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CHAPTER 4

You manage what you measure. Unfortunately, performance assessment systems seldom evolve as fast as businesses do. - Andrew Likierman, Dean, London Business School (Harvard

Business Review, October 2009)

103

4. Data Analysis and Interpretation

The present study attempts to analyze the financial performance of

sample commercial banks involved in mergers during the period 1994 to

2009. To evaluate the financial performance, statistical tools like ratio

analysis, mean, standard deviation and t-test have been employed.

4.1 Evaluation of post-merger Performance of select

commercial banks in India employing ratio analysis

approach

The financial performance of the 11 acquiring commercial banks

(constituting the sample) before and after the merger has been analyzed

below with the help of various financial ratios(Please refer to Table 4.1)

which characterize a commercial bank’s performance.

In order to test the validity of null hypotheses stated in chapter 1, the

following parameters/ratios have been selected to test the results of pre

and post -merger periods (Average of three years).

104

Table 4.1 Classification of financial ratios

Class Code Variable/Parameter

Business Parameters V1 Aggregate deposits

V2 Average working funds (AWF)

V3 Operating profits

V4 Net profits(NI)

Operational Parameters

V5 Total Debt to Net worth

V6 Interest income to AWF

V7 Net interest income to AWF

V8 Operating expenses to AWF

V9 Capital adequacy ratio

V10 Net interest Income to Average assets

V11 Operating expenses to total expenses

V12 Efficiency Ratio

Profitability

Parameters

V13 Operating profit to AWF

V14 Net profit to AWF

V15 Net Profit to average net worth

V16 Operating profit to average net worth

V17 Asset utilization(AU)

V18 Equity multiplier(EM)

V19 Net Interest Margin (NIM)

V20 Burden ratio

V21 Earnings per share(EPS)

V22 Price-Earnings(PE) ratio

Productivity

Parameters

V23 Business per employee

V24 Business per branch

V25 Operating profit per branch

V26 Operating profit per employee

V27 Assets per employee

V28 Loans and Advances per employee

V29 Net income per employee

Source: Author’s perspective

105

Table 4.2

Business Parameters (Rs in Crores)

Business Parameter Analysis: Pre-Merger and Post-Merger Mean

Parameter for acquiring banks

Pre-

Merger

(3-year avg)

Post-

Merger

(3-year avg)

t-statistic

(0.05 significance)

p- values

Aggregate Deposits 29759.265 62499.693 -4.548 0.000(s)

Average Working Funds(AWF) 37715.340 76153.234 -4.686 0.000(s)

Operating Profit 734.746 878.520 -0.347 0.262

Net Profit 270.958 655.168 -4.056 0.004(s)

Source: Results of data analysis

The significance of the each parameter/ratio3 is explained below by

plotting a graph of the mean parameter/ratio (on vertical axis) and the

relative time (in years) on horizontal axis.

Aggregate Deposits (AD):

Aggregate deposits include deposits from public (fixed, savings and

current) and deposits from banks (fixed and current). From a different

angle, aggregate deposits equal the total of all demand and time deposits.

A high deposit figure signifies a bank’s brand equity, branch network and

deposit mobilization strength.

3 All the parameter/ratio values used for plotting graphs (4.1 to 4.27) are averages over

three years before and after the merger year (financial year).

106

0

10000

20000

30000

40000

50000

60000

70000

80000

T-3 T-2 T-1 T0 T+1 T+2 T+3

Ave

rage

De

po

sits

(R

s. c

rore

s)

Relative Time (Yrs.)

Graph 4.1

Aggregate Deposits versus Relative Time

.

Source: Processed Data

Average Working Funds (AWF):

The average of the working funds at the beginning and at the close of an

accounting year. Working funds are total resources (total liabilities or

total assets) of a bank on a particular date. Total resources include

capital, reserves and surplus, deposits, borrowings, other liabilities and

provisions. A higher AWF shows a bank’s total resource strength. This

definition of working funds is in line with capital adequacy calculations

to include all resources, not just deposits and borrowings and is more

pragmatic.

107

Graph 4.2

Average Working Funds versus Relative Time


0

10000

20000

30000

40000

50000

60000

70000

80000

90000

100000

T-3 T-2 T-1 T0 T+1 T+2 T+3

AW

F (R

s. C

rs.)


108

0

200

400

600

800

1000

1200

1400

T-3 T-2 T-1 T0 T+1 T+2 T+3

Ave

rage

Op

era

tin

g P

rofi

t (R

s. C

rs.)

Relative Time(Yrs.)

Operating Profit (OP):

It is Net profit before provisions and contingencies. This is an indicator of

a bank’s profitability at the operating level. In other words, Operating

Profit is a measure of a bank’s operating efficiency.

Graph 4.3

Operating Profit versus Relative Time


109

Net Profit (NP):

This is profit net of provisions, amortization and taxes. Net Profit is the

basic indicator of a bank’s profitability.

Graph 4.4

Net Profit versus Relative Time


4.1.1 Analysis of Business Parameters:

It would be observed that there is significant difference between average

pre- and post-merger figures of Aggregate Deposits, Average Working

Funds (AWF) and Net Profits at 5% level of significance, while it is not so

in respect of Operating Profits(p-value=0.262). While the percentage

growth between average pre and post merger aggregate deposits, average

0

100

200

300

400

500

600

700

800

900

T-3 T-2 T-1 T0 T+1 T+2 T+3

Net

Pro

fit

(Rs.

Crs

.)


110

working funds and Net profits is 110%, 102% and 142% respectively, the

corresponding growth rate for Operating profit is only 19%, justified by p-

value of 0.262. In today’s intensely competitive and increasingly

deregulated financial markets, both the cost and amount of deposits with

the banks are crucial in maintaining a sustainable competitive

advantage.

The financial management implication of the two features of the deposits-

stability and low cost source of funds- makes them the preferred source

of funds by banks. All else being equal, banks with stronger deposit base

are more valuable than those with a weak deposit base. The above

advantages are reflected in the Net profit that has grown significantly

though the Operating profit has not shown such a high growth rate.

111

Table 4.3

OPERATIONAL PARAMETERS

Source: Results of data analysis; * denotes that the variable in question is significant

The significance of the each ratio is explained below by plotting a

graph between the average ratio (on vertical axis) and the relative time (in

years) on horizontal axis.

Total debt to Net worth:

This ratio is expressed as a number. The corresponding ratio in a

manufacturing company is termed as debt- equity ratio. A higher ratio is

a proof of bank’s ability to leverage its net worth effectively. Debt-Equity

Ratio is arrived at by dividing the total borrowings and deposits by

shareholders’ net worth, which includes equity capital and reserves and

surpluses less revaluation reserves and miscellaneous expenses not

Operational Parameter Analysis: Pre- Merger and Post Merger Mean Ratio for

acquiring banks

Pre-

Merger

(3 years

avg. %)

Post-

Merger

(3 year

avg. %)

t-statistic

(0.05

significance) p-values

Total Debt to Net Worth 91.682 112.272 -0.442 0.850

Interest Income to AWF 8.584 8.157 0.702 0.206

Net Interest Income to AWF 1.754 2.383 -0.898 0.924

Operating Expenses to AWF 3.695 3.387 1.842 0.005*

Capital Adequacy

Ratio(CAR) 9.550 11.362 -0.955 0.272

Net Interest Income to Assets 1.754 2.383 -0.898 0.924

Operating expenses to total

expenses 35.934 37.185 -0.545 0.049*

Efficiency Ratio 79.921 77.757 0.697 0.008*

112

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

T-3 T-2 T-1 T0 T+1 T+2 T+3

Tota

l Deb

t to

Net

wo

rth

Rat

io

(tim

es)


written off. This is one of the measures of capital adequacy under the

highly popular CAMEL Model, a world-renowned model for evaluating the

financial health of a bank.

Graph 4.5

Total Debt to Net worth versus Relative Time


113

Interest Income to AWF:

Expressed as a percentage, this ratio shows bank’s ability to leverage its

average total resources in enhancing its main stream operational interest

income.

Graph 4.6

Interest Income to Average Working Funds versus Relative Time


0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

T-3 T-2 T-1 T0 T+1 T+2 T+3

Inte

rest

Inco

me

to A

WF


114

Net Interest Income to AWF:

It is a measure of bank’s operational profitability as a percentage of

average working funds.

Graph 4.7

Net Interest Income to Average Working Funds versus Relative Time


0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

T-3 T-2 T-1 T0 T+1 T+2 T+3

Ne

t In

tere

st In

com

e t

o A

WF


115

Operating expenses to AWF:

The operating expense to AWF ratio explains the overall operational

efficiency of a bank. In fact, this ratio is one of the indicators of the

operating profitability of a bank.

Graph 4.8

Operating Expenses to Average Working Funds versus Relative Time


0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

T-3 T-2 T-1 T0 T+1 T+2 T+3

Op

era

tin

g Ex

pen

ses

to A

WF


116

Capital adequacy Ratio (CAR):

This ratio relates a bank’s core net worth to its risk weighted assets. This

ratio is an internationally accepted risk- driven measure of a bank’s

degree of capitalization. This ratio indicates the risk exposure of the

bank, the quality of assets and the capacity of the bank’s capital to

sustain the risk level. A higher ratio indicates that a bank is well

capitalized vis-à-vis its perceived risks. It is an excellent indicator of a

bank’s long term solvency. The minimum CAR prescribed by the RBI is

9%.

Graph 4.9

Capital Adequacy Ratio versus Relative Time


0

2

4

6

8

10

12

14

T-3 T-2 T-1 T0 T+1 T+2 T+3

Cap

ita

l Ad

eq

ua

cy R

ati

o %


117

Net Interest Income (NII) to Assets:

Net interest income is equal to the interest received minus the

interest paid. The NII when expressed as a percentage of earning assets

gives the NIM (Net interest margin) of the bank. This is an extremely

important measure in evaluating a bank’s ability to manage interest rate

risk.

Graph 4.10

Net Interest Income to Avg.Total Assets versus Relative Time


0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

T-3 T-2 T-1 T0 T+1 T+2 T+3

Net

Inte

rest

Inco

me

to A

vg.T

ota

l Ass

ets

Relative time (yrs.)

118

Operating expenses to total Expenses:

Operating expenses equals non-interest expenses. It is also called

overhead expense. This can be decomposed into components like

establishment expenditure etc which as a percentage of total overhead

expense indicate where cost efficiencies are being realized or where a

bank has a comparative disadvantage. Non-interest expenses vary

between banks and are a function of the composition of liabilities.

(Timothy W. Koch & S. Scott Macdonald, 2003)

Graph 4.11

Operating Expenses to Total Expenses versus Relative Time


0.27

0.28

0.29

0.3

0.31

0.32

0.33

0.34

0.35

0.36

0.37

T-3 T-2 T-1 T0 T+1 T+2 T+3

Op

era

tin

g ex

pen

ses

to T

ota

l Exp

ense

s


119

Efficiency Ratio:

Efficiency ratio measures a bank’s ability to control non-interest expense

relative to adjusted operating income. This is given by the formula

Efficiency Ratio = Non-interest expense/ (NII+Non-interest income)

Banks use this ratio to measure the success of efforts to control non-

interest expense while supplementing earnings from increasing fees. The

smaller the efficiency ratio, the more profitable is the bank, all other

factors being equal (Timothy W. Koch & S. Scott MacDonald, 2003).

Graph 4.12

Efficiency Ratio versus Relative Time


4.1.2 Analysis of Operational Parameters:

Of the eight operational parameters explained above, a significant

difference has been observed only in respect of three i.e average i)

Operating Expenses to AWF ii) Operating expenses to total expenses and

0.65

0.7

0.75

0.8

0.85

0.9

0.95

T-3 T-2 T-1 T0 T+1 T+2 T+3

Effi

cien

cy R

atio

(t

imes

)


120

iii) Efficiency ratio. It may be further observed that average operating

expenses to AWF ratio has declined from 3.67% to 3.39% (t=1.842,

p=0.005) in the post-merger situation. The other operating performance

ratio that has registered a marginal improvement is the efficiency ratio

which has declined from 79.92% to 77.76% (t=0.697, p=0.008) in the

post-merger situation. However, the average operating expenses to total

expenses ratio has slightly increased (from 35.93% to 37.18%) in post

merger period (t=-0.545, p=0.049). There is no significant difference in

respect of other parameters i.e. average i) Total Debt to Net worth ii)

Interest income to AWF iii)Net Interest income to AWF iv) Net interest

income to assets and v) Capital adequacy ratio, at 5% level of

significance.

These results suggest that commercial bank mergers in India have, on

balance, resulted in a slight decline in operating efficiency. The bank

mergers have also not significantly impacted the Interest and Net interest

income to Average Working Funds ratios. Net-interest income (NII) =

Interest income minus interest expense, highlights a few basic risks in

banking. It maps into interest rate risk, liquidity risk and prepayment

risk. (Joseph.S.Sinkey Jr, 2002). The efficiency ratio is quite popular and

measures a bank’s ability to control non-interest expense relative to

adjusted operating income [Non-interest expense/(NII+ Non-interest

income)]. Conceptually, it indicates how much a bank pays in non-

interest expense for one rupee of operating income. The smaller the

121

efficiency ratio, the more profitable is the bank, all other factors being

equal. (Timothy W. Koch & S. Scott MacDonald, 2003)

Table 4.4

PROFITABILITY PARAMETERS

Profitability Parameter Analysis : Pre-Merger and Post-Merger Mean

Ratio for acquiring banks

Pre- Merger

(3 year avg. %)#

Post- Merger (3

year avg.

%)#

t-statistic

(0.05

sig)

p-values

Operating Profit to

AWF 1.8 1.6 1.192 0.158

Net Profit to

AWF(ROA) 1 0.9 0.437 0.036*

Net Profit to Avg Net

Worth(ROE) 13.7 15.8 0.539 0.978

Operating Profit to

average Net Worth 30.7 27.7 0.685 0.495

Asset

Utilization(times) 10.1 9.1 2.07 0.211

Equity Multiplier(Times)

13.7 17.56 0.465 0.007*

Net Interest

Margin(NIM) 1.9 2.3 0.711 0.983

Burden ratio 0.8 1.6 1.217 0.64

Earnings per

Share(EPS)(Rs.) 9.23 19.65 2.47 0.036*

PE ratio** 7.26 9.83 1.80 0.105

Source: Results of data analysis; ** Valuation ratio; #unless stated

otherwise; * denotes the variable in question is significant.

The significance of the each ratio is explained below by plotting a



122

Operating Profit to AWF:

Operating profit is net profit before provisions and contingencies. This

ratio is a measure of a bank’s operating efficiency. Profitability of the

bank and also its ability to earn consistently can be easily determined by

its earning quality measures. The ratio PBDITATA (OP/AWF) measures

the effectiveness of the bank in employing its working funds to generate

profits. This measure also finds place in the world-renowned CAMEL

model generally adopted to evaluate the financial performance of the

commercial banks. CAMEL stands for Capital Adequacy, Asset Quality,

Management, Earnings Quality and Liquidity. Working funds is

computed as the average of total assets during the year. (For Indian

Bank this ratio was 2.61%, topping the list among PSBs in 2006-2007).

Graph 4.13 Operating Profit to AWF versus Relative Time


0.000

0.005

0.010

0.015

0.020

0.025

T-3 T-2 T-1 T0 T+1 T+2 T+3

Op

. Pro

fit

to A

WF

RELATIVE TIME (YRS)

123

Net Profit to AWF:

This ratio is a foolproof indicator of excellent utilization of resources and

optimum leveraging of funds.

Graph 4.14

Net Profit to AWF versus Relative Time


0.000

0.002

0.004

0.006

0.008

0.010

0.012

T-3 T-2 T-1 T0 T+1 T+2 T+3

NP

to

AW

F

RELATIVE TIME (YRS)

124

Net Profit to Average Net worth:

This ratio is the equivalent of the return on net worth ratio used in other

industries. It is a good indicator of profitability and return on

shareholder’s funds.

Graph 4.15

Net Profit to Average NW versus Relative Time


0.000

0.050

0.100

0.150

0.200

0.250

T-3 T-2 T-1 T0 T+1 T+2 T+3

NP

to

Avg

. N

W

RELATIVE TIME (YRS)

125

Operating Profit to Net worth:

This ratio is corollary to the NP/ANW ratio and another indicator of the

shareholder’s returns.

Graph 4.16

Operating Profit to ANW versus Relative Time


0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450

T-3 T-2 T-1 T0 T+1 T+2 T+3

Op

. Pro

fit

to A

NW

RELATIVE TIME (YRS)

126

Asset Utilization:

A bank’s ROA is composed of asset utilization (AU), the expense

ratio(ER) and the tax ratio. ROA = AU – ER—TAX where AU= Total

Revenue /Average total assets. The greater the AU and lower are ER and

TAX, the higher is the ROA.

Graph 4.17

Asset Utilization versus Relative Time


Equity Multiplier:

We have ROE= ROA * EM. A bank’s equity multiplier compares assets

with equity such that large values indicate a large amount of debt

financing relative to stockholders’ equity. EM thus measures financial

leverage and represents both a profit and risk measure. EM influences a

bank’s profits as it has a multiplier effect on ROA in determining a

0.000

0.020

0.040

0.060

0.080

0.100

0.120

T-3 T-2 T-1 T0 T+1 T+2 T+3

Ass

et U

tiliz

atio

n

RELATIVE TIME (YRS)

127

bank’s ROE. Financial leverage works in bank’s favor when the earnings

are positive, but the other side is that it also magnifies the negative

impact of losses. EM is also a risk measure because it reflects how many

assets can go into default before a bank becomes insolvent. A high EM

raises ROE when net income is positive but also implies a high solvency

or capital risk.

Graph 4.18

Equity Multiplier versus Relative Time


0

5

10

15

20

25

30

35

T-3 T-2 T-1 T0 T+1 T+2 T+3

Equ

ity

Mu

ltip

lier

(tim

es)


128

Net interest margin (NIM):

NIM is a summary measure of the net interest return on income

producing assets. Spread, which equals the average yield on earnings

assets minus the average cost of interest bearing liabilities, is a

measure of the rate spread or funding differential. These two measures

are extremely crucial in evaluating a bank’s ability to manage interest-

rate risk.

Graph 4.19 NIM versus Relative Time


Burden Ratio:

NIM and spread must be large enough to cover burden, loan loss

provisions, securities losses and taxes for a bank to grow profitably. The

burden ratio measures the amount of non-interest expense covered by

fees, service charges, securities gains, and other income as a fraction of

129

average total assets. The greater is this ratio, the greater the non-interest

expense exceeds non-interest income for the bank’s balance sheet size. A

bank is obviously better off with a smaller burden ratio, ceteris paribus.

Graph 4.20

Burden Ratio versus Relative Time


Earnings per share (EPS) and PE ratio

While the EPS has increased from Rs.9.22 to Rs.19.65 (over 100%) post-

merger and the rise is statistically significant, the Price-Earnings ratio

has increased marginally from 7.26 to 9.83 and the change is

statistically not significant.

4.1.3 Analysis of Profitability Parameters

It is observed that while three profitability ratios are significant, the

remaining seven are not at 5% level of significance. The ratios which

show significant difference in performance between pre and post-merger

130

situations are i)Net profit to AWF ii) Equity Multiplier and iii)the EPS.

While the ratio of Net profit to AWF (ROA) has shown a 10% decline from

1% to 0.90%, the Equity multiplier (EM) (Total assets/Total equity) and

the EPS have increased from 13.7 to 17.56. (t= -0.465, p=0.007) and

from Rs 9.23 to Rs 19.65 respecively. The increase in EM is psioitive and

significant. A high EM increases ROE when net income is positive but is

also indicative of a high solvency or capital risk.As regards EPS, it is

observed that while the EPS has risen by over 100% post-merger, the

valuation ratio PE has not kept pace with it in as much as it has

increased by only Rs.2.57 post-merger and the change is not significant.

The interest spread to AWF ratio , which shows how well a bank is

managing and matching its interest income and interest expenditure

effectively has increased only slightly by 0.40% (1.90% to 2.30%,

p=0.983). Spread management is critical in successful bank management

because of its impact on the bottom-line. Similarly, the operating profit

to AWF has also come down from 1.8% to 1.60%. The change in average

net profit to average net worth (ROE)(increase by about 2%) is also not

significant( p=0.978) as also the Operating profit to average net

worth.(p=0.495). The change in AU ratio is not significant in as much as

it has come down by just 1% (p=0.211). The changes in NIM and burden

ratios are not significant. While the Net interest margin (NIM), which is a

summary measure of the net interest return on income producing assets,

has not changed significantly (p=0.983), the burden ratio, has increased

131

from 0.8 to 1.6 (p=0.640) not indicating clearly towards improved

performance in the post-merger scenario.

Table 4.5

PRODUCTIVITY PARAMETERS

(Rs. in Crores)

Productivity Parameter Analysis: Pre-Merger and Post-Merger Mean for acquiring banks

Pre Merger

(3 years avg.)

Post

Merger

(3 year avg.)

t-statistic

(0.05 significance) p-values

Business Per

employee 2.750 4.828 -2.952 0.014*

Business per branch 48.085 76.351 -1.355 0.205

Operating Profit per

branch 2.581 2.502 0.120 0.907

Operating profit per

employee 0.142 0.179 -3.964 0.003*

assets per employee 2.254 5.199 -2.740 0.021*

Loans per employee** 0.937 2.209 -2.235 0.049*

Net income per employee .027 .034 -1.476 0.171

Source: Results of data analysis; **Loans & Advances per employee;

*denotes that the variable in question is significant.

The significance of the each ratio is explained below by means of a



132

Business per Employee:

This ratio indicates the degree of labor (employee) productivity of banks.

This reflects the contribution of employees towards the business growth

which in turn impacts the organizational growth.

Graph 4.21 Business per Employee versus Relative Time


0

0.5

1

1.5

2

2.5

3

3.5

T-1 T-2 T-3 0 T+1 T+2 T+3

Bu

sin

ess

Pe

r e

mp

loye

e

RELATIVE TIME (YRS)

133

Business per Branch:

This ratio indicates how well a bank’s branches are being managed and

reflects the degree of branch productivity of banks. The commercial

banks over the years have been mainly concentrating on deposit

mobilization and credit deployment activities. Of late there has been a

marked shift towards non-fund based activities to supplement the

income streams.

Graph 4.22

Business per Branch versus Relative Time


0

50

100

150

200

250

T-1 T-2 T-3 0 T+1 T+2 T+3

Bu

sin

ess

pe

r b

ran

ch

RELATIVE TIME (YRS)

134

Operating Profit per Branch:

This ratio indicates how well a bank’s branches are being managed and

reflects the degree of branch productivity of banks.

Graph 4.23

Operating Profit per Branch versus Relative Time


0

1

2

3

4

5

6

7

T-1 T-2 T-3 0 T+1 T+2 T+3

Op

era

tin

g P

rofi

t p

er

bra

nch

RELATIVE TIME (YRS)

135

Operating Profit per Employee:

This ratio indicates the degree of labor (employee) productivity of banks.

In a service industry like banking, human resources play a crucial role in

extending quality services needed for overall development and making

the banking profitable and enduring.

Graph 4.24

Operating Profit per Employee versus Relative Time


0

1

2

3

4

5

6

7

T-1 T-2 T-3 0 T+1 T+2 T+3

Op

era

tin

g p

rofi

t p

er

em

plo

yee

RELATIVE TIME (YRS)

136

Assets per Employee: (Self Explanatory)

Graph 4.25

Assets per Employee versus Relative Time


0

0.1

0.2

0.3

0.4

0.5

0.6

T-1 T-2 T-3 0 T+1 T+2 T+3

Ass

ets

pe

r e

mp

loye

e

RELATIVE TIME (YRS)

137

Loans and advances per employee: (Self Explanatory)

Graph 4.26

Loans& Advances per Employee versus Relative Time


Net Income per Employee (Self Explanatory)

Graph 4.27

Net Income per Employee versus Relative Time


0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

T-1 T-2 T-3 0 T+1 T+2 T+3 Loan

s p

er

em

plo

yee

RELATIVE TIME (YRS)

0

10

20

30

40

50

60

T-1 T-2 T-3 0 T+1 T+2 T+3

Net

inco

me

pe

r e

mp

loye

e

RELATIVE TIME (YRS)

138

4.1.4 Analysis of productivity parameters:

Out of the seven productivity parameters that have been considered for

analysis, the improvement in four of them has been found to be

statistically significant, post- merger. These are i) Business per employee

ii) Operating profit per employee iii) Assets per employee and iv) Loans

and advances per employee. The business per employee ratio has

increased from Rs.2.75 crores to Rs.4.828 crores (t = -2.952, p= 0.014)

i.e by about 75.55 % which is quite impressive and indicative of the

significant contribution made by the employees of the banks towards

business growth in the post-merger period. While the operating profit per

employee has grown from Rs. 0.142 crores to Rs. 0.179 crores (t= -3.964,

p=0.003) (by about 26.05%), the Assets per employee ratio has grown

from Rs. 2.254 crores Rs. 5.199 crores (by about 130.65% which is quite

overwhelming) and the increase is statistically signifcant. The Loans per

employee ratio has registered a massive increase of over 135% (from

Rs.0.937 to Rs. 2.209 crores) (t=-2.235,p=0.049). The other two

productivity ratios which have shown an increase are i) Business per

branch(BPB) and ii) Net income per employee. The increase in BPB is not

significant possibly because of the speed of branch expansion to meet the

competition and enhance the reach & the need to meet regulatory

requirements of the RBI in the post liberalization/merger period. The

marginal decline in operating profit per branch can also be attributed to

these factors. The net income per employee ratio has increased

139

marginally from 0.027 to 0.034 possibly due to the fact that growth in NI

(PAT) has not kept pace with the increase in the number of employees.

On balance it can be concluded, that the productivity of commercial

banks has shown a healthy increase in the post-merger period.

4.2 Evaluation of post-merger efficiencies of select commercial banks in India using Data Envelopment

Analysis (DEA) approach

The impact of mergers on the Technical (TE= crste), Pure Technical

(PTE= vrste) Scale (SE-se), Cost(X-or CE) and Profit (PE)

efficiencies(Annexure-B) of the acquiring Indian commercial banks is

investigated below, merger-wise. The tables 4.6 to 4.21 summarize DEA

TE, PTE, SE, CE and PE scores for 6 public sector and 2 private sector

commercial banks as acquiring banks in the respective commercial bank

mergers constituting the sample. This could help shed some light on the

sources of inefficiency of the Indian banking sector in general as well as

to differentiate between the public and private sector banks in terms of

their relative efficiencies. DEA analysis has been conducted using the

computer program (DEAP version 2.1) written by Professor Tim Coelli

(1996). This program has been used to construct DEA frontiers for the

calculation of various efficiency scores and also for the calculation of

Malmquist Total factor productivity (TFP) Indices.

140

DEA data analysis

Table 4.6 Oriental Bank of Commerce (OBC) -Bari Doab Bank (BDB) Merger

(Technical Efficiency)

Total Sample

Year Pre-merger

Merger Year Post-merger

Mean pre-

merger efficiency

Mean post-

merger efficiency

1995 1996 1997 1998 1999 2000 2001

Technical Efficiency MODEL1

TE 0.764 0.981 0.873 1.000 1.000 1.000 0.987 0.873 0.996

PTE 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

SE 0.764 0.981 0.873 1.000 1.000 1.000 0.987 0.873 0.996

Technical Efficiency MODEL 2

TE 0.422 0.598 0.739 0.728 0.570 0.621 0.725 0.586 0.639

PTE 0.885 0.935 1.000 1.000 1.000 1.000 0.863 0.940 0.954

SE 0.477 0.639 0.739 0.728 0.570 0.621 0.840 0.618 0.677

Source: Appendix A, Tables A1, A2

Table 4.7 Oriental Bank of Commerce (OBC) -Bari Doab Bank (BDB) Merger

(Cost & Profit Efficiencies)

Total Sample

Year Pre-merger


Mean pre-

merger efficiency

Mean post-

merger efficiency

1995 1996 1997 1998 1999 2000 2001

Cost(X-) Efficiency CE

0.884 1.000 1.000 1.000 1.000 1.000 0.960 0.961 0.987

Profit Efficiency PE

0.890 1.000 1.000 1.000 1.000 1.000 0.951 0.963 0.984


DEA model decomposes Technical Efficiency (TE) in two parts, one due to

Pure technical efficiency (PTE) and the other due to Scale efficiency (SE).

Pure technical efficiency refers to the firm’s (bank’s) ability to avoid waste

by producing as much output as input usage allows, or by using as little

141

input as output production allows. Scale efficiency refers to the ability of

the firm to operate at its optimal scale.(Coelli, 1998).

The above results indicate that the Oriental Bank of Commerce-Bari

Doab Bank Ltd merger led to an increase in mean post-merger TE and

SE of the acquiring bank (OBC) under both the Models, 1 and 2(see table

4.6). A similar improvement has been observed post-merger in regard to

the CE and PE also (see table 4.7). While the PTE has remained at 100%

all along under the Model1, it has gone up from 94.00% to 95.40% under

model 2 post-merger. The scale efficiency has increased from 87.30% to

99.60% under Model, 1 and from 61.80% to 67.70% under Model, 2. The

cost and profit efficiencies of the acquiring bank (OBC) have improved

from 96.10% to 98.70% and 96.30% to 98.40% respectively. The results

suggest that the acquiring bank has performed relatively well in

transforming expenditure into income under Model 1.This follows from

the mean TE scores under Model 1,which are 87.30% pre-merger and

99.60% post-merger. This also indicates that the acquiring bank has

reduced the input waste by 12.30% post-merger. The results compare

favorably with Chu and Lim (1998) where the average overall efficiency of

Singapore banks was found to be 95.30% during the period 1992-

1996.The results also compare well with the14%-25% average input

wastes exhibited by Indian commercial banks (Bhattacharyya et al, 1997)

and the study of Fukuyama (1993) on Japanese banks (14%). However,

142

under Model 2, the mean technical efficiency of the acquiring bank has

gone up by only 5.30% post-merger. While the PTE hovered around 95%

during the period under consideration, the mean SE which was 61.80%

per-merger had gone up to only 67.70% post-merger. It may therefore be

concluded that the primary cause of marginal increase in mean TE under

Model 2 was SE only and the acquiring bank was pure-technically fairly

efficient (95%) during the period under consideration as could be seen

from the above table. The merger has also resulted in a post-merger

increase of 2%- 2.50 %( from 96% to 98.5%) in both mean Cost(X-) and

Profit efficiencies for the acquiring bank(Models 3 and 4 respectively).

Large banks may be more X-efficient than small banks if they are better

able to attract and retain capable managers, and because they tend to be

located in highly competitive metropolitan areas where competitive

pressures are higher (Robert De Young, 1997). OBC’s X-efficiency at a

very high level of 98.50% post-merger, is somewhat in line with this

proposition. The mean profit efficiency of OBC (which was 96.30% before

merger) had gone up by about 2.50% to 98.40%, which is quite

impressive. However the marginal increase in PE of OBC may be

attributed to the very small size of BDB in comparison with that of OBC.

Hence the impact of merger on cost and profit efficiencies of the

acquiring bank OBC does not appear to be significant.

143

Table 4.8

Oriental Bank Of Commerce (OBC)-Global Trust Bank (GTB) Merger (Technical Efficiency)

Total Sample

Year Pre-merger


Mean pre-

merger efficiency

Mean post-

merger efficiency 2001 2002 2003 2004 2005 2006 2007


TE 0.987 0.975 1.000 0.995 1.000 1.000 0.961 0.987 0.987

PTE 1.000 0.984 1.000 1.000 1.000 1.000 0.961 0.995 0.987

SE 0.987 0.990 1.000 0.995 1.000 1.000 1.000 0.992 1.000


TE 0.340 0.420 0.613 0.591 0.744 0.804 0.915 0.458 0.821

PTE 0.484 0.461 0.638 0.600 0.746 0.820 0.921 0.528 0.829

SE 0.703 0.911 0.960 0.985 0.997 0.981 0.994 0.858 0.991


Table 4.9 Oriental Bank Of Commerce (OBC)-Global Trust Bank (GTB)Merger

(Cost & Profit Efficiencies )

Total Sample

Year Pre-merger


Mean pre-

merger efficiency

Mean post-



0.985 0.976 0.968 0.881 0.984 0.952 0.855 0.976 0.930


0.978 0.980 1.000 1.000 0.994 0.912 0.867 0.986 0.924


The table 4.8 clearly indicates that the mean TE of the acquiring bank

(OBC) under Model 1 has remained stable at 98.70% even after the

merger. The mean PTE has declined slightly from 99.50% to 98.70%

post-merger despite the increase in mean SE from 99.20% to 100%.

Under Model 2, there is a quantum jump in mean TE and mean PTEs of

the acquiring bank from 45.80% to 82.10% & from 52.80% to 82.90%

respectively post-merger. The mean SE of the acquiring bank OBC has

jumped from 85.80% to 99.10% post-merger under the Model 2.

144

The mean cost and profit efficiencies have declined by 4.60% and 6.20%

respectively. Hence the increased mean PTE has a significant role in

enhancing the mean TE under Model 2. The Cost and profit

efficiencies(Models 3 and 4 respectively) at a considerably high level of

around 98% before merger and at around 93% post-merger (the first two

years average being 96.50%) (See table 4.9) point to the superior

managerial capabilities displayed in running the organization. However,

the merger does not seem to have helped the acquiring bank (OBC) in

improving its mean X-efficiency or Profit efficiency.

OBC had very strong fundamentals. As on March 31, 2004 it had a

deposit base of Rs.35, 674 crore, advances amounting to Rs.19,681

crore, and total assets worth Rs.41,701 crore. Its gross non-performing

assets (NPAs) were Rs.1, 211 crore and it had no net NPAs. Its operating

profit was Rs.1, 533 crore. Its net profit for the said period was Rs.686

crore. Its investment to deposit ratio worked out to 47.08% and spread to

assets ratio stood at 3.55%. Both its business per employee at Rs.4.16

crore and profit per employee at Rs.5.10 crore were quite impressive.

Based on these statistics, one could have forecasted that OBC’s

operational efficiency would not be unduly affected post-merger. In fact it

consolidated its position in the southern and western parts of the

country by leveraging on the GTB’s branch net work, strong ATM base

and excellent customer service synergies. Further the merger added one

145

million retail deposit holders to OBC’s tally. While the profitability of the

acquiring bank (OBC) could have taken a hit to some extent on account

of additional NPA provisioning, the increase in technical efficiency could

be attributed to the fact that both banks had the same technology

platform, Finacle from Infosys, which facilitated the smooth transition

and integration of operations in good time.

Table 4.10

Bank Of Baroda (BOB)-Bareilly Corporation Bank Ltd (BCB) Merger


Total

Sample Year

Pre- merger

Merger

Year Post -merger

Mean

pre-

merger

efficiency

Mean

post-

merger

efficiency 1996 1997 1998 1999 2000 2001 2002

Technical

Efficiency

MODEL1

TE 0.824 0.882 0.939 0.93 0.969 0.938 0.951 0.882 0.953

PTE 1 1 1 1 1 0.988 0.991 1 0.993

SE 0.824 0.882 0.939 0.93 0.969 0.95 0.959 0.882 0.959

Technical

Efficiency

MODEL

2

TE 0.788 0.779 0.602 0.643 0.8 0.25 0.352 0.723 0.467

PTE 1 1 1 1 0.954 0.483 0.484 1 0.64

SE 0.788 0.779 0.602 0.643 0.838 0.518 0.727 0.723 0.694


Table 4.11

Bank Of Baroda (BOB)-Bareilly Corporation Bank Ltd (BCB) Merger


Total Sample

Year end

Pre merger Merger Year Post merger

Mean pre-

merger efficiency

Mean post-



0.945 0.974 0.967 0.977 1.000 1.000 0.958 0.962 0.986


0.945 0.935 0.948 0.962 0.990 0.999 0.950 0.943 0.980


146

The Bareilly- headquartered bank(BCB) established in 1927, had a Rs

307 crore deposit base and a Rs.344 crore asset base at the time of

merger(i.e for the financial year 1997-98). The bank’s net profit was

Rs.94.05 lakhs in 1997-98 as against Rs.25 lakh in the year before. The

bank despite two successive profit years, had recorded an accumulated

loss of Rs 3 crore. The rationale given by a senior executive of BOB for

the merger of BCB with BOB was that it (BCB) was not viable as an

independent unit. BCB’s Capital adequacy ratio (CAR) was as low as 3%

against the RBI stipulated CAR of 8%.

Post the merger, the mean TE and mean SE of the acquiring bank BOB

have increased by 7.10% and 7.70% respectively under Model 1.However

the PTE has declined by a marginal 0.70%. Under Model 2, there is a

steep decline in mean TE and PTE levels i.e of the order of 25.60% and

36% ( see table 4.10). The results therefore do not show convincingly that

the merger has resulted in improvements in TE and PTE scores of the

acquiring bank (BOB). However, the mean X-efficiency and Profit

efficiency scores have registered a slender increase in the range of 2%-4%

(see table 4.11). One way of looking at the results is that the target bank

is very small in size (in terms of assets and deposits) to make a

significant impact on the efficiency of the acquiring bank BOB.

147

Table 4.12

Bank Of Baroda (BOB)-Banaras State Bank(BSB) Merger (Technical Efficiency)

Total Sample

Year Pre-merger


Mean pre-

merger efficiency

Mean post-



TE 0.969 0.938 0.951 0.964 0.915 0.917 0.933 0.953 0.922

PTE 1.000 0.988 0.991 0.983 0.993 0.970 0.954 0.993 0.972

SE 0.969 0.950 0.959 0.980 0.921 0.945 0.978 0.959 0.948


TE 0.643 0.800 0.250 0.352 0.558 0.574 0.690 0.564 0.607

PTE 1.000 0.954 0.483 0.484 0.564 0.597 0.718 0.812 0.626

SE 0.643 0.838 0.518 0.727 0.989 0.961 0.961 0.666 0.970

Source Appendix A, Tables A1, A2

Table 4.13

Bank Of Baroda (BOB)-Banaras State Bank (BSB) Merger


Total Sample

Year Pre-merger


Mean pre-

merger efficiency

Mean post-



0.977 1.000 1.000 0.958 1.000 0.913 0.934 0.992 0.949


0.962 0.990 0.999 0.950 1.000 0.899 0.873 0.984 0.924


Banaras State Bank was the second beleaguered UP-based bank to be

merged with BOB, the first being the Bareilly Corporation Bank, in

accordance with the scheme of amalgamation drawn up by RBI under

Section 45 of the Banking Regulation Act. BOB gained 105 branches

across the country following the merger, taking its branch network to

over 2,500. As on March 31, 2001 BOB’s deposits accounted for Rs

148

53,985 crore, it had an advance portfolio of Rs.27, 420 crore and an

investment portfolio of Rs.19, 857 crore.

The asset base of BOB stood at Rs.62, 462 crore as on 31st March, 2001.

In contrast, BSB had assets worth Rs.1, 134 crore and its deposits,

advances and investments amounted to Rs.1, 031 crore, Rs.230 crore

and Rs.631 crore respectively. BSB had posted a net loss of Rs.13.38

crore as on March 31, 2001. As on the date of amalgamation 19th june

2002, BSB’s deposits were Rs. 1096 Crore and advances Rs. 151 Crore.

The bank had a total branch network of 105 of which 91 were located in

UP and Uttaranchal while that of BOB’s strength in those two states was

554 branches before the merger approval in June 2002.

DEA analysis(see table 4.12 ) indicates that under Model 1, the mean TE

of the acquirer had declined by 3.10% due to around 1%-2% decline in

mean PTE and SE. Under Model 2 (table 4.12), the mean TE of the

acquiring bank had improved by about 4.30% despite the decline in

mean PTE by 18.60% due to a massive increase in mean SE by over 30%.

This lends credence to the hypothesis that mergers can result in scale

and scope economies. While the mean cost efficiency declined post-

merger by about 4%, the mean profit efficiency declined by 6% (see table

4.13). This could be explained by the fact that the merger was more in

the nature of a rescue exercise under the mandate of the RBI, to salvage

an ailing bank and it is possible that the positive effects of merger from

the marginally increased size and reach would have been experienced by

149

the merged bank only in the long run in terms of increase in cost and

profit efficiencies.

Table 4.14 Union Bank Of India (UBI)-Sikkim Bank (SB) Merger


Total Sample

Year Pre -merger


Mean pre-

merger efficiency

Mean post-



TE 0.949 0.946 0.930 0.943 0.973 0.988 0.979 0.942 0.980

PTE 0.994 1.000 1.000 0.962 1.000 0.997 0.987 0.998 0.995

SE 0.955 0.946 0.930 0.980 0.973 0.991 0.992 0.944 0.985


TE 0.694 0.673 0.484 0.636 0.79 0.246 0.397 0.617 0.478

PTE 0.929 0.883 0.857 0.854 0.829 0.413 0.513 0.890 0.585

SE 0.748 0.762 0.565 0.744 0.953 0.596 0.775 0.692 0.775


Table 4.15 Union Bank Of India (UBI)-Sikkim Bank(SB) Merger


Total Sample

Year Pre-merger


Mean pre-

merger efficiency

Mean post-



0.968 0.977 0.951 0.931 0.973 0.975 0.970 0.965 0.973


0.968 0.963 0.948 0.925 0.967 0.967 0.962 0.960 0.965


Under the merger scheme Union Bank of India (UBI) was required to

absorb the accumulated losses of Sikkim Bank (SB) as well as their

total staff. SB’s entire loan outstandings of Rs.60 crore had turned bad.

Its net worth was negative at Rs.-40.11 crore. The only attraction to UBI

150

in the merger proposition was that Sikkim bank had 8 branches in the

North-East and this could give UBI the needed foothold in the North

Eastern region where it did not have a significant presence. On the other

hand, UBI was among the top public sector banks in India in terms of

business mix and customer profile, with a net profit of Rs.250.10 crore

for the financial year ended 1997-98.

It may be observed from the above table, that under Model 1(see table

4.14), the mean TE has increased by 3.8% which is accounted for by a

marginal increase of 4.10% in mean SE. The mean PTE remained high all

along at around 99.50%, an impressive feature in its own right. But

under Model 2 (see table 4.14), (inputs: Deposits and Employee

compensation and outputs: Loans and Advances & Non-interest income),

the mean Technical Efficiency (TE) had received a major hit, declining as

it did, by about 14% prompted by the decline in mean PTE by a

whopping 30.50%. However, the mean SE under Model 2, had gone up

by 8.30% and under the Model 1, it had increased by 4.10%, which may

be attributed to the impact of merger. The pre- and post-merger Cost and

profit efficiencies had remained stable at around 97%. Though the figure

appears to be healthy in itself, the absence of any increase in this regard

might be attributed to the fact that the target bank was a small and

ailing bank with just 8 branches that too in the North Eastern region of

India besides having accumulated losses leading to a negative net worth.

151

Table 4.16

Punjab National Bank (PNB)-Nedungadi Bank Ltd (NB) Merger (Technical Efficiency)

Total Sample

Year Pre-merger


Mean pre-

merger efficiency

Mean post-



TE 0.944 0.949 0.958 0.987 0.921 0.953 0.974 0.950 0.949

PTE 0.983 1.000 1.000 1.000 1.000 1.000 1.000 0.994 1.000

SE 0.960 0.949 0.958 0.987 0.921 0.953 0.974 0.956 0.949


TE 0.541 0.777 0.226 0.325 0.597 0.627 0.669 0.515 0.631

PTE 0.789 0.807 0.475 0.484 0.600 0.656 0.702 0.690 0.653

SE 0.686 0.963 0.475 0.671 0.994 0.955 0.954 0.708 0.968


Table 4.17

Punjab National Bank(PNB)-Nedungadi Bank Ltd(NB) Merger (Cost & Profit Efficiencies)

Total Sample

Year Pre-merger


Mean pre-

merger efficiency

Mean post-



0.969 0.976 0.950 0.996 0.962 1.000 0.965 0.965 0.976


0.961 0.976 0.950 0.999 0.963 1.000 0.939 0.962 0.967


Public sector Punjab National Bank (PNB) took over Kozhicode (Kerala)

based troubled Nedungadi Bank Ltd (NB) the oldest private sector bank

in Kerala, along with its 1,619 employees under a scheme of

amalgamation prepared by the RBI in the year 2003. The merger added

173 additional branches to PNB’s branch network taking it to around

4,000. Of the 173 branches of Nedungadi bank, 110 branches were in

152

Kerala with a pool of NRI accounts. Nedungadi Bank (NB) had a deposit

base of about Rs.1, 400 crore and advances of over Rs.750 crore as on

March 31, 2002. On the other hand, PNB’s deposits and advances figures

stood at Rs.66, 680 crore and Rs.34, 450 crore respectively. The merger

added only 2.24% to PNB’s business which was over Rs.1, 00,000 crore

at the time of merger.

The meagre addition of 2.24% to the acquiring bank (PNB)’s business

from the merger explains the around 1 % (relatively small) change in

mean cost and profit efficiencies of PNB post-merger. Under Model 1 (see

table 4.16), while the mean TE had not changed much, the mean PTE

change had placed the bank on the efficient frontier post-merger.

However, there was an insignificant decline in the mean SE of PNB to the

extent of 0.07%. Referring to Model 2 (see table 4.16), we find that the

mean SE had increased by a massive 26%, which would speak well of the

scale economies that are theoretically expected to result from the merger.

This had in turn resulted in an increase of mean TE of the acquiring

bank (PNB) by 11.60% despite a drop in mean PTE of PNB by 3.70%. The

marginal decline in mean PTE post-merger reflects the inability of the

merged bank in converting the deposits and employee potential into

Loans and Advances and Non-interest income (fee-based income) on a

substantial basis. It is observed from the table 4.17 that the cost and

profit efficiencies have increased from 96.5% and 96.2% respectively to

97.6% and 96.7% respectively post-merger indicating a marginal, though

153

positive, impact of the merger on the cost and profit efficiencies of the

acquiring bank(PNB).

Table 4.18 ICICI Bank (ICICIB)-Bank Of Madura (BOM) Merger


Total Sample

Year Pre-merger


Mean pre-

merger efficiency

Mean post-



TE 1.000 1.000 1.000 1.000 0.998 0.971 1.000 1.000 0.990

PTE 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

SE 1.000 1.000 1.000 1.000 0.998 0.971 1.000 1.000 0.990


TE 0.732 1.000 0.596 0.714 1.000 1.000 1.000 0.776 1.000

PTE 0.734 1.000 0.799 1.000 1.000 1.000 1.000 0.844 1.000

SE 0.996 1.000 0.746 0.714 1.000 1.000 1.000 0.914 1.000


Table 4.19 ICICI Bank(ICICIB)-Bank Of Madura(BOM) Merger


Total Sample

Year Pre-merger


Mean pre-

merger efficiency

Mean post-



1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000


1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000


The merger between ICICI Bank and Bank of Madura (BOM) was a

remarkable one. This merger was the first between an old generation

private sector bank and a new generation private sector bank.

154

Pre–merger status of ICICI Bank: Assets: Rs.12, 063 crore; Deposits:

Rs.9, 728 crore; Equity market capitalization: Rs.2, 466 crore; Capital

adequacy ratio: 17.59%; Branch network and extension counters: 106;

Net worth: Rs.1, 219 crore; Number of employees: 1,700; one of the most

tech-savvy and fastest growing private sector banks in the country.

Pre-merger status of Bank of Madura (BOM): Assets: Rs.3, 988 crore;

Deposits: Rs.3, 395 crore; capital adequacy ratio: 15.80%; Equity market

capitalization: Rs.100 crore; Branch net work: 263; Number of

employees:2,700;A 57-year old South India based private sector

commercial bank.

Synergies expected from the merger: ICICI Bank was looking at a

branch network of 350-400, which would have taken 4 to 5 years to

achieve, given the pace of branch expansion. This merger provided the

much needed network immediately and also provided opportunities to

ICICI Bank to spread its network to several other states. BOM had a

customer base of 1.20 million. Hence the merger enabled ICICI Bank to

have an aggregate customer base of 2.70 million on an asset base of

Rs.16,000 crore(providing the needed economies of scale and scope) in

addition to cross selling opportunities for assets and other products &

services, like cash management services. The merger was also expected

to be favorable to BOM shareholders in term of value creation besides

providing technology based and sophisticated banking services to the

customers.BOM looked at the merger favorably because size was a major

155

consideration in the highly competitive banking scenario emerging in

India in the aftermath of economic reforms launched by the Government

of India. Size (critical mass) was a necessity in the context of compliance

with the capital adequacy norms stipulated by the RBI and the risk

management measures to be put in place by the commercial banks as

mandated by the Basel committee.

It is observed from the above table that under Model 1(see table 4.18),

the mean TE came down by 1% due to the decline in mean SE by

1%.However, post the merger, while the mean TE came down by just 1%,

the PTE continued to remain at 100%, an impressive performance in

deed. Even under Model 2(see table 4.18), the mean TE, PTE and SE

remained at 100% level post the merger as was the case before merger.

Even the cost(X-) and profit efficiencies, remained at the level of 100%

(see table 4.19) post-merger. These facts clearly show that ICICIB was an

efficient bank (it was on the cost and profit frontiers) and could gainfully

exploit the synergies predicted before the BOM’s merger with itself.

156

Table 4.20

HDFC Bank (HDFCB)-Times Bank (TB) Merger (Technical Efficiency)

Total Sample

Year Pre-merger


Mean pre-

merger efficiency

Mean post-



TE 0.976 0.970 0.957 0.961 0.986 0.925 0.971 0.968 0.961

PTE 1.000 1.000 1.000 1.000 1.000 0.976 1.000 1.000 0.992

SE 0.976 0.970 0.957 0.961 0.986 0.948 0.971 0.968 0.968


TE 0.700 0.645 0.668 0.643 0.654 0.508 0.556 0.671 0.573

PTE 0.731 0.645 0.671 0.701 0.722 0.590 0.567 0.682 0.626

SE 0.958 0.999 0.996 0.917 0.906 0.861 0.981 0.984 0.916


Table 4.21

HDFC Bank (HDFCB)-Times Bank (TB) Merger (Cost & Profit Efficiencies)

HDFC BANK(HDFCB)-TIMES BANK(TB) MERGER

Total Sample

Year end

Pre-merger Merger Year Post-merger

Mean pre-

merger efficiency

Mean post-



* 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000


* 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000


*Efficiency could not be calculated for the values of input or output for the said period.

(Please refer limitations).

The takeover of the Times Bank (TB) by HDFC Bank (HDFCB) was

unique in the sense that it was the first merger deal between two new

generation private sector banks. In a milestone transaction in the Indian

157

banking sector, Times Bank Ltd promoted by Bennett, Coleman &Co

(Times Group) was merged with HDFC Bank effective February 26,

2000.The shareholders of the Times Bank received 1 share of HDFC

Bank for every 5.75 shares of Times Bank.

The merger with Times Bank had catapulted HDFC Bank into a different

league, providing it with greater muscle in terms of retail client base as

well as mid-market corporate clientele. The bank had nearly 8.5 lakh

retail accounts post-merger. While the lending focus continued to be on

top-end corporate clientele, it had an added advantage (diversification

benefits) of serving the mid-market clientele that came as a part of the

Times Bank baggage.

Times Bank had an asset base of Rs.3,274.46 crore;deposits:Rs.3011.18

crore, Capital adequacy ratio:9.97;Advances: Rs.1,311.90 crore; Fee

based income to total income ratio:24.58% and Credit-deposit

ratio:44%;Investment-deposit ratio:35% as on 31.3.1999.

HDFC Bank: The bank’s total assets increased almost three fold post-

merger to Rs.11, 656.14 crore. Pre-merger investment/deposit ratio:

58.23%; Assets: Rs.4349.96crore Deposits: Rs.2915.51crore

Advances: Rs 1400.56 crore; Paid-up capital: Rs.424.60 crore.

Synergies expected from the merger: As per the scheme of amalgamation

issued by the HDFC Bank to its shareholders, the following synergies

were expected to be realized from the deal:

158

Branch network to increase by over 50%

Increase geographical coverage and ATM numbers which allow multi-

branch access to retail clients.

Increase in retail customer base and improvement in product portfolio

Increase in shareholders’ wealth

Cost savings from centralized processing and scale and scope economies

Complementary business practices

Improved infrastructure facilities

While the mean TE under Model 1 (see table 4.20),declined slightly by

0.70% due to decline in mean PTE by 0.80%,the mean SE remained

steady at 96.80%( a healthy figure) post-merger. Under Model 2 (see

table 4.20), the mean TE, PTE and SE dropped by 9.80%, 5.60% and

6.80% respectively. Hence it would appear that the merger had not

improved the PTE and SE under Model 2, which involved conversion of

deposits &compensation to employees into Advances and Non-interest

income. Hence the merger could not leverage the resource base available

in terms of employee potential and deposits for the acquiring bank,

HDFCB. Coming to the Cost and Profit efficiencies (see table 4.21), they

had remained at 100% both pre and post merger which could be

construed as the hallmark of efficiency. The ability to sustain the cost

and profit efficiency post-merger could be attributed to the three fold

increase in size, increase in geographical and improved access to retail

clients through increased ATM numbers.

159

4.2.1 Analysis of Technological and Technical Efficiency changes

post-merger employing DEA Malmquist Productivity Index

Malmquist index of total factor productivity (TFPCH) examines whether

firms (banks) are using the resources efficiently to produce goods and

services, and if they are using the existing technology to produce goods

and services. Values greater than one means increases in productivity,

while values less than one indicate decreases in productivity over time.

Farrell et al (1992) decomposed this index into sub indexes measuring

changes in technical efficiency and changes in technology:

TFPCH= TEFFCH * TECHCH

The first term on the right hand side of the above equation represents the

change in technical efficiency (TEFFCH); and the second term is the

change in technology (TECHCH). A value greater than one means

increases in output technical efficiency, value less than one means

decrease and a value of one indicates no change. The second term

represents the technological change.

Using the data envelopment analysis computer program written by Coelli

(1996), the input oriented Malmquist Total Factor Productivity Change

(TFPCH) index has been computed.

160

The table 4.22 provides the summary of efficiency scores of mean

TEFFCH, TECHCH and TFPCH before and after merger of the merged

banks under Model 1.

Table 4.22

DEA Malmquist Productivity Index (TFPCH) (Model 1)

Merged banks(Acquiring bank

in brackets)

Mean pre-merger efficiency change

Mean post-merger efficiency change

TEFFCH TECHCH TFPCH TEFFCH TECHCH TFPCH

OBC-BDB(OBC) 1.096 0.867 0.943 0.996 0.983 0.979

OBC-GTB(OBC) 1.000 0.968 0.969 0.989 1.020 1.008

BOB-BCB(BOB) 1.060 0.933 0.990 1.008 0.987 0.995

BOB-BSB(BOB) 1.008 0.987 0.995 0.990 0.999 0.988

UBI-SB(UBI) 1.041 0.978 1.018 1.013 0.985 0.997

PNB-NB(PNB) 1.003 0.986 0.989 0.996 1.013 1.007

ICICIB-BOM(ICICIB) 0.998 0.987 0.985 1.011 1.010 1.021

HDFCB –TB(HDFCB) 1.000 0.992 0.992 1.000 0.980 0.980

Source: Data processed

It is observed from the table 4.22 that the Malmquist Productivity Index

(MPI) of the acquiring bank post-merger has increased substantially in

respect of to five out of eight bank mergers listed above under Model 1.

On further examination by decomposing the MPI into its components,

Technical efficiency change (TEFFCH) and Technological change

(TECHCH)(also known as Frontier Shift), it follows that the technical

efficiency change has declined post-merger in six out of eight cases and

in one case, it has remained stable. However, the rate of technological

change has increased in seven out of eight cases. Fare et al.(1992) define

that MPI>1indicates productivity gain; MPI<1 indicates productivity loss;

and MPI=1 means no change in productivity from time t to t+1. They also

161

state that a value of TECHCH greater than one indicates a positive shift

or technical progress where as a value of TECHCH less than one

indicates a negative shift or technical regress, and a value of TECHCH

which equals is indicative of no shift in technology frontier. From this

perspective, it may be inferred that the total factor productivity index

(TFPCH) has increased in five out of eight cases. Hence, on an average, it

is found that there has been an increase in MPI post-merger prompted

more by a technological frontier shift rather than technical efficiency

change.

The table 4.23 provides the summary of efficiency scores of mean

TEFFCH, TECHCH and TFPCH before and after merger of the merged

banks under Model 2.

Table 4.23

DEA Malmquist Productivity Index (TFPCH)(Model 2)

Merged banks(Acquiring bank in brackets)

Mean pre-merger efficiency change

Mean post-merger efficiency change

TEFFCH TECHCH TFPCH TEFFCH TECHCH TFPCH

OBC-BDB(OBC) 1.218 0.756 0.919 0.950 1.055 0.989

OBC-GTB(OBC) 0.882 1.651 1.059 1.276 0.881 1.067

BOB-BCB(BOB) 1.142 0.840 0.945 1.026 0.990 1.016

BOB-BSB(BOB) 1.026 0.990 1.016 1.158 0.993 1.140

UBI-SB(UBI) 1.044 0.963 0.938 0.817 1.803 0.992

PNB-NB(PNB) 1.186 1.001 1.177 1.288 0.855 1.075

ICICIB-BOM(ICICIB) 0.924 0.927 0.820 1.844 0.788 1.376

HDFCB –TB(HDFCB) 1.041 0.963 0.989 0.878 1.713 1.068

Source: Data processed

162

It is be observed from the table 4.23 that the total factor productivity

change (TFPCH) has increased post-merger in seven out of eight mergers

listed above. While the TEFFCH has increased in four cases, it has

declined in the remaining four cases. The technological frontier has

shifted positively in five out of eight cases. Hence the change in MPI post-

merger cannot be solely attributed to either technical efficiency change

increase or positive frontier shift change. Both have played their role.

4.2.2 Tobit Analysis

Model specification

To determine the influence of different factors on the efficiency estimated

using Data Envelopment Analysis (DEA), Tobit model has been employed.

Important variables considered for the analysis (based on the literature

review) exclude those considered as input and output variables for

determining the respective efficiencies i.e. Technical Efficiency, Cost

Efficiency(X-Efficiency) and Profit Efficiency using DEA methodology. The

variables chosen are both qualitative and quantitative in nature.

Qualitative variables are included to capture the effect of

merger/likelihood of merger and the ownership (the bank in question is a

public sector bank or a private sector bank).

163

Table 4.24

Selected Quantitative and Qualitative Variables

Predictor Symbol Description

Size(Market share of

business)

SIZE ln (Average total assets)

Return on net worth RONW Net income/Average total equity

Return on capital

employed

ROCE Net income/Average capital

employed

Capitalization SHFATA Shareholders fund / Average total

assets

Employee(Staff) cost COE ln(Compensation to employees)

Level of fee-based

activity

NIITI Non- interest income / Total

income Proxy for fee-based

activity

NIINI Non – interest income / Net income

Earning power or

Operating Profitability

PBDITATA Profit before depreciation, interest

and tax / Average total assets

A measure of fund’s cost

( Cost Of funds)

INTEXPTE Interest expenses/Total expenses

Significance of Bank

ownership

DSECTOR Public / Private sector

Significance of the year before merger year

DYBMY Year before merger year

Significance of the year after merger year

DYAMY Year after merger year

Source: Author’s perspective

4.2.2.1 Factors influencing the Technical Efficiency (TE) of

commercial banks in India (Model 1)

The following Tobit model has been employed to develop an average

relationship between the technical efficiency scores obtained under CRS

(Model 1) and the factors affecting it.

Y it = α + β1 SIZE it +β2 RONW it +β3 ROCE it + β4 ANWATA it

+β5COE it +β6 NIITI it +β7 PBDITATA it +β8 DSECTOR it + β9

DYBMY it +β10 DYAMYit + ε it

164

Y it (Dependent variable) = Technical efficiency score obtained by i-th

(acquiring) bank in time period t under CRS under Model 1.

SIZE it = Natural logarithm of average total assets of the i-th bank in time

period t.

RONW it = Return on net worth of the i-th bank in time period t.

ANWATA it = Capitalization (Shareholders equity ratio) of the of the i-th

bank in time period t. Capital refers to the Tier-I capital of the

commercial bank computed in accordance with the Basel norms

circulated by the RBI.

COEit = Natural logarithm of compensation to employees (salaries,

wages and bonus) paid by the i-th bank in time period t.

NIITI it = Non-interest income to Total income ratio of the i-th bank

in time period t.

PBDITATA it = Profit before depreciation, interest and tax to average

total assets ratio of the i-th bank in time period t.

DSECTOR it= 1 if i-th bank in time period t is a public sector bank

otherwise zero.

DYBMY it = 1 if the time period t represents the year before merger year

for the i-th bank, otherwise zero.

DYAMYit = 1 if the time period t represents the year after merger year for

the i-th bank, otherwise zero.

165

α, β1, β2…………………………………………………….. β10 are the regression parameters to

be estimated by using the Tobit regression model. And ε it is the error

term.

It is expected that all the explanatory variables except dummies for

public sector banks and the year before merger year will have positive

impact on the technical efficiency of the bank.

Results of Tobit Analysis

The results of the Tobit estimation using the STATA software are

presented in the Tables 4.25 to 4.28. In suggested specifications,

nominal values of the variables are used. As inflation has proportionate

impact on the values of input and output of the commercial banks, it is

not necessary to adjust the effect of inflation while computing the

efficiency scores using DEA methodology. For the efficiency estimation of

commercial banks, those banks with negative values of considered input

and output variables have been excluded. Quantitative explanatory

variables which characterize the commercial banks are considered along

with dummies (to capture over time the performance characteristics of

the commercial banks) in the suggested Tobit models. These

quantitative explanatory variables exclude those variables which are

considered as input and output variables in determining the efficiency in

the respective specification(Coelli et al,1998), as otherwise they would be

highly correlated with input and output variables of DEA leading to

biased results. In a similar fashion, in Tobit models, quantitative

166

explanatory variables have been transformed to remove the skewness in

the distribution and to accomplish this objective, logarithmic

transformation is applied on these variables. This transformation is also

intended to compress the difference among the values of these variables.

Four models (TE1, TE2, CE and PE) are presented in the following tables

4.25 to 4.284. For each of the four models used, the Prob > χ2 is zero,

implying that the set of independent variables considered together

satisfactorily explain the variations in the dependent variable.

The results of Tobit regression for Model 1 are presented in Table 4.25.

Table 4.25

Model 1: Technical Efficiency Explanatory

Variables Coefficient Std.

Error z-

statistic P>|z|

SIZE 0.076601 0.015877 4.82 0.0000*

RONW -4.48E-06 1.11E-05 -0.4 0.6870

ROCE 5.36E-05 7.24E-05 0.74 0.4590

COE 0.002341 0.019682 0.12 0.9050

NIITI 0.037467 0.013611 2.75 0.0060*

ANWATA 0.423263 0.110307 3.84 0.0000*

PBDITATA 3.256965 0.310189 10.5 0.0000*

DSECTOR -0.12803 0.03791 -3.38 0.0010*

DYBM 0.011918 0.028082 0.42 0.6710

DYAM 0.019394 0.029444 0.66 0.5100

Constant -0.00195 0.107372 -0.02 0.9860

No. of observations = 24*15 = 360

* Significant at 1% level

Prob > χ2 = 0.00000

Log likelihood = 282.72733

Source: Appendix A, Table A1

4 None of these four models indicate the existence of muti-collinearirty among the independent

variables.

167

A positive regression coefficient implies an efficiency increase whereas a

negative coefficient reflects a decline in efficiency.

Size has a highly significant positive effect on technical efficiency of the

bank indicating that larger banks on an average would be more

technically efficient, possibly because of the scale economies derived

from the bank merger. This is in line with the efficiency theory of Mergers

and Acquisitions (Weston 2000). Capitalization variable (ANWATA)’s

impact is positive and significant in explaining the technical efficiency.

Theoretically, better capitalized banks should enjoy a higher level of

efficiency (Sufian et al, 2007).

The variables (ratios) Non-interest income to total income (NIITI) & PBDIT

to average total assets (PBDITATA) are highly significant and the signs of

their regression coefficients are positive indicating that they have a

positive influence on the technical efficiency of the commercial banks.

The dummy variable, ownership of the bank (DSECTOR) is also highly

significant with its regression coefficient taking a negative sign. This

implies that the impact of owner ship on the technical efficiency of the

bank, though highly significant, is negative. To state differently, private

sector banks are more technically efficient than public sector banks,

which is in line with our earlier findings. The other explanatory variables

are, however, not significant in their contribution to the technical

efficiency of the bank under Model 1.

168

4.2.2.2 Factors influencing the Technical efficiency (TE) of

commercial banks in India (Model 2)


relationship between the technical efficiency scores obtained under CRS


Y it = α + β1 SIZE it +β2 RONW it +β3 ROCE it +β4 ANWATA it +β5

NIITI it +β6 PBDITATA it +β7 INTEXPTEit +β8 DSECTOR it +β9

DYBMY it +β10 DYAMYit + ε it

Y it (Dependent variable) = Technical efficiency score obtained by i-th

bank in time period t under CRS under Model 2.


period t.







in time period t.

INTEXPTEit = Interest expenses to total expenses ratio of the i-th bank

in time period t.



169


otherwise zero.






be estimated by using the Tobit regression model and ε it is the error

term.

Table 4.26

Model 2: Technical Efficiency

Explanatory

Variables Coefficient Std.

Error z-statistic P>|z|

SIZE 0.0718964 0.010502 6.85 0.0000*

RONW -0.0000591 2.62E-05 -2.26 0.0240**

ROCE 0.0003514 0.000179 1.96 0.0499**

INTEXPTE -0.2586989 0.191868 -1.35 0.1780

NIITI 0.0515453 0.032075 1.61 0.1080

ANWATA 0.5792328 0.262388 2.21 0.0270**

PBDITATA 1.805336 0.884924 2.04 0.0410**

DSECTOR -0.3070882 0.045147 -6.8 0.0000*

DYBM -0.1426229 0.06201 -2.3 0.0210**

DYAM 0.0199808 0.062278 0.32 0.7480

Constant 0.0866815 0.18823 0.46 0.6450


* Significant at 1% level;**Significant at 5% level

Prob > χ2 = 0.00000

Log likelihood = 95.350563 Source: Appendix A, Table A2

170



impact on the technical efficiency of the bank.

Analysis of results

Bank size has a positive and highly significant impact on it’s technical

efficiency computed under, Model 2 as was the case under Model 1.While

the Return on net worth (RONW) and Capitalization (ANWATA) ratios

have been found to be significant in determining the technical efficiency

of commercial banks under Model 2, the former has a negative impact

and the latter has a positive impact on the technical efficiency of the

banks as seen from the above table. That a better capitalized bank will

have a higher level of technical efficiency is in line with the theory. The

negative impact of RONW on technical efficiency though, is counter

intuitive; it can be treated as negligible because of the extremely small

value of the corresponding regression coefficient. The negative sign of the

regression coefficient RONW can be explained as under:

ROE=ROA X EM where ROA stands for the Return on Assets and EM

for equity multiplier or the financial leverage. The ROA of the banks is

around 1% which is very low. To earn a decent ROE the banks have to

increase the EM to say, 15% to 20%. According to finance theory,

financial leverage is a double edged sword. In good times it supercharges

the profit and in bad times its effect is just reversed. The profit does not

fall, but plummets. Further in their effort to achieve a healthy ROA if the

171

banks push up the EM, it might result in negative consequences to the

bank if the level of debt is sub-optimal, despite the increase in ROE or

RONW.

The dummy variable DSECTOR has been found to be significant but its

regression coefficient has a negative sign. This implies that public

ownership of a commercial bank though significant, is associated with a

decline in technical efficiency as was observed under Model 1. Though

the last explanatory variable DYBM is significant in explaining technical

efficiency under Model 2, its regression coefficient is negative. This can

be explained by the theories of merger motives which state that

acquisition of complementary resources(synergies) implying the absence

of certain resources which are crucial for continued survival and growth

is inferred from the bank’s characteristics before merger in the year

preceding the year in which the bank merger has taken place. The other

explanatory variables have not been found to be significant in their

contribution to the technical efficiency of the bank under Model 2.

4.2.2.3 Factors influencing the Cost Efficiency (CE) of commercial

banks in India


relationship between the Cost efficiency(X-efficiency) scores obtained

under CRS (Model 3) and the factors affecting it.

172

Y it = α + β1 SIZE it +β2 RONW it +β3 ROCE it +β4 ANWATA it +β5COE

it +β6 NIINI it +β7 PBDITATA it +β8 DSECTOR it +β9 DYBMY it +β10

DYAMYit + ε it

Y it (Dependent variable) = Cost efficiency score obtained by i-th bank in

time period t under CRS under Model 3.


period t.






COEit = Natural logarithm of Compensation to Employees (COE:

Salaries, wages and bonus) paid by the i-th bank in time period t.

NIINI it = Non-interest income to Total income ratio of the i-th bank

in time period t.




otherwise zero.



173





term.



impact on the Cost efficiency of the bank.

Table 4.27

Model 3: Cost Efficiency(X-Efficiency)

Explanatory variables Coefficient Std. Error

z-

statistic P>|z|

SIZE 0.0226994 0.0098182 2.31 0.021**

RONW 0.000044 0.0000115 3.83 0.000*

ROCE 0.0000123 0.0000656 0.19 0.852

COE -0.0081477 0.0098715 -0.83 0.409

NIINI 0.0419832 0.0187763 2.24 0.025**

ANWATA 0.0826686 0.1210511 0.68 0.495

PBDITATA 0.9785573 0.2722773 3.59 0.000*

DSECTOR -0.0494715 0.0213897 -2.31 0.021**

DYBM 0.0313207 0.0253443 1.24 0.217

DYAM 0.0426646 0.0259696 1.64 0.100

Constant 0.7367723 0.0664688 11.08 0.000



Prob > χ2 = 0.00000



174

Results analysis

It is observed from the table 4.27 that while SIZE, RONW, NIINI and

PBDITATA have positive and significant influence on the Cost

Efficiency(X-Efficiency) of the banks, RONW and PBDIATA have highly

significant influence on the CE of the banks. The dummy variable

DSECTOR is also significant but the negative sign of the corresponding

regression coefficient indicates that public sector banks are less cost

efficient as compared to the new generation banks in the private sector.

4.2.2.4 Factors influencing the Profit Efficiency (PE) of commercial

banks in India


relationship between the Profit efficiency scores obtained under CRS


Y it = α + β1 SIZE it +β2 RONW it +β3 ROCE it +β4 ANWATA it +β5COE

it +β6 NIITI it +β7 PBDITATA it +β8 DSECTOR it +β9 DYBMY it +β10

DYAMYit + ε it

Y it (Dependent variable) = Profit efficiency score obtained by i-th bank in

time period t under CRS under Model 4.


period t.


175





COEit = Natural logarithm of Compensation to Employees (Salaries,

wages and bonus) paid by the i-th bank in time period t.


in time period t.




otherwise zero.







term.

It is expected that all the explanatory variables except the dummies for

public sector banks and the year before merger year will have a positive

impact on the Profit efficiency of the bank.

176

Table 4.28

Model 4: Profit Efficiency

Explanatory variables Coefficient

Std. Error

z-

statistic P>|z|

SIZE 0.0164065 0.010074 1.63 0.1030

RONW 0.0000297 9.49E-06 3.13 0.0020*

ROCE 0.0000346 6.52E-05 0.53 0.5960

COE -0.0033713 0.010031 -0.34 0.7370

NIITI -0.0130974 0.012011 -1.09 0.2760

ANWATA 0.0720379 0.121646 0.59 0.5540

PBDITATA 1.0600000 0.269153 3.94 0.0000*

DSECTOR -0.0526502 0.021825 -2.41 0.0160**

DYBM 0.0332491 0.025484 1.3 0.1920

DYAM 0.0431395 0.026157 1.65 0.0990

Constant 0.7833794 0.069471 11.28 0.0000



Prob > χ2 = 0.00000



Analysis of the results

Both the explanatory variables RONW and PBDITATA are highly

significant and positive in their impact on profit efficiency. This is of

course intuitive. It is interesting to note that the variable Compensation

paid to Employees (COE) though not significant has a negative impact on

the profit efficiency, which is again intuitive. The last significant

explanatory variable is the dummy variable DSECTOR which has a

negative impact on the profit efficiency of the banks thereby indicating

177

public sector banks are less profit efficient as compared to the new

generation banks in the private sector like HDFC Bank. This is also

borne out of our observations in the context of other efficiencies referred

to above.

4.3 Marketing implications of commercial bank mergers

The data has been analyzed for significant association between the

variables influencing the customer perception of bank mergers (items in

the questionnaire) and the Demographic/Behavioral Variables (DBV) of

the respondents employing Chi-square test.

178

Relationship between Demographic (S.Nos: 1-4)/Behavioral Variables (S.Nos:5&6) (DBV) and customer perception of

Service quality in the face of bank mergers in India

The results of analysis are summarized below, item (variable) wise.

Cohran recommends that, while performing Chi-square test, at least 80%

of the expected cell count be five or more and that no expected cell count be

less than one(Cohran’s criterion)

Table 4.29 Relationship between DBV and customer perception regarding

mergers of commercial banks improving dependability of service

S. NO. DEMOGRAPHIC/BEHAVIORAL

VARIABLES(DBV)

CHI- SQUARE

VALUE

P- VALUE

1 Gender 14.353 0.001(s)

2 Age ( years) 7.720 0.102

3 Educational Qualification 6.81 0.146

4 Yearly Income(Rs. lakhs) 24.190 0.000(s)

5 Association with Bank (years) 29.154 0.000(s)

6 Monthly Transaction Frequency 9.17 0.057

Source: Appendix C, Tables C1 to C6

The table 4.29 provides a summary of broad perception of customer-

respondents on the impact of bank mergers on dependability of customer

service. It is observed that there is highly significant association between

the two sets in terms of three out of six demographic/behavioral

179

variables. These are gender, yearly income and the length of association

with bank (in years). There is however no significant relationship

between the opinions of individuals and their age based on the results of

the Chi-square test (p=0.102).This is in line with the observation of

Urban &Prat (2000) in their studies in US. A similar conclusion follows in

respect of the other variables Educational qualification and Monthly

transaction frequency also.

There is no significant difference between the within group male and

female respondents who are highly optimistic that merged banks serve

better. However, there is significant discrimination among the

respondents who are pessimistic. Only 2.5% of the female respondents

feel that there will be very insignificant change in the service quality of

merged banks. On the other hand, 19% of the male respondents perceive

the same. While among those respondents with an income level of Rs

1.50 to Rs.2.50 lakhs, only 45% opined that the service quality would

improve after merger, respondents from all other income classes

expressed in favor of bank mergers improving their service quality. It is

possible that middle income groups are more conservative in expressing

their views as compared to the younger and older generation

respondents.

Among those respondents who have more than 10 years of association

with the bank, 58.6% feel that the dependability of service of the banks

would improve after merger, whereas 20.7% of the people think otherwise

180

and the same percentage of people remain neutral in their perceptions. A

significant observation is that among the respondents with less than 2

years of association with the bank, 88.2% of the people feel that merged

banks provide more dependable service to the customers. It has also

been found that the frequency of monthly transactions is also a

significant study variable. 59.7% of those with higher number of bank

transactions are optimistic about improvement in dependability of the

banking service with mergers as compared to 14.9% who perceive the

improvement in dependability of service post-merger as insignificant.

These relationships are further investigated below.

Table 4.30

Relationship between DBV and customer perception regarding the

increase in number of banking services provided post-merger


VARIABLES(DBV)

CHI- SQUARE

VALUE P - VALUE

1 Gender 2.645 0.266

2 Age ( years) 12.00 0.017(s)

3 Educational Qualification Not calculated ***

4 Yearly Income( Rs.lakhs) 6.600 0.159

5 Association with Bank (years) 10.9 0.028(s)

6 Monthly Transaction Frequency 12.010 0.017(s)

***Cohran’s criterion not satisfied.


It is observed from the above table 4.30, that there is significant

association between the respondents’ opinions and their age, length of

their association with the bank and their monthly transaction frequency.

181

It is observed that 42.1% of the customers who have optimistic views in

this regard are of the age group of more than 45 years and 29.3% of the

people are of the age group of 30-44 years. Further, the impact of

customer’s association on their perception is very much on the expected

lines, as customers whose association with the bank is long would

generally be more knowledgeable about the great variety of products and

services offered by the bank and perceive the implications of bank

mergers on customer service equally well. It is however, interesting to

note that while of those with over 10 years association 75% strongly

opined that bank mergers result in increase in the number of services

provided, a much higher percentage i.e. about 83% to 93% of those

customers with an association of 10 years or less agreed with this view

strongly.

85% of the total respondents feel that a merger results in an increase

in the number of services provided, in contrast to the 5.7% of

respondents who think that a merger is unlikely to prompt an increase in

the number of new services. 9.3% of the respondents have a neutral

opinion on the issue. Another interesting observation is that out of the

84.3% of ihe respondents whose length of association influences their

perception as to the increase in number of services provided post-merger;

only 15% have more than 10 years of association with their respective

banks.

182

Table 4.31

Relationship between DBV and customer perception regarding the increase in range of banking products available post-merger

S. NO.

DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV)

CHI- SQUARE VALUE

P - VALUE

1 Gender 4.249 0.119

2 Age ( years) Not calculated ***

3 Educational Qualification Not calculated ***

4 Yearly Income( Rs.lakhs) 8.22 0.084

5 Association with Bank (years) Not calculated ***

6

Monthly transaction

Frequency 14.1 0.007(S)

***Cohran’s criterion not satisfied.


The above table 4.31 indicates significant association between the

respondents’ monthly transaction frequency and in perceiving the

implications of bank mergers on customer service in so far as the range

of products available is concerned.

88.1% of the respondents with a transaction frequency of more than 5

opine that the transaction frequency brings about new product

introductions by the banks in contrast to the 12% of the people who

either think this is unlikely or remain neutral. Also, a larger percentage

of respondents (65.4%) agree with this view compared to the 7.7% of

respondents who do not agree and 26.9% who remain neutral. 35% of

the customers who agree with the above mentioned view fall in the

income slab of up to Rs. 2.5 lakh, 25.7% of the people under Rs. 2.5-5

lakh and 19.3% come under less than Rs. 5 lakh income category. This

183

is an indication that more people from low income groups expect bank

mergers to give rise to new products.

Table 4.32 Relationship between DBV and customer perception regarding the

increased size of bank loan limits post-merger

S.

NO.

DEMOGRAPHIC/BEHAVIORAL

VARIABLES(DBV)

CHI- SQUARE

VALUE

P-VALUE

1 Gender 4.036 0.133

2 Age ( years) 5.69 0.0224

3 Educational Qualification 14.800 0.005(S)

4 Yearly Income(Rs. lakhs) 16.519 0.011(S)

5 Association with Bank (years) 14.158 0.028(S)



Mergers are supposed to enhance the ability of the banks to lend

more because of the enhanced financial and other resources of the

merged entity. Again we find(See Table 4.32) that the customers’

educational qualification in addition to their yearly income and length of

association with the bank are significant in influencing their perception

of the merged bank’s ability to offer larger loan limits. It is observed that

only 9.3% of the respondents strongly agreeing with the above view have

an association of more than 10 years with the bank. Further, while 40%

of the professionals strongly agree with the above opinion, 48% of the

post-graduates do not entertain this view. A widely varying view of

perception emerges in terms of variation in annual incomes as well.

184

Table 4.33

Relationship between DBV and customer perception regarding the increased accessibility to conventional bank services (not online)

post-merger

S.

NO.


VARIABLES(DBV)

CHI- SQUARE

VALUE

P- VALUE

1 Gender 14.078 0.001(S)

2 Age ( years)

Not

calculated ***


4 Yearly Income( Rs. lakhs) 36.810 0.000(S)


6 Monthly Transaction Frequency 14.144 0.028(S)


S***Cohran’s criterion not satisfied

In regard to the improvement in the accessibility to conventional

banking (not online) services post-merger, the above table 4.33 indicates

significant association between all the demographic variables except age

and the perception of customers. It is generally expected that

accessibility to conventional banking services(not online) would improve

after merger because of the merged entity’s increased human, financial

and technological resources which if deployed intelligently would enable

it to take it closer to this goal. However, it is found that the perception of

customers’ is influenced significantly by their Gender and other

demographic/behavioral variables.

65% of the male respondents and 55% of female respondents feel that

accessibility to conventional banking services improves significantly after

mergers. However, it is interesting to note that out of the 62.1% people

185

who agreed with this opinion, only 15.7% were female respondents and

46.4% were male respondents. While, a majority of the male respondents

(65%) agreed with the view, only 12% of them were strongly opposed to

this line of thinking. 69% of the respondents who had more than 10

years of association with the bank strongly agreed with the above view

while 10.3% felt otherwise. 61.2% of the respondents who had greater

frequency of transaction also agreed that access to banking services

would improve after merger. Another important observation is that out of

the total respondents, who agreed with the view, 17.1% were

professionals, 38.6% were post graduates and 6.4% were graduates.

62.9% of the respondents with income exceeding Rs. 5 lakhs also opined

in favor of this view.

Table 4.34 Relationship between DBV and customer perception regarding

improvement in Online Banking Services after merger

S.

NO.


VARIABLES(DBV)

CHI- SQUARE

VALUE

P-

VALUE

1 Gender 1.530 0.465

2 Age ( years) 6.000 1.990






Online banking services which require deployment of advanced and

latest technology & more financial resources are generally expected to

186

improve after the merger as the combined entities are better off because

of the possible realization of financial and technological synergies.

However, it is observed from the above table 4.34 that there is significant

association between customer’s educational qualification, yearly income

and length of association with the bank, and their perception of the

improvement in merged entity’s ability to provide improved online

banking services.

48.6% of the respondents opine that online banking improves with

mergers. It is observed that among these, 10% have more than 10 years

of association with the banks concerned, 16.4% have 6-10 years of

association and remaining 22.2% have less than 6 years of association. It

is interesting to note that as high as 60% of the respondents whose

transaction frequency is as low as once in a month have strongly

supported the view that online banking services will improve/expand

post-merger.

187

TABLE 4.35

Relationship between DBV and customer perception regarding the reduction in service time of bank after merger

S.

NO.


VARIABLES(DBV)

CHI-

SQUARE VALUE

P- VALUE

1 Gender 2.826 0.243

2 Age ( years) 13.50 0.009(S)


4 Yearly Income( Rs.lakhs) 13.541 0.035(S)




Again strong association has been observed (Table 4.35) between all the

demographic variables except gender and the perception of customers on

the reduction of service time post merger. It is generally expected that the

perception in this regard should not be influenced by the demographic

variables. But the above results of Chi-square test show significant

relationship between the two.

Out of the 29% of people who agree with the statement that service time

is reduced after merger, it is interesting to note that an overwhelmingly

high percentage i.e. 25% of them are in the age group of 18-29.This

shows that the younger generation is more optimistic in their perception.

Significant divergence of opinion has also been observed in terms of the

differences in the length of association, educational qualification,

yearly income and the frequency of transaction.

188

Table: 4.36

Relationship between DBV and customer perception regarding increased safety of deposits after merger

S.

NO.


VARIABLES(DBV)

CHI-

SQUARE VALUE

P-

VALUE

1 Gender 8.576 0.014(S)

2 Age ( years) 6.370 0.173






Safety of deposits is one of the primary factors about which all the bank

customers are concerned. It is normally expected that the safety of the

deposits of the customers will improve post merger because of the

enhanced capital base and other financial resources of the combined

entity. It is however found (Table 4.36) that there is significant

association between all the variables in question except age and the

perception of customers about the increased safety of their deposits post-

merger.

47.1% of the respondents are of the view that safety of deposits increases

after merger. Out of this, it is significant to note, that 37.1% are male

and only 10% are female. It is also interesting to note that 23.6% of the

respondents who share this view are post graduates. While about 67.5%

of the professionals have opined that safety of deposits is enhanced after

189

the merger, an equally high percentage i.e. 63% of the respondents

whose income is in the range of Rs 2.6-5 lakh have strongly veered

around this view. 60% of the respondents with frequency of transaction

as low as once in a month have also exuded optimism in this regard.

Table: 4.37 Relationship between DBV and customer perception regarding bank

mergers resulting in less competitive interest rates

S.

NO.


VARIABLES(DBV)

CHI-

SQUARE VALUE

P-

VALUE

1 Gender 0.809 0.667

2 Age ( years) 11.000 0.027(S)


4 Yearly Income( Rs.lakhs) 20.818 0.002(S)




It is hypothesized that bank mergers result in less competitive interest

rates in view of the reduction in number of banks that follows leading to

greater monopolistic tendencies which bring in their wake systemic

rigidities. But the same line of thinking is not visible across the different

demographic groups as seen from the above table 4.37. Educational

qualification, age, association with the banks and yearly income

differences seem to be strongly influencing the perception of customers in

regard to the movement of interest rates of the banks in the post-merger

scenario.

190

68.8% of the people, who are of the age group 45 years and more, opine

that bank mergers result in less competitive interest rates. This shows

that elderly respondents are less positive in their approach. While 78.9%

of the post-graduates and 61.5% of graduates entertain this perception, a

lesser percentage i.e about 45% of the professionals agree with this view.

Further as high as 68.2% of the respondents whose income level is Rs.

1.5-2.5 lakh (middle income groups) are supportive of this view. However,

on taking a comprehensive view, it is observed that only 47.90% of the

total respondents strongly entertain the opinion (52.10% do not

subscribe strongly to this view) that the bank interest rates will become

less competitive post-merger. The rationale for this view is that a larger

bank can better exploit financial synergies post-merger and will be in a

stronger position to raise cheaper funds which in turn will enable it to

quote more competitive rates of interest.

Table: 4.38

Relationship between DBV and customer perception regarding fee

reduction for different banking services post-merger

S. NO. DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV)

CHI-

SQUARE

VALUE

P - VALUE

1 Gender 12.700 0.002(S)

2 Age ( years) 5.650 0.227



5 Association with Bank (years) 1.094 0.982



191

Mergers are broadly expected to result in fee reductions following the

merged bank’s ability to achieve economies of scale and scope. It is

however observed that the respondents’ opinion in this regard is shaped

by their demographic segmentation. The gender, educational

qualification and income levels seem to be strongly associated with their

thinking in this regard. More educated respondents (and probably they

belong to higher income groups) might possess deeper understanding of

the implications of mergers and hence are probably in a better position to

look at the issue from other perspectives as well.

It is observed that 60.7% of the respondents think that mergers do not

result in fee reduction for different services. Out of this, 39.3% of them

are males. It is however significant to note that 75% of the female

respondents entertain a similar view.68.2% of the graduates 60% of the l

professionals and 78.4% of the respondents whose income is less than

Rs 1.5 lakh feel that fee reduction for banking services is not likely to

follow bank mergers.

192

Table 4.39

Relationship between DBV and customer perception regarding enhancement in Goodwill of the bank post-merger

S.

NO.


VARIABLES(DBV)

CHI-

SQUARE VALUE

P-

VALUE

1 Gender 0.067 0.967

2 Age ( years) 10.325 0.262






The thinking of the customers in regard to the enhancement of goodwill

post-merger appears to be very strongly influenced by their educational

qualification, annual income and length of association with the bank as

can be seen from the above table 4.39.

68.3% of the respondents who have an association of more than 10 years

opine that the goodwill of the bank is enhanced significantly after the

merger in contrast to only 19.5% of them who think otherwise and 12.2%

who have neutral views. While 67.9% of the post graduates and 50% of

the professionals are very optimistic about the improvement in the

goodwill of the bank, only 23% of the graduates strongly entertain this

view. In terms of the annual income levels also significant divergence in

opinion has been observed.

193

Table: 4.40

Relationship between DBV and customer perception regarding bank’s technological advancement after merger


VARIABLES(DBV)

CHI-

SQUARE VALUE

P - VALUE

1 Gender 14.706 0.001(S)

2 Age ( years)

Not

calculated ***





***Cohran’s criterion not satisfied


Banks are generally expected to be technologically sound after merger

because of the improved access/ability of the merged bank to access the

financial markets, state of the art technologies and human talent. The

above table 4.40 indicates that there is a strong relationship between the

demographic/behavioral variables, except age and the opinion of the

respondents as to the technological soundness of the bank post-merger.

While 61% of the male respondents are of the opinion that there will be a

rapid technological advancement in the banks after merger, only 13.6%

of the female respondents strongly entertain this view. Out of the 57.1%

of the respondents who share this view, 26.4% of them account for

maximum frequency in transactions (more than five per month). It is also

significant to note that a sizeable percentage i.e. 70% of the professionals

and 50% of the post-graduates are optimistic in this regard.

194

Table: 4.41

Relationship between DBV and customer perception regarding the increased availability of bank’s ATM services after merger

S. NO.


CHI-

SQUARE

VALUE

P- VALUE

1 Gender 0.122 0.941

2 Age ( years) 3.138 0.719






The above table 4.41 shows significant association between the yearly

income, length of association of the customer with the bank, monthly

frequency of transaction and the perception of the customers in regard to

improved customer service following an increase in number of ATMs that

may be made available post-merger.

84.6% of the respondents have opined that a rise in ATM number post-

merger is a positive sign in improving customer service. 90% of the

respondents who have up to 5 years of association with the banks feel

that a greater number of ATMs after merger help in improving customer

service. 87.8% of people having 6-10 years of association have also

expressed their agreement with this view. Again, 91% of the respondents,

whose frequency of transaction is more than ten, are also of the same

view. It is also significant to note that 91.5% of the respondents, whose

income is less than Rs 2.5 lakh (low income group), also share this

195

opinion. Low income groups generally draw small amounts and quite

often because of the nature of their needs. Their opinion is expected to

carry more weight in view of their more intense use of ATMs as compared

to others.

Table: 4.42


quality of bank’s customer relationship management (CRM) after merger

S.

NO.


VARIABLES(DBV)

CHI-

SQUARE VALUE

P-

VALUE

1 Gender 1.363 0.506

2 Age ( years) 1.670 0.796






A very strong association has been observed (Table 4.42) between the

variables, educational qualification, yearly income & the length of

association with the bank and the customer perception regarding the

improvement in Customer Relationship Management (CRM) after the

merger. However, the gender, age and the monthly transaction frequency

do not seem to significantly influence the opinions of the customers in

this regard.

196

58.5% of the respondents having an association of 6-10 years are of the

opinion that customer relationship management gets better after merger,

compared to only 9.1% of them who think otherwise. As regards the

impact of income differentials, it is found that about 25% of the total of

45% expressing themselves strongly in favor the improvement in CRM

post-merger are those with income levels below Rs.1.5 lacs & Rs.5 lacs

and above in equal proportion. While 55% of the professionals have

expressed strongly in favor of this view, only 48.7% of the post-graduates

and much less i.e. 13.6% of the graduates seem to veer around this view

indicating that the level of education is impacting the customer

perception significantly.

Table: 4.43

Relationship between DBV and customer perception regarding the impact of change of staff members of the bank after merger


VARIABLES(DBV)

CHI-

SQUARE VALUE

P- VALUE

1 Gender 2.609 0.271

2 Age ( years) 9.050 0.060


4 Yearly Income( Rs. lakhs) 6.638 0.356




197

The general human tendency is the preference for continuous dealing

with the bank’s staff members whom they know well as polite and

knowledgeable over time. It is however revealed from the results of Chi-

square test (Table 4.43) that there is significant association between

respondents’ educational qualifications, their length of association with

the bank, monthly transaction frequency and their perception as to

whether mergers result in change of staff giving an impersonal feel.

76.5% of the respondents having less than 2 years of association with

the banks are of the opinion that mergers do not result in change of staff

and hence the personal feel is not lost, when compared to the 17.6% of

respondents who think otherwise. It is of interest to note that 70% of the

respondents with minimum frequency of monthly transactions also share

the same opinion.

Table: 4.44 Relationship between DBV and customer perception regarding

change in competition scenario of banks post-merger

S.

NO.


VARIABLES(DBV)

CHI- SQUARE

VALUE

P- VALUE

1 Gender 0.056 0.972

2 Age ( years) 24.400 0.000(S)






198

Mergers are generally expected to result in big players resulting in

monopolistic tendencies and reduced competition. The above table (Table

4.44) clearly hints at strong association between the customer

perceptions in this regard and their age, yearly income levels, the length

of association with the bank in years and their annual income levels. But

no significant association has been observed between the customer

perception in this regard and the gender, monthly transaction frequency

and educational qualifications.

57.3% of the respondents of the age group 18-29 and 52.4% of the

respondents of age group 30-49 opine that mergers result in big players

and significantly reduce competition. While a significant proportion, as

high as, 68.3% of respondents with 6-10 years of association with

banks concerned also share this opinion, 52.9% of the respondents

having less than 2 years of association with banks think otherwise.

Further, a sizeable proportion i.e 60.9% of the respondents having

income of Rs 2.6-5 lakh (middle income groups) are of the view that

mergers result in reduced competition and emergence of big players.

199

Table: 4.45


increase in opportunities for the merged bank to cross-sell banking

products post-merger

S. NO.


CHI-

SQUARE

VALUE

P- VALUE

1 Gender 2.385 0.304

2 Age ( years) 12.000 0.017(S)





Source: Appendix C, Table C91 to C96

Mergers facilitate cross-selling of products of the merging banks which

will result in exploitation of synergies arising out of complementary

strengths/resources. It would appear from the above table(Table 4.45)

that there is significant association between the respondents’ age,yearly

income, & their association with bank and their opinion in regard to the

merged entity acquiring added ability to cross sell products and thereby

enrich its product offerings.

While 76.5% of the respondents having an association of less than 2

years with the bank and 54.1% of respondents having income less than

Rs 1.5 lakh (low income groups) are of the view that merged bank will be

able to better cross-sell, the corresponding percentages are relatively less

ranging between 30% to 40% for other categories determined by annual

income levels and association with the bank (in terms of years).

200

Table: 4.46

Relationship between DBV and customer perception regarding the improvement in innovative ability of the bank after merger

S.

NO.


VARIABLES(DBV)

CHI-

SQUARE VALUE

P-

VALUE

1 Gender 2.000 0.368

2 Age ( years) 2.930 0.569






As depicted in the above table 4.46, there is very strong association

between the respondents’ educational qualification (in years) and their

customer perception of improvement in innovative ability of banks

following merger. 63.4% of the respondents having 6-10 years of

association with the banks do not opine that merged banks provide

innovative products and redefine the way banks interact with customers,

while 64.7% of the respondents having less than 2 years of association

have a neutral opinion. Also, it is significant to note that 60% of the

professionals also do not opine that merged banks redefine the way the

bank interacts with the customers. Of the total respondents, 53.60%

were of the view (medium to strong) that mergers could improve the

innovative ability of the banks and redefine the way they interact with

the customers. This makes the reasearcher conclude that the customer

opinion in this regard is evenly divided. This calls for further exploration

201

by the banks as to the reasons and how best they can convince their

customers of their ability to research and innovate in the post-merger

scenario.

Table: 4.47


importance of communication about merger to the bank customers

S.

NO.


VARIABLES(DBV)

CHI-

SQUARE VALUE

P- VALUE

1 Gender 10.470 0.005(S)

2 Age ( years) 22.842 0.001(S)






Communication to the bank customers about the merger is considered to

be important so that they are not exposed to undue stress and strain in

perceiving the implications of the merger to the safety and security of

their deposits and the lasting relationships which they have come to

develop with the bank’s employees and the bank itself over the years. It

may be inferred from the above table 4.47 that there is significant

association between the customer perceptions in this regard and the

variables listed above, except for yearly income.

69% of the male respondents and 62.5% of the female respondents opine

that communication about mergers to customers is not very important.

70.60% of the respondents having less than 2 years of association feel

202

that it is important to communicate to the customers about the merger.

However, 75.5% of respondents having 2-5 years association and 75.6%

of respondents with 6-10 years of association have responded otherwise.

Another important observation is that 72.3% of respondents with 3-4

transactions and 67.2% of respondents with more than 5 transactions

per month feel that the communication on bank merger to the customers

is not generally important. This view has been supported by 75% of the

professionals.

On balance, while it makes the researcher conclude that, a major chunk

of the respondents opine that communication to bank customers about

impending merger is not very important, it is also possible that a

majority of the customers could not have fully captured/appreciated the

intricacies or importance of this communication.

Table: 4.48

Relationship between DBV and customer perception regarding his/her switching preference after the bank’s merger

S. NO.


CHI -

SQUARE

VALUE

P - VALUE

1 Gender 1.231 0.540

2 Age ( years) 5.210 0.267






203

It is observed from the above table 4.48 that the opinions of the

customers as to whether they will continue with their present bank after

merger or not is influenced by their association with the bank in years

and their yearly income. The other demographic variables like gender

and age etc do not have significant association with the opinion of the

bank customers on this issue.

63.4% and 65.5% of respondents with 6-10 and more than 10 years of

association respectively prefer to switch to some other bank if their bank

gets merged. However, only 9.3% of the total respondents opined against

switching to another bank, while 35% of them are unsure about it.

67.6% of respondents with income less than Rs 1.5 lakh and 60.9% of

respondents with income in range Rs 2.6-5 lakh were in favor of

switching to another bank in case of merger. These switching tendencies

evidenced by bank customers should be examined and analyzed by the

banks concerned if they are not to lose sizeable chunks of customers in

the wake of bank mergers as profitability and customer loyalty are

strongly linked.

204

Table: 4.49

Difference in Customer Perception of Service Quality based on Whether Customer’s Bank Has Experienced Any Merger

S.

NO. VARIABLES

CHI-

SQUARE VALUE

P

VALUE

1

Mergers improve dependability of bank

service(say, improvement in after –sales

service)

1.4180 0.4920

2

It results in increase in number of services

provided 3.2850 0.1940

3

It increases the range of products available 2.1280 0.3450

4

It results in larger loan limits

0.5580 0.7560

5

After merger better accessibility to services

is possible 4.4860 0.1060

6

Online banking does not improves after

merger 2.4650 0.2910

7

Service time is not reduced after merger

1.8970 0.3870

8

Safety of deposits increases after merger 6.8930 0.3310

9

Bank mergers result in less competitive

interest rates 5.3110 0.0700

10

Mergers generally do not result in fee

reduction for different services 2.7070 0.2580

11

Goodwill of the bank is not enhanced after

merger 4.5220 0.1040

12

Post merger, banks get quickly

technologically advanced 0.3540 0.8380

205

13

More number of ATMs after merger help

in improving customer service. 8.2240 0.0160(

s)

14

Customer relationship management does

not get better after merger 4.4880 0.1060

15

Mergers result in change of staff which gives an impersonal feel 0.9460 0.6230

16

Mergers results in big players and reduces competition 5.5010 0.0640

17

I don’t like cross selling (ex: banks selling

insurance products) undertaken by the merged bank

0.4960 0.7800

18

Merged banks provide innovative products

and redefine the way banks interact with

customers.

0.6580 0.7190

19

Communication about merger to the

customers in general is not very important 2.1570 0.3400

20

I prefer to switch to some other bank if my bank gets merged 0.6590 0.7190


It is clear from the above table 4.49 that except for a larger number of

ATMs helping in improved customer service after merger, there is no

significant association between the customer perception about the

marketing implications of commercial bank mergers and whether the

respondent is a customer of the bank which has undergone any merger

or not. This conclusion has significant implications to our study as quite

a few respondents in our sample belong to commercial banks which have

not gone through any merger.

206

Table: 4.50

Difference in Customer Perception Of Service Quality Based On The Nature Of Bank’s Ownership (Public Sector /Private Sector)

S.

NO. VARIABLES

CHI -

SQUARE VALUE

P -

VALUE

1

Mergers improve dependability of bank

service(say, improvement in after –sales

service)

3.2770 1.9400

2

It results in increase in number of

services provided 1.9730 0.3730

3

It increases the range of products

available 7.1010 0.0290(s)

4

It results in larger loan limits 16.0750 0.0000(s)

5

After merger better accessibility to

services is possible 1.4210 0.4910

6

Online banking does not improves after

merger 21.4490 0.0000(s)

7

Service time is not reduced after merger 2.6900 0.2610

8

Safety of deposits increases after merger 2.4770 0.2900

9

Bank mergers result in less competitive interest rates 7.7890 0.0200(s)

10

Mergers generally do not result in fee

reduction for different services 4.1080 0.1280

11

Goodwill of the bank is not enhanced

after merger 10.4640 0.0050(s)

12

Post merger, banks get quickly technologically advanced 0.1220 0.9410

13

More number of ATMs after merger

help in improving customer service. 18.9160 0.0000(s)

207

14

Customer relationship management

does not get better after merger 5.3770 0.0680

15

Mergers result in change of staff which gives an impersonal feel 1.2090 0.5460

16

Mergers results in big players and

reduces competition 6.8630 0.0320(s)

17

I don’t like cross selling (ex: banks

selling insurance products) undertaken by the merged bank

4.6920 0.0960

18

Merged banks provide innovative

products and redefine the way banks

interact with customers.

2.7430 0.2540

19

Communication about merger to the customers in general is not very

important

0.1900 0.9090

20

I prefer to switch to some other bank if my bank gets merged 8.2920 0.0160(s)


In respect of eight out of twenty items listed in the questionnaire (Table

4.50), it is found that there is significant association between the

customer perception of bank service quality post-merger and the nature

of ownership of the bank of the customer, i.e. whether he/she is a

customer of a public or private sector bank. It is possible that the

customer perception is broadly influenced by the diversified range of

products and services, tech-savvy nature and the promptness in services

of the private sector banks as compared to those in the public sector

where the process of technological upgradation started relatively late.

208

Graph 4.28

Influence of merger on the commercial bank services: Customer response wise break-up


While 62% of the respondents opined that the bank service would get

better after the merger, 21% were of the view that it would remain about

the same. Only 15% of the respondents were pessimistic in this regard

stating that it would worsen after merger (Graph 4.28). On balance, it

can be seen that the customers of the banks expect an improvement in

service quality post-merger.

62%

21%

2% 15%

Get Better Remain about the same Get worse Don’t know

209

Graph: 4.29

Customers classified based on their opinion on the strategy to be followed in commercial bank mergers in India


It is interesting to note that only 4% of the respondents feel that mergers

of banks should not take place. While 44% of the respondents expressed

the view that the merged bank (Combined entity) should be a private

sector bank, 38% maintained that public sector banks should be

preferably merge with public sector banks only (Graph 4.29).

44%

38%

14% 4%

PSU Banks should merge with private sector banks

PSU Banks should merge with PSU banks

Private sector Banks should merge with private sector banks

Merger should not take place at all

210

4.3.1 Factor analysis

Factor analysis has been performed with the 20 statements (items) in the

questionnaire using Statistical Package for Social Sciences (SPSS)

version 16. [Cronbach Alpha coefficient, which demonstrates internal

consistency and reliability of the established scale turned out to be

0.6697 which is sufficiently larger than the acceptable standard of

0.50(Kline, 1998)]

Table 4.51 shows KMO and Bartlett’s test

Table: 4.51


The Kaiser-Meyer-Olkin (KMO) measures the sampling adequacy which

should be greater than 0.50 for a satisfactory factor analysis. Looking at

the above table, the KMO measure is 0.653. From the same table, we can

see that the Bartlett’s Test of Sphericity is significant. The approximate

chi-square statistic is 1348.388 with 190 degrees of freedom which is

significant at the 0.01 level.This implies that the population correlation

matrix is not an identity matrix. The determinant’s value of R-matrix

211

(correlation matrix) is 0.007 which is much greater than the necessary

value of 0.00001 and hence the matrix does not suffer from the problem

of multicollinearity. Also that no correlation coefficient in the R-matrix is

greater than 0.9 implying that the data set does not suffer from

singularity problem either.

The communalities furnished below measure the percent of variance in a

given variable explained by all the factors. Communality for a variable is

the sum of squared factor loadings for that variable (row), and thus is the

percent of variance in a given variable explained by all of the factors. For

full orthogonal PCA, the communality will be 1.0 and all of the variance

in the variables will be explained by all of the factors, which will be as

many as there are variables. In the communalities chart, SPSS labels

this column the “initial” communalities. The extracted “communalities” is

the percent of variance in a given variable explained by the factors which

are extracted, which will usually be fewer than all the possible factors,

resulting in coefficients less than 1.0.

212

The “Total Variance Explained” table shows the eigen values. A factor’s

eigen value may be computed as the sum of its squared factor loadings

for all the variables. The ratio of Eigen values is the ratio of explanatory

importance of the factors with respect to the variables. If a factor’s eigen

value is low, then its contribution little to the explanation of variances in

the variables is small and hence may be ignored vis-à-vis more important

factors. Though the table shows 20 factors, one for each variable, only

the first six are extracted for analysis because, under the Extraction

options, SPSS was directed to extract only those factors with Eigen

values of 1.0 or higher.

The Initial Eigen values and Extraction Sums of Squared Loadings are

the same except that the latter only lists factors which have actually

been extracted in the solution. The Rotation Sums of Squared Loadings

(Varimax rotation has been used) gives the eigen values which improve

the interpretability of the factors. This means that after rotation each

extracted factor counts for a different percentage of variance explained,

even though the total variance explained is the same.

213

Table: 4.52

Total Variance Explained


214

The Cattell Scree test, below, plots (Graph 4.31) the components on the

X-axis and the corresponding eigen values on the Y-axis. As one moves to

the right, towards the later components, the eigen values drop. When the

drop ceases and the curve makes an elbow towards less steep decline,

Cattell’s scree test says “Drop all further components after the one

starting the elbow”. Such a change in the slope in the graph is known as

scree and the point is known as scree point. The factors which are

marked up to the scree point from the origin are to be retained for the

study and all the factors to the right of the scree point are to be dropped

from the study. Based on this criterion and the eigen value criterion

stated earlier, six factors have been retained.

Graph: 4.30

Scree Plot


0

0.5

1

1.5

2

2.5

3

3.5

4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Eige

n V

alu

e

Component Number

215

Table: 4.53


Component Matrix: The above Component Matrix (Table 4.53) gives the

factor loadings. This is the central output for factor analysis. The factor

loadings which are also called the component loadings in Principal

Components Analysis(PCA) methodology are the coefficients of correlation

between the variables (rows) and factors (columns).Factor loadings are

the basis for imputing a label to the different factors. The above table

216

gives the unrotated solution and the one below (Table 4.54) gives the

rotated solution.

Table: 4.54


Interpretation: A look at the rotated component matrix indicates that

the first factor has fairly high loadings from six primary banking service

quality variables X2, X3, X13, X1, X5, X6. Because these six service

quality items sort on the same factor, these items may be combined in a

scale which might be called “Primary Banking Service Quality

Determinants”. It may however be noted that naming a factor is a matter

of subjectivity and at times, even disputes arise on this issue. X4, X8,

217

X12, and X16 are associated strongly with the second factor which

might be called Size/Scale benefits. X7, X10, X14 and X9* are strongly

associated with the third factor which might be called “Customer

Relationship Management”(CRM). The fourth factor is strongly

associated with X15, X11 and X20 all of which have a bearing on Brand

image and might be called Brand image scale. As one goes on, the factors

become harder to interpret. The fifth factor is strongly associated with

the variable X19 (Importance of communication about merger to the

customers) which is a very critical aspect in integration (of the merging

entities) implementation. The sixth factor is strongly associated with

variables X17 and X18 which have a bearing on the ability of merged

banks to provide innovative products. This factor might therefore be

labeled as “Opportunities for innovation”.

* Though the loading for the variable X9 (movement in bank interest

rates post-merger) is -0.417(less than 0.50), it has been clubbed, having

regard to the fact that the difference is small, with the remaining three

variables falling under the construct/factor Customer Relationship

Management(CRM). Alternatively, this variable may be excluded as well.

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