0.26 1.08 0.77 .333 Significant

Group5


4.84 11.97 4.52 1.070Not

Significant


147

Table 4.25 revealed the ratio analysis of the indicator return on advances.

Among the nationalized bank group(Group 1), it was evident that the Canara Bank

had the highest growth rate of 145. All the other banks in this group revealed lower

growth rate. The reason for lower growth rate could be noted that, though the rates of

interest were quite high, but due to the poor recovery rate and bad debt, the return on

advances stood relatively low. A detailed intra bank-wise analysis of SBI & its

Associates bank group (Group 2) revealed that the growth rate of State bank of India

(5) and the State Bank of Indore (13) was very low in the IT enabled era. But both

these banks revealed a CGR of 7.15% in the IT enabled era.

Return on advances of old private sector Bank group (Group 2) revealed an

increasing trend in the IT enabled era when compared to partially computerized era.

The Jammu Kashmir Bank exhibited the highest growth rate (85), followed by the

Federal bank and the Karnataka bank with a growth rate of 66. In the New private

sector bank group (Group 3), the ICICI Bank had the highest growth rate of 99.But

the HDFC had the highest CGR of 17.48% in its group. Similarly, in the foreign bank

group (Group 3), the Barclays Bank which had a growth rate of 285, followed by the

HSBC with a growth rate of 132. All the other banks in its group revealed lower

growth rate. Fall in return on advances had two implications. Firstly, it meant that

the bank loans have become cheaper and thus easily affordable. And secondly, that

the interest income of the banks had shrunk further.

Table 4.26 recorded the correlation analysis of five bank groups taken under

study. It could be inferred that both SBI its associates bank group (Group 2) and the

new private sector bank group (Group 3) registered high positive correlation of 1. The

foreign bank group (Group 5) revealed negative correlation co – efficient. Thus the

analysis revealed that the information technology had a positive impact on majority of

the bank groups under study with regard to the indicator return on advances.

It could be concluded from Table 4.27 that the introduction of information

Technology had a positive impact on the indicator return on advances with regard to

new private sector bank group (Group 4) with a ‘t’ value of .333. All the other bank

groups revealed insignificant values at 5% level of significance. Thus it could be

concluded that information technology had a positive impact on the indicator return

on advances on the new private sector bank group (Group 4).

148

Thus it was concluded from the ratio analysis that in the nationalized bank

group (Group 1), the Canara Bank had the highest growth rate of 145, in the SBI& its

Associates bank group (Group 2), the growth rate of State bank of India was 5 and

that of the State Bank of Indore was 13. Of old private sector Bank group (Group 2)

,the Jammu Kashmir Bank exhibited the highest growth rate of 85, In the New

private sector bank group (Group 3), the ICICI Bank had the highest growth rate of

99, in the foreign bank group (Group 3), the Barclays Bank which had a growth rate

of 285. The correlation analysis revealed that both SBI its associates bank group

(Group2) and the new private sector bank group (Group 3) registered high positive

correlation of 1. It could be concluded that the introduction of information

Technology had a positive impact on the indicator return on advances with regard to

new private sector bank group (Group 4) with a‘t’ value of .333.

4.4.3 PROFITABILITY PARAMETERS: - Profitability is a ratio of earnings to the

funds utilized. It stands for profits deflated by the size of the unit and indicates the

efficiency with which a bank deploys its total resources to maximize its profits. The

study seeks to analyze the following profitability ratios. The profitability parameter

had been analyzed by using certain indicators which were compared between partially

computerized era and IT enabled era.

149

I. INTEREST EARNED RATIOTABLE: 4.28

INTEREST EARNED RATIO

(Value in ratio) (Value in ratio)

Bank



ValueGrowth


Rate CGR

Nationalized BanksBank of Baroda 0.07 30 -2.27 0.05 30 1.62Bank of India 0.08 26 -2.27 0.05 47 -1.59Canara Bank 0.07 27 -2.27 0.05 39 1.62Indian Overseas Bank 0.08 14 -2.27 0.06 26 1.62Oriental Bank of Commerce 0.08 29 -2.27 0.06 29 1.62Punjab Nationalized Bank 0.08 29 -2.27 0.06 29 1.62

SBI & its Associates BanksState Bank of India 0.08 29 -2.27 0.06 38 -1.82State Bank of Indore 0.08 38 -4.50 0.08 33 -2.27

Old Private Sectors BanksFederal Bank 0.08 28 -2.27 0.06 14 4.71Jammu & Kashmir Bank 0.07 38 -4.50 0.05 24 4.71Karnataka Bank 0.08 34 -4.50 0.06 36 2.32South Indian Bank 0.08 31 -4.50 0.06 29 2.32Tamilnadu Mercantile Bank 0.09 24 -2.27 0.07 42 -4.50


Foreign BanksBank of America 0.10 38 - 0.10 100 4.71Bank of Nova Scotia 0.07 26 - 0.07 10 9.64Barclays Bank 1.31 343 - 0.28 -80 -41.11Citibank 0.09 12 -1.37 0.09 11 2.32Hongkong& ShanghaiBanking Corpn.

0.09 5 -0.11 0.09 11 7.15

JP Morgan Chase Bank 0.50 83 -25.86 0.17 -93 -10.87Standard Chartered Bank 0.09 24 -2.27 0.09 4 4.71


150

TABLE: 4.29

PAIRED SAMPLES STATISTICSINTEREST EARNED RATIO



.076 .005 .002.707

Nationalized Banks (IT Enabled) .055 .005 .002

Group 2


.080 .000 .000.541


.070 .014 .010

Group 3


.080 .007 .003.948


.060 .007 .003

Group 4


.115 .063 .045.481


.075 .007 .005


.321 .462 .174.991

Foreign Banks (IT Enabled) .127 .074 .028Level of Significance: 5 per cent


INTEREST EARNED RATIO

Groups Variables Mean SD StdError t value Sig

Group1


.0216 .004 .001 1.300Not

Significant

Group2


.0100 .014 .010 1.000Not

Significant

Group3


.0120 .025 .142 1.452 Significant

Group4


.0400 .056 .040 1.000Not

Significant

Group5


.1942 .388 .146 1.323Not

Significant


151

The interest income of banks consists of interest on advances and discount on

bills, which includes interest and discount on all type of loans and advances like cash

credit, demand loans, overdrafts, export loans, term loans, domestic and foreign bills

purchased and interest subsidy, if any, relating to such advances/bills.

It was evident from table 4.28 that interest earned ratio of nationalized bank

group (Group 1) revealed a marginal growth rate in the IT enabled era. The Bank of

India had the highest growth rate of 47 but had a negative CGR of – 1.59. Similarly in

the SBI and its Associates bank group (Group 2) the state Bank of India and the State

Bank of Indore had a growth rate of 38 and 33 respectively. A higher interest earned

ratio is a sign of higher spread and also a better financial position of the bank. But at

the same time it is not healthy from the point of view of a banks customer, since it

implies that on account of monopoly position, the bank may be paying less rate of

interest on deposits and charging high on loans.

In the old private sector Bank group (Group 3), except the Tamilnadu

Mercantile Bank (growth rate of 42) and the Karnataka Bank (growth rate of 36) all

the other banks in its group revealed marginal growth rate only. In the new private

sector Bank group (Group 4), the HDFC Bank had exceptional growth rate of 100.

But the ICICI Bank had the highest CGR of 9.64%. In the foreign Bank group

(Group 5) as well, except the Bank of America, which had a growth rate of 100 all the

other banks in its group had marginal growth rate only. It could be indicated that in

the IT enable era, the gradual levering of rate of interest, pre-emption of the increased

competition within the banking sector, the increased product sophistication demanded

by customers, the growth of financial maturity of the corporate sector might be the

reasons for the decline in rate of growth in the interest earned ratio of all the banks

taken under study.

The correlation analysis was depicted in table 4.29. It was clear that the

foreign bank group (Group 5) revealed the highest positive correlation of, 991

followed by old private sector bank group (Group 3) with a correlation co – efficient

of .948. The other entire bank group revealed positive correlation thus it was

concluded that information technology had a positive effect on the indicator interest

earned ration of all banks under study.

152

The paired‘t’ test analysis values with regard to the indicator interest earned

ratio were exhibited in Table 4.30. It could be understood that the old private sector

bank group (Group 3) had the highest significant‘t’ value of 1.452. It was followed by

foreign bank group (Group 5) 1.323. It was inferred that the entire bank group had

significant‘t’ values. Thus it was proved that information technology had a significant

impact on the indicator interest earned ratio.

Thus it was concluded from the ratio analysis that of nationalized bank group

(Group 1) the Bank of India had the highest growth rate of 47, in the SBI and its

Associates bank group (Group 2) the state Bank of India and the State Bank of Indore

had a growth rate of 38 and 33 respectively. In the old private sector Bank group

(Group 3), the Tamilnadu Mercantile Bank had a growth rate of 42, in the new private

sector Bank group (Group 4), the HDFC Bank had a growth rate of 100 and in the

foreign Bank group (Group 5) as well the Bank of America had a growth rate of 100.

The correlation analysis revealed that the foreign bank group (Group 5) had the

highest positive correlation of, 991 of all the bank groups under study. The paired‘t’

test analysis with regard to the indicator interest earned ratio revealed that the old

private sector bank group (Group 3) had the highest significant‘t’ value of 1.452.

153

II. INTEREST PAID RATIO

TABLE: 4.31

INTEREST PAID RATIO


Bank



Value GrowthRate


CGR

Nationalized BanksBank of Baroda 0.06 -39 -6.67 0.04 -47 -2.27Bank of India 0.05 -25 -2.27 0.04 -25 -Canara Bank 0.05 -27 -2.27 0.04 -25 2.32Indian Overseas Bank 0.06 -23 -2.27 0.05 -23 4.71Oriental Bank of Commerce 0.05 -27 -4.50 0.04 -20 2.32Punjab Nationalized Bank 0.14 -21 -16.82 0.04 -35 2.32

SBI & its Associates BanksState Bank of India 0.79 -56 -10.87 0.31 -95 -33.93State Bank of Indore 0.00 -240 - 0.01 -1000 44.54

Old Private Sectors BanksFederal Bank 0.06 -33 -4.50 0.04 -25 2.32Jammu & Kashmir Bank 0.05 -28 -4.50 0.04 -21 4.71Karnataka Bank 0.06 -34 -4.50 0.05 -14 7.15South Indian Bank 0.06 -27 -2.27 0.04 -29 2.32Tamilnadu Mercantile Bank 0.07 -24 -2.27 0.05 -32 0.69

New Private Sectors BanksHDFC Bank 0.05 -20 -2.27 0.05 -6 4.71ICICI Bank 0.06 -9 2.32 0.06 7 7.15

Foreign BanksBank of America 0.06 -49 - 0.05 -43 -2.27Bank of Nova Scotia 0.05 -32 - 0.04 -53 -2.05Barclays Bank 0.37 105 - 0.12 -69 -24.14Citibank 0.06 -23 -2.27 0.05 -26 -Hongkong& ShanghaiBanking Corpn.

0.06 -34 -4.50 0.05 -28 4.71

JP Morgan Chase Bank 0.26 -89 -30.81 0.06 -87 12.20Standard Chartered Bank 0.06 -27 -4.50 0.05 -25 2.32


154


INTEREST PAID RATIOGroups Variables Mean SD Std Error Correlation


.068 .035 .014.115


Group 2


.395 .558 .395.841


.160 .212 .150

Group 3


.060 .007 .003.645


.044 .005 .002

Group 4


.055 .007 .005.724


.055 .007 .005


.131 .129 .048.894


TABLE: 4.33PAIRED SAMPLES TESTINTEREST PAID RATIO


Group1


.026 .0361 .014 1.807 Significant

Group2


.235 .346 .245 .959Not

Significant

Group3


.016 .005 .002 6.532Not

Significant

Group4


.045 .002 .010 7.645Not

Significant

Group5


.071 .105 .040 1.785Not

Significant


155

The interest paid relates to the funds raised by the banks. Banks raise funds

from public as deposits, from money or capital market and RBI or Bank as

borrowings. The interest expenditure of banks depends on the size of the deposit

portfolio, the term structure and the interest rate etc. Interest expenditure of banks is

lower than that of other companies or institutions as banks are able to raise low cost

deposits through their branch network.

The ratio analysis of interest paid ratio for all five bank groups had been

exhibited in Table 4.31. In the Nationalized bank group (Group 1) almost all banks

revealed negative growth rate, however the compound growth rate recorded some

improvement in the IT Enabled era, then compared to the partially computerized era

of all the nationalized banks under study, the Indian Overseas Bank had the highest

CGR of 4.71%. It could be further observed from the table that SBI and its

Associates bank group also revealed a negative growth rate with respect to interest

paid ratio. But the State Bank of Indore had a CGR of 44.54% in the IT enabled era.

Further in the old private sector bank group (Group 3), the Karnataka Bank

had the Maximum CGR of 7.15% followed by the Jammu and Kashmir Bank (4.71

%). Almost all the banks in this bank group registered negative growth rate thus the

reason could be that the interest paid expenses had come down considerably than

compared to the partially computerized era. In the New Private Sector Bank group

(Group 4) the ICICI had a positive growth rate of 7and a CGR of 7.15% in the IT

Enabled era. Similarly in the foreign bank group (Group 5) a negative trend in growth

rate of interest paid ratio could be observed. The JP Morgan chase Bank registered

the highest positive CGR of 12.20% followed by the HSBC Bank with a CGR of

4.71%. Thus it was concluded that the interest paid ratio revealed negative growth

rate.

Table 4.32 exhibited the correlation analysis of the indicator interest paid ratio

and it could be understood that almost all the five bank group under study had high

positive correlation. It was understood that the Foreign bank group (Group 5) had a

correlation co – efficient of .894 followed by SBI &its Associates bank group

(Group2) with a correlation co – efficient of .841.Thus the analysis revealed that

information technology had a positive impact on the indicator interest paid ratio of

these banks.

156

An analysis of paired‘t’ test was exhibited in Table 4.33 with respect of the

indicator interest paid ratio. The‘t’ valves at 5% level of significance revealed that

there existed significant difference between the two periods under study. It was

proved from the analysis that the nationalized bank group (Group 1) had a significant

‘t’value of 1.807. The other bank group which revealed significant‘t’ value was the

foreign bank group with a ‘t’ value of 1.785.The other bank group revealed

insignificant values.

Thus from the above analysis it was concluded that In the Nationalized bank

group (Group 1) the Indian Overseas Bank had the highest CGR of 4.71%. In the SBI

& its Associates bank group (Group 2), the State Bank Of Indore had a CGR of

44.54% in the IT enabled era, in the old private sector bank group (Group 3), the

Karnataka Bank had the Maximum CGR of 7.15%, in the New Private Sector Bank

group (Group 4) the ICICI had a positive growth rate of 7 and a CGR of 7.15%, in the

foreign bank group (Group 5) , the JP Morgan chase Bank registered the highest

positive CGR of 12.20%. The correlation analysis of the indicator interest paid ratio

revealed that the Foreign bank group (Group 5) had the highest correlation co –

efficient of .894. An analysis of paired‘t’ test proved that the nationalized bank group

(Group 1) had a significant ‘t’ value of 1.807.

157

III. NON-INTEREST INCOME RATIO

TABLE: 4.34

NON-INTEREST INCOME RATIO


Bank



ValueGrowth


Rate CGR

Nationalized BanksBank of Baroda 0.01 -29 -2.27 0.01 -50 1.15Bank of India 0.01 -40 -4.50 0.01 -53 1.15Canara Bank 0.01 -41 -4.50 0.01 -59 1.39Indian Overseas Bank 0.01 -25 -2.27 0.01 -50 9.64Oriental Bank of Commerce 0.01 -50 -6.67 0.01 -57 -2.27Punjab Nationalized Bank 0.01 -23 -1.82 0.01 -38 2.32

SBI & its Associates BanksState Bank of India 0.01 -13 -0.68 0.01 -33 -2.27State Bank of Indore 0.02 -56 -6.67 0.03 -61 -20.56

Old Private Sectors BanksFederal Bank 0.01 -47 -6.67 0.01 -53 2.32Jammu & Kashmir Bank 0.01 -85 -16.82 0.00 -46 20.22Karnataka Bank 0.02 -50 -8.79 0.01 -69 2.32South Indian Bank 0.02 -67 -10.87 0.01 -76 -Tamilnadu Mercantile Bank 0.02 -18 -8.79 0.01 -18 -

New Private Sectors BanksHDFC Bank 0.01 -8 -1.59 0.01 8 2.32ICICI Bank 0.02 0 2.32 0.02 11 9.64

Foreign BanksBank of America 0.03 0 - 0.03 116 9.64Bank of Nova Scotia 0.01 40 - 0.02 60 12.20Barclays Bank 28.54 163 - 3.10 -100 -81.37Citibank -0.07 -41 - -0.05 -119 -Hongkong& ShanghaiBanking Corpn.

0.02 29 7.15 0.03 59 7.15

JP Morgan Chase Bank 0.31 -81 -29.20 0.07 -103 -Standard Chartered Bank 0.05 -89 -30.81 0.03 -71 14.81


158


NON-INTEREST INCOME RATIOGroups Variables Mean SD Std Error Correlation


.010 .000 .000.781


Group 2


.015 .007 .005.954


.020 .014 .010

Group 3


.016 .005 .002.612


.008 .004 .002

Group 4


.015 .007 .005.416


.015 .007 .005


4.127 10.765 4.069.256

Foreign Banks (IT Enabled) .461 1.164 .439Level of Significance: 5 per cent


NON-INTEREST INCOME RATIO


Group1


.005 .007 .005 -1.000Not

Significant

Group2


.008 .004 .002 4.000 Significant

Group3


3.665 9.600 3.629 1.010Not

Significant

Group4


.718 .102 .009 7.582Not

Significant

Group5


.849 .104 .015 8.652Not

Significant


159

The Non – interest income of banks arises from sources other than credit

deployed by banks. It includes commission exchange and brokerage, profit on sale or

revaluation of investments, profit on foreign exchange transactions, dividend from

subsidiaries and lease income from lease rental, management fee, financial charges,

overdue charges etc. The importance of non – interest income for banks cannot be

over emphasized. Since it is earned without addition to assets it contributes to a large

increase in return on assets (ROA).

An analysis of growth rate of the indicator Non – interest income was

exhibited in Table 4.34. It could be inferred that in the Nationalized bank group

(Group 1) almost all the banks under study revealed negative growth rate. With

regard to CGR, the Indian Overseas Bank registered the highest CGR of 9.64% on the

other hand; the SBI and its Associates Bank group (Group 2) revealed a negative

trend in its growth rate. Both State Bank of India and State Bank of Indore had to

improve their income from other sources in order to increase their return on assets.

The same trend was witnessed in the case of old private sector banks (Group

3), but its compound growth rate revealed some improvement. The Jammu and

Kashmir Bank had the highest CGR of 20.22% in the IT Enabled era. With regard to

the New Private Sector banks (Group 4), both HDFC Bank and ICICI Bank had

positive growth rate of 8 and 11 and a CGR of 2.32% and 9.64% respectively.

Similarly in the Foreign bank group (Group 5) a mixed trend in growth rate could be

witnessed during the IT Enabled era. The Bank of America had the highest growth

rate of 116 in its group, while the Standard Chartered Bank had the highest CGR of

14.81%, followed by Bank of Nova Scotia (12.20%). It was thus concluded that both

the private sector banks and foreign banks had to improve their income from other

sources in order increase their spread ratio and also to sustain its competency.

A statistical analysis of correlation with regard to the indicator Non – interest

income ratio had been presented in Table 4.35. It was evident that information

technology had a positive impact on almost all the five bank groups under study. The

SBI &its Associates bank group (Group 2) had the highest correlation co – efficient of

.954, followed by the Nationalized bank group (Group 1) with a correlation co –

160

efficient of.781. Thus the correlation analysis concluded that the introduction of

information technology had a positive impact on the indicator Non – interest income

ratio.

A paired’t’ test analysis of the indicator Non – interest income ratio had been

exhibited in Table 4.36. It was evident that SBI & its Associates bank group (Group

2) had the highest significant‘t’ value of 4.000. The other bank group which had a

significant‘t’ was old private sector bank group (Group 3). The rest of the bank group

revealed insignificant‘t’ values.

Thus it was concluded from the ratio analysis that in the Nationalized bank

group (Group 1), the Indian Overseas Bank registered the highest CGR of 9.64%, in

the SBI and its Associates Bank group (Group 2) a negative trend in its growth rate

was revealed, of old private sector banks (Group 3),the Jammu and Kashmir Bank had

the highest CGR of 20.22%,in the Foreign bank group (Group 5), the Bank of

America had the highest growth rate of 116 in its group. A statistical analysis of

correlation revealed that the SBI & its Associates bank group (Group 2) had the

highest correlation co – efficient of .954. The paired’t’ test analysis of the indicator

Non – interest income ratio had been exhibited in Table 4.36. It was evident that SBI

& its Associates bank group (Group 2) had the highest significant ‘t’ value of 4.000.

161

IV. NON-INTEREST EXPENSES RATIO

TABLE: 4.37

NON-INTEREST EXPENSES RATIO


Bank



ValueGrowth


Rate CGR

Nationalized BanksBank of Baroda 0.02 -25 -4.50 0.01 -55 -10.87Bank of India 0.00 67 4.71 0.00 200 12.20Canara Bank 0.02 -35 -4.50 0.01 -55 -6.67Indian Overseas Bank 0.02 -19 -2.28 0.01 -38 -2.27Oriental Bank of Commerce 0.01 -27 -2.27 0.01 -47 -8.79Punjab Nationalized Bank 0.02 -18 -2.27 0.01 -50 -6.67

SBI & its Associates BanksState Bank of India 0.02 -14 -2.27 0.01 -33 -6.67State Bank of Indore 0.02 -36 -4.50 0.01 -59 -10.87

Old Private Sectors BanksFederal Bank 0.01 -13 -2.27 0.01 -27 -2.27Jammu & Kashmir Bank 0.01 -47 -8.79 0.01 -47 2.09Karnataka Bank 0.01 -8 -1.37 0.01 -23 0.92South Indian Bank 0.01 -19 -2.27 0.01 -44 -6.67Tamilnadu Mercantile Bank 0.02 6 0.69 0.02 -25 -2.27

New Private Sectors BanksHDFC Bank 0.02 29 2.32 0.02 43 1.85ICICI Bank 0.02 13 2.32 0.02 0 -1.14

Foreign BanksBank of America 0.02 -6 - 0.02 50 2.32Bank of Nova Scotia 0.01 25 - 0.01 -38 -6.45Barclays Bank 0.24 369 - 0.08 -52 -29.20Citibank 0.03 11 0.92 0.03 -7 -20.51Hongkong& ShanghaiBanking Corpn.

0.02 -4 0.06 0.03 4 0.23

JP Morgan Chase Bank 0.19 -91 -30.81 0.02 -92 58.48Standard Chartered Bank 0.02 25 2.32 0.03 69 7.15


162


NON-INTEREST EXPENSES RATIOGroups Variables Mean SD Std Error Correlation


.015 .008 .003.878


Group 2


.020 .000 .000.221


.010 .000 .000

Group 3


.012 .004 .002.495


.012 .004 .002

Group 4


.020 .000 .000.741


.020 .000 .000


.075 .096 .036.682



NON-INTEREST EXPENSES RATIO


Group1


.006 .005 .002 3.162Not

Significant

Group2


.326 .583 .336 .970Not

Significant

Group3


.008 .003 .001 3.416 Significant

Group4


5.627 1.453 4.635 2.001Not

Significant

Group5


.004 .082 .031 1.418Not

Significant


163

The non – interest expenses are also called as operating expenses or

establishment cost. It is incurred for maintaining the staff, premises etc and for

carrying out day to day operations. Major type of operating expenditure are

employees’ salaries / wages, allowances, bonus and staff benefits, rent, taxes and

lighting, printing and stationary, Advertisement and publicity, depreciation on banks

property, director’s fees and Auditors fees.

The ratio analysis of the indicator non – interest expense ratio had been

exhibited in Table 4.37. It was evident that most of the banks taken under study

revealed a negative growth rate with respect to this indicator. An increase in Non –

interest expenses ultimately creates additional burden to the bank, which in turns

affects the profitability position. Thus in the Nationalized bank group (Group 1) Bank

of Baroda, Canara Bank and Punjab National Bank revealed a growth rate of -55, -55,

-50 respectively. Thus they had performed satisfactorily in controlling operating

expenses. Similarly the SBI & its Associates Bank group (Group2)was able to control

the operating expenses to a great extend in the IT enabled era. The State Bank of India

and the State Bank of Indore had negative growth rates of -33 and -59 respectively.

As far as the old private sector Bank group (Group 3) was concerned, almost

all the banks under study had negative growth rate thus proving that they were able to

control their operating expenses. The Jammu & Kashmir Bank had a CGR of 2.09 %.

But in the new private sector bank group (Group 4) the HDFC Bank exhibited a

growth rate of 43% similarly in the foreign bank group, a mixed trend was observed

,that the JP Morgan chase Bank(-92), Barclays Bank(-52) and Bank of Nova Scotia(-

38) had controlled their Non – interest expenses and thus were able to improve their

profitability.

The results of correlation analysis with regard to Non-interest expenses ratio is

given in Table 4.38. The Nationalized bank group (Group 1), new private sector bank

group(Group 4) and the foreign bank group(Group 5) revealed highest positive

correlation of .878, .741 and .682 respectively. Thus it was proved that with the

introduction of information technology; these banks were able to control their

operating expenses and were able to put themselves in a better profitable position.

164

Similarly the paired‘t’ test at 5% level of significance (Table 4.39) proved that

the old private sector bank group (Group 3) had a significant ‘t’ value 3.416. It was

followed by the nationalized bank group (Group1) with a‘t’ value of 3.162. It was

found that the entire bank group had significant values thus proving that the

introduction of information technology had a positive impact on the indicator non-

interest expenses ratio.

Thus it could be concluded that with regard to the indicator non – interest

expenses ratio, Thus in the Nationalized bank group (Group 1) almost all banks in its

group revealed negative growth rate In the SBI & its Associates Bank group (Group2)

the State Bank of India and the State Bank of Indore had negative growth rates of -33

and -59 respectively. In the old private sector Bank group (Group 3) the Jammu &

Kashmir Bank had a CGR of 2.09 %, in the new private sector bank group (Group 4)

the HDFC Bank exhibited a growth rate of 43%, in the foreign bank group, and the JP

Morgan chase Bank had a negative growth rate of 92. The results of correlation

analysis revealed that the Nationalized bank group (Group 1), had the highest positive

correlation of .878. The paired‘t’ test at 5% level of significance (Table 4.39) proved

that the old private sector bank group (Group 3) had a significant ‘t’ value 3.416.

165

V. SPREAD RATIOTABLE: 4.40

SPREAD RATIO


Bank



Value GrowthRate


CGR

Nationalized BanksBank of Baroda 0.008 100 12.20 0.013 200 14.81Bank of India 0.033 25 -4.50 0.014 -75 -6.67Canara Bank 0.022 -23 -4.50 0.010 -65 -2.05Indian Overseas Bank 0.015 25 2.32 0.012 -38 -12.90Oriental Bank of Commerce 0.024 -29 -2.27 0.013 -43 -2.27Punjab Nationalized Bank 0.018 -50 -6.67 0.016 -5 -0.91

SBI & its Associates BanksState Bank of India 0.707 -58 - -0.253 -101 -State Bank of Indore 0.081 -48 -6.67 0.065 -84 -24.14

Old Private Sectors BanksFederal Bank 0.019 -11 -0.11 0.019 28 12.2Jammu & Kashmir Bank 0.022 -56 -6.67 0.014 -28 9.64Karnataka Bank 0.025 -33 -4.50 0.013 -83 -18.71South Indian Bank 0.019 -38 -4.50 0.012 -29 7.15Tamilnadu Mercantile Bank 0.026 -26 -4.50 0.017 -65 -20.56

New Private Sectors BanksHDFC Bank 0.022 5 -0.68 0.022 20 1.15ICICI Bank 0.012 1400 41.25 0.019 2800 23.02

Foreign BanksBank of America 0.041 -14 - 0.054 97 12.2Bank of Nova Scotia 0.020 -10 - 0.028 105 28.82Barclays Bank 0.939 634 - 0.158 -94 -60.18Citibank 0.029 16 2.32 0.035 28 9.64Hongkong& ShanghaiBanking Corpn.

0.022 167 17.48 0.039 242 9.64

JP Morgan Chase Bank 0.242 -75 -22.37 0.102 -100 -56.34Standard Chartered Bank 0.034 -19 -0.68 0.040 39 7.15


166


SPREAD RATIOGroups Variables Mean SD Std Error Correlation


.020 .008 .003.771


Group 2


.394 .442 .313.954


.094 .224 .159

Group 3


.022 .003 .001.826


.015 .002 .001

Group 4


.017 .007 .005.632


.020 .002 .001


.189 .339 .128.950



SPREAD RATIO


Group1


.007 .008 .003 1.993Not

Significant

Group2


.488 .667 .472 1.034Not

Significant

Group3


.007 .004 .001 3.627 significant

Group4


.003 .004 .003 1.425Not

Significant

Group5


.124 .294 .111 1.116Not

Significant


167

The spread ratio is one of the important indicators to determine the

profitability of banks. This ratio is calculated by taking the difference between the

previously computed two ratios namely, interest earned ratio and interest paid ratio.

This ratio functions as a cushion for meeting expenses of management and

administration.

It was observed from Table 4.40 that in the nationalized bank group (Group

1), the Bank of Baroda had a growth rate of 200 and a CGR of 14.81%. All the other

banks in its group revealed negative growth rate. It was evident from the table that in

the SBI & its Associates bank group (group 2), the State Bank of India (-101) and the

State bank of Indore (-84) revealed negative growth rate. Thus it could be inferred

that they should improve their spread in order to improve their profitability position.

In the Old private sector bank group (Group 3), the Federal Bank revealed a

growth rate of 28, while all the other banks in its group revealed negative growth rate.

But in the new private sector bank group (Group 4), the ICICI bank had the highest

growth rate of 2,800 and a CGR of 23.02% in the IT enabled era .It was thus clear that

the ICICI bank’s profitability would be better off than compared to all the other banks

in its group taken understudy. In the foreign bank group (Group 5), the Bank of Nova

Scotia registered the highest CGR of 28.82% in its group followed by the Bank of

America with a CGR of 12.2%. Thus it could be concluded from the analysis that

both New private sector banks and Foreign banks had fared well with regard to this

indicator.

A correlation analysis of the indicator spread ratio had been presented in Table

4.41.It was evident from the analysis that the SBI & its Associates Bank group (Group

2) and Foreign Bank group (Group 5) had the highest correlation values of .954 and

.950 respectively, followed by the old private sector bank group(.826) and the

nationalized bank group(.771).Thus it was concluded that the introduction of

information technology had a positive impact with regard to the indicator , the spread

ratio.

The paired‘t’ test analysis exhibited in table 4.42revealed that the old private

sector bank group (Group 3) had a significant ‘t’ value of 3.627. Similarly all bank

groups under study exhibited significant ‘t’ values. Thus it was concluded that the

168

introduction of information technology had a positive impact on the spread ratio and

indirectly on the profitability of banks taken under study.

Thus it was concluded from the above analysis that in the nationalized bank

group (Group 1), the Bank of Baroda had a growth rate of 200 and a CGR of 14.81%,

in the SBI & its Associates bank group (Group 2), the State Bank of India and the

State bank of Indore revealed negative growth rate of -101 and -84 respectively. In the

Old private sector bank group (Group 3), the Federal Bank revealed a growth rate of

28, in the new private sector bank group (Group 4), the ICICI bank had the highest

growth rate of 2,800, in the foreign bank group (Group 5), the Bank of Nova Scotia

registered the highest CGR of 28.82%. A correlation analysis of the indicator spread

ratio revealed that the SBI & its Associates Bank group (Group 2) and Foreign Bank

group (Group 5) had the highest correlation values of .954 and .950 respectively. The

paired‘t’ test analysis revealed that the old private sector bank group (Group 3) had a

significant ‘t’ value of 3.627.

169

VI. Burden RatioTABLE: 4.43

BURDEN RATIO


Bank



Value GrowthRate


CGR

Nationalized BanksBank of Baroda 0.006 0 2.32 0.003 -60 -32.39Bank of India -0.009 -75 - -0.003 -125 -Canara Bank 0.002 300 - 0.003 100 -22.37Indian Overseas Bank 0.007 -13 -4.50 0.006 -13 -14.88Oriental Bank of Commerce 0.001 300 - 0.003 100 -20.56Punjab Nationalized Bank 0.007 -11 -4.50 0.006 67 -22.37

SBI & its Associates BanksState Bank of India 0.006 -29 -8.79 0.005 -43 -20.56State Bank of Indore 0.000 75 - -0.018 -50 -

Old Private Sectors BanksFederal Bank -0.001 -500 - 0.003 -300 -24.14Jammu & Kashmir Bank 0.000 800 - 0.004 -400 -12.90Karnataka Bank -0.009 -92 - 0.002 -115 -South Indian Bank -0.001 -250 - 0.006 -200 -16.82Tamilnadu Mercantile Bank 0.006 60 7.15 0.006 -40 -6.67

New Private Sectors BanksHDFC Bank 0.004 250 23.02 0.008 250 2.32ICICI Bank -0.004 -75 - -0.001 0 -

Foreign BanksBank of America -0.015 17 - -0.007 350 -Bank of Nova Scotia -0.004 150 - -0.009 400 -Barclays Bank -28.310 162 - -3.027 -100 -Citibank 0.100 -30 -4.50 0.078 -93 -36.90Hongkong& ShanghaiBanking Corpn.

0.004 -83 -20.56 0.000 -133 -

JP Morgan Chase Bank -0.124 -65 - -0.050 -122 -Standard Chartered Bank 0.002 75 - -0.003 -225 -


170


BURDEN RATIOGroups Variables Mean SD Std Error Correlation


.002 .006 .002.954


Group 2


.003 .004 .003.864


.006 .016 .011

Group 3


.001 .005 .002.785


.004 .001 .000

Group 4


.000 .005 .004.564


.003 .006 .004


4.049 10.694 4.043.843



BURDEN RATIO


Group1


.001 .003 .001 .520Not

Significant

Group2


.009 .012 .008 1.118Not

Significant

Group3


.005 .004 .001 2.845 Significant

Group4


.003 .001 .000 7.000Not

Significant

Group5


3.618 9.553 3.610 1.002Not

Significant


171

Burden is the difference between non – interest expenditure and non – income

of the banks. It represents non – interest expenditure not covered by non- interest

income and is an important factor in determining the profitability of banks. The

difference between spread and burden determine the profit or loss of a bank. Thus, in

order to improve its profitability, the banks should endeavor to reduce its burden to

the minimum possible extent by exercising efficient and effective control over non –

interest expenses.

An analysis of the burden ratio was represented in Table 4.43.It could be

observed from the table that in the nationalized bank group(Group 1), the Bank of

India and the Bank of Baroda exhibited negative growth rate of -125 and -60.

Similarly, in the SBI & its Associates bank group (Group 2), the State Bank of India

(-43) and the State Bank of Indore revealed negative growth rates, thus indicating that

these banks were able to control its non - interest expenditure in an efficient manner.

As far as the old private sector banks (Group 3) are concerned, almost all the

five banks taken under study revealed negative CGR. The Jammu & Kashmir Bank

had the highest negative growth rate of -400 in its group. The other banks which were

able to restrict its non – interest expenditure were Federal Bank (-300), South Indian

Bank (-200) and Karnataka Bank (-115). With regard to the new private sector bank

group (Group 4), the HDFC bank registered a growth rateof250 in the IT enabled era.

Similarly, in the foreign bank group (Group 5), Standard Chartered Bank had the

highest negative growth rate of -225 followed by HSBC Bank(-133) and JP Morgan

Chase Bank(-122). Thus these banks were able to employ adequate control over its

non – interest expenditure and in turn the Burden ratio.

A statistical analysis of Burden Ratio had been presented in Table 4.44. The

correlation analysis revealed that their existed high positive correlation among all the

bank group under study, the highest being that of Nationalized bank group (Group 1)

with a correlation co- efficient of .954 followed by SBI & its Associates bank group

(.864) and foreign bank group (.843). Thus it was concluded that the introduction of

information technology had a positive impact on the indicator burden ratio.

172

The paired “t” test analysis for the indicator burden ratio was presented in

Table 4.45. The old private sector bank group (Group 3) had a significant ‘t’ value of

2.845. Similarly all the bank groups revealed significant ‘t’ values. Thus it was

concluded that the introduction of information technology had a significant impact on

all the bank groups

Thus it was concluded that in the nationalized bank group (Group 1), the Bank

of India and the Bank of Baroda exhibited negative growth rate of -125 and -60 , in

the SBI & its Associates bank group (Group2),the State Bank of India (-43) and the

State Bank of Indore (-50)revealed negative growth rates, in the old private sector

banks (Group 3) the analysis revealed that the Jammu & Kashmir Bank had the

highest negative growth rate of -400 in its group, in the new private sector bank group

(Group 4), the HDFC bank registered a growth rate of 250 in the foreign bank group

(Group 5), Standard Chartered Bank had the highest negative growth rate of -225. The

correlation analysis revealed that the Nationalized bank group (Group 1) had the

highest correlation co- efficient of .954. The paired “t” test analysis for the indicator

burden ratio revealed that the old private sector bank group (Group 3) had a

significant ‘t’ value of 2.845.

173

VII. PROFITABILITY RATIOTABLE: 4.46

PROFITABILITY RATIO


Bank



Value GrowthRate


CGR

Nationalized BanksBank of Baroda 0.004 400 - 0.010 100 25.89Bank of India 0.043 -38 -4.50 0.017 -94 -14.88Canara Bank 0.020 -33 -4.50 0.007 -92 9.64Indian Overseas Bank 0.008 63 4.71 0.006 -13 -6.67Oriental Bank of Commerce 0.023 -43 -4.50 0.010 -93 1.39Punjab Nationalized Bank 0.025 -82 -20.56 0.010 -73 14.81

SBI & its Associates BanksState Bank of India -0.713 -58 - -0.258 -101 -State Bank of Indore 0.081 -54 -6.67 0.082 -86 -20.56

Old Private Sectors BanksFederal Bank 0.019 -37 -2.27 0.017 11 20.22Jammu & Kashmir Bank 0.021 -85 -16.82 0.010 -42 25.89Karnataka Bank 0.034 -52 -8.79 0.011 -93 -22.37South Indian Bank 0.021 -73 -12.90 0.006 -58 -Tamilnadu Mercantile Bank 0.020 -38 -6.67 0.011 -67 -

New Private Sectors BanksHDFC Bank 0.017 -22 -2.27 0.015 -6 -0.22ICICI Bank 0.017 33 9.64 0.019 175 25.89

Foreign BanksBank of America 0.057 -9 - 0.061 133 14.81Bank of Nova Scotia 0.024 4 - 0.037 130 25.89Barclays Bank 29.248 168 - 3.186 -100 -Citibank -0.071 -42 - -0.043 -126 -Hongkong& ShanghaiBanking Corpn.

0.018 417 25.89 0.039 617 14.81

JP Morgan Chase Bank 0.366 -72 -20.56 0.152 -107 -Standard Chartered Bank 0.068 -31 14.88 0.043 85 14.81


174


PROFITABILITY RATIOGroups Variables Mean SD Std Error Correlation


.020 .013 .005.784


Group 2


.316 .561 .397.452


.088 .240 .170

Group 3


.023 .006 .002.123


.011 .003 .001

Group 4


.017 .000 .000.654


.017 .002 .002


4.244 11.026 4.167.999


TABLE: 4.48PAIRED SAMPLES TESTPROFITABILITY RATIO


Group1


.010 .011 .004 2.314Not

Significant

Group2


.228 .321 .227 1.004Not

Significant

Group3


.012 .007 .003 3.464 Significant

Group4


.000 .002 .002 .000Not

Significant

Group5


3.747 9.839 3.719 1.008Not

Significant


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



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


178


CREDIT -DEPOSIT RATIOGroups Variables Mean SD Std Error Correlation


49.75 5.19 2.12.900


Group 2


51.96 5.38 3.811.000


72.64 1.22 0.86

Group 3


49.02 7.53 3.37.866


64.68 2.88 1.29

Group 4


57.97 19.55 13.831.000


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



CREDIT -DEPOSIT RATIOGroups Variables Mean SD Std Error t value Sig

Group1


-17.71 2.57 1.05 -16.884Not

Significant

Group2


-20.69 4.16 2.95 -7.024Not

Significant

Group3


-15.66 5.24 2.34 -6.688Not

Significant

Group4


-19.07 0.46 0.32 -58.662Not

Significant

Group5


-7.37 28.78 10.88 -.677Not

Significant


179

The adjustment behaviour of the banking sector, consequent upon the

introduction of information technology could be studied both at macro as well as

micro level. The Macro behaviour could be best studied in terms of credit deposit

ratio.

The ratio analysis of the indicator Credit-deposit ratio was exhibited in table

4.49and the analysis revealed that of all the nationalized bank group (Group 1) taken

under study, a minimal growth rate was revealed in the IT enabled era. Except the

Oriental Bank of commerce, which had the highest growth rate of 180 in the IT

enabled era, all the other banks under study had a decline in the growth rate. The SBI

&its Associates bank group (Group 2) registered a higher credit-deposit ratio in IT

enabled era than compared to the partially computerized era. The State Bank of India

had a growth rate of 43 and in case of State Bank of Indore the growth rate was 30.

The State Bank of India being the government treasurer had a marginal increase in the

overall credit- deployment during the IT enabled era.

The ratio analysis of Credit-deposit ratio of the old private sector bank group

(Group 3) revealed that in the IT enabled era, the Jammu & Kashmir Bank recorded

the highest growth rate of 228. In the old private sector bank group all the other banks

revealed minimal growth rate thus indicating lower rate of credit deployment by these

banks. As far as the new private sector bank group (Group 4) is concerned, the ICICI

Bank had the highest credit deposit growth of 154 and the other bank in its group, the

HDFC bank showed a growth rate of 118, in the IT enabled era. It was further

revealed from the table that in the foreign Bank group (Group 5), the Barclays Bank

had outperformed all the other banks in its group with a growth rate of 471, followed

by HSBC Bank (129) and Citi Bank (101) with an exception of these banks, the other

banks in the foreign bank group revealed a low growth rate. The Barclays Bank had

the highest CGR of 151.18 in the IT enabled era.

The correlation analysis for the indicator Credit-Deposit ratio was exhibited in

Table 4.50. The SBI &its Associates bank group (Group 2) and new private sector

bank group (Group 4) revealed highest correlation value of 1. The analysis further

revealed that almost all the rest of the bank group under study had high positive

correlation. Thus it could be concluded that information technology had a positive

impact on the credit deployment by banks in the IT enabled era.

180

It was evident from Table 4.51 that all the five bank groups under study

revealed and insigficant ‘t’ value. The RBI had been modifying the SLR and CRR

from time to time . But it was found that during the study period the bank groups did

not reveal a satisfactory relation between credit and deposits.

Thus it was concluded from the ratio analysis that of the nationalized bank

group (Group 1), the Oriental Bank of commerce, had the highest growth rate of 180

in the IT enabled era, in the SBI & its Associates bank group (Group 2), the State

Bank of India had a growth rate of 43 and the State Bank of Indore had a growth rate

of 30. Of the old private sector bank group (Group 3), the Jammu & Kashmir Bank

recorded the highest growth rate of 228. In the new private sector bank group (Group

4), the ICICI Bank had the highest credit deposit growth of 154. The correlation

analysis for the indicator Credit-Deposit ratio revealed that the SBI & its Associates

bank group (Group 2) and new private sector bank group (Group 4) had highest

correlation value of 1.

181

II. INVESTMENT-DEPOSIT RATIO

TABLE: 4.52

INVESTMENT-DEPOSIT RATIO


Bank



Value GrowthRate


CGR

Nationalized BanksBank of Baroda 40.09 45 4.71 32.09 40 -10.87Bank of India 36.16 8 1.62 30.77 24 -6.67Canara Bank 48.74 24 2.09 34.74 4 -4.50Indian Overseas Bank 50.23 5 2.32 32.04 33 -6.67Oriental Bank of Commerce 50.23 81 -2.05 32.04 49 -4.50Punjab Nationalized Bank 45.88 101 -0.20 34.93 36 -8.79

SBI & its Associates BanksState Bank of India 51.54 14 2.32 39.79 27 -8.79State Bank of Indore 52.12 10 2.32 31.82 40 -6.67

Old Private Sectors BanksFederal Bank 41.17 7 -0.13 36.39 15 0.69Jammu & Kashmir Bank 41.83 67 7.15 35.01 20 -6.67Karnataka Bank 47.48 8 2.32 40.19 8 -0.68South Indian Bank 42.86 17 0.92 31.40 18 -1.14Tamilnadu Mercantile Bank 47.74 26 2.32 38.02 21 -8.79

New Private Sectors BanksHDFC Bank 65.30 11 -1.82 45.68 42 -4.50ICICI Bank 61.79 48 9.64 47.69 11 -0.68

Foreign BanksBank of America 64.58 114 9.64 89.68 116 0.46Bank of Nova Scotia 37.78 45 7.15 51.27 85 1.15Barclays Bank 225.22 149 25.89 175.20 102 -36.90Citibank 39.43 24 -2.27 42.83 11 4.71Hongkong& ShanghaiBanking Corpn.

58.68 110 2.32 47.10 78 -

JP Morgan Chase Bank 203.06 84 - 157.98 108 25.89Standard Chartered Bank 66.11 130 4.71 38.60 96 -4.50


182

TABLE: 4.53PAIRED SAMPLES STATISTICSINVESTMENT-DEPOSIT RATIO



45.22 5.86 2.39.461


Group 2


51.83 0.41 0.291.000


35.81 5.64 3.99

Group 3


44.22 3.16 1.41.675


36.20 3.31 1.48

Group 4


63.55 2.48 1.76-1.000


46.69 1.42 1.01


99.27 79.53 30.06.960



INVESTMENT-DEPOSIT RATIOGroups Variables Mean SD Std Error t value Sig

Group1


12.45 5.30 2.16 5.761Not

Significant

Group2


16.03 6.04 4.28 3.749 Significant

Group3


8.01 2.61 1.17 6.873Not

Significant

Group4


16.86 3.90 2.76 6.109Not

Significant

Group5


13.17 28.97 10.95 1.203Not

Significant


183

The investment – deposit indicator revealed the banks investment in various

income earning avenues apart from credit deployment by commercial banks. A higher

ratio reveals that there could be a fall in credit deposit ratio of banks.

The analysis of the indicator investment-deposit ratio was reported in Table

4.52. It was clear from the analysis that in the Nationalized bank group (Group 1), the

Oriental Bank of Commerce had the highest growth rate of 49 followed by the Bank

of Baroda (40) and Punjab National Bank (36). Almost all the banks in this bank

group revealed negative CGR indicating that these banks have diverted its deposits

towards credit deployment rather than investment. In the SBI & its Associates Bank

group, the State Bank of Indore had outperformed the State Bank of India with a

growth rate of 40.

An analysis of old private sector bank group (Group 3) revealed that both

Tamil Nadu Mercantile Bank and Jammu & Kashmir Bank had performed well with a

growth rate of 21&20 respectively, followed by South Indian Bank (18) and Federal

Bank (15). But the CGR of all banks revealed negative values. With regard to new

private sector banks (Group 4), the HDFC Bank revealed a highest growth rate of 42.

In the foreign bank group (Group 5), the Bank of America had the highest growth rate

in investment – Deposit ratio of116, followed by JP Morgan chase Bank (108) and

Barclays Bank (102).

The correlation analysis (Table 4.53) of the indicator investment-deposit ratio

revealed that the foreign bank group (Group 5) had the highest positive correlation of

.960. Both SBI & its Associates Bank group (Group 2) and new private sector Bank

group (Group 4) had negative correlation co - efficient. Thus is could be concluded

that information technology had a positive impact on three of the bank groups (Group

2, Group 4, Group5) taken under study.

It could be inferred from Table 4.54 that the introduction of Information

Technology had a positive impact on SBI & its Associates bank group (Group 2) with

a ‘t’ value of 3.749. The other bank group which had a significant ‘t’ value was the

foreign bank group (Group 5) with a ‘t’ value of 1.203. The rest of the bank group

revealed insignificant values. Hence it could be concluded that bank group 2 & 5 had

184

significant impact an investment deposit ratio after the introduction of information

technology in the banking sector.

Thus from the ratio analysis it was concluded that in the Nationalized bank

group (Group 1), the Oriental Bank of Commerce had the highest growth rate of 49,

in the SBI & its Associates Bank group, the State Bank of Indore had a growth rate of

40, in old private sector bank group (Group 3) Tamil Nadu Mercantile Bank had a

growth rate of 21,of the new private sector banks (Group 4), the HDFC Bank revealed

a highest growth rate of 42. In the foreign bank group (Group 5), the Bank of America

had the highest growth rate in investment – Deposit ratio of 116. It could be inferred

from 4.54 that the introduction of Information Technology had a positive impact on

SBI & its Associates bank group (Group 2) with a ‘t’ value of 3.749.

185

III. Ratio of Net NPA to Net Advances

TABLE: 4.55

RATIO OF NET NPA TO NET ADVANCES


Bank



ValueGrowth


Rate CGR

Nationalized BanksBank of Baroda 4.73 12 -2.27 0.67 79 -30.81Bank of India 6.47 42 -6.67 1.25 84 -36.90Canara Bank 8.50 44 -8.79 1.53 87 -4.50Indian Overseas Bank 2.80 64 -12.90 0.80 83 0.11Oriental Bank of Commerce 2.80 100 - 0.80 79 -4.50Punjab Nationalized Bank 6.06 88 -25.86 0.43 98 4.71

SBI & its Associates BanksState Bank of India 5.55 38 -6.67 1.90 68 -7.95State Bank of Indore 4.71 100 - 1.10 92 -10.87

Old Private Sectors BanksFederal Bank 7.89 70 -12.90 0.77 97 -41.11Jammu & Kashmir Bank 1.69 429 17.48 1.03 393 0.92Karnataka Bank 5.19 188 17.48 1.33 43 -16.82South Indian Bank 7.53 102 -12.90 1.42 75 -33.93Tamilnadu Mercantile Bank 5.34 218 20.22 1.18 78 -36.90

New Private Sectors BanksHDFC Bank 0.54 65 -16.82 0.42 37 20.22ICICI Bank 2.81 112 41.25 1.53 106 12.20

Foreign BanksBank of America 0.57 - - 0.00 - -Bank of Nova Scotia 3.70 171 51.35 1.02 616 -Barclays Bank 4.89 975 - 1.69 80 -Citibank 0.97 69 44.54 1.50 130 23.02Hongkong& ShanghaiBanking Corpn.

1.24 41 -4.50 0.52 34 -

JP Morgan Chase Bank 0.00 100 - 1.41 100 -Standard Chartered Bank 1.76 88 -20.56 1.32 68 -0.06


186


RATIO OF NET NPA TO NET ADVANCESGroups Variables Mean SD Std Error Correlation


5.23 2.24 0.91.601


Group 2


5.13 0.59 0.42.000


1.50 0.57 0.40

Group 3


5.53 2.47 1.11.981


1.15 0.26 0.12

Group 4


1.68 1.61 1.14.000


0.98 0.78 0.56


1.88 1.77 0.67.396



RATIO OF NET NPA TO NET ADVANCESGroups Variables Mean SD Std Error t value Sig

Group1


4.31 2.02 0.82 5.233Not

Significant

Group2


3.63 0.03 0.02 1.815Not

Significant

Group3


4.38 2.48 1.11 3.946 Significant

Group4


0.70 0.82 0.58 1.207Not

Significant

Group5


0.81 1.64 0.62 1.307Not

Significant


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)


Bank



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


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


190


RATIO OF NET INTEREST INCOME TO TOTAL ASSETS (NET INTERESTMARGIN)



3.10 0.38 0.16.431


Group 2


3.10 0.38 0.27-1.000


2.69 0.16 0.12

Group 3


2.57 0.58 0.26.676


2.99 0.55 0.24

Group 4


2.68 1.29 0.921.000


3.14 1.58 1.12


3.17 0.99 0.37.605



RATIO OF NET INTEREST INCOME TO TOTAL ASSETS (NET INTERESTMARGIN)


Group1


0.43 0.38 0.15 2.799 Significant

Group2


0.42 0.54 0.39 1.078Not

Significant

Group3


-0.43 0.46 0.20 -2.092Not

Significant

Group4


-0.47 0.29 0.21 -2.268Not

Significant

Group5


-0.51 0.84 0.32 -1.595Not

Significant


191

The results of the ratio analysis of the indicator Ratio of Net Interest Income

to Total Assets was evident from Table 4.58 that in the IT enabled era, most of the

Nationalized bank group (Group 1), revealed a decrease in growth rate with regard to

interest income to total assets ratio. The Indian Overseas Bank and Oriental Bank of

commerce registered a growth rate of 47 and 37 and a CGR of -8.79% and -10.87%

respectively. As far as the SBI & its Associate Bank group (Group 2) are concerned,

the State Bank of Indore had the highest growth rate of 99.

In case of the old private sector Bank group (Group 3) the South Indian Bank

(736), Karnataka Bank (468) and Tamil Nadu Mercantile Bank (315) recorded higher

growth rate in its group. With regard to New Private sector Bank group (Group 4)

both HDFC Bank (32) and ICICI Bank (35) had performed equally well. In the

foreign Bank group, Barclays Bank registered the highest growth rate of 4738, which

was the highest among all the banks taken under study. It also had the highest CGR

of 25.89% followed by the Bank of America with a CGR of 20.22%. Majority of

other banks in Group 5 revealed marginal increase only.

The results of correlation analysis had been recorded in Table 4.59 with regard

to ratio of Net interest income to Total Assets. The impact of information technology

had a highest positive impact on new private sector bank group (Group 4) with a

correlation co – efficient of 1 followed by the Foreign bank group (Group 5) with a

correlation co – efficient of .825. It was concluded that the introduction of

information technology had a positive impact on majority of bank group under study.

Table 4.60 exhibited the analysis of paired‘t’ test at 5% level of significance

with regard to the indicator Ratio of Net interest income to Total Assets. It could be

understood from the analysis that the nationalized bank group (Group 1) had the

highest significant ‘t’ value of 2.799 followed by the SBI & its Associates (Group 2)

which had a significant value of 1.078. The other entire bank group had insignificant

values. Thus it was concluded that the introduction of information Technology had a

positive impact on Group 1 and Group 2 taken under study.

192

Hence it was concluded from the ratio analysis that, in the Nationalized bank

group (Group 1), the Indian Overseas Bank registered a growth rate of 47, in the SBI

& its Associate Bank group (Group 2), the State Bank of Indore had the highest

growth rate of 99. Of the old private sector Bank group (Group 3) the South Indian

Bank (736), recorded highest growth rate in its group. In the New Private sector Bank

group (Group 4) both HDFC Bank (32) and ICICI Bank (35) had performed equally

well. In the foreign Bank group, Barclays Bank registered the highest growth rate of

4738. The results of correlation analysis with regard to ratio of Net interest income to

Total Assets revealed that the new private sector bank group (Group 4) had the

highest correlation co – efficient of 1. The analysis of paired‘t’ test at 5% level of

significance with regard to the indicator Ratio of Net interest income to Total Assets

revealed that the nationalized bank group (Group 1) had the highest significant ‘t’

value of 2.799

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II. RATIO OF NON-INTEREST INCOME TO TOTAL ASSETS

TABLE: 4.61

RATIO OF NON-INTEREST INCOME TO TOTAL ASSETS


Bank



ValueGrowth


Rate CGR

Nationalized BanksBank of Baroda 1.43 78 9.64 1.24 7 -0.22Bank of India 1.71 29 7.15 1.26 17 4.71Canara Bank 0.99 400 20.22 0.94 147 -10.87Indian Overseas Bank 1.32 38 4.71 0.95 18 -4.50Oriental Bank of Commerce 1.32 90 12.20 0.95 4 -2.27Punjab Nationalized Bank 1.52 21 2.32 1.22 16 0.46


Old Private Sectors BanksFederal Bank 1.88 14 4.71 1.32 24 2.32Jammu & Kashmir Bank 1.31 66 9.64 0.68 23 14.81Karnataka Bank 1.88 386 25.89 1.38 173 -2.27South Indian Bank 1.72 687 58.48 0.87 687 -1.37Tamilnadu Mercantile Bank 1.56 14 -2.27 1.36 16 1.62

New Private Sectors BanksHDFC Bank 1.55 6 0.23 1.84 68 9.64ICICI Bank 2.01 13 2.32 2.23 17 -2.27

Foreign BanksBank of America 1.84 51 2.32 3.88 309 14.81Bank of Nova Scotia 1.80 15 -4.50 1.92 56 0.92Barclays Bank 4.88 952 25.89 7.92 231 -20.56Citibank 3.16 28 2.32 2.78 44 -2.27Hongkong& ShanghaiBanking Corpn.

2.57 129 2.32 2.80 137 -

JP Morgan Chase Bank 4.39 255 58.48 5.00 635 -2.27Standard Chartered Bank 2.78 32 -4.50 2.67 12 25.89


194


RATIO OF NON-INTEREST INCOME TO TOTAL ASSETS OF BANKS



1.38 0.24 0.10.809


Group 2


3.96 3.46 2.45-1.000


1.34 0.19 0.14

Group 3


1.67 0.24 0.11.691


1.12 0.32 0.15

Group 4


1.78 0.33 0.231.000


2.04 0.28 0.20


3.06 1.19 0.45.825



RATIO OF NON-INTEREST INCOME TO TOTAL ASSETS OF BANKSGroups Variables Mean SD Std Error t value Sig

Group1


0.29 0.15 0.06 4.849 Significant

Group2


2.63 3.66 2.59 1.015Not

Significant

Group3


0.55 0.24 0.11 5.207Not

Significant

Group4


-0.26 0.05 0.04 -7.286Not

Significant

Group5


-0.79 1.26 0.48 -1.658Not

Significant


195

The results of the ratio analysis of the indicator Non- interest income to Total

Assets ratio had been exhibited in Table 4.61. The analysis revealed that, in the

Nationalized bank group (Group 1), the Canara bank had registered the highest

growth rate of 147 with regard to the indicator non-interest income to total assets ratio

in the IT enabled era. Except Canara bank all the other banks revealed minimal

growth rate in its group. The fall in growth rate could be due to reasons like – growing

level of NPAs, market recession and growing competition from the private and

foreign banks. Similarly in the SBI and its Associates Bank group (Group 2) the State

Bank of Indore had a growth rate of 53 followed by the State bank of India with a

growth of 44 in the IT enabled era.

The Non – interest income to Total Assets ratio of almost all old private sector

Bank group (Group 3) revealed a marginal growth in the IT enabled era. The South

Indian Bank revealed the highest growth rate of 687 followed by the Karnataka bank

(173). The Jammu & Kashmir had the highest CGR of 14.81%. As far as the new

private sector Bank group (Group 4) was concerned the HDFC Bank had out

performed its counterpart ICICI Bank with a growth rate of 68 in the IT enabled era.

Among all the Foreign Bank group (Group 5) taken for study, the JP Morgan chase

Bank had the highest growth rate of 635 followed by Bank of America with a growth

rate 309. The Standard Chartered Bank had the highest CGR of 25.89%.

The results of correlation analysis were exhibited in Table 4.62. The analysis

revealed that the new private sector bank group (Group 4) had the highest positive

correlation of 1, followed by the Foreign bank group (Group 5) with a correlation co –

efficient of .825. Thus there was a positive correlation for majority of bank groups

which concluded that information technology has a positive impact on the indicator

Non-interest income to Total Assets ratio.

An analysis of paired‘t’ test at 5% level of significance (Table 4.63) revealed

that the introduction of information technology had a significant impact on

nationalized bank group (Group 1) which had a significant ‘t’ value of 4.849 followed

by a significant the SBI & its Associates bank group (Group 2) with a significant

value of 1.015. The rest of the bank groups also revealed in significant values with

regard to the indicator Ratio of Non-interest income to Total Assets. It could thus be

196

concluded that the banking sector had a significant impact on the indicator Ratio of

non – interest income to Total Assets.

Thus it was concluded from the ratio analysis that in the Nationalized bank

group (Group 1), the Canara bank had registered the highest growth rate of 147 in the

IT enabled era. In the SBI and its Associates Bank group (Group 2) the State Bank of

Indore had a growth rate of 53, in the new private sector Bank group (group 3) the

South Indian Bank revealed the highest growth rate of 687. As far as the new private

sector Bank group(Group 4) are concerned, the HDFC Bank had a growth rate of 68

in the IT enabled era. In the Foreign Bank group (Group 5), the JP Morgan chase

Bank had the highest growth rate of 635. The correlation analysis revealed that the

new private sector bank group (Group 4) had the highest positive correlation of 1, An

analysis of paired‘t’ test at 5% level of significance revealed that information

technology had a significant impact on nationalized bank group (Group 1) with a

significant ‘t’ value of 4.849

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III. RATIO OF OPERATING PROFITS TO TOTAL ASSETS

TABLE: 4.64

RATIO OF OPERATING PROFITS TO TOTAL ASSETS


Bank



Value GrowthRate


CGR

Nationalized BanksBank of Baroda 2.12 57 4.71 2.06 18 -2.27Bank of India 1.83 160 17.48 2.01 67 12.20Canara Bank 1.17 3529 58.48 1.60 1414 -18.71Indian Overseas Bank 2.83 26 12.20 1.95 5 -6.67Oriental Bank of Commerce 2.83 72 9.64 1.95 31 -10.87Punjab Nationalized Bank 2.13 145 12.20 2.34 89 2.32


Old Private Sectors BanksFederal Bank 2.15 290 25.89 2.83 330 7.15Jammu & Kashmir Bank 2.39 1224 41.25 2.02 780 7.15Karnataka Bank 2.27 196 20.22 2.19 104 -4.50South Indian Bank 2.01 287 - 1.82 175 -4.50Tamilnadu Mercantile Bank 2.49 449 25.89 3.02 319 -4.50


Foreign BanksBank of America 3.56 50 -8.79 5.66 57 23.02Bank of Nova Scotia 3.09 10 -4.50 3.11 64 17.48Barclays Bank 3.77 230 25.89 6.71 35 -20.56Citibank 4.04 149 7.15 4.30 192 7.15Hongkong& ShanghaiBanking Corpn.

2.74 73 -2.27 4.03 122 -

JP Morgan Chase Bank 0.47 25 - 6.18 77 1.62Standard Chartered Bank 4.00 39 4.71 4.16 41 7.15


198


RATIO OF OPERATING PROFITS TO TOTAL ASSETSGroups Variables Mean SD Std Error Correlation


2.15 0.63 0.26.416


Group 2


2.46 0.84 0.601.000


2.06 0.10 0.07

Group 3


2.26 0.19 0.08.476


2.38 0.52 0.23

Group 4


2.51 0.64 0.461.000


2.69 0.67 0.48


3.10 1.25 0.47-.274



RATIO OF OPERATING PROFITS TO TOTAL ASSETSGroups Variables Mean SD Std Error t value Sig

Group1


0.17 0.57 0.23 .711Not

Significant

Group2


0.40 0.94 0.67 .494 Significant

Group3


-0.11 0.46 0.21 -.550Not

Significant

Group4


-0.18 0.03 0.02 -9.000Not

Significant

Group5


-1.78 2.05 0.77 -2.303Not

Significant


199

Operating Profits are an important tool to measure the financial efficiency of

banking companies and the ratio of operating profits to total assets represents the

share of assets occupied by Net Profits. It was observed from Table 4.64 that the

Canara Bank was at the top with a growth rate of 1414, among all the banks in the

nationalized bank group (Group 1) in the IT enabled era. The Bank of India had the

highest CGR of 12.20%. As far as the SBI and its Associate Bank group (Group 2) are

concerned, the State Bank of Indore revealed the highest growth rate of 96.

An analysis of growth rate of old private sector Bank group (Group 3)

revealed that the Jammu and Kashmir Bank had outperformed all the other banks in

its group with a growth rate of 780 followed by Federal Bank(330) and Tamilnadu

Mercantile Bank(319). In the New private Sector Bank group (Group 4) both the

banks under study revealed only minimal growth rate. The HDFC bank had a growth

rate of 10 and a CGR of 2.32%, while the ICICI bank registered a growth rate of 2

and a CGR of 1.85%. On the other hand, in the Foreign Bank group (Group 5) the Citi

Bank had performed well with a growth rate of 192 followed by HSBC Bank (122).

The results of correlation analysis were depicted in Table 4.65and it was

inferred that, the new private sector bank (Group 4) had the highest positive

correlation co- efficient of 1. The SBI & its Associates bank group (group 2) and

foreign bank group (Group 5) had negative correlation co – efficient values. Thus it

was concluded that the introduction of information Technology had a positive impact

on three groups under study namely, Group 4, Group3, Group 1 with regard to the

indicator ratio of operating profits to total assets.

The results of the paired‘t’ test was depicted in Table 4.66 and it was proved

that the SBI & its Associates bank group (Group2) had the highest significant value of

.494. The other bank groups revealed insignificant ‘t’values at 5% level of

significance. Thus it is clear that the Nationalized bank group (Group1), the old

private sector bank group (Group 3), the new private sector bank group (Group 4) and

the foreign bank group (group 5) had to improve their operating profits to improve

their competency in the highly competitive banking sector.

200

Thus it was concluded from the ratio analysis of the indicator ratio of

operating profits to total assets that, in the nationalized bank group (Group 1) the

Canara Bank had a growth rate of 1414 in the IT enabled era. In the SBI and its

Associates Bank group (Group 2), the State Bank of Indore revealed the highest

growth rate of 96. In the old private sector Bank group (Group 3) the Jammu and

Kashmir Bank had growth rate of 780. In the New private Sector Bank group (Group

4) the HDFC bank had a growth rate of 10 and a CGR of 2.32%, of the Foreign Bank

group (Group 5) the Citi Bank had the highest growth rate of 192. The results of

correlation analysis revealed that the new private sector bank (Group 4) had the

highest positive correlation of 1. The results of the paired‘t’ test revealed that the SBI

& its Associates bank group (Group2) had the highest significant value of .494.

4.4 CONCLUSION OF DIAGNOSTIC ANALYSIS:

From the above diagnostic analysis it could be concluded that the introduction

of information technology had a positive impact on all the parameters taken under

study. Thus the performance of the entire bank groups had improved in the IT enabled

era.

201

4.5 EMPIRICAL STUDY ON CUSTOMERS’ PERCEPTION TOWARDS IT –

ENABLED SERVICES IN COIMBATORE CITY.

4.5.1 Introduction

The global financial scene is witnessing a substantial change and Indian

financial sector is influenced by these developments where banking is the most

triggered area. The recent transformation is basically because of influential changes in

global economic environment where innovative information technology, innovative

banking products/services, deregulation and liberalization etc. are the major driving

forces. These external forces and some internal forces influence the banks to adjust to

these changes. Therefore, Indian banks, especially private sector, have rapidly

introduced innovative technologies. Almost all banks have invested in establishing IT

systems especially e-services. Now, introduction of Information Technology is one of

the core strategies for banking developments. The major banks, who have

successfully implemented IT, are on the top of the emerging competitive markets

because IT further intensify competition, better efficiency of operations, risk

management, better customer relations etc. The first and foremost set of application of

IT that could benefit from technology advances, relates to payment system.

Traditionally, payments were made in cash but now various electronic based

payments through ATMs, Cards, EFTs, and ECS etc. have been slowly making their

appearance. IT as an enabler has broken all bounds of cost, distance and time and

hence, efficiency of the banks has been improved especially with quality

improvement, timely delivery of services at affordable cost.

The banks’ efficiency does not depend on banks performance only, but

customer is a king to make the banks successful in market. Today’s customer has

become more aware and hence, customer is the focal point of success of every

business. Moreover, there is enough evidence that the overall performance of a firm is

linked with customer satisfaction. According to numerous thinkers; customer is the

fundamental reason for the existence of business. Once good service is extended to a

customer, a loyal customer will work as an Ambassador to the bank and facilitate

growth of business. For delivering quality service, it is imperative to have customer

orientation as a culture in banks. The customer orientation builds long term

relationships resulting in customer satisfaction and cash flows to the banks. It is in

202

this context that customer service has to be analyzed and appropriate strategies drawn

up, not only to attract new customers, but also to retain existing ones, because

customer behaviour helps to judge the efficiency of the banks. The key success for IT

enabled serves lies in knowing the customers perception towards such services.

Therefore, it is important to understand about customer’s perception and how it

develops the behaviour of an individual.

Perception is very important facet of behaviour to evaluate anything about

products and services. S. Robbins defines that perception is a process by which

individuals organize and interpret their sensory impressions in order to give meaning

to their environment. In its simple sense, perception is understood as the act of seeing

what is there to be seen. But what is seen is influenced by the perceiver, the object

and the environment.

Research has adequately established that people perceive the world and

approaches the life problems differently. The opinion about a particular object differs

from person to person and more particularly people have different reactions to

different situations. The reason is that people behave on the basis of what they

perceive reality to be and not necessarily as what reality is. The difference in

perception of different people is mainly because of number of factors like social,

psychological, and demographic and so forth that influence the perception of an

individual. That is why perception is one of the most important psychological factors

affecting human behaviour. Customer perception has an important relationship with

customer to evaluate their satisfaction because customer satisfaction is a mental state

which results from the customer’s comparison of expectations prior to a purchase with

performance perceptions after a purchase.

4.5.2 Reliability test

In this context, the analysis results presented in this chapter is based mainly on

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

203

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.

4.5.3 DEMOGRAPHIC PROFILE OF RESPONDENT

TABLE 4.66 COMPOUND TABLES REPRESENTING DEMOGRAPHIC PROFILEOF RESPONDENTS

S.NoAge Group Frequency Percent

1 Less than 25 87 28.62 25 to 35 years 151 49.63 35 to 45 years 29 9.64 45 and above years 37 12.2

Total 304 100.0S.No Gender Frequency Percent

1 Male 197 64.82 Female 107 35.2

Total 304 100.0

S.No EducationalQualification

Frequency Percent

1 High school 74 24.32 Bachelor Degree 91 29.93 Master Degree 69 22.74 Doctorate Degree 70 23.0

Total 304 100.0S.No Occupational Status Frequency Percent

1 Professional 74 24.32 Business class 48 15.83 Industrialist 104 34.24 Service class 70 23.05 Agriculturist 8 2.66 Total 304 100.0

S.No Type of account Frequency Percent1 savings bank account 52 17.12 current account 91 29.93 overdraft account 29 9.54 cash credit account 86 28.35 loan account 46 15.16 Total 304 100.0

204

Table 4.66 explains the age group of the respondents. Out of the sample

respondents taken for study, 49.67 percent of them belong to the age group of 25 to 35

years, 28.28 percent of the respondents belong to the age group of less than 25 years,

12.5 percent of the respondent belong to the age group of 45 years and above and the

remaining 9.54 percent of the respondents belong to the age group of 35 to 45 years.

When concentrating on the Gender group of the respondents, it could be

understood that 64.8 percent of the respondents were male and the remaining 35.2

percent of the respondents were female

While analyzing the educational qualification of the respondents, it is clear

that 24.3 percent of the respondents have completed High school education, 29.9

percent of the respondents have completed Bachelor’s Degree, 22.7 percent of the

respondents have completed Post graduate degree and 23 percent of the respondents

have completed their Doctorate degree.

When observing the occupational status of the respondents it was understood

that 34.2 percent of the respondents were working in the service oriented jobs, 24.3

percent of them were professionals, 2.3 percent of them were agriculturists, 15.8

percent of them belong to business class, and the remaining 2.6 percent of them were

in other forms of occupation.

While taking into account, the type of account operated by the respondents.

29.9 percent of the respondents were operating Current account, 28.3 percent of the

respondents were operating cash/credit account, 17.1 percent of them were operating

savings bank account, 15.1 percent of them were operating loan account and the

remaining 9.5 percent of them were operating over draft account.

205

Figure 4.1 Chart showing age group of respondents

Figure 4.2 Chart showing Educational Qualification of Respondents

Figure 4.3 Chart showing Occupational Status of respondents

Figure 4.4 Chart showing gender of respondents

206

Figure 4.5 Chart showing type of account held by respondents

4.5.4 ANALYSIS OF THE SURVEY RESULTS

Table 4.67 Factors Considered for Selecting a Bank Communalities

S. No Factors Initial Extraction1. Availability Of More Products/Services 1.000 .470

2. Better Customer Relationship Management 1.000 .802

3. Convenient Location Of A Bank 1.000 .697

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

To Lose Information1837 289 213 70 46 2455 III

6. Time Saving 1253 188 188 36 56 1623 VSource: Primary Data.

Table 4.69 unveils the fact that, out of the various factors selected which are

encouraging the customers in preferring e-channels, the variable X4-Providning

209

Efficient Services has got the Ist rank with a total score of 3592 variable X3-Provide

Accurate Information has got IInd rank with a total score of 2588, the variable X5-

provide security for threats to lose information, the variable X2-Convenient

Accessibility, has got IIIrd and IVth rank respectively. The Vth and VIth ranks were

secured by the variables of X6-Time Saving and X1-Cost Effectiveness with a total

score of 1623 and 1307 respectively.

Thus it was concluded the respondents were encouraged to prefer the e –

channels because these channels provide efficient services and accurate information.

Figure 4.7 Motivational factors encouraging the customers to prefer particular e-

channels

Table 4.70 Level of satisfaction towards e-channel performance

S. no e- channels HS S NE DS HDS Total Rank1. ATM 70 588 111 208 2 979 VII2. Credit card 40 332 627 4 2 1005 V3. Debit card 380 164 342 72 37 995 VI4. Internet banking 260 123 128 64 50 625 VIII5. Mobile banking 50 716 219 80 2 1067 IV6. Smart card 890 184 228 8 4 1314 I7. Tele-banking 385 712 18 4 41 1160 III8. All e-channels 890 160 228 20 15 1313 II

Source: Primary Data.

210

From the above table 4.70, it could be understood that out of the various e-

channels, Smart Card has secured the Ist rank with a total score of 1314, All e-

channels has secured the IInd rank with a total score of 1313, Tele-banking has bagged

the IIIrd rank with a total score of 1160, Mobile Banking services has secured the IVth

rank with a total score of 1067, Credit Card and Debit card has bagged the Vth and

VIth rank with total scores of 1005 and 995 respectively. The VIIth and VIIIth rank

was secured by ATM and internet banking with total scores of 979 and 625

respectively.

It could be understood that Smart cards and all the other e – delivery channels

have predominantly occupied the minds of customers.

Figure 4.8 Level of satisfaction towards e-channel performance

4.71 Time taken by the banks to respond towards the customer services

S.no Services

Partially IT-oriented banks e-banks

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an30

day

s

No

resp

onse

Tot

al

1. Demand For Cash 4 40 180 80 304 180 36 40 48 304

2.To Encash BankDraft

2 56 200 46 304 204 30 70 - 304

3. To Deposit Cash 10 85 108 101 304 236 18 24 26 304

4.To Get New ChequeBook

15 98 76 115 304 113 126 46 19 304

5.To Purchase A BankDraft

20 156 68 60 304 183 46 58 17 304

6.To Open FixedDeposit Account

27 138 79 60 304 158 73 42 31 304

Source: Primary Data

211

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


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.

S. No Variables Un StandardizedCo-efficient

StandardizedCo efficient

T Value Sig

Beta Std. error Beta

(Constant) 3.610 0.196 18.413 1%1 ATM 0.875 0.014 0.891 62.553 5%2 Credit card 0.924 0.011 0.932 80.865 1%3 Debit card 0.924 0.025 0.932 80.967 1%4 Internet banking 0.176 0.048 0.118 3.657 1%5 Mobile banking -0.221 0.018 0.018 12.388 1%6 Smart card -0.157 0.042 0.042 -3.750 1%7 Tele-banking 6.931 0.026 0.003 0.270 NS

R value R2 value Degree of

freedom-v1

Degree of

freedom-v2

F value Sig

0.895 0.801 13 304 311.568 1% level

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

Total % of Variance Cumulative % Total% of

VarianceCumulative

%

123456789

10111213

4.123 31.714 31.714 4.123 31.714 31.7142.819 21.686 53.400 2.819 21.686 53.4001.716 13.201 66.601 1.716 13.201 66.6011.160 8.922 75.523 1.160 8.922 75.523.981 7.550 83.073.731 5.625 88.698.485 3.733 92.430.346 2.658 95.089.262 2.013 97.102.207 1.593 98.694.086 .663 99.357.056 .432 99.790.027 .210 100.000

Extraction Method: Principal Component analysis

215

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


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


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


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


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


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


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


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


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


99.63 301 .340

Total100.075 303


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


96.648 300 .322

Total 106.234 303

3 Debit card BetweenGroups

145.875 3 48.625 38.894 .000


375.059 300 1.250

Total 520.934 303

4 Mobile banking BetweenGroups

123.129 3 41.043 54.175 .000


227.279 300 .758

Total 350.408 303

5 Online banking BetweenGroups

24.727 3 8.242 16.595 .000


149.006 300 .497

Total 173.734 303

6 Smart card BetweenGroups

3.001 3 1.000 1.288 .279


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


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

224

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


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

225

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


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


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


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

S.No Major complaints Total Score Rank

1 Less facilities 1754 V2 Inappropriate operational activities 3428 IV3 Procedural rigidities 6356 I4 Rude staff behavior 4380 III5 Improper seating arrangements 5346 II


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It was observed from the above table that the factor X3 - Procedural rigidities was

the major complaint regarding e – channels with a total score of 6356, followed by the

factor X5 - Improper seating arrangements with a total score of 5346. The IIIrd rank

was secured by the factor X4 - Rude staff behavior followed by the factor X2 -

Inappropriate operational activities and the factor X1- Less facility with a least total

score of 1754.

Figure 4.16 Major complaints regarding e - channels

Table 4.88 Suggestions for the improvement of E-channels

S.No. Factors Mean Score Mean rank

1.ATM s, should be located at moreconvenient and easily accessible places

979 VI

2.Draft making facility through ATMs shouldbe introduced

1005 IV

3. e-cheques should be introduced 995 V

4.Fast network services should be provided tomake e-banking more popular

625 VII

5.Issue e-channels to the customers with lessertime

1067 III

6. Make e-channels more secure 1314 I

7.Service charges for e-channels should bereduced to make them more popular

1160 II

Table 4.88 explains the suggestions for the improvement of e-channels and it

was understood that, out of the various factors selected which were the suggestion

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given by the respondents, the factor X6 - Make e-channels more secure has secured

the Ist rank with a mean score of 1314, the factor X7 - Service charges for e-channels

should be reduced to make them more popular has secured the IInd rank with a mean

score of 1160, the factor X5 - Issue e-channels to the customers with lesser time has

secured the IIIrd rank with a mean score of 1067, the factor X2 - Draft making facility

through ATMs should be introduced has secured the IVth rank with a mean score of

1005, the factor X3 - e-cheques should be introduced has secured the Vth rank with a

mean score of 995, the factor X1 - ATM s, should be located at more convenient and

easily accessible places has secured the VIth rank with a mean score of 979 and the

factor X4 - Fast network services should be provided to make e-banking more popular

has secured the VIIth rank with a mean score of 625.

Figure 4.17 Suggestions for the improvement on e-channels

4.7 Conclusion of empirical analysis

Overall, survey results concluded that customers are not much aware about all

e-channels though they prefer ATMs due to cost effectiveness and convenient access.

Customers like better ebanks for prompt and innovative services. Even the majority

customers want to shift towards ebanks which is also an indication for dominance of

ebanks over partially IT-oriented banks. They favour introduction of information

technology for its positive impact on banks’ competence where IT plays a crucial role.

The key issues that the banks must widen awareness, give knowledge about new

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technology through presentation, material should be logical and in regional language.

Time is opportune for Indian banking sector to institute sound electronic system to

have strong hold on the market. No doubt, it requires profound investment but RBI

and the Government should come forward to facilitate the banks especially rural

banks to establish technically advanced infrastructure. At the time when the industry

is showing signs of picking up in transformation, banks should labors to enrich the

electronic infrastructure along with service quality because delivering quality services

is the survival factor for the banks in today’s changing environment. Ultimately,

serving the customers as per their predictive requirements is the robust policy to bring

the banks out of the woods.

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